Paola Forabosco

@article{botia_additional_2017,

title = {An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks},

author = {Juan A Bot{í}a and Jana Vandrovcova and Paola Forabosco and Sebastian Guelfi and Karishma D'Sa and United Kingdom Brain Expression Consortium and John Hardy and Cathryn M Lewis and Mina Ryten and Michael E Weale},

doi = {10.1186/s12918-017-0420-6},

issn = {1752-0509},

year  = {2017},

date = {2017-04-01},

journal = {BMC systems biology},

volume = {11},

number = {1},

pages = {47},

abstract = {BACKGROUND: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). 

RESULTS: We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. 

CONCLUSIONS: The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bettencourt_gene_2016,

title = {Gene co-expression networks shed light into diseases of brain iron accumulation},

author = {Concei{ç}{ã}o Bettencourt and Paola Forabosco and Sarah Wiethoff and Moones Heidari and Daniel M Johnstone and Juan A Bot{í}a and Joanna F Collingwood and John Hardy and UK Brain Expression Consortium (UKBEC) and Elizabeth A Milward and Mina Ryten and Henry Houlden},

doi = {10.1016/j.nbd.2015.12.004},

issn = {1095-953X},

year  = {2016},

date = {2016-03-01},

journal = {Neurobiology of Disease},

volume = {87},

pages = {59--68},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mencacci_missense_2015,

title = {A missense mutation in KCTD17 causes autosomal dominant myoclonus-dystonia},

author = {Niccolo E Mencacci and Ignacio Rubio-Agusti and Anselm Zdebik and Friedrich Asmus and Marthe H R Ludtmann and Mina Ryten and Vincent Plagnol and Ann-Kathrin Hauser and Sara Bandres-Ciga and Concei{ç}{ã}o Bettencourt and Paola Forabosco and Deborah Hughes and Marc M P Soutar and Kathryn Peall and Huw R Morris and Daniah Trabzuni and Mehmet Tekman and Horia C Stanescu and Robert Kleta and Miryam Carecchio and Giovanna Zorzi and Nardo Nardocci and Barbara Garavaglia and Ebba Lohmann and Anne Weissbach and Christine Klein and John Hardy and Alan M Pittman and Thomas Foltynie and Andrey Y Abramov and Thomas Gasser and Kailash P Bhatia and Nicholas W Wood},

doi = {10.1016/j.ajhg.2015.04.008},

issn = {1537-6605},

year  = {2015},

date = {2015-06-01},

journal = {American Journal of Human Genetics},

volume = {96},

number = {6},

pages = {938--947},

abstract = {Myoclonus-dystonia (M-D) is a rare movement disorder characterized by a combination of non-epileptic myoclonic jerks and dystonia. SGCE mutations represent a major cause for familial M-D being responsible for 30%-50% of cases. After excluding SGCE mutations, we identified through a combination of linkage analysis and whole-exome sequencing KCTD17 c.434 G>A p.(Arg145His) as the only segregating variant in a dominant British pedigree with seven subjects affected by M-D. A subsequent screening in a cohort of M-D cases without mutations in SGCE revealed the same KCTD17 variant in a German family. The clinical presentation of the KCTD17-mutated cases was distinct from the phenotype usually observed in M-D due to SGCE mutations. All cases initially presented with mild myoclonus affecting the upper limbs. Dystonia showed a progressive course, with increasing severity of symptoms and spreading from the cranio-cervical region to other sites. KCTD17 is abundantly expressed in all brain regions with the highest expression in the putamen. Weighted gene co-expression network analysis, based on mRNA expression profile of brain samples from neuropathologically healthy individuals, showed that KCTD17 is part of a putamen gene network, which is significantly enriched for dystonia genes. Functional annotation of the network showed an over-representation of genes involved in post-synaptic dopaminergic transmission. Functional studies in mutation bearing fibroblasts demonstrated abnormalities in endoplasmic reticulum-dependent calcium signaling. In conclusion, we demonstrate that the KCTD17 c.434 G>A p.(Arg145His) mutation causes autosomal dominant M-D. Further functional studies are warranted to further characterize the nature of KCTD17 contribution to the molecular pathogenesis of M-D.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cruchaga_rare_2014,

title = {Rare coding variants in the phospholipase Đ3 gene confer risk for Alzheimer's disease},

author = {Carlos Cruchaga and Celeste M Karch and Sheng Chih Jin and Bruno A Benitez and Yefei Cai and Rita Guerreiro and Oscar Harari and Joanne Norton and John Budde and Sarah Bertelsen and Amanda T Jeng and Breanna Cooper and Tara Skorupa and David Carrell and Denise Levitch and Simon Hsu and Jiyoon Choi and Mina Ryten and UK Brain Expression Consortium and John Hardy and Mina Ryten and Daniah Trabzuni and Michael E Weale and Adaikalavan Ramasamy and Colin Smith and Celeste Sassi and Jose Bras and Raphael J Gibbs and Dena G Hernandez and Michelle K Lupton and John Powell and Paola Forabosco and Perry G Ridge and Christopher D Corcoran and Joann T Tschanz and Maria C Norton and Ronald G Munger and Cameron Schmutz and Maegan Leary and Yesim F Demirci and Mikhil N Bamne and Xingbin Wang and Oscar L Lopez and Mary Ganguli and Christopher Medway and James Turton and Jenny Lord and Anne Braae and Imelda Barber and Kristelle Brown and Alzheimer's Research UK Consortium and Peter Passmore and David Craig and Janet Johnston and Bernadette McGuinness and Stephen Todd and Reinhard Heun and Heike K{ö}lsch and Patrick G Kehoe and Nigel M Hooper and Emma R L C Vardy and David M Mann and Stuart Pickering-Brown and Kristelle Brown and Noor Kalsheker and James Lowe and Kevin Morgan and A {David Smith} and Gordon Wilcock and Donald Warden and Clive Holmes and Pau Pastor and Oswaldo Lorenzo-Betancor and Zoran Brkanac and Erick Scott and Eric Topol and Kevin Morgan and Ekaterina Rogaeva and Andrew B Singleton and John Hardy and Ilyas M Kamboh and Peter {St George-Hyslop} and Nigel Cairns and John C Morris and John S K Kauwe and Alison M Goate},

doi = {10.1038/nature12825},

issn = {1476-4687},

year  = {2014},

date = {2014-01-01},

journal = {Nature},

volume = {505},

number = {7484},

pages = {550--554},

abstract = {Genome-wide association studies (GWAS) have identified several risk variants for late-onset Alzheimer's disease (LOAD). These common variants have replicable but small effects on LOAD risk and generally do not have obvious functional effects. Low-frequency coding variants, not detected by GWAS, are predicted to include functional variants with larger effects on risk. To identify low-frequency coding variants with large effects on LOAD risk, we carried out whole-exome sequencing (WES) in 14 large LOAD families and follow-up analyses of the candidate variants in several large LOAD case-control data sets. A rare variant in PLD3 (phospholipase D3; Val232Met) segregated with disease status in two independent families and doubled risk for Alzheimer's disease in seven independent case-control series with a total of more than 11,000 cases and controls of European descent. Gene-based burden analyses in 4,387 cases and controls of European descent and 302 African American cases and controls, with complete sequence data for PLD3, reveal that several variants in this gene increase risk for Alzheimer's disease in both populations. PLD3 is highly expressed in brain regions that are vulnerable to Alzheimer's disease pathology, including hippocampus and cortex, and is expressed at significantly lower levels in neurons from Alzheimer's disease brains compared to control brains. Overexpression of PLD3 leads to a significant decrease in intracellular amyloid-β precursor protein (APP) and extracellular Aβ42 and Aβ40 (the 42- and 40-residue isoforms of the amyloid-β peptide), and knockdown of PLD3 leads to a significant increase in extracellular Aβ42 and Aβ40. Together, our genetic and functional data indicate that carriers of PLD3 coding variants have a twofold increased risk for LOAD and that PLD3 influences APP processing. This study provides an example of how densely affected families may help to identify rare variants with large effects on risk for disease or other complex traits.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{forabosco_insights_2013,

title = {Insights into TREM2 biology by network analysis of human brain gene expression data},

author = {Paola Forabosco and Adaikalavan Ramasamy and Daniah Trabzuni and Robert Walker and Colin Smith and Jose Bras and Adam P Levine and John Hardy and Jennifer M Pocock and Rita Guerreiro and Michael E Weale and Mina Ryten},

doi = {10.1016/j.neurobiolaging.2013.05.001},

issn = {1558-1497},

year  = {2013},

date = {2013-12-01},

journal = {Neurobiology of Aging},

volume = {34},

number = {12},

pages = {2699--2714},

abstract = {Rare variants in TREM2 cause susceptibility to late-onset Alzheimer's disease. Here we use microarray-based expression data generated from 101 neuropathologically normal individuals and covering 10 brain regions, including the hippocampus, to understand TREM2 biology in human brain. Using network analysis, we detect a highly preserved TREM2-containing module in human brain, show that it relates to microglia, and demonstrate that TREM2 is a hub gene in 5 brain regions, including the hippocampus, suggesting that it can drive module function. Using enrichment analysis we show significant overrepresentation of genes implicated in the adaptive and innate immune system. Inspection of genes with the highest connectivity to TREM2 suggests that it plays a key role in mediating changes in the microglial cytoskeleton necessary not only for phagocytosis, but also migration. Most importantly, we show that the TREM2-containing module is significantly enriched for genes genetically implicated in Alzheimer's disease, multiple sclerosis, and motor neuron disease, implying that these diseases share common pathways centered on microglia and that among the genes identified are possible new disease-relevant genes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}