biostudies-arrayexpress00630Stephen PopperHomo sapienshttps://www.ebi.ac.uk/biostudies/studies/E-GEOD-15297ABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr) disease_state_designbiostudies-arrayexpressNucleic Acid Extraction - Paxgene Ambion amp 4ugLabeling - not providedNucleic Acid Extraction - Stratagene Universal Human Reference RNA, 1st rnd amp AW 11/01/01, 3ugHybridization - not providedMIAME ScoreOrganizationAssays and DataProcessed DataMAGE-TAB FilesArray DesignsFeature Extraction - VALUE is Log (base 2) of the ratio of the median of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm)Assay Data Transformation - ID_REF = ID_REF<br>CH1I_MEAN = Mean feature pixel intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>CH2I_MEAN = Mean feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>CH1B_MEDIAN = The median feature background intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel; Background<br>CH2B_MEDIAN = The median feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>CH1D_MEAN = The mean feature pixel intensity at wavelength 532 nm with the median background subtracted.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel<br>CH2D_MEAN = .The mean feature pixel intensity at wavelength 635 nm with the median background subtracted.; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH1I_MEDIAN = Median feature pixel intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>CH2I_MEDIAN = Median feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>CH1B_MEAN = The mean feature background intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Background<br>CH2B_MEAN = The mean feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Background<br>CH1D_MEDIAN = The median feature pixel intensity at wavelength 532 nm with the median background subtracted.; Type: integer; Scale: linear_scale<br>CH2D_MEDIAN = The median feature pixel intensity at wavelength 635 nm with the median background subtracted.; Type: integer; Scale: linear_scale<br>CH1_PER_SAT = The percentage of feature pixels at wavelength 532 nm that are saturated.; Type: integer; Scale: linear_scale<br>CH2_PER_SAT = The percentage of feature pixels at wavelength 635 nm that are saturated.; Type: integer; Scale: linear_scale<br>CH1I_SD = The standard deviation of the feature intensity at wavelength 532 nm.; Type: integer; Scale: linear_scale; Channel: Cy3 Channel<br>CH2I_SD = The standard deviation of the feature pixel intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH1B_SD = The standard deviation of the feature background intensity at wavelength 532 nm.; Type: float; Scale: linear_scale; Channel: Cy3 Channel; Background<br>CH2B_SD = The standard deviation of the feature background intensity at wavelength 635 nm.; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>PERGTBCH1I_1SD = The percentage of feature pixels with intensities more than one standard deviation above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH2I_1SD = The percentage of feature pixels with intensities more than one standard deviation above the background pixel intensity, at wavelength 635 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH1I_2SD = The percentage of feature pixels with intensities more than two standard deviations above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>PERGTBCH2I_2SD = The percentage of feature pixels with intensities more than two standard deviations above the background pixel intensity, at wavelength 532 nm.; Type: integer; Scale: linear_scale<br>SUM_MEAN = The sum of the arithmetic mean intensities for each wavelength, with the median background subtracted.; Type: integer; Scale: linear_scale<br>SUM_MEDIAN = The sum of the median intensities for each wavelength, with the median background subtracted.; Type: integer; Scale: linear_scale<br>RAT1_MEAN = Ratio of the arithmetic mean intensities of each spot for each wavelength, with the median background subtracted. Channel 1/Channel 2 ratio, (CH1I_MEAN - CH1B_MEDIAN)/(CH2I_MEAN - CH2B_MEDIAN) or Green/Red ratio.; Type: float; Scale: linear_scale<br>RAT2_MEAN = The ratio of the arithmetic mean intensities of each feature for each wavelength, with the median background subtracted.; Type: float; Scale: linear_scale<br>RAT2_MEDIAN = The ratio of the median intensities of each feature for each wavelength, with the median background subtracted.; Type: float; Scale: linear_scale<br>PIX_RAT2_MEAN = The geometric mean of the pixel-by-pixel ratios of pixel intensities, with the median background subtracted.; Type: float; Scale: linear_scale<br>PIX_RAT2_MEDIAN = The median of pixel-by-pixel ratios of pixel intensities, with the median background subtracted.; Type: float; Scale: linear_scale<br>RAT2_SD = The geometric standard deviation of the pixel intensity ratios.; Type: float; Scale: linear_scale<br>TOT_SPIX = The total number of feature pixels.; Type: integer; Scale: linear_scale<br>TOT_BPIX = The total number of background pixels.; Type: integer; Scale: linear_scale<br>REGR = The regression ratio of every pixel in a 2-feature-diameter circle around the center of the feature.; Type: float; Scale: linear_scale<br>CORR = The correlation between channel1 (Cy3) & Channel 2 (Cy5) pixels within the spot, and is a useful quality control parameter. Generally, high values imply better fit & good spot quality.; Type: float; Scale: linear_scale<br>DIAMETER = The diameter in um of the feature-indicator.; Type: integer; Scale: linear_scale<br>X_COORD = X-coordinate of the center of the spot-indicator associated with the spot, where (0,0) is the top left of the image.; Type: integer; Scale: linear_scale<br>Y_COORD = Y-coordinate of the center of the spot-indicator associated with the spot, where (0,0) is the top left of the image.; Type: integer; Scale: linear_scale<br>TOP = Box top: int(((centerX - radius) - Xoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>BOT = Box bottom: int(((centerX + radius) - Xoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>LEFT = Box left: int(((centerY - radius) - yoffset) / pixelSize).; Type: integer; Scale: linear_scale<br>RIGHT = Box right: int(((centerY + radius) - yoffset) / pixelSize); Type: integer; Scale: linear_scale<br>FLAG = The type of flag associated with a feature: -100 = user-flagged null spot; -50 = software-flagged null spot; 0 = spot valid.; Type: integer; Scale: linear_scale<br>CH2IN_MEAN = Normalized value of mean Channel 2 (usually 635 nm) intensity (CH2I_MEAN/Normalization factor).; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>CH2BN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) background (CH2B_MEDIAN/Normalization factor).; Type: integer; Scale: linear_scale; Channel: Cy5 channel; Background<br>CH2DN_MEAN = Normalized value of mean Channel 2 (usually 635 nm) intensity with normalized background subtracted (CH2IN_MEAN - CH2BN_MEDIAN).; Type: integer; Scale: linear_scale; Channel: Cy5 channel<br>RAT2N_MEAN = Type: float; Scale: linear_scale<br>CH2IN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) intensity (CH2I_MEDIAN/Normalization factor).; Type: integer; Scale: linear_scale<br>CH2DN_MEDIAN = Normalized value of median Channel 2 (usually 635 nm) intensity with normalized background subtracted (CH2IN_MEDIAN - CH2BN_MEDIAN).; Type: integer; Scale: linear_scale<br>RAT1N_MEAN = Ratio of the means of Channel 1 (usually 532 nm) intensity to normalized Channel 2 (usually 635 nm) intensity with median background subtracted (CH1D_MEAN/CH2DN_MEAN). Channel 1/Channel 2 ratio normalized or Green/Red ratio normalized.; Type: float; Scale: linear_scale<br>RAT2N_MEDIAN = Channel 2/Channel 1 ratio normalized, RAT2_MEDIAN/Normalization factor or Red/Green median ratio normalized.; Type: float; Scale: linear_scale<br>LOG_RAT2N_MEAN = Log (base 2) of the ratio of the mean of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm) [log (base 2) (RAT2N_MEAN)].; Type: float; Scale: log_base_2<br>VALUE = Log (base 2) of the ratio of the median of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm) [log (base 2) (RAT2N_MEDIAN)] with flagged values removed<br>UNF_VALUE = Log (base 2) of the ratio of the median of Channel 2 (usually 635 nm) to Channel 1 (usually 532 nm) [log (base 2) (RAT2N_MEDIAN)].; Type: float; Scale: log_base_2Image Adquisition - Scanner Model: GenePix 4000AUnknownTranscriptomicsGenomicsProteomics<h4>Background</h4>Acute Kawasaki disease (KD) is difficult to distinguish from other illnesses that involve acute rash or fever, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed.<h4>Methods</h4>We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with 3 illnesses that resemble KD.<h4>Results</h4>Genes associated with platelet and neutrophil activation were expressed at higher levels in patients with KD than in patients with acute adenovirus infections or systemic adverse drug reactions, but levels in patients with KD were not higher than those in patients with scarlet fever. Genes associated with B cell activation were also expressed at higher levels in patients with KD than in control subjects. A striking absence of interferon-stimulated gene expression in patients with KD was confirmed in an independent cohort of patients with KD. Using a set of 38 gene transcripts, we successfully predicted the diagnosis for 21 of 23 patients with KD and 7 of 8 patients with adenovirus infection.<h4>Conclusions</h4>These findings provide insight into the molecular features that distinguish KD from other febrile illnesses and support the feasibility of developing novel diagnostic reagents for KD based on the host response.transcription profiling by arrayHomo sapiensGene transcript abundance profiles distinguish Kawasaki disease from adenovirus infection.Popper SJ, Watson VE, Shimizu C, Kanegaye JT, Burns JC, Relman DAStephen Popper63falseKawasaki Disease and Other Febrile IllnessesABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr) disease_state_designE-GEOD-15297GSE1529719583510EFO_000276819583510