Clozapine modulation of zebrafish swimming behavior and gene expression as a case study to investigate effects of atypical drugs on aquatic organisms

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.

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Case Study: Differences in HumanPer2 Gene Expression, Body Temperature, Cortisol, and Melatonin Parameters in Remission and Hypersomnia in a Patient with Recurrent Hypersomnia

Chronobiology International ◽

10.1081/cbi-120024213 ◽

2003 ◽

Vol 20 (5) ◽

pp. 893-900 ◽

Cited By ~ 8

Author(s):

Akemi Tomoda ◽

Takako Joudoi ◽

Junko Kawatani ◽

Takafumi Ohmura ◽

Akinobu Hamada ◽

...

Keyword(s):

Gene Expression ◽

Body Temperature

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A Case Study on Disease Diagnosis Using Gene Expression Data Classification with Feature Selection: Application of Data Science Techniques in Health Care

Data Science and Its Applications ◽

10.1201/9781003102380-12 ◽

2021 ◽

pp. 239-254

Author(s):

Abhilasha Chaudhuri ◽

Tirath Prasad Sahu

Keyword(s):

Gene Expression ◽

Health Care ◽

Feature Selection ◽

Gene Expression Data ◽

Data Science ◽

Data Classification ◽

Disease Diagnosis ◽

Expression Data

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Context‐dependency of synthetic minimal promoters in driving gene expression: a case study

Microbial Biotechnology ◽

10.1111/1751-7915.13489 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1476-1486 ◽

Cited By ~ 2

Author(s):

Liyuan Jin ◽

Said Nawab ◽

Mengli Xia ◽

Xiaoyan Ma ◽

Yi‐Xin Huo

Keyword(s):

Gene Expression ◽

Context Dependency

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A tank system for studying benthic aquatic organisms at predictable levels of turbidity and sedimentation: Case study examining coral growth

Limnology and Oceanography ◽

10.4319/lo.1999.44.6.1415 ◽

1999 ◽

Vol 44 (6) ◽

pp. 1415-1422 ◽

Cited By ~ 19

Author(s):

Kenneth R. N. Anthony

Keyword(s):

Aquatic Organisms ◽

Coral Growth ◽

Tank System

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Gene expression profiles based flux balance model to predict the carbon source for Bacillus subtilis

10.1101/842518 ◽

2019 ◽

Author(s):

Kulwadee Thanamit ◽

Franziska Hoerhold ◽

Marcus Oswald ◽

Rainer Koenig

Keyword(s):

Gene Expression ◽

Carbon Source ◽

Drug Targets ◽

Expression Profiles ◽

Carbon Sources ◽

Gene Expression Profiles ◽

Fast Method ◽

Flux Balance ◽

Metabolic Fluxes

ABSTRACTFinding drug targets for antimicrobial treatment is a central focus in biomedical research. To discover new drug targets, we developed a method to identify which nutrients are essential for microorganisms. Using 13C labeled metabolites to infer metabolic fluxes is the most informative way to infer metabolic fluxes to date. However, the data can get difficult to acquire in complicated environments, for example, if the pathogen homes in host cells. Although data from gene expression profiling is less informative compared to metabolic tracer derived data, its generation is less laborious, and may still provide the relevant information. Besides this, metabolic fluxes have been successfully predicted by flux balance analysis (FBA). We developed an FBA based approach using the stoichiometric knowledge of the metabolic reactions of a cell combining them with expression profiles of the coding genes. We aimed to identify essential drug targets for specific nutritional uptakes of microorganisms. As a case study, we predicted each single carbon source out of a pool of eight different carbon sources for B. subtilis based on gene expression profiles. The models were in good agreement to models basing on 13C metabolic flux data of the same conditions. We could well predict every carbon source. Later, we applied successfully the model to unseen data from a study in which the carbon source was shifted from glucose to malate and vice versa. Technically, we present a new and fast method to reduce thermodynamically infeasible loops, which is a necessary preprocessing step for such model-building algorithms.SIGNIFICANCEIdentifying metabolic fluxes using 13C labeled tracers is the most informative way to gain insight into metabolic fluxes. However, obtaining the data can be laborious and challenging in a complex environment. Though transcriptional data is an indirect mean to estimate the fluxes, it can help to identify this. Here, we developed a new method employing constraint-based modeling to predict metabolic fluxes embedding gene expression profiles in a linear regression model. As a case study, we used the data from Bacillus subtilis grown under different carbon sources. We could well predict the correct carbon source. Additionally, we established a novel and fast method to remove thermodynamically infeasible loops.

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An Integrative Analysis of microRNA and mRNA Expression–-A Case Study

Cancer Informatics ◽

10.4137/cin.s633 ◽

2008 ◽

Vol 6 ◽

pp. CIN.S633 ◽

Cited By ~ 15

Author(s):

Li-Xuan Qin

Keyword(s):

Gene Expression ◽

Mrna Expression ◽

Hierarchical Clustering ◽

Human Cancer ◽

Microrna Expression ◽

Tissue Type ◽

Integrated Analysis ◽

Tumor Type ◽

Cancer Data

Background MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression. Results We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression. Discussion Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure–the co-clustering frequency–to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general.

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Gene Expression and Manipulation in Aquatic Organisms. S. J. Ennion , G. Goldspink

Journal of the North American Benthological Society ◽

10.2307/1468161 ◽

1997 ◽

Vol 16 (3) ◽

pp. 724-725

Author(s):

Bruce J. Turner

Keyword(s):

Gene Expression ◽

Aquatic Organisms

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Differential Expression of Inflammation-Related Genes in Down Syndrome Patients with or without Periodontal Disease

Mediators of Inflammation ◽

10.1155/2019/4567106 ◽

2019 ◽

Vol 2019 ◽

pp. 1-6

Author(s):

M. Baus-Domínguez ◽

R. Gómez-Díaz ◽

D. Torres-Lagares ◽

J. R. Corcuera-Flores ◽

J. C. Ruiz-Villandiego ◽

...

Keyword(s):

Gene Expression ◽

Down Syndrome ◽

Periodontal Disease ◽

Differential Expression ◽

Clinical Feature ◽

Related Gene ◽

Thermo Fisher Scientific ◽

Oral Inflammation ◽

Fisher Scientific

Aim. Aware that Down Syndrome patients present among their clinical characteristics impaired immunity, the aim of this study is to identify the statistically significant differences in inflammation-related gene expression by comparing Down Syndrome patients with Periodontal Disease (DS+PD+) with Down Syndrome patients without Periodontal Disease (DS+PD-), and their relationship with periodontitis as a chronic oral inflammatory clinical feature. Materials and Methods. Case study and controls on eleven Down Syndrome patients (DS+PD+ vs. DS+PD-). RNA was extracted from peripheral blood using a Qiagen PAXgene Blood miRNA Kit when performing an oral examination. A search for candidate genes (92 selected) was undertaken on the total genes obtained using a Scientific GeneChip® Scanner 3000 (Thermo Fisher Scientific) and Clariom S solutions for human, mouse, and rat chips, with more than 20,000 genes annotated for measuring expression levels. Results. Of the 92 inflammation-related genes taken initially, four genes showed a differential expression across both groups with a p value of <0.05 from the data obtained using RNA processing of the patient sample. Said genes were TNFSF13B (p=0.0448), ITGB2 (p=0.0033), ANXA3 (p=0.0479), and ANXA5 (p=0.016). Conclusions. There are differences in inflammation-related gene expression in Down Syndrome patients when comparing patients who present a state of chronic oral inflammation with patients with negative rates of periodontal disease.

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