Gene co-expression networks shed light into diseases of brain iron accumulation

Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention

Critical role for iron accumulation in the pathogenesis of fibrotic lung disease

Increased iron levels and dysregulated iron homeostasis, or both, occur in several lung diseases. Here, the effects of iron accumulation on the pathogenesis of pulmonary fibrosis and associated lung function decline was investigated using a combination of murine models of iron overload and bleomycin-induced pulmonary fibrosis, primary human lung fibroblasts treated with iron, and histological samples from patients with or without idiopathic pulmonary fibrosis (IPF). Iron levels are significantly increased in iron overloaded transferrin receptor 2 (Tfr2) mutant mice and homeostatic iron regulator (Hfe) gene–deficient mice and this is associated with increases in airway fibrosis and reduced lung function. Furthermore, fibrosis and lung function decline are associated with pulmonary iron accumulation in bleomycin-induced pulmonary fibrosis. In addition, we show that iron accumulation is increased in lung sections from patients with IPF and that human lung fibroblasts show greater proliferation and cytokine and extracellular matrix responses when exposed to increased iron levels. Significantly, we show that intranasal treatment with the iron chelator, deferoxamine (DFO), from the time when pulmonary iron levels accumulate, prevents airway fibrosis and decline in lung function in experimental pulmonary fibrosis. Pulmonary fibrosis is associated with an increase in Tfr1+ macrophages that display altered phenotype in disease, and DFO treatment modified the abundance of these cells. These experimental and clinical data demonstrate that increased accumulation of pulmonary iron plays a key role in the pathogenesis of pulmonary fibrosis and lung function decline. Furthermore, these data highlight the potential for the therapeutic targeting of increased pulmonary iron in the treatment of fibrotic lung diseases such as IPF

Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset

Background: Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD. Methods and Findings: We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3×10−13). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering “meta-features,” representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%. Conclusions: Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages

Proceedings of the 13th annual conference of INEBRIA

CITATION: Watson, R., et al. 2016. Proceedings of the 13th annual conference of INEBRIA. Addiction Science & Clinical Practice, 11:13, doi:10.1186/s13722-016-0062-9.The original publication is available at https://ascpjournal.biomedcentral.comENGLISH SUMMARY : Meeting abstracts.https://ascpjournal.biomedcentral.com/articles/10.1186/s13722-016-0062-9Publisher's versio

Genome-wide microarray analysis of brain gene expression in mice on a short-term high iron diet

The effects of systemic iron overload on the brain are unclear. Microarray analysis of brain gene expression in mice following short-term iron supplementation revealed altered expression of 287 genes, although most changes were small. Transcripts for the iron storage protein ferritin light chain increased 20% (p = 0.002) and transcripts for iron regulatory protein 1, which negatively regulates ferritin translation, decreased 28% (p = 0.048). There were expression changes for genes involved in important brain functions such as neurotransmission and nitric oxide signaling, which is dependent on iron. Few changes related to reactive oxygen species, inflammation or apoptosis, however expression changes were observed for genes causatively linked to neurological disorders, including Charcot-Marie-Tooth disease, neuronal ceroid lipofuscinosis and mucolipidosis. The latter involve intralysosomal lipofuscin build-up that may reflect lysosomal iron accumulation. The findings suggest that high iron intake may cause subtle brain effects of clinical relevance in some circumstances

Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset

Background Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD. Methods and Findings We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3×10−13). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering “meta-features,” representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%. Conclusions Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages

Multivariate protein signatures of pre-clinical Alzheimer's disease in the Alzheimer's disease neuroimaging initiative (ADNI) plasma proteome dataset.

BackgroundRecent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD.Methods and findingsWe focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3 × 10(-13)). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering "meta-features," representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%.ConclusionsApplying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages

Validation of proposed signatures on data collected at 12 month follow-up.

a<p>The full set of samples contained data on 50 Controls and 92 MCI Progressors.</p>b<p>The size-matched groups contained data on 50 Controls and 50 MCI Progressors.</p>c<p>The full set of samples contained data on 50 Controls and 97 AD patients.</p>d<p>The size-matched groups contained data on 50 Controls and 50 AD patients.</p