Specific antimicrobial antibodies present in the sera of patients with inflammatory bowel disease (IBD) have been proven to be valuable serological biomarkers for diagnosis/prognosis of the disease. Herein we describe the use of a whole Escherichia coli proteome microarray as a novel high throughput proteomics approach to screen and identify new serological biomarkers for IBD. Each protein array, which contains 4,256 E. coli K12 proteins, was screened using individual serum from healthy controls (n = 39) and clinically well characterized patients with IBD (66 Crohn disease (CD) and 29 ulcerative colitis (UC)). Proteins that could be recognized by serum antibodies were visualized and quantified using Cy3-labeled goat anti-human antibodies. Surprisingly significance analysis of microarrays identified a total of 417 E. coli proteins that were differentially recognized by serum antibodies between healthy controls and CD or UC. Among those, 169 proteins were identified as highly immunogenic in healthy controls, 186 proteins were identified as highly immunogenic in CD, and only 19 were identified as highly immunogenic in UC. Using a supervised learning algorithm (k-top scoring pairs), we identified two sets of serum antibodies that were novel biomarkers for specifically distinguishing CD from healthy controls (accuracy, 86 +/- 4%; p < 0.01) and CD from UC (accuracy, 80 +/- 2%; p < 0.01), respectively. The Set 1 antibodies recognized three pairs of E. coli proteins: Era versus YbaN, YhgN versus FocA, and GabT versus YcdG, and the Set 2 antibodies recognized YidX versus FrvX. The specificity and sensitivity of Set 1 antibodies were 81 +/- 5 and 89 +/- 3%, respectively, whereas those of Set 2 antibodies were 84 +/- 1 and 70 +/- 6%, respectively. Serum antibodies identified for distinguishing healthy controls versus UC were only marginal because their accuracy, specificity, and sensitivity were 66 +/- 5, 69 +/- 5, and 61 +/- 7%, respectively (p < 0.04). Taken together, we identified novel sets of serological biomarkers for diagnosis of CD versus healthy control and CD versus UC.