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

      As above, so below: Whole transcriptome profiling demonstrates strong molecular similarities between avian dorsal and ventral pallial subdivisions

      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.

          Abstract

          Over the last two decades, beginning with the Avian Brain Nomenclature Forum in 2000, major revisions have been made to our understanding of the organization and nomenclature of the avian brain. However, there are still unresolved questions on avian pallial organization, particularly whether the cells above the vestigial ventricle represent distinct populations to those below it or similar populations. To test these two hypotheses, we profiled the transcriptomes of the major avian pallial subdivisions dorsal and ventral to the vestigial ventricle boundary using RNA sequencing and a new zebra finch genome assembly containing about 22,000 annotated, complete genes. We found that the transcriptomes of neural populations above and below the ventricle were remarkably similar. Each subdivision in dorsal pallium (Wulst) had a corresponding molecular counterpart in the ventral pallium (dorsal ventricular ridge). In turn, each corresponding subdivision exhibited shared gene co‐expression modules that contained gene sets enriched in functional specializations, such as anatomical structure development, synaptic transmission, signaling, and neurogenesis. These findings are more in line with the continuum hypothesis of avian brain subdivision organization above and below the vestigial ventricle space, with the pallium as a whole consisting of four major cell populations (intercalated pallium, mesopallium, hyper‐nidopallium, and arcopallium) instead of seven (hyperpallium apicale, interstitial hyperpallium apicale, intercalated hyperpallium, hyperpallium densocellare, mesopallium, nidopallium, and arcopallium). We suggest adopting a more streamlined hierarchical naming system that reflects the robust similarities in gene expression, neural connectivity motifs, and function. These findings have important implications for our understanding of overall vertebrate brain evolution.

          Abstract

          In 2013, our group examined the expression profiles of 50 genes to develop the continuum hypothesis of avian brain organization. It states that the subdivisions of the dorsal pallium develop continuously with those of the ventral pallium, resulting in a “partial mirror image” organization around the vestigial ventricle divide. However, these claims were challenged due to the small number of genes profiled. The present study uses RNA sequencing to profile the whole transcriptome (~20,000 genes) of the principal subdivisions of the avian telencephalon and confirms the remarkable molecularly similarities between the dorsal and ventral pallium. We recommend adopting a hierarchal nomenclature to reflect these robust molecular similarities.

          Related collections

          Most cited references78

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

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
              Bookmark
              • 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

                Author and article information

                Contributors
                ggedman@rockefeller.edu
                ejarvis@rockefeller.edu
                Journal
                J Comp Neurol
                J Comp Neurol
                10.1002/(ISSN)1096-9861
                CNE
                The Journal of Comparative Neurology
                John Wiley & Sons, Inc. (Hoboken, USA )
                0021-9967
                1096-9861
                07 May 2021
                August 2021
                : 529
                : 12 ( doiID: 10.1002/cne.v529.12 )
                : 3222-3246
                Affiliations
                [ 1 ] Laboratory of the Neurogenetics of Language The Rockefeller University New York New York USA
                [ 2 ] Vertebrate Genome Laboratory The Rockefeller University New York New York USA
                [ 3 ] Behavioural Genomics, Max Planck Institute for Evolutionary Biology Plön Germany
                [ 4 ] Howard Hughes Medical Institute Chevy Chase Maryland USA
                Author notes
                [*] [* ] Correspondence

                Erich D. Jarvis, and Gregory Gedman, Laboratory of the Neurogenetics of Language, The Rockefeller University, Box 54, 1230 York Avenue, New York, NY 10065.

                Email: ejarvis@ 123456rockefeller.edu ; (E. D. J.) and ggedman@ 123456rockefeller.edu (G. G.)

                Author information
                https://orcid.org/0000-0001-6819-2019
                https://orcid.org/0000-0001-8945-7282
                https://orcid.org/0000-0003-4331-9890
                https://orcid.org/0000-0002-6450-7551
                https://orcid.org/0000-0001-8931-5049
                Article
                CNE25159
                10.1002/cne.25159
                8251894
                33871048
                76d61551-e15e-41ae-bb22-701a6d23ade0
                © 2021 The Authors. The Journal of Comparative Neurology published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 16 March 2021
                : 15 October 2020
                : 19 March 2021
                Page count
                Figures: 12, Tables: 3, Pages: 25, Words: 17904
                Funding
                Funded by: Howard Hughes Medical Institute , open-funder-registry 10.13039/100000011;
                Award ID: OSU1013377
                Funded by: National Science Foundation Graduate Research Fellowship , open-funder-registry 10.13039/100000001;
                Award ID: 2015202850
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                August 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.4 mode:remove_FC converted:02.07.2021

                Neurology
                avian,brain evolution,pallium,rna‐seq,transcriptomics,wgnca,rrids,scr_014583,scr_017036,scr_001905,scr_015954,scr_010943,scr_015687,scr_021063,scr_003092,scr_012988,scr_003302,scr_018190,scr_021061,scr_021062,scr_004277,scr_000432

                Comments

                Comment on this article