Headwater Influences on Downstream Water Quality

We investigated the influence of riparian and whole watershed land use as a function of stream size on surface water chemistry and assessed regional variation in these relationships. Sixty-eight watersheds in four level III U.S. EPA ecoregions in eastern Kansas were selected as study sites. Riparian land cover and watershed land use were quantified for the entire watershed, and by Strahler order. Multiple regression analyses using riparian land cover classifications as independent variables explained among-site variation in water chemistry parameters, particularly total nitrogen (41%), nitrate (61%), and total phosphorus (63%) concentrations. Whole watershed land use explained slightly less variance, but riparian and whole watershed land use were so tightly correlated that it was difficult to separate their effects. Water chemistry parameters sampled in downstream reaches were most closely correlated with riparian land cover adjacent to the smallest (first-order) streams of watersheds or land use in the entire watershed, with riparian zones immediately upstream of sampling sites offering less explanatory power as stream size increased. Interestingly, headwater effects were evident even at times when these small streams were unlikely to be flowing. Relationships were similar among ecoregions, indicating that land use characteristics were most responsible for water quality variation among watersheds. These findings suggest that nonpoint pollution control strategies should consider the influence of small upland streams and protection of downstream riparian zones alone is not sufficient to protect water quality.
Introduction
Nonpoint source pollution is a serious problem that degrades surface waters and aquatic ecosystems. Loading of nutrients, sediment, and other pollutants from the landscape may compromise the integrity of freshwaters (Hunsaker and Levine 1995). In particular, excessive inputs of nitrogen and phosphorus result in eutrophication and fundamental changes in trophic state of lakes and streams (Carpenter and others 1998; Dodds and others 2002; Dodds 2006) and the impairment of surface waters for uses such as drinking, recreation, and support of aquatic life (Dodds and Welch 2000). These problems are pervasive; almost 40% of classified stream miles in the United States may be impaired, with diffuse pollutants responsible for a large percentage of impairments (U.S. Environmental Protection Agency [EPA] 2000). In response to these problems, research has focused on identifying and testing practices that reduce excessive pollutant loading and help restore the health of aquatic ecosystems.
The development of remote sensing and geographic information systems (GIS) technologies has facilitated quantitative assessment of landscape influences on aquatic ecosystems and watershed-scale approaches to the study of water quality (Johnson and Gage 1997). Watershed land cover is strongly correlated with water chemistry parameters, especially nutrient concentrations (e.g., Hunsaker and Levine 1995; Johnson and others 1997; Jones and others 2001; Osborne and Wiley 1988; Sliva and Williams 2001). Riparian land use may be particularly influential and, in some cases, a better predictor of in-stream water quality than land cover in the entire catchment (Johnson and others 1997; Osborne and Wiley 1988). Intact riparian zones provide water quality benefits and help preserve the biological integrity of watersheds (Gregory and others 1991).
In areas such as the Midwestern United States large-scale land use conversion has resulted in some of the worst water pollution in the United States (U.S. EPA 2000) and imperilment of many native aquatic species (Fausch and Bestgen 1997). Establishing or protecting riparian zones or large watershed areas that mitigate impacts of human land use on water quality may be costly or politically difficult, particularly in areas where much of the land is privately owned. In such instances, it is essential that scientists and managers identify areas within watersheds where protection would produce the most substantial water quality benefits, and prioritize these areas for protection. Geographic information systems are ideally suited to provide such identification because landscape analyses encompass the full range of spatial scales across which stream processes are regulated (Allan and others 1997) and allow for multiscale examinations of riparian (e.g., Johnson and Gage 1997) or headwater impacts on water quality.
We examined relationships between riparian and whole watershed land cover and water chemistry metrics in streams in Kansas at spatial scales ranging from several kilometers to the entire watershed, with the objective of testing areas where land use may strongly affect water quality in downstream reaches of the watershed (herein referred to as “downstream water quality”). We hypothesized that land use adjacent to small headwater streams would have a disproportionately large impact on water quality, because these streams provide the predominant hydrologic contributions to the watershed (Lowrance and others 1997), and substantial in-stream nutrient processing and retention in upland streams and rivers can regulate downstream water quality (Alexander and others 2000; Peterson and others 2001).
Natural geological and topographic features also influence surface water quality at landscape scales, in addition to anthropogenic factors such as land use conversion (Johnson and others 1997; Sliva and Williams 2001). To assess regional differences related to these features, we compared riparian-water chemistry relationships among four U.S. EPA level III ecoregions. Ecoregions denote general similarities in ecosystem types, serve as a spatial framework for research, assessment, and management of ecosystems (Omernik 1995), and can correspond well with principal factors that may influence surface water quality (e.g., Brown and Brown 1994; Rohm and others 2002). We assessed the degree to which relationships between surface water quality and land cover were affected by landscape heterogeneity (as indicated by ecoregions) by evaluating regional variation in riparian-water chemistry relationships. To our knowledge, no previous studies have examined the importance of headwater riparian zones, compared to other riparian areas within watersheds, at these scales of analysis across multiple watersheds.
Methods
Sixty-eight small watersheds (mean watershed area, 280 km2; range, 19–1400 km2) were identified in four level III U.S. EPA ecoregions (U.S. EPA 1998a) across eastern Kansas (Fig. 1). These ecoregions also represent 4 of the 14 regions developed for the National Nutrient Strategy (U.S. EPA 1998b), which were classified by both anthropogenic and natural characteristics (i.e., geology, geomorphology, land use, soils, vegetation) associated with nutrient concentrations in streams. Sites were selected across the four ecoregions so results would not be as tied to within-ecoregion characteristics. Sites were chosen from those regularly sampled by the Kansas Department of Health and Environment within the ecoregions such that the watersheds did not cross ecoregion boundaries and none of the sites were nested.
Fig. 1Location of study watersheds in Kansas, grouped by level III U.S. EPA ecoregion, and example of land cover classification scheme, in which riparian and catchment land cover was quantified for the subcatchment of each stream segment in the watersheds
Twenty-four watersheds were located in the Flint Hills (FH) ecoregion, characterized by rolling hills, coarse soils, and relatively intact tracts of tallgrass prairie predominantly used as cattle pasture. Because of topography and geology, little of this region has been converted to cropland agriculture. Eighteen watersheds were located in the Central Irregular Plains (CIP), characterized by irregular topography, loam soils, and a variety of land use types, including cropland agriculture, tallgrass prairie, and oak-hickory forests. Fourteen watersheds were located in the Western Corn Belt Plains (WCBP), a region that was historically covered with tall and mixed-grass prairie but has now been almost entirely converted to cropland agriculture. Finally, 12 watersheds were located in the eastern part of the Central Great Plains (CGP) ecoregion, characterized by reduced topography, mixed-grass prairie, and large tracts of cropland agriculture. Criteria for inclusion in the study were as follows: (1) watersheds were sampled for water chemistry parameters a minimum of 12 times, and (2) watersheds were entirely contained within one U.S. EPA level III ecoregion. Watersheds were located across a precipitation gradient, with average rainfall ranging from 610 to 1016 mm/year. No watersheds were chosen that had very large livestock feeding operations or municipal point sources. The few smaller feeding operations (∼1000 animals) included were in all cases at least 0.1 km upstream of the stream chemistry site, and the total area of these operations was included in the analysis (see section Statistical Analyses, below).
Relationships between riparian land cover and water chemistry parameters were assessed at four spatial scales (Fig. 2). Riparian land cover throughout entire watersheds was quantified to examine cumulative impacts on water quality. Because small streams exert a large influence on downstream water quality (Alexander and others 2000; Peterson and others 2001), we examined correlations between riparian land cover adjacent to only the smallest (first-order) streams and water chemistry parameters sampled in downstream reaches of these watersheds. In addition, we examined localized riparian impacts on water quality by quantifying riparian land cover both 2 and 4 km upstream of the sampling site. The results of the above analyses were compared to correlations between water chemistry parameters and catchment-scale land cover at both the watershed and the first-order streams scales. In this way, we assessed the relative impact of riparian land cover on water chemistry parameters, compared to catchment land cover. Temporal variation was explored by partitioning water chemistry data seasonally, which allowed for examination of riparian-water chemistry relationships during both high and base flow conditions.
Fig. 2Riparian land cover assessed at four spatial scales: (A) land cover in the whole watershed, (B) land cover adjacent to the first-order streams of watersheds, and (C) land cover 2 and 4 km upstream of the water chemistry sampling point
We examined a subset of 39 study watersheds where water chemistry measurements were taken on a fourth-order reach of stream to directly compare the influence of riparian land cover on streams of similar sizes within watersheds. Riparian land cover was quantified by stream order (Strahler 1957) and correlated with downstream water chemistry values separately, so comparisons could be made between stream sizes. In addition, we analyzed riparian land cover-water chemistry relationships among ecoregions to determine if differences existed, or if these relationships held constant across ecosystem types. These analyses also help to show that watershed size and natural factors captured by ecoregions (geology, precipitation, elevation, gradient, etc.) did not confound the interpretations of land use effects.
Water Chemistry Data
Water chemistry data were collected and analyzed by the Kansas Department of Health and Environment (KDHE) as part of their stream chemistry monitoring network (KDHE 2000). Total nitrogen (TN), nitrate (NO3−), ammonium (NH4+), total phosphorus (TP), total suspended solids (TSSs), atrazine (AT), fecal coliform bacteria (FC), and dissolved oxygen (DO) data were used to assess the impact of riparian land cover on water chemistry. Samples are collected every 2 months between 0900 and 1700 hr at each site on a rotational schedule. Extreme weather (river icing, very high floods) precludes sampling occasionally. Water chemistry samples were collected from the thalweg of each stream, frozen, and stored in acid-washed bottles in the dark, prior to analysis. All TN, NH4+, and TP samples were analyzed within 28 days of collection, NO3− samples were analyzed within 48 hr of collection, TSS and AT samples were analyzed within 7 days of collection, FC samples were analyzed within 24 hr of collection, and DO measurements were taken in the field using a membrane electrode probe. Total nitrogen and phosphorus were analyzed by a colorimetric automated phenate method, following digestion by metal-catalyzed acid and persulfate techniques, respectively (U.S. EPA 1983). Nitrate was analyzed by ion chromatography; NH4+, by semiautomated colorimetry; TSS, by a residue, nonfilterable and TSSs method; and AT, by gas chromatography (U.S. EPA 1983). Fecal coliform bacteria samples were analyzed by a membrane filter procedure (APHA 1992). Field duplicate samples and internal spikes were used to assess the reliability and recovery efficiencies of the assays.
Water chemistry data for NO3−, NH4+, TP, TSS, AT, FC, and DO were collected from 1990 to 2001 for all study watersheds. Total nitrogen data were collected from January 2000 to May 2003 for 57 of the 68 study watersheds. Collection of TN data began in 2000 to assist establishment of nutrient criteria for Kansas’ surface waters. For all analyses, mean concentrations of TN, NO3−, NH4+, TP, TSS, AT, and FC were taken for each watershed across sampling dates. Minimum and maximum DO concentrations were quantified by averaging minimum and maximum concentrations by year for all years in which at least five samples were taken, then taking a mean of these concentrations across years.
To examine temporal variability in riparian-water chemistry relationships, we first classified seasons using mean monthly discharge measurements (1990–2001) from 30 USGS gauging stations across the study region. Seasons were classified as the month or months in which 0%–25%, 26%–50%, 51%–75%, and 76%–100% of the annual water volume across the region was discharged. Mean water chemistry concentrations of NO3−, NH4+, TP, TSS, AT, and FC were taken for each of the four seasons. Insufficient data prevented analysis of total nitrogen and DO for temporal differences.
Digital and Land Cover Data
Digital stream networks were derived for each watershed using 30-m digital elevation models, ARCGIS (Arcview version 8.2, 2002), and ArcHydro (Maidment, 2002) software. This method accounts for permanent streams and all but the smallest intermittent streams. Catchment area above each KDHE monitoring site was delineated using catchment-processing tools in ArcHydro software. Using the same processing tools, a subcatchment was delineated for each stream segment of the watersheds. A stream segment was defined as a section of stream from its upstream confluence to its downstream confluence with other tributaries. By overlaying catchment and subcatchment layers with digitized riparian and catchment land cover data, we quantified land cover for each watershed and watershed subcatchment (Fig. 1).
Riparian land cover was classified from the Kansas Riparian Areas Inventory dataset (NRCS 2001). The riparian ecotone in this dataset was defined as the 33 m adjacent to the stream and was digitized at a 1:24,000 scale from USGS Digital Orthophotograph Quarter Quadrangles that reflected land cover conditions in 1991. Land cover was identified from the beginning of the period of water chemistry sampling. Large socioeconomic changes did not occur in Kansas over this time period (e.g., only ∼10% population increase). This dataset contained 11 land cover classes (animal production area (holding pens or feeding areas), barren land, cropland, crop/tree mix, forest, grassland, grass/tree mix, shrub/scrub land, urban land, urban/tree mix, water), and riparian areas were classified by the land cover type occurring in ≥51% of the 33-m ecotone. Of the 11 land cover classes, 3 (shrub/scrub land, barren land, and animal production area) did not account for more than 1% of the riparian land cover in any watershed and were not included in the analyses. The remaining eight classifications were aggregated into five categories (cropland, forest, grassland, urban land, and water) following the level I classification scheme developed by Anderson et al. (1976). Water was not included as a land cover type in analyses. While this scheme can create problems with colinearity, the primary goal of this paper was to determine the best-fit model at different spatial scales within the watershed. Colinearity influences the ability to ascribe causation by individual categories of land use (e.g., cropland, urban, forest, or grassland), but this was not the primary goal of our analysis.
Catchment land cover was classified from the Kansas Land Cover dataset (KARS 1993). This dataset was digitized at a 1:100,000 scale from Landsat Thematic Mapper imagery and, also, contained 11 land cover classes that reflected conditions in 1991. Land cover classes were reclassified in the same way as the riparian dataset. Comparison of the riparian dataset to a 33-m “buffer” clipped from the catchment dataset showed highly significant correlations (average Kendal τ correlation = 0.93, p < 0.01) between the two datasets for all land cover types. Information on permitted point sources and confined livestock feeding operations within watersheds was obtained from KDHE and incorporated into GIS to ensure that point sources were not in close proximity to sampling sites.
Statistical Analyses
Forward stepwise linear regression models were used to predict water chemistry parameters with land cover data (animal production area [holding pens or feeding areas], barren land, cropland, crop/tree mix, forest, grassland, grass/tree mix, shrub/scrub land, urban land, urban/tree mix, water) at four spatial scales (watershed, first-order streams, 2 km upstream, 4 km upstream). Separate regressions were done at each scale. F-values of 1 and 0 were used as thresholds to include and exclude land cover classifications from regression models. We investigated the predictive ability of riparian land cover independent of catchment effects by examining partial correlations (r) among riparian land cover classifications that were significant predictors in regression models and water chemistry parameters, controlling for predictor catchment land cover classifications. Analysis of variance (ANOVA) was used to test for differences among ecoregions. Since ecoregions were correlated with land use, slopes of relationships were compared among ecoregions at all four spatial scales using general linear model (GLM) analysis of variance (ANCOVA) to assess whether riparian-water chemistry relationships held constant across ecoregions. Results of comparisons of intercepts on these data were presented in a prior publication (Dodds and Oakes 2004). Least-squares means were used to compare slopes of regression lines. Slopes represent the fundamental response to anthropogenic effects (most relevant to this paper) and intercepts indicate the baseline nutrient or pollutant level. Response data appeared normally distributed and were not transformed prior to analyses. All relationships among the data were plotted and no clear outliers or leveraged relationships were observed.
Results
Riparian-Water Chemistry Relationships
Strahler ordering showed the smallest (first-order) digitized streams on average comprised >60% of the stream miles within study watersheds, with larger streams accounting for sequentially fewer percentages of stream miles. Across all studied watersheds, riparian land cover was a significant predictor of among-site variation in water chemistry concentrations at the watershed and first-order streams scales, particularly for nutrients (Table 1). Less variance was explained at local scales represented as riparian cover 2 or 4 km upstream from the sampling site (Fig. 3). Total nitrogen, TP, and NO3− were the parameters with the greatest R2 values related to riparian land cover, and all three had slightly greater R2 values using land cover adjacent to first-order streams of watersheds than using riparian land cover across the whole watershed.
Table 1Multiple regression models showing correlations between water chemistry parameters and riparian land cover in both the whole watersheds the first-order streams of watershedsWater chemistry parameterCropForestGrasslandUrban InterceptR2Watershed  TN−0.4400.2601.932 0.355  NO3-N0.6230.490−0.5000.525  NH4-N−0.466−0.6620.2030.327  TP0.2640.7120.0950.507  AT0.4280.5580.171  FC0.3781621.5700.199  DO (max)0.50812.0850.247First order  TN0.3880.5760.5510.406  NO3-N0.6500.538−0.0330.606  NH4-N−0.445−0.6830.1950.304  TP0.3200.7800.0870.634  AT0.4130.6050.158  FC0.458798.8320.198  DO (max)0.52212.1130.261Note. Significant regression coefficients are presented, illustrating the magnitude and direction of importance of land cover classes in models. TN analyses based on 57 watersheds; all other analyses based on 68 watersheds. Nutrient parameters and dissolved oxygen expressed as milligrams per liter, atrazine (AT) expressed as micrograms per liter, microbiological parameters expressed as colony forming units/100 ml, and land cover classifications expressed as percentages. All values reported were significant at p < 0.05Fig. 3Variance in water chemistry variables (R2 values) accounted for by (A) land cover in the riparian ecotone (33 m) at multiple scales and (B) catchment land cover at two scales, using multiple linear regression analyses. TN analyses based on 57 watersheds; all other analyses based on 68 watersheds. Bars for R2 values were not plotted when there was not a significant relationship (p > 0.05)
Riparian land cover 2 and 4 km upstream explained no significant variance in TP concentrations, and riparian land cover 2 km upstream of the sampling point explained no significant variance in AT concentrations. Total suspended solids and minimum DO concentrations did not have significant relationships with riparian cover in any analyses and are not discussed further in this section.
Catchment land cover showed similar relationships to water chemistry parameters as riparian land cover (Fig. 3). In all comparisons between catchment and riparian land cover, the magnitude of differences was small. Partial correlations indicated that riparian land cover classifications were still significantly correlated with some water chemistry parameters after controlling for variance explained by catchment land cover classifications that were significant predictors in regression models (Table 2). Removal of the effect of land use cover by using partial correlations can actually remove riparian effects from the overall correlation so these data should not be interpreted to suggest that riparian cover only explains a small portion of the variance in water quality.
Table 2Partial correlations among nutrient concentrations and riparian land cover classificationsWater chemistry parameterCatchment land cover Riparian land coverrp-valueWatershed  TNGrass, forestGrass, urbanGrass = −0.060.687Urban = 0.200.134  NO3−Crop, urbanCrop, urbanCrop = 0.220.071Urban = 0.480.000  NH4+Grass, forestGrass, forestGrass = −0.030.803Wood = −0.110.370  TPCrop, urban, forestCrop, urbanCrop = 0.040.779Urban = 0.580.000First order  TNCrop, grassCrop, urbanCrop = 0.250.068Urban = 0.330.013  NO3−Crop, urbanCrop, urbanCrop = 0.260.033Urban = 0.500.000  NH4+Grass, forestGrass, forestGrass = −0.000.994Forest = −0.080.543  TPCrop, urbanCrop, urbanCrop = 0.040.724Urban = 0.680.000Note. Correlations controlled for catchment land cover classifications that were significant predictors in regression models and were used to partition additional variance explained by riparian land cover from variance explained by catchment land cover. Partial correlations (r) for which riparian crop land (crop), forest, grassland (grass), and urban land (urban) explained >30% of the variation in water chemistry parameters among sites (see Table 1 and Fig. 3) are presented
Temporal Variation
Examination of regional discharge patterns revealed that 25% of annual water volume was discharged from January to April, 50% by June, 75% by August, and the remainder in the August–December time period. Thus, the periods of January–April, May, June–July, and August–December were designated as seasons in temporal analyses. Seasons in which a quarter of annual water volume was discharged in 1 or 2 months (i.e., May, June–July) represented periods of high flow and high connectivity across the landscape, while seasons encompassing more than 2 months (January–April, August–December) represented predominantly base flow conditions (with most of the upper reaches of the first-order streams dry).
Most water chemistry parameters exhibited temporal changes in the degree that they were statistically related to riparian land cover. Total P and NH4+ were significantly correlated with riparian land cover in all seasons except May (Fig. 4); in particular, riparian land cover at both the watershed and the first-order streams scales explained most variance in TP concentrations in January–April compared to other seasons. Conversely, AT and FC concentrations were best explained during the high flow period of May, and did not have significant relationships with riparian land cover during some base flow seasons. Nitrate exhibited comparatively less temporal variation; riparian land cover at the watershed scale explained a minimum of 30%, and at the first-order streams scale a minimum of 45%, of among-site variance in NO3− concentrations across seasons.
Fig. 4Temporal variation (R2 values) in relationships between water chemistry parameters and (A) total riparian land cover in watersheds and (B) riparian land cover adjacent to the first-order streams of watersheds. Seasons were designated from quartiles of annual discharge occurring across the study region. Total nitrogen and DO were not analyzed for temporal differences (see Methods). Bars for R2 values were not plotted when there was not a significant relationship (p > 0.05)
A particularly interesting aspect of these data is that even when first-order streams are not very likely to flow (August–December), the riparian land cover around them yielded somewhat greater R2 values than did the whole watershed riparian cover for TP and NO3−.
Impact of Stream Size
Different stream sizes were used in the analyses to this point. To control for this a subset of sites was chosen from which data were taken only for fourth-order streams. Total N and NO3− were most closely correlated with first-order riparian land cover (Fig. 5). In general, the most variance was explained by riparian land cover adjacent to first-order streams and less variance was explained by riparian cover near larger-order streams closer to sampling sites. Atrazine and maximum DO concentrations were not significantly correlated with riparian land cover near streams of any size in this subset of watersheds.
Fig. 5Variance in water chemistry variables (R2 values) explained by riparian land cover adjacent to different sized streams within watersheds. Analyses performed with a subset of 39 fourth-order watersheds; TN analyses preformed with 38 fourth-order watersheds. Comparisons between riparian land cover and AT and maximum DO concentrations were not significant and are not presented
Ecoregion Effects
ANOVA indicated some variation in TN, NO3−, NH4+, TP, FC, and maximum DO concentrations among ecoregions. Comparison of least-squares means showed TN and NO3− concentrations were significantly different (p < 0.05) among all ecoregions except the CGP and CIP (Fig. 6). The Western Corn Belt Plains was the only ecoregion that exhibited significantly different NH4+, FC, and maximum DO concentrations, which were all higher than mean concentrations in other ecoregions.
Fig. 6Mean values for selected water chemistry parameters and riparian cropland, grouped by ecoregion (WCBP, Western Corn Belt Plains; CGP, Central Great Plains; FH, Flint Hills; CIP, Central Irregular Plains). TN data for WCBP were available for only 3 of 12 study watersheds. Significant differences are labeled with different letters; error bars represent 1 SE
The Flint Hills was the only ecoregion that exhibited significantly different TP concentrations, which were lower than those of other ecoregions. Atrazine concentrations did not differ significantly among ecoregions.
The percentage of riparian land in agricultural production also varied by ecoregion and closely mirrored nutrient concentrations. Least-squares means comparing slopes of regression lines between water chemistry parameters and significant predictor land cover classifications among the four ecoregions showed that slopes were generally similar across all water chemistry parameters (e.g., Fig. 7), and differences that did exist most often occurred when comparing the Flint Hills to other ecoregions (Table 3).
Fig. 7Example of typically observed relationships between riparian land cover and water chemistry parameters among the four ecoregions analyzed (WCBP, Western Corn Belt Plains; CGP, Central Great Plains; FH, Flint Hills; CIP, Central Irregular Plains). The percentage of riparian cropland in the watersheds is plotted versus in-stream NO3− concentrations. Slopes of regression lines fitted through each of the four ecoregions were not significantly differentTable 3Comparisons of least-squares means using general linear model analyses to assess differences in slopes of riparian-water chemistry relationships at four spatial scales, across level III U.S. EPA ecoregionsSpatial scaleResponse variableEcoregions with different slopesp-valueWatershedTPFH & CGP0.010First orderTNFH & CGP0.019First orderTPFH & CGP0.007First orderTPCIP & CGP0.007First orderTPCIP & WCBP0.0412 km upstreamTNFH & CGP0.0222 km upstreamNO3−FH & CIP0.0142 km upstreamNO3−FH & WCBP0.0024 km upstreamNO3−FH & WCBP0.0014 km upstreamFCFH & WCBP0.023Note. Significantly different slope comparisons between Central Great Plains (CGP), Central Irregular Plains (CIP), Flint Hills (FH), and Western Corn Belt Plains (WCBP) ecoregions are listed. All other comparisons were not significantly different at p < 0.05
Discussion
Land Cover-Water Chemistry Relationships
Riparian and whole watershed land cover was significantly correlated with water quality metrics, particularly nutrient concentrations. Land cover explained greater variance at landscape scales (watershed and first-order streams) than riparian cover at local scales (2 and 4 km upstream of sampling), which is consistent with the idea that nutrient loading and retention occurs at larger spatial scales (Allan and others 1997). Given that NO3− uptake lengths are often less than 2 km in this region (O’Brien and others 2007), it is possible that local riparian cover would influence NO3− concentrations, but the effect was small. Differences in correlations between nitrogen species (NO3− and NH4+) may have occurred because NO3− inputs from the watershed are often greater than NH4+ inputs (Peterson and others 2001) and NH4+ is a preferred nitrogen source for aquatic organisms that can use inorganic N and cycles more quickly than NO3− (Dodds and others 2000). Seasonal differences in relationships between riparian land cover and both NH4+ and TP may be attributable to their strong relationship to particulate dynamics (Johnson and others 1997). Phosphate and NH4+ both adsorb readily to sediments, and are primarily transported into streams via surface runoff (Novotny and Olem 1994).
We wanted to remove the potential problem that the proportion of length of first order streams would vary by stream order. But if we only used our fourth-order sites, then we had about half the total number of sites and our statistical power decreased. Thus we analyzed the subset of fourth-order stream sites (Fig. 5) to be certain that our results were not an artifact of sampling sites occurring at different order streams. Since our results were similar with this subset, all other analyses used the full dataset.
Variation in nutrient concentrations during high flow may have resulted from “pulses” of sediment-bound nutrients entering from the landscape which were not effectively captured by our method of analyzing mean seasonal concentrations. This could explain the lack of correlation between riparian land cover and NH4+ and TP in May compared to other seasons. Conversely, the primary mode of NO3− transport to surface water is generally via subsurface flow (Hill 1996), and this consistent connectivity to the landscape may explain the comparatively low temporal variability seen in riparian-NO3− relationships.
Discrepancies in numbers of sampling dates and sites made it difficult to directly compare riparian-TN relationships with those of other parameters. However, we felt it was important to include TN in these analyses because of its importance in establishing nutrient criteria (Dodds and Welch 2000) and because other available parameters, such as dissolved inorganic nitrogen, can be unsuitable substitutes (Dodds 2003). A disproportionate number of watersheds for which TN data were not available were primarily agricultural and contained some of the highest observed concentrations of both NO3− and TP; the absence of these sites in TN analyses may explain why TN was not as strongly correlated with riparian land cover compared to NO3− or TP.
Nonnutrient water chemistry parameters had weaker correlations with riparian land cover. Although AT, FC, and maximum DO concentrations were significantly correlated with riparian land cover, relationships were weak across all spatial and temporal scales and preclude conjecture into the mechanisms underlying correlations. Lack of correlation between TSS and riparian land cover contrasts with results of previous studies (Johnson and others 1997; Sliva and Williams 2001) and, as with relationships observed in sediment-bound nutrients, may be a function of averaging TSS concentrations into one measurement.
Although permitted livestock operations and other point sources were not substantial in each watershed, point sources falling below Kansas’ permitting regulations (e.g., confined livestock operations under 300 animals) were likely present in some watersheds and may have accounted for unexplained variance in the observed relationships. Our results were consistent with previous studies (Johnson and others 1997; Jones and others 2001; Osborne and Wiley 1988; Sliva and Williams 2001), suggesting that agricultural and/or urban lands were the most important predictors of water quality variability.
Maintaining buffers or other passive land uses in headwater streams may effectively reduce diffuse pollution downstream. The importance of these streams and their riparian zones is due in part to their sheer numbers; small streams often comprise the majority of stream miles within a drainage network (Horton 1945; Leopold and others 1964), and in this study the smallest (first-order) streams on average comprised more than 60% of the stream miles in the study watersheds. Riparian land cover near the first-order streams of watersheds explained greater variance in TN, NO3−, and TP concentrations than did riparian land cover immediately upstream from sampling sites. First-order riparian land cover was statistically related to most water quality measures, even when all potential correlation related to watershed land cover was controlled for. Our results suggest that headwater riparian areas could have an important impact on downstream water quality.
Our study was correlative in nature and does not unequivocally confirm causation. Such an approach is required at the spatial scales of our study. Previous work suggests several possible causes for our observed associations. First, lower-order streams have the greatest potential for interactions between water and the adjacent landscape (Lowrance and others 1997). Second, the large benthic surface area-to-volume ratio of small streams favors rapid in-stream uptake, processing, and retention of nitrogen (Alexander and others 2000; Dodds and others 2000; Peterson and others 2001), which in larger streams increases in proportion to depth (Alexander and others 2000) or discharge (Wollheim and others 2001). Because high nitrogen inputs may overwhelm this ability (O’Brien and others 2007; Wollheim and others 2001), riparian zones adjacent to small streams may be particularly important in regulating nutrient inputs and allowing natural in-stream processes to significantly impact nutrient concentrations.
Several studies have addressed the relative importance of riparian versus whole catchment land use in regulating water quality. Reports in the literature have been mixed; some researchers (Hunsaker and Levine 1995; Sliva and Williams 2001) found that catchment land cover was better correlated with water quality, while others (Osborne and Wiley 1988; Johnson and others 1997) reported that land cover in the riparian ecotone was more influential. Likewise, although partial correlations indicated riparian land cover classifications were significantly related to TN, NO3−, and TP even after accounting for catchment effects, this did not hold true for all water chemistry parameters. Overall, it is difficult to separate the effects of land cover in the riparian ecotone and land cover in the catchment because they are highly correlated, and in many altered landscapes, riparian land cover may simply reflect the dominant catchment land cover types.
Significant partial correlations between riparian land cover and TN, NO3−, and TP concentrations correspond with previous work (e.g., Karr and Schlosser 1978; Lowrance and others 1997) identifying riparian zones as key regulators of nutrient inputs to surface waters. These results, in addition to strong relationships among water quality metrics and riparian land use that have been previously reported at both field (e.g., Karr and Schlosser 1978; Peterjohn and Correll 1984) and landscape (e.g., Johnson and others 1997; Osborne and Wiley 1988) scales, suggest that intact riparian zones could influence landscape impacts on surface water quality.
Ecoregion Effects
The finding that slopes of the relationships were not significantly different in most ecoregion comparisons may be attributable to several factors. Exceptionally variable relationships could preclude the statistical power to determine differences. It is possible that the study regions were not sufficiently distinct to allow detection of differences in riparian interactions, although this is unlikely given their previous classification as both separate ecoregions (U.S. EPA 1998a) and nutrient regions (U.S. EPA 1998b). Because U.S. EPA ecoregion designations encompass human impacts such as land use in addition to natural geological, climatic, and soil characteristics (Omernik 1995), observed intraregion differences in riparian land cover classification and nutrient concentrations were expected. However, since land use is often a dominant factor regulating surface water quality (Hunsaker and Levine 1995; Johnson and others 1997; Osborne and Wiley 1988), riparian-water chemistry relationships would be expected to remain relatively constant across ecoregions if designations were partially dependent on land use, as was the case in this study.
Conclusions
The data suggest that riparian cover near sampling sites is generally less well correlated with water quality parameters than riparian cover or land use in first-order streams. Because watershed cover and riparian land use were correlated, it is difficult to determine how important first-order riparian cover is related to water quality. Our results suggest a statistically significant effect of riparian cover of first-order streams on water quality because partial correlations among riparian land cover classifications were significant predictors in regression models when controlling for predictor catchment land cover classifications. We take the conservative approach in our interpretation, but it is possible that riparian cover has much stronger effects than whole-watershed land cover and that most of the correlation is driven by riparian effects. The effect of first-order land cover may not be too surprising; first-order streams make up the majority of stream length in watersheds. Our approach shows that a correlation with land uses in small headwater streams does hold, and holds even in seasons when many of the first-order stream channels are not flowing.

water quality
riparian zones
nonpoint source pollution
geographic information systems
headwater streams
watershed management


The relevance of the IgG subclass of autoantibodies for blister induction in autoimmune bullous skin diseases

Autoimmune bullous skin diseases are characterized by autoantibodies and T cells specific to structural proteins maintaining cell–cell and cell–matrix adhesion in the skin. Existing clinical and experimental evidence generally supports a pathogenic role of autoantibodies for blister formation. These autoantibodies belong to several IgG subclasses, which associate with different functional properties and may thus determine the pathogenic potential of IgG antibodies. In pemphigus diseases, binding of IgG to keratinocytes is sufficient to cause intraepidermal blisters without engaging innate immune effectors and IgG4 autoantibodies seem to mainly mediate acantholysis. In contrast, in most subepidermal autoimmune blistering diseases, complement activation and recruitment and activation of leukocytes by autoantibodies are required for blister induction. In these conditions, tissue damage is thought to be mainly mediated by IgG1, but not IgG4 autoantibodies. This review summarizes the current knowledge on the pathogenic relevance of the IgG subclass of autoantibodies for blister formation. Characterization of the pathogenically relevant subclass(es) of autoantibodies not only provides mechanistic insights, but should greatly facilitate the development of improved therapeutic modalities of autoimmune blistering diseases.
Introduction
Autoimmune blistering diseases are associated with an autoimmune response directed to structural proteins mediating cell–cell and cell–matrix adhesion in the skin [62, 66]. Both autoantibodies and autoreactive T cells have been found in patients with these organ-specific autoimmune diseases. However, blister induction is mainly mediated by autoantibodies. Autoimmune blistering diseases are classified based on the ultrastructural site of deposition of immunoreactants and on the molecular target of autoantibodies. Diseases of the pemphigus group are associated with autoantibodies to epidermal components mediating cell–cell adhesion and are characterized by acantholytic blisters within the epidermis [39, 71]. Tissue-bound and circulating autoantibodies to the dermal–epidermal junction are characteristic immunopathological features of subepidermal autoimmune bullous diseases [62, 85]. Target antigens of autoantibodies have been identified for the majority of autoimmune blistering diseases. In most of these diseases, the pathogenicity of autoantibodies is supported by clinical observations and extensive experimental evidence [62].
Antibodies are effector molecules of the adaptive immune system secreted by plasmablasts and long-lived plasma cells. Antibody responses are physiologically mounted following an infection or vaccination and protect against various pathogens. Occasionally, in the setting of an autoimmune disease, antibodies to autologous structures may develop and cause different forms of tissue damage. The immunopathology induced by autoantibodies, similar to the immunity mediated by antibodies to pathogens, relies on several mechanisms of action of antibodies, including direct mechanisms, which are mediated by the antibody’s variable regions (e.g., by steric hindrance and signal transduction), and indirect mechanisms, which are triggered by the constant regions of antibodies. For the latter, (auto)antibodies typically interact through their Fc portions with other factors of the innate immune system, including the complement system and inflammatory cells [62].
Antibodies of the IgG isotype predominate in the systemic immune response, as reflected in serum immunoglobulin concentration, and activate a wide range of effector functions. Four subclasses of IgG are defined, originally from the antigenic uniqueness of their heavy chains, which are products of distinct genes [20, 27, 77]. The subclasses are designated as IgG1, IgG2, IgG3 and IgG4 in order of their serum concentration ∼60, 25, 10 and 5%, respectively. Although the heavy chains show >95% sequence homology, each IgG subclass expresses a unique profile of effector activities [35, 56, 59, 76, 80, 82]. Protein antigens characteristically provoke IgG1 and IgG3 responses and these isotypes are able to activate all types of Fc receptors and the C1 component of complement. The IgG4 subclass may be characteristic of chronic antigen stimulation, as in autoimmune disease; it has restricted Fc receptor activating abilities and does not activate C1q. The IgG2 subclass often predominates in responses to carbohydrate antigens; it has restricted Fc receptor and C1 activating abilities [35, 56, 80, 82].
The pathogenic potential unfolded by autoantibodies is determined not only by their specificity and affinity, but also by their isotype. Autoantibodies against cutaneous proteins in autoimmune blistering diseases belong to different IgG subclasses. This paper summarizes the current knowledge on the relevance of IgG subclasses for tissue injury in autoimmune bullous diseases.
Pemphigus diseases
Pemphigus designates a group of life-threatening-autoimmune blistering diseases characterized by intraepithelial blister formation caused by loss of cell–cell adhesion [39, 54, 71]. IgG autoantibodies in patients with pemphigus seem to mediate their pathogenic functions independently of their Fc portions [62]. Patients’ IgG autoantibodies are pathogenic in C5-deficient mice and F(ab’)2, Fab, and scFv fragments of autoantibodies induce acantholysis by passive transfer in wild type mice showing that complement activation or other Fc-mediated effects are not required for pathogenicity [4, 21, 45, 55].
Numerous studies clearly demonstrated that tissue bound and circulating autoantibodies in pemphigus patients mainly belong to the IgG1 and IgG4 subclasses [2, 5, 8, 9, 16–18, 23, 28, 36, 38, 40, 49, 61, 79]. The IgG subclass distribution of autoantibodies in a representative pemphigus patient is shown in Fig. 1. While it is generally agreed upon the subclass distribution of IgG autoantibodies, the relevance of autoantibodies of different IgG isotypes for acantholytic blistering in pemphigus is still a matter of debate. IgG4 autoantibodies, known to have poor complement- and leucocyte-activating properties, predominate in pemphigus vulgaris and foliaceus. While several studies suggest a pathogenic role of IgG4 in pemphigus, the capacity of IgG1 autoantibodies to induce acantholysis has not yet been ruled out. In patients with active pemphigus vulgaris, IgG4 autoantibodies against desmogleins were found to predominate [5, 17, 23, 40]. The transplacental transfer of these autoantibodies in mothers with pemphigus induces acantholytic skin disease in neonates [52]. In endemic pemphigus foliaceus the early antibody response in normal subjects living in the endemic area and in patients before the onset of clinical disease is mainly IgG1. Acquisition of an IgG4 response seems to be a key step in the development of clinical disease [78]. Clinical and experimental data suggests that IgG1 differ from IgG4 autoantibodies in terms of both their epitope specificity and pathogenic potential [9, 42]. A monoclonal IgG4 antibody against desmoglein 3 generated from a patient with active pemphigus vulgaris induces acantholysis in cultured skin and when injected in neonatal mice [86]. The fact that IgG4 antibodies purified from patients with fogo selvagem, an endemic form of pemphigus foliaceus, are pathogenic in mice further supports the notion that IgG4 is pathogenic and complement activation is not required for blister formation [57], but does not exclude a pathogenic potential of IgG autoantibodies belonging to other subclasses. Indeed, in several pemphigus foliaceus patients, only IgG1 autoantibodies were found that caused intraepidermal blisters by passive transfer into neonatal mice [29]. This observation clearly shows that autoantibodies of different subclasses may display blister-inducing activity. In addition, in paraneoplastic pemphigus, autoantibodies against desmoglein 3 mainly belong to the IgG1 and IgG2 subclasses suggesting that the IgG subclass per se is not a direct determinant of the antibodies’ pathogenic potential in pemphigus [24].
Fig. 1IgG subclass distribution of circulating pemphigus autoantibodies. A 1:10 dilution of serum from a patient with pemphigus foliaceus was incubated with 6 μm-thick cryostat sections of normal human skin for 30 min at room temperature. Bound antibodies, visualized using an FITC-labeled antibody specific to human IgG, were of a IgG1 and d IgG4 subclasses. In contrast, no binding of b IgG2 and c IgG3 autoantibodies was evidenced
Experimental evidence using a murine model of pemphigus vulgaris shows that, similar to human pemphigus, the autoimmune response in mice is biased toward non-complement fixing autoantibodies. In this model, in immunodeficient mice infused with splenocytes from desmoglein-deficient mice immunized against this antigen, IgG autoantibodies are produced by homeostatically expanded antigen-specific B cells under T cell control and mice develop a phenotype reminiscent of pemphigus vulgaris. These IgG autoantibodies predominantly belong to the IgG1 subclass, which is a non-complement fixing antibody in the mouse [3, 50]. These results demonstrate that non-complement-fixing autoantibodies can induce acantholysis and suggest a similar mechanism in patients, but they do not exclude a pathogenic potential of patients’ IgG1 autoantibodies in pemphigus.
In conclusion, in pemphigus diseases the autoantibodies mainly belong to the IgG4 and IgG1 subclasses. Extensive experimental evidence demonstrates the blister-inducing potential of IgG4 autoantibodies. The pathogenic activity of autoantibodies of other subclasses seems likely, but needs further investigation.
Subepidermal autoimmune blistering diseases
Bullous pemphigoid and pemphigoid gestationis
Bullous pemphigoid is an autoimmune blistering disease characterized by subepidermal blisters and associated with linear deposits of C3 and IgG at the epidermal basement membrane zone. Autoantibodies in bullous pemphigoid are directed against two hemidesmosomal antigens, BP230 and BP180/type XVII collagen [87]. Pemphigoid gestationis, also referred to as herpes gestationis, is a subepidermal blistering disease associated with pregnancy and characterized by linear deposition of C3 and, to a lesser extent of IgG at the dermal–epidermal junction, as detected by immunofluorescence microscopy [62, 66]. The autoimmune response in bullous pemphigoid and pemphigoid gestationis is mainly directed against epitopes clustered within the immunodominant 16th non-collagenous (NC16) A region of type XVII collagen [48, 65, 88].
Experimental evidence generally supports the pathogenic role of autoantibodies against type XVII collagen for blister formation. Data from passive transfer animal models strongly suggest that antibodies to type XVII collagen are directly involved in the pathogenesis of bullous pemphigoid [44, 84]. In addition, in an ex vivo model utilizing cryosections of human skin, it has been demonstrated that binding of autoantibodies to the immunodominant NC16A domain of type XVII collagen, is the first critical step in subepidermal blister formation [31, 64].
Analysis of the subclass distribution of IgG autoantibodies in the skin of patients with bullous pemphigoid by immunofluorescence microscopy, revealed IgG4 as being the predominant subclass of autoantibodies in bullous pemphigoid, followed by IgG1 autoantibodies, while IgG2 and IgG3 autoantibodies were found only occasionally [1, 10–12, 22, 61, 83]. In addition, serum autoantibodies binding to the dermal–epidermal junction by immunofluorescence microscopy also mainly belong to the IgG4 and IgG1 subclasses [10, 11, 61, 83]. Subsequent molecular analysis of IgG autoantibodies by immunoblotting and ELISA generally confirmed the predominance of IgG1 and IgG4 autoantibodies reactive with type XVII collagen and BP230 (Fig. 2) [6, 19, 32, 41, 69].
Fig. 2IgG1 and IgG4 autoantibodies mainly target type XVII collagen in bullous pemphigoid. Immunoblot analysis of serum from a bullous pemphigoid patient with recombinant type XVII collagen revealed that reactivity against its immunodominant domain consists mainly of IgG1 (lane 1) and IgG4 (lane 4), and less IgG2 (lane 2) and IgG3 (lane 3) autoantibodies
In contrast to bullous pemphigoid, in pemphigoid gestationis tissue-bound and circulating autoantibodies seem to mainly belong to the IgG1 and IgG3 subclasses [14, 37]. However, a recent study challenged these reports revealing IgG4 as the predominant IgG subclass of tissue-bound autoantibodies in pemphigoid gestationis patients [53], a pattern similar to the one found in bullous pemphigoid. Further studies should solve this contradiction.
Data from several studies in patients suggested a pathogenic role of IgG1 autoantibodies for blister formation (briefly reviewed in [43]). In a recent study, ELISA analysis showed that autoantibodies against the N-terminus of the extracellular domain of type XVII collagen predominantly belong to the IgG1 subclass. More importantly, a NC16A-specific IgG1 response was predominant in the acute phase of bullous pemphigoid, while IgG4 was predominantly detected in bullous pemphigoid patients in remission [32]. Using immunoaffinity purified IgG subclasses, it has been shown that IgG1, but not IgG4 autoantibodies from bullous pemphigoid patients activate the complement system in vitro (Fig. 3) [46, 72]. This observation is in line with the currently accepted view that IgG4 is unable to activate the classical pathway of complement. However, until recently it was unclear which IgG subclass is actually pathogenic in bullous pemphigoid. Using the ex vivo cryosection model, we demonstrated that, in addition to IgG1, IgG4 autoantibodies are also able to activate leukocytes and to induce leukocyte-dependent tissue damage (Fig. 4) [46]. Our results are in line with recent studies demonstrating that both polyclonal human IgG1 and IgG4 from patients with Wegener’s granulomatosis and chronic urticaria can activate leukocytes [33, 70]. Although the pathogenic potential of IgG4 autoantibodies was significantly lower compared to IgG1, IgG4 autoantibodies, which generally predominate, may activate the inflammatory cells already recruited into the upper dermis by complement-fixing IgG1 autoantibodies and thus amplify the recruitment of additional leukocytes and the extent of blister formation. Therefore, when associated with IgG1 and/or IgG3 autoantibodies, IgG4 may significantly contribute to the pathology induced by autoantibodies in antibody-induced granulocyte-mediated autoimmune blistering diseases [46].
Fig. 3IgG4 autoantibodies, in contrast to IgG1, do not fix complement to the dermal–epidermal junction in bullous pemphigoid. Cryosections of normal human skin were incubated with serum and immunoaffinity purified IgG1 and IgG4 antibody preparations from a bullous pemphigoid patient and, subsequently, treated with normal human serum as a source of complement. Both a serum and b purified IgG1 autoantibodies fixed complement C3 at the dermal–epidermal junction in a linear fashion. c In contrast, incubation of cryosections with IgG4 specific for the dermal–epidermal junction does not result in C3 deposition (all magnifications, ×200)Fig. 4IgG4 autoantibodies from bullous pemphigoid patients induce dermal–epidermal separation in sections of human skin. Dermal–epidermal separation in sections of normal human skin is induced by a IgG1 and b IgG4 autoantibodies from a bullous pemphigoid patient. c IgG antibodies from a healthy control (NHS) do not induce subepidermal splits (all magnifications, ×200)
Several reports suggested that binding of bullous pemphigoid antibodies to keratinocytes triggers a signal transduction [58, 73–75]. Bullous pemphigoid autoantibodies trigger a signal-transducing event that leads to expression and secretion of interleukin-6 and interleukin-8 from human cultured keratinocytes [58]. A series of studies from another group demonstrated that IgG1 autoantibodies from bullous pemphigoid patients and rabbit IgG against type XVII collagen induces Ca2+ release from intracellular storage sites [73–75]. Interestingly, complement activation by these IgG1 autoantibodies did not result in lysis of keratinocytes [73]. While the relevance of these findings is not yet fully understood, IgG2 and IgG4 patients’ autoantibodies were found to inhibit the transient increase of intracellular Ca2+ induced by bullous pemphigoid IgG1 antibody [74].
Mucous membrane pemphigoid
Mucous membrane pemphigoid is a heterogeneous disease with regard to the clinical phenotype and the target antigens. Different target antigens have been identified in mucous membrane pemphigoid, including BP180, laminins 5 (epiligrin) and 6, β4 integrin [62, 66]. In general, autoantibodies in mucous membrane pemphigoid mainly belong to the IgG4 and IgG1 subclasses [7, 34]. Interestingly, in anti-epiligrin cicatricial pemphigoid, autoantibodies against laminin 5 almost exclusively belong to the IgG4 subclass [34]. Consistent with these findings, sera from patients with anti-laminin 5 IgG autoantibodies do not fix C3 to the epidermal basement membranes and do not induce leukocyte-dependent dermal–epidermal separation in vitro [34, 60]. These data suggest that complement activation does not play a major role in this disease and subepidermal blisters in these patients may develop via a direct effect of anti-laminin 5 IgG itself [34, 60].
Diseases associated with autoimmunity against type VII collagen
Epidermolysis bullosa acquisita is a chronic subepidermal blistering disease characterized by circulating and tissue-bound antibodies targeting the non-collagenous domain 1 (NC1) of type VII collagen. The pathogenic relevance of antibodies against type VII collagen is supported by compelling evidence: (1) autoantibodies from patients with epidermolysis bullosa acquisita were shown to recruit and activate leukocytes ex vivo resulting in dermal–epidermal separation in cryosections of human skin [60, 63], (2) antibodies against type VII collagen induce subepidermal blisters when passively transferred into mice [67, 81], (3) immunization with recombinant autologous type VII collagen induces an autoimmune response to this protein resulting in a blistering phenotype closely resembling human epidermolysis bullosa acquisita [68].
Tissue-bound and circulating antibodies in epidermolysis bullosa acquisita patients mainly belong to the IgG1 and IgG4 subclasses [7, 15, 26, 47]. A similar distribution of IgG subclasses of autoantibodies is found also in SJL mice immunized against murine type VII collagen [68]. In these mice, while both non-complement-fixing IgG1 and complement-fixing IgG2a and IgG2b autoantibodies are produced after immunization, IgG2a/b autoantibodies seem to induce blistering [68].
Systemic lupus erythematosus and inflammatory bowel diseases may be also associated with autoantibodies against type VII collagen [13, 25, 30, 51]. However, in contrast to epidermolysis bullosa acquisita, autoantibodies from patients with bullous systemic lupus erythematosus and inflammatory bowel diseases mainly belong to IgG2 and IgG3, respectively [30, 51]. The pathogenic relevance of autoantibodies against type VII collagen in inflammatory bowel diseases has not yet been addressed [51]. IgG autoantibodies from patients with bullous systemic lupus erythematosus were shown to induce leukocyte-dependent dermal–epidermal separation in cryosections of human skin ex vivo [30]. This findings suggest that the presence of complement-fixing autoantibodies is not a strict requirement for blistering in patients.
Conclusion and perspectives
The polyclonal antibody response against structural skin proteins in autoimmune bullous diseases is heterogeneous, but shows a skewing in subclass distribution of autoantibodies. The strong bias toward production of IgG4 autoantibodies in these organ-specific autoimmune diseases suggests chronic antigenic stimulation. In pemphigus, IgG4 autoantibodies that dominate the autoimmune response are clearly pathogenic. However, IgG1 autoantibodies also likely possess blister-inducing potential that requires further investigation. In subepidermal autoimmune blistering diseases, the effector functions of autoantibodies are important for blistering. Thus, in bullous pemphigoid and epidermolysis bullosa acquisita, complement-fixing IgG1 autoantibodies may show a significantly higher pathogenic potential when compared with IgG4 autoantibodies. Characterization of the blister-inducing capacity of different subclasses of autoantibodies in autoimmune bullous diseases will not only provide relevant mechanistic insights, but should also greatly facilitate the development of improved therapeutic modalities of autoimmune blistering diseases. Detailed knowledge on the pathogenic IgG isotype(s) will serve as a basis for the development of IgG subclass-specific immunoapheresis, skewing autoantibody production toward non-pathogenic subclasses by immunotherapy or blocking of complement or leukocytes activation by targeting specific IgG subclasses. A promising approach is represented by interventions aimed at inhibiting the production of autoantibodies in general or skewing the production of autoantibodies toward non-pathogenic subclasses. The molecular targets of these approaches may include different cytokines (e.g., IL-12 and IL-17) and their activity could be modulated using inhibitory antibodies, small peptide inhibitors or peptidomimetics as well as immunization with the autoantigen together with adjuvants known to induce a Th2 immune response.

igg subclasses
complement
autoimmune bullous diseases


Adiponectin receptor genes: mutation screening in syndromes of insulin resistance and association studies for type 2 diabetes and metabolic traits in UK populations

Aims/hypothesis Adiponectin is an adipokine with insulin-sensitising and anti-atherogenic properties. Several reports suggest that genetic variants in the adiponectin gene are associated with circulating levels of adiponectin, insulin sensitivity and type 2 diabetes risk. Recently two receptors for adiponectin have been cloned. Genetic studies have yielded conflicting results on the role of these genes and type 2 diabetes predisposition. In this study we aimed to evaluate the potential role of genetic variation in these genes in syndromes of severe insulin resistance, type 2 diabetes and in related metabolic traits in UK Europid populations.
Introduction
Adiponectin, encoded by the gene ADIPOQ (also known as 30-kDa adipocyte complement-related protein, Acrp30, APM-1, APM1, ACDC, and gelatin-binding protein-28 or GBP28), is an adipokine with insulin-sensitising [1, 2] and anti-atherogenic actions [3]. Its levels correlate strongly with insulin sensitivity in humans and animal models, and increasing levels of plasma adiponectin produce a sensitising effect to the biological action of insulin [4]. Several genetic reports have detected association between adiponectin gene variants and obesity, insulin resistance, type 2 diabetes, and adiponectin levels [5–9].
Recently, two adiponectin receptors were identified, adiponectin receptor 1 (ADIPOR1), cloned from a human skeletal muscle expression library, and adiponectin receptor 2 (ADIPOR2), identified using computational tools by Yamauchi et al. [10]. In mice, Adipor1 is expressed ubiquitously, with higher levels in skeletal muscle, and has a higher affinity for the globular form of adiponectin. Adipor2, on the other hand, is most abundant in the liver and preferentially binds the full-length form of adiponectin [10]. In contrast, both human ADIPOR1 (375aa) and ADIPOR2 (311aa) were predominantly expressed in skeletal muscle [10, 11].
In Mexican Americans, glucose-tolerant individuals with a family history of type 2 diabetes were reported to exhibit significantly lower levels of mRNA for ADIPOR1 and ADIPOR2 in skeletal muscle than subjects without a family history of diabetes. mRNA levels of both receptors were also reported to positively correlate with glucose disposal [11]. It is possible therefore that lower expression or altered function of the receptors would predispose to increased insulin resistance and type 2 diabetes. In fact, common variants in the ADIPOR1 gene were recently tested for association in a case–control study with white and African American individuals, but no association was reported [12]. Two additional studies have evaluated the role of adiponectin receptor variants and risk of type 2 diabetes. In the Old Order Amish population, two intronic variants in ADIPOR1 were reported to associate with risk of type 2 diabetes, while in ADIPOR2 an extended haplotype block was associated with increased risk of disease [13]. In contrast, in a Japanese population no associations between polymorphisms in adiponectin receptor genes and risk of type 2 diabetes were detected [14]. More recently, studies in French and Finnish populations reported no association between ADIPOR1 single nucleotide polymorphisms (SNPs) and type 2 diabetes [15, 16], although evidence for association between rs767870 in ADIPOR2 and type 2 diabetes in a French population has been suggested [15]. In light of these studies, and the potential role of these receptors in insulin action and diabetes, we sought to identify and investigate the effects of genetic variants in these genes in UK populations.
Subjects and methods
Participants
Severe insulin resistance cohort A cohort of human patients with severe insulin resistance (SIR cohort) was collected at the University of Cambridge, UK. The inclusion criteria for this cohort were: (1) fasting insulin >150 pmol/l or exogenous insulin requirement >200 U/day; (2) acanthosis nigricans; and (3) BMI <33 kg/m2. In the present study, 129 patients from this cohort were screened for mutations in exons and splice junctions of ADIPOR1 and ADIPOR2 genes. Cambridge Local Research Ethics Committee approval was obtained, and informed consent was received from all individuals before participation.
Cambridgeshire Case–Control Study The Cambridgeshire Case–Control Study has been described previously [17]. Briefly, this population-based case–control study consists of 552 type 2 diabetes patients and matched control subjects. DNA was available from 516 cases and control subjects for this study. The cases were a random sample of Europid men and women with type 2 diabetes, aged 47 to 75 years, from a population-based diabetes register in a geographically defined region in Cambridgeshire, UK. The presence of type 2 diabetes in these participants was defined as onset of diabetes after the age of 30 years without use of insulin therapy in the first year after diagnosis. The control participants were individually matched to each of the diabetic subjects by age, sex and geographical location, but not by BMI. Potential control subjects with HbA1c levels greater than 6% were excluded, as this subgroup could have contained a higher proportion of individuals with previously undiagnosed diabetes. Ethical approval for the study was granted by the Cambridge Local Research Ethics Committee.
EPIC-Norfolk participants This is a nested case–control study within the EPIC-Norfolk prospective cohort study; both the case–control and full cohort study [18, 19] have been described in detail previously. Briefly, the case–control study consists of 417 incident type 2 diabetes cases and two sets of 417 control subjects, matched on age, sex, time in study and family physician, with the second set additionally matched for BMI. A case was defined by a physician’s diagnosis of type 2 diabetes, with no insulin prescribed within the first year after diagnosis, and/or HbA1c >7% at baseline or the follow-up health check. Controls were selected from those in the cohort who had not reported diabetes, cancer, stroke or myocardial infarction at baseline, and who had not developed diabetes by the time of selection. Potential control subjects with measured HbA1c levels >6% were excluded. DNA was available for this analysis from 354 cases and 741 control subjects. Ethical approval for the study was granted by the Norwich Local Research Ethics Committee.
Ely Study This is a population-based cohort study of the aetiology and pathogenesis of type 2 diabetes and related metabolic disorders in the UK [20]. It uses an ethnically homogeneous Europid population, in which phenotypic data were recorded at the outset and after 4.5 years. The cohort was recruited from a population sampling frame with a high response rate (74%), making it representative of the general population for this area in Eastern England. This analysis included 1,721 men and women, aged 35–79 years and without diagnosed diabetes, who attended the study clinic for a health check between 2000 and 2004. Of these, 1,005 were attending a follow-up health check, while the remaining 716 were newly recruited in 2000 from the original population sampling frame. Participants attending the health check underwent standard anthropometric measurements and a 75-g oral glucose tolerance test. Plasma glucose was measured using the hexokinase method. Plasma insulin was measured by two-site immunometric assays with either 125I or alkaline phosphatase labels. Cross-reactivity with intact proinsulin was less than 0.2% and interassay CVs were less than 7%. Ethical approval for the study was granted by the Cambridge Local Research Ethics Committee.
PCR and sequencing
Genomic DNA from patients was randomly preamplified in a GenomiPhi reaction (GE Healthcare UK, Chalfont St. Giles, UK) prior to amplification with gene-specific primers. Primers were designed using Primer3 software [21] to cover all coding exons and splice junctions. PCR primers and expected product sizes are described in Electronic supplementary material (ESM) Table 1. Following PCR, performed using standard conditions, products were purified using exonuclease I and shrimp alkaline phosphatase (USB Corporation, Cleveland, OH, USA), and bi-directional sequencing was performed using a DNA sequencing kit (Big Dye Terminator 3.1; Applied Biosystems, Foster City, CA, USA). Sequencing reactions were run on ABI3700 capillary machines (Applied Biosystems) and sequences were analysed using Mutation Surveyor version.2.20 (SoftGenetics LLC, State College, PA, USA).
Genotyping
SNP selection All SNPs with a minor allele frequency greater than 2% in our SIR cohort were selected for genotyping. To increase coverage in areas not re-sequenced, a number of dbSNPs were selected in an attempt to eliminate gaps, between genotyped SNPs, of greater than 2.5–3 kb on average (additional SNPs were selected prior to HapMap phase I data release). SNP choice was based on the following criteria: (1) all putative non-synonymous SNPs in dbSNP were selected irrespective of whether or not there was frequency or validation information for the SNP; (2) SNPs with frequency information were selected if their minor allele frequency was greater than or equal to 5%; (3) for SNPs with no frequency information the choice was based on whether the SNP was a double-hit SNP, had been validated by-cluster or by-submitter; and finally (4) some SNPs with no validation information were included to try and eliminate gaps of greater than 2.5–3 kb between SNPs selected for genotyping.
Genotyping and quality control Samples were arrayed on 96-well plates with three replicates and one water control per plate. For the case–control populations, cases and control samples were randomly distributed across each 96-well plate, with approximately the same number of cases and controls per plate. Genotyping of samples was performed in 384-well plates at the Wellcome Trust Sanger Institute, Cambridge, using an adaptation of the homogenous MassExtend protocol for the MassArray system (Sequenom, San Diego, CA, USA) [22]. Assay results for case–control are described in ESM Table 2. The following criteria were used to pass assays resulting from genotyping: (1) call rates had to be greater than or equal to 90% (in one case a call rate of 88% was accepted); (2) concordance rates between duplicate samples had to be greater than or equal to 98%; (3) minor allele frequency had to be greater than or equal to 1% in the genotyped populations; (4) agreement with Hardy–Weinberg equilibrium was tested separately in cases and controls using a χ2 goodness-of-fit test, and if p < 0.01 in controls, the assay was failed, while if p < 0.01 in cases, the assay was flagged but included in primary analysis. Assays that failed quality control were excluded from further analysis. In total we analysed results from nine SNPs in ADIPOR1 and 15 SNPs from ADIPOR2.
Statistical analysis
All analyses used SAS 8.02 (SAS Institute, Cary, NC, USA) or Stata 7.0 (Stata Corporation, College Station, TX, USA) statistical programs, unless otherwise stated. All used genotypes were in Hardy–Weinberg equilibrium. The pair-wise linkage disequilibrium (LD) coefficient for the controls (r2) was calculated for genotyped SNPs and is represented in Fig. 1. For each SNP, two primary models were used to assess association with diabetes and quantitative traits, the linear trend (additive model) on 1df and the general model on 2df. Since the results from these analyses were not materially different, we present only the results from the linear trend test. Tests for association were performed by logistic regression combined in the two case–control populations adjusting for age, sex and population. Between-study heterogeneity was tested by log-likelihood ratio tests. Quantitative trait analysis was undertaken in the Ely Study population. Association between fasting plasma insulin, fasting and 2-h post-load plasma glucose, 30-min insulin increment (30-min insulin minus fasting insulin over 30-min glucose in an OGTT), BMI and genotype was tested in a multiple regression model, adjusted for age and sex.
Fig. 1Genomic structure and pair-wise marker LD in ADIPOR1 (a) and ADIPOR2 (b). The location of SNPs identified in this study and/or genotyped is represented along the gene (bold type: SNPs selected for genotyping, and failed or monomorphic SNPs). Exons are represented in boxes (black for coding and open for untranslated). Introns and flanking sequences appear as lines. The pair-wise marker LD measured by r2 statistics is shown below the genomic structures and indicated by the shade of grey blocks (white to black) and the r2 value. a, b, isoforms a and b in ADIPOR2
Results
To evaluate whether genetic variation in ADIPOR1 and ADIPOR2 contributed to severe insulin resistance in humans, we sequenced exons and splice junctions in both genes in 129 individuals from our SIR cohort. These individuals were unrelated and had a variety of syndromes of severe insulin resistance [23]. We identified 13 and 29 polymorphisms in ADIPOR1 and ADIPOR2 respectively, none of which altered the protein sequence of either gene (ESM Table 3). This suggests that no functional mutations were identified in these genes. We next tested whether common variants at these genes impacted on type 2 diabetes predisposition or related quantitative traits in UK Europid populations. We selected all variants with a minor allele frequency greater than 2% in the SIR cohort and supplemented our SNP selection with additional variants from the dbSNP database plus SNPs with significant association results in other published studies. Thirteen polymorphisms from ADIPOR1 and 23 polymorphisms from ADIPOR2 were selected for genotyping in two type 2 diabetes case–control studies (n=2,127) and one metabolic quantitative trait study (n=1,721) (Fig. 1). Those SNPs that passed our genotyping quality control criteria (described in methods) were used to investigate the degree of LD in control individuals across ADIPOR1 and ADIPOR2. In total nine SNPs in ADIPOR1 and 15 SNPs in ADIPOR2 were included in the analysis. LD was measured by r2 statistic and is depicted in Fig. 1. Under both general and linear trend models no evidence was found for statistically significant associations between SNPs and disease risk (Table 1). In ADIPOR1, SNPs rs2275738, rs2275735 and rs10581 were removed from the quantitative trait analysis as they were out of Hardy–Weinberg equilibrium (p<0.01). For the remaining SNPs there was also no evidence for association of the SNPs tested with BMI, fasting and 2-h glucose levels, fasting insulin or 30-min insulin incremental response (ESM Table 4). In ADIPOR2 a few SNPs showed nominally significant association with BMI and 2-h glucose levels. However, these results are likely to be chance findings given the number of statistical tests performed (ESM Table 4).
Table 1ADIPOR1 and ADIPOR2 SNP association results with type 2 diabetesGene SNPAlleleCasesControlsOdds ratiop value trendp value homogeneity1/211 (%)12 (%)22 (%)11 (%) 12 (%) 22 (%)(95% CI)ADIPOR1rs6666089G/A378 (47.6)340 (42.8)76 (9.6)536 (46.2)525 (45.2)100 (8.6)1.00 (0.86,1.15)0.96010.750rs1539355A/G374 (46.6)346 (43.1)82 (10.2)558 (45.9)547 (45.0)111 (9.1)1.02 (0.89,1.18)0.73500.649rs2275738C/T277 (34.4)379 (47.1)149 (18.5)395 (34.0)551 (47.4)216 (18.6)0.97 (0.85,1.1)0.61090.496IVS3+85C/G707 (92.1)61 (7.9)0 (0)1046 (92.6)82 (7.3)2 (0.2)1.03 (0.73,1.45)0.87210.654rs2275735C/T763 (92.0)66 (8.0)0 (0)1152 (93.5)79 (6.4)1 (0.1)1.24 (0.88,1.75)0.22390.228rs1342387C/T249 (30.4)414 (50.5)156 (19.0)362 (29.5)634 (51.7)231 (18.8)0.96 (0.85,1.1)0.59090.603rs10581G/A741 (93.8)46 (5.8)3 (0.4)1079 (95.7)46 (4.1)3 (0.3)1.35 (0.92,1.97)0.12810.466rs7539542C/G373 (47.4)336 (42.7)78 (9.9)554 (46.9)510 (43.2)117 (9.9)1.00 (0.87,1.15)0.95660.193rs2185781C/T532 (63.6)256 (30.6)49 (5.9)789 (63.8)398 (32.2)50 (4.0)1.08 (0.92,1.26)0.34250.495ADIPOR2rs1029629A/C353 (44.6)372 (47.0)67 (8.5)537 (44.9)528 (44.1)132 (11.0)0.94 (0.81,1.08)0.34970.265rs11061971A/T228 (28.1)409 (50.4)174 (21.5)349 (29.5)589 (49.7)246 (20.8)1.05 (0.93,1.20)0.43940.092rs4766415A/T207 (25.6)411 (50.7)192 (23.7)323 (27.1)586 (49.2)282 (23.7)1.04 (0.92,1.19)0.51590.074rs767870A/G575 (71.3)214 (26.5)18 (2.2)857 (72.3)310 (26.1)19 (1.6)1.08 (0.9,1.3)0.38430.095rs2286384G/C214 (25.7)429 (51.5)190 (22.8)329 (26.8)618 (50.3)282 (22.9)1.03 (0.9,1.17)0.68370.037*rs2286383C/T237 (28.6)418 (50.4)174 (21.0)368 (30.1)602 (49.2)253 (20.7)1.05 (0.92,1.19)0.45980.068I290C/A618 (75.2)193 (23.5)11 (1.3)940 (77.9)252 (21.0)14 (1.2)1.18 (0.97,1.44)0.09850.118rs9805042C/T603 (75.8)180 (22.6)12 (1.5)900 (79.0)225 (19.8)14 (1.2)1.17 (0.96,1.43)0.12820.051rs2286382G/A791 (94.5)45 (5.4)1 (0.1)1159 (94.2)71 (5.8)0 (0)0.98 (0.67,1.43)0.90540.664rs12342C/T370 (44.6)376 (45.3)84 (10.1)543 (44.3)540 (44.1)142 (11.6)0.96 (0.84,1.1)0.54610.398rs1044471C/T227 (27.8)420 (51.5)169 (20.7)334 (27.6)592 (49.0)282 (23.3)0.93 (0.82,1.05)0.25000.115rs2286380A/T621 (77.0)176 (21.8)10 (1.2)947 (79.0)238 (19.8)15 (0.0125)1.12 (0.91,1.37)0.27620.155rs13219T/C251 (30.2)403 (48.4)178 (21.4)369 (30.1)618 (50.4)238 (19.4)1.04 (0.91,1.18)0.56100.276rs2286379T/C259 (31.0)399 (47.7)178 (21.3)376 (30.6)614 (50.0)239 (19.4)1.03 (0.91,1.17)0.64270.269rs3815325G/A490 (60.6)273 (33.7)46 (5.7)712 (58.7)437 (36.0)64 (5.3)0.96 (0.82,1.11)0.57850.26311, homozygous for allele 1, 12, heterozygous, 22, homozygous for allele 2. Genotype counts are shown with frequency (%), odds ratios are shown per allele 2 with significance calculated for the linear trend (p value trend). Homogeneity in results from association between EPIC and Cambridge Case–Control populations was tested (p value homogeneity)*p values ≤ 0.05
Discussion
The current study, including sequencing of 129 patients with syndromes of severe insulin resistance and genotyping of both population-based type 2 diabetes case–control studies (n=2,127) and a metabolic quantitative trait study (n=1,721), suggests that ADIPOR1 and ADIPOR2 genetic variants are unlikely to be major risk factors for type 2 diabetes and insulin resistance in UK Europid populations.
Although sequencing of ADIPOR1 and ADIPOR2 genes in a cohort of patients with syndromes of severe insulin resistance (n=129) led to the identification of 42 polymorphisms, including 21 novel rare variants, none altered the protein sequence. Given that this group of patients comprises a heterogeneous cohort representative of a variety of syndromes of extreme insulin resistance, the lack of variants affecting the protein sequence suggests that functional mutations in the genes ADIPOR1 and ADIPOR2 are not major causes of extreme insulin resistance in humans.
For ADIPOR1, our data in case–control studies are consistent with previous reports showing that SNPs in this gene are not associated with type 2 diabetes risk in Europid [12, 15, 16], African [12] or Japanese populations [14]. This is in contrast to evidence from the Old Order Amish, where an association of rs2275738 (and rs2275737 which is in perfect LD with it) and rs1342387 with type 2 diabetes risk has been reported [13]. Since the Amish represent an isolated population, it is possible that variants in ADIPOR1 play a role in type 2 diabetes predisposition among them, which is not apparent in more heterogeneous populations. However, given that both reported associated SNPs are present in all populations, it is unlikely that either is the true causal variant, although they could be detecting, through LD, the effect of an untested SNP. Alternatively, given the relative small sample sizes used and lack of adjustment for multiple testing, the authors may have reported a false–positive association.
For ADIPOR2 the data are less consistent. While no evidence of association between ADIPOR2 SNPs and type 2 diabetes was present in a Japanese population [14], an association has been suggested between SNP rs767870 and type 2 diabetes risk in French populations [15]. Our data, and those from the Old Order Amish [13], do not support this finding. Notably, in the French population, meta-analysis of rs767870, including 1,380 individuals with type 2 diabetes and 1,496 controls, demonstrated allelic association of nominal significance only (p=0.02), while the most significant result was under a recessive model (p=0.0018). Our study has only 23% power to detect such small recessive effects (odds ratio 1.3) with an allele frequency of 0.15, and this could explain our discrepant result. Further large scale studies of this SNP in additional populations will be required to elucidate its role in conferring risk of disease. A haplotype was also reported to increase risk of diabetes in the Old Order Amish [13]. However, when we performed haplotype analyses in both ADIPOR1 and ADIPOR2, we were unable to detect any significant associations with diabetes risk (data not shown), and this discrepancy might also be accounted for by differences in the populations studied.
With regard to quantitative metabolic traits, to date two studies have reported nominally significant results with insulin sensitivity and body size [16, 24]. The first study showed nominally significant associations between two tightly linked SNPs (rs6666089 and an intronic –1927 SNP) in ADIPOR1 and decreased insulin sensitivity and increased HbA1c levels [24]. We did not test association with HbA1c, but have not replicated any association with insulin sensitivity (including with SNP rs6666089) as assessed by fasting insulin measurements. Recently Kantartzis and colleagues reported that the association between rs6666089 and insulin sensitivity is observed only in more obese, but not in lean individuals [25]. This dependence on the degree of adiposity could explain some of the discrepant results observed for this SNP, if there are substantial differences in mean BMI between populations tested. To explore this hypothesis, we performed SNP×BMI interaction tests (using BMI both as a continuous trait and splitting the population above and below the median) on all ADIPOR1 SNPs tested. Our data provided no evidence for such an interaction (data not shown). We also specifically tested for association between rs6666089, and measures of insulin and glucose in participants with BMI above and below 25. Again we found no statistically significant difference between the two groups (p>0.3). Therefore, it is unlikely that BMI differences between populations are at the root of our discrepant results. Of note, our data cannot exclude possible small effects of this SNP on insulin sensitivity, in particular if this effect is only observed in subjects with higher BMI. The second study suggested there was evidence of association between two markers (rs10920534 and rs2275738) and BMI, but this evidence came from men only. Furthermore, three other markers (rs10920534, rs12045862 and rs7539542) were reported to associate with fasting and 2-h insulin levels, particularly in men at baseline [16]. We did not test rs10920534, but did test rs6666089 (D′=r2=1 with rs10920534) and found no evidence for SNP×sex interaction on any of the quantitative traits we analysed. Our data also did not replicate the sex effect of SNP rs7539542 on insulin measurements. Although we did not test SNP rs12045862, previously published data was conflicting. Thus while the C allele was suggested to be associated with higher 2-h insulin levels in men (p=0.027), in women the T allele was associated with higher levels (p=0.029) [16]. Given that neither we, nor others [24], have found evidence for sex×SNP interaction effects on measures of insulin sensitivity, we suggest that further confirmatory studies are required to test this hypothesis.
Until recently, remaining uncertainty regarding the identity of the true physiological receptors for adiponectin [26] had hampered interpretation of the functional relevance of polymorphisms in ADIPOR1 and ADIPOR2 with respect to adiponectin’s insulin sensitising effects. However, a recent yeast two-hybrid screen identified an ADIPOR1 interacting molecule, APPL1, thought to mediate many of the effects of adiponectin [27]. This molecule was shown to interact with both ADIPOR1 and ADIPOR2 in an adiponectin-sensitive manner, and was shown to mediate many of adiponectin’s insulin-sensitising effects. This suggests that ADIPOR1 and ADIPOR2 could be therapeutic targets for drug development and should renew interest in association studies, such as those we present here, testing polymorphisms in ADIPOR1 and ADIPOR2 for effects on type 2 diabetes risk and metabolic traits.
In summary, sequencing of ADIPOR1 and ADIPOR2 genes in a cohort of patients with syndromes of severe insulin resistance (n=129) suggests that functional mutations in these genes are not a major cause of extreme insulin resistance in humans. Furthermore, testing of common genetic variants (n=24) did not find evidence for association of these genes with type 2 diabetes risk (n=2,127) or with five additional quantitative metabolic traits (n=1,721). These data suggest that ADIPOR1 and ADIPOR2 variants are unlikely to be major risk factors for type 2 diabetes and insulin resistance in UK Europid populations, although more detailed analyses of gene variants may be required to exclude a potential minor role of these genes in insulin resistance and glucose homeostasis.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Table 1
Primer sequences and PCR product sizes used for sequencing ADIPOR1 and ADIPOR2 (DOC 46 kb)
Table 2
Genotyping assay results for case-control studies (DOC 61 kb)
Table 3
SNPs detected by sequencing ADIPOR1 and ADIPOR2 in severely insulin-resistant patients (DOC 78 kb)
Table 4
ADIPOR1 and ADIPOR2 SNP association results with quantitative traits (DOC 215 kb)

insulin resistance
association studies
type 2 diabetes
adipor1
adipor2
polymorphisms