Publications

@article{pmid26477495,

title = {Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases},

author = {Jialiang Yang and Tao Huang and Francesca Petralia and Quan Long and Bin Zhang and Carmen Argmann and Yong Zhao and Charles V Mobbs and Eric E Schadt and Jun Zhu and Zhidong Tu and {GTEx Consortium}, {incl. Michael Sammeth}},

doi = {10.1038/srep15145},

issn = {2045-2322},

year  = {2015},

date = {2015-10-01},

urldate = {2015-10-01},

journal = {Sci Rep},

volume = {5},

pages = {15145},

abstract = {Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed, and some essential tissues (e.g., heart and lung) show much stronger "co-aging" than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.},

keywords = {},

pubstate = {published},

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}

@article{pmid26484571,

title = {A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project},

author = {Latarsha J Carithers and Kristin Ardlie and Mary Barcus and Philip A Branton and Angela Britton and Stephen A Buia and Carolyn C Compton and David S DeLuca and Joanne Peter-Demchok and Ellen T Gelfand and Ping Guan and Greg E Korzeniewski and Nicole C Lockhart and Chana A Rabiner and Abhi K Rao and Karna L Robinson and Nancy V Roche and Sherilyn J Sawyer and Ayellet V Segrè and Charles E Shive and Anna M Smith and Leslie H Sobin and Anita H Undale and Kimberly M Valentino and Jim Vaught and Taylor R Young and Helen M Moore and {GTEx Consortium}, {incl. Michael Sammeth}},

doi = {10.1089/bio.2015.0032},

issn = {1947-5543},

year  = {2015},

date = {2015-10-01},

urldate = {2015-10-01},

journal = {Biopreserv Biobank},

volume = {13},

number = {5},

pages = {311--319},

abstract = {The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25954002,

title = {Human genomics. The human transcriptome across tissues and individuals},

author = {Marta Melé and Pedro G Ferreira and Ferran Reverter and David S DeLuca and Jean Monlong and Michael Sammeth and Taylor R Young and Jakob M Goldmann and Dmitri D Pervouchine and Timothy J Sullivan and Rory Johnson and Ayellet V Segrè and Sarah Djebali and Anastasia Niarchou and {GTEx Consortium}, and Fred A Wright and Tuuli Lappalainen and Miquel Calvo and Gad Getz and Emmanouil T Dermitzakis and Kristin G Ardlie and Roderic Guigó},

doi = {10.1126/science.aaa0355},

issn = {1095-9203},

year  = {2015},

date = {2015-05-01},

urldate = {2015-05-01},

journal = {Science},

volume = {348},

number = {6235},

pages = {660--665},

abstract = {Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25954003,

title = {Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome},

author = {Manuel A Rivas and Matti Pirinen and Donald F Conrad and Monkol Lek and Emily K Tsang and Konrad J Karczewski and Julian B Maller and Kimberly R Kukurba and David S DeLuca and Menachem Fromer and Pedro G Ferreira and Kevin S Smith and Rui Zhang and Fengmei Zhao and Eric Banks and Ryan Poplin and Douglas M Ruderfer and Shaun M Purcell and Taru Tukiainen and Eric V Minikel and Peter D Stenson and David N Cooper and Katharine H Huang and Timothy J Sullivan and Jared Nedzel and {GTEx Consortium}, and Carlos D Bustamante and Jin Billy Li and Mark J Daly and Roderic Guigo and Peter Donnelly and Kristin Ardlie and Michael Sammeth and Emmanouil T Dermitzakis and Mark I McCarthy and Stephen B Montgomery and Tuuli Lappalainen and Daniel G MacArthur},

doi = {10.1126/science.1261877},

issn = {1095-9203},

year  = {2015},

date = {2015-05-01},

urldate = {2015-05-01},

journal = {Science},

volume = {348},

number = {6235},

pages = {666--669},

abstract = {Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25954001,

title = {Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans},

author = {{GTEx Consortium}, {incl. Michael Sammeth}},

doi = {10.1126/science.1262110},

issn = {1095-9203},

year  = {2015},

date = {2015-05-01},

urldate = {2015-05-01},

journal = {Science},

volume = {348},

number = {6235},

pages = {648--660},

abstract = {Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25970446,

title = {Sharing and Specificity of Co-expression Networks across 35 Human Tissues},

author = {Emma Pierson and {GTEx Consortium}, {incl. Michael Sammeth} and Daphne Koller and Alexis Battle and Sara Mostafavi and Kristin G Ardlie and Gad Getz and Fred A Wright and Manolis Kellis and Simona Volpi and Emmanouil T Dermitzakis},

doi = {10.1371/journal.pcbi.1004220},

issn = {1553-7358},

year  = {2015},

date = {2015-05-01},

urldate = {2015-05-01},

journal = {PLoS Comput Biol},

volume = {11},

number = {5},

pages = {e1004220},

abstract = {To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25577392,

title = {Analysis of alternative splicing events in custom gene datasets by AStalavista},

author = {Sylvain Foissac and Michael Sammeth},

doi = {10.1007/978-1-4939-2291-8_24},

issn = {1940-6029},

year  = {2015},

date = {2015-01-01},

journal = {Methods Mol Biol},

volume = {1269},

pages = {379--392},

abstract = {Alternative splicing (AS) is a eukaryotic principle to derive more than one RNA product from transcribed genes by removing distinct subsets of introns from a premature polymer. We know today that this process is highly regulated and makes up a large part of the differences between species, cell types, and states. The key to compare AS across different genes or organisms is to tokenize the AS phenomenon into atomary units, so-called AS events. These events then usually are grouped by common patterns to investigate the underlying molecular mechanisms that drive their regulation. However, attempts to decompose loci with AS observations into events are often hampered by applying a limited set of a priori defined event patterns which are not capable to describe all AS configurations and therefore cannot decompose the phenomenon exhaustively. In this chapter, we describe working scenarios of AStalavista, a computational method that reports all AS events reflected by transcript annotations. We show how to practically employ AStalavista to study AS variation in complex transcriptomes, as characterized by the human GENCODE annotation. Our examples demonstrate how the inherent and universal AStalavista paradigm allows for an automatic delineation of AS events in custom gene datasets. Additionally, we sketch an example of an AStalavista use case including next-generation sequencing data (RNA-Seq) to enrich the landscape of discovered AS events.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25249621,

title = {Nova1 is a master regulator of alternative splicing in pancreatic beta cells},

author = {Olatz Villate and Jean-Valery Turatsinze and Loriana G Mascali and Fabio A Grieco and Tatiane C Nogueira and Daniel A Cunha and Tarlliza R Nardelli and Michael Sammeth and Vishal A Salunkhe and Jonathan L S Esguerra and Lena Eliasson and Lorella Marselli and Piero Marchetti and Decio L Eizirik},

doi = {10.1093/nar/gku861},

issn = {1362-4962},

year  = {2014},

date = {2014-10-01},

journal = {Nucleic Acids Res},

volume = {42},

number = {18},

pages = {11818--11830},

abstract = {Alternative splicing (AS) is a fundamental mechanism for the regulation of gene expression. It affects more than 90% of human genes but its role in the regulation of pancreatic beta cells, the producers of insulin, remains unknown. Our recently published data indicated that the 'neuron-specific' Nova1 splicing factor is expressed in pancreatic beta cells. We have presently coupled specific knockdown (KD) of Nova1 with RNA-sequencing to determine all splice variants and downstream pathways regulated by this protein in beta cells. Nova1 KD altered the splicing of nearly 5000 transcripts. Pathway analysis indicated that these genes are involved in exocytosis, apoptosis, insulin receptor signaling, splicing and transcription. In line with these findings, Nova1 silencing inhibited insulin secretion and induced apoptosis basally and after cytokine treatment in rodent and human beta cells. These observations identify a novel layer of regulation of beta cell function, namely AS controlled by key splicing regulators such as Nova1.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid25121733,

title = {Gene expansion shapes genome architecture in the human pathogen Lichtheimia corymbifera: an evolutionary genomics analysis in the ancient terrestrial mucorales (Mucoromycotina)},

author = {Volker U Schwartze and Sascha Winter and Ekaterina Shelest and Marina Marcet-Houben and Fabian Horn and Stefanie Wehner and Jörg Linde and Vito Valiante and Michael Sammeth and Konstantin Riege and Minou Nowrousian and Kerstin Kaerger and Ilse D Jacobsen and Manja Marz and Axel A Brakhage and Toni Gabaldón and Sebastian Böcker and Kerstin Voigt},

doi = {10.1371/journal.pgen.1004496},

issn = {1553-7404},

year  = {2014},

date = {2014-08-01},

journal = {PLoS Genet},

volume = {10},

number = {8},

pages = {e1004496},

abstract = {Lichtheimia species are the second most important cause of mucormycosis in Europe. To provide broader insights into the molecular basis of the pathogenicity-associated traits of the basal Mucorales, we report the full genome sequence of L. corymbifera and compared it to the genome of Rhizopus oryzae, the most common cause of mucormycosis worldwide. The genome assembly encompasses 33.6 MB and 12,379 protein-coding genes. This study reveals four major differences of the L. corymbifera genome to R. oryzae: (i) the presence of an highly elevated number of gene duplications which are unlike R. oryzae not due to whole genome duplication (WGD), (ii) despite the relatively high incidence of introns, alternative splicing (AS) is not frequently observed for the generation of paralogs and in response to stress, (iii) the content of repetitive elements is strikingly low (<5%), (iv) L. corymbifera is typically haploid. Novel virulence factors were identified which may be involved in the regulation of the adaptation to iron-limitation, e.g. LCor01340.1 encoding a putative siderophore transporter and LCor00410.1 involved in the siderophore metabolism. Genes encoding the transcription factors LCor08192.1 and LCor01236.1, which are similar to GATA type regulators and to calcineurin regulated CRZ1, respectively, indicating an involvement of the calcineurin pathway in the adaption to iron limitation. Genes encoding MADS-box transcription factors are elevated up to 11 copies compared to the 1-4 copies usually found in other fungi. More findings are: (i) lower content of tRNAs, but unique codons in L. corymbifera, (ii) Over 25% of the proteins are apparently specific for L. corymbifera. (iii) L. corymbifera contains only 2/3 of the proteases (known to be essential virulence factors) in comparison to R. oryzae. On the other hand, the number of secreted proteases, however, is roughly twice as high as in R. oryzae.},

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