Identification of amyloid antibodies for Alzheimer disease - immunotherapy.

The current study identified the specific antibodies that recognise amyloid protein for Alzheimer disease - immunotherapy. The immune-selection of random sequences from a phage display library and sequencing to obtain the random 12 amino acids peptide library for each antibody, and then we analysed these peptides for unique and common sequences, relation to Aß42 sequence and shape and pattern of the amino acid reaction to the antibody to predict the epitopes. Data obtained for 4G8 showed that, the sequence segment related to the putative epitope of 4G8 was LVFFAED. Nine of the ten top sequences contain the sequence RHD corresponding to the Aß sequence from residues 5-7. Peptide 7 has the sequence IRYDTGSYHIH, which has a RYD. It was concluded that, 4G8 and 6E10 can tolerate the binding the sequences that explain it is able to recognise amyloid aggregates.

An "epitomic" analysis of the specificity of conformation-dependent, anti-Aß amyloid monoclonal antibodies.

Antibodies against Aß amyloid are indispensable research tools and potential therapeutics for Alzheimer's disease. They display several unusual properties, such as specificity for aggregated forms of the peptide, the ability to distinguish polymorphic aggregate structures, and the ability to recognize generic aggregation-related epitopes formed by unrelated amyloid sequences. Understanding the mechanisms underlying these unusual properties and the structures of their corresponding epitopes is crucial for the understanding why antibodies display different therapeutic activities and for the development of more effective therapeutic agents. Here we employed a novel "epitomic" approach to map the fine structure of the epitopes of 28 monoclonal antibodies against amyloid-beta using immunoselection of random sequences from a phage display library, deep sequencing, and pattern analysis to define the critical sequence elements recognized by the antibodies. Although most of the antibodies map to major linear epitopes in the amino terminal 1 to 14 residues of Aß, the antibodies display differences in the target sequence residues that are critical for binding and in their individual preferences for nontarget residues, indicating that the antibodies bind to alternative conformations of the sequence by different mechanisms. Epitomic analysis also identifies discontinuous, nonoverlapping sequence Aß segments that may constitute the conformational epitopes that underlie the aggregation specificity of antibodies. Aggregation-specific antibodies recognize sequences that display a significantly higher predicted propensity for forming amyloid than antibodies that recognize the monomer, indicating that the ability of random sequences to aggregate into amyloid is a critical element of their binding mechanism.

Epitomic Characterization of the Specificity of the Anti-Amyloid Aß Monoclonal Antibodies 6E10 and 4G8.

The monoclonal antibodies 6E10 and 4G8 are among the first anti-amyloid monoclonal antibodies against Aß and the most widely used antibodies in Alzheimer's disease research. Although the epitopes for 6E10 and 4G8 have been reported to correspond to residues 1-16 and 17-24, a more recent high-resolution mapping approach indicates that 6E10 maps to residues 4-10 while 4G8 maps to residues 18-23. To characterize the binding specificity of both antibodies in greater detail, we used immunoselection of random sequences from phage display library followed by deep sequencing and analysis of resulting patterns from thousands of immunoselected sequences. We found that the minimum sequence required for 6E10 binding is R-x-D with over half (53%) of the immunoselected sequences conforming to this pattern. The vast majority of these sequences contain an H at position x (R-H-D), corresponding to residues 5-7 of the Aß target sequences, but Y is also permitted at this position in a minority of sequences. For 4G8 we found that the most frequent pattern is F-x-A contained in approximately 30% of the sequences, followed by F-A, L-x(3)-A, L-x-F, and F-F each accounting for approximately 18% of the sequences. The F-x-A motif also occurs in islet amyloid poly peptide which may explain why 4G8 also recognizes amyloid fibrils of this peptide. Immunoselection of random sequences and deep sequencing may also be a facile and efficient means of determining residues critical for antibody binding and validating the specificity of monoclonal antibodies and polyclonal antisera.

Comparison of microarray expression profiles between follicular variant of papillary thyroid carcinomas and follicular adenomas of the thyroid.

BACKGROUND: Follicular variant of papillary thyroid carcinoma (FVPTC) and follicular adenoma (FA) are histologically closely related tumors and differential diagnosis remains challenging. RNA expression profiling is an established method to unravel molecular mechanisms underlying the histopathology of diseases. METHODS: BRAF mutational status was established by direct sequencing the hotspot region of exon 15 in six FVPTCs and seven FAs. Whole-transcript arrays were employed to generate expression profiles in six FVPTCs, seven FAs and seven normal thyroid tissue samples. The threshold of significance for differential expression on the gene and exon level was a p-value with a false discovery rate (FDR) < 0.05 and a fold change cutoff > 2. Two dimensional average linkage hierarchical clustering was generated using differentially expressed genes. Network, pathway, and alternative splicing utilities were employed to interpret significance of expression data on the gene and exon level. RESULTS: Expression profiling in FVPTCs and FAs, all of which were negative for a BRAF mutation, revealed 55 transcripts that were significantly differentially expressed, 40 of which were upregulated and 15 downregulated in FVPTCs vs. FAs. Amongst the most significantly upregulated genes in FVPTCs were GABA B receptor, 2 (GABBR2), neuronal cell adhesion molecule (NRCAM), extracellular matrix protein 1 (ECM1), heparan sulfate 6-O-sulfotransferase 2 (HS6ST2), and retinoid X receptor, gamma (RXRG). The most significantly downregulated genes in FVPTCs included interaction protein for cytohesin exchange factors 1 (IPCEF1), G protein-coupled receptor 155 (GPR155), Purkinje cell protein 4 (PCP4), chondroitin sulfate N-acetylgalactosaminyltransferase 1 (CSGALNACT1), and glutamate receptor interacting protein 1 (GRIP1). Alternative splicing analysis detected 87 genes, 52 of which were also included in the list of 55 differentially expressed genes. Network analysis demonstrated multiple interactions for a number of differentially expressed molecules including vitamin D (1,25- dihydroxyvitamin D3) receptor (VDR), SMAD family member 9 (SMAD9), v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT), and RXRG. CONCLUSIONS: This is one of the first studies using whole-transcript expression arrays to compare expression profiles between FVPTCs and FAs. A set of differentially expressed genes has been identified that contains valuable candidate genes to differentiate both histopathologically related tumor types on the molecular level.