scholarly journals Cellular heterogeneity and MTH1 play key roles in galactose mediated signaling of the GAL switch to utilize the disaccharide melibiose

2021 ◽  
Author(s):  
Rajesh Kumar Kar ◽  
Paike Jayadeva Bhat
Keyword(s):  
Gene Expression ◽  
Glucose Repression ◽  
Expression Patterns ◽  
Negative Regulator ◽  
Cellular Heterogeneity ◽  
Growth Defect ◽  
Gene Expressions ◽  
Gal1 Promoter ◽  
Mel Gene

Yeast metabolizes the disaccharide melibiose by hydrolyzing it into equimolar concentrations of glucose and galactose by MEL1-encoded α-galactosidase. Galactose metabolizing genes (including MEL1) are induced by galactose and repressed by glucose, which are the products of melibiose hydrolysis. Therefore, how melibiose catabolization and utilization take place by circumventing the glucose repression is an enigma. Other than the galactose metabolizing genes MTH1, a negative regulator of glucose signal pathway has Gal4p binding sites and is induced by galactose and repressed by high glucose concentration. But, at low or no glucose MTH1 along with its paralogue STD1 represses hexose transporters, that are involved in glucose transport. This sort of tuning of glucose and galactose regulation motivated us to delineate the role of MTH1 as a regulator of MEL1 expression and melibiose utilization. The deletion mutant of MTH1 shows growth defect on melibiose and this growth defect is enhanced upon the deletion of both MTH1 and its paralogue STD1. Microscopy and flowcytometry analysis, suggest, that even though MEL1 and GAL1 promoter are under Gal4p and Gal80p regulation, upon deletion of MTH1 it hampers only MEL1 expression, but not the GAL1 gene expression. By using 2-Deoxy galactose toxicity assay, we observed phenotypic heterogeneity in cells grown on melibiose i.e. after cleaving of melibiose a fraction of cell population utilizes glucose and another fraction utilizes galactose and coexist together. Understanding GAL/MEL gene expression patterns in melibiose will have great implication to understand various other complex sugar utilizations, tunable gene expressions and complex feedback gene regulations.

scholarly journals Understanding the Growth Phenotype of the Yeastgcr1 Mutant in Terms of Global Genomic Expression Patterns

2000 ◽  
Vol 182 (17) ◽  
pp. 4970-4978 ◽  
Author(s):  
M. Cecilia López ◽  
Henry V. Baker
Keyword(s):  
Gene Expression ◽  
Glucose Repression ◽  
Expression Patterns ◽  
Carbon Sources ◽  
Glycolytic Enzyme ◽  
Growth Defect ◽  
Wild Type ◽  
Genomic Expression ◽  
Enzyme Gene ◽  
Severe Growth

ABSTRACT The phenotype of an organism is the manifestation of its expressed genome. The gcr1 mutant of yeast grows at near wild-type rates on nonfermentable carbon sources but exhibits a severe growth defect when grown in the presence of glucose, even when nonfermentable carbon sources are available. Using DNA microarrays, the genomic expression patterns of wild-type and gcr1 mutant yeast growing on various media, with and without glucose, were compared. A total of 53 open reading frames (ORFs) were identified asGCR1 dependent based on the criterion that their expression was reduced twofold or greater in mutant versus wild-type cultures grown in permissive medium consisting of YP supplemented with glycerol and lactate. The GCR1-dependent genes, so defined, fell into three classes: (i) glycolytic enzyme genes, (ii) ORFs carried by Ty elements, and (iii) genes not previously known to beGCR1 dependent. In wild-type cultures,GCR1-dependent genes accounted for 27% of the total hybridization signal, whereas in mutant cultures, they accounted for 6% of the total. Glucose addition to the growth medium resulted in a reprogramming of gene expression in both wild-type and mutant yeasts. In both strains, glycolytic enzyme gene expression was induced by the addition of glucose, although the expression of these genes was still impaired in the mutant compared to the wild type. By contrast, glucose resulted in a strong induction of Ty-borne genes in the mutant background but did not greatly affect their already high expression in the wild-type background. Both strains responded to glucose by repressing the expression of genes involved in respiration and the metabolism of alternative carbon sources. Thus, the severe growth inhibition observed in gcr1 mutants in the presence of glucose is the result of normal signal transduction pathways and glucose repression mechanisms operating without sufficient glycolytic enzyme gene expression to support growth via glycolysis alone.

Distinct gene expression patterns in chronic lymphocytic leukemia defined by usage of specific VH genes

Blood ◽  
2006 ◽  
Vol 107 (5) ◽  
pp. 2090-2093 ◽  
Author(s):  
Dirk Kienle ◽  
Axel Benner ◽  
Alexander Kröber ◽  
Dirk Winkler ◽  
Daniel Mertens ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Lymphocytic Leukemia ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Cell Receptor ◽  
Lymphocytic Leukemia ◽  
Expression Of Genes ◽  
Vh Genes ◽  
Signaling Activation

The mutation status and usage of specific VH genes such as V3-21 and V1-69 are potentially independent pathogenic and prognostic factors in chronic lymphocytic leukemia (CLL). To investigate the role of antigenic stimulation, we analyzed the expression of genes involved in B-cell receptor (BCR) signaling/activation, cell cycle, and apoptosis control in CLL using these specific VH genes compared to VH mutated (VH-MUT) and VH unmutated (VH-UM) CLL not using these VH genes. V3-21 cases showed characteristic expression differences compared to VH-MUT (up: ZAP70 [or ZAP-70]; down: CCND2, P27) and VH-UM (down: PI3K, CCND2, P27, CDK4, BAX) involving several BCR-related genes. Similarly, there was a marked difference between VH unmutated cases using the V1-69 gene and VH-UM (up: FOS; down: BLNK, SYK, CDK4, TP53). Therefore, usage of specific VH genes appears to have a strong influence on the gene expression pattern pointing to antigen recognition and ongoing BCR stimulation as a pathogenic factor in these CLL subgroups.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

scholarly journals MacroH2A histone variants modulate enhancer activity to repress oncogenic programs and cellular reprogramming

2021 ◽  
Author(s):  
Alexandre Gaspar-Maia ◽  
Wazim Mohammed Ismail ◽  
Amelia Mazzone ◽  
Jagneet Kaur ◽  
Stephanie Safgren ◽  
...  
Keyword(s):  
Regulatory Elements ◽  
Histone Variants ◽  
Cellular Reprogramming ◽  
Negative Regulator ◽  
Cellular Heterogeneity ◽  
Enhancer Activity ◽  
Active Enhancer ◽  
A Cell ◽  
Cell Type Specific

Abstract Considerable efforts have been made to characterize active enhancer elements, which can be annotated by accessible chromatin and H3 lysine 27 acetylation (H3K27ac). However, apart from poised enhancers that are observed in early stages of development and putative silencers, the functional significance of cis-regulatory elements lacking H3K27ac is poorly understood. Here we show that macroH2A histone variants mark a subset of enhancers in normal and cancer cells, which we coined ‘macroH2A-Bound Enhancers’, that negatively modulate enhancer activity. We find macroH2A variants enriched at enhancer elements that are devoid of H3K27ac in a cell type-specific manner, indicating a role for macroH2A at inactive enhancers to maintain cell identity. In following, reactivation of macro-bound enhancers is associated with oncogenic programs in breast cancer and its repressive role is correlated with the activity of macroH2A2 as a negative regulator of BRD4 chromatin occupancy. Finally, through single cell epigenomic profiling, we show that the loss of macroH2A2 leads to increased cellular heterogeneity that may help to explain the role of macroH2A variants in defining oncogenic transcriptional dependencies.

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

Biological Image Analysis via Matrix Approximation

2011 ◽  
pp. 166-170
Author(s):  
Jieping Ye ◽  
Ravi Janardan ◽  
Sudhir Kumar
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Controlled Vocabulary ◽  
Gene Expression Patterns ◽  
Image Content ◽  
Gene Expressions ◽  
Microarray Gene Expression ◽  
Individual Gene

Understanding the roles of genes and their interactions is one of the central challenges in genome research. One popular approach is based on the analysis of microarray gene expression data (Golub et al., 1999; White, et al., 1999; Oshlack et al., 2007). By their very nature, these data often do not capture spatial patterns of individual gene expressions, which is accomplished by direct visualization of the presence or absence of gene products (mRNA or protein) (e.g., Tomancak et al., 2002; Christiansen et al., 2006). For instance, the gene expression pattern images of a Drosophila melanogaster embryo capture the spatial and temporal distribution of gene expression patterns at a given developmental stage (Bownes, 1975; Tsai et al., 1998; Myasnikova et al., 2002; Harmon et al., 2007). The identification of genes showing spatial overlaps in their expression patterns is fundamentally important to formulating and testing gene interaction hypotheses (Kumar et al., 2002; Tomancak et al., 2002; Gurunathan et al., 2004; Peng & Myers, 2004; Pan et al., 2006). Recent high-throughput experiments of Drosophila have produced over fifty thousand images (http://www. fruitfly.org/cgi-bin/ex/insitu.pl). It is thus desirable to design efficient computational approaches that can automatically retrieve images with overlapping expression patterns. There are two primary ways of accomplishing this task. In one approach, gene expression patterns are described using a controlled vocabulary, and images containing overlapping patterns are found based on the similarity of textual annotations. In the second approach, the most similar expression patterns are identified by a direct comparison of image content, emulating the visual inspection carried out by biologists [(Kumar et al., 2002); see also www.flyexpress.net]. The direct comparison of image content is expected to be complementary to, and more powerful than, the controlled vocabulary approach, because it is unlikely that all attributes of an expression pattern can be completely captured via textual descriptions. Hence, to facilitate the efficient and widespread use of such datasets, there is a significant need for sophisticated, high-performance, informatics-based solutions for the analysis of large collections of biological images.

CD72 Expression Patterns in ZAP70 Positive and Negative Chronic Lymphocytic Leukemia: Potential Regulatory Role in BCR Signalling

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4159-4159
Author(s):  
Francisco P. Careta ◽  
Rodrigo A. Panepucci ◽  
Daniel M Matos ◽  
Rodrigo Proto-Siqueira ◽  
Wilson A. Silva-Junior ◽  
...  
Keyword(s):  
B Cells ◽  
Chronic Lymphocytic Leukemia ◽  
Expression Patterns ◽  
Surrogate Marker ◽  
Lymphocytic Leukemia ◽  
Negative Regulator ◽  
Beta Catenin ◽  
Cell Surface Glycoprotein ◽  
Expression Levels

Abstract Introduction: Absence of mutations in IgVH genes or higher number of ZAP70+ cells (as a surrogate marker) in chronic lymphocytic leukemia (CLL) B-cells defines a patient group with a poorer clinical course. These features relate to the role of BCR signalling in the proliferation and survival of CLL B-cells, and establish a link between these markers and the biology of CLL prognostic subgroups. The identification of additional players in this context may help to better understand the molecular basis of this disease and contribute to develop new therapeutic approaches. A search for genes potentially related to BCR signalling, when comparing mutated and unmutated CLL cases using serial analysis of gene expression, revealed a 4-fold increase of CD72 tags in unmutated samples, a specific B cell surface glycoprotein known to transmit both positive and negative signals in BCR signalling. Objective: This finding lead us to explore the potential role of CD72 on BCR signalling in distinct CLL prognostic subgroups, as defined by ZAP70 expression. Methods: Percentage of ZAP70+ and CD72+ cells were evaluated by flow cytometry on gated CD19+CD5+ cells in 25 CLL samples. Positive cases for ZAP70 and CD72 were defined using a cut-off of 35% and 40% positive cells, respectively. Real time PCR was used to quantify the expression levels of 3 genes related to proliferation and survival, RELB, Beta-Catenin (CTNNB1) and AKT1, on 16 CD19+ enriched (purity > 90%) CLL samples. Results: Samples were classified as 11 ZAP70+ and 14 ZAP70−. Median percentage of CD72+ cells in ZAP70+ was significantly higher than for ZAP70− cases (82% compared to 39%, respectively, P=0.0029). Furthermore, percentages of CD72 and ZAP70 were positively correlated (r=0.5930 and P=0.0009). Interestingly, ZAP70+ cases were restricted to CD72+ cases (n=11, CD72+ZAP70+ [+/+]), whereas six CD72+ cases were ZAP70− (ZAP70−CD72+ [−/+]). Finally, there were 8 cases CD72−ZAP70− [−/−]. No differences among these 3 groups were observed in regard to laboratory parameters (white blood cells, total lymphocytes, lymphocyte percentage, haemoglobin, haematocrit and platelet number). Despite the reduced number of samples analysed (6 +/+, 6 −/− and 4 −/+), transcripts for RELB (P<0.05), CTNNB1 (P<0.05), and AKT1(P=0.057) were expressed at higher levels in ZAP70+CD72+ than in ZAP70−CD72+ samples. Additionally, the transcripts were expressed at higher levels in ZAP70−CD72− than in ZAP70−CD72+ samples, and this difference was statistically significant (P<0.05) for CTNB1 and AKT1, but not for RELB (P=0.054). Conclusion: Our data indicate that higher percentages of ZAP70+ cells are associated with higher expression levels of transcripts related to proliferation and survival of CLL B-cells. In the absence of ZAP70 expression, CD72 may act as a negative regulator of the BCR pathway, as indicated by the lowest levels of transcripts on ZAP70−CD72+ cases.

Defining The Role Of Microrna-146a In B Cell Lymphomagenesis

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3805-3805
Author(s):  
Jorge Contreras ◽  
Jayanth Kumar Palanichamy ◽  
Tiffany Tran ◽  
Dinesh S. Rao
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Conflicts Of Interest ◽  
Cell Lymphoma ◽  
Expression Patterns ◽  
Significant Proportion ◽  
B Cell Lymphoma ◽  
Gene Expression Patterns

Abstract Diffuse large B cell lymphoma (DLBCL) is one of the most common Non-Hodgkin lymphomas among adults. It is a heterogeneous disease characterized by multiple mutations and translocations. Gene expression profiling studies have revealed several characteristic gene expression patterns, with two main patterns emerging, namely Germinal Center(GC) type, and Activated B Cell (ABC) type. ABC-type DLBCL shows gene expression patterns that resemble activated B-cells, with increased expression of anti-apoptotic, and pro-proliferative genes. Critically, upregulation of the NF-κB the pathway is a hallmark of ABC-type DLBCL and has been shown to be necessary for survival, and is caused by several different mutations at different levels within the pathway. Recent work has revealed the critical importance of a new class of small RNA molecules, namely microRNAs, in gene regulation. Of these, microRNA-146a (miR-146a) was discovered as an NF-κB induced microRNA that plays a role as a negative feedback regulator of this pathway by targeting adaptor proteins. To further characterize miR-146a, mice deficient for this miRNA were created, and were found to develop lymphadenopathy, splenomegaly, and myeloid proliferation. As expected, immune cells in these mice have an upregulated NF-κB pathway and many of the phenotypes can be ameliorated by inhibition of the NF-κB pathway. Importantly, a significant proportion of the animals develop B-cell lymphoma at older ages. In this study, we examined the role of miR-146a in the development of malignancy in B-cells. To accelerate the role of miR-146a in tumor formation we overlaid the miR-146a deficient allele onto the Eμ-Myc like mouse model. Eμ-Myc mice develop tumors on average by 14weeks of age. The transgenic status of animals was verified by genotyping, RNA and protein expression analyses. miR-146a sufficient and deficient animals on the Eμ-Myc background were followed for tumor latency by peripheral blood analysis and careful physical examination. Based on approved humane criteria for animal discomfort, animals were sacrificed and hematopoietic tissue was harvested for analysis. Mice deficient for miR-146a had a statistically reduced survival in comparison with miR-146a sufficient animals with a p-value of .0098 (Kaplan Meir survival analysis). Complete Blood Count of animals at time of death revealed an increase leukemia presentation in the miR-146a deficient background. FACS analysis of tumor tissue from both groups revealed an increase in the number of IgM positive tumors in the miR-146a-deficient background indicating skewing towards more mature B cell neoplasms when miR-146a is lacking. Lineage analysis of tumors verified them to be of B cell origin although a subset of miR-146a sufficient tumors had higher numbers of infiltrating myeloid cells compared to deficient animals. Furthermore, histologic analysis of hematopoietic organs showed that while infiltration remained similar in kidneys and liver, more spleens in the miR-146a deficient background tended to be less involved. Our extensive histopathologic and immunophenotypic analyses indicate that miR-146a deficiency drives a more aggressive malignant phenotype in the B-cell lineage. In keeping with this, our profiling studies of human DLBCL suggest that a subset of DLBCL show decreased expression of miR-146a. We are currently examining the status of NF-κB in the murine tumors and using high throughput sequencing approaches to delineate gene expression differences between miR-146a sufficient and deficient tumors. We anticipate the discovery of novel gene targets of miR-146a and expect that these studies will lead to improved diagnostic and therapeutic options for patients of B-cell malignancies. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Correlation between Bacillus subtilis scoC Phenotype and Gene Expression Determined Using Microarrays for Transcriptome Analysis

2001 ◽  
Vol 183 (24) ◽  
pp. 7329-7340 ◽  
Author(s):  
Robert Caldwell ◽  
Ron Sapolsky ◽  
Walter Weyler ◽  
Randal R. Maile ◽  
Stuart C. Causey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bacillus Subtilis ◽  
Expression Patterns ◽  
Global Scale ◽  
Complete Sequence ◽  
Dna Arrays ◽  
Genome Wide ◽  
Genes Encoding ◽  
Nucleoside Metabolism

ABSTRACT The availability of the complete sequence of the Bacillus subtilis chromosome (F. Kunst et al., Nature 390:249–256, 1997) makes possible the construction of genome-wide DNA arrays and the study of this organism on a global scale. Because we have a long-standing interest in the effects of scoC on late-stage developmental phenomena as they relate toaprE expression, we studied the genome-wide effects of ascoC null mutant with the goal of furthering the understanding of the role of scoC in growth and developmental processes. In the present work we compared the expression patterns of isogenic B. subtilis strains, one of which carries a null mutation in the scoC locus (scoC4). The results obtained indicate thatscoC regulates, either directly or indirectly, the expression of at least 560 genes in the B. subtilisgenome. ScoC appeared to repress as well as activate gene expression. Changes in expression were observed in genes encoding transport and binding proteins, those involved in amino acid, carbohydrate, and nucleotide and/or nucleoside metabolism, and those associated with motility, sporulation, and adaptation to atypical conditions. Changes in gene expression were also observed for transcriptional regulators, along with sigma factors, regulatory phosphatases and kinases, and members of sensor regulator systems. In this report, we discuss some of the phenotypes associated with the scoCmutant in light of the transcriptome changes observed.

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