scholarly journals Different Gene Expression Patterns of Hexose Transporter Genes Modulate Fermentation Performance of Four Saccharomyces cerevisiae Strains

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 164
Author(s):  
Chiara Nadai ◽  
Giulia Crosato ◽  
Alessio Giacomini ◽  
Viviana Corich
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Fermentation Process ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Facilitated Diffusion ◽  
Fermentation Performance ◽  
Expression Levels

In Saccharomyces cerevisiae, the fermentation rate and the ability to complete the sugar transformation process depend on the glucose and fructose transporter set-up. Hexose transport mainly occurs via facilitated diffusion carriers and these are encoded by the HXT gene family and GAL2. In addition, FSY1, coding a fructose/H+ symporter, was identified in some wine strains. This little-known transporter could be relevant in the last part of the fermentation process when fructose is the most abundant sugar. In this work, we investigated the gene expression of the hexose transporters during late fermentation phase, by means of qPCR. Four S. cerevisiae strains (P301.9, R31.3, R008, isolated from vineyard, and the commercial EC1118) were considered and the transporter gene expression levels were determined to evaluate how the strain gene expression pattern modulated the late fermentation process. The very low global gene expression and the poor fermentation performance of R008 suggested that the overall expression level is a determinant to obtain the total sugar consumption. Each strain showed a specific gene expression profile that was strongly variable. This led to rethinking the importance of the HXT3 gene that was previously considered to play a major role in sugar transport. In vineyard strains, other transporter genes, such as HXT6/7, HXT8, and FSY1, showed higher expression levels, and the resulting gene expression patterns properly supported the late fermentation process.

AML with Translocation T(8;16) Shows Unique Cytomorphological, Cytogenetic, Molecular, and Prognostic Features and Therefore Qualifies as An Own Entity According to WHO Criteria.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1197-1197
Author(s):  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Hans-Ulrich Klein ◽  
Christian Ruckert ◽  
Wolfgang Kern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Differentiation Potential ◽  
Who Criteria ◽  
Prognostic Features

Abstract Balanced chromosomal rearrangements define distinct biological subsets in acute myeloid leukemia (AML). It is recognized that recurrent balanced aberrations, such as t(15;17), t(8;21), inv(16), and 11q23/MLL translocations, show a close correlation to cytomorphology and also harbor specific gene expression signatures. We here present a cohort of 13 AML cases with t(8;16)(p11;p13). This translocation is rare with only 13 cases (6 males, 7 females) diagnosed from our overall cohort of 6124 cases of AML over recent years, and is more frequently found in therapy-related AML than in de novo AML (7/438 t-AML, and 6/5686 de novo, p=0.00001). Prognosis was poor with median overall survival of 4.7 months. Five patients deceased within the first month after diagnosis. AML with t(8;16) is characterized by striking cytomorphologic features: In all 13 cases the positivity for myeloperoxidase (MPO) on bone marrow smears was >30% (median: 85%) and intriguingly, in parallel also >40% (median: 88%) of blast cells stained strongly positive for non-specific esterase (NSE) in the same cell, suggesting that AML with t(8;16) arise from a very early stem cell with both myeloid and monoblastic differentiation potential. Therefore, AML with t(8;16) cases can not be classified according to standard FAB categories. Morphologically we also detected erythrophagocytosis in 7/13 cases, a specific feature in AML with t(8;16) that was previously described. With respect to cytogenetics, 6/13 patients had t(8;16)(p11;p13) as sole abnormality. 7/13 patients demonstrated additional non-recurrent abnormalities, 4 cases with single additional aberrations, and 3 cases with two or more additional aberrations. Molecular analyses detected the MYST3- CREBBP fusion transcript in all cases tested (12/12). We then compared gene expression patterns in 7 cases of AML with t(8;16) to: (i) AML FAB subtypes M1 and M4/5 with strong MPO or NSE with normal karyotype and to (ii) distinct AML subtypes with balanced chromosomal aberrations according to WHO classification. In a first series using Affymetrix HG-U133A+B microarrays 4 cases of AML with t(8;16) were compared to FAB M1 (n=46), M4 (n=41), M5a (n=9), and M5b (n=16). Hierarchical clustering and principal component analyses revealed that AML with t(8;16) were intercalating rather with FAB subtypes M4 and M5b and did not cluster near to FAB M1, although strong positivity for MPO was seen in all t(8;16) cases. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid features. When further compared to AML WHO subtypes t(15;17) (n=43), t(8;21) (n=43), inv(16) (n=49), and 11q23/MLL (n=50), AML with t(8;16) samples were repeatedly grouped in the vicinity of the 11q23/MLL cases. This can be explained by a similar expression of genes such as EAF2, HOXA9, HOXA10, PRKCD, or HNMT. Yet, in a subsequent pairwise comparison AML with t(8;16) could also be clearly discriminated from 11q23/MLL with differentially expressed genes including CAPRIN1, RAN, SMARCD2, LRRC41, or H2BFS, higher expressed in AML with t(8;16) and SOCS2, PRAME, RUNX3, or TPT1, lower expressed in AML with t(8;16), respectively. Moreover, the respective FAB-type or WHO-type signatures were validated on a separate cohort of patients (n=3 AML with t(8;16); n=107 other AML subtypes as above), all prospectively analyzed with the successor HG-U133 Plus 2.0 microarray. Again, in direct comparison to FAB-type or WHO-type cases, dominant and unique gene expression patterns were seen for AML with t(8;16), confirming the molecular distinctiveness of this rare AML entity. Using a classification algorithm we were able to correctly predict all AML with t(8;16) cases by their gene expression pattern. This accuracy was observed not only for both FAB-type and WHO-type signatures, but also correctly classified the cases across the different patient cohorts and microarray designs. In conclusion, AML with t(8;16) is a specific subtype of AML with very poor prognosis that often presents as treatment-related AML and with particular characteristics not only in morphology and clinical profile, but also on a molecular level. Due to these unique features, it qualifies as a specific recurrent entity according to WHO criteria.

scholarly journals Unique Gene Expression Patterns in Human T-cell Lines Generated from Multiple Sclerosis Patients by Stimulation with a Synthetic MOG Peptide

2005 ◽  
Vol 12 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Mathilda Mandel ◽  
Michael Gurevich ◽  
Gad Lavie ◽  
Irun R. Cohen ◽  
Anat Achiron
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cell Lines ◽  
Healthy Subjects ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
T Cell Lines ◽  
Myelin Antigens

Multiple sclerosis (MS) is an autoimmune disease where T-cells activated against myelin antigens are involved in myelin destruction. Yet, healthy subjects also harbor T-cells responsive to myelin antigens, suggesting that MS patient-derived autoimmune T-cells might bear functional differences from T-cells derived from healthy individuals. We addressed this issue by analyzing gene expression patterns of myelin oligodendrocytic glycoprotein (MOG) responsive T-cell lines generated from MS patients and healthy subjects. We identified 150 transcripts that were differentially expressed between MS patients and healthy controls. The most informative 43 genes exhibited >1.5-fold change in expression level. Eighteen genes were up-regulated including BCL2, lifeguard, IGFBP3 and VEGF. Twenty five genes were down-regulated, including apoptotic activators like TNF and heat shock protein genes. This gene expression pattern was unique to MOG specific T-cell lines and was not expressed in T-cell lines reactive to tetanus toxin (TTX). Our results indicate that activation in MS that promotes T-cell survival and expansion, has its own state and that the unique gene expression pattern that characterize autoreactive T-cells in MS represent a constellation of factors in which the chronicity, timing and accumulation of damage make the difference between health and disease.

scholarly journals Mapping global shifts in Saccharomyces cerevisiae gene expression across asynchronous time trajectories with diffusion maps

2021 ◽  
Author(s):  
Taylor Reiter ◽  
Rachel Montpetit ◽  
Ron Runnebaum ◽  
C. Titus Brown ◽  
Ben Montpetit
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Expression Patterns ◽  
Pinot Noir ◽  
Wine Fermentation ◽  
Gene Expression Patterns ◽  
Expression Data ◽  
Site Specific ◽  
Time Series Gene Expression ◽  
Specific Factors

AbstractGrapes grown in a particular geographic region often produce wines with consistent characteristics, suggesting there are site-specific factors driving recurrent fermentation outcomes. However, our understanding of the relationship between site-specific factors, microbial metabolism, and wine fermentation outcomes are not well understood. Here, we used differences in Saccharomyces cerevisiae gene expression as a biosensor for differences among Pinot noir fermentations from 15 vineyard sites. We profiled time series gene expression patterns of primary fermentations, but fermentations proceeded at different rates, making analyzes of these data with conventional differential expression tools difficult. This led us to develop a novel approach that combines diffusion mapping with continuous differential expression analysis. Using this method, we identified vineyard specific deviations in gene expression, including changes in gene expression correlated with the activity of the non-Saccharomyces yeast Hanseniaspora uvarum, as well as with initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated wine fermentation outcomes. In addition, we demonstrate that our analysis approach can extract biologically relevant gene expression patterns in other contexts (e.g., hypoxic response of Saccharomyces cerevisiae), indicating that this approach offers a general method for investigating asynchronous time series gene expression data.ImportanceWhile it is generally accepted that foods, in particular wine, possess sensory characteristics associated with or derived from their place of origin, we lack knowledge of the biotic and abiotic factors central to this phenomenon. We have used Saccharomyces cerevisiae gene expression as a biosensor to capture differences in fermentations of Pinot noir grapes from 15 vineyards across two vintages. We find that gene expression by non-Saccharomyces yeasts and initial nitrogen content in the grape must correlates with differences in gene expression among fermentations from these vintages. These findings highlight important relationships between site-specific variables and gene expression that can be used to understand, or possibly modify, wine fermentation outcomes. Our work also provides a novel analysis method for investigating asynchronous gene expression data sets that is able to reveal both global shifts and subtle differences in gene expression due to varied cell – environment interactions.

scholarly journals Saccharomyces cerevisiae gene expression during fermentation of Pinot noir wines at industrially relevant scale

2021 ◽  
Author(s):  
Taylor Reiter ◽  
Rachel Montpetit ◽  
Shelby Byer ◽  
Isadora Frias ◽  
Esmeralda Leon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Expression Patterns ◽  
Pilot Scale ◽  
Pinot Noir ◽  
Wine Fermentation ◽  
Gene Expression Program ◽  
Gene Expression Patterns ◽  
Grape Must ◽  
Expression Program

Saccharomyces cerevisiae metabolism produces ethanol and other compounds during the fermentation of grape must into wine. Thousands of genes change expression over the course of a wine fermentation, allowing S. cerevisiae to adapt to and dominate the fermentation environment. Investigations into these gene expression patterns have previously revealed genes that underlie cellular adaptation to the grape must and wine environment involving metabolic specialization and ethanol tolerance. However, the majority of studies detailing gene expression patterns have occurred in controlled environments that may not recapitulate the biological and chemical complexity of fermentations performed at production scale. Here, an analysis of the S. cerevisiae RC212 gene expression program is presented, drawing from 40 pilot-scale fermentations (150 liters) using Pinot noir grapes from 10 California vineyards across two vintages. A core gene expression program was observed across all fermentations irrespective of vintage similar to that of laboratory fermentations, in addition to novel gene expression patterns likely related to the presence of non-Saccharomyces microorganisms and oxygen availability during fermentation. These gene expression patterns, both common and diverse, provide insight into Saccharomyces cerevisiae biology critical to fermentation outcomes under industry-relevant conditions. Importance This study characterized Saccharomyces cerevisiae RC212 gene expression during Pinot noir fermentation at pilot scale (150 liters) using industry-relevant conditions. The reported gene expression patterns of RC212 are generally similar to that observed in laboratory fermentation conditions, but also contain gene expression signatures related to yeast-environment interactions found in a production setting (e.g., presence of non-Saccharomyces microorganisms). Key genes and pathways highlighted by this work remain under-characterized, raising the need for further research to understand the roles of these genes and their impact on industrial wine fermentation outcomes.

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.

scholarly journals Regional and cell-specific gene expression patterns during petal development.

1992 ◽  
Vol 4 (11) ◽  
pp. 1383-1404 ◽  
Author(s):  
G N Drews ◽  
T P Beals ◽  
A Q Bui ◽  
R B Goldberg
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Petal Development

FOXA1 in breast cancer

2009 ◽  
Vol 11 ◽  
Author(s):  
Harikrishna Nakshatri ◽  
Sunil Badve
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Signature ◽  
Specific Gene ◽  
Breast Cancers ◽  
Transcription Factor Network ◽  
Histopathological Classification

Breast cancer is a heterogeneous disease and classification is important for clinical management. At least five subtypes can be identified based on unique gene expression patterns; this subtype classification is distinct from the histopathological classification. The transcription factor network(s) required for the specific gene expression signature in each of these subtypes is currently being elucidated. The transcription factor network composed of the oestrogen (estrogen) receptor α (ERα), FOXA1 and GATA3 may control the gene expression pattern in luminal subtype A breast cancers. Breast cancers that are dependent on this network correspond to well-differentiated and hormone-therapy-responsive tumours with good prognosis. In this review, we discuss the interplay between these transcription factors with a particular emphasis on FOXA1 structure and function, and its ability to control ERα function. Additionally, we discuss modulators of FOXA1 function, ERα–FOXA1–GATA3 downstream targets, and potential therapeutic agents that may increase differentiation through FOXA1.

scholarly journals Microgenomic analysis reveals cell type-specific gene expression patterns between ray and fusiform initials within the cambial meristem ofPopulus

2008 ◽  
Vol 180 (1) ◽  
pp. 45-56 ◽  
Author(s):  
Nadia Goué ◽  
Marie-Claude Lesage-Descauses ◽  
Ewa J. Mellerowicz ◽  
Elisabeth Magel ◽  
Philippe Label ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Fusiform Initials ◽  
Cell Type Specific

scholarly journals Plant-parasitic nematodes respond to root exudate signals with host-specific gene expression patterns

2019 ◽  
Vol 15 (2) ◽  
pp. e1007503 ◽  
Author(s):  
Christopher A. Bell ◽  
Catherine J. Lilley ◽  
James McCarthy ◽  
Howard J. Atkinson ◽  
P. E. Urwin
Keyword(s):  
Gene Expression ◽  
Root Exudate ◽  
Expression Patterns ◽  
Parasitic Nematodes ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Plant Parasitic Nematodes ◽  
Specific Gene Expression ◽  
Plant Parasitic
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