Limits...
Resources, challenges and way forward in rare mitochondrial diseases research.

Rajput NK, Singh V, Bhardwaj A - F1000Res (2015)

Bottom Line: Over 300 million people are affected by about 7000 rare diseases globally.Hence, innovative approaches are needed to identify potential solutions.This review focuses on the resources developed over the past years for analysis of genome data towards understanding disease biology especially in the context of mitochondrial diseases, given that mitochondria are central to major cellular pathways and their dysfunction leads to a broad spectrum of diseases.

View Article: PubMed Central - PubMed

Affiliation: Open Source Drug Discovery (OSDD) Unit, Council of Scientific and Industrial Research, New Delhi, 110001, India.

ABSTRACT
Over 300 million people are affected by about 7000 rare diseases globally. There are tremendous resource limitations and challenges in driving research and drug development for rare diseases. Hence, innovative approaches are needed to identify potential solutions. This review focuses on the resources developed over the past years for analysis of genome data towards understanding disease biology especially in the context of mitochondrial diseases, given that mitochondria are central to major cellular pathways and their dysfunction leads to a broad spectrum of diseases. Platforms for collaboration of research groups, clinicians and patients and the advantages of community collaborative efforts in addressing rare diseases are also discussed. The review also describes crowdsourcing and crowdfunding efforts in rare diseases research and how the upcoming initiatives for understanding disease biology including analyses of large number of genomes are also applicable to rare diseases.

No MeSH data available.


Related in: MedlinePlus

(A) This section describes the challenges involved in dissecting the impact of genomic variation in disease association. As shown, mitochondria are involved in a large number of cellular processes including energy metabolism. Genes encoded by mitochondrial and nuclear genomes carry out these functions. The protein subunits of the complexes of the electron transport chain are encoded both by mtDNA (red) and nDNA (green). In order to understand the complex genotype-phenotype correlation it is imperative to identify the molecular interactions at systems level (protein-protein interaction network, metabolic network, signaling network, regulatory network). It is important to curate the information needed to generate these networks from literature and existing resources. (B) The resources currently available for mitochondrial dysfunction include databases, web servers and analysis pipelines as shown. For generating systems level models, these resources may be integrated systematically. (C) There are various ongoing efforts involving researchers, clinicians and or patient groups. It is proposed that a community collaborative open access platform is a must to interface these communities. In order to establish such a platform that allows geographically different communities to work together, globally accepted ontologies in a language independent representation are needed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4490798&req=5

f1: (A) This section describes the challenges involved in dissecting the impact of genomic variation in disease association. As shown, mitochondria are involved in a large number of cellular processes including energy metabolism. Genes encoded by mitochondrial and nuclear genomes carry out these functions. The protein subunits of the complexes of the electron transport chain are encoded both by mtDNA (red) and nDNA (green). In order to understand the complex genotype-phenotype correlation it is imperative to identify the molecular interactions at systems level (protein-protein interaction network, metabolic network, signaling network, regulatory network). It is important to curate the information needed to generate these networks from literature and existing resources. (B) The resources currently available for mitochondrial dysfunction include databases, web servers and analysis pipelines as shown. For generating systems level models, these resources may be integrated systematically. (C) There are various ongoing efforts involving researchers, clinicians and or patient groups. It is proposed that a community collaborative open access platform is a must to interface these communities. In order to establish such a platform that allows geographically different communities to work together, globally accepted ontologies in a language independent representation are needed.

Mentions: There is a need for innovation in technology that allows for interfacing the different stakeholders, namely, researchers, clinicians and patient groups. Such resources will go a long way in future to allow patients to perform diagnostic procedures with greater accuracy and help clinicians identify the best possible route for therapeutic interventions. This is only possible if the participating members use standard terms to share the data. Various ontologies have been developed over years for capturing function of genes like the Gene Ontology37, PAGE-OM38 and VariO39 for capturing features of variation, UMLS which includes MeSH40, RxNORM41 and SNOMED CT42 for capturing clinical details and Human Phenotype Ontology43 for phenotype data, to name a few. These ontologies are meant to function as a common set of vocabulary that needs to be shared on a community collaborative platform. The semantic network of these terms will allow deciphering and interpreting novel patterns in understanding disease biology. Such a semantic-based platform will be amenable for plugging in data and new methods with increasing understanding of disease biology. It will also ensure that the data generated as part of negative results are also shared systematically with scientific community. Orphanet Rare Disease Ontology (ORDO) (http://www.orphadata.org/cgi-bin/inc/ordo_orphanet.inc.php) is an effort to achieve these goals. ORDO offers definitions, classification of rare diseases, gene-disease relations, epidemiological data and connections with other terminologies like MeSH, UMLS (http://www.nlm.nih.gov/research/umls/), OMIM44, UniProtKB45, HGNC46, ensembl47, Reactome48, etc. It is imperative that existing resources utilize these ontologies for data sharing allowing for data interoperability across different platforms.Figure 1 illustrates the scope of the proposed integrative platform describing the scientific challenges involved in establishing genotype-phenotype correlations and how the existing resources and community collaborations may be converged towards a systems level understanding of the disease biology.


Resources, challenges and way forward in rare mitochondrial diseases research.

Rajput NK, Singh V, Bhardwaj A - F1000Res (2015)

(A) This section describes the challenges involved in dissecting the impact of genomic variation in disease association. As shown, mitochondria are involved in a large number of cellular processes including energy metabolism. Genes encoded by mitochondrial and nuclear genomes carry out these functions. The protein subunits of the complexes of the electron transport chain are encoded both by mtDNA (red) and nDNA (green). In order to understand the complex genotype-phenotype correlation it is imperative to identify the molecular interactions at systems level (protein-protein interaction network, metabolic network, signaling network, regulatory network). It is important to curate the information needed to generate these networks from literature and existing resources. (B) The resources currently available for mitochondrial dysfunction include databases, web servers and analysis pipelines as shown. For generating systems level models, these resources may be integrated systematically. (C) There are various ongoing efforts involving researchers, clinicians and or patient groups. It is proposed that a community collaborative open access platform is a must to interface these communities. In order to establish such a platform that allows geographically different communities to work together, globally accepted ontologies in a language independent representation are needed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4490798&req=5

f1: (A) This section describes the challenges involved in dissecting the impact of genomic variation in disease association. As shown, mitochondria are involved in a large number of cellular processes including energy metabolism. Genes encoded by mitochondrial and nuclear genomes carry out these functions. The protein subunits of the complexes of the electron transport chain are encoded both by mtDNA (red) and nDNA (green). In order to understand the complex genotype-phenotype correlation it is imperative to identify the molecular interactions at systems level (protein-protein interaction network, metabolic network, signaling network, regulatory network). It is important to curate the information needed to generate these networks from literature and existing resources. (B) The resources currently available for mitochondrial dysfunction include databases, web servers and analysis pipelines as shown. For generating systems level models, these resources may be integrated systematically. (C) There are various ongoing efforts involving researchers, clinicians and or patient groups. It is proposed that a community collaborative open access platform is a must to interface these communities. In order to establish such a platform that allows geographically different communities to work together, globally accepted ontologies in a language independent representation are needed.
Mentions: There is a need for innovation in technology that allows for interfacing the different stakeholders, namely, researchers, clinicians and patient groups. Such resources will go a long way in future to allow patients to perform diagnostic procedures with greater accuracy and help clinicians identify the best possible route for therapeutic interventions. This is only possible if the participating members use standard terms to share the data. Various ontologies have been developed over years for capturing function of genes like the Gene Ontology37, PAGE-OM38 and VariO39 for capturing features of variation, UMLS which includes MeSH40, RxNORM41 and SNOMED CT42 for capturing clinical details and Human Phenotype Ontology43 for phenotype data, to name a few. These ontologies are meant to function as a common set of vocabulary that needs to be shared on a community collaborative platform. The semantic network of these terms will allow deciphering and interpreting novel patterns in understanding disease biology. Such a semantic-based platform will be amenable for plugging in data and new methods with increasing understanding of disease biology. It will also ensure that the data generated as part of negative results are also shared systematically with scientific community. Orphanet Rare Disease Ontology (ORDO) (http://www.orphadata.org/cgi-bin/inc/ordo_orphanet.inc.php) is an effort to achieve these goals. ORDO offers definitions, classification of rare diseases, gene-disease relations, epidemiological data and connections with other terminologies like MeSH, UMLS (http://www.nlm.nih.gov/research/umls/), OMIM44, UniProtKB45, HGNC46, ensembl47, Reactome48, etc. It is imperative that existing resources utilize these ontologies for data sharing allowing for data interoperability across different platforms.Figure 1 illustrates the scope of the proposed integrative platform describing the scientific challenges involved in establishing genotype-phenotype correlations and how the existing resources and community collaborations may be converged towards a systems level understanding of the disease biology.

Bottom Line: Over 300 million people are affected by about 7000 rare diseases globally.Hence, innovative approaches are needed to identify potential solutions.This review focuses on the resources developed over the past years for analysis of genome data towards understanding disease biology especially in the context of mitochondrial diseases, given that mitochondria are central to major cellular pathways and their dysfunction leads to a broad spectrum of diseases.

View Article: PubMed Central - PubMed

Affiliation: Open Source Drug Discovery (OSDD) Unit, Council of Scientific and Industrial Research, New Delhi, 110001, India.

ABSTRACT
Over 300 million people are affected by about 7000 rare diseases globally. There are tremendous resource limitations and challenges in driving research and drug development for rare diseases. Hence, innovative approaches are needed to identify potential solutions. This review focuses on the resources developed over the past years for analysis of genome data towards understanding disease biology especially in the context of mitochondrial diseases, given that mitochondria are central to major cellular pathways and their dysfunction leads to a broad spectrum of diseases. Platforms for collaboration of research groups, clinicians and patients and the advantages of community collaborative efforts in addressing rare diseases are also discussed. The review also describes crowdsourcing and crowdfunding efforts in rare diseases research and how the upcoming initiatives for understanding disease biology including analyses of large number of genomes are also applicable to rare diseases.

No MeSH data available.


Related in: MedlinePlus