scholarly journals Transcriptional Regulators and Human-Specific/Primate-Specific Genes in Neocortical Neurogenesis

2020 ◽  
Vol 21 (13) ◽  
pp. 4614 ◽  
Author(s):  
Samir Vaid ◽  
Wieland B. Huttner
Keyword(s):  
Gene Expression ◽  
Cognitive Abilities ◽  
Temporal Dynamics ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Transcriptional Regulators ◽  
New Genes ◽  
Recent Part ◽  
Spatio Temporal

During development, starting from a pool of pluripotent stem cells, tissue-specific genetic programs help to shape and develop functional organs. To understand the development of an organ and its disorders, it is important to understand the spatio-temporal dynamics of the gene expression profiles that occur during its development. Modifications in existing genes, the de-novo appearance of new genes, or, occasionally, even the loss of genes, can greatly affect the gene expression profile of any given tissue and contribute to the evolution of organs or of parts of organs. The neocortex is evolutionarily the most recent part of the brain, it is unique to mammals, and is the seat of our higher cognitive abilities. Progenitors that give rise to this tissue undergo sequential waves of differentiation to produce the complete sets of neurons and glial cells that make up a functional neocortex. We will review herein our understanding of the transcriptional regulators that control the neural precursor cells (NPCs) during the generation of the most abundant class of neocortical neurons, the glutametergic neurons. In addition, we will discuss the roles of recently-identified human- and primate-specific genes in promoting neurogenesis, leading to neocortical expansion.

Gene Expression Profiles in Acute Myeloid Leukemia (AML): From Diagnosis to Prognosis.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2996-2996
Author(s):  
Sanggyu Lee ◽  
Jianjun Chen ◽  
Goulin Zhou ◽  
Run Shi ◽  
Masha Kocherginsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Major Change ◽  
Molecular Signature ◽  
Human Leukemia ◽  
Prognostic Information ◽  
Myeloid Progenitor Cells ◽  
Novel Transcripts

Abstract Chromosome translocations are among the most common genetic abnormalities in human leukemia. The abnormally expressed genes from each translocation can be used to identify specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow(BM) samples from 22 patients with four types of AML, [de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11)].We made SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patient’s samples and normal CD15+ BM; they were also statistically significantly different at the 5% level. Using these strict criteria, we identified 2,381 unique tags, of which 2,053 were known genes and ESTs, and 328 were novel transcripts that were either specific for each translocation or were common(55) SAGE tags for all 4 translocations. The major change in all translocations was a decrease in expression in leukemia cells compared with normal cells; the decrease was least in the t(8;21) cells. Changes in expression of these known genes, which fall into different gene ontology functional categories, varied by translocation. Those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial microarray contained 349 probes including 212 known genes, 61 ESTs, 28 novel sequences based on our data and 48 genes reported by others. We have now included 65 additional probes that appeared to be correlated with survival. Using 63 samples with the four translocations [16 inv(16), 4 t(9;11), 20 t(15;17), 4 t(8;21) and 19 other translocations], we are validating which genes provide a robust, reproducible “fingerprint” for each translocation, for all translocations, and which ones provide reliable information related to prognosis and survival. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype as well as which genes appear to provide valid prognostic information.

Gene Expression Profiling Reveals Fundamental Differences between Leukemic Stem Cells (Lin-CD34+CD38−) and Mature Blasts (Lin−CD34+CD38+) of Acute Myeloid Leukaemia (AML) Patients.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1377-1377
Author(s):  
Kazem Zibara ◽  
Daniel Pearce ◽  
David Taussig ◽  
Spyros Skoulakis ◽  
Simon Tomlinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Populations ◽  
Leukemic Stem Cells ◽  
Significant Differential Expression ◽  
New Genes

Abstract The identification of LSC has important implications for future research as well as for the development of novel therapies. The phenotypic description of LSC now enables their purification and should facilitate the identification of genes that are preferentially expressed in these cells compared to normal HSC. However, gene-expression profiling is usually conducted on mononuclear cells of AML patients from either peripheral blood and/or bone marrow. These samples contain a mixture of blasts cells, normal hematopoietic cells and limited number of leukemic stem cells. Thus, this results in a composite profile that obscure differences between LSC and blasts cells with low proliferative potential. The aim of this study was to compare the gene expression profile of highly purified LSC versus leukemic blasts in order to identify genes that might have important roles in driving the leukemia. For this purpose, we analyzed the gene expression profiles of highly purified LSCs (Lin−CD34+CD38−) and more mature blast cells (Lin−CD34+CD38+) isolated from 7 adult AML patients. All samples were previously tested for the ability of the Lin−CD34+CD38− cells but not the Lin−CD34+CD38+ fraction to engraft using the non-obese diabetic/severe combined immuno-deficiency (NOD-SCID) repopulation assay. Affymetrix microarrays (U133A chip), containing 22,283 genes, were used for the analysis. Comparison of Lin-CD34+CD38- cell population to the Lin−CD34+CD38+ cell fraction showed 5421 genes to be expressed in both fractions. Comparative analysis of gene-expression profiles showed statistically significant differential expression of 133 genes between the 2 cell populations. Most of the genes were downregulated in the LSC-enriched fraction, compared to the more differentiated fraction. Gene ontology was used to determine the categories of the up-regulated transcripts. These transcripts, which are selectively expressed, include a number of known genes (e.g., receptors, signalling genes, proliferation and cell cycle genes and transcription factors). These genes play important roles in differentiation, self-renewal, migration and adhesion of HSCs. Among the genes showing the highest differences in expression levels were the following: ribonucleotide reductase M2 polypeptide, thymidylate synthetase, ZW10 interactor, cathepsin G, azurocidin 1, topoisomerase II, CDC20, nucleolar and spindle associated protein 1, Rac GTPase activating protein 1, leukocyte immunoglobulin-like receptor, proliferating cell nuclear antigen, myeloperoxidase, cyclin A1 (RRM2, TYMS, ZWINT, CTSG, AZU1, TOP2A, CDC20, NUSAP1, RACGAP1, LILRB2, PCNA, MPO, CCNA1). Some transcripts detected have not been implicated in HSC functions, and others have unknown function so far. This work identifies new genes that might play a role in leukemogenesis and cancer stem cells. It also leads to a better description and understanding of the molecular phenotypes of these 2 cell populations. Hence, in addition to being a more efficient way to further understand the biology of LSC, this should also provide a more efficient way of identifying new therapeutics and diagnostic targets.

scholarly journals Gammaherpesvirus Lytic Gene Expression as Characterized by DNA Array

2002 ◽  
Vol 76 (12) ◽  
pp. 6244-6256 ◽  
Author(s):  
Joo Wook Ahn ◽  
Kenneth L. Powell ◽  
Paul Kellam ◽  
Dagmar G. Alber
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Cdna Array ◽  
Gene Expression Profiles ◽  
Hierarchical Cluster ◽  
Murine Gammaherpesvirus ◽  
Transcription Profiles ◽  
A Genome

ABSTRACT Gammaherpesviruses are associated with a number of diseases including lymphomas and other malignancies. Murine gammaherpesvirus 68 (MHV-68) constitutes the most amenable animal model for this family of pathogens. However experimental characterization of gammaherpesvirus gene expression, at either the protein or RNA level, lags behind that of other, better-studied alpha- and beta-herpesviruses. We have developed a cDNA array to globally characterize MHV-68 gene expression profiles, thus providing an experimental supplement to a genome that is chiefly annotated by homology. Viral genes started to be transcribed as early as 3 h postinfection (p.i.), and this was followed by a rapid escalation of gene expression that could be seen at 5 h p.i. Individual genes showed their own transcription profiles, and most genes were still being expressed at 18 h p.i. Open reading frames (ORFs) M3 (chemokine-binding protein), 52, and M9 (capsid protein) were particularly noticeable due to their very high levels of expression. Hierarchical cluster analysis of transcription profiles revealed four main groups of genes and allowed functional predictions to be made by comparing expression profiles of uncharacterized genes to those of genes of known function. Each gene was also categorized according to kinetic class by blocking de novo protein synthesis and viral DNA replication in vitro. One gene, ORF 73, was found to be expressed with α-kinetics, 30 genes were found to be expressed with β-kinetics, and 42 genes were found to be expressed with γ-kinetics. This fundamental characterization furthers the development of this model and provides an experimental basis for continued investigation of gammaherpesvirus pathology.

scholarly journals Proteasome inhibitor induced gene expression profiles reveal overexpression of transcriptional regulators ATF3, GADD153 and MAD1

Oncogene ◽  
2000 ◽  
Vol 19 (25) ◽  
pp. 2913-2920 ◽  
Author(s):  
Johann Zimmermann ◽  
Dirk Erdmann ◽  
Isabelle Lalande ◽  
Rita Grossenbacher ◽  
Maria Noorani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Proteasome Inhibitor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Transcriptional Regulators

scholarly journals De novo characterization of the Lycium ruthenicum transcriptome and analysis of its digital gene expression profiles during fruit development and ripening

2017 ◽  
Vol 69 (1) ◽  
pp. 181-190 ◽  
Author(s):  
Yong Peng ◽  
Huiqin Ma ◽  
Shangwu Chen
Keyword(s):  
Gene Expression ◽  
Developmental Stages ◽  
De Novo ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Digital Gene Expression ◽  
Orthologous Group ◽  
Functional Studies ◽  
Lycium Ruthenicum

Lycium ruthenicum Murr., which belongs to the family Solanaceae, is a resource plant for Chinese traditional medicine and nutraceutical foods. In this study, RNA sequencing was applied to obtain raw reads of L. ruthenicum fruit at different stages of ripening, and a de novo assembly of its sequence was performed. Approximately 52.45 million 100-bp paired-end raw reads were generated from the samples by deep RNA-seq analysis. These short reads were assembled to obtain 164814 contigs, and the contigs were assembled into 84968 non-redundant unigenes using the Trinity method. Assembled sequences were annotated with gene descriptions, gene ontology, clusters of orthologous group and KEGG (Kyoto Encyclopedia of Genes and Genomes)pathway terms. Digital gene expression analysis was applied to compare gene-expression patterns at different fruit developmental stages. These results contribute to existing sequence resources for Lycium spp. during the fruit-ripening stages, which is valuable for further functional studies of genes involved in L. ruthenicum fruit nutraceutical quality.

Abstract P744: Gene Transcript Clusters Distinguish Time-Dependent Expression Patterns in Monocytes, Neutrophils and Whole Blood After Ischemic Stroke Injury

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Paulina Carmona-Mora ◽  
Glen C Jickling ◽  
Xinhua Zhan ◽  
Marisa Hakoupian ◽  
Heather Hull ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ischemic Stroke ◽  
Whole Blood ◽  
Temporal Dynamics ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Time Dependent ◽  
Gene Transcript ◽  
Time Points

Introduction: After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. We previously showed that peripheral blood cells display different gene expression profiles after IS and these transcriptional programs reflect the changes in immune processes in response to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of the changes of molecular and cellular pathways involved in acute brain injury. Methods: We analyzed the transcriptomic profiles of 33 IS patients in isolated monocytes, neutrophils and whole blood. RNA-sequencing was performed on all the stroke samples as well as 12 controls with vascular risk factors (diabetes and/or hypertension and/or hypercholesterolemia). To identify differentially expressed genes, subjects were split into time points (TPs) from stroke onset (TP1= 0-24 h; TP2= 24-48 h; and TP3= > 48 h), and controls were assigned TP0. A linear regression model including time and the interaction of diagnosis x TP with cutoff of p<0.02 and fold-change>|1.2| was used. Time dependent changes were analyzed using artificial neural networks to identify clusters of genes that behave in a similar way across TPs. Results: Unique patterns of temporal expression were distinguished for the three sample types. These include genes not expressed in TP0 that peak only within the first 24 h, others that peak or decrease in TP2 and TP3, and more complex patterns. Genes that peak at TP1 in monocytes and neutrophils are related to cell adhesion and leukocyte differentiation/migration, respectively. Early peaks in whole blood occur in genes related to transcriptional regulation. In monocytes, interleukin pathways are enriched across all TPs, whereas there is a trend of suppression after 24 h in neutrophils. The inflammasome pathway is enriched in the earlier TPs in neutrophils, while not enriched in monocytes until over 48 hours. Conclusion: Our analyses on gene expression dynamics and cluster patterns allow identification of key genes and pathways at different time points following ischemic injury that are valuable as IS biomarkers and may be possible treatment targets.

Co-Expression of BAALC with ERG, TCF4, and CD133 Indicates a Shared Pathway in Acute Myeloid and Lymphoblastic Leukemia.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2755-2755 ◽  
Author(s):  
Claudia D. Baldus ◽  
Michael Radmacher ◽  
Guido Marcucci ◽  
Dieter Hoelzer ◽  
Eckhard Thiel ◽  
...  
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Newly Diagnosed ◽  
Time Points ◽  
Normal Cytogenetics ◽  
Acute Myeloid

Abstract The human gene BAALC (Brain And Acute Leukemia, Cytoplasmic) is a molecular marker of hematopoietic progenitor cells and is aberrantly expressed in subsets of acute myeloid (AML) and lymphoblastic (ALL) leukemias. High mRNA expression levels of BAALC have been shown to adversely impact outcome in newly diagnosed AML patients (pts) with normal cytogenetics. To gain insight into the functional role of BAALC and its significance to normal hematopoiesis and leukemogenesis we compared gene expression profiles of normal CD34+ progenitors with those of AML and ALL blasts (using oligonucleotide microarrays; HG-U133 plus 2.0, Affymetrix, Santa Clara, CA). First we explored the regulation of BAALC expression during lineage specific maturation of in vitro differentiated human CD34+ bone marrow cells selected from healthy individuals. Microarray analyses were carried out using CD34+ cells stimulated in vitro with EPO, TPO, or G/GM-CSF to induce lineage-specific differentiation. At day 0 of culture and at three different time points during differentiation (days 4, 7, 11) cells were harvested, and if necessary purified by immunomagnetic beads and used for microarray studies. Experiments of all lineages and time points were done in triplicates. A total of 276 genes were identified showing similar changes in expression (with downregulation during differentiation) as BAALC at the three time points in all lineages with a correlation coefficient of R&gt;0.95. This set of 276 BAALC co-expressed genes was investigated in an AML expression dataset generated from 51 adult pts with newly diagnosed de novo AML and normal cytogenetics (Cancer and Leukemia Group B). After exclusion of probesets expressed in fewer than 20% of pt samples, 21 probesets representing 14 named genes 6 of which are known to be involved in AML (BAALC, CD34, CD133, SOX4, ERG, SEPT6) and 4 implicated in lymphoid development (TCF4, SH2D1A, ITM2A, ITM2C) were found to be overexpressed (a significance level of P=0.01 was used) in pts of the highest third compared to pts of the lowest third of BAALC expression values as measured by real-time RT-PCR. We next applied these same 21 BAALC co-expressed probesets to an ALL expression dataset generated from 66 adult pts with newly diagnosed standard risk B-lineage precursor ALL (from the German ALL GMALL study group). A BAALC specific cluster uncovered 7 probesets representing 4 different co-expressed genes: BAALC, CD133, and the transcription factors ERG and TCF4. Thus, applying a BAALC specific expression signature to AML and ALL gene expression profiles revealed 3 genes (CD133, ERG, TCF4), which are highly associated with BAALC in myeloid and lymphoid blasts. Interestingly in non-malignant lymphoid and myeloid cells the oncogeneic ETS transcription factor ERG has shown specificity to immature cells, while its mechanistical role in leukemogenesis remains unknown. ERG and TCF4 may directly regulate BAALC and indicate a specific pathway implicated in leukemogenesis, while co-expression of CD133 and BAALC suggests shared stem cell characteristics. Functional studies are in progress to further explore these findings.

Gene Expression Model of Survival and Transformation in Follicular Lymphoma (FL): A Study by the LLMPP.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2606-2606
Author(s):  
N.A. Johnson ◽  
T. Nayar ◽  
S.S. Dave ◽  
G. Wright ◽  
A. Rosenwald ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Cross Validation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Survival Model ◽  
Transformation Model ◽  
Previous Model ◽  
New Genes

Abstract Background: FL is a common NHL that has a broad spectrum of clinical outcomes. Over time some pts will transform to an aggressive histology (Tly) associated with inferior survival. In 2004, the LLMPP constructed a model that was predictive of overall survival (OS) based on the gene expression profiles (GEP) of 191 specimens taken from pts with untreated FL. The genes associated with survival were derived from the non-neoplastic immune response (IR) cells. However the risk of developing Tly was not addressed in this study. Thus we re-analyzed the GEP with updated clinical data. Our goal was to validate our previous model with extended follow-up and to create a model that would predict the risk of developing TLy. Methods: 170 of 191 previously untreated FL pts had updated clinical information but only 142 had transformation outcome. Transformation was defined as biopsy proven DLBCL or clinically based on the presence of at least one of the following: hypercalcemia, a sudden rise in LDH &gt;twice baseline, unusual extranodal growth or rapid discordant nodal growth. Raw CEL files from Affymetrix U133A arrays were pre-processed and normalized using Bioconductor’s GCRMA package. Models were developed using SignS package (http://signs/bioinfo.cnio.es/), with 10 times cross-validation. All gene lists produced in these analyses were then re-tested for association with outcome using Bioconductor’s Globaltest package. Over Representation Analysis of signature components was performed using Dchip. Results: The median OS of these patients was 8 yrs. A new 7-component survival model (85 genes) was developed that was significantly associated with survival (p= 2.9×10−13). In Globaltest, these gene lists were associated with survival at a level of (p=2.6×10−5). The previous model using IR-1 and IR-2 signatures was associated with survival at a level of p=2.6×10−4. Although there is little overlap between the 2 models, the new model confirms the importance of IR genes and extracellular matrix genes as being prognostically important. Interestingly, one component containing 10 genes on chromosome 6q was associated with a superior survival (p&lt;1×107). 27% developed Tly over a median follow-up time of 11.2 yrs (69% biopsy proven). Our transformation model included 53 genes divided into 3 components (p=0.001). The Globaltest analysis for association of these genes with transformation was significant (p=0.018). 54 genes overlapped between the survival genes and transformation genes that were present in &gt;1 cross validation run. These were significantly enriched in genes important in immune response like T cell and macrophage activation. Conclusion: Our survival model is stable and confirms the importance of key genes involved in the immune response and lymph node remodeling. It also introduces new genes that are potentially important for survival. Our transformation model may shed light on the mechanisms involved in the progression of FL to DLBCL but it is less stable and less reliable than our survival model at predicting outcome.

Metabolic and transcriptional patterns accompanying glutamine depletion and repletion in mouse hepatoma cells: a model for physiological regulatory networks

2004 ◽  
Vol 16 (2) ◽  
pp. 247-255 ◽  
Author(s):  
Matthew S. Wong ◽  
R. Michael Raab ◽  
Isidore Rigoutsos ◽  
Gregory N. Stephanopoulos ◽  
Joanne K. Kelleher
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Dna Microarrays ◽  
Cell Function ◽  
De Novo ◽  
Expression Profiles ◽  
Mammalian Cell Culture ◽  
Gene Expression Profiles ◽  
Tca Cycle ◽  
Glycolytic Flux

An important objective in postgenomic biology is to link gene expression to function by developing physiological networks that include data from the genomic and functional levels. Here, we develop a model for the analysis of time-dependent changes in metabolites, fluxes, and gene expression in a hepatic model system. The experimental framework chosen was modulation of extracellular glutamine in confluent cultures of mouse Hepa1-6 cells. The importance of glutamine has been demonstrated previously in mammalian cell culture by precipitating metabolic shifts with glutamine depletion and repletion. Our protocol removed glutamine from the medium for 24 h and returned it for a second 24 h. Flux assays of glycolysis, the tricarboxylic acid (TCA) cycle, and lipogenesis were used at specified intervals. All of these fluxes declined in the absence of glutamine and were restored when glutamine was repleted. Isotopomer spectral analysis identified glucose and glutamine as equal sources of lipogenic carbon. Metabolite measurements of organic acids and amino acids indicated that most metabolites changed in parallel with the fluxes. Experiments with actinomycin D indicated that de novo mRNA synthesis was required for observed flux changes during the depletion/repletion of glutamine. Analysis of gene expression data from DNA microarrays revealed that many more genes were anticorrelated with the glycolytic flux and glutamine level than were correlated with these indicators. In conclusion, this model may be useful as a prototype physiological regulatory network where gene expression profiles are analyzed in concert with changes in cell function.

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