Research data from the University of British Columbia Michael Smith Laboratories
Dr. Paul Pavlidis
Dr. Paul Pavlidis
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Studies: 9 | Downloads: 62
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
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:

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:

Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.

Production Date:2012
Related Publications:Gillis J, Pavlidis P. "“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks." PLOS Computational Biology 8(3): e1002444. https://doi.org/10.1371/journal.pcbi.1002444
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/guilt-by-association-in-gene-networks/
hdl:11272/10545
0 downloads
Last Released: Feb 22, 2018
Description:

The most dramatic growth of the human brain occurs in utero and during the first 2 years of postnatal life. Genesis of the cerebral cortex involves cell proliferation, migration, and apoptosis, all of which may be influenced by prenatal environment. Here, we show that variation in KCTD8 (potassium channel tetramerization domain 8) is associated with brain size in female adolescents (rs716890, P = 5.40 × 10(-09)). Furthermore, we found that the KCTD8 locus interacts with prenatal exposure to maternal cigarette smoking vis-à-vis cortical area and cortical folding: In exposed girls only, the KCTD8 locus explains up to 21% of variance. Using head circumference as a proxy of brain size at 7 years of age, we have replicated this gene-environment interaction in an independent sample. We speculate that KCTD8 might modulate adverse effects of smoking during pregnancy on brain development via apoptosis triggered by low intracellular levels of potassium, possibly reducing the number of progenitor cells.

Production Date:2012
Related Publications:Paus T, Bernard M, Chakravarty MM, et al. "KCTD8 Gene and Brain Growth in Adverse Intrauterine Environment: A Genome-wide Association Study." Cerebral Cortex (New York, NY). 2012;22(11):2634-2642. doi:10.1093/cercor/bhr350.
Related Material:Pavlidis Lab supplementary information page: http://msl-pavlidis-lab.sites.olt.ubc.ca/data-and-supplementary-information/coexpression-of-kctd8/
hdl:11272/10547
0 downloads
Last Released: Feb 22, 2018
Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Databy Mancarci, B. Ogan; Toker, Lilah; Tripathy, Shreejoy J; Li, Brenna; Rocco, Brad; Sibille, Etienne; Pavlidis, Paul
Description:

Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single cell RNA-sequencing studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at Neuroexpresso.org.

Related Publications:Mancarci BO, Toker L, Tripathy SJ, et al. "Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data." eNeuro. 2017;4(6):ENEURO.0212-17.2017. doi:10.1523/ENEURO.0212-17.2017.
Related Material:Pavlidis Lab supplementary information page: http://pavlab.msl.ubc.ca/supplement-to-mancarci-et-al-neuroexpresso/
hdl:11272/10527
1 download
Last Released: Feb 22, 2018
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