4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Synaptic protein interaction networks encode experience by assuming stimulus-specific and brain-region-specific states

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          SUMMARY

          A core network of widely expressed proteins within the glutamatergic post-synapse mediates activity-dependent synaptic plasticity throughout the brain, but the specific proteomic composition of synapses differs between brain regions. Here, we address the question, how does proteomic composition affect activity-dependent protein-protein interaction networks (PINs) downstream of synaptic activity? Using quantitative multiplex co-immunoprecipitation, we compare the PIN response of in vivo or ex vivo neurons derived from different brain regions to activation by different agonists or different forms of eyeblink conditioning. We report that PINs discriminate between incoming stimuli using differential kinetics of overlapping and non-overlapping PIN parameters. Further, these “molecular logic rules” differ by brain region. We conclude that although the PIN of the glutamatergic post-synapse is expressed widely throughout the brain, its activity-dependent dynamics show remarkable stimulus-specific and brain-region-specific diversity. This diversity may help explain the challenges in developing molecule-specific drug therapies for neurological disorders.

          Graphical Abstract

          In brief

          Molecular encoding of sensory stimuli occurs through modification of synaptic protein networks, but the underlying rules are poorly defined. Lautz et al. demonstrate that four brain regions respond to eyeblink conditioning by modifying excitatory synapse protein interaction networks using different encoding strategies, despite employing a similar set of proteins.

          Related collections

          Most cited references95

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Adjusting batch effects in microarray expression data using empirical Bayes methods.

            Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Principles of connectivity among morphologically defined cell types in adult neocortex.

              Since the work of Ramón y Cajal in the late 19th and early 20th centuries, neuroscientists have speculated that a complete understanding of neuronal cell types and their connections is key to explaining complex brain functions. However, a complete census of the constituent cell types and their wiring diagram in mature neocortex remains elusive. By combining octuple whole-cell recordings with an optimized avidin-biotin-peroxidase staining technique, we carried out a morphological and electrophysiological census of neuronal types in layers 1, 2/3, and 5 of mature neocortex and mapped the connectivity between more than 11,000 pairs of identified neurons. We categorized 15 types of interneurons, and each exhibited a characteristic pattern of connectivity with other interneuron types and pyramidal cells. The essential connectivity structure of the neocortical microcircuit could be captured by only a few connectivity motifs.
                Bookmark

                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                11 December 2021
                30 November 2021
                03 January 2022
                : 37
                : 9
                : 110076
                Affiliations
                [1 ]Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA, USA
                [2 ]Department of Pediatrics, University of Washington, Seattle, WA, USA
                [3 ]Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA
                [4 ]Lead contact
                Author notes
                [* ]Correspondence: seps@ 123456uw.edu

                AUTHOR CONTRIBUTIONS

                Except for the survival surgery and EBC experiments performed by J.P.W. and the two-photon imaging performed by K.B.T., J.D.L. performed all experiments with technical assistance from E.P.G. and Z.Z. J.D.L., K.B.T., S.E.P.S., and J.P.W. analyzed the data. J.D.L., S.E.P.S., and J.P.W. wrote the manuscript. All authors read and approved the manuscript.

                Article
                NIHMS1760957
                10.1016/j.celrep.2021.110076
                8722361
                34852231
                e15bbbf0-6dd4-499a-9a3e-1bb2ae3de79a

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Article

                Cell biology
                Cell biology

                Comments

                Comment on this article