Tissue-specific regulatory elements in mammalian promoters Transcription factor-binding sites and the cis-regulatory modules they compose are central determinants of gene expression . We previously showed that binding site motifs and modules in proximal promoters can be used to predict a significant portion of mammalian tissue-specific transcription . Here , we report on a systematic analysis of promoters controlling tissue-specific expression in heart , kidney , liver , pancreas , skeletal muscle , testis and CD4 T cells , for both human and mouse . We integrated multiple sources of expression data to compile sets of transcripts with strong evidence for tissue-specific regulation . The analysis of the promoters corresponding to these sets produced a catalog of predicted tissue-specific motifs and modules , and cis-regulatory elements . Predicted regulatory interactions are supported by statistical evidence , and provide a foundation for targeted experiments that will improve our understanding of tissue-specific regulatory networks . In a broader context , methods used to construct the catalog provide a model for the analysis of genomic regions that regulate differentially expressed genes . Introduction Reverse engineering mammalian transcriptional regulatory circuits can be achieved using systematic methodology that includes both computational and experimental techniques , working in tandem to generate , refine and verify hypotheses . Understanding tissue-specific transcription is a necessary step for extending regulatory circuit reverse-engineering efforts from single-cell eukaryotes to metazoans . We recently demonstrated that the information in proximal promoters can predict a significant portion of tissue-specific elevated or inhibited expression -LRB- Smith et al , 2006 -RRB- . Here , focusing on tissue-specific regulatory pattern identification and prediction accuracy instead of proof of existence , we use refined analysis and data curation methods to discover and catalog high-confidence regulatory interactions and sites . This catalog will assist experimental efforts to reverse engineer tissue-specific transcriptional regulatory networks from the bottom up . Numerous techniques for analysis of regulatory sequences have been proposed , and the problem of module identification is now receiving due attention -LRB- Zhou and Wong , 2004 ; Gupta and Liu , 2005 ; Zhu et al , 2005 -RRB- . Previously characterized binding site motifs have been used to infer transcription factor function in certain tissues -LRB- Nelander et al , 2005 -RRB- . Xie et al -LRB- 2005 -RRB- identified conserved motifs across ortholog promoters of four mammalian genomes . Robertson et al -LRB- 2006 -RRB- describe cisRed , a database that integrates genome annotation data , homology data and genome alignments to identify motifs with conserved sites across mammals . We analyzed proximal promoters with evidence for tissue-specific regulation in order to identify tissue-specific motifs , modules and their sites in proximal promoters . We developed a new technique for characterizing tissue-specific modules that ensures that each module component significantly improves tissue-specific module enrichment . We integrated multiple sources of expression data to identify reliable sets of transcripts that are under tissue-specific regulation in human and mouse . Using transcription start site -LRB- TSS -RRB- annotation in Cold Spring Harbor Mammalian Promoter Database -LRB- CSHLmpd -RRB- -LRB- Xuan et al , 2005 -RRB- , we compiled sets of proximal promoters corresponding to transcripts with evidence for specific regulation in the selected tissues . Our analysis was based on motifs discovered de novo -LRB- called novel motifs -RRB- using DME -LRB- Smith et al , 2005a -RRB- and DME-B -LRB- Smith et al , 2006 -RRB- , as well as previously characterized vertebrate binding-site motifs -LRB- called known motifs -RRB- from TRANSFAC -LRB- Matys et al , 2003 -RRB- and JASPAR -LRB- Sandelin et al , 2004 -RRB- . We evaluated motifs according to enrichment in tissue-specific promoters relative to other promoters from the same species . We showed that motifs associated with factors with known tissue-specific roles rank high for enrichment , that motif ranks are significantly correlated between human and mouse and that this same set of motifs and their corresponding cis-elements are unlikely to be identified using traditional , order-preserving alignments of ortholog promoter sequences . We constructed modules of interacting motifs -LRB- both novel and known motifs -RRB- , ensuring that each component contributed significantly to the enrichment of the whole module . We annotated tissue-specific promoters with predicted tissue-specific regulatory elements and demonstrated that these sites are in excellent agreement with experimentally annotated liver-specific sites in the human albumin promoter and skeletal-muscle-specific sites in the human α-actin promoter . Both promoters are particularly well annotated with experimentally verified tissue-specific regulatory elements and permit an informative comparison . In other tissues , we gave predicted sites for tissue-specific motifs in representative promoters . The complete data and analysis are available in TCat : The Catalog of Tissue-Specific Regulatory Motifs -LRB- http://rulai.cshl.edu/tcat -RRB- . Results We describe first steps toward cataloging high-confidence tissue-specific motifs , modules and their sites . We first collected and integrated expression and function data from various sources , and identified transcripts that are likely to be under tissue specific regulation . We demonstrated that transcripts with evidence for tissue-specific regulation from multiple expression sources in one species -LRB- human or mouse -RRB- are significantly more likely to have evidence for tissue-specificity in the other species . We analyzed and annotated proximal-promoter sets in seven representative tissues from both human and mouse , demonstrating that motifs and predicted binding sites are in agreement with experimentally verified data and that analyses in human and mouse are significantly correlated . We also showed that the top-scoring sites in orthologous tissue-specific promoters from human and mouse rarely have significant conservation of site order , suggesting that comparative genomics alone may not be sufficient to decode the regulatory signals in these proximal promoters . Transcripts under tissue-specific regulation Few transcripts have expression restricted to a single tissue , but many transcripts appear to be regulated in a tissue-specific manner -LRB- Su et al , 2004 -RRB- , and the corresponding promoters are likely to contain tissue-specific regulatory elements . To circumvent problems associated with individual sources of information , we used a voting system that combined information about expression , function and tissue specificity from different sources . Table I gives the number of transcripts with single and multiple sources of evidence -LRB- votes -RRB- for tissue-specific regulation in each tissue . Orthologs of transcripts with multiple votes for tissue-specific regulation were more likely to have evidence for specific regulation in that tissue , suggesting that the false-positive rate for calling a transcript tissue specific is lower when based on multiple votes . The number of ortholog transcript pairs with multiple votes for tissue-specific regulation in both species ranged from 1 in CD4 T cells to 69 in liver . Table II lists genes and orthologous transcripts with votes for skeletal-muscle-specific regulation in both human and mouse . Gene and transcript lists for other tissues are given in Supplementary Section 1.5 . Enrichment of known tissue-specific motifs Knowledge of factors and corresponding binding sites that regulate tissue-specific transcription can be used to evaluate motif ranking . We measured motif enrichment in tissue-specific promoter sets using balanced error rates , evaluating motifs for their ability to distinguish tissue-specific sets -LRB- foreground sets -RRB- from background sets that are composed of non-tissue-specific promoter samples from CSHLmpd . Balanced error rates measure proportions of misclassified promoters after normalization of foreground and background sizes . We ranked motifs according to enrichment and determined whether the ranks assigned to binding-site motifs for factors with known tissue-specific roles are significantly elevated in the corresponding tissues . The results presented in Table III demonstrate that binding-site motifs for these factors ranked significantly high -LRB- P < 0.01 -RRB- according to a Wilcoxon signed-ranks test in almost all tissues tested . Excluding DBP in human liver and HNF-3 in mouse liver , these factors had evidence for expression in their respective tissues , and their binding-site motifs were highly enriched in our foreground sets . Results are summarized in Table IV , and the motifs with greatest enrichment in each tissue are given in TCat . Table IV also includes information for HNF-6 in liver . HNF-6 is a known liver regulator , but there is no evidence for its expression in liver based on our data , and its binding-site motif was not enriched in our liver foreground sets . In addition , C/G - or A/T-rich motifs are likely to be enriched in foreground sets that are C/G or A/T rich relative to promoter base composition . To eliminate this potential bias , we adjusted the GC content in background sets to match foreground sets . Some known tissue-specific motifs were identified as enriched only after GC content correction . Nuclear receptor binding-site motifs and E-box motifs are among the top enriched motifs in 11 and 10 of the 14 human and mouse tissues , respectively . A Wilcoxon signed-ranks test showed that nuclear receptor and E-box motifs -LRB- represented by 54 and 39 TRANSFAC motifs , respectively -RRB- are enriched in the union of our foreground sets with P-values below 6.07 E − 14 and 2.02 E − 13 -LRB- nuclear-receptor motifs -RRB- and 2.22 E − 10 and 4.57 E − 03 -LRB- E-box -RRB- in human and mouse , respectively . These results suggest a diversity of tissue-specific roles for nuclear receptors and E-box binders , likely mediated by tissue-specific cofactors . Tissue-specific cis-regulatory elements Highly enriched motifs , and the associated score thresholds identified by our methods , provide a starting point for targeted experimental annotation of tissue-specific promoters . Figure 1 shows known and predicted sites mapped on the − 500 to +100 region of the human albumin promoter and the − 250 to +50 region of the human skeletal muscle α-actin promoter . Human albumin has known functional binding sites for HNF-1 , C/EBP , AFP and NF-Y -LRB- Paonessa et al , 1988 ; Sawadaishi et al , 1988 ; Frain et al , 1990 ; Li et al , 1990 -RRB- , all of which were identified among the top predicted motifs or included in top modules for liver . Locations of known sites for C/EBP -LRB- at − 437 -RRB- and NF-Y -LRB- at − 125 -RRB- do not perfectly align with the corresponding predicted sites -LRB- at − 462 and − 143 , respectively -RRB- , but current knowledge about binding sites for those factors raises the possibility that the predicted locations are more accurate . The only predicted binding site for these factors in the human albumin promoter that is not depicted in the figure is a C/EBP site at − 956 . Human skeletal muscle α-actin has known sites for SRF , TEF and a known TATA box -LRB- Boxer et al , 1989 ; MacLellan et al , 1994 -RRB- , all among top predicted motifs for human skeletal muscle -LRB- TATA box motifs have high similarity with MEF-2 motifs -RRB- . Figure 2 gives predictions in a representative human promoter in each of the remaining tissues . Comparison to previous results Previous analysis of tissue-specific patterns in regulatory regions includes analysis based on cross-species conservation -LRB- Xie et al , 2005 -RRB- and coexpression -LRB- Smith et al , 2006 -RRB- . Xie et al -LRB- 2005 -RRB- identified conserved elements in orthologous promoters of four mammalian genomes . They found 59 experimentally validated motifs that are significantly conserved and enriched in at least one human tissue . These include E-box , ETS , MEF2 , MEIS1 and NF-1 in skeletal muscle ; Chx10 in kidney ; NRF-1 , ELK-1 , GABP and E12 in CD4 T cells ; AP-4 and MEF-2 in heart ; and NRF-1 in testis . Our results agree with Xie et al -LRB- 2005 -RRB- on the enrichment of E-Box and MEF-2 in skeletal muscle , ETS in CD4 T-cells and E-box in pancreas . In previous work -LRB- Smith et al , 2006 -RRB- , we tested the hypothesis that proximal promoters contain information that can be used to predict tissue-specific expression . We were not concerned with identifying the most significant tissue-specific motifs , modules and sites . Considering the difference between the goals of the two projects , it is not surprising that the predictive models described by Smith et al -LRB- 2006 -RRB- have little similarity to our top motifs and modules . The most significant similarities between our top tissue-specific patterns and the predictive models of Smith et al -LRB- 2006 -RRB- include the enrichment of ETS in CD4 T-cell-specific promoters and the enrichment of Smith et al -LRB- 2006 -RRB- motifs Novel3 and Novel6 in mouse testis and Novel1 in human testis . The three novel testis motifs are very similar to motifs that rank in the top 100 in our analysis , but the enrichment of these motifs was not sufficiently high for inclusion in TCat . Correlation between human and mouse regulatory regions We compared motif enrichment ranks in each human foreground set to ranks in the corresponding mouse foreground set using Spearman 's rank correlation test , and found that enrichment ranks across species are highly correlated -LRB- P < 0.001 -RRB- for all but CD4 T cells -LRB- Supplementary Table 15 -RRB- . In CD4 T cells , motif enrichment ranks are similar only for few highly enriched motifs . Despite the motif-enrichment ranks correlation , the order of the top predicted binding sites is not usually conserved between orthologous promoters . Fewer than 10 % of orthologous pairs showed significant -LRB- P < 0.01 -RRB- conservation of site order . Weak site-order conservation suggests that the top tissue-specific sites would be difficult to identify using traditional cross-species alignment alone , and methods that rely on co-linear promoter alignment may have high false-negative detection rates . This evidence is in agreement with Frith et al -LRB- 2006 -RRB- , who found that homologous transcription start sites can be separated by more than 100 nucleotides . A list of the nine genes -LRB- out of 102 candidates -RRB- with significant conservation of site order is given in Supplementary Section 2.3 . Materials and methods The steps used in creating the catalog include -LRB- 1 -RRB- identifying tissue-specific transcripts , -LRB- 2 -RRB- identifying factors that are expressed in each tissue , -LRB- 3 -RRB- obtaining promoter sequences for tissue-specific transcript , and -LRB- 4 -RRB- identifying individual motifs and modules -LRB- i.e. sets of interacting motifs -RRB- that characterize tissue-specific promoter sets . Identifying tissue-specific transcripts To identify motifs and modules that regulate tissue-specific transcription , we analyzed promoters of transcripts that appear to be regulated in a tissue-specific manner . If an information source indicated that a transcript has restricted expression , unusually high expression , or a specific function in the tissue , that source voted for tissue specificity of the transcript . For each tissue , we sorted the transcripts according to the number of votes received , retaining the top 100 with distinct TSS as tissue specific . Ties in the ranking were broken according to intensity values from the GNF SymAtlas expression data -LRB- discussed below -RRB- , which we have found to be the most complete and the most reliable source of tissue-based expression information . We used the same number of transcripts for each tissue to facilitate comparison across tissues , and 100 sequences provided sufficient information for our analysis while allowing identification of well-known tissue-specific motifs . Microarray data The GNF SymAtlas microarray data were generated using Affymetrix HG-U133A array and the custom GNF1H and GNF1M Affymetrix arrays , and include expression profiles for 79 human and 61 mouse tissues -LRB- Su et al , 2004 -RRB- . Among these are the seven tissues we selected to include in the catalog . Tissues were selected with consideration to data availability in GNF and other sources and interest from Zhang lab members and collaborators . A transcript received a vote for tissue specificity from this information source if it was called present and its intensity exceeded its mean across all tissues by 3 standard deviations . The Hughes Toronto microarray data -LRB- Zhang et al , 2004 -RRB- , which was generated using custom-built oligonucleotide arrays , provide mouse expression profiles for 55 tissues , including all of our tissues but CD4 T cells . A transcript received a vote for tissue specificity if it was called present in the tissue , and had intensity at least 10 standard deviations above its mean across all 55 tissues . This large number of standard deviations was required to limit the number of transcripts receiving positive votes . The GeneNote expression profiles -LRB- Shmueli et al , 2003 -RRB- , which were generated using the Affymetrix GeneChip HG-U95 , provide human expression data for 12 tissues , including all of our tissues but CD4 T cells and testis . The GeneNote data were used in the same way as the GNF SymAtlas data , with a transcript being called tissue specific if it is present in that tissue and has intensity at least 3 standard deviations above its mean across all 12 tissues . EST data dbEST is a database of expressed sequence tags -LRB- Boguski et al , 1993 -RRB- , and contains source information , such as the tissue of origin , for each EST. . This information is used to annotate UniGene clusters with the source data , and a UniGene is said to have restricted expression in a tissue if more than half of the ESTs contributing to that UniGene have the same source tissue . A transcript received one vote for specificity in a particular tissue if the corresponding UniGene cluster is annotated as having expression restricted to that tissue . GO terms We associated a set of GO Terms with each tissue . This was performed by compiling a set of keywords for each tissue -LRB- e.g. ` renal ' was associated with kidney ; ` sperm ' was associated with testis -RRB- , and searching GO Term names and definitions for those keywords . This produced , for each tissue , a set of GO Terms that were subsequently reviewed to ensure that the context of the keywords was appropriate . A transcript of a gene annotated with a GO Term that is associated with a tissue received a vote for specificity in that tissue . Selecting promoter sequences Although regulatory elements can exist almost anywhere in the genome , they are concentrated near the TSS -LRB- Cooper et al , 2006 -RRB- . We used the CSHLmpd to map transcripts to promoters , using experimentally confirmed promoters from EPD -LRB- Perier et al , 1998 -RRB- , DBTSS -LRB- Suzuki et al , 2002 -RRB- and GenBank , as well as computationally predicted promoters . For each promoter , we used the proximal sequence region of − 1000 to +100 relative to the TSS . Each part of our analysis is based on comparing the tissue-specific promoter sets to a background of random promoters from the same species . For each tissue , a background set was constructed by selecting 1000 transcripts uniformly at random from the set of RefSeqs for the corresponding species with TSS annotation in CSHLmpd . For each tissue , transcripts with at least one vote for specificity in that tissue were removed from consideration before selecting the background . Because our analysis focused on proximal promoters , − 1000 to +100 relative to the TSS , if the TSS annotation is off by several hundred base pairs , important promoter regions might be excluded . AKR1D1 , identified as liver specific by our voting system , has two known TSSs within 500 bp of the first exon for the corresponding RefSeq -LRB- NM_005989 -RRB- -LRB- Charbonneau and Luu-The , 1999 -RRB- . We used the TSS located upstream of the first exon , but could have chosen to use the other promoter , which was annotated by a generally more reliable source -LRB- DBTSS versus GenBank -RRB- . Currently , in such situations , there is little information that identifies the promoter responsible for observed tissue-specific regulation , but comparative genomics and rapidly improving arrays promise better 5 ′ end identification , thus improving proximal promoter annotation and association between transcripts and promoters -LRB- Kim et al , 2005 ; Carninci et al , 2006 -RRB- . Negative promoter sets can be used to cancel out patterns that are not related to tissue-specific transcription regulation . We use random negative promoter sets with and without GC-content correction ; this correction cancels the inuence of genomic GC-content isochore variability . Identifying and evaluating motifs Given a motif M -LRB- represented as a position -- frequency matrix -RRB- and a sequence S , the max-score of M in S , max-score -LRB- M , S -RRB- is the score of the top scoring subsequence of S when aligned against the scoring matrix for M. Details on constructing and using scoring matrices can be found in Stormo -LRB- 2000 -RRB- . For a fixed threshold λ , the max-score classification method classifies S as belonging to the foreground if max-score -LRB- M , S -RRB- ⩾ λ , and the background otherwise . Given a set of foreground sequences FG -LRB- i.e. the tissue-specific promoters -RRB- and a set of background sequences BG , the sensitivity of M and λ under max-score classification is and the specificity is The balanced error rate for M and λ under max-score classification is then The quantity of interest in our analysis corresponds to the optimal value of λ for M in distinguishing FG from BG : Many known motifs are similar to each other , usually owing to similar binding specificities for distinct factors or distinct origins for mofits associated with a single factor . We used MATCOMPARE -LRB- Schones et al , 2005 -RRB- to eliminate redundancies in the sets of known and novel motifs . Identifying and evaluating modules Modules are used to classify sequences based on the max-score values of the motifs they contain . Let ℳ = -LCB- M1 , ... , Mk -RCB- be a module and Λ = -LCB- λ1 , ... , λk -RCB- be an associated set of thresholds . The max-score classification for modules assigns sequence S to the foreground if and only if max-score -LRB- Mi , S -RRB- ⩾ λi holds for all i -LRB- 1 ⩽ i ⩽ k -RRB- . Given sets of sequences FG and BG , the sensitivity of ℳ and Λ under max-score classification is and the specificity is The balanced-error rate for ℳ and Λ under the max-score classification is As with mofits , we are interested in the optimal value of Λ and define Because modules are intended to describe synergistic function of a set of motifs , we are interested in modules whose performance is better than expected given the performance of the individual motifs composing the module . For a module ℳ composed of k motifs , let ℳ ′ ⊂ ℳ minimize B -LRB- ℳ ′ , FG , BG -RRB- over all size k − 1 modules built from motifs in ℳ , and let ℳ ′ = ℳ \ ℳ ′ . To assess whether ℳ , with balanced-error rate u , significantly improves over ℳ ′ and ℳ ′ , we use the probability We estimated this probability empirically , by sampling from the distribution of balanced-error rates resulting from intersections of sets with balanced-error rates B -LRB- ℳ ′ , FG , BG -RRB- and B -LRB- ℳ ′ , FG , BG -RRB- . We used MODULATOR , which is available in CREAD -LRB- Smith et al , 2005b -RRB- , to construct modules . Given a set of motifs , a set of foreground sequences and a set of background sequences , MODULATOR identifies those modules composed of the given motifs that have the best balanced-error rates . A branch-and-bound algorithm is used to simultaneously optimize the score thresholds for the motifs in a module . Modules are constructed by adding motifs to existing modules until a user-specified module size is reached or until motif addition does not significantly improve enrichment . Each time a motif is added to a module , the resulting larger module is retained only if the balanced-error rate of the larger module is improved significantly above expectation . The initial modules of size two are obtained by combining pairs of motifs . For modules that are entirely composed of known motifs , the top 100 motifs -LRB- before eliminating redundancies -RRB- were used . Modules were allowed to contain up to four motifs for reasons of computational feasibility , but many top modules are smaller . Novel modules , which must contain at least one novel motif , were constructed using the top 100 novel motifs and the top 100 known motifs . Redundancies were removed from the lists of top modules using a procedure described in Supplementary Section 2 . Measuring the significance of motifs and modules To measure significance of enrichment for top known motifs we used known motifs to classify randomly assembled promoter sets . We constructed 1000 foreground/background pairs for each species by selecting 100 sequences for each foreground and 1000 for each background uniformly at random from CSHLmpd . For each foreground/background pair we calculated the balanced-error rate of each known motif . The best balanced-error rates overall obtained on random samples for human and mouse were 0.364 and 0.368 , respectively . We used the distribution of these error rates to identify the q-value -LRB- Storey and Tibshirani , 2003 -RRB- significance of the error rate of each motif . Tissues whose highest ranking motifs fail the q < 0.05 test include CD4 T cells and heart in human , and CD4 T cells in mouse . TCat includes q-value annotation for each ranked known motif . The full set of motifs for which q < 0.05 is estimated to include five false leads per 100 predictions . We did not obtain statistical significance measures for novel motifs , because this will require running DME and DME-B more times than is computationally feasible . Modules were identified by combining motifs whose cooccurrence was enriched in the foreground sets . We measured enrichment of modules using the balanced-error rate -LRB- analogous to that of motifs -RRB- , and we required that each motif in a module contributes significantly to the enrichment of the module as a whole . To test significance , we randomly selected 100 of the 1000 foreground/background pairs used to evaluate individual known motifs , and performed the module identification procedure on each of the 100 selected pairs . The best balanced-error rates for modules that are entirely composed of known motifs -LRB- called known modules -RRB- in random human and mouse sets were 0.3145 and 0.304 , respectively . We used these balanced-error rates as an estimate of the critical value for P < 0.01 . We opted for using P-value cutoffs instead of computing q-values because accurate q-value estimation for modules is computationally prohibitive . Top known modules in human kidney -LRB- 0.2955 -RRB- , liver -LRB- 0.3105 -RRB- , pancreas -LRB- 0.3125 -RRB- and testis -LRB- 0.3 -RRB- scored better than the cutoff , as did top known modules in mouse kidney -LRB- 0.3005 -RRB- , liver -LRB- 0.3025 -RRB- and testis -LRB- 0.2945 -RRB- . Supplementary Material Supplementary information

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

cis-regulatory modules
tissue-specific regulation


The

different

effector

function

capabilities

of

the

seven

equine

IgG

subclasses

have

implications

for

vaccine

strategies

Recombinant

versions

of

the

seven

equine

IgG

subclasses

were

expressed

in

CHO

cells

All

assembled

into

intact

immunoglobulins

stabilised

by

disulphide

bridges

although

reminiscent

of

human

a

small

proportion

of

equine

and

were

held

together

by

non-covalent

bonds

alone

All

seven

IgGs

were

N-glycosylated

In

addition

appeared

to

be

O-glycosylated

and

could

bind

the

lectin

jacalin

Staphylococcal

protein

A

displayed

weak

binding

for

the

equine

IgGs

in

the

order

Streptococcal

protein

G

bound

strongly

to

and

moderately

to

weakly

to

and

and

not

at

all

to

Analysis

of

antibody

effector

functions

revealed

that

and

but

not

and

were

able

to

elicit

a

strong

respiratory

burst

from

equine

peripheral

blood

leukocytes

predicting

that

the

former

five

IgG

subclasses

are

able

to

interact

with

Fc

receptors

on

effector

cells

and

but

not

and

were

able

to

bind

complement

and

activate

complement

via

the

classical

pathway

The

differential

effector

function

capabilities

of

the

subclasses

suggest

that

for

maximum

efficacy

equine

vaccine

strategies

should

seek

to

elicit

antibody

responses

of

the

and

subclasses

Introduction

Only

recently

has

the

full

complement

of

horse

Ig

heavy

-LRB-

H

-RRB-

chain

constant

region

genes

-LRB-

one

δ

one

μ

seven

γ

one

α

and

one

ɛ

-RRB-

been

described

-LRB-

Wagner

et

al.

-RRB-

The

horse

has

the

highest

number

of

IgG

constant

region

-LRB-

γ

or

IGHG

-RRB-

genes

of

any

mammalian

species

examined

to

date

and

all

the

seven

IgG

subclasses

they

encode

appear

to

be

expressed

in

vivo

-LRB-

Wagner

et

al.

-RRB-

Early

studies

described

five

equine

IgG

subclasses

named

IgGa

IgGb

IgGc

IgG

-LRB-

T

-RRB-

and

IgG

-LRB-

B

-RRB-

or

aggregating

immunoglobulin

Following

identification

of

the

seven

horse

heavy

chain

constant

region

genes

the

IgG

subclasses

have

been

reassigned

as

to

-LRB-

Wagner

-RRB-

Of

the

originally

described

IgG

subclasses

IgGa

corresponds

to

IgGb

to

and

IgGc

to

and

IgG

-LRB-

T

-RRB-

to

both

and

-LRB-

Wagner

et

al.

-RRB-

IgG

is

the

predominant

antibody

class

in

equine

serum

and

colostrum

-LRB-

Sheoran

et

al.

-RRB-

and

is

present

at

the

mucosal

surfaces

where

it

is

the

most

abundant

isotype

in

the

equine

urinary

tract

lower

respiratory

tract

and

lung

-LRB-

Butler

-RRB-

In

equine

serum

IgGb

-LRB-

and

-RRB-

is

the

most

prevalent

isotype

followed

by

IgG

-LRB-

T

-RRB-

-LRB-

and

-RRB-

IgGa

-LRB-

-RRB-

and

IgGc

-LRB-

-RRB-

In

colostrum

IgGb

is

predominant

followed

by

IgGa

and

IgG

-LRB-

T

-RRB-

while

IgGc

is

barely

detectable

In

nasal

wash

samples

from

adult

horses

only

the

IgGa

and

IgGb

subclasses

have

been

detected

-LRB-

Sheoran

et

al.

-RRB-

Systemic

and/or

mucosal

IgG

antibody

responses

are

known

to

play

an

important

role

in

protection

against

several

equine

pathogens

including

equine

influenza

virus

-LRB-

Nelson

et

al.

Breathnach

et

al.

-RRB-

and

Streptococcus

equi

-LRB-

Galan

and

Timoney

Galan

et

al.

Sheoran

et

al.

-RRB-

and

may

limit

the

severity

and

spread

of

equine

herpes

virus

-LRB-

EHV

-RRB-

-LRB-

Kydd

et

al.

-RRB-

Although

a

role

for

equine

IgG

antibodies

in

protection

against

disease

has

long

been

recognised

the

structures

and

functions

of

the

individual

IgG

subclasses

are

not

well

characterised

Identification

and

cloning

of

the

full

complement

of

IgG

H

chain

genes

has

provided

a

fresh

resource

for

the

study

of

equine

IgG

proteins

Here

we

describe

the

first

expression

of

recombinant

versions

of

all

seven

equine

IgG

subclasses

and

present

an

analysis

of

their

individual

physical

and

biological

properties

Materials

and

methods

Construction

of

equine

γ

H

chain

expression

vectors

The

mouse

VH

gene

specific

for

NIP

was

subcloned

as

a

HindIII-BamHI

fragment

from

a

previously

described

human

expression

vector

-LRB-

Morton

et

al.

-RRB-

into

Hygro

-LRB-

-RRB-

-LRB-

Invitrogen

Paisley

UK

-RRB-

to

produce

Vnip

Isolation

of

genomic

DNA

for

equine

and

and

cDNA

for

equine

and

has

been

previously

described

-LRB-

Wagner

et

al.

-RRB-

For

construction

of

the

equine

H

chain

vector

a

kb

BamHI

fragment

containing

the

gene

along

with

bp

of

UTR

and

bp

of

UTR

was

placed

downstream

of

the

mouse

VH

gene

in

Vnip

To

construct

mammalian

expression

vectors

for

the

remaining

six

horse

IgGs

each

γ

constant

region

was

amplified

by

PCR

and

placed

downstream

of

the

bp

horse

UTR

-LRB-

also

amplified

by

PCR

-RRB-

within

the

cloning

vector

pBluescript

II

-LRB-

Stratagene

Amsterdam

The

Netherlands

-RRB-

For

each

subclass

the

UTR

and

γ

constant

region

cassette

was

then

subcloned

into

VNip

downstream

of

the

mouse

VH

gene

Expression

of

reqIgGs

in

cells

cells

which

had

previously

been

stably

transfected

with

the

mouse

λ

L

chain

specific

for

NIP

were

transfected

with

one

of

the

γ1

γ7

H

chain

expression

vectors

as

previously

described

-LRB-

Morton

et

al.

-RRB-

Supernatant

from

individual

resistant

CHO

clones

was

screened

for

Ig

production

by

antigen-capture

ELISA

as

previously

described

-LRB-

Morton

et

al.

-RRB-

except

that

the

detection

antibodies

used

were

either

rabbit

anti-mouse

λ

L

chain

HRP

conjugate

-LRB-

μg/ml

in

Tween

-LSB-

PBS-T

-RSB-

-RRB-

or

goat

anti-horse

IgG

Fc-HRP

conjugate

-LRB-

μg/ml

in

PBS-T

-RRB-

-LRB-

both

Rockland

Gilbertsville

PA

USA

-RRB-

Purification

and

analysis

of

reqIgGs

ReqIgGs

were

purified

using

NIP-affinity

chromatography

as

previously

described

-LRB-

Morton

et

al.

-RRB-

then

subjected

to

gel

filtration

on

a

column

using

an

ÄKTA

FPLC

system

-LRB-

GE

Healthcare

Little

Chalfont

UK

-RRB-

Only

fractions

corresponding

to

intact

antibody

-LRB-

-RRB-

were

pooled

for

further

analysis

The

antibodies

were

analysed

by

SDS-PAGE

and

Western

blotting

on

acrylamide

gels

under

reducing

and

non-reducing

conditions

Gels

were

stained

with

Coomassie

brilliant

blue

and

Western

blots

were

probed

with

HRP-conjugated

rabbit

anti-mouse

λ

L

chain

-LRB-

μg/ml

-RRB-

or

anti-horse

IgG

antibodies

Horse

serum

IgG

-LRB-

Sigma

Aldrich

Poole

UK

-RRB-

and

recombinant

human

-LRB-

Pleass

et

al.

-RRB-

were

used

as

controls

Reactivity

of

commercially

available

anti-horse

IgG

antibodies

with

the

reqIgGs

The

following

antibodies

were

tested

for

reactivity

against

the

reqIgGs

in

ELISA

goat

HRP-conjugated

polyclonal

antibodies

specific

for

horse

IgGa

IgGb

IgGc

or

IgG

-LRB-

T

-RRB-

-LRB-

kindly

provided

by

Serotec

Oxford

UK

and

Bethyl

Laboratories

Montgomery

TX

USA

-RRB-

and

mouse

monoclonal

antibodies

-LRB-

mAb

-RRB-

against

horse

IgGa

-LRB-

-RRB-

IgGb

-LRB-

-RRB-

IgGc

-LRB-

-RRB-

and

IgG

-LRB-

T

-RRB-

-LRB-

-RRB-

-LRB-

kindly

provided

by

Serotec

-RRB-

Secondary

antibody

used

to

detect

binding

of

the

mAb

was

a

goat

anti-mouse

conjugate

diluted

-LRB-

kindly

provided

by

Serotec

-RRB-

In

addition

HRP-conjugated

goat

polyclonal

anti-horse

IgG

H

L

chain

-LRB-

kindly

provided

by

Bethyl

Laboratories

-RRB-

and

goat

anti-horse

IgG

Fc

-LRB-

Rockland

-RRB-

were

tested

in

ELISA

Antigen-capture

ELISAs

were

performed

as

above

using

ng/ml

of

each

purified

reqIgG

subclass

Detection

antibodies

were

diluted

-LRB-

goat

polyclonals

-RRB-

or

-LRB-

anti-IgGa

mAb

-RRB-

-LRB-

anti-IgGb

mAb

-RRB-

-LRB-

anti-IgGc

mAb

-RRB-

and

-LRB-

anti-IgG

-LRB-

T

-RRB-

mAb

-RRB-

The

goat

polyclonal

anti-horse

IgGa

IgGb

IgGc

and

IgG

-LRB-

T

-RRB-

antibodies

were

also

tested

in

immunoblotting

Purified

reqIgGs

-LRB-

μg

-RRB-

were

electrophoresed

under

reducing

and

non-reducing

conditions

and

the

detection

antibodies

were

diluted

as

for

ELISA

Analysis

of

reqIgG

glycosylation

ReqIgGs

-LRB-

μg

-RRB-

were

electrophoresed

on

acrylamide

gels

under

reducing

conditions

alone

or

following

reduction

and

de-glycosylation

Removal

of

N-linked

sugars

was

carried

out

using

a

GlycoPro

enzymatic

deglycosylation

kit

-LRB-

Prozyme

Inc.

Leandro

CA

USA

-RRB-

according

to

the

manufacturer

's

instructions

Following

transfer

to

nitrocellulose

membranes

were

probed

with

the

appropriate

HRP-conjugated

goat

anti-horse

IgG

subclass

antibody

or

either

Con

A-biotin

-LRB-

diluted

-RRB-

or

jacalin-biotin

-LRB-

diluted

-RRB-

-LRB-

both

Vector

Laboratories

Inc.

Burlingame

CA

USA

-RRB-

followed

by

streptavidin-AP

-LRB-

Dako

Ely

UK

-RRB-

Recombinant

human

and

-LRB-

Pleass

et

al.

-RRB-

were

used

as

positive

controls

Interaction

with

staphylococcal

protein

A

and

streptococcal

protein

G

Interaction

of

reqIgGs

with

protein

A

and

protein

G

was

analysed

by

ELISA

NIP-BSA

coated

wells

were

incubated

with

μl

-LRB-

μg/ml

-RRB-

of

one

of

the

purified

reqIgGs

or

recombinant

human

for

comparison

Binding

to

protein

A

or

protein

G

was

detected

by

incubation

with

μl

of

serial

dilutions

of

either

HRP-conjugated

protein

A

-LRB-

μg/ml

in

PBS-T

-RRB-

or

HRP-conjugated

protein

G

-LRB-

μg/ml

in

PBS-T

-RRB-

-LRB-

both

Sigma

Poole

UK

-RRB-

Plates

were

developed

with

ABTS

and

absorbance

read

at

nm

Functional

assays

Chemiluminescence

assays

were

carried

out

as

previously

described

-LRB-

Pleass

et

al.

-RRB-

using

μg/ml

of

each

reqIgG

and

equine

PBLs

isolated

from

blood

drawn

under

appropriate

licence

from

the

UK

Home

Office

Complement

binding

and

activation

was

assessed

by

ELISA

on

NIP-BSA

coated

wells

incubated

with

recombinant

human

-LRB-

Bruggemann

et

al.

-RRB-

or

each

of

the

reqIgGs

-LRB-

μg/ml

in

PBS-T

μl/well

-RRB-

for

h

at

room

temperature

Following

washing

plates

were

incubated

with

human

serum

diluted

in

veronal

buffered

saline

containing

mM

mM

gelatin

and

BSA

for

h

at

room

temperature

After

washing

plates

were

incubated

with

either

a

dilution

of

sheep

-LRB-

Serotec

-RRB-

or

a

dilution

of

biotin-conjugated

-LRB-

Quidel

Santa

Clara

CA

USA

-RRB-

followed

by

streptavidin-HRP

-LRB-

Dako

-RRB-

diluted

in

PBS-T

BSA

for

h

at

room

temperature

Plates

were

developed

as

above

Results

Analysis

of

purified

antibody

by

SDS-PAGE

and

Western

blotting

Transfection

of

each

of

the

equine

γ1

γ7

H

chain

expression

vectors

into

CHO

cells

stably

expressing

a

compatible

mouse

λ

light

-LRB-

L

-RRB-

chain

allowed

expression

of

all

seven

subclasses

Expressed

Ig

was

purified

by

antigen

affinity

chromatography

followed

by

FPLC

All

seven

IgGs

eluted

from

FPLC

as

a

single

major

peak

representing

monomer

Minor

peaks

eluting

earlier

were

attributed

to

antibody

that

had

aggregated

during

purification

For

each

subclass

fractions

corresponding

to

the

major

peak

were

pooled

and

used

for

all

subsequent

experiments

SDS-PAGE

analysis

of

FPLC

purified

reqIgGs

confirmed

that

the

reqIgs

assembled

into

intact

Igs

stabilised

by

interchain

disulphide

bridges

-LRB-

Fig.

-RRB-

Under

reducing

conditions

analysis

of

the

seven

IgGs

revealed

that

the

H

chain

migrated

the

most

quickly

followed

closely

by

those

of

and

then

and

-LRB-

Fig.

-RRB-

Under

the

more

sensitive

conditions

of

Western

blotting

using

an

anti-mouse

λ

L

chain

antibody

to

detect

the

horse

IgGs

-LRB-

Fig.

-RRB-

some

lower

molecular

weight

species

were

noted

for

and

under

non-reducing

conditions

These

may

represent

species

such

as

half

molecules

-LRB-

HL

-RRB-

and

L

chain

dimers

-LRB-

-RRB-

suggesting

that

a

small

proportion

of

the

and

molecules

lack

inter-H

and/or

H

L

disulphide

bonds

and

are

stabilized

by

non-covalent

interactions

alone

Hence

under

the

native

conditions

of

FPLC

only

a

single

major

peak

of

fully

assembled

IgG

was

seen

Reactivity

of

anti-horse

IgG

antibodies

with

the

reqIgG

subclasses

Currently

available

anti-horse

IgG

antibodies

are

categorised

on

the

basis

of

reactivity

against

horse

IgG

subclasses

defined

under

the

previous

nomenclature

of

IgGa

IgGb

IgGc

and

IgG

-LRB-

T

-RRB-

We

sought

to

analyse

the

reactivity

of

these

antibodies

with

the

seven

reqIgG

subclasses

The

results

obtained

by

ELISA

are

summarised

in

Table

The

polyclonal

antibody

directed

against

horse

IgGa

was

specific

for

a

single

subclass

namely

Similarly

a

polyclonal

antibody

preparation

directed

against

IgGc

was

found

to

recognise

just

one

subclass

in

this

case

Goat

anti-horse

IgGb

showed

strong

reactivity

with

and

but

also

weakly

recognised

The

goat

anti-horse

IgG

-LRB-

T

-RRB-

polyclonal

antibody

reacted

most

strongly

with

but

also

recognised

and

The

pattern

of

recognition

of

the

IgG

subclasses

by

the

mAbs

was

similar

to

that

of

the

polyclonal

antibodies

except

that

anti-horse

IgGb

mAb

recognised

and

but

not

and

anti-horse

IgG

-LRB-

T

-RRB-

mAb

strongly

recognised

weakly

recognised

but

showed

no

reactivity

with

Reactivity

of

both

the

polyclonal

and

monoclonal

anti-IgGb

antibodies

with

and

is

not

surprising

as

these

two

subclasses

show

amino

acid

identity

The

polyclonal

anti-horse

IgG

-LRB-

H

L

-RRB-

and

anti-horse

IgG

Fc

antibodies

recognised

all

seven

IgG

subclasses

but

gave

varying

signal

strengths

in

the

ELISA

-LRB-

see

Table

-RRB-

Anti-horse

IgG

-LRB-

H

L

-RRB-

gave

a

strong

ELISA

signal

with

and

an

intermediate

signal

with

and

and

only

a

weak

signal

with

The

observed

reactivity

is

directed

towards

the

heavy

chain

of

the

recombinant

IgGs

only

because

no

binding

of

the

anti-horse

IgG

-LRB-

H

L

-RRB-

was

seen

with

recombinant

forms

of

either

equine

IgA

or

human

IgA

-LRB-

Morton

et

al.

-RRB-

both

of

which

carry

identical

light

chains

to

those

of

the

eqIgGs

-LRB-

data

not

shown

-RRB-

Anti-horse

IgG

Fc

gave

strong

ELISA

signals

with

and

intermediate

signals

with

and

and

only

weak

signals

with

and

The

reactivities

of

the

anti-horse

polyclonal

Abs

with

the

horse

IgG

subclasses

when

assessed

by

immunoblotting

under

non-reducing

conditions

-LRB-

Fig.

-RRB-

mirrored

those

of

ELISA

However

under

reducing

conditions

-LRB-

Fig.

-RRB-

goat

anti-horse

IgGb

recognised

and

but

no

longer

recognised

suggesting

that

the

epitope

-LRB-

s

-RRB-

recognised

by

this

antibody

may

be

discontinuous

and

depend

on

the

intact

disulphide-stabilised

structure

of

the

antibody

Several

previously

described

anti-equine

IgG

monoclonal

antibodies

were

thought

to

recognise

conformational

epitopes

on

equine

IgG

as

they

functioned

under

native

conditions

but

not

in

immunoblotting

-LRB-

Sheoran

et

al.

-RRB-

Under

reducing

conditions

goat

anti-horse

IgG

-LRB-

T

-RRB-

used

at

a

dilution

recognised

and

but

in

contrast

to

non-reducing

SDS-PAGE

and

ELISA

no

longer

reacted

with

but

with

instead

However

recognition

of

under

reducing

conditions

could

be

achieved

with

a

higher

concentration

-LRB-

dilution

-RRB-

of

detection

antibody

-LRB-

data

not

shown

-RRB-

Under

non-reducing

conditions

immunoblotting

with

goat

anti-IgGb

-LRB-

Fig.

-RRB-

revealed

additional

molecular

weight

species

for

and

consistent

with

those

observed

with

anti-L

chain

blotting

These

additional

bands

may

represent

species

such

as

free

H-chain

H-chain

dimers

-LRB-

-RRB-

and

half

molecules

-LRB-

HL

-RRB-

suggesting

again

an

absence

of

inter-H

and/or

H

L

disulphides

in

small

fractions

of

these

two

subclasses

Analysis

of

the

glycosylation

of

reqIgGs

All

seven

reqIgGs

contain

a

conserved

potential

N-glycosylation

site

within

the

domain

Additional

potential

sites

are

found

in

the

domain

of

and

and

in

the

domain

of

To

determine

whether

N-glycans

were

attached

to

any

of

these

sites

the

reqIgGs

were

probed

under

reducing

conditions

with

concanavalin

A

-LRB-

Con

A

-RRB-

which

is

specific

for

N-linked

glycans

Con

A

bound

to

all

reqIgG

subclasses

confirming

that

the

IgGs

are

indeed

N-glycosylated

-LRB-

Fig.

-RRB-

There

was

however

some

difference

in

the

intensity

of

the

bands

with

and

blotting

more

weakly

than

the

other

subclasses

most

likely

a

reflection

of

the

number

type

or

accessibility

of

N-glycans

present

By

contrast

jacalin

which

is

specific

for

O-linked

glycans

bound

only

to

and

to

the

O-glycosylated

human

used

as

a

positive

control

-LRB-

Fig.

-RRB-

Treatment

of

reqIgGs

with

N-glycanase

resulted

in

a

noticeable

reduction

in

the

molecular

weights

of

their

H

chains

-LRB-

Fig.

-RRB-

confirming

their

N-glycosylation

status

For

the

subclass

reactivity

of

the

detecting

antibody

-LRB-

goat

anti-horse

IgG

-LRB-

T

-RRB-

-RRB-

was

lost

following

the

removal

of

N-glycans

suggesting

that

the

epitopes

recognized

by

this

anti-horse

reagent

are

in

some

way

glycan-dependent

The

H

chain

migrated

as

two

discrete

bands

following

treatment

with

N-glycanase

possibly

reflecting

partial

removal

of

the

two

potential

N-linked

glycans

in

a

proportion

of

the

molecules

treated

Interaction

with

staphylococcal

protein

A

and

streptococcal

protein

G

Binding

of

protein

A

to

reqIgGs

was

generally

much

weaker

than

the

binding

of

protein

G

-LRB-

Fig.

-RRB-

in

keeping

with

earlier

reports

-LRB-

Sheoran

and

Holmes

Burton

and

Woof

-RRB-

A

higher

concentration

range

of

protein

A

than

protein

G

was

required

to

see

significant

binding

to

equine

and

human

IgG

Protein

A

showed

the

highest

level

of

binding

to

and

relatively

low

levels

of

binding

to

the

other

subclasses

in

the

order

Sheoran

and

Holmes

-LRB-

-RRB-

reported

moderate

adherence

of

protein

A

to

serum

IgGa

-LRB-

i.e.

-RRB-

but

saw

no

binding

of

IgGb

-LRB-

and

-RRB-

or

IgG

-LRB-

T

-RRB-

-LRB-

and

-RRB-

The

fact

that

slight

binding

of

protein

A

to

and

was

observed

in

our

study

may

be

explained

by

the

use

of

high

concentrations

of

protein

A

-LRB-

up

to

μg/ml

-RRB-

and

a

sensitive

method

of

assessing

the

interaction

Sugiura

et

al.

-LRB-

-RRB-

also

reported

some

binding

of

equine

IgGb

and

IgG

-LRB-

T

-RRB-

to

protein

A

In

agreement

with

previous

work

-LRB-

Sheoran

and

Holmes

Sugiura

et

al.

-RRB-

binding

of

to

protein

A

was

barely

detectable

above

background

levels

Protein

G

showed

strong

binding

to

and

closely

followed

by

displayed

intermediate

binding

while

binding

to

and

was

low

and

did

not

bind

Sheoran

and

Holmes

-LRB-

-RRB-

also

showed

that

the

binding

affinity

of

IgGb

-LRB-

and

-RRB-

to

protein

G

was

higher

than

that

of

IgGa

-LRB-

-RRB-

Furthermore

they

identified

two

fractions

of

IgG

-LRB-

T

-RRB-

one

which

bound

strongly

to

protein

G

and

one

that

bound

weakly

which

most

likely

correspond

to

and

respectively

Functional

assays

We

studied

FcγR-mediated

cellular

responses

by

assessing

the

capacity

of

the

antibodies

to

trigger

a

respiratory

burst

in

equine

peripheral

blood

leukocytes

-LRB-

PBL

-RRB-

Recombinant

and

and

to

a

lesser

extent

all

triggered

strong

respiratory

bursts

whereas

and

elicited

only

very

weak

responses

-LRB-

Fig.

-RRB-

Binding

of

and

activation

of

the

classical

complement

pathway

by

the

reqIgGs

was

assessed

using

ELISA

Human

serum

was

used

as

a

source

of

complement

due

to

the

ready

availability

of

antibodies

to

detect

human

and

human

complex

The

IgG

interaction

is

highly

conserved

throughout

evolution

and

is

known

to

react

with

IgG

from

different

species

-LRB-

Burton

and

Woof

-RRB-

The

ability

of

the

reqIgGs

to

bind

-LRB-

Fig.

-RRB-

was

reflected

in

their

ability

to

activate

the

classical

complement

pathway

to

its

terminal

components

-LRB-

Fig.

-RRB-

In

control

experiments

both

binding

and

deposition

were

reduced

to

zero

for

all

IgGs

following

heat

inactivation

of

serum

-LRB-

data

not

shown

-RRB-

was

the

most

potent

activator

of

complement

followed

by

and

which

all

bound

and

activated

complement

to

the

same

extent

Recombinant

and

did

not

bind

or

activate

complement

Discussion

Our

studies

show

that

the

CHO

expression

system

previously

described

for

human

antibodies

-LRB-

Morton

et

al.

-RRB-

can

be

used

to

generate

a

stable

supply

of

equine

IgG

antibodies

These

recombinant

IgGs

retain

affinity

for

their

antigen

are

recognised

by

anti-mouse

λ

L-chain

and

anti-horse

IgG

antibodies

are

glycosylated

and

assemble

as

covalently

stabilised

four

chain

-LRB-

-RRB-

molecules

with

the

expected

relative

molecular

weight

of

approximately

kDa

Hence

in

terms

of

structure

they

appear

to

be

representative

of

natural

horse

IgGs

Interestingly

horse

appears

to

have

O-linked

as

well

as

N-linked

glycans

attached

to

the

H

chain

O-glycosylated

forms

of

rabbit

IgG

and

mouse

in

which

the

O-glycans

are

attached

to

the

hinge

region

have

been

described

-LRB-

Fanger

and

Smyth

Kim

et

al.

Kabir

et

al.

-RRB-

Serine

and

threonine

residues

often

in

clusters

are

known

sites

for

O-glycan

attachment

A

prominent

feature

of

O-glycosylation

sites

is

an

increased

frequency

of

proline

residues

especially

at

positions

or

relative

to

the

glycosylated

residue

-LRB-

Wilson

et

al.

-RRB-

The

horse

hinge

is

rich

in

proline

and

threonine

and

contains

two

Thr

residues

with

a

Pro

at

position

and

a

number

of

Thr

residues

with

a

Pro

at

position

Hence

it

is

probable

that

O-glycans

are

attached

to

the

hinge

region

of

horse

O-glycosylation

of

the

hinge

region

of

IgG

confers

an

increased

resistance

to

proteolysis

by

various

proteases

including

pepsin

papain

and

elastase

-LRB-

Fanger

and

Smyth

Kabir

et

al.

Parham

-RRB-

and

therefore

may

play

a

role

in

protection

of

from

degradation

A

minor

feature

of

both

equine

and

was

the

presence

of

species

such

as

free

H

chain

H

chain

dimers

-LRB-

-RRB-

half

molecules

-LRB-

HL

-RRB-

and

L

chain

dimers

-LRB-

-RRB-

under

denaturing

conditions

suggesting

that

a

small

proportion

of

the

molecules

lack

H

L

or

H

H

disulphide

bonds

akin

to

human

-LRB-

-RRB-

and

human

respectively

The

human

and

subclasses

are

disulphide

bonded

to

the

L

chain

through

in

the

domain

The

equivalent

Cys

is

missing

in

equine

and

and

may

account

for

the

tendency

for

a

minor

proportion

of

these

subclasses

to

exist

in

forms

lacking

H

L

bonds

The

majority

of

equine

and

molecules

are

present

at

molecular

weights

corresponding

to

intact

-LRB-

-RRB-

antibodies

suggesting

that

H

L

disulphide

bonds

can

exist

Human

which

lacks

in

the

domain

instead

forms

a

disulphide

bond

to

the

L

chain

through

in

the

upper

hinge

-LRB-

Burton

and

Woof

-RRB-

Hence

it

is

feasible

that

hinge

cysteines

in

equine

and

may

participate

in

H

L

disulphide

bond

formation

Likewise

this

could

be

the

case

for

equine

which

also

lacks

the

corresponding

Cys

residue

The

appearance

of

half

molecules

of

equine

and

would

suggest

that

a

fraction

of

both

these

subclasses

lack

inter-H

chain

disulphide

bonds

with

the

molecules

being

stabilised

by

non-covalent

interactions

instead

This

arrangement

has

already

been

noted

in

a

fraction

of

human

molecules

which

are

seen

as

HL

half

molecules

under

denaturing

non-reducing

conditions

-LRB-

Aalberse

and

Schuurman

-RRB-

The

deficiency

in

inter-H-chain

bonds

in

human

has

been

attributed

to

a

Ser

within

the

core

hinge

sequence

-LRB-

CPSC

-RRB-

which

replaces

the

Pro

-LRB-

CPPC

-RRB-

found

in

isotypes

such

as

human

and

that

do

not

have

this

deficiency

This

Ser

seems

to

increase

the

likelihood

that

the

two

core

hinge

Cys

residues

form

intra

rather

than

interchain

disulphide

bonds

possibly

due

to

greater

hinge

flexibility

-LRB-

Angal

et

al.

Bloom

et

al.

-RRB-

In

equine

and

which

all

form

disulphide

bonded

molecules

the

corresponding

core

hinge

sequence

is

CPKC

In

contrast

the

core

hinge

sequences

of

and

are

CPTC

and

CPAEC

respectively

suggesting

that

deviations

from

central

Lys

to

either

Thr

or

Ala-Glu

may

decrease

the

efficiency

of

interchain

disulphide

bridge

formation

in

some

way

Our

results

for

the

binding

of

the

reqIgGs

to

protein

A

and

protein

G

agree

well

with

those

for

the

binding

of

horse

IgG

subclasses

which

had

been

purified

from

serum

and

classified

under

the

previous

nomenclature

-LRB-

Sheoran

and

Holmes

Sugiura

et

al.

-RRB-

The

binding

sites

for

protein

A

and

G

on

the

human

IgG

Fc

lie

at

the

domain

interface

with

considerable

but

incomplete

overlap

-LRB-

Deisenhofer

Sauer-Eriksson

et

al.

-RRB-

-LRB-

Fig.

-RRB-

The

amino

acid

sequences

of

equine

and

all

contain

at

least

one

amino

acid

difference

from

human

within

the

regions

contributing

to

protein

A

binding

-LRB-

Fig.

-RRB-

These

differences

may

be

sufficient

to

explain

the

low

affinity

of

these

IgGs

for

protein

A

because

a

single

amino

acid

substitution

of

His

to

Arg

at

position

in

a

human

allotype

is

known

to

prevent

binding

to

protein

A

-LRB-

Burton

and

Woof

-RRB-

Similarly

the

differential

reactivity

of

the

subclasses

with

protein

G

can

be

rationalized

on

the

basis

of

amino

acid

substitutions

at

key

positions

in

the

protein

G

interaction

site

-LRB-

Fig.

-RRB-

The

fact

that

the

highest

level

of

binding

is

seen

with

and

which

like

human

all

possess

and

may

suggest

that

these

two

residues

play

key

discriminatory

roles

in

the

binding

of

horse

IgGs

to

protein

G.

IgGs

mediate

effector

function

through

interaction

with

leukocyte

FcγRs

and

activation

of

the

classical

pathway

of

complement

These

effector

functions

differ

with

IgG

subclass

-LRB-

Burton

and

Woof

-RRB-

There

is

little

available

information

with

regard

to

comparative

binding

of

the

horse

IgG

subclasses

to

FcγRs

An

early

study

found

that

eqIgG

-LRB-

subclass

unspecified

-RRB-

but

not

eqIgG

-LRB-

T

-RRB-

-LRB-

i.e.

and/or

-RRB-

was

able

to

interact

via

the

Fc

region

with

equine

monocytes

and

neutrophils

-LRB-

Banks

and

McGuire

-RRB-

In

the

present

study

we

have

demonstrated

that

equine

and

are

able

to

elicit

a

strong

respiratory

burst

from

equine

PBL

predicting

that

these

five

subclasses

can

interact

with

FcγR

on

the

surface

of

these

cells

and

stimulated

little

or

no

response

suggesting

that

these

two

subclasses

are

unable

to

interact

efficiently

with

FcγR

Several

amino

acids

in

the

lower

hinge

region

of

human

at

the

N-terminus

of

the

domain

-LRB-

Leu234-Leu235-Gly236-Gly237-Pro238-Ser239

-RRB-

-LRB-

Fig.

-RRB-

play

a

central

role

in

binding

to

human

FcγR

and

deviations

from

this

motif

help

to

explain

human

IgG

subclass

specificity

for

FcγR

-LRB-

Burton

and

Woof

Woof

and

Burton

-RRB-

In

the

horse

IgGs

and

which

elicit

bursts

retain

this

motif

while

and

which

do

not

elicit

bursts

display

significant

differences

from

the

motif

-LRB-

Fig.

-RRB-

Thus

amino

acid

differences

in

this

region

may

account

at

least

in

part

for

the

observed

differences

in

ability

to

trigger

a

respiratory

burst

However

despite

and

lacking

the

full

motif

these

subclasses

are

still

capable

of

eliciting

strong

respiratory

bursts

suggesting

that

regions

other

than

the

lower

hinge

region

may

influence

interaction

with

at

least

some

classes

of

equine

FcγRs

Activation

of

the

classical

complement

pathway

by

IgG

may

be

initiated

through

the

binding

of

the

first

complement

component

to

two

or

more

adjacent

IgG

molecules

Human

IgG

subclasses

bind

in

the

rank

order

while

shows

no

significant

binding

-LRB-

Burton

and

Woof

-RRB-

Early

studies

of

equine

anti-Lac

antibodies

-LRB-

Klinman

et

al.

-RRB-

using

guinea

pig

complement

showed

that

a

preparation

comprising

IgGa

and

IgGb

-LRB-

i.e.

and

-RRB-

was

able

to

fix

complement

whilst

IgGc

-LRB-

i.e.

-RRB-

was

not

The

current

study

confirmed

and

extended

these

results

We

found

that

was

the

most

potent

activator

of

complement

followed

by

and

while

and

failed

to

bind

or

activate

complement

While

our

results

were

obtained

using

human

serum

as

a

source

of

complement

and

we

can

not

rule

out

the

possibility

of

subtle

differences

of

reactivity

with

horse

complement

we

feel

that

our

findings

provide

a

good

reflection

of

the

likely

relative

complement-activating

capabilities

of

the

different

eqIgG

subclasses

The

binding

site

lies

within

the

domain

of

IgG

but

appears

not

to

be

conserved

precisely

across

species

and

are

necessary

for

binding

by

mouse

whilst

in

human

or

and

appear

important

with

minimal

contribution

from

and

-LRB-

Duncan

and

Winter

Idusogie

et

al.

Thomessen

et

al.

-RRB-

A

comparison

of

the

equine

IgG

amino

acid

sequences

does

not

reveal

a

common

difference

between

the

complement

activating

and

non-activating

subclasses

at

the

residues

corresponding

to

or

-LRB-

Fig.

-RRB-

However

all

of

the

complement-activating

subclasses

have

a

Lys

corresponding

to

The

fact

that

this

residue

is

a

serine

in

and

may

account

at

least

in

part

for

the

inability

of

these

subclasses

to

activate

complement

Curiously

which

possesses

a

lysine

at

position

does

not

activate

complement

underlining

the

subtle

species

differences

that

are

apparent

in

complement

activation

Our

data

on

the

effector

function

capabilities

of

the

IgG

subclasses

have

important

new

implications

for

the

design

of

effective

horse

vaccines

It

is

clear

that

to

achieve

maximal

protection

via

FcγR

and

complement-mediated

elimination

mechanisms

vaccines

should

seek

to

elicit

IgG

antibodies

of

the

and

subclasses

Vaccines

that

trigger

only

or

antibodies

are

predicted

to

offer

less

effective

protection

Since

IgG

plays

key

roles

in

both

serum

and

mucosal

compartments

in

the

horse

these

considerations

are

applicable

to

both

systemic

and

mucosal

vaccination

strategies

Further

our

findings

on

the

relative

effector

function

capabilities

of

the

different

IgG

subclasses

lend

themselves

to

a

rationalization

of

the

protection

offered

by

natural

IgG

immune

responses

against

various

infectious

agents

Thus

investigations

into

the

role

of

individual

subclasses

in

protective

immunity

have

demonstrated

that

IgGa

and

IgGb

-LRB-

i.e.

the

FcγR

and

complement-engaging

isotypes

and

-RRB-

contribute

to

protection

against

equine

influenza

virus

-LRB-

Nelson

et

al.

Breathnach

et

al.

Soboll

et

al.

-RRB-

Streptococcus

equi

-LRB-

Sheoran

et

al.

-RRB-

and

Rhodococcus

equi

-LRB-

Lopez

et

al.

-RRB-

Indeed

IgGb

-LRB-

and

-RRB-

has

been

suggested

to

be

most

important

in

equine

protective

antibody-mediated

immune

responses

to

intracellular

pathogens

-LRB-

Nelson

et

al.

Lopez

et

al.

Goodman

et

al.

-RRB-

whereas

IgGc

-LRB-

-RRB-

is

suggested

to

be

least

important

-LRB-

Sheoran

et

al.

-RRB-

The

current

study

helps

rationalize

this

observation

as

both

and

are

able

to

stimulate

a

potent

respiratory

burst

and

activate

complement

via

the

classical

pathway

By

contrast

these

effector

functions

are

absent

in

the

IgGc

-LRB-

-RRB-

subclass

The

requirement

of

equine

IgGs

to

recruit

effector

molecules

for

effective

immunity

is

illustrated

by

several

studies

For

example

IgG-mediated

neutralisation

of

equine

arteritis

virus

-LRB-

EAV

-RRB-

-LRB-

Balasuriya

and

MacLachlan

-RRB-

and

acute

phase

-LRB-

Snyder

et

al.

-RRB-

is

complement

dependent

and

opsonisation

by

specific

IgG

is

necessary

for

efficient

phagocytosis

of

Rhodococcus

equi

Escherichia

coli

and

Actinobacillus

equuli

by

equine

neutrophils

or

alveolar

macrophages

-LRB-

Hietala

and

Ardans

Grondahl

et

al.

Cauchard

et

al.

-RRB-

Drawing

all

this

information

together

it

is

clear

that

the

enhanced

effector

function

capabilities

of

equine

and

equip

these

subclasses

for

key

protective

roles

International

meetings

have

highlighted

the

need

for

further

research

into

the

functional

roles

of

equine

IgG

subclasses

and

for

the

development

of

tools

to

study

equine

Igs

-LRB-

Lunn

et

al.

-RRB-

The

reqIgGs

described

here

are

a

reliable

source

of

pure

and

homogeneous

equine

IgG

subclasses

and

will

serve

as

useful

reference

proteins

for

the

production

and

screening

of

equine

specific

reagents

In

particular

the

generation

of

mAb

able

to

discriminate

between

the

seven

subclasses

is

an

ongoing

project

Moreover

they

will

provide

a

valuable

resource

for

future

research

in

particular

in

delineation

of

the

function

of

individual

IgG

subclasses

in

antibody-mediated

immunity

of

the

horse

O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O B O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O B O O O O O O O O O B B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O B O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O

horse
igg
complement
respiratory burst
protein a

fcγr, receptor specific for the fc region of igg