TSdb: a database of transporter substrates linking metabolic pathways and transporter systems on a genome scale via their shared substrates

Consistent analysis of differentially expressed genes across 7 cell types in papillary thyroid carcinoma

Single-cell transcriptome sequencing (scRNA-seq) provides a higher resolution of cellular differences than bulk RNA-seq, enabling the dissection of cell-type-specific responses to perturbations in papillary thyroid carcinoma (PTC). However, cellular genomic features are highly heterogeneous and have a large number of genes without any expression signals, which hinders the statistical power to identify differentially expressed genes and may generate many false-positive results. To overcome this challenge, we conducted an integrative analysis on two PTC scRNA-seq datasets and cross-validated consistent differential expression. By combining results from 32 common cell types in the two studies, we identified 31 consistently differentially expressed genes (DEGs) across seven cell types, including B cells, endothelial cells, epithelial cells, monocytes, NK cells, smooth muscle cells, and T cells. Functional enrichment analysis revealed that these genes are important for the adaptive immune response and autoimmune thyroid diseases. The additional disease-free survival analysis also confirmed that these 31 genes significantly affected patient survival time in large scale thyroid cancer cohort. Furthermore, we experimentally validated one of the top consistent DEGs as a potential biomarker gene of PTC epithelial cells, KRT7, which may be a upstream gene for the NF-κB signaling pathway. The result shows that KRT7 may promote thyroid cancer metastasis through the epithelial-mesenchymal transition and NF-κB signaling pathway. In summary, our single-cell transcriptome integration-based approach may provide insights into the important role of NF-κB in the underlying biology of the PTC.

Identifying the Common Cell-Free DNA Biomarkers across Seven Major Cancer Types

Blood-based detection of circulating cell-free DNA (cfDNA) is a non-invasive and easily accessible method for early cancer detection. Despite the extensive utility of cfDNA, there are still many challenges to developing clinical biomarkers. For example, cfDNA with genetic alterations often composes a small portion of the DNA circulating in plasma, which can be confounded by cfDNA contributed by normal cells. Therefore, filtering out the potential false-positive cfDNA mutations from healthy populations will be important for cancer-based biomarkers. Additionally, many low-frequency genetic alterations are easily overlooked in a small number of cfDNA-based cancer tests. We hypothesize that the combination of diverse types of cancer studies on cfDNA will provide us with a new perspective on the identification of low-frequency genetic variants across cancer types for promoting early diagnosis. By building a standardized computational pipeline for 1358 cfDNA samples across seven cancer types, we prioritized 129 shard genetic variants in the major cancer types. Further functional analysis of the 129 variants found that they are mainly enriched in ribosome pathways such as cotranslational protein targeting the membrane, some of which are tumour suppressors, oncogenes, and genes related to cancer initiation. In summary, our integrative analysis revealed the important roles of ribosome proteins as common biomarkers in early cancer diagnosis.

A Database of Lung Cancer-Related Genes for the Identification of Subtype-Specific Prognostic Biomarkers

The molecular subtype is critical for accurate treatment and follow-up in patients with lung cancer; however, information regarding subtype-associated genes is dispersed among thousands of published studies. Systematic curation and cross-validation of the scientific literature would provide a solid foundation for comparative genetic studies of the major molecular subtypes of lung cancer. Here, we constructed a literature-based lung cancer gene database (LCGene). In the current release, we collected and curated 2507 unique human genes, including 2267 protein-coding and 240 non-coding genes from comprehensive manual examination of 10,960 PubMed article abstracts. Extensive annotations were added to aid identification of differentially expressed genes, potential gene editing sites, and non-coding gene regulation. For instance, we prepared 607 curated genes with CRISPR knockout information in 43 lung cancer cell lines. Further comparison of these implicated genes among different subtypes identified several subtype-specific genes with high mutational frequencies. Common tumor suppressors and oncogenes shared by lung adenocarcinoma and lung squamous cell carcinoma, for example, exhibited different mutational frequencies and prognostic features, suggesting the presence of subtype-specific biomarkers. Our retrospective analysis revealed 43 small cell lung cancer-specific genes. Moreover, 52 tumor suppressors and oncogenes shared by lung adenocarcinoma and squamous cell carcinoma confirmed the different molecular mechanisms of these two cancer subtypes. The subtype-based genetic differences, when combined, may provide insight into subtype-specific biomarkers for genetic testing.

Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome

BACKGROUND

Clostridium thermocellum is a gram-positive thermophile that can directly convert lignocellulosic material into biofuels. The metabolism of C. thermocellum contains many branches and redundancies which limit biofuel production, and typical genetic techniques are time-consuming. Further, the genome sequence of a genetically tractable strain C. thermocellum DSM 1313 has been recently sequenced and annotated. Therefore, developing a comprehensive, predictive, genome-scale metabolic model of DSM 1313 is desired for elucidating its complex phenotypes and facilitating model-guided metabolic engineering.

RESULTS

We constructed a genome-scale metabolic model iAT601 for DSM 1313 using the KEGG database as a scaffold and an extensive literature review and bioinformatic analysis for model refinement. Next, we used several sets of experimental data to train the model, e.g., estimation of the ATP requirement for growth-associated maintenance (13.5 mmol ATP/g DCW/h) and cellulosome synthesis (57 mmol ATP/g cellulosome/h). Using our tuned model, we investigated the effect of cellodextrin lengths on cell yields, and could predict in silico experimentally observed differences in cell yield based on which cellodextrin species is assimilated. We further employed our tuned model to analyze the experimentally observed differences in fermentation profiles (i.e., the ethanol to acetate ratio) between cellobiose- and cellulose-grown cultures and infer regulatory mechanisms to explain the phenotypic differences. Finally, we used the model to design over 250 genetic modification strategies with the potential to optimize ethanol production, 6155 for hydrogen production, and 28 for isobutanol production.

CONCLUSIONS

Our developed genome-scale model iAT601 is capable of accurately predicting complex cellular phenotypes under a variety of conditions and serves as a high-quality platform for model-guided strain design and metabolic engineering to produce industrial biofuels and chemicals of interest.

GCGene: a gene resource for gastric cancer with literature evidence

Gastric cancer (GC) is the fifth most common cancer and third leading cause of cancer-related deaths worldwide. Its lethality primarily stems from a lack of detection strategies for early stages of GC and a lack of noninvasive detection strategies for advanced stages. The development of early diagnostic biomarkers largely depends on understanding the biological pathways and regulatory mechanisms associated with putative GC genes. Unfortunately, the GC-implicated genes that have been identified thus far are scattered among thousands of published studies, and no systematic summary is available, which hinders the development of a large-scale genetic screen. To provide a publically accessible resource tool to meet this need, we constructed a literature-based database GCGene (Gastric Cancer Gene database) with comprehensive annotations supported by a user-friendly website. In the current release, we have collected 1,815 unique human genes including 1,678 protein-coding and 137 non-coding genes curated from extensive examination of 3,142 PubMed abstracts. The resulting database has a convenient web-based interface to facilitate both textual and sequence-based searches. All curated genes in GCGene are downloadable for advanced bioinformatics data mining. Gene prioritization was performed to rank the relative relevance of these genes in GC development. The 100 top-ranked genes are highly mutated according to the cohort of published studies we reviewed. By conducting a network analysis of these top-ranked GC-associated genes in the human interactome, we were able to identify strong links between 8 highly connected genes with low expression and patient survival time. GCGene is freely available to academic users at http://gcgene.bioinfo-minzhao.org/.

Multi-tissue transcriptomics for construction of a comprehensive gene resource for the terrestrial snail Theba pisana

The land snail Theba pisana is native to the Mediterranean region but has become one of the most abundant invasive species worldwide. Here, we present three transcriptomes of this agriculture pest derived from three tissues: the central nervous system, hepatopancreas (digestive gland), and foot muscle. Sequencing of the three tissues produced 339,479,092 high quality reads and a global de novo assembly generated a total of 250,848 unique transcripts (unigenes). BLAST analysis mapped 52,590 unigenes to NCBI non-redundant protein databases and further functional analysis annotated 21,849 unigenes with gene ontology. We report that T. pisana transcripts have representatives in all functional classes and a comparison of differentially expressed transcripts amongst all three tissues demonstrates enormous differences in their potential metabolic activities. The genes differentially expressed include those with sequence similarity to those genes associated with multiple bacterial diseases and neurological diseases. To provide a valuable resource that will assist functional genomics study, we have implemented a user-friendly web interface, ThebaDB (http://thebadb.bioinfo-minzhao.org/). This online database allows for complex text queries, sequence searches, and data browsing by enriched functional terms and KEGG mapping.

CSGene: a literature-based database for cell senescence genes and its application to identify critical cell aging pathways and associated diseases

Cell senescence is a cellular process in which normal diploid cells cease to replicate and is a major driving force for human cancers and aging-associated diseases. Recent studies on cell senescence have identified many new genetic components and pathways that control cell aging. However, there is no comprehensive resource for cell senescence that integrates various genetic studies and relationships with cell senescence, and the risk associated with complex diseases such as cancer is still unexplored. We have developed the first literature-based gene resource for exploring cell senescence genes, CSGene. We complied 504 experimentally verified genes from public data resources and published literature. Pathway analyses highlighted the prominent roles of cell senescence genes in the control of rRNA gene transcription and unusual rDNA repeat that constitute a center for the stability of the whole genome. We also found a strong association of cell senescence with HIV-1 infection and viral carcinogenesis that are mainly related to promoter/enhancer binding and chromatin modification processes. Moreover, pan-cancer mutation and network analysis also identified common cell aging mechanisms in cancers and uncovered a highly modular network structure. These results highlight the utility of CSGene for elucidating the complex cellular events of cell senescence.

An evidence-based knowledgebase of metastasis suppressors to identify key pathways relevant to cancer metastasis

Metastasis suppressor genes (MS genes) are genes that play important roles in inhibiting the process of cancer metastasis without preventing growth of the primary tumor. Identification of these genes and understanding their functions are critical for investigation of cancer metastasis. Recent studies on cancer metastasis have identified many new susceptibility MS genes. However, the comprehensive illustration of diverse cellular processes regulated by metastasis suppressors during the metastasis cascade is lacking. Thus, the relationship between MS genes and cancer risk is still unclear. To unveil the cellular complexity of MS genes, we have constructed MSGene (http://MSGene.bioinfo-minzhao.org/), the first literature-based gene resource for exploring human MS genes. In total, we manually curated 194 experimentally verified MS genes and mapped to 1448 homologous genes from 17 model species. Follow-up functional analyses associated 194 human MS genes with epithelium/tissue morphogenesis and epithelia cell proliferation. In addition, pathway analysis highlights the prominent role of MS genes in activation of platelets and coagulation system in tumor metastatic cascade. Moreover, global mutation pattern of MS genes across multiple cancers may reveal common cancer metastasis mechanisms. All these results illustrate the importance of MSGene to our understanding on cell development and cancer metastasis.

METSP: a maximum-entropy classifier based text mining tool for transporter-substrate identification with semistructured text

The substrates of a transporter are not only useful for inferring function of the transporter, but also important to discover compound-compound interaction and to reconstruct metabolic pathway. Though plenty of data has been accumulated with the developing of new technologies such as in vitro transporter assays, the search for substrates of transporters is far from complete. In this article, we introduce METSP, a maximum-entropy classifier devoted to retrieve transporter-substrate pairs (TSPs) from semistructured text. Based on the high quality annotation from UniProt, METSP achieves high precision and recall in cross-validation experiments. When METSP is applied to 182,829 human transporter annotation sentences in UniProt, it identifies 3942 sentences with transporter and compound information. Finally, 1547 confidential human TSPs are identified for further manual curation, among which 58.37% pairs with novel substrates not annotated in public transporter databases. METSP is the first efficient tool to extract TSPs from semistructured annotation text in UniProt. This tool can help to determine the precise substrates and drugs of transporters, thus facilitating drug-target prediction, metabolic network reconstruction, and literature classification.

dbEMT: an epithelial-mesenchymal transition associated gene resource

As a cellular process that changes epithelial cells to mesenchymal cells, Epithelial-mesenchymal transition (EMT) plays important roles in development and cancer metastasis. Recent studies on cancer metastasis have identified many new susceptibility genes that control this transition. However, there is no comprehensive resource for EMT by integrating various genetic studies and the relationship between EMT and the risk of complex diseases such as cancer are still unclear. To investigate the cellular complexity of EMT, we have constructed dbEMT (http://dbemt.bioinfo-minzhao.org/), the first literature-based gene resource for exploring EMT-related human genes. We manually curated 377 experimentally verified genes from literature. Functional analyses highlighted the prominent role of proteoglycans in tumor metastatic cascades. In addition, the disease enrichment analysis provides a clue for the potential transformation in affected tissues or cells in Alzheimer's disease and Type 2 Diabetes. Moreover, the global mutation pattern of EMT-related genes across multiple cancers may reveal common cancer metastasis mechanisms. Our further reconstruction of the EMT-related protein-protein interaction network uncovered a highly modular structure. These results illustrate the importance of dbEMT to our understanding of cell development and cancer metastasis, and also highlight the utility of dbEMT for elucidating the functions of EMT-related genes.

Metrabase: a cheminformatics and bioinformatics database for small molecule transporter data analysis and (Q)SAR modeling

ABSTRACT

Both metabolism and transport are key elements defining the bioavailability and biological activity of molecules, i.e. their adverse and therapeutic effects. Structured and high quality experimental data stored in a suitable container, such as a relational database, facilitates easy computational processing and thus allows for high quality information/knowledge to be efficiently inferred by computational analyses. Our aim was to create a freely accessible database that would provide easy access to data describing interactions between proteins involved in transport and xenobiotic metabolism and their small molecule substrates and modulators. We present Metrabase, an integrated cheminformatics and bioinformatics resource containing curated data related to human transport and metabolism of chemical compounds. Its primary content includes over 11,500 interaction records involving nearly 3,500 small molecule substrates and modulators of transport proteins and, currently to a much smaller extent, cytochrome P450 enzymes. Data was manually extracted from the published literature and supplemented with data integrated from other available resources. Metrabase version 1.0 is freely available under a CC BY-SA 4.0 license at http://www-metrabase.ch.cam.ac.uk.

Pedican: an online gene resource for pediatric cancers with literature evidence

Pediatric cancer (PC), that is cancer occurring in children, is the leading cause of death among children worldwide, with an incidence of 175,000 per year. Elucidating the genetic abnormalities and underlying cellular mechanisms may provide less toxic curative treatments. Therefore, it is important to understand the pathology of pediatric cancer at the genetic, genomic and epigenetic level. To unveil the cellular complexity of PC, we have developed a database of pediatric cancers (Pedican), the first literature-based pediatric gene data resource by comprehensive literature curation and data integration. In the current release, Pedican contains 735 human genes, 88 gene fusion and 24 chromosome abnormal events curated from 2245 PubMed abstracts. Pedican provides detailed annotations for each gene, such as Entrez gene information, involved pathways, protein–protein interactions, mutations, gene expression, methylation sites, TF regulation, and post-translational modification. Additionally Pedican has a user-friendly web interface, which allows sophisticated text query, sequence searches, and browsing by highlighted literature evidence and hundreds of cancer types. Overall, our curated pediatric cancer-related gene list maps the genomic and cellular landscape for various pediatric cancers, providing a valuable resource for further experiment design. The Pedican is available at http://pedican.bioinfo-minzhao.org/.

Transporter taxonomy - a comparison of different transport protein classification schemes

Currently, there are more than 800 well characterized human membrane transport proteins (including channels and transporters) and there are estimates that about 10% (approx. 2000) of all human genes are related to transport. Membrane transport proteins are of interest as potential drug targets, for drug delivery, and as a cause of side effects and drug–drug interactions. In light of the development of Open PHACTS, which provides an open pharmacological space, we analyzed selected membrane transport protein classification schemes (Transporter Classification Database, ChEMBL, IUPHAR/BPS Guide to Pharmacology, and Gene Ontology) for their ability to serve as a basis for pharmacology driven protein classification. A comparison of these membrane transport protein classification schemes by using a set of clinically relevant transporters as use-case reveals the strengths and weaknesses of the different taxonomy approaches.

An evidence-based knowledgebase of pulmonary arterial hypertension to identify genes and pathways relevant to pathogenesis

Pulmonary arterial hypertension (PAH) is a major progressive form of pulmonary hypertension (PH) with more than 4800 patients in the United States. In the last two decades, many studies have identified numerous genes associated with this disease. However, there is no comprehensive research resource for PAH or other PH types that integrates various genetic studies and their related biological information. Thus, the number of associated genes, and their strength of evidence, is unclear. In this study, we tested the hypothesis that a web-based knowledgebase could be used to develop a biological map of highly interrelated, functionally important genes in PAH. We developed the pulmonary arterial hypertension knowledgebase (PAHKB, ), a comprehensive database with a user-friendly web interface. PAHKB extracts genetic data from all available sources, including those from association studies, genetic mutation, gene expression, animal model, supporting literature, various genomic annotations, gene networks, cellular and regulatory pathways, as well as microRNAs. Moreover, PAHKB provides online tools for data browsing and searching, data integration, pathway graphical presentation, and gene ranking. In the current release, PAHKB contains 341 human PH-related genes (293 protein coding and 48 non-coding genes) curated from over 1000 PubMed abstracts. Based on the top 39 ranked PAH-related genes in PAHKB, we constructed a core biological map. This core map was enriched with the TGF-beta signaling pathway, focal adhesion, cytokine-cytokine receptor interaction, and MAPK signaling. In addition, the reconstructed map elucidates several novel cancer signaling pathways, which may provide clues to support the application of anti-cancer therapeutics to PAH. In summary, we have developed a system for the identification of core PH-related genes and identified critical signaling pathways that may be relevant to PAH pathogenesis. This system can be easily applied to other pulmonary diseases.

Human transporter database: comprehensive knowledge and discovery tools in the human transporter genes

Transporters are essential in homeostatic exchange of endogenous and exogenous substances at the systematic, organic, cellular, and subcellular levels. Gene mutations of transporters are often related to pharmacogenetics traits. Recent developments in high throughput technologies on genomics, transcriptomics and proteomics allow in depth studies of transporter genes in normal cellular processes and diverse disease conditions. The flood of high throughput data have resulted in urgent need for an updated knowledgebase with curated, organized, and annotated human transporters in an easily accessible way. Using a pipeline with the combination of automated keywords query, sequence similarity search and manual curation on transporters, we collected 1,555 human non-redundant transporter genes to develop the Human Transporter Database (HTD) (http://htd.cbi.pku.edu.cn). Based on the extensive annotations, global properties of the transporter genes were illustrated, such as expression patterns and polymorphisms in relationships with their ligands. We noted that the human transporters were enriched in many fundamental biological processes such as oxidative phosphorylation and cardiac muscle contraction, and significantly associated with Mendelian and complex diseases such as epilepsy and sudden infant death syndrome. Overall, HTD provides a well-organized interface to facilitate research communities to search detailed molecular and genetic information of transporters for development of personalized medicine.

EDdb: a web resource for eating disorder and its application to identify an extended adipocytokine signaling pathway related to eating disorder

Eating disorder is a group of physiological and psychological disorders affecting approximately 1% of the female population worldwide. Although the genetic epidemiology of eating disorder is becoming increasingly clear with accumulated studies, the underlying molecular mechanisms are still unclear. Recently, integration of various high-throughput data expanded the range of candidate genes and started to generate hypotheses for understanding potential pathogenesis in complex diseases. This article presents EDdb (Eating Disorder database), the first evidence-based gene resource for eating disorder. Fifty-nine experimentally validated genes from the literature in relation to eating disorder were collected as the core dataset. Another four datasets with 2824 candidate genes across 601 genome regions were expanded based on the core dataset using different criteria (e.g., protein-protein interactions, shared cytobands, and related complex diseases). Based on human protein-protein interaction data, we reconstructed a potential molecular sub-network related to eating disorder. Furthermore, with an integrative pathway enrichment analysis of genes in EDdb, we identified an extended adipocytokine signaling pathway in eating disorder. Three genes in EDdb (ADIPO (adiponectin), TNF (tumor necrosis factor) and NR3C1 (nuclear receptor subfamily 3, group C, member 1)) link the KEGG (Kyoto Encyclopedia of Genes and Genomes) "adipocytokine signaling pathway" with the BioCarta "visceral fat deposits and the metabolic syndrome" pathway to form a joint pathway. In total, the joint pathway contains 43 genes, among which 39 genes are related to eating disorder. As the first comprehensive gene resource for eating disorder, EDdb ( http://eddb.cbi.pku.edu.cn ) enables the exploration of gene-disease relationships and cross-talk mechanisms between related disorders. Through pathway statistical studies, we revealed that abnormal body weight caused by eating disorder and obesity may both be related to dysregulation of the novel joint pathway of adipocytokine signaling. In addition, this joint pathway may be the common pathway for body weight regulation in complex human diseases related to unhealthy lifestyle.

IQdb: an intelligence quotient score-associated gene resource for human intelligence

Intelligence quotient (IQ) is the most widely used phenotype to characterize human cognitive abilities. Recent advances in studies on human intelligence have identified many new susceptibility genes. However, the genetic mechanisms involved in IQ score and the relationship between IQ score and the risk of mental disorders have won little attention. To address the genetic complexity of IQ score, we have developed IQdb (http://IQdb.cbi.pku.edu.cn), a publicly available database for exploring IQ-associated human genes. In total, we collected 158 experimental verified genes from literature as a core dataset in IQdb. In addition, 46 genomic regions related to IQ score have been curated from literature. Based on the core dataset and 46 confirmed linked genomic regions, more than 6932 potential IQ-related genes are expanded using data of protein–protein interactions. A systematic gene ranking approach was applied to all the collected and expanded genes to represent the relative importance of all the 7090 genes in IQdb. Our further systematic pathway analysis reveals that IQ-associated genes are significantly enriched in multiple signal events, especially related to cognitive systems. Of the 158 genes in the core dataset, 81 are involved in various psychotic and mental disorders. This comprehensive gene resource illustrates the importance of IQdb to our understanding on human intelligence, and highlights the utility of IQdb for elucidating the functions of IQ-associated genes and the cross-talk mechanisms among cognition-related pathways in some mental disorders for community.

Database URL: http://IQdb.cbi.pku.edu.cn.

Study on the networks of "Nature-Family-Component" of Chinese medicinal herbs based on association rules mining

OBJECTIVE

To explore appropriate methods for the research of the theory of Chinese medicine nature property and find the relationship between Nature-Family-Component of Chinese herbs.

METHODS

From perspective of systems biology, we used Associate Network to identify useful relationships among "Nature-Family-Component" of Herbs. In this work, Associate Network combines association rules mining method and network construction method to evaluate the complicate relationship among "Nature-Family-Component" of herbs screened.

RESULTS

The results of association rules mining showed that the families had a close relationship with nature properties of herbs. For example, the families of Magnoliaceae, Araceae had a close relationship with hot nature with confidence of 100%, the families of Cucurbitaceae has a close relationship to cold nature with confidence of 90.91%. Moreover, the results of constructed Associate Network implied that herbs belonging to the same families generally had the same natures. In addition, some herbs belonging to different families may also have same natures when they contain the same main components.

CONCLUSION

These results implied that the main components of herbs might affect their natures; the relationships between families and natures were based on the main compounds of herbs.

Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective

Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such 'ecosystems biology' approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. This mini review aims to address selected opportunities and challenges of modern metagenomics from a bioinformatics perspective and hopefully will serve as a useful resource for microbial ecologists and bioinformaticians alike.