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Open Access

Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses

  • Sabrina Humbert1, 4,
  • Sanjeena Subedi2,
  • Jonathan Cohn3,
  • Bin Zeng1,
  • Yong-Mei Bi1,
  • Xi Chen3,
  • Tong Zhu3,
  • Paul D McNicholas2 and
  • Steven J Rothstein1Email author
BMC Genomics201314:3

DOI: 10.1186/1471-2164-14-3

Received: 8 August 2012

Accepted: 27 December 2012

Published: 16 January 2013

Water and nitrogen are two of the most critical inputs required to achieve the high yield potential of modern corn varieties. Under most agricultural settings however they are often scarce and costly. Fortunately, tremendous progress has been made in the past decades in terms of modeling to assist growers in the decision making process and many tools are now available to achieve more sustainable practices both environmentally and economically. Nevertheless large gaps remain between our empirical knowledge of the physiological changes observed in the field in response to nitrogen and water stresses, and our limited understanding of the molecular processes leading to those changes.

This work examines in particular the impact of simultaneous stresses on the transcriptome. In a greenhouse setting, corn plants were grown under tightly controlled nitrogen and water conditions, allowing sampling of various tissues and stress combinations. A microarray profiling experiment was performed using this material and showed that the concomitant presence of nitrogen and water limitation affects gene expression to an extent much larger than anticipated. A clustering analysis also revealed how the interaction between the two stresses shapes the patterns of gene expression over various levels of water stresses and recovery.

Overall, this study suggests that the molecular signature of a specific combination of stresses on the transcriptome might be as unique as the impact of individual stresses, and hence underlines the difficulty to extrapolate conclusions obtained from the study of individual stress responses to more complex settings.

Drought Maize Nitrogen Microarray Clustering analysis Abiotic stress

Last year, the world population reached 7 billion and it is expected to exceed 10 billion by 2100 [1]. Food supply is critical if we are to sustain this population. Twentieth century agriculture met the growing food demand by achieving sustained increases in crop yields. In the past two decades however, the difficulty in maintaining the required yield increases along with the high environmental and economical price of intensive production methods have become strong incentives for a profound change. The challenge lies not only in increasing production but also in developing economically and environmentally sustainable practices.

Agriculture is by far the biggest user of water, and accounts for almost 70 percent of all withdrawals worldwide. According to the International Panel on Climate Change, water availability will be problematic in the years to come as the timing and geographical patterns of precipitations are predicted to undergo significant changes, along with the increased frequency of droughts, the amount of snowmelt runoff and temperature-induced evaporation [2]. Access to irrigation water will be one of the main challenges of this century and its use will have to be carefully managed to maintain crop production in drought-prone areas.

The use of fertilizer in agriculture has increased fivefold since the 1960s and about 65 percent of it is used on cereals. Nitrogen plays a major role in plant nutrition but it is the mineral element most often deficient in arable soils. Although it is directly linked to cereal crop yield, inadequate or inefficient fertilization is a major contributor to soil degradation and represents substantial financial losses worldwide. Further, the very significant loss of added nitrogen fertilizer to the air and water causes very significant global environmental damage. Therefore efficient use is required both to enhance yields as well as to limit the environmental damage caused by crop production.

Corn (Zea mays L.) is the most important crop in terms of area planted (almost 160 million ha in 2009) and production (820 tonnes in 2009) before rice and wheat. Although it is highly demanding in water and fertilizer, it is widely grown in areas where environmental conditions are far from ideal, and productivity therefore depends on large amounts of inputs. While in some places excessive irrigation and fertilization are clearly common practice in order to guarantee yield, in other regions large gaps remain between achievable and realized yield. In both cases, inadequate use of inputs comes at high environmental and economical costs and a better management of available resources is needed to maximize profits and meet the challenges of modern agriculture.

To implement better agricultural practices, numerous models have been built that help predict productivity while optimizing the timing and quantity of inputs required (for example [3, 4]). Their empirical formulas integrate various parameters such as water availability, carbon and nitrogen pools, as well as the dynamics between them, based on the analysis of large experimental datasets obtained in the field. Studies focusing on the effects of irrigation in combination with nitrogen fertilizer application for example have been instrumental in designing those models. They have shown in particular that the dynamic interaction between the two factors clearly impacts plant physiology and leads to a multiplicative increase in grain yield and water use efficiency (for example, [5]). Yet very little is known about the underlying molecular mechanisms that contribute to those increases in response to the availability of water and nitrogen. With the development of genomic resources, numerous transcript profiling experiments have been performed on plant response to nutrient or water deficiency. Lists of genes responsive to nitrogen have been generated in Arabidopsis[611], rice [12, 13] and other species (see [14] for review). Similarly, the effects of drought have been surveyed in Arabidopsis[1517] rice [18, 19], and corn [20, 21]. The few studies that examined the potential interactive effects of abiotic stresses, which are usually present simultaneously during the field season, mostly focused on the combination of heat and water stresses (for example, [22, 23]) and allowed the making of significant progress in that area. To our knowledge, the present study is unique in exploring the genome-wide expression profile of corn plants subjected to individual and combined nitrogen and water stresses in a controlled environment. The information generated is relevant in the context of crop improvement and should provide valuable insight more specifically for current research and biotech approaches.

Experimental Design

Plants were grown in the greenhouse using a setup that aimed at controlling the amount of both nutrient and water while trying to best mimic natural conditions. To achieve proper development of the root system while being able to supply precise amounts of fertilizer and water, a clay-based substrate (Turface) was chosen instead of a complete hydroponic system. Stress and recovery conditions were empirically determined as explained below.

Optimal and limiting nitrogen conditions were defined respectively as 20 mM and 8 mM NH4NO3 and applied at the beginning of the experiment as indicated in the Methods. The limiting nitrogen condition caused a chronic, moderate stress that translated into a phenotype visible around week 4. Nitrogen-deprived plants were smaller and lighter green compared to control plants (Additional file 1), but since the stress applied was only moderate, these plants were able to develop normally and go through a complete life cycle as would be expected in a field setting.

Water conditions were also defined empirically so as to achieve visible but potentially reversible physiological changes. The chosen conditions were not lethal and allowed complete recovery once water was supplied again, as might typically be the case several times over the course of a growing season. Two drought levels were achieved by withdrawing water for 3 d (mild stress) and 5 d (severe stress) before sampling. Leaf rolling and wilting were clearly observable on severely stressed plants, while moderately stressed plants only displayed intermediate effects (Additional file 1). Two re-watering time points were tested to assess plant recovery from drought stress. Severely stressed plants were watered again 2 h and 5 h before sampling. After 2 h rehydration, plants could barely be visually distinguished from the well-watered controls while after 5 h rehydration they could not be distinguished at all from the controls.

To assess the physiological state of stressed plants compared to untreated controls, a series of measurements were performed. Dry weight loss was quantified as a measure of chronic nitrogen limitation. The data in Figure 1-A show the ratio of dry weights measurements relative to the corresponding optimal nitrogen averages. The limiting nitrogen treatment resulted in 15 to 30% loss in dry weight. The loss was more pronounced under water stress conditions than under optimal water supply although the difference was not found to be statistically significant.
Figure 1

Physiological measurements. A. Plant dry weight under different water regimes relative to the average value of the corresponding optimal nitrogen treatment. Bars represent the standard error of 3 biological replicates. B. Relative Water Content in the first fully developed leaf under different water regimes relative to the average value of the corresponding optimal water treatment. Bars represent the standard error of 4 biological replicates.

The impact of water limitation was estimated by measuring the relative water content (RWC) in the first fully expanded leaf. Under our experimental conditions, the RWC under optimal water supply were 96.1% (+/− 2.0%) and 93.6% (+/− 2.0%) when plants were grown respectively at optimal and limiting nitrogen concentrations. In Figure 1-B, the results are expressed as ratios of the calculated RWC in each condition relative to the corresponding value under optimal water regime. Under optimal nitrogen conditions, RWC was found to decrease markedly in response to water stress, respectively about 15% drop under mild and 30% under severe stresses. The decrease was less drastic when plants were grown under limiting nitrogen conditions but still significant under severe water stress (about 15% reduction in RWC compared to the corresponding optimal water treatment). When plants were watered again, the RWC was restored gradually to non-stress levels. This trend was in agreement with the phenotypes described previously upon re-watering.

Applied independently, the chosen nitrogen and water limitation conditions allowed visible physiological changes and led to measurable effects on dry weight and RWC, which were in the same range of about 15 to 30% loss relative to the optimal.

Microarray Analysis

A transcript profiling experiment was performed using a customized Affymetrix maize microarray containing 46784 entities. About 70% of all entities on the chip were associated with functional annotation at the time of the analysis (see Methods for a more detailed description of this experiment). Thirty conditions were studied, each being a unique combination of three parameters: organ (leaf, root or stem), nitrogen supply (optimal or limiting) and water supply (optimal, mildly stressed, severely stressed, severely stressed followed by 2 h re-watering, or severely stressed followed by 5 h re-watering). Each condition was run in a set of three biological replicates, totalling 90 samples. Raw data files (.cel files) were imported in Genespring GX (Agilent, CA, USA). Eighty-eight out of the 90 samples were exploitable and used in the analysis, so that each unique combination of conditions was represented by at least 2 replicates. The data were normalized using RMA (Robust Multichip Average) and log-transformed. Baseline transformation of the normalized signal was then performed to the median of all samples. Additional file 2 shows the Box Whisker plot of the normalized intensities for all chips. Pre-filtering was performed on the raw data to filter out probes that were consistently detected at low levels, and an arbitrary detection threshold of 20 signal intensity unit was used. Out of 46784 entities, 39664 had a raw signal above 20 in 100% of the replicate values in at least one out of the 30 conditions. This list was used in all subsequent analyses. For homogeneity, differentially expressed genes were identified by first filtering on a fold change ≥ 2 and then performing statistical tests using the Benjamini-Hochberg multiple testing method with a false discovery rate set at 0.05.

Identification of differentially regulated entities in response to nitrogen limitation and different levels of water stresses and recovery

In this experiment, plant transcriptional responses to stress were examined in the leaf, stem and root. The different roles of these three organs in the plant as well as the transcriptional trends observed suggested very distinct responses to stress at the molecular level and we therefore performed separate analyses for each organ. The response to nitrogen and water stresses were first analysed independently, keeping the other parameter optimal.

The impact of nitrogen limitation on the transcriptome of each organ was studied using the six treatment combinations where plants did not undergo any water stress (i.e. optimal and limiting nitrogen supply in leaf, root and stem). For each organ, genes that displayed a fold change ≥ 2 when comparing optimal vs. limiting nitrogen and a t-test p-value corrected for multiple testing below 0.05 were retained (see Methods for a detailed description of the procedure). As shown in Table 1-A, very few genes passed these criteria under our conditions: only 68, 17 and 6 entities respectively were found to be responsive to chronic nitrogen limitation in leaf, root and stem.
Table 1

Impact of nitrogen and water stresses on leaf, root and stem transcriptomes

Nitrogen limitation

Mild Water Stress

Severe Water stress

Number of entities differentially regulated under limiting nitrogen (water being optimal) (A) and drought stress (nitrogen being optimal) (B). Numbers in parentheses indicate percentages relative to the total number of entities on the chip.

The impact of drought was then assessed using 9 of the 30 conditions, where nitrogen supply was optimal and dismissing re-watered samples from the analysis (i.e. optimal water supply, mild and severe water stresses in leaf, root and stem). For each organ, a similar procedure as described above was followed. Respectively 6841, 6262 and 8903 entities were found to be differentially regulated under mild or severe water stress in leaf, root and stem (Table 1-B).

While both nitrogen and water stresses induced observable phenotypical effects of similar magnitude compared to their optimal, our data show that the impact of those treatments on the transcriptome was extremely different (Table 1). The number of genes differentially expressed under nitrogen limitation was very low (0.2% of all chip entities) whereas the various water stress treatments widely affected gene expression. Overall, 15.8% and 29.8% of chip entities respectively were found to vary significantly from the well-watered control under mild and severe water stresses. This result was most likely due to our particular experimental setup. In an attempt to mimic field conditions, nitrogen stress was applied in a mild and chronic fashion while water stress was much more sudden. Under nitrogen stress, transcriptional changes are thus expected to be limited at the time of sampling since the plant has had a considerable time to adjust to stress, which is obviously not the case under water stress.

Each tissue displayed a distinct behaviour at the transcriptome level in response to drought. Quantitatively, the leaf transcriptome was the most strongly affected by a mild water stress (5151 entities differentially expressed) but quite surprisingly this response was not aggravated by a severe stress (4954 entities) (Table 1-B and Figure 2-B). This was the case however for root and stem, where the number of differentially expressed entities increased with the acuteness of the stress (2370 to 5994 entities for root, and 1539 to 8767 entities for stem).
Figure 2

Impact of water stresses on leaf, root and stem. A. Venn diagrams showing the overlap of the response to water stresses between organs. B. Graph showing the response to mild and severe water stresses in each organ. Numbers correspond to entities differentially expressed under the indicated conditions.

The extent of the induced genes overlap between different organs was found to be rather limited relative to the total number of induced entities (Figure 2-A) and therefore also supported the idea that the response to drought is unique to each organ. Under mild water stress, most differentially regulated genes were unique to leaf (4059 entities, Figure 2-A). Under severe stress however, most differentially regulated genes were identified solely in stem (4468 entities) and the overlap of regulated genes was highest between stem and root (2321 entities in common, Figure 2-A). In addition, Figure 2-B shows that in leaf, most entities were common between mild and severe stresses, whereas in root and stem most entities were only identified under severe stress. This would suggest that the transcriptional response of the plant to water stress might have been first initiated in leaf, which already expressed most transcriptional changes under milder stress conditions, while in root and stem, gene regulation was triggered under more acute stress conditions and to a larger extent than in leaf.

Qualitatively, a gene ontology (GO) analysis also supported the idea that the processes involved in each organ in response to drought stress were quite unique. An overview of the metabolic processes transcriptionally affected by severe water stress was obtained using Mapman [24] and shown in Figure 3. A similar analysis under mild water stress is shown in Additional file 3. A gene enrichment analysis using AgriGO SEA (Singular Enrichment Analysis) Compare tool (Table 2) confirmed that the functional categories affected by the transcriptional changes were very distinct across tissues. Most of the GO terms significantly enriched in the lists of entities differentially regulated under mild or severe water stress compared to the optimal water control were organ-specific.
Figure 3

Metabolism overview under severe water stress (optimal nitrogen). Genes differentially regulated under severe water stress in leaf, root, and stem as visualized in Mapman (Thimm et al., 2004). In leaf, root and stem respectively, 4954, 5994, and 8767 entities were found to be differentially regulated under severe water stress (FC≥2, p-value≤0.05) and corresponded to 4476, 5401 and 7843 unique transcript identifiers imported in Mapman. Transcripts up- and down-regulated are represented with blue and red squares respectively. Values are log2-transformed fold changes.

Table 2

Gene Ontology (GO) analysis

Mild water stress

Severe water stress

Mild water stress

Severe water stress

Mild water stress

Severe water stress

GO Term

Cellular Component

extracellular region

Molecular Function

catalytic activity

transporter activity

Biological Process

metabolic process

cellular amino acid and derivative metabolic process

generation of precursor metabolites and energy

carbohydrate metabolic process

response to abiotic stimulus

lipid metabolic process

response to stimulus

response to stress

homeostatic process

regulation of biological quality

multicellular organismal process

biological regulation

The analysis was performed using the Singular Enrichment Analysis (SEAcompare) on the AgriGO website (Du et al., 2010, This tool allowed the identification of GO terms that were significantly enriched in the lists of entities differentially regulated between mild or severe water stress and well-watered control (FC≥2, p≤0.05). For each condition, the false discovery rate (FDR) and the number of entities (Num) are shown where the GO term enrichment was significant.

Clearly the photosynthetic machinery and its related processes (sucrose and starch metabolisms, Calvin cycle) were significantly affected by mild and severe drought in leaf. While transcripts associated with light reactions and Calvin cycle were down-regulated, those pertaining to starch and sucrose breakdown were up-regulated. These trends were accompanied by an increase in gene expression for the biosynthesis of a few amino acids, such as proline and asparagine. Overall, gene expression under drought suggested a decrease in photosynthetic assimilation to benefit remobilization of sugars and synthesis of targeted amino acids (Figure 3).

By contrast, components of the cell wall machinery were mostly affected in root and stem under severe water stress. Transcripts encoding enzymes from the pectin esterase bins, which include genes for arabinogalacatan synthesis, expansins, glucan transferases and leucin-rich repeat proteins, were highly down-regulated, converging towards the global remobilization of cell wall components (Figure 3).

It is worth noting that transcripts involved in nitrogen assimilation were also significantly affected during water stress in all three tissues, converging towards a down-regulation of nitrate reduction. The assimilation of ammonium was also affected but to slightly different extents depending on the tissue, with a decrease in glutamine and glutamate synthases transcripts in stem and root and an increase in leaf (Figure 3).

The dynamic interaction between nitrogen and water treatments is responsible for substantial changes in the transcriptome

Our experiment was designed to study specifically the interactive effects of nitrogen and water stresses in a way that attempted to best mimic field conditions: chronic nitrogen limitation and transient drought. Under such conditions, the expression of some genes might be influenced by the nitrogen status, the water status, both, or the dynamic interaction between nitrogen and water. The latter category is of particular interest. For simplicity, only combinations of the following conditions were considered: optimal/limiting nitrogen, optimal/severe water stress (4 combinations for each organ). Only entities that had a mean fold change ≥ 2 in at least one of the comparisons in Table 3 were retained. An analysis of variance (ANOVA) was performed for each organ separately to simplify the model. P-values were corrected for multiple testing as described in the Methods section. Entities that had a p-value (nitrogen x water) ≤ 0.05 were considered significant in that their variation across the above set of conditions was explained by an interaction effect of nitrogen and water status. Results from the ANOVA were compared to the pairwise t-test results. Entities that had a p-value (nitrogen) > 0.05 and a p-value (water) > 0.05 in the t-tests and had a p-value (nitrogen x water) ≤ 0.05 were considered to be responsive to both nitrogen and water status. Results are summarized in Table 4.
Table 3

Pair-wise comparisons performed prior to the ANOVA

Condition 1

Condition 2

Fold change

Optimal Nitrogen – Optimal Water

Optimal Nitrogen – Severe drought

Optimal Nitrogen – Optimal Water

Limiting Nitrogen – Optimal Water

Optimal Nitrogen – Severe drought

Limiting Nitrogen - Severe drought

Limiting Nitrogen – Optimal Water

Limiting Nitrogen – Severe drought

Entities that displayed a fold change ≥ 2 in at least one of the comparisons below were used in the ANOVA.

Table 4

Number of differentially regulated entities per organ explained by nitrogen, water or the interactive effect of both stresses

Nitrogen x Water

1703 (122)

7260 (5016)

1905 (99)

1575 (48)

6474 (4307)

1668 (20)

6309 (109)

9458 (1527)

6693 (188)

Numbers in brackets denote entities exclusively regulated by either nitrogen, water or the interaction. The percentages in the last row indicate the proportion of differentially regulated entities relative to the total number of entities on the chip.

In leaf, 1904 entities out of the 8042 that passed the 2-fold change cut-off were found to be significantly affected by the interaction of nitrogen and water status, 99 of which were exclusively explained by this interaction effect. In root, 1668 of the 6983 entities that passed the 2-fold change cut-off were found to be explained by the interaction and among those 20 were exclusively explained by the interaction. In stem, 6693 of the 10017 entities that passed the 2-fold change cut-off were explained by the interaction between nitrogen and water, 188 of which were responsive to the interaction effect.

Although water stress had much greater effect on gene expression than nitrogen stress, our results indicate that the interaction between nitrogen and water stresses affects regulation of a number of maize genes.

The nitrogen x water interaction shapes the transcriptional response to a series of water treatments

The fact that we identified entities with different responses to water stress depending on the plant N status may have a profound impact on experimental designs and their results. A clustering analysis was performed to look into this issue in more detail. Only results corresponding to leaf tissue are shown here for clarity but similar trends were obtained in roots and stem unless otherwise stated. As indicated in the experimental procedure, the samples were taken simultaneously on different plants that had undergone various stress treatments. This design was driven by practical limitations and the fact that we wanted to avoid capturing the circadian component of gene expression in order to concentrate solely on stress-related responses. Nevertheless the effect of various stress conditions observed for a particular gene were not considered independent from each other. For example, the effects of the mild stress treatment may be considered intermediate to severe stress; the same goes for the 2- and 5-h re-watering time points. Hence, the data was treated as longitudinal to take into account the correlation among the measurements of gene expression over various stress conditions. A model-based clustering algorithm using a mixture of Gaussian distributions for longitudinal data, typically used to model time course gene expression studies, was used to group the genes that behaved in a similar manner over various conditions. For each entity, the median of observed values was used, as it is less susceptible to outlier than the mean. Similarly to what was performed in our previous analysis, entities were filtered based on a minimum fold-change threshold. The entities that met this criterion were then used in the model-based clustering approach. After running the data a few times, it was found that a model selecting for 1 to 8 optimal groups was adapted to our particular dataset while minimizing the computational burden. The algorithm is symmetric, resulting in both up- and down-regulated entities being grouped in the same cluster. While it makes biological sense that genes involved in similar pathways may be affected in opposite manners, subgrouping was performed a posteriori for clarity of the figures using a splitting criterion based on up- or down-regulation (Figure 4 and Figure 5, lower panels).
Figure 4

1203 genes were selected for leaves under optimal nitrogen condition based on a fold change above 3 under either mild or severe water stress compared to optimal water. Those genes were then classified into 4 groups shown in the graphs in the first row, where the black lines represent the respective means. The vertical axis corresponds to the normalized expression levels in the log-scale, the horizontal axis corresponds to the water treatments: optimal water supply (1), mild water stress (2), severe water stress (3), 2h after rewatering (4), 5h after rewatering (5). For each group, subgroups were arbitrarily created by splitting a posteriori (the second row). The color-coding used is blue for genes up-regulated under mild water stress (condition 2) compared to optimal water supply (condition 1) and red for genes down-regulated under the same conditions.
Figure 5

1408 genes were selected for leaves under limiting nitrogen condition based on a fold change above 2 under either mild or severe water stress compared to optimal water. Those genes were then classified into 4 groups shown in the graphs in the first row, where the black lines represent the respective means. The vertical axis corresponds to the normalized expression levels in log scale, the horizontal axis corresponds to the water treatments: optimal water supply (1), mild water stress (2), severe water stress (3), 2h after rewatering (4), 5h after rewatering (5). For each group, subgroups were arbitrarily created by splitting a posteriori (the second row). The color-coding used is blue for genes up-regulated under severe water stress (condition 3) compared to mild water stress (condition 2) and red for genes down-regulated under the same conditions.

The first step was to identify clusters of gene expression in response to water treatment, under optimal or limiting nitrogen conditions. Under optimal nitrogen conditions, 1203 entities were found to meet a 3 fold-change cut-off for leaf and classified into four optimal gene clusters (Figure 4). A similar procedure was followed under the limiting nitrogen conditions (Figure 5). However only 417 entities met this 3 fold-change criterion. A similar scenario was found for stem and root as well, illustrating the fact that the amplitude of gene variation was smaller under limiting nitrogen compared to optimal nitrogen treatment. In order to run the algorithm with a number of entities comparable to the ones identified under optimal nitrogen, a more relaxed cut-off was set. Using a 2 fold-change threshold, 1408 entities were identified for leaf under limiting nitrogen conditions, for which the algorithm identified 4 optimal clusters of expression.

The second step of the cluster analysis was to compare the genes identified as belonging to a cluster under either optimal or limiting nitrogen conditions. For leaf, 697 entities met the fold change criteria under both optimal and limiting nitrogen, and hence were assigned a cluster in both cases. The expression levels of the entities that belonged to a particular group were taken both from limiting and optimal nitrogen conditions and a model based clustering approach was run. The goal was to identify entities that had different transcriptional trends in response to water treatment depending whether nitrogen was limiting or optimal. Among the 697 entities matching the fold change threshold under both optimal and limiting nitrogen conditions for leaf, 228 entities (32.7%) were found to follow different trends under optimal and limiting nitrogen. The behaviour of those entities is shown in Figure 6. A similar analysis was run for stem and root tissue: respectively 28 out of 45 entities (62.2%) and 121 out of 277 entities (43.7%) were found to behave differently in response to the series of water treatments under limiting and optimal nitrogen supply. This analysis shows that both expression levels and patterns of expression are influenced by nitrogen and water availability. Therefore, a large proportion of entities present in one cluster under optimal nitrogen may be separated into a significantly different cluster when nitrogen is limiting.
Figure 6

The 228 leaves genes that showed different trends in different nitrogen conditions with black showing the gene expression under limiting nitrogen and red showing the same gene expression under optimal nitrogen. The vertical axis corresponds to the normalized expression levels in log scale, the horizontal axis corresponds to the water treatments: optimal water supply (1), mild water stress (2), severe water stress (3), 2h after rewatering (4), 5h after rewatering (5).

In this study, we set out to examine the dynamic interaction between two of the most common abiotic stresses encountered in the field, nitrogen limitation and drought. Our experimental setup was established empirically in the greenhouse so as to achieve meaningful and controllable levels of stresses. Recovery from drought was also studied as it is a typical occurrence in the field. Under our particular experimental design, nitrogen stress was applied in a chronic and mild manner, translating into limited transcriptional changes. By contrast, drought was applied in a more severe and transient manner, leading to profound changes in transcript levels in all tissues analyzed. These changes are in agreement with the data obtained by other groups (for example, [17, 21, 2527]) and with our current understanding of drought response in corn and other plant species. The main changes observed suggested a reduction of photosynthetic activity, an increase in the breakdown of complex sugars and cell wall components for remobilization, the biosynthesis of a few specific amino acids such as proline, and an adjustment of nitrogen metabolism likely in response to the decrease in carbon fixation.

Although nitrogen assimilation and therefore its metabolism are directly affected by limiting water conditions, the extent and the nature of the interaction between nitrogen stress and drought have not been looked at on a global scale to date. By focusing on the transcriptional changes occurring under limiting nitrogen conditions and water stress applied in combination, our study enabled us to establish the existence of a statistically significant interaction effect on several genes. The interaction effect was most pronounced under mild drought stress. It was surprising to find that, despite the fact that the individual effect of nitrogen limitation was not severe in this study, the N stress applied had a large impact on gene expression changes in response to water stress; especially mild water stress. This underlines the importance of a pre-existing stress such as nitrogen limitation on plants ability to respond to drought.

Our results led us to conduct a more detailed analysis of the interaction by looking at the overall pattern of gene expression during a series of drought and recovery treatments in combination with nitrogen limitation. A clustering analysis was performed that identified principal transcriptional behaviours of entities over the course of five water conditions (optimal supply, mild water stress, severe water stress, 2h recovery and 5h recovery), under optimal or limiting nitrogen supply. The defined patterns were then compared and our results indicated that almost a third of those entities identified in leaf were found to follow statistically significantly different patterns of expression in response to drought and recovery depending whether nitrogen was limiting or not. Similar trends were obtained in stem and root tissue. This finding supports the idea that nitrogen and water status were not only significant factors affecting gene expression in terms of fold change but also in terms of overall pattern of expression over a range of conditions.

From a methodological standpoint, our results demonstrate the importance of carefully controlling for various parameters when conducting expression analyses. As most studies focus mainly on a single stress and therefore often closely monitor a single parameter, the existence of additional, potentially significant secondary stresses, is often overlooked. This could explain why it can sometimes be challenging to reproduce results in different laboratories, greenhouses and growth chamber conditions.

With the large amount of data available, the agricultural biosciences are now facing new challenges, which lie in our analysis and synthesis capabilities. As this study exemplifies, taking multiple factors into account is necessary to produce accurate and meaningful data. Developing tools that can integrate not only multiple environmental factors but also diverse genetic backgrounds and levels of analysis will be instrumental in finding the answers to those challenges. While such tools can already be used to predict some aspects of plant physiology for example, there is still a large gap between the impressive amount of available data and our limited understanding of biological networks and phenomena. A sustained collaborative effort between agronomists, molecular biologists, biochemists and computer scientists is the first step to provide those answers.

Growth conditions and physiological measurements

Zea mays (inbred line SRG-200) plants were grown in a semi-hydroponic system consisting of 2 L plastic pails filled with “turface”, a baked montmorillonite clay (International Minerals and Chemical, Blue Mountain, ON, Canada), and placed in trays irrigated with controlled amounts of solution. Plants were fertilized once at the beginning of the experiment with a 6 L-solution containing 187 mg/L HPO4 (850–1), 375 mg/L KHCO3 (0-0-47), 400 mg/L MgSO4.7H2O (100–1), 30 mg/L of Plant-Prod chelated micronutrient mix (all nutrients supplied by Plant Products, Bramalea, ON, Canada), and supplemented with ammonium nitrate (NH4NO3, 34-0-0). Concentrations of 20 mM and 8 mM NH4NO3 were defined respectively as optimal and limiting nitrogen conditions. The pH was adjusted to 5.5 to 6.0 with HCl. Plants were subsequently irrigated with purified water up to a 6 L-mark every 2 to 3 d and grown in the greenhouse under a long-day regime of 16 h light (~500 μmol m-2.s-1) at 29°C, and 8 h dark at 23°C.

At week 4, plants were treated with one of the three water treatments defined as optimal water supply, mild water stress or severe water stress. Plants grown under optimal water conditions were watered regularly up to the 6 L-mark throughout week 4. Plants grown under mild and severe stress conditions were watered regularly up to the 6 L-mark until respectively day 3 and day 5 before sampling. At that time, any residual water at the bottom of the trays was drained and the plants were not irrigated until sampling. Two re-watering treatments were performed the day of sampling by re-supplying water to severely stressed plants respectively 2 h and 5 h prior to sampling.

Stems, leaves and roots were harvested separately at the end of the fourth week. Three biological replicates were collected for each condition and frozen in liquid nitrogen. The tissue collected from the leaves consisted of the top 30 cm of the first fully expanded leaf (which corresponded to leaf 6 or 7 depending on the developmental stage of the plants given their stress treatment). The tissue collected from the stem consisted in the 15-cm region below the first node. The root system was extracted from the Turface substrate and collected.

Leaf discs of 1-inch diameter were removed using a leaf punch about 15 cm from the tip of first and second fully expanded leaves, taking care to avoid the mid-vein. Each sample was placed in a pre-weighed airtight tube and weighed to obtain the fresh weight (FW). The samples were then hydrated in distilled water with gentle shaking at 4°C overnight and weighed to obtain the turgid weight (TW). Water was then discarded and the samples were finally dried overnight in an oven and weighed again to obtain the dry weight (DW). The RWC was calculated according to the formula: RWC (%) = [(FW-DW) / (TW-DW)] x 100. The results shown are averages of four biological replicates.

All tissues were ground in liquid nitrogen and RNA was extracted using TRI-Reagent (Sigma-Aldrich Co. MO, USA) following the manufacturer’s instructions. Samples were DNase-treated using RQ1 RNase-free DNase (Promega Co., WI, USA). Total RNA was quantified using a Nanodrop 2000c spectrophotometer (Thermo Fisher Scientific Inc., MA, USA). Samples were further processed at Syngenta. Briefly, total RNA was reverse transcribed. Complementary RNA was synthesized in vitro from double stranded cDNA, biotin-labeled and hybridized to a Syngenta custom-designed maize GeneChip microarray, manufactured by Affymetrix. The GeneChip was originally designed to represent 82,000 unique maize expressed sequence tag (EST) clusters and genes as well as various negative, spike, and transgenic control genes [28]. A customized Chip Definition File (CDF) was created for this GeneChip to produce probe sets that represented gene models from the completed maize genome [29]. In addition, the custom CDF was mapped to a series of full-length cDNAs [30], as well as Maize Illumina Expressed Sequence Tags (runs corresponding to leaf, shoot apical meristem and seedlings, NCBI Sequence Read Archive The chip definition file contains coordinate information on 558,801 probes assigned to 46,681 probe sets. Probe sets were annotated either directly from the maize genome site (Gene Ontology and Interproscan, or using BLAST (Gene ontology and KEGG pathways) and InterProScan (Zdobnov and Apweiler, 2001). Data analysis and subsequent statistical tests were performed using Genespring GX version 11 (Agilent Technologies, CA, USA). In all analyses entities were filtered as described in the text based on their raw signal intensities. To identify genes regulated by nitrogen, entities were filtered based on a fold change above 2 between the two conditions: optimal nitrogen-optimal water and limiting nitrogen-optimal water. The statistical test performed was an unpaired t-test based on equal variance. P-values were corrected for multiple comparison using the Benjamini-Hochberg method. To identify genes regulated by mild water stress and severe water stress respectively, entities were filtered based on a fold change above 2 between the two conditions: optimal nitrogen-optimal water and optimal nitrogen-mild water stress, or optimal nitrogen-optimal water and optimal nitrogen-severe water stress. Similar statistical tests were performed as in the identification of nitrogen-regulated genes. To identify genes regulated by nitrogen, water stress, and their interaction, entities were first filtered based on a fold change above 2 in at least one of the comparisons described in Table 3. The statistical test performed was a 2-way analysis of variance (ANOVA), which uses type-III sum of square. Similarly to single factor analyses, a correction of the p-values for multiple testing was performed using the Benjamini-Hochberg method. Pathway analyses were performed in Mapman (Thimm et al., 2004, version 3.5.0 beta using Zea mays mapping Zm_B73_5b_FGS_cds_2011) and AgriGO (Du et al., 2010,

Clustering analysis

Model-based clustering (see [31]) was utilized for this analysis. The model-based approach assumes that the population consists of a finite mixture of subpopulations and provides the flexibility to model these complex data through the imposition of a mixture of component densities. One (or more) of these component densities is then taken to represent a cluster. In this analysis, we utilized a mixture of Gaussian distributions so each subpopulation was modeled using a Gaussian distribution. The covariance structure takes into account the relationship between expression levels at different water conditions (cf. [32]). Genes are clustered into groups (or subpopulations) which have similar patterns over different water / nitrogen / water and nitrogen conditions. The cluster membership was estimated using an expectation-maximization algorithm (see [32] for details). For the analysis, expression levels of genes over different water stress levels were clustered for both nitrogen conditions and all organs separately to identify the genes with similar expression profiles. These genes could potentially have similar functions and /or may be involved in a similar biological pathway. The expression levels of the genes that met the prescreening criterion for both nitrogen conditions were clustered to check if they had the same expression profiles. Genes showing no change in their expression pattern under different nitrogen conditions should belong to the same cluster whereas genes showing different expression patterns under limiting and optimal nitrogen should belong to different clusters. With this in mind, we ran a two-component mixture model to identify genes showing different expression patterns under limiting and optimal nitrogen conditions. For illustration, if a gene X had a similar expression under both nitrogen conditions, the expression level of gene X for both limiting and optimal nitrogen belonged to group 1, say. However, if a gene Y had a change in expression pattern under limiting and optimal nitrogen, the expression pattern under optimal nitrogen was clustered in group 1 and the expression pattern under limiting nitrogen was clustered in group 2, say. Mathematical details on the algorithm can be found in [32]. The algorithm was implemented in R [33].

We would like to thank Dr. Alex Feltus, Clemson University, for creating the custom Chip Description File used for analyzing the microarray data.

Authors’ Affiliations

Department of Molecular and Cellular Biology, University of Guelph
Department of Mathematics and Statistics, University of Guelph
Syngenta Biotechnology Inc
Pioneer Hi-Bred

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Open Access

Symptoms predicting remission after divalproex augmentation with olanzapine in partially nonresponsive patients experiencing mixed bipolar I episode: a post-hoc analysis of a randomized controlled study

  • John P Houston1, 2Email author,
  • Jennifer L Gatz1,
  • Elisabeth K Degenhardt1 and
  • Hassan H Jamal1
BMC Research Notes20103:276

DOI: 10.1186/1756-0500-3-276

Received: 16 June 2010

Accepted: 2 November 2010

Published: 2 November 2010

Rating scale items in a 6-week clinical trial of olanzapine versus placebo augmentation in patients with mixed bipolar disorder partially nonresponsive to ≥14 days of divalproex monotherapy were analyzed to characterize symptom patterns that could predict remission. At baseline, the two treatment groups were similar.

Factor analysis with Varimax rotation was performed post hoc on baseline items of the 21-Item Hamilton Depression Rating Scale (HDRS-21) and Young Mania Rating Scale (YMRS). Backwards-elimination logistic regression ascertained factors predictive of protocol-defined endpoint remission (HDRS-21 score ≤ 8 and YMRS score ≤ 12) with subsequent determination of optimally predictive factor score cutoffs.

Factors for Psychomotor activity (YMRS items for elevated mood, increased motor activity, and increased speech and HDRS-21 agitation item) and Guilt/Suicidality (HDRS-21 items for guilt and suicidality) significantly predicted endpoint remission in the divalproex+olanzapine group. No factor predicted remission in the divalproex+placebo group. Patients in the divalproex+olanzapine group with high pre-augmentation psychomotor activity (scores ≥10) were more likely to remit compared to those with lower psychomotor activity (odds ratio [OR] = 3.09, 95% confidence interval [CI] = 1.22-7.79), and patients with marginally high Guilt/Suicidality (scores ≥2) were less likely to remit than those with lower scores (OR = 0.37, 95% CI = 0.13-1.03). Remission rates for divalproex+placebo vs. divalproex+olanzapine patients with high psychomotor activity scores were 22% vs. 45% (p = 0.08) and 33% vs. 48% (p = 0.29) for patients with low Guilt/Suicidality scores.

Patients who were partially nonresponsive to divalproex treatment with remaining high vs. low psychomotor activity levels or minimal vs. greater guilt/suicidality symptoms were more likely to remit with olanzapine augmentation.

Trial Registration;, Identifier: NCT00402324

Variable pharmacologic treatment response has led to analyses of associations with previous clinical course [14] and baseline clinical characteristics [1, 3, 5, 6] to develop tailored therapies. A post hoc factor analysis of baseline components from the 21-item Hamilton Depression Rating Scale (HDRS-21 [7]) and the Young Mania Rating Scale (YMRS) [8] in bipolar I disorder patients with a current mixed episode characterized specific baseline mood symptoms which potentially predicted remission with treatment augmentation. Data were from a 6-week, double-blind, placebo-controlled trial, of olanzapine (OLZ) augmentation in patients with inadequate response to divalproex (DPX). The primary efficacy endpoint for the original clinical trial was a change in YMRS and HDRS-21 from baseline across 6 weeks by therapy. The primary study was reviewed and approved by the institutional review board at each site, and was conducted in accordance with the International Conference on Harmonisation Good Clinical Practice guidelines. Verbal and written informed consent was obtained from all subjects prior to participation. The study, which demonstrated a statistically significant difference between overall treatment groups in improvement over 6 weeks (in both YMRS and HDRS-21 scores), primary endpoints for the original clinical trial, was not adequately powered to show a difference in treatment arms for dual remission rate (31% vs. 26%, p = .437 for OLZ augmentation vs. continued DPX monotherapy) [9]. Part of the rationale for performing the present analysis was to determine whether baseline symptoms effectively determined sub-groups of patients more likely to remit with olanzapine augmentation vs. continued DPX monotherapy, given the low overall remission rate.

Outpatients aged 18 to 60 years with bipolar I disorder, current mixed episode (with or without psychotic features), as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, (DSM-IV-TR; diagnostic criteria 296.6x) were included. Those (n = 202) with inadequate response to ≥14 days of oral DPX monotherapy (blood levels 75 to 125 μg/mL) were randomly assigned to 6 weeks of placebo (PLA; [n = 101]) or OLZ (mean modal daily dose 14.6 mg; n = 101]) augmentation of DPX treatment. Inadequate response was defined as a HDRS-21 score ≥16 and a YMRS score ≥16. Clinical Global Impressions of Severity (CGI-S) for Bipolar Illness scale data were also collected at baseline. Study details are available elsewhere [9]. Briefly, baseline characteristics of the two treatment groups at randomization were similar in the DPX+PLA vs. DPX+OLZ study arms: 55% vs. 46% white, 29% vs. 38% African American, and 43% vs. 40% male with mean HDRS-21 scores of 21.9 vs. 22.5 and YMRS scores of 20.4 vs. 21.4 [9].

Factor analysis is a statistical technique that combines individual variables varying in the same manner or direction into representative summary factors. Factor analysis permits a reduction in the dimensionality of the original data and the identification of clinically-defined patient subgroups. Factor analysis with Varimax rotation was performed on HDRS-21 and YMRS items, obtained prior to augmentation therapy [10]. Items with factor-loading scores ≥0.4 were included in the one factor for which they had the highest factor-loading score. Items with a negative loading factor were reverse-scored. Only factors with an eigenvalue > 1 were included in the analyses. Pearson correlation coefficients between these factors and CGI-S scores were determined at baseline.

Backwards-elimination logistic regression models were run for DPX+OLZ and DPX+PLA separately for remission (HDRS-21 score ≤ 8 and YMRS score ≤ 12) at 6 weeks. Investigative site location (US vs. Puerto Rico) and all factors derived from the factor analysis were included in the initial model as potential predictors. Variables were eliminated from the model at the 0.05 level of statistical significance.

Several potential cut-off scores (median, median ± 1, 2) for each factor that were significant in the logistic regression models were investigated as predictors of remission. T-tests of each of these potential cut-off scores against remission were performed to find the factor cut-off score that best divided the remitters from the non-remitters within that treatment group based on statistical significance. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also determined. Logistic regression models were performed using the categorical factor cut-off scores to predict ultimate remission.

Analyses of the YMRS and HRDS-21 total scores over time in sub-groups separated by initial factor cut-off scores utilized the mixed model repeated measures (MMRM) approach over 6 weeks. The models included the fixed, categorical effects of factor cut-off score, visit, baseline total score, investigative site, and visit-by-factor cut-off interaction. The optimal within-subject covariance matrix in each MMRM model was determined by Bayesian Information Criterion after testing the following options: unstructured, toeplitz, auto-regressive, and compound-symmetric. SAS version 9.1.3 was used for all analyses.

Remission rates for statistically significant factors in patients sub-grouped by optimal cut-off factor scores were compared between DPX+PLA and DPX+OLZ groups by Fisher's exact test.

Using principal factor analysis with Varimax rotation, the 32 individual HDRS-21 and YMRS items were reduced into 11 factors with eigenvalues > 1 as shown in Table 1. These 11 factors explained 62% of the total variance of the baseline HDRS-21 and YMRS item scores. Eigenvalues did not drop substantially in value across the sequence. Psychomotor activity (Factor 2), Work impairment/somatization (Factor 4), Irritability (Factor 5), Guilt/Suicidality (Factor 8), and Appearance/Gastrointestinal symptoms (Factor 9) correlated significantly with CGI-S (Table 1). Sleep disturbance (Factor 1) contributed significantly to the total scores from HDRS-21 and YMRS items, but did not correlate significantly with CGI-S. In a logistic regression model that included these 11 factors at baseline and investigative site as independent variables (and remission as the dependent variable), only Psychomotor activity and Guilt/Suicidality and investigative site were significant predictors of remission in DPX+OLZ-treated patients (Table 2). In the DPX-PLA treated patients, no factors remained in the backwards elimination model at a 0.05 significance level.
Table 1

Factor Analysis [12] of Mania and Depression Symptoms

Factor No. Name

Scale Items

Mean Contribution To HDRS-21 + YMRS Total Score

Pearson Correlation Coefficient with CGI

(p value)

1. Sleep disturbance

YMRS 4 Sleep HDRS-21 4 Early Insomnia HDRS-21 5 Middle Insomnia HDRS-21 6 Late Insomnia

0.073 (0.301)

2. Psychomotor activity

YMRS 1 Elevated Mood YMRS 2 Increased Psychomotor Activity YMRS 6 Rapid Speech HDRS-21 9 Agitation

0.253 (< .001)*

3. Insight

YMRS 11 Lack of Insight HDRS 17 Lack of Insight

-0.043 (0.541)

4. Work impairment/Somatization

HDRS-21 7 Work and Activities HDRS-21 11 Somatic Anxiety HDRS-21 13 General Somatic Symptoms

0.293 (< .001)*

5. Irritability

YMRS 5 Irritability YMRS 9 Disruptive Aggressive Behavior HDRS-21 1 Depressed Mood

0.181 (< .01)*

6. Variation/Derealization/Obsession

HDRS-21 18b Diurnal Variation HDRS-21 19 Depersonalization and Derealization HDRS-21 21 Obsessional and Compulsive Symptoms

0.073 (0.303)

7. Sexuality

YMRS 3 Sexual Interest HDRS-21 14 Genital Symptoms (reverse)

-0.120 (0.089)

8. Guilt/Suicidality

HDRS-21 2 Feelings of Guilt HDRS-21 3 Suicide

0.256 (< .001)*

9. Appearance/GI

YMRS 10 Appearance HDRS-21 12 Somatic Symptoms (gastrointestinal) HDRS-21 16 Loss of Weight

0.298 (< .001)*

10. Thought disorder

YMRS 8 Content HDRS-21 20 Paranoid Symptoms

0.012 (0.871)

11. Retardation/Hypochondriasis

HDRS-21 8 Psychomotor Retardation (reverse) HDRS-21 15 Hypochondriasis

0.118 (0.094)

* Statistically-significant (p < .05). GI: Gastrointestinal

Table 2

Details of Two Factors Predictive of Remission with Olanzapine Augmentation of Divalproex Partially Nonresponsive Patients.

Factor No:

Odds Ratio

95% CI

P value

Score Cutoff

Odds Ratio

95% CI

P value

Sensitivity %

2. Psychomotor activity

1.01 - 1.38

1.22 - 7.79

8. Guilt/Suicidality

0.39 - 0.94

0.13 - 1.03

The logistic regression models also included investigative site location (USA vs. Puerto Rico)

Further analysis of Psychomotor activity and Guilt/Suicidality factors revealed that baseline scores below 10 and 2, respectively, best separated patients who achieved remission from those who did not (non-remitters) with DPX+OLZ treatment. Logistic regression models using these categorical cut-offs showed that those with Psychomotor activity (Factor 2) scores ≥10 were more likely to remit at endpoint (p = 0.02). Those with Guilt/Suicidality (Factor 8) scores ≥2 were less likely to remit at endpoint, although this relationship did not quite reach significance (p = 0.06). The sensitivity, specificity, PPV, and NPV of these cut-off scores are shown in Table 2. As defined by these scores, only 22% of patients without higher levels of psychomotor activity and 26% of patients with even modest levels of suicidality or guilt feelings remaining after non-response to DPX treatment for at least 2 weeks remitted with OLZ augmentation.

In an MMRM model of changes in HDRS-21 and YMRS total scores over time, those with Psychomotor activity (Factor 2) scores ≥10 had significantly lower totals on the HDRS-21 score from 2 weeks post-randomization onwards (Figure 1A) and a significantly lower YMRS score at 6 weeks, despite a significantly higher YMRS score at baseline (Figure 1B). For Guilt/Suicidality (Factor 8), significant outcome differences were observed only at approximately 5 weeks post-randomization for HDRS-21 and YMRS scores (Figures 1C and 1D).
Figure 1

Total HDRS-21 (A and C) and YMRS (B and D) scores in sub-categorized patients. Patients were sub-categorized by cut-off scores for baseline Psychomotor activity (Factor 2) and Guilt/suicidality (Factor 8) during olanzapine augmentation of divalproex-partially nonresponsive patients. HDRS-21: 21-Item Hamilton Depression Rating Scale; YMRS: Young Mania Rating Scale.

Remission rates in the DPX+PLA vs. DPX+OLZ groups were 21.9% vs. 44.7%, p = 0.08 for patients with high psychomotor activity (score ≥10) and 33.3% vs. 47.8% (p = 0.29) for patients with low Guilt/Suicidality (score < 2).

The categorical cut-off scores for Psychomotor activity (Factor 2) and Guilt/Suicidality (Factor 8) were also compared to the endpoint CGI-S score to evaluate their association with a measure of remission not based on YMRS or HDRS-21. Both of these categorical cut-offs in the OLZ treatment group were associated with a nearly half-point difference in the endpoint CGI-S scale though these relationships did not reach statistical significance (Psychomotor activity [Factor 2] < 10: mean CGI-S = 3.11 versus Psychomotor activity ≥10 mean CGI-S = 2.68, p = .08; Guilt/Suicidality (Factor 8) < 2, mean CGI-S = 2.57 versus Guilt/Suicidality ≥2, mean CGI-S = 3.06, p = .05).

We reduced 32 items of the HDRS-21 and YMRS at baseline into 11 factors, of which 2 factors predicted remission at 6 weeks following OLZ augmentation in patients previously partially nonresponsive to DPX monotherapy. Patients with greater baseline Psychomotor activity (Factor 2 ≥10) were more likely to remit with OLZ augmentation. These patients had lower final YMRS and HDRS-21 scores after 6 weeks of OLZ augmentation despite significantly higher YMRS and similar HDRS-21 scores at baseline. In contrast, patients with even marginally high Guilt/Suicidality (Factor 8≥2) were less likely to remit than those with lower levels of symptomatology. Guilt/Suicidality (Factor 8) correlated significantly with CGI-S scores despite a relatively low contribution to overall HDRS-21 and YMRS total scores (Table 1).

These findings along with relatively higher (although statistically nonsignificant) remission rates for high psychomotor activity and low Guilt/Suicidality groups with OLZ augmentation vs. continued DPX treatment alone suggest that these subgroups of patients may be better candidates for OLZ augmentation after inadequate response to DPX monotherapy. While Psychomotor activity and Guilt/suicidality had only moderate PPVs for remission (44.7% and 47.8%, respectively), in part related to the low overall rate of remission, they both had high NPVs (77.4% and 74.0%, respectively). Such predictors could increase the overall remission rate with OLZ augmentation through pre-treatment exclusion of patients likely to be non-remitters.

It is difficult to compare factors from our analysis with others in the literature because our population was restricted to bipolar disorder patients experiencing a mixed state. The factors we identified may be related to the presence of substantial depressive and manic symptoms. Also, a greater number of eigenvalues resulted from lack of at clear drop in eigenvalue magnitude between factors that would have allowed the elimination of factors associated with lower eigenvalues. Additionally, the methodology of other analyses differs from ours. One analysis developed a bivariate symptom rating scale based on 2 factors identified from a principal factor analysis in a retrospective study in which patients completed a symptom questionnaire based on their last manic episode [11]. Another analysis that used principal components factor analysis of HDRS-21 and YMRS scores in a group of patients with bipolar disorder who had experienced either mixed or manic states, yielded 5 factors corresponding to depression, dysphoria, hedonism, psychosis, and activation [10].

The likelihood of a type I error exists due to multiple testing (individual item analysis). Results cannot be generalized to other settings for characterization of baseline symptoms which predicted overall remission after OLZ augmentation. Although YMRS and HDRS-21 scales were used to define both predictors and outcomes, baseline factors composed of YMRS and HDRS-21 individual items defined predictors, and YMRS and HDRS-21 totals defined outcomes. YMRS and HDRS-21 measures used for predictors vs. outcomes were at different time points separated by up to six weeks. CGI-S scores also showed similar differences in endpoint changes from baseline when patients were sub-classified according to the categorical factor cut-off scores. The percent contribution of Factors 2 and 8 to total HDRS-21 and YMRS scores at baseline were 20% and 5%, respectively (Table 1). Since the study was not adequately powered to show a remission rate difference in treatment arms based upon the above cut-off scores, comparison of remission rates between treatments in high Psychomotor activity and low Suicidality/Guilt subgroups is not fully conclusive. While another limitation is that backwards elimination logistic regression is inferior to hypothesis-driven modeling, this exploratory post-hoc analysis sought the best predictive model using newly generated factors as predictors. Because no specific hypotheses regarding the factors were being tested, backwards elimination model building was an appropriate strategy.

In this post hoc analysis of patients with bipolar disorder mixed episodes, factor analysis suggested two groups of HDRS-21 and YMRS items that predict remission in patients receiving OLZ augmentation following an inadequate response to DPX. Patients were more likely to remit with OLZ augmentation vs. continued DPX monotherapy if they scored lower on items related to guilt and suicidality and higher on items related to psychomotor activity.

List of abbreviations

Clinical Global Impressions of Severity

95% CI

95% confidence interval

21-Item Hamilton Depression Rating Scale

negative predictive value

positive predictive value

Young Mania Rating Scale.

The authors would like to thank David Williams and Michael Witte (Lilly USA, LLC); Rebekah Conway and Caron Modeas (i3 Statprobe); and Yingbin Su (inVentiv Clinical Solutions, LLC) for review. Study funding was provided by Lilly USA, LLC. The funding body was involved in the design, collection, analysis, and interpretation.

Authors’ Affiliations

US Medical Neuroscience, Lilly USA, LLC; Drop Code
Department of Psychiatry, Indiana University School of Medicine

  1. Suppes T, Brown E, Schuh LM, Baker RW, Tohen M: Rapid versus non-rapid cycling as a predictor of response to olanzapine and divalproex sodium for bipolar mania and maintenance of remission: post hoc analyses of 47-week data. J Affect Disord. 2005, 89: 69-77. 10.1016/j.jad.2005.07.011.PubMedView ArticleGoogle Scholar
  2. Ketter TA, Houston JP, Adams DH, Risser RC, Meyers AL, Williamson DJ, Tohen M: Differential efficacy of olanzapine and lithium in preventing manic or mixed recurrence in patients with bipolar I disorder based on number of previous manic or mixed episodes. J Clin Psychiatry. 2006, 67: 95-101. 10.4088/JCP.v67n0113.PubMedView ArticleGoogle Scholar
  3. Perlis RH, Ostacher MJ, Patel JK, Marangell LB, Zhang H, Wisniewski SR, Ketter TA, Miklowitz DJ, Otto MW, Gyulai L: Predictors of recurrence in bipolar disorder: primary outcomes from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Am J Psychiatry. 2006, 163: 217-224. 10.1176/appi.ajp.163.2.217.PubMedView ArticleGoogle Scholar
  4. Lipkovich IA, Houston JP, Ahl J: Identifying patterns in treatment response profiles in acute bipolar mania: a cluster analysis approach. BMC Psychiatry. 2008, 8 (65): 65-10.1186/1471-244X-8-65.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Keller MB, Lavori PW, Kane JM, Gelenberg AJ, Rosenbaum JF, Walzer EA, Baker LA: Subsyndromal symptoms in bipolar disorder. A comparison of standard and low serum levels of lithium. Arch Gen Psychiatry. 1992, 49: 371-376.PubMedView ArticleGoogle Scholar
  6. Houston JP, Lipkovich IA, Ahl J, Rotelli MD, Baker RW, Bowden CL: Initial symptoms of manic relapse in manic or mixed-manic bipolar disorder: post hoc analysis of patients treated with olanzapine or lithium. J Psychiatr Res. 2007, 41: 616-621. 10.1016/j.jpsychires.2005.08.009.PubMedView ArticleGoogle Scholar
  7. Hamilton M: A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960, 23: 56-62. 10.1136/jnnp.23.1.56.PubMed CentralPubMedView ArticleGoogle Scholar
  8. Young RC, Biggs JT, Ziegler VE, Meyer DA: A rating scale for mania: reliability, validity, and sensitivity. Br J Psychiatry. 1978, 133: 429-435. 10.1192/bjp.133.5.429.PubMedView ArticleGoogle Scholar
  9. Houston JP, Tohen M, Degenhardt EK, Jamal HH, Liu LL, Ketter TA: Olanzapine-Divalproex combination versus divalproex monotherapy in the treatment of bipolar mixed episodes: a double-blind, placebo-controlled study. J Clin Psychiatry. 2009, 70: 1540-1547. 10.4088/JCP.08m04895yel.PubMedView ArticleGoogle Scholar
  10. Gonzalez-Pinto A, Ballesteros J, Aldama A, Perez de Heredia JL, Gutierrez M, Mosquera F, Gonzalez-Pinto A: Principal components of mania. J Affect Disord. 2003, 76: 95-102. 10.1016/S0165-0327(02)00070-8.PubMedView ArticleGoogle Scholar
  11. Cavanagh J, Schwannauer M, Power M, Goodwin GM: A novel scale for measuring mixed states in bipolar disorder. Clin Psychol Psychother. 2009, 16: 497-509. 10.1002/cpp.633.PubMedView ArticleGoogle Scholar
  12. Manly BFJ: Multivariate Statistical Methods: A Primer. 2005, Chapman & Hall/CRC, Boca Raton, FLGoogle Scholar

© Houston et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


text:
i drove a boxster
লিখেছেন- Mutley the dog 19 de ডিসেম্বার de 1999
Photos & Videos
Top Definition
An extremely quick and fun to drive sports car that has great handling and priced reasonably.
That guy in the Boxster gets a lot of girls.
লিখেছেন- Dee 1 de জানুয়ারি de 2005
Beginnng production in 1997, the Boxster is Porsche's entry level sports car. Since it's introduction, the Boxster has gotten numerous upgrades and in 2005 was remodeled. An "S" version was also introduced that has a more powerful engine and various other upgrades. Being a Porsche, the car is wonderful to drive and is considered by many to be among the most fun cars to drive.
That guy in the Boxster fast around the track.
লিখেছেন- Nagi0000 18 de নভেম্বার de 2007
A piece of shit. A porsche that is so damn ugly, you cant tell if it is driving down the road backwards or not. these frequently get their asses handed to them by all other porsches, including the ones 20 years old, such as a 944 turbo or a Carrera
Hahaha... That guy who showed up at the Porsche meet with a boxster was laughed at, looked down upon, and had his car pee'd on by some kid,
লিখেছেন- boxster killer 19 de জানুয়ারি de 2007
Silly little car for people who lack the money for a new 996, and the brains to get a used 993. Silly little hairdressers car, ponces only.
Haha, look at the little twat in his Boxster, thinks he's the shit with his entry level Porsche!
লিখেছেন- Him 10 de নভেম্বার de 2003
A poor mans porsche. Cost about 53K-60K but is the cheapiest and ugliest porsche.
I bought a boxster which goes 0-60 in 5.9 , just to own the porsche insgnia name.....when I could have bought a corvette C6 and gone 0-60 in less than 4.5
লিখেছেন- BobbaFart 19 de জুলাই de 2005
ফ্রী দৈনিক ই-মেইল

ফ্রী Urban প্রতিদিনের নির্বাচিত শব্দ পেতে নিচে আপনার ই-মেইল ঠিকানা লিখুন! থেকে ই-মেইল পাঠানো হয়ে। আমারা আপনাকে কখনো স্প্যাম করব না।


text:
Brown-headed Nuthatch
Sitta pusilla
– Family
Authors: Withgott, James H., and Kimberly G. Smith
Revisors: Slater, Gary L., and John D. Lloyd

Welcome to the Birds of North America Online!

Welcome to BNA Online, the leading source of life history information for North American breeding birds. This free, courtesy preview is just the first of 14 articles that provide detailed life history information including Distribution, Migration, Habitat, Food Habits, Sounds, Behavior and Breeding. Written by acknowledged experts on each species, there is also a comprehensive bibliography of published research on the species.

A subscription is needed to access the remaining articles for this and any other species. Subscription rates start as low as $5 USD for 30 days of complete access to the resource. To subscribe, please visit the Cornell Lab of Ornithology E-Store.

If you are already a current subscriber, you will need to sign in with your login information to access BNA normally.

Subscriptions are available for as little as $5 for 30 days of full access! If you would like to subscribe to BNA Online, just visit the Cornell Lab of Ornithology E-Store.

Brown-headed Nuthatch, Wekiwa Springs SP, FL, 24 November.
Figure 1. Distribution of the Brown-headed Nuthatch.

Endemic to pine forests of the southeastern United States, and rarely seen far from pine-dominated areas, the Brown-headed Nuthatch is one of the few cooperatively-breeding birds native to North America, and one of the few for which tool use has been documented (individuals use chips of pine bark to pry off other bark chips while foraging).

This nuthatch’s habit of staying high in the canopy often makes it difficult to observe, but its tendency to nest lower has encouraged studies of its breeding biology. Norris’s (1958) pioneering work detailed the breeding biology of a Georgia population in the course of a comparative study with its sister species, the Pygmy Nuthatch (Sitta pygmaea). Many aspects of Brown-headed Nuthatch biology, including its cooperative breeding behavior and its population demography, have recently been investigated in color-marked populations, providing a wealth of information upon which to base further study.

The Brown-headed’s association with pines, particularly mature pines, and its reliance on snags for nesting may make it a good indicator species for the health of southeastern pine forests, which have been extensively logged over the last century. The failure of this species to recolonize areas where populations were extirpated because of habitat change, and the near disappearance of populations on Grand Bahama Island in the Bahamas (S. p. insularis), highlight the vulnerability of this species to habitat alteration by humans. Nonetheless, important conservation actions have been accomplished -- this is one of the few North American landbirds that has been successfully reintroduced to habitat it formerly occupied.


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Francisco Bernal and his wife, Altagracia Gonzales -- from Pueblonuevo

Replies: 12

Re: Bernals from Pueblonuevo

Posted: 15 Aug 2013 7:15PM GMT
Classification: Query
Edited: 17 Aug 2013 7:42PM GMT
Surnames: Barreneche, Etcheverry
When did your Grandfather own Huatzimitiro? Meaning, what years. Thanks.

I am a descendent of:

Jean Baptiste ETCHEVERRY, born October 27, 1852, died November 27, 1907 in Bayonne, France & Clara TEILLERY, born August 12, 1858, died February 28, 1897 in Iholdy, France

...the Parents of my great-grandmother Marie ETCHEVERRY, born November 5, 1888

And Martin BARRENECHE and Gracieuse BERHO, born July 23, 1852.

...the Parents of my great-grandfather Basilio Gaspar BARRENECHE born January 2, 1881 in Ascain, France, died in Mexico on March 11, 1943

Basilio Gaspar BARRENECHE and Marie ETCHEVERRY were Married June 3, 1912 in Itxassou, France

Their son, is my maternal grandfather, Joseph Laurent BARRENECHE ETCHEVERRY whom I am searching for. Born September 17, 1915 in Mexico

Other names of interest are:

Emile Dominique BARRENECHE, born May 27, 1918 in Mexico
Dominique BERHO, born 1847
Laurent BERTERECHE, born 1822
Salvat TEILLERCHE, born 1828

Francisco RAMOS, born 1927
Bicente JASSO, born 1898
Elpidio RAMOS, born 1917

Please visit my blog to browse the photos and names and let me know of you recognize any of them

Thank you.
SubjectAuthorDate Posted
rangerpoo1 19 Jul 2010 8:26PM GMT
rangerpoo1 19 Jul 2010 11:36PM GMT
Barreneche_Et... 16 Aug 2013 1:15AM GMT
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Family Connections

Replies: 1

Family Connections

Posted: 13 Dec 2012 4:20PM GMT
Classification: Query
I am presently doing family research of the Elmores trying to locate family records of ancestors who lived on Bateman Plantation and/ Lagoanda Planation in Louisiana during the 1800s. If you have information about slave listing and such please contact me. This would be highly appreciated.
SubjectAuthorDate Posted
elmoreratliff... 13 Dec 2012 11:20PM GMT
blastfromthep... 13 Dec 2012 11:46PM GMT
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Deshazo family of Henry Co

Replies: 12

Deshazo family of Henry Co

Posted: 22 Dec 2001 4:21PM GMT
Classification: Query
Edited: 21 Jun 2002 6:53AM GMT
I am distantly related to the Deshazo family of Henry Co and have a question for anyone who might be a descendant of this line. I have two William Thomas Deshazos from Henry Co. One was the s/o William Thomas Deshazo Sr & Sallie Ann Finney and was b. 1 July 1863; the other was the s/o Richard Tazewell Deshazo & Mary Jane Napier and was b. 27 Nov 1867. My question - which of these was the William Thomas Deshazo who married Nannie Eugenia Pratt?
Thanks! Peggy
SubjectAuthorDate Posted
Chesney3 22 Dec 2001 11:21PM GMT
bruce7911 23 Dec 2001 5:06PM GMT
Chesney3 24 Dec 2001 5:08AM GMT
DebbyDD49 26 Feb 2012 3:10AM GMT
nanny_cathy 11 Oct 2012 10:01PM GMT
pherman142 29 Oct 2012 10:40PM GMT
nanny_cathy 31 Oct 2012 5:28PM GMT
MerrylynS 19 Oct 2013 4:20PM GMT
pherman142 28 Oct 2013 1:44AM GMT
Catherine Wil... 12 Apr 2015 4:07AM GMT
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Claude Huddleston

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Claude Huddleston

Maria (View posts)
Posted: 19 Sep 2000 3:25PM GMT
My Grandfather was Claude Huddleston (1903-1984) He lived in South Carolina. He was married to Faye Baker and has 3 children. Anyone with any info please e-mail me.

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Jatzlau Family in America

Replies: 1

Jatzlau Family in America

Jeanne (View posts)
Posted: 15 Mar 2005 7:24AM GMT
Classification: Query
Surnames: Jatzlau, Locke
Ok, we know the first Jatzlau in America was Christian he also brought along Hans and Maria. I believe Hans and Maria were his younger siblings, but I can find NO record of them once they left the port in Galveston. They all came over on the ship Ben Nevis, and Christin ended up settling around Serbin Texas. I have traced all of my family and the Jatzlaus around the Giddings area all to Christian and Magdalena Locke she was called Lena.I am hoping to find out what happened to Hans and Maria and also find out about Christians parents as well as his life before he came to the states.
Jeanne Jatzlau Cook
SubjectAuthorDate Posted
Jeanne 15 Mar 2005 2:24PM GMT
jeannie5555 24 Apr 2008 10:05PM GMT
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Posted: 6 Jun 2009 3:20AM GMT
Classification: Query
Surnames: Roboch
Seeking information on the Roboch surname from Libusin, Slany, Bohemia. The names I have are: Frank Roboch b. 1884, Joseph (Father), James (also listed as father of Frank, Henry (a brother to Frank), and Alice (possibly a sister of Frank and Henry). Would appreciate any information anyone has.

Re: Roboch

Posted: 8 Jun 2009 12:26AM GMT
Classification: Query
No ROBOCH but RABOCH in CZ's phone book.
Map of Bohemia:
Possible this RABOCH arriving to America thru EI from Kladno might be related to your ROBOCH.
Raboch, Karel Kladno, Austria 20 1889 1909
Raboch, Karel Kladno, Aus. 18 1889 1907

Re: Roboch

Posted: 12 Jan 2012 2:19AM GMT
Classification: Query
Wow, I just saw this now! Thank you for doing that research! I am going to sift through all the links you sent. I should have mentioned in my original post that Frank left Libusin for Canada, not the US. He also served three years in the Bohemian Army ( incase anyone knows how to access that information). Thank you again!!

Re: Roboch

Posted: 12 Jan 2012 2:52PM GMT
Classification: Query
I have only found some info about Frank Roboch in Canada:

Frank Roboch, b. 1884, m. 1922 Frank, Alberta, Canada, d. Aug 1960 Canada
wife: Anna Poch nee Mullar, b. 31 Oct 1889 Kopisty-Julius, Czechoslovakia, d. 22 Dec 1964 Blairmore, Alberta
children: Edward Roboch, b. 1930 Frank, Alberta, Canada, d. 1963 Calgary, Alberta, Canada

Unfortunately, Libušín birth records for 1884 year are not online yet:
Posted: 9 Feb 2013 7:32AM GMT
Classification: Query
Hello, Jan Roboch and Anna Valenta were married 15.05.1838 in Libušín.
Posted: 26 Jan 2014 9:07AM GMT
Classification: Query
I believe you are referring to my grandfather. He was married to Anna Mullar nee Poch. Her first husband was killed in a mine accident.
Frank & Anna Roboch had the following children, which are in order of birth: Frank, Annie, Josephine, Mary (my mom now deceased), Edward and Victoria, who died when she was very young.
Anna had two children from her first marriage to a gent with the last name of Poch. The eldest was Louis, who was killed overseas in WW2, and Tony who passed away in Nelson, British Columbia, Canada.
Posted: 30 Apr 2014 6:10AM GMT
Classification: Query
Surnames: Bauer
Hello, I am doing my family tree and there František Václav Roboch, born 28 September 1884 in Libušín. I have the exact data and photocopies of records in registers. Frank's mother: Josepha Roboch nee Bauer (2 April 1852 - 11 April 1904). She was the mother of my great-grandfather. If interested, I can provide other details. Note: I can not English and this is the translation via Google Translate. Sorry about that. Regards Josef Bauer

Re: Roboch

Posted: 11 Aug 2014 3:39AM GMT
Classification: Query
Hi Sherry,
Thanks for your response! Frank would be my great grandfather and your Uncle Edward is my grandfather. We know very little about this side of the family and I would appreciate any info or especially pictures if you have any! Thank you again! Daleanne Roboch Czarnecki (
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Thread: Niners fans crying "outcoached"

  1. #1
    Join Date
    Dec 2007

    Niners fans crying "outcoached"

    Crying they got outcoached and they gave up on the running game too soon.
    Saying Gore was getting 5 yards on every touch.

    Not sure what they are talking about. Gore was getting some runs, but
    he got stuffed for no gain on a 3rd and 6 at our 26 in the first drive.

    Second drive he went -1 on first down, then 6 yards second down.
    3rd is obvious pass play since you just ran it twice for a total of 5 yards.

    3rd drive smith took one shot down the field got intercepted.
    How is that giving up on the run after one play.

    4th drive gore for 3, false start penalty, then gore for 8.
    short pass failed on 3rd and 4, but you can't keep running
    the ball and never passing. (unless your the giants in
    the 4th quarter, HAHAHA!!). so 3rd and 4, missed
    short pass.

    5th drive they are first and 20 after holding penalty.
    Gave it to gore he got 3 yards. Had to pass then,
    got 12 yards on second, now they were 3rd and 5.
    Maybe gore gets the 5 and maybe not, but
    usually passing down on 3 and 5, pretty manageable.
    missed pass, had to punt.

    6th drive give it to kendal got 2 yards.
    2nd down pass to MH for 7 yards.
    3rd down intercepted on 3rd and 6 (after 5 yard penalty).

    Up until then, smith was completing short passes, he was doing OK.
    But at that point, they were down 20-3 and had to pass.
    Overall I don't see when they gave up on run, unless the whiners
    wanted their team to run EVERY down......

  2. #2
    I would chalk that win up to a severe ***** beating...

  3. #3
    Join Date
    Sep 2010
    They absolutely did get out coached, that's not even a question.
    And they only gave Gore 8 carries all game. Regardless whether he was getting stuffed or not they were extremely stupid to abandon the run. AB wasn't having a monster game in the beginning but we kept feeding him the ball and eventually he started breaking some nice runs.

  4. #4
    All-Pro Moke's Avatar
    Join Date
    Aug 2012
    Gore was gaining yards on us at the beginning. Looks like he started to get stuff. But they did abandon the run after awhile. Might have been when the Giants were gaining on them.

  5. #5
    All-Pro TheAnalyst's Avatar
    Join Date
    Jul 2012
    Original Member since 04-04-04
    They got manhandled in every aspect of the game. They did not execute, we did. The 49ers are obviously a great team. We just dominated them on this day. I'm sure this isnt the last time these 2 will match up.

  6. #6
    All-Pro Sarcasman's Avatar
    Join Date
    Nov 2009
    Manussa Loka
    I thought they got out-coached simply because they started to abandon the things that have served them so well this year and I think his inability (or unwillingness) to adapt his game plan earlier in the game coupled with his petulant "punishment" of Smith by insisting on bringing on the crap-cat offense regardless of whether it made sense or not....

    Harbaugh's lack of confidence in Smith could well become an issue this year. He can talk all he wants, he doesn't act like a coach who is confident in his protege. It's ironic: given what a flat out crappy QB he was one would think he'd be more patient of his own QB.

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Thread: Under my skin

  1. #1

    Under my skin

    I couldnt help but notice the way the Packers fans blog about this game. Since when did the Packers become the Eagles? I'm at the point where I just want to see the Giants win just to shut them up!

  2. #2

    Re: Under my skin

    I have to guess there are a lot of hangers-on because the Packer fanbase is amongst the classiest.
    8-Time NFL Champions - 1927 1934 1938 1956 1986 1990 2007 2011

    "You win close games because you're prepared to do it. It doesn't just come down to luck." -- Bill Parcells

  3. #3

    Re: Under my skin

    [quote user="SweetZombieJesus"]I have to guess there are a lot of hangers-on because the Packer fanbase is amongst the classiest.

    As much as I hate tagging anything "classy" ... I'll agree with you Sweet in that the 'hangers-on' irritate the living hell out of me too ... an irritant roughly similar to consuming a bad laxative.

    There are a lot of blowholes that hit the internet these days and I have a nagging notion that many of them are of what I call the ESPN generation...everyone HAS to be controversial...everyone HAS to have that one SINGLE observation that seems to have eluded the mainstream media (and they're a story in and of themselves). Seems to always be a matter of who can yell their opinion the loudest.

    One thing I'm reasonably sure of, a good number of those "irritants" aren't residents of north eastern Wisconsin. By and large, that is not how we're built. So be it, we can't control drunk people who decide they want to blog.

  4. #4
    All-Pro TroyArcher's Avatar
    Join Date
    Sep 2001

    Re: Under my skin

    My guess is some disgruntled Eagles & Cowboys fans in disguise.

  5. #5

    Re: Under my skin

    Unfortunately when you win a SB you get 'em. We had plenty of our own for a good year after 07. After we flopped in 08 most toddled back under their rocks looking for the next champ to be a fan of and strangely enough they are usually the most outspoken on things they know pretty much nothing about.

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text:
Boat Specifications

Make: Wellcraft Boats
Model: 210 Sportsman
Year: 2011
Length: 21 ft. 0 in.
Type: Bowrider Boats
Listing: 722213
Buyer's Tools:

Looking for a multi-purpose boat that can handle the family and your fishing friends? Here's a winner. With an enclosed head compartment, tons of storage, great versatility,  including removable aft seats,  no one will feel cheated no matter what their desired activity. Rod holders and a baitwell, a Bimini top and a huge cooler: this model is ready for anything.
Draft: 2 ft. 9 in.
Beam: 8 ft. 6 in.
Fuel tank capacity: 101
Water tank capacity: 9
Standard features:
Aft seats, removable, port/stbd
Baitwell, w/light and overboard drain
Beverage holders, stainless steel (9)
Bow seating w/backrests, port/stbd
Courtesy lights, LED
Overboard scupper
Cooler, portside w/cushion (NA w/lounge seat)
Fish boxes, port/stbd w/overboard pumpout (2)
Glovebox, lockable
Pedestal seats
Raw water washdown
Storage bin, stbd/aft
Ski/wakeboard storage
Skid-resistant surfaces
Storage, finished compartment under helm
Tackle storage trays

Rod holders, stainless steel (4)
Anchor rope locker w/hawse pipe
Anchor roller w/8" cleat
Bow and stern eyes, stainless steel
Half dive platform
Rub rail, heavy duty w/stainless steel insert
Rails, stainless steel
Thru hulls, stainless steel
Windshield, curved w/tempered Solex®: glass
Rod racks, under gunwale, port/stbd
Hullside color
Cleats, pull-up, stainless steel

Accessory switches
Accessory plug, 12V
Baitwell pump
Battery switch, dual, w/2 trays
Circuit breakers, helm, weather-resistant
Bilge pump w/auto float switch
Instrumentation, digital
Horn, flush-mounted
Hoses, steel-reinforced
Ignition, safety interlock system w/lanyard
Navigation lights
Propeller, aluminum
Remote oil fill (2-stroke)
Steering, hydraulic tilt
Steering wheel, stainless steel w/power knob
Connections, electrical, submersible-rated
Switches, weather-resistant

10-year structural hull warranty
Complies w/Coast Guard safety regulations

Optional features:

Raymarine Package A65
Baitwell cushion
Cockpit bolsters, port/stbd
Freshwater system, pressurized w/transom shower and aft sink (port)
Lounge seat, back-to-back, port, w/cooler (replaces std ped seat and 48-qt cooler)
AM/FM CD player w/MP3 jack at helm, 4 speakers
Sport arch, powdercoated, white

Freshwater system, pressurized w/transom shower and aft sink (port)
Ski pylon
Midnite Blue

Raymarine Package A65
Canada certification
CE certification, export only
Freshwater system, pressurized w/transom shower and aft sink (port)
Trim tabs w/indicators

Marine head w/dockside pumpout

Evinrude 150 E-Tec
Evinrude 200 E-Tec
Evinrude 250 E-Tec
Yamaha F 150 4-stroke
Yamaha F 200 4-stroke
Yamaha F 250 4-stroke

Australian certification

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1651 Whitfield Avenue
Sarasota, FL 34243
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