scholarly journals Co-expression of synaptic genes in the sponge Amphimedon queenslandica uncovers ancient neural submodules

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Eunice Wong ◽  
Jan Mölter ◽  
Victor Anggono ◽  
Sandie M. Degnan ◽  
Bernard M. Degnan
Keyword(s):  
Common Ancestor ◽  
Expression Profiles ◽  
Life Cycle Stage ◽  
Developmental Expression ◽  
Last Common Ancestor ◽  
Cell Type ◽  
Amphimedon Queenslandica ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Receptor Clusters

Abstract The synapse is a complex cellular module crucial to the functioning of neurons. It evolved largely through the exaptation of pre-existing smaller submodules, each of which are comprised of ancient sets of proteins that are conserved in modern animals and other eukaryotes. Although these ancient submodules themselves have non-neural roles, it has been hypothesized that they may mediate environmental sensing behaviors in aneural animals, such as sponges. Here we identify orthologues in the sponge Amphimedon queenslandica of genes encoding synaptic submodules in neural animals, and analyse their cell-type specific and developmental expression to determine their potential to be co-regulated. We find that genes comprising certain synaptic submodules, including those involved in vesicle trafficking, calcium-regulation and scaffolding of postsynaptic receptor clusters, are co-expressed in adult choanocytes and during metamorphosis. Although these submodules may contribute to sensory roles in this cell type and this life cycle stage, total synaptic gene co-expression profiles do not support the existence of a functional synapse in A. queenslandica. The lack of evidence for the co-regulation of genes necessary for pre- and post-synaptic functioning in A. queenslandica suggests that sponges, and perhaps the last common ancestor of sponges and other extant animals, had the ability to promulgate sensory inputs without complete synapse-like functionalities. The differential co-expression of multiple synaptic submodule genes in sponge choanocytes, which have sensory and feeding roles, however, is consistent with the metazoan ancestor minimally being able to undergo exo- and endocytosis in a controlled and localized manner.

scholarly journals Auxiliary subunits of the CKAMP family differentially modulate AMPA receptor properties

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Paul Farrow ◽  
Konstantin Khodosevich ◽  
Yechiam Sapir ◽  
Anton Schulmann ◽  
Muhammad Aslam ◽  
...  
Keyword(s):  
Ampa Receptor ◽  
Expression Profiles ◽  
Developmental Expression ◽  
Interacting Proteins ◽  
Cell Type ◽  
Specific Expression ◽  
New Members ◽  
Auxiliary Subunits ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AMPA receptor (AMPAR) function is modulated by auxiliary subunits. Here, we report on three AMPAR interacting proteins—namely CKAMP39, CKAMP52 and CKAMP59—that, together with the previously characterized CKAMP44, constitute a novel family of auxiliary subunits distinct from other families of AMPAR interacting proteins. The new members of the CKAMP family display distinct regional and developmental expression profiles in the mouse brain. Notably, despite their structural similarities they exert diverse modulation on AMPAR gating by influencing deactivation, desensitization and recovery from desensitization, as well as glutamate and cyclothiazide potency to AMPARs. This study indicates that AMPAR function is very precisely controlled by the cell-type specific expression of the CKAMP family members.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

Evolution of biosynthetic diversity

2017 ◽  
Vol 474 (14) ◽  
pp. 2277-2299 ◽  
Author(s):  
Anthony J. Michael
Keyword(s):  
Common Ancestor ◽  
Polyamine Metabolism ◽  
Last Common Ancestor ◽  
Protein Fold ◽  
Last Universal Common Ancestor ◽  
Endosymbiotic Gene Transfer ◽  
Evolutionary Mechanisms ◽  
Universal Common Ancestor ◽  
Genes Encoding ◽  
Domains Of Life

Since the emergence of the last common ancestor from which all extant life evolved, the metabolite repertoire of cells has increased and diversified. Not only has the metabolite cosmos expanded, but the ways in which the same metabolites are made have diversified. Enzymes catalyzing the same reaction have evolved independently from different protein folds; the same protein fold can produce enzymes recognizing different substrates, and enzymes performing different chemistries. Genes encoding useful enzymes can be transferred between organisms and even between the major domains of life. Organisms that live in metabolite-rich environments sometimes lose the pathways that produce those same metabolites. Fusion of different protein domains results in enzymes with novel properties. This review will consider the major evolutionary mechanisms that generate biosynthetic diversity: gene duplication (and gene loss), horizontal and endosymbiotic gene transfer, and gene fusion. It will also discuss mechanisms that lead to convergence as well as divergence. To illustrate these mechanisms, one of the original metabolisms present in the last universal common ancestor will be employed: polyamine metabolism, which is essential for the growth and cell proliferation of archaea and eukaryotes, and many bacteria.

Cell-type specific organization of glycine receptor clusters in the mammalian spinal cord

1997 ◽  
Vol 379 (1) ◽  
pp. 150-169 ◽  
Author(s):  
Francisco J. Alvarez ◽  
Dianne E. Dewey ◽  
Deborah A. Harrington ◽  
Robert E.W. Fyffe
Keyword(s):  
Spinal Cord ◽  
Glycine Receptor ◽  
Cell Type ◽  
Cell Type Specific ◽  
Receptor Clusters

scholarly journals PRISM: Recovering cell type specific expression profiles from composite RNA-seq data

2019 ◽  
Author(s):  
Antti Häkkinen ◽  
Kaiyang Zhang ◽  
Amjad Alkodsi ◽  
Noora Andersson ◽  
Erdogan Pekcan Erkan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Expression Profiles ◽  
Cell Type ◽  
Specific Expression ◽  
Patient Response ◽  
Statistical Framework ◽  
Sample Composition ◽  
Cell Type Specific Expression ◽  
Treatment Naïve ◽  
Cell Type Specific

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2020 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that modulates and maintains patterns of gene expression through the cell cycle

2020 ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

ABSTRACTBackgroundChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear.To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL) to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3.ResultsChromosome regions (bands) of 10-50Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. We show that they comprise 1-5Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely.We found little change between cell cycle phases, whether compared by 5Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains.Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription.ConclusionsModified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

Integration of Single-Cell RNA- and CAGE-seq Reveals Tooth-Enriched Genes

2021 ◽  
pp. 002203452110497
Author(s):  
Y. Chiba ◽  
K. Yoshizaki ◽  
T. Tian ◽  
K. Miyazaki ◽  
D. Martin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Tooth Development ◽  
Expression Profiles ◽  
Cell Type ◽  
Bioinformatics Analyses ◽  
Data Set ◽  
Cell Type Specific

Organ development is dictated by the regulation of genes preferentially expressed in tissues or cell types. Gene expression profiling and identification of specific genes in organs can provide insights into organogenesis. Therefore, genome-wide analysis is a powerful tool for clarifying the mechanisms of development during organogenesis as well as tooth development. Single-cell RNA sequencing (scRNA-seq) is a suitable tool for unraveling the gene expression profile of dental cells. Using scRNA-seq, we can obtain a large pool of information on gene expression; however, identification of functional genes, which are key molecules for tooth development, via this approach remains challenging. In the present study, we performed cap analysis of gene expression sequence (CAGE-seq) using mouse tooth germ to identify the genes preferentially expressed in teeth. The CAGE-seq counts short reads at the 5′-end of transcripts; therefore, this method can quantify the amount of transcripts without bias related to the transcript length. We hypothesized that this CAGE data set would be of great help for further understanding a gene expression profile through scRNA-seq. We aimed to identify the important genes involved in tooth development via bioinformatics analyses, using a combination of scRNA-seq and CAGE-seq. We obtained the scRNA-seq data set of 12,212 cells from postnatal day 1 mouse molars and the CAGE-seq data set from postnatal day 1 molars. scRNA-seq analysis revealed the spatiotemporal expression of cell type–specific genes, and CAGE-seq helped determine whether these genes are preferentially expressed in tooth or ubiquitously. Furthermore, we identified candidate genes as novel tooth-enriched and dental cell type–specific markers. Our results show that the integration of scRNA-seq and CAGE-seq highlights the genes important for tooth development among numerous gene expression profiles. These findings should contribute to resolving the mechanism of tooth development and establishing the basis for tooth regeneration in the future.

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