Cellular Component Ontology Research Articles

ProFun-SOM: Protein Function Prediction for Specific Ontology Based on Multiple Sequence Alignment Reconstruction.

Protein function prediction is crucial for understanding species evolution, including viral mutations. Gene ontology (GO) is a standardized representation framework for describing protein functions with annotated terms. Each ontology is a specific functional category containing multiple child ontologies, and the relationships of parent and child ontologies create a directed acyclic graph. Protein functions are categorized using GO, which divides them into three main groups: cellular component ontology, molecular function ontology, and biological process ontology. Therefore, the GO annotation of protein is a hierarchical multilabel classification problem. This hierarchical relationship introduces complexities such as mixed ontology problem, leading to performance bottlenecks in existing computational methods due to label dependency and data sparsity. To overcome bottleneck issues brought by mixed ontology problem, we propose ProFun-SOM, an innovative multilabel classifier that utilizes multiple sequence alignments (MSAs) to accurately annotate gene ontologies. ProFun-SOM enhances the initial MSAs through a reconstruction process and integrates them into a deep learning architecture. It then predicts annotations within the cellular component, molecular function, biological process, and mixed ontologies. Our evaluation results on three datasets (CAFA3, SwissProt, and NetGO2) demonstrate that ProFun-SOM surpasses state-of-the-art methods. This study confirmed that utilizing MSAs of proteins can effectively overcome the two main bottlenecks issues, label dependency and data sparsity, thereby alleviating the root problem, mixed ontology. A freely accessible web server is available at http://bliulab.net/ ProFun-SOM/.

Antagonistic Activity of Streptomyces alfalfae 11F against Fusarium Wilt of Watermelon and Transcriptome Analysis Provides Insights into the Synthesis of Phenazine-1-Carboxamide.

Streptomyces alfalfa strain 11F has inhibitory effects on many phytopathogenic fungi and improves the establishment and biomass yield of switchgrass. However, the antagonistic effects of strain 11F on Fusarium wilt of watermelon and its secondary metabolites that contribute to its biocontrol activity are poorly understood. We evaluated the antagonistic and growth-promoting effects of strain 11F and conducted a transcriptome analysis to identify the metabolites contributing to antifungal activity. Strain 11F had marked inhibitory effects on six fungal pathogens. The incidence of Fusarium wilt of watermelon seedlings was decreased by 46.02%, while watermelon seedling growth was promoted, as indicated by plant height (8.7%), fresh weight (23.1%), and dry weight (60.0%). Clean RNA-sequencing data were annotated with 7553 functional genes. The 2582 differentially expressed genes (DEGs) detected in the Control vs. Case 2 comparison were divided into 42 subcategories of the biological process, cellular component, and molecular function Gene Ontology categories. Seven hundred and forty functional genes (55.47% of the DEGs) were assigned to Kyoto Encyclopedia of Genes and Genomes metabolic pathways, reflecting the complexity of the strain 11F metabolic regulatory system. The expression level of the gene phzF, which encodes an enzyme essential for phenazine-1-carboxylic acid (PCA) synthesis, was downregulated 3.7-fold between the 24 h and 48 h fermentation time points, suggesting that strain 11F can produce phenazine compounds. A phenazine compound from 11F was isolated and identified as phenazine-1-carboxamide (PCN), which contributed to the antagonistic activity against Fusarium oxysporum f. sp. niveum. PCA was speculated to be the synthetic precursor of PCN. The downregulation in phzF expression might be associated with the decrease in PCA accumulation and the increase in PCN synthesis in strain 11F from 24 to 48 h. Streptomyces alfalfae 11F protects watermelon seedlings from Fusarium wilt of watermelon and promotes seedling growth. The transcriptome analysis of strain 11F provides insights into the synthesis of PCN, which has antifungal activity against F. oxysporum f. sp. niveum of watermelon.

TEMPROT: protein function annotation using transformers embeddings and homology search

BackgroundAlthough the development of sequencing technologies has provided a large number of protein sequences, the analysis of functions that each one plays is still difficult due to the efforts of laboratorial methods, making necessary the usage of computational methods to decrease this gap. As the main source of information available about proteins is their sequences, approaches that can use this information, such as classification based on the patterns of the amino acids and the inference based on sequence similarity using alignment tools, are able to predict a large collection of proteins. The methods available in the literature that use this type of feature can achieve good results, however, they present restrictions of protein length as input to their models. In this work, we present a new method, called TEMPROT, based on the fine-tuning and extraction of embeddings from an available architecture pre-trained on protein sequences. We also describe TEMPROT+, an ensemble between TEMPROT and BLASTp, a local alignment tool that analyzes sequence similarity, which improves the results of our former approach.ResultsThe evaluation of our proposed classifiers with the literature approaches has been conducted on our dataset, which was derived from CAFA3 challenge database. Both TEMPROT and TEMPROT+ achieved competitive results on F_{max }, S_{min }, AuPRC and IAuPRC metrics on Biological Process (BP), Cellular Component (CC) and Molecular Function (MF) ontologies compared to state-of-the-art models, with the main results equal to 0.581, 0.692 and 0.662 of F_{max } on BP, CC and MF, respectively.ConclusionsThe comparison with the literature showed that our model presented competitive results compared the state-of-the-art approaches considering the amino acid sequence pattern recognition and homology analysis. Our model also presented improvements related to the input size that the model can use to train compared to the literature methods.

Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning

The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene’s function is not independent of its location, the few available examples of gene function prediction based on gene location rely on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function. Here we predict thousands of gene functions in five model eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models exclusively trained with features derived from the location of genes in the genomes to which they belong. Our aim was not to obtain the best performing method to automated function prediction but to explore the extent to which a gene's location can predict its function in eukaryotes. We found that our models outperform BLAST when predicting terms from Biological Process and Cellular Component Ontologies, showing that, at least in some cases, gene location alone can be more useful than sequence to infer gene function.

PANDA2: protein function prediction using graph neural networks.

High-throughput sequencing technologies have generated massive protein sequences, but the annotations of protein sequences highly rely on the low-throughput and expensive biological experiments. Therefore, accurate and fast computational alternatives are needed to infer functional knowledge from protein sequences. The gene ontology (GO) directed acyclic graph (DAG) contains the hierarchical relationships between GO terms but is hard to be integrated into machine learning algorithms for functional predictions. We developed a deep learning system named PANDA2 to predict protein functions, which used the cutting-edge graph neural network to model the topology of the GO DAG and integrated the features generated by transformer protein language models. Compared with the top 10 methods in CAFA3, PANDA2 ranked first in cellular component ontology (CCO), tied first in biological process ontology (BPO) but had a higher coverage rate, and second in molecular function ontology (MFO). Compared with other recently-developed cutting-edge predictors DeepGOPlus, GOLabeler, and DeepText2GO, and benchmarked on another independent dataset, PANDA2 ranked first in CCO, first in BPO, and second in MFO. PANDA2 can be freely accessed from http://dna.cs.miami.edu/PANDA2/.

NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.

With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large-scale multi-label classification. Under the learning to rank (LTR) framework, our previous NetGO tool integrated massive networks and multi-type information about protein sequences to achieve good performance by dealing with all possible GO terms (>44 000). In this work, we propose the updated version as NetGO 2.0, which further improves the performance of large-scale AFP. NetGO 2.0 also incorporates literature information by logistic regression and deep sequence information by recurrent neural network (RNN) into the framework. We generate datasets following the critical assessment of functional annotation (CAFA) protocol. Experiment results show that NetGO 2.0 outperformed NetGO significantly in biological process ontology (BPO) and cellular component ontology (CCO). In particular, NetGO 2.0 achieved a 12.6% improvement over NetGO in terms of area under precision-recall curve (AUPR) in BPO and around 2.6% in terms of n}{}mathbf {F_{max}} in CCO. These results demonstrate the benefits of incorporating text and deep sequence information for the functional annotation of BPO and CCO. The NetGO 2.0 web server is freely available at http://issubmission.sjtu.edu.cn/ng2/.

GODoc: high-throughput protein function prediction using novel k-nearest-neighbor and voting algorithms

BackgroundBiological data has grown explosively with the advance of next-generation sequencing. However, annotating protein function with wet lab experiments is time-consuming. Fortunately, computational function prediction can help wet labs formulate biological hypotheses and prioritize experiments. Gene Ontology (GO) is a framework for unifying the representation of protein function in a hierarchical tree composed of GO terms.ResultsWe propose GODoc, a general protein GO prediction framework based on sequence information which combines feature engineering, feature reduction, and a novel ​k​-nearest-neighbor algorithm to resolve the multiple GO prediction problem. Comprehensive evaluation on CAFA2 shows that GODoc performs better than two baseline models. In the CAFA3 competition (68 teams), GODoc ranks 10th in Cellular Component Ontology. Regarding the species-specific task, the proposed method ranks 10th and 8th in the eukaryotic Cellular Component Ontology and the prokaryotic Molecular Function Ontology, respectively. In the term-centric task, GODoc performs third and is tied for first for the biofilm formation of Pseudomonas aeruginosa and the long-term memory of Drosophila melanogaster, respectively.ConclusionsWe have developed a novel and effective strategy to incorporate a training procedure into the k-nearest neighbor algorithm (instance-based learning) which is capable of solving the Gene Ontology multiple-label prediction problem, which is especially notable given the thousands of Gene Ontology terms.

Genomewide analysis of microsatellite markers based on sequenced database in two anuran species

Eukaryotic and prokaryotic cell genomes exhibit multiple microsatellites. In this study, we characterized microsatellites in genomes and genes of Nanorana parkeri and Xenopus laevis. This characterization was used for gene ontology (GO) analysis of coding sequences (CDS). Compared to the genome of N. parkeri, the genome of X. laevis is larger and contains more number of microsatellites, but the diversity of both species are similar. Trinucleotide repeats in the genome of N. parkeri and dinucleotide and tetranucleotide repeats inthe genome of X. laevis were the most diverse. In both the species, diversity of microsatellites was highest in intergenic regions, followed by intron and exon regions, and lowest in coding regions. Microsatellites in CDS are thus subject to higher selective pressure. Many microsatellites are concentrated upstream and downstream of genes in both species, suggesting suppression of repeats in the middle of protein-CDS. Repeats are enriched in regions near gene termini purely due to the biophysical constraints of protein structure. In GO analysis, two and five unique GO terms, only found in N. parkeri and X. laevis, respectively, indicate advantageous mutations during species evolution. Biological process, cellular component and molecular function ontology reflected in the GO analysis predicted that the microsatellites located in CDS can alter protein function and may provide a molecular basis for species adaptation to new and changing environments.

Quantifying Gene Essentiality Based on the Context of Cellular Components.

Different genes have their protein products localized in various subcellular compartments. The diversity in protein localization may serve as a gene characteristic, revealing gene essentiality from a subcellular perspective. To measure this diversity, we introduced a Subcellular Diversity Index (SDI) based on the Gene Ontology-Cellular Component Ontology (GO-CCO) and a semantic similarity measure of GO terms. Analyses revealed that SDI of human genes was well correlated with some known measures of gene essentiality, including protein–protein interaction (PPI) network topology measurements, dN/dS ratio, homologous gene number, expression level and tissue specificity. In addition, SDI had a good performance in predicting human essential genes (AUC = 0.702) and drug target genes (AUC = 0.704), and drug targets with higher SDI scores tended to cause more side-effects. The results suggest that SDI could be used to identify novel drug targets and to guide the filtering of drug targets with fewer potential side effects. Finally, we developed a user-friendly online database for querying SDI score for genes across eight species, and the predicted probabilities of human drug target based on SDI. The online database of SDI is available at: http://www.cuilab.cn/sdi.

Molecular signal communication during arbuscular mycorrhizal formation induces significant transcriptional reprogramming of wheat (Triticum aestivum) roots.

Arbuscular mycorrhizal (AM) symbiosis begins with molecular signal communication (MSC) between AM fungi and the roots of the host plant. We aimed to test the hypothesis that the transcriptional profiles of wheat roots can be changed significantly by AM symbiotic signals, without direct contact. Non-mycorrhizal (NM) and MSC treatments involved burying filter membrane bags containing sterilized and un-sterilized inoculum of the AM fungus Rhizophagus irregularis, respectively. The bags physically separated roots and AM structures but allowed molecular signals to pass through. Extracted RNA from wheat roots was sequenced by high-throughput sequencing. Shoot total nitrogen and phosphorus content of wheat plants was decreased by the MSC treatment. A total of 2360 differentially expressed genes (DEGs), including 1888 up-regulated DEGs and 472 down-regulated DEGs, were found dominantly distributed on chromosomes 2A, 2B, 2D, 3B, 5B and 5D. The expression of 59 and 121 genes was greatly up- and down-regulated, respectively. Only a portion of DEGs could be enriched into known terms during gene ontology analysis, and were mostly annotated to 'catalytic activity', 'protein metabolic process' and 'membrane' in the molecular function, biological process and cellular component ontology categories, respectively. More than 120 genes that may be involved in key processes during AM symbiosis development were regulated at the pre-physical contact stages. The transcriptional profiles of wheat roots can be changed dramatically by MSC. Much of the information provided by our study is of great importance for understanding the mechanisms underlying the development of AM symbiosis.

Transcriptome-based analysis of carotenoid accumulation-related gene expression in petals of Chinese cabbage (Brassica rapa L.).

To identify genes associated with carotenoid accumulation in petals of Chinese cabbage, the composition and content of carotenoids were analyzed, and comparative transcriptome sequencing was performed between the yellow flower line, 92S105, and the orange flower line, 94C9. High-performance liquid chromatography (HPLC) revealed that petals of 92S105 were high in violaxanthin as well as lutein, whereas petals of 94C9 showed considerable levels of lutein and β-carotene. Transcriptome analysis showed that 3534 and 3833 genes were up- and down-regulated in 94C9, respectively. Among these differentially expressed genes (DEGs), many related to carotenoid accumulation were identified, including 12 carotenoid biosynthesis pathway genes, 4 transcription factor genes, and 1028 specifically expressed genes. β-carotene hydroxylase 1 (BrBCH1), BrBCH2, zeaxanthin epoxidase (BrZEP), and MYB transcription factor gene (BrGAMYB) were down-regulated in petals of 94C9 when compared with petals of 92S105, which caused β-carotene accumulation and may lead to orange petal color in 94C9. Expression levels of 20 DEGs were verified by qPCR and the results were highly consistent with those of transcriptome sequencing. Moreover, Gene Ontology (GO) enrichment analysis revealed that membrane, binding, and metabolic processes were the most significantly enriched GO terms in cellular component, molecular function, and biological process ontologies, respectively. In conclusion, our study analyzed the differences in composition and content of carotenoids between 92S105 and 94C9 and identified potential candidate genes related to carotenoid accumulation in petals, thereby creating a solid foundation for future studies on the mechanism regulating carotenoid accumulation in petals of Chinese cabbage.

Identification of SNPs potentially related to immune responses and growth performance in Litopenaeus vannamei by RNA-seq analyses.

Litopenaeus vannamei is one of the most important shrimp species for worldwide aquaculture. Despite this, little genomic information is available for this penaeid and other closely related taxonomic crustaceans. Consequently, genes, proteins and their respective polymorphisms are poorly known for these species. In this work, we used the RNA sequencing technology (RNA-seq) in L. vannamei shrimp evaluated for growth performance, and exposed to the White Spot Syndrome Virus (WSSV), in order to investigate the presence of Single Nucleotide Polymorphisms (SNPs) within genes related to innate immunity and growth, both features of great interest for aquaculture activity. We analyzed individuals with higher and lower growth rates; and infected (unhealthy) and non-infected (healthy), after exposure to WSSV. Approximately 7,000 SNPs were detected in the samples evaluated for growth, being 3,186 and 3,978 exclusive for individuals with higher and lower growth rates, respectively. In the animals exposed to WSSV we found about 16,300 unique SNPs, in which 9,338 were specific to non-infected shrimp, and 7,008 were exclusive to individuals infected with WSSV and symptomatic. In total, we describe 4,312 unigenes containing SNPs. About 60% of these unigenes returned GO blastX hits for Biological Process, Molecular Function and Cellular Component ontologies. We identified 512 KEGG unique KOs distributed among 275 pathways, elucidating the majority of metabolism roles related to high protein metabolism, growth and immunity. These polymorphisms are all located in coding regions, and certainly can be applied in further studies involving phenotype expression of complex traits, such as growth and immunity. Overall, the set of variants raised herein enriches the genomic databases available for shrimp, given that SNPs originated from nextgen are still rare for this relevant crustacean group, despite their huge potential of use in genomic selection approaches.

Transcriptome Analysis Reveals Silver Nanoparticle-Decorated Quercetin Antibacterial Molecular Mechanism.

Facile and simple method is developed to synthesize silver-nanoparticle-decorated quercetin nanoparticles (QA NPs). Modification suggests that synergistic quercetin (Qe) improves the antibacterial effect of silver nanoparticles (Ag NPs). Characterization experiment indicates that QA NPs have a diameter of approximately 10 nm. QA NPs show highly effective antibacterial activities against drug-resistant Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). We explore antibacterial mechanisms using S. aureus and E. coli treated with QA NPs. Through morphological changes in E. coli and S. aureus, mechanisms are examined for bacterial damage caused by particulate matter from local dissociation of silver ion and Qe from QA NPs trapped inside membranes. Moreover, we note that gene expression profiling methods, such as RNA sequencing, can be used to predict discover mechanisms of toxicity of QA NPs. Gene ontology (GO) assay analyses demonstrate the molecular mechanism of the antibacterial effect of QA NPs. Regarding cellular component ontology, "cell wall organization or biogenesis" (GO: 0071554) and "cell wall macromolecule metabolic process" (GO: 0044036) are the most represented categories. The present study reports that transcriptome analysis of the mechanism offers novel insights into the molecular mechanism of antibacterial assays.

Minimal metabolic pathway structure is consistent with associated biomolecular interactions.

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated.

Deregulated expression of cytoskeleton related genes in the spinal cord and sciatic nerve of presymptomatic SOD1(G93A) Amyotrophic Lateral Sclerosis mouse model.

Early molecular events related to cytoskeleton are poorly described in Amyotrophic Lateral Sclerosis (ALS), especially in the Schwann cell (SC), which offers strong trophic support to motor neurons. Database for Annotation, Visualization and Integrated Discovery (DAVID) tool identified cytoskeleton-related genes by employing the Cellular Component Ontology (CCO) in a large gene profiling of lumbar spinal cord and sciatic nerve of presymptomatic SOD1G93A mice. One and five CCO terms related to cytoskeleton were described from the spinal cord deregulated genes of 40 days (actin cytoskeleton) and 80 days (microtubule cytoskeleton, cytoskeleton part, actin cytoskeleton, neurofilament cytoskeleton, and cytoskeleton) old transgene mice, respectively. Also, four terms were depicted from the deregulated genes of sciatic nerve of 60 days old transgenes (actin cytoskeleton, cytoskeleton part, microtubule cytoskeleton and cytoskeleton). Kif1b was the unique deregulated gene in more than one studied region or presymptomatic age. The expression of Kif1b [quantitative polymerase chain reaction (qPCR)] elevated in the lumbar spinal cord (40 days old) and decreased in the sciatic nerve (60 days old) of presymptomatic ALS mice, results that were in line to microarray findings. Upregulation (24.8 fold) of Kif1b was seen in laser microdissected enriched immunolabeled motor neurons from the spinal cord of 40 days old presymptomatic SOD1G93A mice. Furthermore, Kif1b was dowregulated in the sciatic nerve Schwann cells of presymptomatic ALS mice (60 days old) that were enriched by means of cell microdissection (6.35 fold), cell sorting (3.53 fold), and primary culture (2.70 fold) technologies. The gene regulation of cytoskeleton molecules is an important occurrence in motor neurons and Schwann cells in presymptomatic stages of ALS and may be relevant in the dying back mechanisms of neuronal death. Furthermore, a differential regulation of Kif1b in the spinal cord and sciatic nerve cells emerged as key event in ALS.

Protein Ontology: a controlled structured network of protein entities

The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO’s organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO’s representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.

Identification and Analysis of Single- and Multiple-Region Mitotic Protein Complexes by Grouping Gene Ontology Terms

Many mitotic proteins are assembled into protein super complexes in three regions - midbody, centrosome and kinetochore (MCK) - with distinctive roles in modulating the mitosis process. However, more than 16% of the mitotic proteins are in multiple regions. Advance identification of mitotic proteins will be helpful to realize the molecular regulatory mechanisms of this organelle. Few ensemble-classifier methods can solve this problem but these methods often fuse various complementary features. In which, Gene ontology (GO) terms play an important role but the GO-term search space is massive and sparse. This motives this work to present an easily implemented method, namely mMck-GO, by identifying a small number of GO terms with support vector machine (SVM) andk-nearest neighbor (KNN) in predicting single-and multiple-region MCK proteins. The mMck-GO method using a simple grouping scheme based on a SVM classifier assembles the GO terms into several groups according to their numbers of annotated proteins in the training dataset, and then measures which top-grouped GO terms performs the best. A new MCK protein dataset containing 701 (611 single-and 90 multiple-region) is established in this work. None of the MCK proteins has a 25% pair-wise sequence identity with any other proteins in the same region. When performing on this dataset, we find that the GO term with the maximum annotation number annotates 49.2% of the training protein sequences; contrarily, 56.5% of the GO terms annotate single one protein sequence. This shows the sparse character of GO terms and the effectiveness of top-grouped GO terms in distinguishing MCK proteins. Accordingly, a small group of top 134 GO terms is identified and mMck-GO fuses the GO terms with amino acid composition (AAC) as input features to yield and independent-testing accuracies of 71.66% and 69.18%, respectively. Top 30 GO terms contain eight, eight, and 14 GO terms belonging to molecular function, biological process and cellular component branches, respectively. The 14 GO terms in cellular-component ontology in addition to centrosome and kinetochore are reverent to subcellular compartments, microtubule, membrane, and spindle, where GO:0005737 (cytoplasm) is ranked first. The eight GO terms enabling molecular functions comprise GO:0005515 (protein binding), GO:0000166 (nucleotide binding), and GO:0005524 (ATP binding). Most of the eight GO terms in biological-process ontology are reverent to cell cycle, cell division and mitosis but two GO terms, GO:0045449 and GO:0045449, are reverent to regulation of transcription and transport processes, which helps us to clarify the molecular regulatory mechanisms of this organelle. The top-grouped GO terms can be as an indispensable feature set when concerning other feature types to solve multiple-class problems in the investigation of biological functions.

Modular Biological Function Is Most Effectively Captured by Combining Molecular Interaction Data Types

Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

Influence of Aromatase Absence on the Gene Expression and Histology of the Mouse Meibomian Gland

We hypothesize that aromatase, an enzyme that controls estrogen biosynthesis, plays a major role in the sex-related differences of the meibomian gland. To begin to test this hypothesis, we examined the influence of aromatase absence, which completely eliminates estrogen production, on glandular gene expression and histology in male and female mice. Meibomian glands were obtained from adult, age-matched wild-type (WT) and aromatase knockout (ArKO) mice. Tissues were processed for histology or the isolation of total RNA, which was analyzed for differentially expressed mRNAs by using microarrays. Our results show that aromatase significantly influences the expression of more than a thousand genes in the meibomian gland. The nature of this effect is primarily sex-dependent. In addition, the influence of aromatase on sex-related differences in gene expression is predominantly genotype-specific. However, many of the sex-related variations in biological process, molecular function, and cellular component ontologies, as well as in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, are remarkably similar between WT and ArKO mice. The loss of aromatase activity has no obvious effect on the histology of meibomian glands in male or female mice. Our findings demonstrate that aromatase has a significant impact on gene expression in the meibomian gland. The nature of this influence is sex-dependent and genotype-specific; however, many of the sex-related variations in gene ontologies and KEGG pathways are similar between WT and ArKO mice. Consequently, it appears that aromatase, and by extension estrogen, do not play a major role in the sex-related differences of the mouse meibomian gland.

The Gene Ontology (GO) Cellular Component Ontology: integration with SAO (Subcellular Anatomy Ontology) and other recent developments

BackgroundThe Gene Ontology (GO) (http://www.geneontology.org/) contains a set of terms for describing the activity and actions of gene products across all kingdoms of life. Each of these activities is executed in a location within a cell or in the vicinity of a cell. In order to capture this context, the GO includes a sub-ontology called the Cellular Component (CC) ontology (GO-CCO). The primary use of this ontology is for GO annotation, but it has also been used for phenotype annotation, and for the annotation of images. Another ontology with similar scope to the GO-CCO is the Subcellular Anatomy Ontology (SAO), part of the Neuroscience Information Framework Standard (NIFSTD) suite of ontologies. The SAO also covers cell components, but in the domain of neuroscience.DescriptionRecently, the GO-CCO was enriched in content and links to the Biological Process and Molecular Function branches of GO as well as to other ontologies. This was achieved in several ways. We carried out an amalgamation of SAO terms with GO-CCO ones; as a result, nearly 100 new neuroscience-related terms were added to the GO. The GO-CCO also contains relationships to GO Biological Process and Molecular Function terms, as well as connecting to external ontologies such as the Cell Ontology (CL). Terms representing protein complexes in the Protein Ontology (PRO) reference GO-CCO terms for their species-generic counterparts. GO-CCO terms can also be used to search a variety of databases.ConclusionsIn this publication we provide an overview of the GO-CCO, its overall design, and some recent extensions that make use of additional spatial information. One of the most recent developments of the GO-CCO was the merging in of the SAO, resulting in a single unified ontology designed to serve the needs of GO annotators as well as the specific needs of the neuroscience community.