There is a need for an expressible open reading frame (ORF) eome that can be made into an arrayable proteome in the search for disease biomarkers. The number of proteins that are available for screening limits disease biomarker discovery. For example, in the search for human autoantigens, the availability of proteins to use in screening studies has been a bottleneck. The National Institutes of Health (NIH) has a collection of approximately 29,636 human ORFs that contain 17,526 (source: http://mgc.nci.nih.gov/) nonredundant gene sequences. Although Invitrogen has cloned all the human ORFs into their Gateway system, the creation of an assayable human proteome has been limited by the protein purification process. After almost a decade, the number of human proteins contained on their commercial Human ProtoArray chip remains at 8000 (<30% coverage). Recent work has shown that that these ProtoArray human protein chips, although limited in scope, have been used successfully to identify serodiagnostic autoantigens (cancer biomarkers). Hudson et al. profiled serum samples from 30 cancer patients and 30 healthy persons using microarrays containing 5005 human proteins. Ninety-four discriminatory serodiagnostic antigens were identified that exhibited enhanced reactivity from sera in cancer patients relative to control sera [ 7]. This is a good proof-of-principle experiment that should be expanded to verify that the 30 serum samples used for this experiment are actually representative of the ovarian cancer population as a whole, not just markers for families that are predisposed to this type of cancer but may never develop the disease. An additional control would have included serum samples from the sisters of the patients who did not have ovarian cancer.
To assay even a small population with an appropriate number of controls is financially prohibitive using the commercially available human ProtoArray. The array is available for $3000 each. For this reason the Human ProtoArray chip offered by Invitrogen has not been utilized for large- s cale screening. Small-scale experiments may be fine for a publication, but to identify robust biomarkers there needs to be a massive screening that allows profiling as many healthy, predisposed, and diseased persons as possible so that the data derived from the experiment are biologically relevant. It is only at this scale that we can truly make any conclusions about the immune response. The reason that we still have not found the ideal biomarkers is due to the complexity of immune response and its variability from person to person. The current human protein chip is being distributed as a consumable with a sizable price tag. To attempt a study of the size required to determine disease-state discriminatory antigens, or groups thereof, there needs to be a higher-throughput, more cost-effective method of generating the human proteome chip.
Infectious disease biomarker discovery faces challenges as well. Purified proteins of many infectious agents are scarce. Purified protein microarrays of pathogens usually include only a handful of proteins. The lack of content has been the major reason for the slow rate of emergence of new serodiagnostic markers. For example, only 149 out of the 4198 ORFs (3.5%) constituting the Yersinia pestis proteome were used to profile the antibody response to live vaccine  . In addition, 156 out of approximately 1000 (15.6%) Chlamydia trachomatis proteins were used to profile the human humoral immune response to urogenital tract infections in human subjects . The ability to express and purify recombinant proteins in the lab limits the amount of unique content available for serological screening. This low percentage of proteome coverage is common, and most laboratories in this field are studying a few antigens at a time from thousands of possible candidates.
One of the innovative ways is which researchers have overcome the lack of coverage is to apply the lessons learned from whole- cell Western blots and two-dimensional gels to microarray research. Native protein microarrays are heterogeneous protein pools that have been fractionated using chromatogra-phy. Sartain et al. developed a technique for the separation of native Mycobacterium tuberculosis cytosol and culture filtrate proteins that resulted in 960 unique protein fractions that were used to generate protein microarrays. These 960 fractions represented all the expressed proteins, having some proteins represented in different fractions. When these microarrays were used to profile the reactivity of different disease states, various previously characterized proteins as well as some novel proteins were identified from these fractions using mass spectrometry . A similar approach was utilized in a different field to profile the sera of prostate cancer patients and controls. Two-dimensional liquid chromatography was used to separate proteins from the prostate cancer cell line LNCaP into 1760 fractions, which were subsequently spotted onto a microarray. The microarrays were probed with serum samples from 25 men with prostate cancer and 25 male controls. Statistical analysis revealed that 38 of the fractions showed significantly more reactivity in the cancer group. Samples were classified with up to 98% accuracy using the same sera . Although a good academic exercise, the protein fraction microarray needs further studies to uncover the proteins responsible for the reactivity. In the tuberculosis case, mass spectrometry was able to characterize a number of reactive proteins, but the authors conceded that this approach was a stopgap until the complete proteome is cloned, expressed, and arrayed. In the case of prostate cancer the reactive fractions must be analyzed further to determine which of the many proteins represented in the fractions are the important ones. Another challenge that faces users of protein fraction microarrays is that proteins are not all represented equally in these fractions. Antigens that potentially are extremely good biomarkers may be underrepresented, due to an expression level regulated by cell degradation pathways that may target these proteins.
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