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Assessing and selecting gene expression signals based upon the quality of the measured dynamics.

ABSTRACT: BACKGROUND: One of the challenges with modeling the temporal progression of biological signals is dealing with the effect of noise and the limited number of replicates at each time point. Given the rising interest in utilizing predictive mathematical models to describe the biological response of an organism or analysis such as clustering and gene ontology enrichment, it is important to determine whether the dynamic progression of the data has been accurately captured despite the limited number of replicates, such that one can have confidence that the results of the analysis are capturing important salient dynamic features. RESULTS: By pre-selecting genes based upon quality before the identification of differential expression via algorithm such as EDGE, it was found that the percentage of statistically enriched ontologies (p < .05) was improved. Furthermore, it was found that a majority of the genes found via the proposed technique were also selected via an EDGE selection though the reverse was not necessarily true. It was also found that improvements offered by the proposed algorithm are anti-correlated with improvements in the various microarray platforms and the number of replicates. This is illustrated by the fact that newer arrays and experiments with more replicates show less improvement when the filtering for quality is first run before the selection of differentially expressed genes. This suggests that the increase in the number of replicates as well as improvements in array technologies are increase the confidence one has in the dynamics obtained from the experiment. CONCLUSION: We have developed an algorithm that quantifies the quality of temporal biological signal rather than whether the signal illustrates a significant change over the experimental time course. Because the use of these temporal signals, whether it is in mathematical modeling or clustering, focuses upon the entire time series, it is necessary to develop a method to quantify and select for signals which conform to this ideal. By doing this, we have demonstrated a marked and consistent improvement in the results of a clustering exercise over multiple experiments, microarray platforms, and experimental designs.

SUBMITTER: Yang E

PROVIDER: S-EPMC2653486 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Assessing and selecting gene expression signals based upon the quality of the measured dynamics.

Yang Eric E Androulakis Ioannis P IP

BMC bioinformatics 20090210

<h4>Background</h4>One of the challenges with modeling the temporal progression of biological signals is dealing with the effect of noise and the limited number of replicates at each time point. Given the rising interest in utilizing predictive mathematical models to describe the biological response of an organism or analysis such as clustering and gene ontology enrichment, it is important to determine whether the dynamic progression of the data has been accurately captured despite the limited n ...[more]

PMID: 19208252

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Project description:Accurate evaluation of the biological performance of biomaterials requires the correct assessment of their native-like cell ligation properties. However, cell attachment studies often overlook the details of the substrate-cell binding mechanisms, be they integrin-mediated or non-specific, and ignore the class- and species-specificities of the cell adhesion receptor involved. In this work we have used different collagen (Col) substrates (fibrillar collagens I, II and III and network-forming Col IV), containing different affinity cell-recognition motifs, to establish the influence of the receptor identity and species-specificity on collagen-cell interactive properties. Receptor expression was varied by using cells of different origin, or transfecting collagen-binding integrins into integrin-null cells. These include mouse C2C12 myoblasts transfected with human ?1, ?2, ?10 or ?11; human fibrosarcoma HT1080 cells which constitutively express only human ?2?1, and rat glioma Rugli cells, with only rat ?1?1. Using these lines, the nature of integrin binding sites was studied in order to delineate the bioactivity of different collagen substrates. Integrin ligation was studied on collagen coatings alongside synthetic (GFOGER/GLOGEN) and Toolkit (Col II-28/Col III-7) triple-helical peptides to evaluate (1) their affinity towards different integrins and (2) to confirm the activity of the inserted integrin in the transfected cells. Thin films of dermal and tendon Col I were used to evaluate the influence of the carbodiimide (EDC)-based treatment on the cellular response on Col of different origin. The results showed that the binding properties of transfected C2C12 cells to collagens depend on the identity of inserted integrin. Similar ligation characteristics were observed using ?1+ and ?10+ cells, but these were distinct from the similar binding features of ?2+ and ?11+ cells. Recombinant human and rat-?1 I domain binding to collagens and peptides correlated with the cell adhesion results, showing receptor class- and species-specificities. The understanding of the physiologically relevant cell anchorage characteristics of bio-constructs may assist in the selection of (1) the optimum collagen source for cellular supports and (2) the correct cellular model for their biological assessment. This, in turn, may allow reliable prediction of the biological performance of bio-scaffolds in vivo for specific TE applications. STATEMENT OF SIGNIFICANCE:Integrins play a vital role in cellular responses to environmental cues during early-stage cell-substrate interaction. We describe physiologically relevant cell anchorage to collagen substrates that present different affinity cell-recognition motifs, to provide experimental tools to assist in understanding integrin binding. Using different cell types and recombinant integrin ?1-I-domains, we found that cellular response was highly dependent on collagen type, origin and EDC-crosslinking status, as well as on the integrin class and species of origin. This comprehensive study establishes selectivity amongst the four collagen-binding integrins and species-specific properties that together may influence choice of cell type and receptor in different experimental settings. This work offers key guidance in selecting of the correct cellular model for the biological testing of collagen-based biomaterials.

Dataset Information

Assessing and selecting gene expression signals based upon the quality of the measured dynamics.

Publications

Assessing and selecting gene expression signals based upon the quality of the measured dynamics.

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