A pooling-deconvolution strategy for biological network elucidation

Jin, Fulai; Hazbun, Tony R.; Michaud, Gregory A.; Salcius, Michael; Predki, Paul; Fields, Stanley; Huang, Jing

doi:10.1038/nmeth859

Cited by 36 publications

(33 citation statements)

References 33 publications

(19 reference statements)

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“…Interactions among the MMS19 homolog DNA repair/transcription protein MET18/MMS19 (Met18), Cia1 (20), Nar1 (21), and the MIP18 homolog MIP18 family protein YHR122W (Yhr122w) (22) were identified previously in screens for protein complexes in budding yeast. Human MMS19 was shown to be part of the MMXD complex, which also contains CIAO1, MIP18, XPD, and ANT2 (13).…”

Section: Discussionmentioning

confidence: 94%

The Mammalian Proteins MMS19, MIP18, and ANT2 Are Involved in Cytoplasmic Iron-Sulfur Cluster Protein Assembly

Wietmarschen

Moradian

Morin

et al. 2012

Journal of Biological Chemistry

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Background: MMS19 was recently identified as part of the cytosolic iron-sulfur (Fe-S) cluster assembly (CIA) machinery. Results: MMS19 and MIP18 both interact with CIA and Fe-S proteins, and ANT2 binds to this complex. Conclusion: MMS19, MIP18, and ANT2 facilitate Fe-S cluster insertion into cytosolic Fe-S proteins. Significance: Identification of proteins in the CIA complex elucidates aspects of cytoplasmic iron-sulfur cluster assembly.

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Section: Discussionmentioning

confidence: 94%

The Mammalian Proteins MMS19, MIP18, and ANT2 Are Involved in Cytoplasmic Iron-Sulfur Cluster Protein Assembly

Wietmarschen

Moradian

Morin

et al. 2012

Journal of Biological Chemistry

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“…Therefore, it will be detected by the antibody in the microarray format, but not identified by the Q-TOF. Despite this, we also assume the high incidence of false positives in these sorts of devices, as reflected in numerous previous papers (29,32,33).…”

Section: Discussionmentioning

confidence: 95%

Integral Protein Microarrays for the Identification of Lung Cancer Antigens in Sera That Induce a Humoral Immune Response

Madoz‐Gúrpide

Kuick

Wang³

et al. 2008

Molecular & Cellular Proteomics

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The identification of biomarkers (both molecules and profiles) in patient sera offers enormous interest for the diagnosis of cancers. In this context, the detection of antibodies to tumor cell autologous antigens possesses great potential. The humoral immune response represents a form of biological amplification of signals that are otherwise weak because of very low concentrations of antigen, especially in the early stages of cancers. Herein we present the use of integral microarrays spotted with tumorderived proteins to investigate the antibody repertoire in the sera of lung cancer patients and controls. The use of two-dimensional liquid chromatography allowed us to separate proteins from the lung adenocarcinoma cell line A549 into 1760 fractions, which were printed in duplicate, along with various controls, onto nitrocellulose coated slides. The sensitivity and specificity of the microarrays to detect singular antibodies in fluids were first validated through the recognition of fractions containing a lung marker antigen by antibody probing. Twenty fractions were initially selected as highly reactive against the anti-PGP9.5 antibody, and subsequent mass spectrometry analyses confirmed the identity of PGP9.5 protein in four of them. As a result, the importance of neighboring fractions in microarray detection was revealed due to the spreading of proteins during the separation process. Next, the microarrays were individually incubated with 14 serum samples from patients with lung cancer patients, 14 sera from colon cancer patients, and 14 control sera from normal subjects. The reactivity of the selected fractions was analyzed, and the level of immunoglobulin bound to each fraction by each serum sample was quantified. Eight of the 20 fractions offered p values < 0.01 and were recognized by an average of four reacting patients, whereas no serum from normal individuals was positive for those fractions. Protein microarrays from tumor-derived fractions hold the diagnostic potential of uncovering antigens that induce an immune response in patients with certain types of cancers. Molecular & Cellular Proteomics 7:268 -281, 2008.

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“…10,18,20,24,25 Several methods have been proposed for correcting for variation in spot concentration. One common approach is to divide the raw signal by the estimated protein concentration on the array.…”

Section: Introductionmentioning

confidence: 99%

A Concentration-Dependent Analysis Method for High Density Protein Microarrays

et al. 2008

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Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.

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A pooling-deconvolution strategy for biological network elucidation

Cited by 36 publications

References 33 publications

The Mammalian Proteins MMS19, MIP18, and ANT2 Are Involved in Cytoplasmic Iron-Sulfur Cluster Protein Assembly

The Mammalian Proteins MMS19, MIP18, and ANT2 Are Involved in Cytoplasmic Iron-Sulfur Cluster Protein Assembly

Integral Protein Microarrays for the Identification of Lung Cancer Antigens in Sera That Induce a Humoral Immune Response

A Concentration-Dependent Analysis Method for High Density Protein Microarrays

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