Gene Ontology Enrichment Improves Performances of Functional Similarity of Genes

Programmable DNA‐Peptide Conjugated Hydrogel via Click Chemistry for Sequential Modulation of Peripheral Nerve Regeneration

During peripheral nerve regeneration, current deoxyribonucleic acid (DNA)‐based therapeutic platforms face the challenge of precisely regulating Schwann cells (SCs) fate to sustain their repair phenotype due to their inability to stably and precisely integrate multiple bioactive components. Herein, the strain‐promoted azide–alkyne cycloaddition reaction is utilized to integrate the neurotrophic factor mimetic peptide RGI and the laminin‐derived peptide IKVAV into DNA monomers. Through DNA sequence self‐assembly, a programmable DNA‐peptide conjugated hydrogel is constructed for loading bone marrow mesenchymal stem cell‐derived exosomes. This programmable hydrogel can rapidly, stably, and precisely integrate various bioactive components into the hydrogel network, thereby enabling sequential modulation of peripheral nerve repair. In vitro, studies show that this hydrogel, through sequential modulation mechanisms, can activate the neuregulin‐1 (Nrg1)/ErbB pathway to induce the reprogramming of SCs and promote the recruitment and proliferation of repair SCs. The induced repair SCs promote neuronal axon outgrowth and enhance tube formation in endothelial cells. In vivo, this programmable hydrogel can gelate in situ through intraneural injection in a rat sciatic nerve crush injury model, promoting nerve regeneration and functional recovery. In summary, this work provides an effective and practical strategy for peripheral nerve regeneration.

Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here, we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the Catalog of Somatic Mutations In Cancer database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Bioinformatics and system biology approaches to determine the connection of SARS-CoV-2 infection and intrahepatic cholangiocarcinoma

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide, which poses a severe threat to human health. COVID-19 is a systemic ailment affecting various tissues and organs, including the lungs and liver. Intrahepatic cholangiocarcinoma (ICC) is one of the most common liver cancer, and cancer patients are particularly at high risk of SARS-CoV-2 infection. Nonetheless, few studies have investigated the impact of COVID-19 on ICC patients.

METHODS

With the methods of systems biology and bioinformatics, this study explored the link between COVID-19 and ICC, and searched for potential therapeutic drugs.

RESULTS

This study identified a total of 70 common differentially expressed genes (DEGs) shared by both diseases, shedding light on their shared functionalities. Enrichment analysis pinpointed metabolism and immunity as the primary areas influenced by these common genes. Subsequently, through protein-protein interaction (PPI) network analysis, we identified SCD, ACSL5, ACAT2, HSD17B4, ALDOA, ACSS1, ACADSB, CYP51A1, PSAT1, and HKDC1 as hub genes. Additionally, 44 transcription factors (TFs) and 112 microRNAs (miRNAs) were forecasted to regulate the hub genes. Most importantly, several drug candidates (Periodate-oxidized adenosine, Desipramine, Quercetin, Perfluoroheptanoic acid, Tetrandrine, Pentadecafluorooctanoic acid, Benzo[a]pyrene, SARIN, Dorzolamide, 8-Bromo-cAMP) may prove effective in treating ICC and COVID-19.

CONCLUSION

This study is expected to provide valuable references and potential drugs for future research and treatment of COVID-19 and ICC.

OMA orthology in 2024: improved prokaryote coverage, ancestral and extant GO enrichment, a revamped synteny viewer and more in the OMA Ecosystem

In this update paper, we present the latest developments in the OMA browser knowledgebase, which aims to provide high-quality orthology inferences and facilitate the study of gene families, genomes and their evolution. First, we discuss the addition of new species in the database, particularly an expanded representation of prokaryotic species. The OMA browser now offers Ancestral Genome pages and an Ancestral Gene Order viewer, allowing users to explore the evolutionary history and gene content of ancestral genomes. We also introduce a revamped Local Synteny Viewer to compare genomic neighborhoods across both extant and ancestral genomes. Hierarchical Orthologous Groups (HOGs) are now annotated with Gene Ontology annotations, and users can easily perform extant or ancestral GO enrichments. Finally, we recap new tools in the OMA Ecosystem, including OMAmer for proteome mapping, OMArk for proteome quality assessment, OMAMO for model organism selection and Read2Tree for phylogenetic species tree construction from reads. These new features provide exciting opportunities for orthology analysis and comparative genomics. OMA is accessible at https://omabrowser.org.

Anti-inflammation is an important way that Qingre-Huazhuo-Jiangsuan recipe treats acute gouty arthritis

Background: Acute gouty arthritis (AGA) significantly impairs patients' quality of life. Currently, existing therapeutic agents exhibit definite efficacy but also lead to serious adverse reactions. Therefore, it is essential to develop highly efficient therapeutic agents with minimal adverse reactions, especially within traditional Chinese medicine (TCM). Additionally, food polyphenols have shown potential in treating various inflammatory diseases. The Qingre-Huazhuo-Jiangsuan-Recipe (QHJR), a modification of Si-Miao-San (SMS), has emerged as a TCM remedy for AGA with no reported side effects. Recent research has also highlighted a strong genetic link to gout. Methods: The TCM System Pharmacology (TCMSP) database was used to collect the main chemical components of QHJR and AGA-related targets for predicting the metabolites in QHJR. HPLC-Q-Orbitrap-MS was employed to identify the ingredients of QHJR. The collected metabolites were then used to construct a Drugs-Targets Network in Cytoscape software, ranked based on their "Degree" of significance. Differentially expressed genes (DEGs) were screened in the Gene Expression Omnibus (GEO) database using GEO2R online analysis. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The DEGs were utilized to construct a Protein-Protein Interaction (PPI) Network via the STRING database. In vivo experimental validation was conducted using colchicine, QHJR, rapamycin (RAPA), and 3-methyladenine (3-MA) as controls to observe QHJR's efficacy in AGA. Synovial tissues from rats were collected, and qRT-PCR and Western blot assays were employed to investigate Ampk-related factors (Ampk, mTOR, ULK1), autophagy-related factors (Atg5, Atg7, LC3, p62), and inflammatory-related factors (NLRP3). ELISA assays were performed to measure inflammatory-related factor levels (IL-6, IL-1β, TNF-α), and H&E staining was used to examine tissue histology. Results: Network analysis screened out a total of 94 metabolites in QHJR for AGA. HPLC-Q-Orbitrap-MS analysis identified 27 of these metabolites. Notably, five metabolites (Neochlorogenic acid, Caffeic acid, Berberine, Isoliquiritigenin, Formononetin) were not associated with any individual herbal component of QHJR in TCMSP database, while six metabolites (quercetin, luteolin, formononetin, naringenin, taxifolin, diosgenin) overlapped with the predicted results from the previous network analysis. Further network analysis highlighted key components, such as Caffeic acid, cis-resveratrol, Apigenin, and Isoliquiritigenin. Other studies have found that their treatment of AGA is achieved through reducing inflammation, consistent with this study, laying the foundation for the mechanism study of QHJR against AGA. PPI analysis identified TNF, IL-6, and IL-1β as hub genes. GO and KEGG analyses indicated that anti-inflammation was a key mechanism in AGA treatment. All methods demonstrated that inflammatory expression increased in the Model group but was reversed by QHJR. Additionally, autophagy-related expression increased following QHJR treatment. The study suggested that AMPKα and p-AMPKα1 proteins were insensitive to 3 MA and RAPA, implying that AMPK may not activate autophagy directly but through ULK1 and mTOR. Conclusion: In conclusion, this study confirms the effectiveness of QHJR, a modified formulation of SMS (a classic traditional Chinese medicine prescription for treating gout), against AGA. QHJR, as a TCM formula, offers advantages such as minimal safety concerns and potential long-term use. The study suggests that the mechanism by which QHJR treats AGA may involve the activation of the AMPK/mTOR/ULK1 pathway, thereby regulating autophagy levels, reducing inflammation, and alleviating AGA. These findings provide new therapeutic approaches and ideas for the clinical treatment of AGA.

Physiological, metabolomic, and transcriptomic reveal metabolic pathway alterations in Gymnocypris przewalskii due to cold exposure

Teleost fish have evolved various adaptations that allow them to tolerate cold water conditions. However, the underlying mechanism of this adaptation is poorly understood in Tibetan Plateau fish. RNA-seq combined with liquid chromatography‒mass spectrometry (LC‒MS/MS) metabolomics was used to investigate the physiological responses of a Tibetan Plateau-specific teleost, Gymnocypris przewalskii, under cold conditions. The 8-month G. przewalskii juvenile fish were exposed to cold (4 ℃, cold acclimation, CA) and warm (17 ℃, normal temperature, NT) temperature water for 15 days. Then, the transcript profiles of eight tissues, including the brain, gill, heart, intestine, hepatopancreas, kidney, muscle, and skin, were evaluated by transcriptome sequencing. The metabolites of the intestine, hepatopancreas, and muscle were identified by LC‒MS/MS. A total of 5,745 differentially expressed genes (DEGs) were obtained in the CA group. The key DEGs were annotated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. The DEGs from the eight tissues were significantly enriched in spliceosome pathways, indicating that activated alternative splicing is a critical biological process that occurs in the tissues to help fish cope with cold stress. Additionally, 82, 97, and 66 differentially expressed metabolites were identified in the intestine, hepatopancreas, and muscle, respectively. Glutathione metabolism was the only overlapping significant pathway between the transcriptome and metabolome analyses in these three tissues, indicating that an activated antioxidative process was triggered during cold stress. In combination with the multitissue transcriptome and metabolome, we established a physiology-gene‒metabolite interaction network related to energy metabolism during cold stress and found that gluconeogenesis and long-chain fatty acid metabolism played critical roles in glucose homeostasis and energy supply.

Pan-Cancer Analysis Reveals Functional Similarity of Three lncRNAs across Multiple Tumors

Long non-coding RNAs (lncRNAs) are emerging as key regulators in many biological processes. The dysregulation of lncRNA expression has been associated with many diseases, including cancer. Mounting evidence suggests lncRNAs to be involved in cancer initiation, progression, and metastasis. Thus, understanding the functional implications of lncRNAs in tumorigenesis can aid in developing novel biomarkers and therapeutic targets. Rich cancer datasets, documenting genomic and transcriptomic alterations together with advancement in bioinformatics tools, have presented an opportunity to perform pan-cancer analyses across different cancer types. This study is aimed at conducting a pan-cancer analysis of lncRNAs by performing differential expression and functional analyses between tumor and non-neoplastic adjacent samples across eight cancer types. Among dysregulated lncRNAs, seven were shared across all cancer types. We focused on three lncRNAs, found to be consistently dysregulated among tumors. It has been observed that these three lncRNAs of interest are interacting with a wide range of genes across different tissues, yet enriching substantially similar biological processes, found to be implicated in cancer progression and proliferation.

Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Global transcriptome and coexpression network analyses reveal cultivar-specific molecular signatures associated with different rooting depth responses to drought stress in potato

Potato is one of the most important vegetable crops worldwide. Its growth, development and ultimately yield is hindered by drought stress condition. Breeding and selection of deep-rooted and drought-tolerant potato varieties has become a prime approach for improving the yield and quality of potato (Solanum tuberosum L.) in arid and semiarid areas. A comprehensive understanding of root development-related genes has enabled scientists to formulate strategies to incorporate them into breeding to improve complex agronomic traits and provide opportunities for the development of stress tolerant germplasm. Root response to drought stress is an intricate process regulated through complex transcriptional regulatory network. To understand the rooting depth and molecular mechanism, regulating root response to drought stress in potato, transcriptome dynamics of roots at different stages of drought stress were analyzed in deep (C119) and shallow-rooted (C16) cultivars. Stage-specific expression was observed for a significant proportion of genes in each cultivar and it was inferred that as compared to C16 (shallow-rooted), approximately half of the genes were differentially expressed in deep-rooted cultivar (C119). In C16 and C119, 11 and 14 coexpressed gene modules, respectively, were significantly associated with physiological traits under drought stress. In a comparative analysis, some modules were different between the two cultivars and were associated with differential response to specific drought stress stage. Transcriptional regulatory networks were constructed, and key components determining rooting depth were identified. Through the results, we found that rooting depth (shallow vs deep) was largely determined by plant-type, cell wall organization or biogenesis, hemicellulose metabolic process, and polysaccharide metabolic process. In addition, candidate genes responding to drought stress were identified in deep (C119) and shallow (C16) rooted potato varieties. The results of this study will be a valuable source for further investigations on the role of candidate gene(s) that affect rooting depth and drought tolerance mechanisms in potato.

Identification of disease genes and assessment of eye-related diseases caused by disease genes using JMFC and GDLNN

Early detection of disease genes helps humans to recover from certain gene-related diseases, like genetic eye diseases. This work identifies the possibility of eye diseasesfor the disease genes utilizing a Gaussian-activation function (G)-centric deeplearning neural network (GDLNN) model. In this work, human genes are selected by computing structural similarity and genes are clustered as disease genesand normal genes by using the JMFC clustering algorithm. Levy flight and Crossover and Mutation (LCM) centric Chicken Swarm Optimization (LCM-CSO) is employed for feature selection and GDLNN classifies the eye-related diseases for the input genes using the selected features.

Predicting candidate genes from phenotypes, functions and anatomical site of expression

Motivation

Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models.

Results

We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression.

Availability and implementation

Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.

Supplementary information

Supplementary data are available at Bioinformatics online.

A computational framework for modeling functional protein-protein interactions

Protein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three-dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein-complex structures utilizing GO-based semantic similarity technique and co-evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid-body docking and template-based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re-ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template-based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein-protein interactions.

Inducing Apoptosis and Suppressing Inflammatory Reactions in Synovial Fibroblasts are Two Important Ways for Guizhi-Shaoyao-Zhimu Decoction Against Rheumatoid Arthritis

Background and Objectives

Guizhi-Shaoyao-Zhimu decoction (GSZD) is often applied to control rheumatoid arthritis (RA), gout, osteoarthritis, etc. In this study, bioinformatic analysis and experimental verification were used to uncover the integral mechanism profile of GSZD against RA.

Materials and Methods

The chemical compositions of GSZD were identified by UPLC-QTOF-MS/MS. MH7A cell model was established to screen active compounds in GSZD, and potential targets of these compounds were predicted through online database retrieval. The differential expression genes (DEGs) in synovial tissue of RA patients and normal controls were retrieved from the GEO database. DEGs and the predicated compounds targets were overlapped, and the overlapped genes were subsequently enriched by GO and KEGG analysis. The pathways with significant enrichments were further experimentally verified.

Results

A total of 19 constituents were identified from GSZD, and 11 compounds showed obviously antiproliferative effects on MH7A cells with IC₅₀ < 100 μg/mL. Bioinformatic analysis indicated that IL-1β, IL-6, MAPK8, JAK2, CXCL8, and CASP3 were the main targets of GSZD, and the integral pharmacological mechanisms profile of GSZD might be related to anti-inflammation and proapoptosis. GSZD can promote the loss of mitochondrial membrane potential (MOMP) and induce apoptosis in MH7A cells. Furthermore, in vitro experiments showed GSZD can not only downregulate mRNA expressions of IL-1β (p<0.05), IL-6 (p<0.05), MMPs (p<0.05) and CCL5 (p<0.05) but also inhibit the nuclear transcription of NF-κB. GSZD also reduced the expressions of Bcl-2 (p<0.05), JAK2 (p<0.05), STAT-3 (p<0.05), whereas increase Bax (p<0.05), Caspase-3 (p<0.05) and caspase-9 (p<0.05).

Conclusion

Collectively, inducing synovial fibroblast apoptosis and inhibiting inflammatory response are two important ways for GSZD to RA, and our study proved bioinformatic analysis combined with experimental verification is a feasible method to explore the drug targets and mechanism of actions of TCMs.

Graph embeddings on gene ontology annotations for protein-protein interaction prediction

Background

Protein–protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term–term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph.

Results

We compare our method with those methods that are based on information content (IC) and one method that is based on word embeddings, with experiments on three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.

Conclusion

Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GOA graphs for our defined missing and spurious PPI tasks.

Evolutionarily conserved plant genes responsive to root-knot nematodes identified by comparative genomics

Root-knot nematodes (RKNs, genus Meloidogyne) affect a large number of crops causing severe yield losses worldwide, more specifically in tropical and sub-tropical regions. Several plant species display high resistance levels to Meloidogyne, but a general view of the plant immune molecular responses underlying resistance to RKNs is still lacking. Combining comparative genomics with differential gene expression analysis may allow the identification of widely conserved plant genes involved in RKN resistance. To identify genes that are evolutionary conserved across plant species, we used OrthoFinder to compared the predicted proteome of 22 plant species, including important crops, spanning 214 Myr of plant evolution. Overall, we identified 35,238 protein orthogroups, of which 6,132 were evolutionarily conserved and universal to all the 22 plant species (PLAnts Common Orthogroups-PLACO). To identify host genes responsive to RKN infection, we analyzed the RNA-seq transcriptome data from RKN-resistant genotypes of a peanut wild relative (Arachis stenosperma), coffee (Coffea arabica L.), soybean (Glycine max L.), and African rice (Oryza glaberrima Steud.) challenged by Meloidogyne spp. using EdgeR and DESeq tools, and we found 2,597 (O. glaberrima), 743 (C. arabica), 665 (A. stenosperma), and 653 (G. max) differentially expressed genes (DEGs) during the resistance response to the nematode. DEGs' classification into the previously characterized 35,238 protein orthogroups allowed identifying 17 orthogroups containing at least one DEG of each resistant Arachis, coffee, soybean, and rice genotype analyzed. Orthogroups contain 364 DEGs related to signaling, secondary metabolite production, cell wall-related functions, peptide transport, transcription regulation, and plant defense, thus revealing evolutionarily conserved RKN-responsive genes. Interestingly, the 17 DEGs-containing orthogroups (belonging to the PLACO) were also universal to the 22 plant species studied, suggesting that these core genes may be involved in ancestrally conserved immune responses triggered by RKN infection. The comparative genomic approach that we used here represents a promising predictive tool for the identification of other core plant defense-related genes of broad interest that are involved in different plant-pathogen interactions.

Identification of SPRR3 as a novel diagnostic/prognostic biomarker for oral squamous cell carcinoma via RNA sequencing and bioinformatic analyses

Oral squamous cell carcinoma (OSCC) has always been one of the most aggressive and invasive cancers among oral and maxillofacial malignancies. As the morbidity and mortality of the disease have increased year by year, the search for a promising diagnostic and prognostic biomarker for the disease is becoming increasingly urgent. Tumorous and adjacent tissues were collected from three OSCC sufferers and we obtained 229 differentially expressed genes (DEGs) between tumor and normal tissues via high-throughput RNA sequence. Function and pathway enrichment analyses for DEGs were conducted to find a correlation between tumorigenesis status and DEGs. Protein interaction network and molecular complex detection (MCODE) were constructed to detect core modules. Two modules were enriched in MCODE. The diagnostic and prognostic values of the candidate genes were analyzed, which provided evidence for the candidate genes as new tumor markers. Small Proline Rich Protein 3 (SPRR3), a potential tumor marker that may be useful for the diagnosis of OSCC, was screened out. The survival analysis showed that SPRR3 under expression predicted the poor prognosis of OSCC patients. Further experiments have also shown that the expression of SPRR3 decreased as the malignancy of OSCC increased. Therefore, we believe that SPRR3 could be used as a novel diagnostic and prognostic tumor marker.

Transcriptome Analysis of Skeletal Muscle Reveals Altered Proteolytic and Neuromuscular Junction Associated Gene Expressions in a Mouse Model of Cerebral Ischemic Stroke

Stroke is a leading cause of mortality and long-term disability in patients worldwide. Skeletal muscle is the primary systemic target organ of stroke that induces muscle wasting and weakness, which predominantly contribute to functional disability in stroke patients. Currently, no pharmacological drug is available to treat post-stroke muscle morbidities as the mechanisms underlying post-stroke muscle wasting remain poorly understood. To understand the stroke-mediated molecular changes occurring at the transcriptional level in skeletal muscle, the gene expression profiles and enrichment pathways were explored in a mouse model of cerebral ischemic stroke via high-throughput RNA sequencing and extensive bioinformatic analyses. RNA-seq revealed that the elevated muscle atrophy observed in response to stroke was associated with the altered expression of genes involved in proteolysis, cell cycle, extracellular matrix remodeling, and the neuromuscular junction (NMJ). These data suggest that stroke primarily targets muscle protein degradation and NMJ pathway proteins to induce muscle atrophy. Collectively, we for the first time have found a novel genome-wide transcriptome signature of post-stroke skeletal muscle in mice. Our study will provide critical information to further elucidate specific gene(s) and pathway(s) that can be targeted to mitigate accountable for post-stroke muscle atrophy and related weakness.

Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.

Evolving knowledge graph similarity for supervised learning in complex biomedical domains

Background

In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying semantics. An alternative is to use machine learning approaches that explore semantic similarity. However, since ontologies can model multiple perspectives, semantic similarity computations for a given learning task need to be fine-tuned to account for this. Obtaining the best combination of semantic similarity aspects for each learning task is not trivial and typically depends on expert knowledge.

Results

We have developed a novel approach, evoKGsim, that applies Genetic Programming over a set of semantic similarity features, each based on a semantic aspect of the data, to obtain the best combination for a given supervised learning task. The approach was evaluated on several benchmark datasets for protein-protein interaction prediction using the Gene Ontology as the knowledge graph to support semantic similarity, and it outperformed competing strategies, including manually selected combinations of semantic aspects emulating expert knowledge. evoKGsim was also able to learn species-agnostic models with different combinations of species for training and testing, effectively addressing the limitations of predicting protein-protein interactions for species with fewer known interactions.

Conclusions

evoKGsim can overcome one of the limitations in knowledge graph-based semantic similarity applications: the need to expertly select which aspects should be taken into account for a given application. Applying this methodology to protein-protein interaction prediction proved successful, paving the way to broader applications.

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein-protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL: https://github.com/liseda-lab/kgsim-benchmark.

Regulatory Network Analysis to Reveal Important miRNAs and Genes in Non-Small Cell Lung Cancer

Objective

Lung cancer has high incidence and mortality rate, and non-small cell lung cancer (NSCLC) takes up approximately 85% of lung cancer cases. This study is aimed to reveal miRNAs and genes involved in the mechanisms of NSCLC.

Materials and Methods

In this retrospective study, GSE21933 (21 NSCLC samples and 21 normal samples), GSE27262 (25 NSCLC samples and 25 normal samples), GSE43458 (40 NSCLC samples and 30 normal samples) and GSE74706 (18 NSCLC samples and 18 normal samples) were searched from gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were screened from the four microarray datasets using MetaDE package, and then conducted with functional annotation using DAVID tool. Afterwards, protein-protein interaction (PPI) network and module analyses were carried out using Cytoscape software. Based on miR2Disease and Mirwalk2 databases, microRNAs (miRNAs)-DEG pairs were selected. Finally, Cytoscape software was applied to construct miRNA-DEG regulatory network.

Results

Totally, 727 DEGs (382 up-regulated and 345 down-regulated) had the same expression trends in all of the four microarray datasets. In the PPI network, TP53 and FOS could interact with each other and they were among the top 10 nodes. Besides, five network modules were found. After construction of the miRNA-gene network, top 10 miRNAs (such as hsa-miR-16-5p, hsa-let-7b-5p, hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-let-7a-5p and hsa-miR-34a- 5p) and genes (such as HMGA1, BTG2, SOD2 and TP53) were selected.

Conclusion

These miRNAs and genes might contribute to the pathogenesis of NSCLC.