Further defining housekeeping, or "maintenance," genes Focus on "A compendium of gene expression in normal human tissues".
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Adult↗
Biomedical subjects
A J Butte
Publications and source records attributed to A J Butte.
Comparing the similarity of time-series gene expression using signal processing metrics.
Many algorithms have been used to cluster genes measured by microarray across a time series. Instead of clustering, our goal was to compare all pairs of genes to determine whether there was evidence of a phase shift between them. We describe a technique where gene expression is treated as a discrete time-invariant signal, allowing the use of digital signal-processing tools, including power spectral density, coherence, and transfer gain and phase shift. We used these on a public RNA expression set of 2467 genes measured every 7 min for 119 min and found 18 putative associations. Two of these were known in the biomedical literature and may have been missed using correlation coefficients. Digital signal processing tools can be embedded and enhance existing clustering algorithms.
Algorithms↗
Challenges in bioinformatics: infrastructure, models and analytics.
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Computational Biology↗
Strict interpretation of vaccination guidelines with computerized algorithms and improper timing of administered doses.
BACKGROUND: Frequently changing immunization recommendations may lead to incorrectly administered doses. OBJECTIVE: To determine the incidence and characteristics of inappropriately timed vaccinations. METHODS: Prospectively collected immunization histories of patients <5 years old from well-child care encounters with pediatric residents in a large urban clinic during a 3-month study period. New patients or those with no immunization history in the medical record were excluded. Paper records were verified before each visit and served as the immunization history. Immunization records were entered into and analyzed by the Massachusetts Immunization Information System with strict interpretation of minimum spacing and age guidelines to identify invalid vaccine doses. Reasons for invalidity were determined by manual review. Invalid doses were cross-referenced with clinic schedule to determine who delivered doses. RESULTS: Inclusion criteria were met by 690 encounters. Charts were available for review before the encounter for 580, containing 6983 total immunizations. Of these 289 (4.1%) administered doses were invalid; 206 of 580 (35.5%) patients had at least one invalid dose. Common invalid doses given were unnecessary poliovirus vaccine around 18 months (n = 66) and second hepatitis B vaccine given too soon after the first (n = 53). All types of providers gave invalid doses; pediatric residents and fellows delivered significantly more (P < 0.01). CONCLUSIONS: By strict interpretation of immunization guidelines, many patients were immunized incorrectly. Clinicians should be aware of common errors in vaccine dosing and national guidelines should be simplified.
Algorithms↗
Determining significant fold differences in gene expression analysis.
A typical use for RNA expression microarrays is comparing the measurement of gene expression of two groups. There has not been a study reproducing an entire experiment and modeling the distribution of reproducibility of fold differences. Our goal was to create a model of significance for fold differences, then maximize the number of ESTs above that threshold. Multiple strategies were tested to filter out those ESTs contributing to noise, thus decreasing the requirements of what was needed for significance. We found that even though RNA expression levels appears consistent in duplicate measurements, when entire experiments are duplicated, the calculated fold differences are not as consistent. Thus, it is critically important to repeat as many data points as possible, to ensure that genes and ESTs labeled as significant are truly so. We were successfully able to use duplicated expression measurements to model the duplicated fold differences, and to calculate the levels of fold difference needed to reach significance. This approach can be applied to many other experiments to ascertain significance without a priori assumptions.
Expressed Sequence Tags↗
Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks.
In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.
Antineoplastic Agents↗
Severe hypothyroidism caused by type 3 iodothyronine deiodinase in infantile hemangiomas.
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Hemangioma↗
Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.
Increasing numbers of methodologies are available to find functional genomic clusters in RNA expression data. We describe a technique that computes comprehensive pair-wise mutual information for all genes in such a data set. An association with a high mutual information means that one gene is non-randomly associated with another; we hypothesize this means the two are related biologically. By picking a threshold mutual information and using only associations at or above the threshold, we show how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks. The biological significance of each Relevance Network is explained.
Computer Simulation↗
Enrolling patients into clinical trials faster using RealTime Recuiting.
Previous work has been done on both optimizing the clinical trials process, and on sending critical laboratory results and decision support through paging systems. We report the first integration of both these solution, focusing on improving the clinical trial recruitment process. We describe a clinical trial needing a real-time method of recruiting patients in an unbiased manner, quickly enough that study tests can be obtained before patients leave or samples discarded. The report describes how the ten currently recruited patients were found and how diagnoses of potentially life-threatening disorders are being made.
Child, Preschool↗
Unsupervised knowledge discovery in medical databases using relevance networks.
Increasing amounts of data exist in medical databases. When multiple variables are measured for each case in a data set, there exists an underlying relationship between all pairs of variables, some highly correlated and some not. This report describes a technique that creates networks of related variables, or relevance networks, by dropping links with either too weak correlation or too few data points to defend the relationship. The paper describes how applying this methodology to the domain of laboratory results allows the generation of meaningful relations between types of laboratory tests. These relations could be used as the basis of further exploratory research.
Clinical Laboratory Techniques↗
Insulin receptor substrate 1 mediates the stimulatory effect of insulin on GLUT4 translocation in transfected rat adipose cells.
Insulin signaling is initiated at least in part by activation of the insulin receptor tyrosine kinase and subsequent phosphorylation of cellular substrates such as insulin receptor substrate 1 (IRS-1). Previous studies have focused on the role of IRS-1 in the mitogenic actions of insulin. We have now investigated the possible role of IRS-1 in mediating the effect of insulin to stimulate glucose transport in a physiologically relevant insulin target tissue. In this study, we transfected rat adipose cells in primary culture with an antisense ribozyme directed against rat IRS-1. Expression of the ribozyme in these cells caused a 4.4-fold increase in the concentration of insulin required to achieve half-maximal stimulation of the translocation of cotransfected epitope-tagged GLUT4 without changing the maximal insulin response. Overexpression of human IRS-1 increased the basal cell surface GLUT4 to nearly the maximal level in the absence of insulin. When the ribozyme (specific to rat IRS-1) was cotransfected along with human IRS-1, the insulin dose-response curve was shifted to the left when compared with cells transfected with the ribozyme alone. These data provide strong support for the hypothesis that IRS-1 plays a role in insulin-stimulated glucose transport in insulin-responsive cells.
Adipose Tissue↗
Tyrosine kinase-deficient mutant human insulin receptors (Met1153-->Ile) overexpressed in transfected rat adipose cells fail to mediate translocation of epitope-tagged GLUT4.
Insulin regulates essential pathways for growth, differentiation, and metabolism in vivo. We report a physiologically relevant system for dissecting the molecular mechanisms of insulin signal transduction related to glucose transport. This is an extension of our recently reported method for transfection of DNA into rat adipose cells in primary culture. In the present work, cDNA coding for GLUT4 with an epitope tag (HA1) in the first exofacial loop is used as a reporter gene so that GLUT4 translocation can be studied exclusively in transfected cells. Insulin stimulates a 4.3-fold recruitment of transfected epitope-tagged GLUT4 to the cell surface. Cells cotransfected with the reporter gene and the human insulin receptor gene show an increase in cell surface GLUT4 in the basal state (no insulin) to levels comparable to those seen with maximal insulin stimulation of cells transfected with the reporter gene alone. In contrast, cells overexpressing a naturally occurring tyrosine kinase-deficient mutant insulin receptor (Met1153-->Ile) show no increase in the basal cell surface GLUT4 and no shift in the insulin dose-response curve relative to cells transfected with the reporter gene alone. These results demonstrate that insulin receptor tyrosine kinase activity is essential in insulin-stimulated glucose transport in adipose cells.
Adipose Tissue↗
Derivation of a scale-independent parameter which characterizes genetic sequence comparisons.
A new method of quantifying the similarity between genetic sequences is presented. The method makes use of the finding that sequence comparisons expressed in binary vector form have an associated scale-independent parameter, D. This parameter is represented in the function M(S,n) = (N) (en/enD), where S is the vector, n represents the window size which is allowed to vary, N is a constant, and D is the scale-independent measure of homology. By comparing two sequences using this method, a unimodal, symmetric distribution of D values associated with the frameshifted vectors is obtained. The degree of sequence similarity is determined by the distribution of these parameters. A set of sequences of evolutionary interest coding for glyceraldehyde-3-phosphate dehydrogenases and mammalian insulins is compared using this methodology. The results confirm evolutionary tree distances calculated using different procedures. Since a z score can be calculated for each comparison, the method allows for the rapid identification of sequence homologies ranked according to the probability of occurrence. This unique scale-independent measure of similarity allows contrasts and comparisons between any two sequence fragments using all available order information.
Actins↗