Research data from the University of British Columbia Michael Smith Laboratories
UBC Michael Smith Laboratories
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Studies: 30 | Downloads: 66
Modeling sources of inter-laboratory variability in electrophysiological properties of mammalian neuronsby Tebaykin, Dmitry; Tripathy, Shreejoy J.; Binnion, Nathalie; Li, Brenna; Gerkin, Richard C.; Pavlidis, Paul
Description:

Patch-clamp electrophysiology is widely used to characterize neuronal electrical phenotypes. However, there are no standard experimental conditions for in vitro whole-cell patch-clamp electrophysiology, complicating direct comparisons between datasets. Here, we sought to understand how basic experimental conditions differ among labs and how these differences might impact measurements of electrophysiological parameters. We curated the compositions of external bath solutions (ACSF), internal pipette solutions, and other methodological details such as animal strain and age from 509 published neurophysiology articles studying rodent neurons. We found that very few articles used the exact same experimental solutions as any other and some solution differences stem from recipe inheritance from adviser to advisee as well as changing trends over the years. Next, we used statistical models to understand how the use of different experimental conditions impacts downstream electrophysiological measurements such as resting potential and action potential width. While these experimental condition features could explain up to 43% of the study-to-study variance in electrophysiological parameters, the majority of the variability was left unexplained. Our results suggest that there are likely additional experimental factors that contribute to cross-laboratory electrophysiological variability, and identifying and addressing these will be important to future efforts to assemble consensus descriptions of neurophysiological phenotypes for mammalian cell types.

Production Date:November 22, 2017
hdl:11272/10525
1 download
Last Released: Nov 23, 2017
Transcriptomic correlates of neuron electrophysiological diversityby Tripathy, Shreejoy; Toker, Lilah; Li, Brenna; Crichlow, Cindy-Lee; Tebaykin, Dmitry; Mancarci, Ogan; Shreejoy Tripathy
Description:

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.

Production Date:2017
Related Publications:Tripathy SJ, Toker L, Li B, et al. "Transcriptomic correlates of neuron electrophysiological diversity." Ayers J, ed. PLoS Computational Biology. 2017;13(10):e1005814. doi:10.1371/journal.pcbi.1005814.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/transcriptomic-correlates-of-neuron-electrophysiological-diversity/
hdl:11272/10485
41 downloads
Last Released: Feb 22, 2018
Protease-inhibitor interaction predictions: Lessons on the complexity of protein-protein interactionsby Fortelny, Nikolaus; Butler, Georgina; Overall, Christopher; Pavlidis, Paul
Description:

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features. Thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of non-proteolytic and non-inhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task, and thereby highlight limitations of computational interaction prediction methods.

Production Date:2017
Related Publications:Fortelny N, Butler GS, Overall CM, Pavlidis P. "Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein–Protein Interactions." Molecular & Cellular Proteomics : MCP. 2017;16(6):1038-1051. doi:10.1074/mcp.M116.065706.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/protease-inhibitor-interaction-predictions/
hdl:11272/10472
18 downloads
Last Released: Feb 22, 2018
Description:

Background: Prenatal alcohol exposure (PAE) can result in an array of morphological, behavioural and neurobiological deficits that can range in their severity. Despite extensive research in the field and a significant progress made, especially in understanding the range of possible malformations and neurobehavioral abnormalities, the molecular mechanisms of alcohol responses in development are still not well understood. There have been multiple transcriptomic studies looking at the changes in gene expression after PAE in animal models, however there is a limited apparent consensus among the reported findings. In an effort to address this issue, we performed a comprehensive re-analysis and meta-analysis of all suitable, publically available expression data sets. Methods: We assembled ten microarray data sets of gene expression after PAE in mouse and rat models consisting of samples from a total of 63 ethanol-exposed and 80 control animals. We re-analyzed each data set for differential expression and then used the results to perform meta-analyses considering all data sets together or grouping them by time or duration of exposure (pre- and post-natal, acute and chronic, respectively). We performed network and Gene Ontology enrichment analysis to further characterize the identified signatures. Results: For each sub-analysis we identified signatures of differential expressed genes that show support from multiple studies. Overall, the changes in gene expression were more extensive after acute ethanol treatment during prenatal development than in other models. Considering the analysis of all the data together, we identified a robust core signature of 104 genes down-regulated after PAE, with no up-regulated genes. Functional analysis reveals over-representation of genes involved in protein synthesis, mRNA splicing and chromatin organization. Conclusions: Our meta-analysis shows that existing studies, despite superficial dissimilarity in findings, share features that allow us to identify a common core signature set of transcriptome changes in PAE. This is an important step to identifying the biological processes that underlie the etiology of FASD.

Production Date:2016
Related Publications:Rogic S, Wong A, Pavlidis P. "Meta-analysis of gene expression patterns in animal models of prenatal alcohol exposure suggests role for protein synthesis inhibition and chromatin remodeling." Alcoholism, clinical and experimental research. 2016;40(4):717-727. doi:10.1111/acer.13007.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/pae-meta-analysis/
hdl:11272/10543
0 downloads
Last Released: Feb 22, 2018
Description:

We describe the WhiteText project, and its progress towards automatically extracting statements of neuroanatomical connectivity from text. We review progress to date on the three main steps of the project: recognition of brain region mentions, standardization of brain region mentions to neuroanatomical nomenclature, and connectivity statement extraction. We further describe a new version of our manually curated corpus that adds 2,111 connectivity statements from 1,828 additional abstracts. Cross-validation classification within the new corpus replicates results on our original corpus, recalling 67% of connectivity statements at 51% precision. The resulting merged corpus provides 5,208 connectivity statements that can be used to seed species-specific connectivity matrices and to better train automated techniques. Finally, we present a new web application that allows fast interactive browsing of the over 70,000 sentences indexed by the system, as a tool for accessing the data and assisting in further curation. Software and data are freely available at http://www.chibi.ubc.ca/WhiteText/.

Production Date:2015
Related Publications:French L, Liu P, Marais O, et al. "Text mining for neuroanatomy using WhiteText with an updated corpus and a new web application." Frontiers in Neuroinformatics. 2015;9:13. doi:10.3389/fninf.2015.00013.
hdl:11272/10570
0 downloads
Last Released: Feb 22, 2018
Description:

MOTIVATION: The Gene Ontology (GO) is heavily used in systems biology, but the potential for redundancy, confounds with other data sources and problems with stability over time have been little explored. RESULTS: We report that GO annotations are stable over short periods, with 3% of genes not being most semantically similar to themselves between monthly GO editions. However, we find that genes can alter their 'functional identity' over time, with 20% of genes not matching to themselves (by semantic similarity) after 2 years. We further find that annotation bias in GO, in which some genes are more characterized than others, has declined in yeast, but generally increased in humans. Finally, we discovered that many entries in protein interaction databases are owing to the same published reports that are used for GO annotations, with 66% of assessed GO groups exhibiting this confound. We provide a case study to illustrate how this information can be used in analyses of gene sets and networks. AVAILABILITY: Data available at http://chibi.ubc.ca/assessGO.

Production Date:2013
Related Publications:Gillis J, Pavlidis P. "Assessing identity, redundancy and confounds in Gene Ontology annotations over time." Bioinformatics. 2013;29(4):476-482. doi:10.1093/bioinformatics/bts727.
hdl:11272/10571
0 downloads
Last Released: Feb 22, 2018
Description:

Numerous studies have examined gene expression profiles in post-mortem human brain samples from individuals with schizophrenia compared to healthy controls, to gain insight into the molecular mechanisms of the disease. While some findings have been replicated across studies, there is a general lack of consensus of which genes or pathways are affected. It has been unclear if these differences are due to the underlying cohorts, or methodological considerations. Here we present the most comprehensive analysis to date of expression patterns in the prefrontal cortex of schizophrenic compared to unaffected controls. Using data from seven independent studies, we assembled a data set of 153 affected and 153 control individuals. Remarkably, we identified expression differences in the brains of schizophrenics that are validated by up to seven laboratories using independent cohorts. Our combined analysis revealed a signature of 39 probes that are up-regulated in schizophrenia and 86 down-regulated. Some of these genes were previously identified in studies that were not included in our analysis, while others are novel to our analysis. In particular, we observe gene expression changes associated with various aspects of neuronal communication, and alterations of processes affected as a consequence of changes in synaptic functioning. A gene network analysis predicted previously unidentified functional relationships among the signature genes. Our results provide evidence for a common underlying expression signature in this heterogeneous disorder

Production Date:2013
Related Publications:Mistry M, Gillis J, Pavlidis P. "Genome-wide expression profiling of schizophrenia using a large combined cohort." Molecular psychiatry. 2013;18(2):215-225. doi:10.1038/mp.2011.172.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/genome-wide-expression-profiling-of-schizophrenia-using-a-large-combined-cohort/
hdl:11272/10546
0 downloads
Last Released: Feb 22, 2018
Description:

Abstract: An important goal in neuroscience is to understand gene expression patterns in the brain. The recent availability of comprehensive and detailed expression atlases for mouse and human creates opportunities to discover global patterns and perform cross-species comparisons. Recently we reported that the major source of variation in gene transcript expression in the adult normal mouse brain can be parsimoniously explained as reflecting regional variation in glia to neuron ratios, and is correlated with degree of connectivity and location in the brain along the anterior-posterior axis. Here we extend this investigation to two gene expression assays of adult normal human brains that consisted of over 300 brain region samples, and perform comparative analyses of brain-wide expression patterns to the mouse. We performed principal components analysis (PCA) on the regional gene expression of the adult human brain to identify the expression pattern that has the largest variance. As in the mouse, we observed that the first principal component is composed of two anti-correlated patterns enriched in oligodendrocyte and neuron markers respectively. However, we also observed interesting discordant patterns between the two species. For example, a few mouse neuron markers show expression patterns that are more correlated with the human oligodendrocyte-enriched pattern and vice-versa. In conclusion, our work provides insights into human brain function and evolution by probing global relationships between regional cell type marker expression patterns in the human and mouse brain.

Production Date:2013
Related Publications:Tan PPC, French L, Pavlidis P. "Neuron-Enriched Gene Expression Patterns are Regionally Anti-Correlated with Oligodendrocyte-Enriched Patterns in the Adult Mouse and Human Brain." Frontiers in Neuroscience. 2013;7:5. doi:10.3389/fnins.2013.00005.
hdl:11272/10578
0 downloads
Last Released: Feb 22, 2018
FTO, obesity and the adolescent brainby Melka, Melkaye G.; Gillis, Jesse; Bernard, Manon; Abrahamowicz, Michal; Chakravarty, M. Maller; Leonard, Gabriel T.; Perron, Michel; Richer, Louis; Veillette, Suzanne; Banaschewski, Tobias; Barker, Gareth J.; Buchel, Christian; Conrod, Patricia; Flor, Herta; Heinz, Andreas; Garavan, Hugh; Bruhl, Rudiger; Mann, Karl; Artiges, Eric; Lourdusamy, Anbarasu; Lathrop, Mark; Loth, Eva; Schwartz, Yannick; Frouin, Vincent; Rietschel, Marcella; Smolka, Michael N.; Strohle, Andreas; Gallinat, Jurge; Struve, Maren; Lattka, Eva; Waldenberger, Melanie; Schumann, Gunter; Pavlidis, Paul; Gaudet, Daniel; Paus, Tomas; Pausova, Zdenka
Description:

Genetic variations in fat mass- and obesity (FTO)-associated gene, a well-replicated gene locus of obesity, appear to be associated also with reduced regional brain volumes in elderly. Here, we examined whether FTO is associated with total brain volume in adolescence, thus exploring possible developmental effects of FTO. We studied a population-based sample of 598 adolescents recruited from the French Canadian founder population in whom we measured brain volume by magnetic resonance imaging. Total fat mass was assessed with bioimpedance and body mass index was determined with anthropometry. Genotype-phenotype associations were tested with Merlin under an additive model. We found that the G allele of FTO (rs9930333) was associated with higher total body fat [TBF (P = 0.002) and lower brain volume (P = 0.005)]. The same allele was also associated with higher lean body mass (P = 0.03) and no difference in height (P = 0.99). Principal component analysis identified a shared inverse variance between the brain volume and TBF, which was associated with FTO at P = 5.5 × 10(-6). These results were replicated in two independent samples of 413 and 718 adolescents, and in a meta-analysis of all three samples (n = 1729 adolescents), FTO was associated with this shared inverse variance at P = 1.3 × 10(-9). Co-expression networks analysis supported the possibility that the underlying FTO effects may occur during embryogenesis. In conclusion, FTO is associated with shared inverse variance between body adiposity and brain volume, suggesting that this gene may exert inverse effects on adipose and brain tissues. Given the completion of the overall brain growth in early childhood, these effects may have their origins during early development.

Production Date:2013
Related Publications:Melka MG, Gillis J, Bernard M, et al. "FTO, obesity and the adolescent brain." Human Molecular Genetics. 2013;22(5):1050-1058. doi:10.1093/hmg/dds504.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/fto-coexpression-supplement/
hdl:11272/10544
1 download
Last Released: Feb 22, 2018
Description:

The electronic linking of neuroscience information, including data embedded in the primary literature, would permit powerful queries and analyses driven by structured databases. This task would be facilitated by automated procedures that can identify biological concepts in journals. Here we apply an approach for automatically mapping formal identifiers of neuroanatomical regions to text found in journal abstracts, applying it to a large body of abstracts from the Journal of Comparative Neurology (JCN). The analyses yield over 100,000 brain region mentions, which we map to 8,225 brain region concepts in multiple organisms. Based on the analysis of a manually annotated corpus, we estimate mentions are mapped at 95% precision and 63% recall. Our results provide insights into the patterns of publication on brain regions and species of study in JCN but also point to important challenges in the standardization of neuroanatomical nomenclatures. We find that many terms in the formal terminologies never appear in a JCN abstract, and, conversely, many terms that authors use are not reflected in the terminologies. To improve the terminologies, we deposited 136 unrecognized brain regions into the Neuroscience Lexicon (NeuroLex). The training data, terminologies, normalizations, evaluations, and annotated journal abstracts are freely available at http://www.chibi.ubc.ca/WhiteText/.

Production Date:2012
Related Publications:French L, Pavlidis P. "Using text mining to link journal articles to neuroanatomical databases." The Journal of comparative neurology. 2012;520(8):10.1002/cne.23012. doi:10.1002/cne.23012.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/the-whitetext-project/text-mining-of-journal-of-comparative-neurology/
hdl:11272/10548
0 downloads
Last Released: Feb 22, 2018
 
 
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