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Article: The Human Extracellular Matrix Diseasome Reveals Genotype-Phenotype Associations with Clinical Implications for Age-Related Diseases.

Statzer, Cyril / Luthria, Karan / Sharma, Arastu / Kann, Maricel G / Ewald, Collin Y

2023 Volume 11, Issue 4

Abstract: The extracellular matrix (ECM) is earning an increasingly relevant role in many disease states and aging. The analysis of these disease states is possible with the GWAS and PheWAS methodologies, and through our analysis, we aimed to explore the ... ...

Abstract	The extracellular matrix (ECM) is earning an increasingly relevant role in many disease states and aging. The analysis of these disease states is possible with the GWAS and PheWAS methodologies, and through our analysis, we aimed to explore the relationships between polymorphisms in the compendium of ECM genes (i.e., matrisome genes) in various disease states. A significant contribution on the part of ECM polymorphisms is evident in various types of disease, particularly those in the core-matrisome genes. Our results confirm previous links to connective-tissue disorders but also unearth new and underexplored relationships with neurological, psychiatric, and age-related disease states. Through our analysis of the drug indications for gene-disease relationships, we identify numerous targets that may be repurposed for age-related pathologies. The identification of ECM polymorphisms and their contributions to disease will play an integral role in future therapeutic developments, drug repurposing, precision medicine, and personalized care.
Language	English
Publishing date	2023-04-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines11041212
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Retraction notice to <Recruitment of Tiam1 to Semaphorin 4D activates Rac and enhances proliferation, invasion and metastasis in oral squamous cell carcinoma> <[Neoplasia 19 (2016) 65-74]>.

Zhou, Hua / Kann, Maricel G / Mallory, Emily K / Yang, Ying-Hua / Bugshan, Amr / Binmadi, Nada O / Basile, John R

Neoplasia (New York, N.Y.)

2021 Volume 23, Issue 8, Page(s) 835

Language	English
Publishing date	2021-07-19
Publishing country	United States
Document type	Retraction of Publication
ZDB-ID	1483840-0
ISSN	1476-5586 ; 1522-8002
ISSN (online)	1476-5586
ISSN	1522-8002
DOI	10.1016/j.neo.2021.07.003
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Article ; Online: Pathway networks generated from human disease phenome.

Cirincione, Ann G / Clark, Kaylyn L / Kann, Maricel G

BMC medical genomics

2018 Volume 11, Issue Suppl 3, Page(s) 75

Abstract: Background: Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to ...

Abstract	Background: Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases. Methods: We used a compilation of over 80,000 human genetic variants with known disease associations from databases including the Online Mendelian Inheritance in Man (OMIM), Clinical Variance database (ClinVar), Universal Protein Resource (UniProt), and Human Gene Mutation Database (HGMD). Furthermore, we used the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies, mapping 87% of unique genetic variants to phenotypic disorder concepts. Lastly, variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results: By linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. When looking at broader disease categories, our network analysis showed that large complex diseases, such as cancers, are highly linked by their common pathways. In addition, we found Cardiovascular Diseases and Skin and Connective Tissue Diseases to have the highest number of common pathways, among 35 significant main disease category (MeSH) pairings. Conclusions: This study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. For instance, we found that mutations in different genes associated to Noonan Syndrome and Essential Hypertension share a common pathway. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions.
MeSH term(s)	Computational Biology/methods ; Databases, Genetic ; Disease/genetics ; Gene Regulatory Networks ; Genome, Human ; Humans ; Metabolic Networks and Pathways ; Mutation ; Phenotype ; Protein Interaction Maps ; Software ; Unified Medical Language System
Language	English
Publishing date	2018-09-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	1755-8794
ISSN (online)	1755-8794
DOI	10.1186/s12920-018-0386-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Human Extracellular Matrix Diseasome Reveals Genotype–Phenotype Associations with Clinical Implications for Age-Related Diseases

Statzer, Cyril / Luthria, Karan / Sharma, Arastu / Kann, Maricel G. / Ewald, Collin / id_orcid:0 000-0003-1166-4171

Biomedicines, 11 (4)

2023

Abstract	The extracellular matrix (ECM) is earning an increasingly relevant role in many disease states and aging. The analysis of these disease states is possible with the GWAS and PheWAS methodologies, and through our analysis, we aimed to explore the relationships between polymorphisms in the compendium of ECM genes (i.e., matrisome genes) in various disease states. A significant contribution on the part of ECM polymorphisms is evident in various types of disease, particularly those in the core-matrisome genes. Our results confirm previous links to connective-tissue disorders but also unearth new and underexplored relationships with neurological, psychiatric, and age-related disease states. Through our analysis of the drug indications for gene–disease relationships, we identify numerous targets that may be repurposed for age-related pathologies. The identification of ECM polymorphisms and their contributions to disease will play an integral role in future therapeutic developments, drug repurposing, precision medicine, and personalized care. ISSN:2227-9059
Keywords	phenome ; matrisome ; matreotype ; phenotype ; extracellular matrix ; data mining ; SNP ; PheWAS ; GWAS ; electronic health records ; drug repurposing ; precision medicine ; collagen ; human
Subject code	610
Language	English
Publisher	MDPI
Publishing country	ch
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Advances in translational bioinformatics: computational approaches for the hunting of disease genes.

Kann, Maricel G

Briefings in bioinformatics

2009 Volume 11, Issue 1, Page(s) 96–110

Abstract: Over a 100 years ago, William Bateson provided, through his observations of the transmission of alkaptonuria in first cousin offspring, evidence of the application of Mendelian genetics to certain human traits and diseases. His work was corroborated by ... ...

Abstract	Over a 100 years ago, William Bateson provided, through his observations of the transmission of alkaptonuria in first cousin offspring, evidence of the application of Mendelian genetics to certain human traits and diseases. His work was corroborated by Archibald Garrod (Archibald AE. The incidence of alkaptonuria: a study in chemical individuality. Lancert 1902;ii:1616-20) and William Farabee (Farabee WC. Inheritance of digital malformations in man. In: Papers of the Peabody Museum of American Archaeology and Ethnology. Cambridge, Mass: Harvard University, 1905; 65-78), who recorded the familial tendencies of inheritance of malformations of human hands and feet. These were the pioneers of the hunt for disease genes that would continue through the century and result in the discovery of hundreds of genes that can be associated with different diseases. Despite many ground-breaking discoveries during the last century, we are far from having a complete understanding of the intricate network of molecular processes involved in diseases, and we are still searching for the cures for most complex diseases. In the last few years, new genome sequencing and other high-throughput experimental techniques have generated vast amounts of molecular and clinical data that contain crucial information with the potential of leading to the next major biomedical discoveries. The need to mine, visualize and integrate these data has motivated the development of several informatics approaches that can broadly be grouped in the research area of 'translational bioinformatics'. This review highlights the latest advances in the field of translational bioinformatics, focusing on the advances of computational techniques to search for and classify disease genes.
MeSH term(s)	Computational Biology ; Genetic Diseases, Inborn/genetics ; Genetic Predisposition to Disease ; Humans
Language	English
Publishing date	2009-12-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbp048
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Article ; Online: Workshop during the Pacific Symposium of Biocomputing, Jan 3-7, 2019: Reading between the genes: interpreting non-coding DNA in high-throughput.

Berghout, Joanne / Lussier, Yves A / Vitali, Francesca / Bulyk, Martha L / Kann, Maricel G / Moore, Jason H

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

2019 Volume 24, Page(s) 444–448

Abstract: Identifying functional elements and predicting mechanistic insight from non-coding DNA and noncoding variation remains a challenge. Advances in genome-scale, high-throughput technology, however, have brought these answers closer within reach than ever, ... ...

Abstract	Identifying functional elements and predicting mechanistic insight from non-coding DNA and noncoding variation remains a challenge. Advances in genome-scale, high-throughput technology, however, have brought these answers closer within reach than ever, though there is still a need for new computational approaches to analysis and integration. This workshop aims to explore these resources and new computational methods applied to regulatory elements, chromatin interactions, non-protein-coding genes, and other non-coding DNA.
MeSH term(s)	CRISPR-Cas Systems ; Computational Biology/methods ; DNA/genetics ; Epigenesis, Genetic ; Gene Regulatory Networks ; Genetic Variation ; High-Throughput Nucleotide Sequencing/statistics & numerical data ; Humans ; Mutation ; RNA, Untranslated/genetics ; Regulatory Elements, Transcriptional ; Sequence Analysis, DNA/statistics & numerical data ; Systems Biology
Chemical Substances	RNA, Untranslated ; DNA (9007-49-2)
Language	English
Publishing date	2019-03-12
Publishing country	United States
Document type	Congress
ISSN	2335-6936
ISSN (online)	2335-6936
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Pathway networks generated from human disease phenome

Ann G. Cirincione / Kaylyn L. Clark / Maricel G. Kann

BMC Medical Genomics, Vol 11, Iss S3, Pp 1-

2018 Volume 9

Abstract: Abstract Background Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers ... ...

Abstract	Abstract Background Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases. Methods We used a compilation of over 80,000 human genetic variants with known disease associations from databases including the Online Mendelian Inheritance in Man (OMIM), Clinical Variance database (ClinVar), Universal Protein Resource (UniProt), and Human Gene Mutation Database (HGMD). Furthermore, we used the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies, mapping 87% of unique genetic variants to phenotypic disorder concepts. Lastly, variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results By linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. When looking at broader disease categories, our network analysis showed that large complex diseases, such as cancers, are highly linked by their common pathways. In addition, we found Cardiovascular Diseases and Skin and Connective Tissue Diseases to have the highest number of common pathways, among 35 significant main disease category (MeSH) pairings. Conclusions This study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. For instance, we found that mutations in different genes associated to Noonan Syndrome and Essential Hypertension share a common pathway. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions.
Keywords	Networks ; Phenome ; Disease mutations ; Internal medicine ; RC31-1245 ; Genetics ; QH426-470
Language	English
Publishing date	2018-09-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Protein interactions and disease: computational approaches to uncover the etiology of diseases.

Kann, Maricel G

Briefings in bioinformatics

2007 Volume 8, Issue 5, Page(s) 333–346

Abstract: The genomic era has been characterised by vast amounts of data from diverse sources, creating a need for new tools to extract biologically meaningful information. Bioinformatics is, for the most part, responding to that need. The sparseness of the ... ...

Abstract	The genomic era has been characterised by vast amounts of data from diverse sources, creating a need for new tools to extract biologically meaningful information. Bioinformatics is, for the most part, responding to that need. The sparseness of the genomic data associated with diseases, however, creates a new challenge. Understanding the complex interplay between genes and proteins requires integration of data from a wide variety of sources, i.e. gene expression, genetic linkage, protein interaction, and protein structure among others. Thus, computational tools have become critical for the integration, representation and visualization of heterogeneous biomedical data. Furthermore, several bioinformatics methods have been developed to formulate predictions about the functional role of genes and proteins, including their role in diseases. After an introduction to the complex interplay between proteins and genetic diseases, this review explores recent approaches to the understanding of the mechanisms of disease at the molecular level. Finally, because most known mechanisms leading to disease involve some form of protein interaction, this review focuses on the recent methodologies for understanding diseases through their underlying protein interactions. Recent contributions from genetics, protein structure and protein interaction network analyses to the understanding of diseases are discussed here.
MeSH term(s)	Biomarkers ; Computational Biology/methods ; Genetic Predisposition to Disease/genetics ; Genetic Testing/methods ; Humans ; Protein Interaction Mapping/methods ; Proteins/genetics ; Proteins/metabolism
Chemical Substances	Biomarkers ; Proteins
Language	English
Publishing date	2007-09
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Intramural ; Review
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbm031
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Article ; Online: Chapter 4: Protein interactions and disease.

Gonzalez, Mileidy W / Kann, Maricel G

PLoS computational biology

2012 Volume 8, Issue 12, Page(s) e1002819

Abstract: ... with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal ...

Abstract	Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.
MeSH term(s)	Disease ; Humans ; Mutation ; Protein Binding
Language	English
Publishing date	2012-12-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1002819
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Article ; Online: Putting benchmarks in their rightful place: The heart of computational biology.

Peters, Bjoern / Brenner, Steven E / Wang, Edwin / Slonim, Donna / Kann, Maricel G

PLoS computational biology

2018 Volume 14, Issue 11, Page(s) e1006494

Abstract: Research in computational biology has given rise to a vast number of methods developed to solve scientific problems. For areas in which many approaches exist, researchers have a hard time deciding which tool to select to address a scientific challenge, ... ...

Abstract	Research in computational biology has given rise to a vast number of methods developed to solve scientific problems. For areas in which many approaches exist, researchers have a hard time deciding which tool to select to address a scientific challenge, as essentially all publications introducing a new method will claim better performance than all others. Not all of these claims can be correct. Equally, for this same reason, developers struggle to demonstrate convincingly that they created a new and superior algorithm or implementation. Moreover, the developer community often has difficulty discerning which new approaches constitute true scientific advances for the field. The obvious answer to this conundrum is to develop benchmarks-meaning standard points of reference that facilitate evaluating the performance of different tools-allowing both users and developers to compare multiple tools in an unbiased fashion.
MeSH term(s)	Algorithms ; Area Under Curve ; Computational Biology/methods ; Publications
Language	English
Publishing date	2018-11-08
Publishing country	United States
Document type	Editorial
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1006494
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