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AIDBD: AUTOIMMUNE AND INFLAMMATORY DISEASES
Kulwinder Singh1,*, Monika2, Neelam Verma1
1Department of Biotechnology, Punjabi University, Patiala 147002, Punjab, India
2Department of Biotechnology, Mata Gujri College, Fatehgarh Sahib 140406, Punjab, India
: Kulwinder Singh.
Tel: +91-9888695963; E-mail: [email protected]
Database of biomarkers of autoimmune and inflammatory diseases
: June 12, 2016
: August 23, 2016
: August 30, 2016
One of the major challenges facing the healthcare industry is how to personalize, or tailor
healthcare products and services to individuals' unique genetic and biomarker make-ups.
Biomarkers provide information about normal or patho-physiological processes to detect or
define disease progression or to predict or quantify therapeutic responses. Once these footprints
have been identified and measured, they can then be used to personalize or tailor treatment plans,
products and services to each individual's unique makeup and background. Autoimmune and
Inflammatory Diseases Biomarker Database (AIDBD) is one of the first efforts to build an easily
accessible and comprehensive literature-derived database covering information on known
autoimmune and inflammatory diseases, biomarkers and available medications. It allows users to
link autoimmune and inflammatory diseases to protein or gene biomarkers through its user
interface. Currently, AIDBD integrates 206 biomarkers for 21 autoimmune and inflammatory
diseases and data on 516 launched drugs for the treatment these diseases. The database is freely
accessible at http://www.aidbd.in/.
Autoimmune diseases; Inflammatory diseases; Biomarker; Database; Drug
development; Personalized medicine
Autoimmune diseases are a family of more than 80 chronic and often disabling illnesses that
develop when underlying defects in the immune system lead the body to attack its own organs,
tissues and cells. Since cures are not yet available for most autoimmune diseases, patients face a
lifetime of illness and treatment. They often endure debilitating symptoms, loss of organ function,
reduced productivity at work, and high medical expenses (Jacobson et al.
, 1997). And because
most of these diseases disproportionately afflict women, and are among the leading causes of
death for young and middle-aged women, they impose a heavy burden on patients' families and
on society (Walsh and Rau, 2000; US Department of Health and Human Services Report to
Autoimmune diseases are commonly considered complex immune disorders. While many
autoimmune diseases are rare, collectively these diseases afflict millions of patients. Despite their
clinical diversity, they have one similarity, namely the dysfunction of the immune system (Hayter
and Cook, 2012). It is suspected that genetic defects play a role in the etiology of these diseases.
Modern high throughput technologies, like mRNA micro arrays have enabled researchers to
investigate diseases at a genome-wide level (Cotsapas and Hafler, 2013). In contrast to classical
inherited genetic diseases like sickle cell anemia, autoimmune diseases are not caused by the
defect of a single gene but by the dysfunction of the complex interaction of a group of genes.
Although no autoimmune disease has been completely analyzed, there has been tremendous
success in recent years in identifying major players in the development of autoimmune diseases
(Karopka et al.
A biomarker, as defined by the Food and Drug Administration (FDA) of the United States, is
any "characteristic that is objectively measured and evaluated as an indicator of normal biological
processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention"
(Biomarkers Definitions Working Group, 2001). Biomarkers are characteristics that can be
objectively measured and evaluated. They provide information about normal or patho-
physiological processes to detect or define disease progression or to predict or quantify
therapeutic responses (Wilson et al.
, 2007). With the recent explosion of high-performance
‘omic' technologies – genomics, proteomics and metabolomics, among others – the rate at which
biomarker candidates are being discovered is now faster than ever (Moore et al.
In order to advance our understanding of biomarkers and their roles in early autoimmune and
inflammatory processes, we have developed an integrated user-friendly database that catalogs
putative and validated biomarkers and relates them to autoimmune and inflammatory disease
processes. In addition, we have added information on approved medications for the treatment of
autoimmune and inflammatory diseases. This freely accessible resource will be a valuable
research tool and a contribution to improved public heath.
Asthma, rhinitis, arthritis, diabetes, transplantation, biomarker, target, inhibitor,
antagonist, agonist etc. were used as keywords in PubMed Medline Database to search for the
research papers. All the results were screened at the abstract level to segregate the false positive
papers from the hit list. All potential published studies on drugs, candidate protein and gene
biomarkers were evaluated. The true positive papers were collected to perform the manual data
curation process on diseases, biomarkers and drugs. Information on the proteins, taxonomy ID,
lineage, amino acid length, catalytic activity, molecular function, and role in diseases was
retrieved from UniProt, NCBI, OMIM, HGNC, RefSeq, PIR and other biological databases.
Information on the genes, its gene type, taxonomy, identifiers of other databases, its molecular
function, nucleotide length, nucleotide sequence, amino acid length etc. was retrieved from
NCBI, KEGG, HGNC, RefSeq, OMIM, MGI and GenBank databases. Information on drugs, its
mechanism of action, mode of administration was retrieved from literature, Google, PubChem
and clinical trials databases. The collected information was used to create the database using
HTML for creating web pages and CSS, a style sheet language, for enriching the look and format
web pages that display results. PHP, a server-side scripting language for web development was
used for the interface and MySQL for the backend coding of the database.
Early response to autoimmune and inflammatory diseases depends on rapid clinical
diagnosis and detection, which, if in place, are able to ameliorate suffering and economic loss.
Biomarkers, molecules that can be sensitively measured in the human body, are by definition
potentially diagnostic. The efficacy of biomarkers to autoimmune and inflammatory diseases lies
in their capability to provide early detection, establish highly specific diagnosis, determine
accurate prognosis, direct molecular-based therapy and monitor disease progression (Baker,
2005). They are increasingly important in both diagnostic and therapeutic processes, with high
potential to guide preventive interventions. Vast resources have been devoted to identifying and
developing biomarkers that can help determine the treatments for patients. There is growing
consensus that multiple markers will be required for most diagnoses, while single markers may
serve in only selected cases. Despite intensified interest and research, however, the rate of
development of novel biomarkers has been falling (Rifai et al.
, 2006), suggesting that a resource
that leverages existing data is overdue. At present the databases containing information about
biomarkers are focused predominantly on cancer: gastric cancer knowledgebase (Lee et al.
2006), early detection research network (Srinivas et al.
, 2001), integrated cancer biomarker
information system (Feng et al.
, 2005) etc. Furthermore, there are very good resources available
with respect to information on drugs like ChEBI; a freely available dictionary of molecular
entities focused on 'small' chemical compounds (Hastings et al.
, 2013), DrugBank, a
bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical,
pharmacological and pharmaceutical) data with comprehensive drug target information (Wishart
, 2006) etc. but no systematic effort has been described for easily accessible integrating
information from the disease specific biomarker domains and therapies especially for
autoimmune and inflammatory diseases. AIDBD introduces a community annotation database of
biomarkers, with interfaces for users to directly explore the information on autoimmune and
inflammatory diseases, putative and validated biomarkers and known therapies. It was designed
to collect, store and display information about biomarkers, conjoined to identifiers of research
tools for sequence and structural analyses of the data. AIDBD currently includes information on
201 biomarkers from 21 autoimmune and inflammatory diseases and 516 launched drugs for
these diseases. Diseases covered in AIDBD includes autoimmune and inflammatory diseases of
the respiratory tract i.e. Asthma, Chronic Obstructive Pulmonary Disease (COPD), Allergic
Rhinitis & Cystic Fibrosis; rheumatic joint disorders i.e. Rheumatoid Arthritis, Osteoarthritis,
Psoriatic Arthritis & Ankylosing Spondylitis; autoimmune diseases affecting multiple organs i.e.
Systemic Lupus Erythmatosus & Sjogren's Syndrome; skin disorders: Psoriasis & Atopic
Dermatitis; autoimmune disease due to the destruction of insulin-producing beta cells in the
pancreas: Diabetes Type 1; demyelinating disease of central nervous system: Multiple sclerosis;
Digestive disorders & Gastrointestinal diseases: Crohn's disease, Ulcerative colitis, Irritable
bowel syndrome; inflammatory disease of the blood vessels: Vasculitis and autoimmune disease
of the liver i.e. Primary Biliary Cirrhosis. Home page of AIDBD is illustrated in Figure 1.
The record entry in the database contains the following information about the protein targets:
recommended name, its brief description, gene name, organism information, taxonomy ID,
lineage, amino acid length, protein existence, its function, catalytic activity (if any); enzyme
regulation (if any); involvement in disease, family to which it belongs, its molecular function,
identifiers of UniProt, NCBI, Ensembl, OMIM, HGNC, RefSeq, PIR, UNIGENE databases and
tools, AA sequence, molecular weight, summary, inhibitors developed or under development (if
any) and references. An example of information stored in AIDBD on 'Interleukin-4' as a
biomarker of respiratory diseases is listed in Table 1. In the gene biomarker section, AIDBD
provides information on gene name, official symbol, full name, synonyms (if any), summary,
gene type, organism, taxonomy, identifiers of NCBI, KEGG, GENECARDS, HGNC, RefSeq,
OMIM, Ensembl, MGI, HOMOLOGENE databases and tools, its molecular function,
involvement in biological process, nucleotide length, nucleotide FASTA identifier, nucleotide
sequence, amino acid length, amino acid FASTA identifier, amino acid sequence, homologous
genes and references. An example of information stored in AIDBD on 'RNASE3' as a gene
biomarker is listed in Table 2. Drug section of AIDBD provides information on launched drugs
for the treatment of autoimmune and inflammatory diseases. Currently, drugs section of AIDBD
provides information on drug names including brand names, drug class, mechanism of action and
mode of administration. Information stored in AIDBD on some of the drugs is listed in Table 3.
Utility and future directions
AIDBD has been developed as a new resource to help the scientific and medical
community. Currently, AIDBD provide useful targets or biomarkers relevant for clinical
diagnosis of autoimmune and inflammatory diseases. It helps in accelerating the research as it
presents the underlying molecular mechanism of the disease, underpinning the targets. It also
provides information on drugs as well as their mechanism of action for better understanding on
how these drugs are involved in the biological processes for treatment of autoimmune and
inflammatory diseases. Its disease section also provides brief overviews on autoimmune and
inflammatory diseases, symptoms, diagnosis methods, severity levels and available treatment
options. The database content is carefully maintained and updated. Repeated literature searches
and curation are ongoing for identification and periodic update of new data into the database.
Development of a search engine is also planned by accommodating the search based on gene
identifiers, disease name, protein name, drug name etc. Inclusion of data for other related
diseases, to broaden the scope of the database to a larger audience is also under consideration.
AIDBD can be publicly accessed from any Web browser at http://www.aidbd.in/.
Authors are grateful to University Grants Commission (UGC), Government of India for financial
support under the major research project scheme (File No. 39-290/2010 (SR)). The authors report
no conflict of interest.
AIDBD, Autoimmune and Inflammatory Diseases Biomarker Database; mRNA, messenger
Ribonucleic Acid; FDA, Food and Drug Administration; NCBI, National Center for
Biotechnology Information; OMIM, Online Mendelian Inheritance in Man; HGNC, The HUGO
Gene Nomenclature Committee; RefSeq, NCBI Reference Sequence Database; PIR, The Protein
Information Resource; KEGG, Kyoto Encyclopedia of Genes and Genomes; MGI, Mouse
Genome Informatics; COPD, Chronic Obstructive Pulmonary Disease; UniProt, Universal
Protein Resource; GENECARDS, Database of Human Genes; HOMOLOGENE, NCBI tool for
automated detection of homologs among the annotated genes of several completely sequenced
eukaryotic genomes; FASTA, Fast Alignment; RNASE3, Ribonuclease 3; HTML, Hyper Text
Markup Language; CSS, Cascading Style Sheets; ChEBI, Chemical
of Biological Interest.
Baker, M. (2005). In biomarker we trust? Nat. Biotechnol. 23, 297–304.
Biomarkers Definitions Working Group (2001). Biomarkers and surrogate endpoints: preferred
definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89-95.
Cotsapas, C. and Hafler, D. A. (2013). Immune-mediated disease genetics: the shared basis of
pathogenesis. Trends Immunol. 34, 22–26.
Feng, W., Wu, B., Phan, J., Dale, J., Young, A. N. and Wang, M. D. (2005). An integrated cancer
biomarker information system. IEEE Eng. Med. Biol. Soc. 3, 2851–2854.
Hastings, J., de Matos, P., Dekker, A., Ennis, M., Harsha, B., Kale, N., Muthukrishnan, V.,
Owen, G., Turner, S., Williams, M. and Steinbeck, C. (2013). The ChEBI reference
database and ontology for biologically relevant chemistry: enhancements for 2013.
Nucleic Acids Res. 41(Database issue), D456-D463.
Hayter, S. M. and Cook, M. C. (2012). Updated assessment of the prevalence, spectrum and case
definition of autoimmune disease. Autoimmun. Rev. 11, 754–65.
Jacobson, D. L, Gange, S. J, Rose, N. R and Graham, N. M (1997). Epidemiology and estimated
population burden of selected autoimmune diseases in the United States. Clin. Immunol.
Immunopathol. 84, 223–243.
Karopka, T., Fluck, J., Mevissen, H.T. and Glass, A. (2006). The Autoimmune Disease Database:
a dynamically compiled literature-derived database. BMC Bioinform. 27, 325-341.
Lee, B. T. K, Song, C. M., Yeo, B. H., Chung, C. W, Chan, Y. L., Lim, T. T, Chua, Y. B., Loh,
M. C. S., Ang, B. K. Vijayakumar, P., Liew, L., Lim, J., Lim, Y. P., Wong, C. H., Chuon,
D., Rajagopal, G. and Hill, J. (2006). Gastric cancer (biomarkers) knowledgebase
(GCBKB): a curated and fully integrated knowledgebase of putative biomarkers related to
gastric cancer. Biomarker Insights 2, 135–141.
Moore, R.E., Kirwan, J., Doherty, M.K. and Whitfield, P.D. (2007). Biomarker Discovery in
Animal Health and Disease: The Application of Post-Genomic Technologies. Biomarker
Insights 2, 185-196.
Rifai, N., Gillette, M. A. and Carr, S. A. (2006). Protein biomarker discovery and validation: the
long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983.
Srinivas, P. R., Kramer, B. S. and Srivastava, S. (2001). Trends in biomarker research for cancer
detection. Lancet Oncol. 2, 698-704.
The Autoimmune Diseases Coordinating Committee. Progress in Autoimmune Diseases
Research: Report to Congress. US Department of Health and Human Services. 2005.
Walsh, S. J, Rau, L. M. (2000). Autoimmune diseases: a leading cause of death among young and
middle-aged women in the United States. Am. J. Public Health 90, 1463–1466.
Wilson, C.L., Schultz, S. and Waldman, S. A. (2007). Where Medicine, Business, And Public
Policy Intersect. Biotechnol. Healthc. 4, 33-42.
Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z. and
Woolsey, J. (2006). DrugBank: a comprehensive resource for in silico
drug discovery and
exploration. Nucleic Acids Res. 34, D668-D672.
Annotated Protein Sequence Database, available at: http://pir.georgetown.edu/
Chemical Entities of Biological Interest, available at: https://www.ebi.ac.uk/chebi/
Clinical Trials Database, availble at: https://clinicaltrials.gov/
Collection of databases dealing with genomes, biological pathways, diseases, drugs, and
chemical substances, available at: http://www.genome.jp/kegg/
Database resource for the laboratory mouse, providing integrated genetic, genomic, and
biological data to facilitate the study of human health and disease, available at:
DrugBank, available at: http://www.drugbank.ca/
Early Detection Research Network, available at: https://edrn.nci.nih.gov/
Gastric Cancer Knowledgebase, available at: http://biomarkers.bii.a-star.edu.sg/
GenBank Database, available at: http://www.ncbi.nlm.nih.gov/genbank/
Gene Nomenclature & Gene Families Database, available at: http://www.genenames.org/
Nucleotide Sequence Database, available at: http://www.ncbi.nlm.nih.gov/refseq/
Online Mendelian Inheritance in Man: An Online Catalog of Human Genes and Genetic
Disorders, available at: http://www.omim.org/
PubChem Database, available at: https://pubchem.ncbi.nlm.nih.gov/
PubMed Literature Database, available at: http://www.ncbi.nlm.nih.gov/pubmed
Universal Protein Resource Database, available at: http://www.uniprot.org/
Figure 1: Home page of AIDBD displaying various sections of database: Disease, Biomarker
and Drugs section.
Table 1. An example of information stored in AIDBD on 'Interleukin-4' as a biomarker of
Protein is a pleiotropic cytokine produced by activated T cells. The interleukin 4 receptor also binds to IL13, which may contribute to many overlapping functions of this cytokine and IL13.
Eukaryota › Metazoa › Chordata › Craniata › Vertebrata › Euteleostomi › Mammalia › Eutheria › Euarchontoglires › Primates › Haplorrhini › Catarrhini › Hominidae › Homo
Evidence at protein level.
Participates in at least several B-cell activation processes as well as of other cell types. It is a costimulator of DNA-synthesis. It induces the expression of class II MHC molecules on resting B-cells.
Involvement in disease
The TH2-like cytokine interleukin (IL)-4 play a pivotal role in airway wall inflammation in asthma and these cytokines are increased in peripheral blood and bronchoalveolar lavage fluid from asthmatic patients.
cytokine activity, growth factor activity, interleukin-4 receptor binding
ENST00000231449; ENSP00000231449; ENSG00000113520; ENST00000350025; ENSP00000325190; ENSG00000113520;
147780. gene., 601367. phenotype.
Interleukin-4 is a cytokine that induces differentiation of naive helper T cells (Th0 cells) to Th2 cells. Upon activation by IL-4, Th2 cells subsequently produce additional IL-4.
J Allergy Clin Immunol.;107(6):963-70. Jun 2001 Borish LC, Nelson HS, Corren J, Bensch G, Busse WW, Whitmore JB, Agosti JM; IL-4R Asthma Study Group. PMID: 11398072
Table 2. An example of information stored in AIDBD on 'RNASE3' as a gene biomarker.
ribonuclease, RNase A family, 3 (eosinophil cationic protein)
Cytotoxin and helminthotoxin with low-efficiency ribonuclease activity. Possesses a wide variety of biological activities. Exhibits antibacterial activity.
Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini; Catarrhini; Hominidae; Homo
Molecular Function nucleic acid binding, pancreatic ribonuclease activity
RNA catabolic process, defence response to bacterium
>gi 45243506 ref NM_002935.2 Homo sapiens ribonuclease, RNase A family, 3 (eosinophil cationic protein) (RNASE3), mRNA
>gi 147744558 sp P12724.2 ECP_HUMAN RecName: Full=Eosinophil cationic protein; Short=ECP; AltName: Full=Ribonuclease 3; Short=RNase 3; Flags: Precursor
Ear5, Mus musculus- GeneID: 54159 , Rnase2, Rattus norvegicus-GeneID: 474169, Rnase17, Rattus norvegicus-GeneID: 497195, Ear11, Rattus norvegicus-GeneID: 192264
Eosinophil cationic protein in serum from adults with asthma and with chronic obstructive pulmonary disease. Yoshizawa A, Kamimura M, Sugiyama H, Kudo K, Kabe J. Nihon Kyobu Shikkan Gakkai Zasshi. 34(1):24-9. Jan 1996. PMID: 8717287
Table 3. Information stored in AIDBD on drugs.
Mechanism of Action
(-)-cetirizine; Sepracor; levocetirizine;
Antiallergic, non-asthma; Antiasthma;
Histamine H1 receptor
levocetirizine dihydrochloride; Xazal; Xusal;
Xyzal; Xyzala; Xyzall
131I-rituximab; anti-CD20 MAb, Genentech;
anti-CD20 MAb, IDEC; anti-CD20 MAb,
Roche; anti-CD20 MAb, Zenyaku; IDEC
C2B8 (SC); IDEC-C2B8 (IV); MabThera;
MabThera (IV); MabThera (SC)
Monoclonal antibody, chimaeric;
Multiple sclerosis treatment; Ophthalmological; Urological
alemtuzumab; alemtuzumab (IP);
alemtuzumab (IV); alemtuzumab (SC);
alentuzumab; alentuzumab (IP); alentuzumab
(IV); alentuzumab (SC); Campath; Campath
Monoclonal antibody, humanized;
(IP); Campath (IV); Campath (SC); Campath-
Multiple sclerosis treatment
1H; Campath-1H (IV); Campath-1H (SC);
LDP-03; LDP-03 (IP
CNTO 148; CNTO-148; CNTO-148
Tumour necrosis factor
(intravenous); CNTO-148 (subcutaneous);
golimumab; golimumab (intravenous);
golimumab (subcutaneous); Simponi; Simponi inflammatory/bowel disorders;
(intravenous); Simponi (subcutaneous)
Monoclonal antibody, human;
Emlucast; Kipres; L-706631; Lukair; Lukasm;
Antiallergic, non-asthma; Antiasthma
MK-0476; MK-0476 (granules); MK-476;
antagonist; Leucotriene Injectable,
MK0476; montelukast; montelukast
(chewable); montelukast (granules);
montelukast (IV); montelukast sodium; MR-
4524; Romilast; Singulair
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