Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses.

Front Plant Sci. 2016;7:1138

Authors: de Oliveira Dal'Molin CG, Orellana C, Gebbie L, Steen J, Hodson MP, Chrysanthopoulos P, Plan MR, McQualter R, Palfreyman RW, Nielsen LK

Abstract
The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated that this systems approach is powerful enough to complement the functional metabolic annotation of bioenergy grasses.

PMID: 27559337 [PubMed]

Metagenomic applications in environmental monitoring and bioremediation.

J Ind Microbiol Biotechnol. 2016 Aug 24;

Authors: Techtmann SM, Hazen TC

Abstract
With the rapid advances in sequencing technology, the cost of sequencing has dramatically dropped and the scale of sequencing projects has increased accordingly. This has provided the opportunity for the routine use of sequencing techniques in the monitoring of environmental microbes. While metagenomic applications have been routinely applied to better understand the ecology and diversity of microbes, their use in environmental monitoring and bioremediation is increasingly common. In this review we seek to provide an overview of some of the metagenomic techniques used in environmental systems biology, addressing their application and limitation. We will also provide several recent examples of the application of metagenomics to bioremediation. We discuss examples where microbial communities have been used to predict the presence and extent of contamination, examples of how metagenomics can be used to characterize the process of natural attenuation by unculturable microbes, as well as examples detailing the use of metagenomics to understand the impact of biostimulation on microbial communities.

PMID: 27558781 [PubMed - as supplied by publisher]

B chromosomes: from cytogenetics to systems biology.

Chromosoma. 2016 Aug 24;

Authors: Valente GT, Nakajima RT, Fantinatti BE, Marques DF, Almeida RO, Simões RP, Martins C

Abstract
Though hundreds to thousands of reports have described the distribution of B chromosomes among diverse eukaryote groups, a comprehensive theory of their biological role has not yet clearly emerged. B chromosomes are classically understood as a sea of repetitive DNA sequences that are poor in genes and are maintained by a parasitic-drive mechanism during cell division. Recent developments in high-throughput DNA/RNA analyses have increased the resolution of B chromosome biology beyond those of classical and molecular cytogenetic methods; B chromosomes contain many transcriptionally active sequences, including genes, and can modulate the activity of autosomal genes. Furthermore, the most recent knowledge obtained from omics analyses, which is associated with a systemic view, has demonstrated that B chromosomes can influence cell biology in a complex way, possibly favoring their own maintenance and perpetuation.

PMID: 27558128 [PubMed - as supplied by publisher]

Quantitative Analysis of Protein-DNA Interaction by qDPI-ELISA.

Methods Mol Biol. 2016;1482:49-66

Authors: Fischer SM, Böser A, Hirsch JP, Wanke D

Abstract
The specific binding of DNA-binding proteins to their cognate DNA motifs is a crucial step for gene expression control and chromatin organization in vivo. The development of methods for the identification of in vivo binding regions by, e.g. chromatin immunoprecipitation (ChIP) or DNA adenine methyltransferase identification (Dam-ID) added an additional level of qualitative information for data mining in systems biology or applications in synthetic biology. In this respect, the in vivo techniques outpaced methods for thorough characterization of protein-DNA interaction and, especially, of the binding motifs at single base-pair resolution. The elucidation of DNA-binding capacities of proteins is frequently done with methods such as yeast one-hybrid, electrophoretic mobility shift assay (EMSA) or systematic evolution of ligands by exponential enrichment (SELEX) that provide only qualitative binding information and are not suited for automation or high-throughput screening of several DNA motifs. Here, we describe the quantitative DNA-protein-Interaction-ELISA (qDPI-ELISA) protocol, which makes use of fluorescent fusion proteins and, hence, is faster and easier to handle than the classical DPI-ELISA. Although every DPI-ELISA experiment delivers quantitative information, the qDPI-ELISA has an increased consistency, as it does not depend on immunological detection. We demonstrate the high comparability between probes and different protein extracts in qDPI-ELISA experiments.

PMID: 27557760 [PubMed - in process]

Multi-breed and multi-trait co-association analysis of meat tenderness and other meat quality traits in three French beef cattle breeds.

Genet Sel Evol. 2016;48:37

Authors: Ramayo-Caldas Y, Renand G, Ballester M, Saintilan R, Rocha D

Abstract
BACKGROUND: Studies to identify markers associated with beef tenderness have focused on Warner-Bratzler shear force (WBSF) but the interplay between the genes associated with WBSF has not been explored. We used the association weight matrix (AWM), a systems biology approach, to identify a set of interacting genes that are co-associated with tenderness and other meat quality traits, and shared across the Charolaise, Limousine and Blonde d'Aquitaine beef cattle breeds.
RESULTS: Genome-wide association studies were performed using ~500K single nucleotide polymorphisms (SNPs) and 17 phenotypes measured on more than 1000 animals for each breed. First, this multi-trait approach was applied separately for each breed across 17 phenotypes and second, between- and across-breed comparisons at the AWM and functional levels were performed. Genetic heterogeneity was observed, and most of the variants that were associated with WBSF segregated within rather than across breeds. We identified 206 common candidate genes associated with WBSF across the three breeds. SNPs in these common genes explained between 28 and 30 % of the phenotypic variance for WBSF. A reduced number of common SNPs mapping to the 206 common genes were identified, suggesting that different mutations may target the same genes in a breed-specific manner. Therefore, it is likely that, depending on allele frequencies and linkage disequilibrium patterns, a SNP that is identified for one breed may not be informative for another unrelated breed. Well-known candidate genes affecting beef tenderness were identified. In addition, some of the 206 common genes are located within previously reported quantitative trait loci for WBSF in several cattle breeds. Moreover, the multi-breed co-association analysis detected new candidate genes, regulators and metabolic pathways that are likely involved in the determination of meat tenderness and other meat quality traits in beef cattle.
CONCLUSIONS: Our results suggest that systems biology approaches that explore associations of correlated traits increase statistical power to identify candidate genes beyond the one-dimensional approach. Further studies on the 206 common genes, their pathways, regulators and interactions will expand our knowledge on the molecular basis of meat tenderness and could lead to the discovery of functional mutations useful for genomic selection in a multi-breed beef cattle context.

PMID: 27107817 [PubMed - indexed for MEDLINE]

pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts.

J Biosci. 2015 Oct;40(4):671-82

Authors: Rani J, Shah AB, Ramachandran S

Abstract
The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, 'Evolving role of diabetes educators', 'Cancer risk assessment' and 'Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.rproject. org/web/packages/pubmed.mineR.

PMID: 26564970 [PubMed - indexed for MEDLINE]

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/08/25

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/08/25

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

A genomics-based systems approach towards drug repositioning for rheumatoid arthritis.

BMC Genomics. 2016;17(Suppl 7):518

Authors: Xu R, Wang Q

Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. RA affects up to 1 % of the population worldwide. Currently, there are no drugs that can cure RA or achieve sustained remission. The unknown cause of the disease represents a significant challenge in the drug development. In this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between RA and other genetic diseases as well as a large amount of higher-level drug treatment data. We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). We developed a network-based ranking algorithm to prioritize diseases genetically-related to RA (RA-related diseases). We then developed a drug prioritization algorithm to reposition drugs from RA-related diseases to treat RA.
RESULTS: Our algorithm found 74 of the 80 FDA-approved RA drugs and ranked them highly (recall: 0.925, median ranking: 8.93 %), demonstrating the validity of our strategy. When compared to a study that used GWAS data to directly connect RA-associated genes to drug targets ("direct genetics-based" approach), our algorithm ("indirect genetics-based") achieved a comparable overall performance, but complementary precision and recall in retrospective validation (precision: 0.22, recall: 0.36; F1: 0.27 vs. precision: 0.74, recall: 0.16; F1: 0.28). Our approach performed significantly better in novel predictions when evaluated using 165 not-yet-FDA-approved RA drugs (precision: 0.46, recall: 0.50; F1: 0.47 vs. precision: 0.40, recall: 0.006; F1: 0.01).
CONCLUSIONS: In summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating RA.

PMID: 27557330 [PubMed - as supplied by publisher]

Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse phenotypes.

BMC Genomics. 2016;17(Suppl 7):516

Authors: Chen Y, Gao Z, Wang B, Xu R

Abstract
BACKGROUND: Glioblastoma (GBM) is the most common and aggressive brain tumors. It has poor prognosis even with optimal radio- and chemo-therapies. Since GBM is highly heterogeneous, drugs that target on specific molecular profiles of individual tumors may achieve maximized efficacy. Currently, the Cancer Genome Atlas (TCGA) projects have identified hundreds of GBM-associated genes. We develop a drug repositioning approach combining disease genomics and mouse phenotype data towards predicting targeted therapies for GBM.
METHODS: We first identified disease specific mouse phenotypes using the most recently discovered GBM genes. Then we systematically searched all FDA-approved drugs for candidates that share similar mouse phenotype profiles with GBM. We evaluated the ranks for approved and novel GBM drugs, and compared with an existing approach, which also use the mouse phenotype data but not the disease genomics data.
RESULTS: We achieved significantly higher ranks for the approved and novel GBM drugs than the earlier approach. For all positive examples of GBM drugs, we achieved a median rank of 9.2 45.6 of the top predictions have been demonstrated effective in inhibiting the growth of human GBM cells.
CONCLUSION: We developed a computational drug repositioning approach based on both genomic and phenotypic data. Our approach prioritized existing GBM drugs and outperformed a recent approach. Overall, our approach shows potential in discovering new targeted therapies for GBM.

PMID: 27557118 [PubMed - as supplied by publisher]

EDITORIAL: Repurposing Niacin as Antiplatelet Drug?

Curr Clin Pharmacol. 2016;11(1):2-3

Authors: Mangoni AA

PMID: 26860693 [PubMed - indexed for MEDLINE]

Oral treatments of Echinococcus multilocularis-infected mice with the antimalarial drug mefloquine that potentially interacts with parasite ferritin and cystatin.

Int J Antimicrob Agents. 2015 Nov;46(5):546-51

Authors: Küster T, Stadelmann B, Rufener R, Risch C, Müller J, Hemphill A

Abstract
This study investigated the effects of oral treatments of Echinococcus multilocularis-infected mice with the antimalarial drug mefloquine (MEF) and identified proteins that bind to MEF in parasite extracts and human cells by affinity chromatography. In a pilot experiment, MEF treatment was applied 5 days per week and was intensified by increasing the dosage stepwise from 12.5 mg/kg to 200 mg/kg during 4 weeks followed by treatments of 100 mg/kg during the last 7 weeks. This resulted in a highly significant reduction of parasite weight in MEF-treated mice compared with mock-treated mice, but the reduction was significantly less efficacious compared with the standard treatment regimen of albendazole (ABZ). In a second experiment, MEF was applied orally in three different treatment groups at dosages of 25, 50 or 100 mg/kg, but only twice a week, for a period of 12 weeks. Treatment at 100 mg/kg had a profound impact on the parasite, similar to ABZ treatment at 200 mg/kg/day (5 days/week for 12 weeks). No adverse side effects were noted. To identify proteins in E. multilocularis metacestodes that physically interact with MEF, affinity chromatography of metacestode extracts was performed on MEF coupled to epoxy-activated Sepharose(®), followed by SDS-PAGE and in-gel digestion LC-MS/MS. This resulted in the identification of E. multilocularis ferritin and cystatin as MEF-binding proteins. In contrast, when human cells were exposed to MEF affinity chromatography, nicotinamide phosphoribosyltransferase was identified as a MEF-binding protein. This indicates that MEF could potentially interact with different proteins in parasites and human cells.

PMID: 26395219 [PubMed - indexed for MEDLINE]

"Death is my Heir"--Ferroptosis Connects Cancer Pharmacogenomics and Ischemia-Reperfusion Injury.

Cell Chem Biol. 2016 Feb 18;23(2):202-3

Authors: Tonnus W, Linkermann A

Abstract
Although they are key to precision medicine, pharmacokinetics and pharmacogenomics are currently plagued with inconsistent results. In this issue of Cell Chemical Biology, Shimada et al. (2016) use cell line selectivity and appropriate filters to improve the consistency and to identify biomarkers for the selectivity of lethal compounds. These insights may be useful for our understanding of how necrosis and ischemic injury are regulated.

PMID: 26971867 [PubMed - indexed for MEDLINE]

Vincristine pharmacogenomics: 'winner's curse' or a different phenotype?

Pharmacogenet Genomics. 2016 Feb;26(2):51-2

Authors: Diouf B, Crews KR, Evans WE

PMID: 26704670 [PubMed - indexed for MEDLINE]

A genomics-based systems approach towards drug repositioning for rheumatoid arthritis.

BMC Genomics. 2016;17(Suppl 7):518

Authors: Xu R, Wang Q

Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. RA affects up to 1 % of the population worldwide. Currently, there are no drugs that can cure RA or achieve sustained remission. The unknown cause of the disease represents a significant challenge in the drug development. In this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between RA and other genetic diseases as well as a large amount of higher-level drug treatment data. We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). We developed a network-based ranking algorithm to prioritize diseases genetically-related to RA (RA-related diseases). We then developed a drug prioritization algorithm to reposition drugs from RA-related diseases to treat RA.
RESULTS: Our algorithm found 74 of the 80 FDA-approved RA drugs and ranked them highly (recall: 0.925, median ranking: 8.93 %), demonstrating the validity of our strategy. When compared to a study that used GWAS data to directly connect RA-associated genes to drug targets ("direct genetics-based" approach), our algorithm ("indirect genetics-based") achieved a comparable overall performance, but complementary precision and recall in retrospective validation (precision: 0.22, recall: 0.36; F1: 0.27 vs. precision: 0.74, recall: 0.16; F1: 0.28). Our approach performed significantly better in novel predictions when evaluated using 165 not-yet-FDA-approved RA drugs (precision: 0.46, recall: 0.50; F1: 0.47 vs. precision: 0.40, recall: 0.006; F1: 0.01).
CONCLUSIONS: In summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating RA.

PMID: 27557330 [PubMed - as supplied by publisher]

Systems Biology and Clinical Practice in Respiratory Medicine: The Twain Shall Meet.

Am J Respir Crit Care Med. 2016 Aug 24;

Authors: Thamrin C, Frey U, Kaminsky DA, Reddel HK, Seely AJ, Suki B, Sterk PJ

Abstract
Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective article, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, COPD, and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations, as well as predict adverse outcomes in critical care. We highlight new methods which are emerging with this approach, and discuss remaining questions which merit greater attention in the field.

PMID: 27556336 [PubMed - as supplied by publisher]

Intelligent biology and medicine in 2015: advancing interdisciplinary education, collaboration, and data science.

BMC Genomics. 2016;17(Suppl 7):524

Authors: Huang K, Liu Y, Huang Y, Li L, Cooper L, Ruan J, Zhao Z

Abstract
We summarize the 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) and the editorial report of the supplement to BMC Genomics. The supplement includes 20 research articles selected from the manuscripts submitted to ICIBM 2015. The conference was held on November 13-15, 2015 at Indianapolis, Indiana, USA. It included eight scientific sessions, three tutorials, four keynote presentations, three highlight talks, and a poster session that covered current research in bioinformatics, systems biology, computational biology, biotechnologies, and computational medicine.

PMID: 27556295 [PubMed - as supplied by publisher]

Systems Biology, Neuroimaging, Neuropsychology, Neuroconnectivity and Traumatic Brain Injury.

Front Syst Neurosci. 2016;10:55

Authors: Bigler ED

Abstract
The patient who sustains a traumatic brain injury (TBI) typically undergoes neuroimaging studies, usually in the form of computed tomography (CT) and magnetic resonance imaging (MRI). In most cases the neuroimaging findings are clinically assessed with descriptive statements that provide qualitative information about the presence/absence of visually identifiable abnormalities; though little if any of the potential information in a scan is analyzed in any quantitative manner, except in research settings. Fortunately, major advances have been made, especially during the last decade, in regards to image quantification techniques, especially those that involve automated image analysis methods. This review argues that a systems biology approach to understanding quantitative neuroimaging findings in TBI provides an appropriate framework for better utilizing the information derived from quantitative neuroimaging and its relation with neuropsychological outcome. Different image analysis methods are reviewed in an attempt to integrate quantitative neuroimaging methods with neuropsychological outcome measures and to illustrate how different neuroimaging techniques tap different aspects of TBI-related neuropathology. Likewise, how different neuropathologies may relate to neuropsychological outcome is explored by examining how damage influences brain connectivity and neural networks. Emphasis is placed on the dynamic changes that occur following TBI and how best to capture those pathologies via different neuroimaging methods. However, traditional clinical neuropsychological techniques are not well suited for interpretation based on contemporary and advanced neuroimaging methods and network analyses. Significant improvements need to be made in the cognitive and behavioral assessment of the brain injured individual to better interface with advances in neuroimaging-based network analyses. By viewing both neuroimaging and neuropsychological processes within a systems biology perspective could represent a significant advancement for the field.

PMID: 27555810 [PubMed]

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).

Database (Oxford). 2016;2016

Authors: Fluck J, Madan S, Ansari S, Kodamullil AT, Karki R, Rastegar-Mojarad M, Catlett NL, Hayes W, Szostak J, Hoeng J, Peitsch M

Abstract
Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types 'increases' and 'decreases'. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task.Database URL: http://wiki.openbel.org/display/BIOC/Datasets.

PMID: 27554092 [PubMed - in process]

Proteomic definitions of basement membrane composition in health and disease.

Matrix Biol. 2016 Aug 20;

Authors: Randles M, Humphries MJ, Lennon R

Abstract
Basement membranes are formed from condensed networks of extracellular matrix (ECM) proteins. These structures underlie all epithelial, mesothelial and endothelial sheets and provide an essential structural scaffold. Candidate-based investigations have established that predominant components of basement membranes are laminins, collagen type IV, nidogens and heparan sulphate proteoglycans. More recently, global proteomic approaches have been applied to investigate ECM and these analyses confirm tissue-specific ECM proteomes with a high degree of complexity. The proteomes consist of structural as well as regulatory ECM proteins such as proteases and growth factors. This review is focused on the proteomic analysis of basement membranes and illustrates how this approach can be used to build our understanding of ECM regulation in health and disease.

PMID: 27553508 [PubMed - as supplied by publisher]