scholarly journals Coordinated Diel Gene Expression of Cyanobacteria and Their Microbiome

2021 ◽  
Vol 9 (8) ◽  
pp. 1670
Author(s):  
Kai Wang ◽  
Xiaozhen Mou
Keyword(s):  
Gene Expression ◽  
Structural Change ◽  
Vitamin B12 ◽  
Lake Erie ◽  
Expression Patterns ◽  
Microcosm Experiment ◽  
Gene Expressions ◽  
Significant Structural Change ◽  
Set Up ◽  
Whole Water

Diel rhythms have been well recognized in cyanobacterial metabolisms. However, whether this programmed activity of cyanobacteria could elicit coordinated diel gene expressions in microorganisms (microbiome) that co-occur with cyanobacteria and how such responses in turn impact cyanobacterial metabolism are unknown. To address these questions, a microcosm experiment was set up using Lake Erie water to compare the metatranscriptomic variations of Microcystis cells alone, the microbiome alone, and these two together (whole water) over two day-night cycles. A total of 1205 Microcystis genes and 4779 microbiome genes exhibited significant diel expression patterns in the whole-water microcosm. However, when Microcystis and the microbiome were separated, only 515 Microcystis genes showed diel expression patterns. A significant structural change was not observed for the microbiome communities between the whole-water and microbiome microcosms. Correlation analyses further showed that diel expressions of carbon, nitrogen, phosphorous, and micronutrient (iron and vitamin B12) metabolizing genes were significantly coordinated between Microcystis and the microbiome in the whole-water microcosm. Our results suggest that diel fluxes of organic carbon and vitamin B12 (cobalamin) in Microcystis could cause the diel expression of microbiome genes. Meanwhile, the microbiome communities may support the growth of Microcystis by supplying them with recycled nutrients, but compete with Microcystis for iron.

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.

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.

scholarly journals What can we learn from gene expression profiling of mouse oocytes?

Reproduction ◽  
2008 ◽  
Vol 135 (5) ◽  
pp. 581-592 ◽  
Author(s):  
Toshio Hamatani ◽  
Mitsutoshi Yamada ◽  
Hidenori Akutsu ◽  
Naoaki Kuji ◽  
Yoshiyuki Mochimaru ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Oocyte Quality ◽  
Preimplantation Development ◽  
Preimplantation Embryos ◽  
Gene Expressions ◽  
Oocyte Competence

Mammalian ooplasm supports the preimplantation development and reprograms the introduced nucleus transferred from a somatic cell to confer pluripotency in a cloning experiment. However, the underlying molecular mechanisms of oocyte competence remain unknown. Recent advances in microarray technologies have allowed gene expression profiling of such tiny specimens as oocytes and preimplantation embryos, generating a flood of information about gene expressions. So, what can we learn from it? Here, we review the initiative global gene expression studies of mouse and/or human oocytes, focusing on the lists of maternal transcripts and their expression patterns during oogenesis and preimplantation development. Especially, the genes expressed exclusively in oocytes should contribute to the uniqueness of oocyte competence, driving mammalian development systems of oocytes and preimplantation embryos. Furthermore, we discuss future directions for oocyte gene expression profiling, including discovering biomarkers of oocyte quality and exploiting the microarray data for ‘making oocytes’.

scholarly journals Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder

2021 ◽  
Author(s):  
Kangning Dong ◽  
Shihua Zhang
Keyword(s):  
Gene Expression ◽  
Spatial Information ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Identification Accuracy ◽  
Spatial Context ◽  
Gene Expressions ◽  
Spatially Resolved ◽  
Spatial Domains

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining spatial context of tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we developed a graph attention auto- encoder framework STGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validated STGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STGATE could be extended to multiple consecutive sections for reducing batch effects between sections and extracting 3D expression domains from the reconstructed 3D tissue effectively.

scholarly journals Antifreeze Protein Improves the Cryopreservation Efficiency of Hosta capitata by Regulating the Genes Involved in the Low-Temperature Tolerance Mechanism

Horticulturae ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 82
Author(s):  
Phyo Phyo Win Pe ◽  
Aung Htay Naing ◽  
Chang Kil Kim ◽  
Kyeung Il Park
Keyword(s):  
Gene Expression ◽  
Low Temperature ◽  
Cold Exposure ◽  
Expression Patterns ◽  
Antifreeze Protein ◽  
Temperature Tolerance ◽  
Gene Expressions ◽  
Low Temperature Tolerance ◽  
Binding Factor ◽  
Freezing Injuries

In this study, whether the addition of antifreeze protein (AFP) to a cryopreservative solution (plant vitrification solution 2 (PVS2)) is more effective in reducing freezing injuries in Hosta capitata than PVS2 alone at different cold exposure times (6, 24, and 48 h) is investigated. The upregulation of C-repeat binding factor 1 (CBF1) and dehydrin 1 (DHN1) in response to low temperature was observed in shoots. Shoots treated with distilled water (dH2O) strongly triggered gene expression 6 h after cold exposure, which was higher than those expressed in PVS2 and PVS2+AFP. However, 24 h after cold exposure, gene expressions detected in dH2O and PVS2 treatments were similar and higher than PVS2 + AFP. The expression was highest in PVS2+AFP when the exposure time was extended to 48 h. Similarly, nitric reductase activities 1 and 2 (Nia1 and Nia2) genes, which are responsible for nitric oxide production, were also upregulated in low-temperature-treated shoots, as observed for CBF1 and DHN1 expression patterns during cold exposure periods. Based on the gene expression patterns, shoots treated with PVS2+AFP were more likely to resist cold stress, which was also associated with the higher cryopreservation efficiency of PVS2+AFP compared to PVS2 alone. This finding suggests that the improvement of cryopreservation efficiency by AFP could be due to the transcriptional regulation of CBF1, DHN1, Nia1, and Nia2, which might reduce freezing injuries during cryopreservation. Thus, AFP could be potentially used as a cryoprotectant in the cryopreservation of rare and commercially important plant germplasm.

scholarly journals Prognostic value and co-expression patterns of metabolic pathways in cancers

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Dan Zhang ◽  
Yan Guo ◽  
Ni Xie
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Metabolic Pathway ◽  
Metabolic Pathways ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Cancer Prognosis ◽  
Gene Expressions ◽  
Pathway Gene ◽  
Pathway Expression

Abstract Background Abnormal metabolic pathways have been considered as one of the hallmarks of cancer. While numerous metabolic pathways have been studied in various cancers, the direct link between metabolic pathway gene expression and cancer prognosis has not been established. Results Using two recently developed bioinformatics analysis methods, we evaluated the prognosis potential of metabolic pathway expression and tumor-vs-normal dysregulations for up to 29 metabolic pathways in 33 cancer types. Results show that increased metabolic gene expression within tumors corresponds to poor cancer prognosis. Meta differential co-expression analysis identified four metabolic pathways with significant global co-expression network disturbance between tumor and normal samples. Differential expression analysis of metabolic pathways also demonstrated strong gene expression disturbance between paired tumor and normal samples. Conclusion Taken together, these results strongly suggested that metabolic pathway gene expressions are disturbed after tumorigenesis. Within tumors, many metabolic pathways are upregulated for tumor cells to activate corresponding metabolisms to sustain the required energy for cell division.

scholarly journals Identification of the associated expression patterns as potential predictive markers for cancer prognosis

2020 ◽  
Author(s):  
Han Liang ◽  
Cong Lin ◽  
Yong Hou ◽  
Fuqiang Li ◽  
Kui Wu
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Expression Patterns ◽  
Tumor Development ◽  
Cancer Prognosis ◽  
Gene Expressions ◽  
Complex Interactions ◽  
Genetic Abnormalities ◽  
Gene Expression Correlation ◽  
Cancer Types

AbstractDysregulated gene expression can develop as a consequence of uncontrolled alterations of tumor cells. Analysis of these abnormal alterations will improve our understanding of the tumor development and reveal the corresponding clinical associations. It is well known that multiple genetic abnormalities could be observed in the same tumor, however, the interactions between those abnormal events are rarely analyzed. To address this problem, we constructed a novel gene expression correlation network by integrating the transcriptomes of 5,001 cancer patients from 22 cancer types. We investigated how the change of associated expression pattern (AEP), which describe certain associations between gene expression, could affect the cancer patient’s prognosis. Consequently, we identified an AEP composed of mitosis-related gene expressions, which is significantly correlated with overall survival in most cancer types. In particular, the AEPs could present the association between gene expressions and show distinct effects on prognosis prediction for cancer patients, suggesting that AEP analysis is indispensable to uncover the complex interactions of abnormal gene expressions in tumor development.

scholarly journals Achieving k-anonymity in DataMarts used for gene expressions exploitation

2007 ◽  
Vol 4 (1) ◽  
pp. 132-144 ◽  
Author(s):  
Konrad Stark ◽  
Johann Eder ◽  
Kurt Zatloukal
Keyword(s):  
Gene Expression ◽  
Target Identification ◽  
Expression Patterns ◽  
Point Of View ◽  
Patient Specific ◽  
Distributed Data ◽  
Gene Expressions ◽  
Sensitive Data ◽  
Activation Patterns ◽  
Related Data

Abstract Gene expression profiling is a sophisticated method to discover differences in activation patterns of genes between different patient collectives. By reasonably defining patient groups from a medical point of view, subsequent gene expression analysis may reveal disease-related gene expression patterns that are applicable for tumor markers and pharmacological target identification. When releasing patient-specific data for medical studies privacy protection has to be guaranteed for ethical and legal reasons. k-anonymisation may be used to generate a sufficient number of k data twins in order to ensure that sensitive data used in analyses is protected from being linked to individuals. We use an adapted concept of k-anonymity for distributed data sources and include various customisation parameters in the anonymisation process to guarantee that the transformed data is still applicable for further processing. We present a real-world medical-relevant use case and show how the related data is materialised, anonymised, and released in a data mart for testing the related hypotheses.

Gene Expression Profile of Osteoblast-Like Cells on Calcium Phosphate Biomaterials

2007 ◽  
Vol 330-332 ◽  
pp. 1087-1090 ◽  
Author(s):  
Taro Takemura ◽  
Hong Song Fan ◽  
Toshiyuki Ikoma ◽  
M. Tanaka ◽  
Nobutaka Hanagata
Keyword(s):  
Gene Expression ◽  
Calcium Phosphate ◽  
Porous Materials ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Osteoblast Differentiation ◽  
Expression Patterns ◽  
Gene Expressions ◽  
Microarray Analyses ◽  
Cells Cultured

Gene expression profile of osteoblast-like cells cultured on dense disk materials and porous materials of calcium phosphate ceramics was constructed from DNA microarray analyses. The profile revealed that gene expression patterns of porous materials were significantly different from those of dense disk materials. The porous materials had a capacity to induce expressions of genes involved in osteoblast differentiation, while dense disk materials regulated gene expressions related to osteoclastogenesis.

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