scholarly journals Transcriptional Analysis of Clostridium beijerinckii NCIMB 8052 and the Hyper-Butanol-Producing Mutant BA101 during the Shift from Acidogenesis to Solventogenesis

2008 ◽  
Vol 74 (24) ◽  
pp. 7709-7714 ◽  
Author(s):  
Zhen Shi ◽  
Hans P. Blaschek
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Genomic Sequence ◽  
Morphological Changes ◽  
Expression Patterns ◽  
Sugar Transport ◽  
Clostridium Beijerinckii ◽  
Transcriptional Analysis ◽  
Sequence Information ◽  
Phosphotransferase System

ABSTRACT Clostridium beijerinckii is an anaerobic bacterium used for the fermentative production of acetone and butanol. The recent availability of genomic sequence information for C. beijerinckii NCIMB 8052 has allowed for an examination of gene expression during the shift from acidogenesis to solventogenesis over the time course of a batch fermentation using a ca. 500-gene set DNA microarray. The microarray was constructed using a collection of genes which are orthologs of members of gene families previously found to be important to the physiology of C. acetobutylicum ATCC 824. Similar to the onset of solventogenesis in C. acetobutylicum 824, the onset of solventogenesis in C. beijerinckii 8052 was concurrent with the initiation of sporulation. However, forespores and endospores developed more rapidly in C. beijerinckii 8052 than in C. acetobutylicum 824, consistent with the accelerated expression of the sigE- and sigG-regulated genes in C. beijerinckii 8052. The comparison of gene expression patterns and morphological changes in C. beijerinckii 8052 and the hyper-butanol-producing C. beijerinckii strain BA101 indicated that BA101 was less efficient in sporulation and phosphotransferase system-mediated sugar transport than 8052 but that it exhibited elevated expression of several primary metabolic genes and chemotaxis/motility genes.

scholarly journals Signalling pathways and mechanistic cues highlighted by transcriptomic analysis of primordial, primary, and secondary ovarian follicles in domestic cat

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shauna Kehoe ◽  
Katarina Jewgenow ◽  
Paul R. Johnston ◽  
Susan Mbedi ◽  
Beate C. Braun
Keyword(s):  
Gene Expression ◽  
Transforming Growth Factor ◽  
Ovarian Follicle ◽  
Mammalian Species ◽  
Expression Patterns ◽  
Ovarian Follicles ◽  
Domestic Cat ◽  
Transcriptional Analysis ◽  
Ovarian Folliculogenesis

AbstractIn vitro growth (IVG) of dormant primordial ovarian follicles aims to produce mature competent oocytes for assisted reproduction. Success is dependent on optimal in vitro conditions complemented with an understanding of oocyte and ovarian follicle development in vivo. Complete IVG has not been achieved in any other mammalian species besides mice. Furthermore, ovarian folliculogenesis remains sparsely understood overall. Here, gene expression patterns were characterised by RNA-sequencing in primordial (PrF), primary (PF), and secondary (SF) ovarian follicles from Felis catus (domestic cat) ovaries. Two major transitions were investigated: PrF-PF and PF-SF. Transcriptional analysis revealed a higher proportion in gene expression changes during the PrF-PF transition. Key influencing factors during this transition included the interaction between the extracellular matrix (ECM) and matrix metalloproteinase (MMPs) along with nuclear components such as, histone HIST1H1T (H1.6). Conserved signalling factors and expression patterns previously described during mammalian ovarian folliculogenesis were observed. Species-specific features during domestic cat ovarian folliculogenesis were also found. The signalling pathway terms “PI3K-Akt”, “transforming growth factor-β receptor”, “ErbB”, and “HIF-1” from the functional annotation analysis were studied. Some results highlighted mechanistic cues potentially involved in PrF development in the domestic cat. Overall, this study provides an insight into regulatory factors and pathways during preantral ovarian folliculogenesis in domestic cat.

scholarly journals Growth, Development, and Gene Expression in a Persistent Streptococcus gordonii Biofilm

2003 ◽  
Vol 71 (8) ◽  
pp. 4759-4766 ◽  
Author(s):  
Keeta S. Gilmore ◽  
Pravina Srinivas ◽  
Darrin R. Akins ◽  
Kenneth L. Hatter ◽  
Michael S. Gilmore
Keyword(s):  
Gene Expression ◽  
Biofilm Formation ◽  
Bacterial Population ◽  
Morphological Changes ◽  
Expression Patterns ◽  
Nutrient Flux ◽  
Gene Expression Patterns ◽  
Significant Shift ◽  
Streptococcus Gordonii ◽  
Growth Development

ABSTRACT A model for the protracted (30-day) colonization of smooth surfaces by Streptococcus gordonii that incorporates the nutrient flux that occurs in the oral cavity was developed. This model was used to characterize the biphasic expansion of the adherent bacterial population, which corresponded with the emergence of higher-order architectures characteristic of biofilms. Biofilm formation by S. gordonii was observed to be influenced by the presence of simple sugars including sucrose, glucose, and fructose. Real-time PCR was used to quantify changes in expression of S. gordonii genes known or thought to be involved in biofilm formation. Morphological changes were accompanied by a significant shift in gene expression patterns. The majority of S. gordonii genes examined were observed to be downregulated in the biofilm phase. Genes found to be upregulated in the biofilm state were observed to encode products related to environmental sensing and signaling.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

scholarly journals Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Qihua Tan ◽  
Mads Thomassen ◽  
Mark Burton ◽  
Kristian Fredløv Mose ◽  
Klaus Ejner Andersen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Coefficient ◽  
Parametric Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Time Course ◽  
Time Course Data ◽  
Generalized Correlation ◽  
Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

Follistatin alters myostatin gene expression in C2C12 muscle cells

2004 ◽  
Vol 52 (2) ◽  
pp. 135-141 ◽  
Author(s):  
H. Kocams¸ ◽  
N. Gulmez ◽  
S. Aslan ◽  
M. Nazlı
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Time Course ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Pattern ◽  
Muscle Cells ◽  
Treated Group ◽  
Myostatin Gene ◽  
Significant Difference

The objective of the present study was to determine the effects of follistatin addition on myostatin and follistatin gene expression patterns in C2C12 muscle cells. C2C12 cells were administered with 100 ng/ml recombinant human (rh) follistatin in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS), 4 mM glutamine and antibiotics daily for three days. Rh follistatin was not added in the control wells. Follistatin and myostatin gene cDNAs were synthesised by reverse transcriptase polymerase chain reaction (RT-PCR).The time course of follistatin gene expression pattern was similar in both the control and the follistatin-treated group. Myostatin mRNA level significantly increased in the follistatin-treated group after 24 h of culture (Fig. 3, P < 0.01). Amounts then sharply decreased (Fig. 3, P < 0.01) at 48 h of culture, whereas there was no significant difference between the control and the follistatin-treated group at 72 h of culture. Our results demonstrated that myostatin and follistatin mRNA were expressed in C2C12 cells and rh follistatin changed the myostatin expression pattern.

scholarly journals Expansion of the circadian transcriptome in Brassica rapa and genome-wide diversification of paralog expression patterns

2020 ◽  
Author(s):  
Kathleen Greenham ◽  
Ryan C. Sartor ◽  
Stevan Zorich ◽  
Ping Lou ◽  
Todd C. Mockler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Network ◽  
Time Course ◽  
Expression Patterns ◽  
Time Of Day ◽  
Circadian Regulation ◽  
Genome Wide ◽  
Circadian Control ◽  
Gene Regulatory

AbstractAn important challenge of crop improvement strategies is assigning function to paralogs in polyploid crops. Gene expression is one method for determining the activity of paralogs; however, the majority of transcript abundance data represents a static point that does not consider the spatial and temporal dynamics of the transcriptome. Studies in Arabidopsis have estimated up to 90% of the transcriptome to be under diel or circadian control depending on the condition. As a result, time of day effects on the transcriptome have major implications on how we characterize gene activity. In this study, we aimed to resolve the circadian transcriptome in the polyploid crop Brassica rapa and explore the fate of multicopy orthologs of Arabidopsis circadian regulated genes. We performed a high-resolution time course study with 2 h sampling density to capture the genes under circadian control. Strikingly, more than two-thirds of expressed genes exhibited rhythmicity indicative of circadian regulation. To compare the expression patterns of paralogous genes, we developed a program in R called DiPALM (Differential Pattern Analysis by Linear Models) that analyzes time course data to identify transcripts with significant pattern differences. Using DiPALM, we identified genome-wide divergence of expression patterns among retained paralogs. Cross-comparison with a previously generated diel drought experiment in B. rapa revealed evidence for differential drought response for these diverging paralog pairs. Using gene regulatory network models we compared transcription factor targets between B. rapa and Arabidopsis circadian networks to reveal additional evidence for divergence in expression between B. rapa paralogs that may be driven in part by variation in conserved non coding sequences. These findings provide new insight into the rapid expansion and divergence of the transcriptional network in a polyploid crop and offer a new method for assessing paralog activity at the transcript level.SignificanceThe circadian regulation of the transcriptome leads to time of day changes in gene expression that coordinates environmental conditions with physiological responses. Brassica rapa, a morphologically diverse crop species, has undergone whole genome triplication since diverging from Arabidopsis resulting in an expansion of gene copy number. To examine how this expansion has influenced the circadian transcriptome we developed a new method for comparing gene expression patterns. This method facilitated the discovery of genome-wide expansion of expression patterns for genes present in multiple copies and divergence in temporal abiotic stress response. We find support for conserved sequences outside the gene body contributing to these expression pattern differences and ultimately generating new connections in the gene regulatory network.

scholarly journals Transcriptome analysis of sevoflurane exposure effects at the different brain regions

2020 ◽  
Author(s):  
Hiroto Yamamoto ◽  
Yutaro Uchida ◽  
Tomoki Chiba ◽  
Ryota Kurimoto ◽  
Takahide Matsushima ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Neural Development ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Transcriptional Analysis ◽  
Rna Seq ◽  
Ontology Term ◽  
Whole Brain ◽  
Differently Expressed Genes

AbstractBackgroundsSevoflurane is a most frequently used volatile anaesthetics, but its molecular mechanisms of action remain unclear. We hypothesized that specific genes play regulatory roles in whole brain exposed to sevoflurane. Thus, we aimed to evaluate the effects of sevoflurane inhalation and identify potential regulatory genes by RNA-seq analysis.MethodsEight-week old mice were exposed to sevoflurane. RNA from four medial prefrontal cortex, striatum, hypothalamus, and hippocampus were analysed using RNA-seq. Differently expressed genes were extracted. Their gene ontology terms and the transcriptome array data of the cerebral cortex of sleeping mice were analysed using Metascape, and the gene expression patterns were compared. Finally, the activities of transcription factors were evaluated using a weighted parametric gene set analysis (wPGSA). JASPAR was used to confirm the existence of binding motifs in the upstream sequences of the differently expressed genes.ResultsThe gene ontology term enrichment analysis result suggests that sevoflurane inhalation upregulated angiogenesis and downregulated neural differentiation in the whole brain. The comparison with the brains of sleeping mice showed that the gene expression changes were specific to anaesthetized mice. Sevoflurane induced Klf4 upregulation in the whole brain. The transcriptional analysis result suggests that KLF4 is a potential transcriptional regulator of angiogenesis and neural development.ConclusionsKlf4 was upregulated by sevoflurane inhalation in whole brain. KLF4 might promote angiogenesis and cause the appearance of undifferentiated neural cells by transcriptional regulation. The roles of KLF4 might be key to elucidating the mechanisms of sevoflurane induced functional modification in the brain.

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