Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum

Agranoff, Dan; Fernández-Reyes, Delmiro; Papadopoulos, Marios C.; Rojas, Sergio; Herbster, Mark; Loosemore, Alison; Tarelli, Edward; Sheldon, Jo; Schwenk, Achim; Pollok, Richard; Rayner, Charlotte F.J; Krishna, Sanjeev

doi:10.1016/s0140-6736(06)69342-2

Cited by 234 publications

(181 citation statements)

References 26 publications

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“…There is evidence suggesting that it could be a powerful tool for characterizing the proteome of different tissues and identifying potential biomarkers for various diseases, including cancer. [15][16][17][18] At the same time, great concerns have also been raised regarding its reproducibility and other instrumental and technical limitations. 19,20 In the present study, we sought to determine whether serum protein expression profiling with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry platform could be used to search for potential disease-and therapy-associated markers for MDS.…”

Section: Discussionmentioning

confidence: 99%

Identification of disease- and therapy-associated proteome changes in the sera of patients with myelodysplastic syndromes and del(5q)

Chen

Bowen²,

Giagounidis

et al. 2010

Leukemia

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Using ProteinChip array technology, which is based on the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, we performed proteomic analyses on sera from myelodysplastic syndromes (MDSs) patients with an interstitial deletion of the long arm of chromosome 5 (del(5q)) and those from control individuals. One analysis with 80 samples from 29 patients and 51 control subjects resulted in the detection of 61 peak differences. Another analysis with 36 paired-samples from 18 patients collected before and after the treatment with lenalidomide (Revlimid) identified 19 differential peak features. We also observed differential profiles between the pre-treatment samples from the responders and those from the non-responders reflected by eight peak differences. On the basis of these data we developed two classification models that could distinguish between the diseased and the control subjects or between the responders and the non-responders. Efforts were made to purify and identify a range of differential peak proteins. We conclude that inter-a trypsin inhibitor, heavy chain H4 (fragments), serum transferrin, transthyretin (variants), haemoglobin and a protein peak at m/z 2791 could be potential disease-associated markers for del(5q) MDS. Platelet factor 4 (PF-4) and a peak at m/z 8559 may serve as therapy-associated markers and be potentially useful for monitoring and predicting the response to therapy.

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Section: Discussionmentioning

confidence: 99%

Identification of disease- and therapy-associated proteome changes in the sera of patients with myelodysplastic syndromes and del(5q)

Chen

Bowen²,

Giagounidis

et al. 2010

Leukemia

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show abstract

“…Proteomic profiling by mass spectrometry or protein chip technology allows for nontargeted screening of large numbers of peptides between TB patients and controls. Proteomic fingerprinting of serum for host markers identified three markers (transthyretin, C-reactive protein [CRP], and neopterin) that could discriminate with high accuracy between TB patients and controls with other infectious and inflammatory conditions (Agranoff et al 2006). Similarly, serum proteomic profiles could differentiate sputum smear-negative and smear-positive TB patients from controls with reasonable accuracy (Liu et al 2010).…”

Section: Untargeted Approaches and New Technologiesmentioning

confidence: 99%

Clinical Immunology and Multiplex Biomarkers of Human Tuberculosis

Walzl

Haks

Joosten

et al. 2014

Cold Spring Harbor Perspectives in Medicine

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“…Results can be prioritized based on cross-validation, and only those that meet a preset threshold of predictive ability are considered potential 'true positives.' Crossvalidation methods have been widely used over the last decade in genomic, 41,42 metabolomic, 43 proteomic, 44,45 and transcriptomic 46 -48 studies as a method for discriminating between true associations and false-positive associations.…”

Section: Statisticsmentioning

confidence: 99%

The genetic architecture of fasting plasma triglyceride response to fenofibrate treatment

et al. 2008

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Metabolic response to the triglyceride (TG)-lowering drug, fenofibrate, is shaped by interactions between genetic and environmental factors, yet knowledge regarding the genetic determinants of this response is primarily limited to single-gene effects. Since very low-density lipoprotein (VLDL) is the central carrier of fasting TG, identifying factors that affect both total TG and VLDL -TG response to fenofibrate is critical for predicting individual fenofibrate response. As part of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, 688 individuals from 161 families were genotyped for 91 single-nucleotide polymorphisms (SNPs) in 25 genes known to be involved in lipoprotein metabolism. Using generalized estimating equations to control for family structure, we performed linear modeling to investigate whether single SNPs, single covariates, SNP -SNP interactions, and/or SNP -covariate interactions had a significant association with the change in total fasting TG and fasting VLDL -TG after 3 weeks of fenofibrate treatment. A 10-iteration fourfold cross-validation procedure was used to validate significant associations and quantify their predictive abilities. More than one-third of the significant, cross-validated SNP -SNP interactions predicting each outcome involved just five SNPs, showing that these SNPs are of key importance to fenofibrate response. Multiple variable models constructed using the top-ranked SNPcovariate interactions explained 11.9% more variation in the change in TG and 7.8% more variation in the change in VLDL than baseline TG alone. These results yield insight into the complex biology of fenofibrate response, which can be used to target fenofibrate therapy to individuals who are most likely to benefit from the drug.

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Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum

Abstract: The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.

Cited by 234 publications

References 26 publications

Identification of disease- and therapy-associated proteome changes in the sera of patients with myelodysplastic syndromes and del(5q)

Identification of disease- and therapy-associated proteome changes in the sera of patients with myelodysplastic syndromes and del(5q)

Clinical Immunology and Multiplex Biomarkers of Human Tuberculosis

The genetic architecture of fasting plasma triglyceride response to fenofibrate treatment

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