DHD-index scores showed acceptable correlationswith the four combined biomarkers taking energy intake into account. Inconclusion, the DHD-index score based on FFQ data shows similar associations withparticipants' characteristics, energy intake, micronutrient intake and biomarkers comparedwith the score based on 24hR data. Furthermore, ranking of participants was acceptable forboth methods. FFQ data may therefore be used to assess diet quality using the DHD-index inDutch populations.The Dutch Healthy Diet index (DHD-index) was developed using data from two 24 h recalls(24hR) and appeared useful to evaluate diet quality in Dutch adults. As many epidemiologicstudies use FFQ, we now estimated the DHD-index score using FFQ data. We compared whetherthis score showed similar associations with participants' characteristics, micronutrientintakes, and biomarkers of intake and metabolism compared with the DHD-index using 24hRdata. Data of 121 Dutch participants of the European Food Consumption Validation studywere used. Dietary intake was assessed by two 24hR and a 180-item FFQ. Biomarkers measuredwere serum total cholesterol and carotenoids, EPA + DHA in plasma phospholipids and 24 hurinary Na. A correlation of 0·48  was observed between the DHD-indexscore based on 24hR data and on FFQ data. Classification of participants into the sametertiles of the DHD-index was achieved for 57 %. Women showed higher DHD-index scores.Energy intake was inversely associated with both DHD-index scores. Furthermore, age andintakes of folate, Fe, Mg, K, vitamin B To better reflect thecomplexity of dietary intake, an alternative approach is to investigate overall diet quality.This can be assessed through diet indices, which may give insight into the association offoods, combinations of nutrients and other dietary components with healthoutcomes. In that study, we used data from the Dutch National Food ConsumptionSurvey of 2003 (DNFCS-2003) to examine the association of the DHD-index with energy andmicronutrient intakes. We found an inverse association with energy intake and positiveassociations with several micronutrients when adjusted for energy intake. We concluded thatthe DHD-index can be used to estimate adherence to the Dutch dietary guidelines and as amonitoring tool in public health research. In the DNFCS-2003, two non-consecutive 24 h recalls (24hR) were used toassess dietary intake. In many epidemiological studies, however, a FFQ is usedinstead. To evaluate wider applicability of the DHD-index, it is important tocompare the DHD-index based on FFQ data with the index based on 24hR data.We recently developed the Dutch Healthy Diet index (DHD-index) that consists of tencomponents representing the Dutch Guidelines for a Healthy Diet of 2006(–). These significant correlations between diet quality indices and singlebiomarkers ranged between 0.19 and 0.44.A FFQ is designed to assess usual intake whereas 24hR assess detailed information on dietaryintake of 1 d or more. Due to natural day-to-day variation within an individual, comparing theDHD-index score based on FFQ data is expected to differ from the DHD-index score based on24hR. For example, as fish is considered episodically consumed in the Netherlands, estimationsof fish intake from a FFQ are expected to be higher compared with data from two 24hR. Thisexample of measurement error is a feature of the dietary assessment method as such and willinfluence the DHD-index scores. Therefore, it is important to not only compare the DHD-indexbased on FFQ data with the DHD-index based on 24hR data, but also examine associations withobjective urinary and plasma biomarkers of dietary intake and metabolism. Serum totalcholesterol, EPA, DHA and several carotenoids have shown significant associations withexisting indices of diet quality(–) and on the availability of data.Our objective was to assess whether the DHD-index score based on FFQ data showed similarassociations with participants' characteristics, micronutrient intakes, and biomarkers ofdietary intake and metabolism compared with the DHD-index score based on 24hR data.Furthermore, we will compare the ranking of participants between the DHD-index scores based onthe two dietary assessment methods. The biomarkers of dietary intake were selected based onthe literatureData of the Dutch participants of the European Food Consumption Validation (EFCOVAL)study, including 121 men and women aged 45–65 years, were used for the present study. Allsubjects were healthy individuals representing all educational levels. Subjects wereexcluded if they could not speak and write Dutch, were currently taking diuretics, werepregnant or lactating, had diabetes mellitus or kidney disease, and had been donatingblood or plasma less than 4 weeks before the study. All subjects signed an informedconsent and the present study was conducted according to the guidelines laid down in theDeclaration of Helsinki. All procedures involving human subjects were approved by themedical ethical committee in Wageningen.et al.. The aim of the study was to validate the duplicate 24hR method usingEPIC-Soft ; a computerised 24hR program thatfollows standardised procedures.The EFCOVAL study is an observational study in five European countries and has beendescribed in more detail by Crispim  and a general questionnaire on lifestyle, food habits and supplementuse. Fish oil supplement users were identified when at least on one of the recalled daysor during the past 3 months at least one supplement containing EPA or DHA was consumed.Furthermore, body weight and height were measured following standardised protocols at thestudy centre. After that, a 24hR and a 24 h urine collection were obtained covering thesame reference day. The second 24hR and urine collection were obtained at least 1 monthafter the first one. At the end of the study period, all subjects received a FFQ by mailand filled it in at home.At enrolment, all subjects filled in the Short Questionnaire Assessing Health EnhancingPhysical Activity (SQUASH).Two non-consecutive 24hR were collected per subject, one by phone and one face to face atthe research centre. All days of the week and the two modes of administration of 24hR wererandomised among subjects, whereas the intake on Saturdays was recalled 2 d later onMondays. Interviewers were all trained in interviewing techniques and in using EPIC-Soft(version 9.16). Portion size estimation was done using household measures, weight/volume,standard units and portions, bread shapes and photographs. Nutrient intakes werecalculated using the Dutch food composition table. All questionnaires were checked on unusual or missing values, and, ifnecessary, subjects received a telephone call to obtain additional information. Averagedaily nutrient intakes were calculated by multiplying frequency of consumption of fooditems by portion size and nutrient content per g based on the Dutch food compositiontable.The 180-item semi-quantitative FFQ was developed to assess intake of energy,macronutrients, dietary fibre and selected vitamins. Briefly, para-aminobenzoic acid (PABA) was used toverify completeness of 24 h urine collections. Subjects were asked to fill in a shortdiary about time of taking PABA, completeness of the urine collection and medication use.Five urine samples with PABA recoveries below 50 % were excluded from the data analyses.Recoveries between 50 and 85 % were proportionally adjusted to 93 % of PABA recovery, assuggested by Johansson et al.. Recoveries above 85 % were included without adjustments. Urinary Nawas measured by an ion-selective electrode on a Beckman Synchron LX20 analyser (BeckmanCoulter) as the biomarker for dietary Na intake.More detail on the 24 h urine collections, venepuncture, analyses and storage have beendescribed elsewhere. The carotenoids α-carotene, β-cryptoxanthin, β-carotene, lutein andzeaxanthin were analysed as described by Nguyen et al. and their sum was used as the marker of fruit and vegetableintake. Serum total cholesterol was measured spectrophotometrically on aSynchron LX20 clinical analyser (Beckman Coulter) and was used as the biomarker of SFA andtrans-fatty acids (TFA).Non-fasting blood samples were taken by a trained laboratory technician. The percentageof EPA and DHA in relation to the total measured fatty acids (thirty-five fatty acids) wasused as the concentration biomarker of fish intake. Briefly, the required amount of consumption or physical activitiesstated in the Dutch Guidelines for a Healthy Diet were used as cut-off values for themaximum number of points. For the components physical activity, fruit, vegetables, fishand fibre the minimum score of zero was assigned when no intake or activities wereundertaken. For the components SFA, TFA, ADF occasions and Na the minimum score was basedon the 85th percentile of the 2 d average intake of a Dutch referencepopulation. These threshold values are recommended for all future use of theDHD-index to make it possible to compare results between different study populations. Thecut-off value for the component TFA was lower than the dietary recommendation;consequently the component TFA was scored dichotomously. The cut-off values for thecomponent Na were lowered by 30 % to adjust for Na added during cooking and at thetable, which is not taken into account by both dietary assessment methods.The minimum score for the component alcohol was based on the cut-off values ofbinge-drinking. Between zero and 10 points the score was calculated proportionally.The DHD-index consists of ten components , Na and alcohol),representing the ten Dutch Guidelines for a Healthy Diet of 2006 . The maxFor the 24hR data, component scores were based on reported 2 d average intake. Forcalculation with the FFQ data, scores were based on the reported usual intake. Thecomponent ADF could not be estimated with FFQ data, as the number of consumption occasionsper d was not assessed. Therefore, component ADF occasions were omitted from the index inall further analyses. In addition, the component physical activity was omitted fromfurther analyses because the SQUASH was assessed only once; consequently the componentscore was the same for both indices based on the two different dietary assessmentmethods.Z-transformation. Differencesbetween medians were tested with the Wilcoxon signed-rank test and by the χ2test for the dichotomous TFA component. To study the association of the DHD-index withparticipants' characteristics and micronutrient intakes, the DHD-index was divided intosex-specific tertiles. Means, standard deviations and P for trend werecalculated with general linear models.Ranking of the participants between the DHD-index scores based on FFQ data and theDHD-index score based on 24hR data was studied by analysing the correlations andcross-classification of tertiles. Partial correlation coefficients were calculated betweenthe DHD-index score and its components based on FFQ data and the DHD-index score based on24hR data adjusting for energy intake assessed by FFQ and by 24hR. Additional adjustmentfor sex did not alter the results. Pearson correlations were used for normally distributedvariables and Spearman correlations for skewed variables. The 95 % CI of the correlationcoefficients were calculated by Fisher's . The square root of R2 from linearregression models including energy intake as an independent variable was used to calculatethe energy-adjusted correlation coefficient between the DHD-index score and the fourbiomarkers. The 95 % CI for this correlation was estimated with bootstrap analyses using10 000 replications. Partial correlation coefficients for the separate biomarkers werecalculated for the DHD-index scores based on the two dietary assessment methods and forthe component scores of interest adjusting for energy intake. Additional adjustment forsex did not change the results. All statistical analyses were performed using SAS 9·2 (SASInstitute Inc.).The four biomarkers were used as independent variables in a linear regression to provide,hypothetically, the best objective ‘marker’ of diet quality based on available data. Weexpected correlations of 0·4 between the DHD-index and the four linear combinations ofbiomarkers based on published correlations between single biomarkers and dietindicessd 5·1) years and mean BMI was 26.0(sd 4·5) kg/m2. Almost 50 % of the study population completed a levelof higher education and 10 % of the study population followed a diet regimen.The mean age of the study population was 56·2 (P = 0·003), and 5·7 (P = 0·018) points higher for womenwhen the DHD-index was based on 24hR data. The mean DHD-index score for the sum of eightcomponents was 49·9 (sd 13·5) based on 24hR data and 56.0 (sd 11·0) basedon FFQ data . The four components fibre,fish, SFA and Na showed significantly lower median scores when the scores were based on 24hRdata compared with FFQ data. The components fruit, TFA and alcohol showed similar mediansfor both methods, whereas the alcohol component score distributions were different(P < 0·001). The mean DHD-index score based on FFQ data was 6·0 points higher for women than for men. The correlationbetween the DHD-index scores based on FFQ and 24hR data was 0·58  andafter energy adjustment this correlation decreased to 0.48  and for SFA with TFA(r 0·39) were observed when the DHD-index was based on FFQ data.Moderate correlations between the components fruit and vegetables with fibre, whileBMI, supplement use, smoking and educational level did not show a significant trend acrossthe tertiles. Intakes of the micronutrients folate, Fe, Mg, thiamin, vitamin B6and vitamin C expressed per 4·2 MJ were positively associated with the DHD-index score basedon FFQ data. Intakes of the micronutrients Ca, riboflavin, vitamin A, vitamin B12and vitamin E showed no significant trend across tertiles of the FFQ DHD-index score. TheDHD-index score based on 24hR data showed similar positive associations with participants'characteristics and micronutrient intakes. Additionally, vitamin E was positively associated(P < 0·022) with the DHD-index score based on 24hR data (data notshown). The participants' age showed a positive trend across the sex-specific tertiles of theDHD-index score based on FFQ data , and forthe DHD-index score based on FFQ data was 0.51 . The DHD-index scoresbased on FFQ data and 24hR data were positively correlated with serum EPA + DHA , although the correlation was not significant forthe FFQ data , while it was 0.09 and non-significant forthe fruit component score based on 24hR data. Significant correlations were observed betweenserum carotenoids and the fibre component score based on FFQ data (r 0·20)and the fibre component based on 24hR data (r 0·21). Serum EPA + DHA wasassociated with the fish component scores, the correlation being higher when based on FFQdata compared with 24hR data . Urinary Na was inversely correlated with the Na component although, notsignificantly, for the Na component based on 24hR data. These inverse correlations wereexpected, because higher scores on the component Na were expected to be associated withlower dietary Na intake. No significant associations were observed between total cholesteroland the components SFA and TFA for both dietary assessment methods.The vegetable component scores based on FFQ data and 24hR data were both positivelycorrelated with serum carotenoids . Thissuggests that the component ADF consumption occasions may be omitted to arrive at a moresimple form of the index. However, the DHD-index has not yet been evaluated by studyingdiet–disease associations, which might alter this conclusion.In the present study, the component ADF consumption occasions was omitted, because thenumber of consumption occasions could not be assessed by the FFQ. Previously, the componentADF consumption occasions showed not to be discriminating in ranking subjects according tothe guideliner 0·48)reported by Benítez-Arciniega et al., who compared the ‘Modified Mediterranean diet score’ based on FFQ datawith the score based on twelve 24hR. However, our observed correlation was lower than thecorrelation (r 0·72) reported by Newby etal.. The latter correlation, however, compared the ‘Diet Quality IndexRevised’ based on FFQ data with the index based on two 1-week diet records. The referenceperiods covered by these two dietary assessment methods are probably more comparable witheach other than the reference periods covered by our FFQ and two 24hR, which could explainthe lower correlation in the present study.Ranking of the participants based on the two DHD-index scores was studied by examining thecorrelations and cross-classification. The correlation between the DHD-index based on FFQdata and based on 24hR data was comparable with the correlation . Furthermore, Kendall's τ-b coefficient showed a moderate agreementbetween the tertiles of the DHD-index based on FFQ and 24hR data. Based on the presentresults, we can conclude that ranking of participants was acceptable for both DHD-indexscores.Well over a half of the participants in the present study were classified into the sametertile; this result is similar to the results from cross-classifications between a FFQ and24hR on food groups. The correlation between the vegetable components was rather low. Inmost validation studies the FFQ tends to overestimate vegetable intake compared withvegetable intake assessed by multiple 24hR; however, the results of the present study showed the opposite. We couldnot explain this discrepancy. The correlation between Na components was rather low, probablybecause the FFQ was not specifically designed to assess Na intake levels. Furthermore, fishcomponents were also rather poorly correlated, probably due to the fact that two recalls areunable to assess the usual intake of episodically consumed foods such asfish.The DHD-index component scores based on 24hR data and the DHD-index component scores basedon FFQ data were all significantly correlated with each other, except for the component TFA.This might be due to the fact that the component TFA was scored dichotomously and thusshowed little variation. The component alcohol showed the highest correlation between 24hRand FFQ; this could be due to the fact that FFQ and 24hR are both known for a satisfactoryranking between individuals according to alcohol intake. These methods eliminate intra-individual variability from the data.Unfortunately, the estimation of usual intakes requires a bigger samplesize and the statistical methods may have their limitations. Age and energy intake showed significant trends across the sex-specifictertiles of both DHD-index scores. The inverse association of the DHD-index score withenergy intake was also observed in the population of the DNFCS-2003. The positive association with age, however, was not seen in thatpopulation. This may be due to the smaller age range (19–30 years) in the DNFCS-2003population compared with the age range (45–65 years) of the EFCOVAL study population.To improve comparability between the two DHD-index scores based on FFQ and 24hR data, usualintakes could be estimated for 24hR data by statistical models, like the National CancerInstitute Method and the multiple source method. Newby et al. found similar associations for the ‘DietQuality Index Revised’ with the micronutrients vitamin A, vitamin B6, vitamin C,folate, Mg and Fe. In the present study, however, vitamin E also showed a positiveassociation across tertiles of the DHD-index based on 24hR(P < 0·022), which was comparable with others(–). We assumed that the combination of the four biomarkers was the bestavailable approach to evaluate diet quality as estimated by the DHD-index. The magnitude ofthe correlations was higher than the expected correlations of 0·4 based on publishedcorrelations of diet indices with single biomarkers. Based on these results we may conclude that for both dietary assessmentmethods the DHD-index can be used to assess diet quality at the population level.The positive associations of micronutrient intakes with the DHD-index score based on FFQdata in the present study were similar to the associations with the DHD-index based on 24hRand to our earlier findings based on DNFCS-2003 data. Furthermore, the FFQ used was not specifically designed for theestimation of Na and did not include questions on all Na-rich food products such as soyasauce. By lowering the cut-off values by 30 %, we tried to adjust for these measurementerrors. In the present study, however, we also measured urinary Na, the preferred method ofestimating dietary Na intake. The mean Na component score was 2·4 (sd 3·5) when based onurinary Na, 3.5 (sd 4·1) when based on 24hR data, and 4·8 (sd 4·3) whenbased on FFQ data. These differences are quite substantial; consequently, conclusionsregarding the DHD-index component score based on Na intake assessed by FFQ or 24hR data mustbe drawn with caution. Preferably, data of urinary Na are used for estimation of thecomponent Na to overcome measurement errors. If urinary Na is used, the original cut-offvalues without additional adjustment should be used; maximum points will be assigned when Naintake is lower or equals 2400 mg and zero points will be assigned when Na intake is above3600 mg.A limitation of both dietary assessment methods is the inaccurate assessment of dietary Naintake. Dietary Na intake assessed by the two methods is probably underestimated due tolacking data on salt added during cooking or at the table, also observed in the present study. This can be explained by theinfluence of many other factors such as absorption and metabolism on plasma carotenoidconcentrations. Additional adjustment for serum total cholesterol and smoking did notimprove the results for plasma carotenoids with the components fruit, vegetables and fibre.Unfortunately, a more accurate biomarker for fruit and vegetable intake is not available.In the present study, the biomarkers used were initially selected to validate twonon-consecutive 24hR using EPIC-Soft within the EFCOVAL study. Unfortunately, the biomarkerscarotenoids and total cholesterol have some limitations for the present study. First, thebiomarker plasma carotenoids is already known for its modest correlation with fruit andvegetable intake. In some other studies, however, significant associations wereobserved. Suggested explanations for these discrepancies were the differencesbetween intake levels of populations, the differences between dietary assessment methods,and the differences between indices used(–). Preferably, serum LDL-cholesterol concentrations should be used tostudy associations with types of fat intake, but these were not available in the present study.Second, serum total cholesterol was used as a biomarker for SFA and TFA intake. In thepresent study, no significant correlations were observed between the DHD-index and serumtotal cholesterol, which was comparable with the results of othersIn conclusion, the DHD-index based on a 180-item FFQ showed similar associations withparticipants' characteristics, micronutrient intake and biomarkers of dietary intake andmetabolism compared with the DHD-index based on two non-consecutive 24hR. Furthermore, theranking of participants was acceptable for both DHD-index scores. Therefore, both dietaryassessment methods can be used to assess diet quality by using the DHD-index in Dutchpopulations. Future research should focus on the evaluation of the DHD-index by studyingassociations with disease outcomes.


Patients with chronic obstructive pulmonary disease (COPD) often suffer concomitant disorders that worsen significantly their health status and vital prognosis. The pathogenic mechanisms underlying COPD multimorbidities are not completely understood, thus the exploration of potential molecular and biological linkages between COPD and their associated diseases is of great interest.We developed a novel, unbiased, integrative network medicine approach for the analysis of the diseasome, interactome, the biological pathways and tobacco smoke exposome, which has been applied to the study of 16 prevalent COPD multimorbidities identified by clinical experts.Our analyses indicate that all COPD multimorbidities studied here are related at the molecular and biological level, sharing genes, proteins and biological pathways. By inspecting the connections of COPD with their associated diseases in more detail, we identified known biological pathways involved in COPD, such as inflammation, endothelial dysfunction or apoptosis, serving as a proof of concept of the methodology. More interestingly, we found previously overlooked biological pathways that might contribute to explain COPD multimorbidities, such as hemostasis in COPD multimorbidities other than cardiovascular disorders, and cell cycle pathway in the association of COPD with depression. Moreover, we also observed similarities between COPD multimorbidities at the pathway level, suggesting common biological mechanisms for different COPD multimorbidities. Finally, chemicals contained in the tobacco smoke target an average of 69% of the identified proteins participating in COPD multimorbidities.The network medicine approach presented here allowed the identification of plausible molecular links between COPD and comorbid diseases, and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.The online version of this article (doi:10.1186/s12931-014-0111-4) contains supplementary material, which is available to authorized users. The bidities ,11.shared component hypothesis in COPD. Under this hypothesis, the multimorbidities of COPD most frequently seen in the clinic would be related between them and with COPD at the molecular level by common genes, proteins and biological pathways. To this end, we used a network medicine approach that included: (1) data mining of the diseasome (or disease network), the interactome (as defined by a protein-protein interaction (PPI) network) to enrich this diseasome and the tobacco smoking exposome ; and, (2) a functional analysis to identify the biological pathways potentially involved in COPD multimorbidity. Our findings indicate that the most prevalent COPD multimorbidities are likely to be related at the molecular level, and highlight some previously overlooked pathways that might contribute to explain their co-occurrence. Remarkably, the results here presented propose new hypothesis for explaining the molecular underpinning of COPD multimorbidities.In this study, we sought to test the Clinical experts selected 16 prevalent diseases frequently associated with COPD (Table http://www.pathwaycommons.org/), gathering biological pathway information collected from public pathway databases and HIPPIE [http://cbdm.mdc-berlin.de/tools/hippie/information.php), a human PPI database that integrates multiple experimental PPI datasets  Figure . DisGeNEd HIPPIE   as defined by:When building the diseasome based on proteins in common between two diseases, it could be argued that it is more likely to find shared proteins among diseases that have been characterized more extensively. In order to minimize this bias when estimating the strength of the association between two given diseases dis1 and dis2) in the diseasome ), ∩ is the intersection operator and ∪ is the union operator between two sets of elements (proteinsdis1 and proteinsdis2). The sets resulting in both numerator and denominator are written within vertical bars to indicate their cardinality (number of element).where q-values ≤ 0.05 , an adjusted equivalent to the standard statistical p-value incorporated in ReactomePA).To identify the most significant biological functions of the shared proteins between two diseases, a functional enrichment analysis with biological pathways from Reactome was performed . The ReaJaccard coefficient, which is defined as:To evaluate the number of biological pathways shared by COPD multimorbidities, we used the multimorbidity1 and multimorbidity2 represent two pairs of COPD multimorbidities (for instance lung cancer-COPD and diabetes-COPD) whereas pathways of multimorbidity1 and pathways of multimorbidity2 represent the biological pathways in which the proteins associated with the pairs multimorbidity1 and multimorbidity2 participate respectively. The Jaccard Coefficient is a measure of the degree of similarity between two COPD multimorbidities (for instance lung cancer-COPD and diabetes-COPD) at the level of biological pathways. Results were visualized as heat-maps using Gitools [where  Gitools .Using the Comparative Toxicogenomics Database (CTD), a database gathering information about gene/protein and chemical interactions, we investigated if the genes and proteins shared by COPD multimorbidities were potential biological targets for chemical compounds present in the tobacco smoke ,23.COPD has been associated in the literature with alterations in 187 genes, whereas the number of genes associated with the 16 comorbidities analyzed here range from 19 (cachexia) to 1,369 (lung cancer)  to more than 700 (COPD and lung cancer) Figure . In termThe proposed network medicine workflow not only allows a global analysis of the genes and proteins supporting a particular multimorbidity (see next sections), but it also provides detailed information on specific genes and their association to the multimorbid diseases under study. For example, the gene ADRB2, is associated to several multimorbidities: COPD with cardiovascular diseases , and with diabetes, lung cancer and obesity. Interestingly, the protein encoded by this gene has been shown to participate in smooth muscle relaxation in bronchi resulting in a facilitated respiration ; in blooAnother study in a Danish population showed that the ADRB2 T164I polymorphism is associated with a reduced lung function and an increased risk of COPD in the general population . This poFurther analysis using haplotype frequencies indicated that haplotypes G16-Q27-I164 and (non-G16-Q27)-T164, as compared to the reference haplotype G16-Q27-T164, were significantly associated with a decreased risk of myocardial infarction . In summq-values ≤ 0.05, and results are visualized here as heat-maps. We observed that “Innate immune response” pathways were enriched in almost all of the multimorbidity pairs studied, particularly in those of COPD with diabetes, osteoporosis, lung cancer, heart failure or ischemic heart disease  at the level of biological pathways, we used the Jaccard coefficient see . We founAccording to the CTD , the 106shared component hypothesis of the COPD diseasome, and allow the identification of specific molecular links  between COPD and its comorbid diseases.The network medicine analysis of COPD multimorbidities presented here shows that 16 prevalent COPD multimorbidities often share genes, proteins and biological pathways with COPD, and that many of these proteins are targets for tobacco smoke chemicals. Overall, these observations support the The vast amount of information available today in different areas of science makes it necessary to properly mine and connect it in a network of information that allows the generation of new knowledge. This strategy has the potential to bring out new hypotheses that are not self-evident when using more traditional scientific approaches. In the present study, we performed an integrative mining approach developed to exploit information from different databases  and experts in COPD multimorbidities  and bioinformatics , who participated in the analysis phases. Future studies will have to explore experimentally the new hypotheses generated in this analysis.shared component hypothesis [COPD is a paradigmatic gene-environment disease since tobacco smoking, and other environmental factors, are well established risk factors, but not all exposed subjects develop the disease, suggesting a role for the genetic background of the exposed individual . The facpothesis –9 as a pOur findings suggest that the most prevalent COPD multimorbidities are related at the molecular level, and highlight some previously overlooked pathways that might contribute to explain their co-occurrence. In this context, that COPD shares certain molecular mechanisms with cardiovascular diseases or lung cancer is probably not surprising. By contrast, that it also shares these mechanisms with depression or diabetes is more intriguing. Likewise, it is of note that proteins involved in the “Nitric Oxide Metabolism” pathway were associated with multimorbidities involving the cardiovascular system, but also with metabolic diseases, as well as with osteoporosis, pulmonary hypertension, lung cancer and depression , and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.


The role of microRNAs as key regulators of a wide variety of fundamental cellular processes, such as apoptosis, differentiation, proliferation and cell cycle is increasingly recognized in most aspects of biology and biomedicine. Accretion of results from multiple microRNA studies over multiple pathway networks, led us to hypothesize that microRNAs target molecular pathways. As we show here, this is a network-wide phenomenon. The work presented, uses statistical tools that show how single microRNAs target molecular pathways. We demonstrate that this targeting could not be the result of random associations and cannot be the result of the sheer numeracy of microRNA targets. Furthermore, the strongest evidence for the association microRNA-pathway, is in a demonstration of the way by which these associations are disease-relevant. In our analyses we study ten different types of cancer involving thousands of samples, and show that the identified microRNA–pathway associations demonstrate a clinical affiliation and an ability to stratify patients. The work presented here shows the first evidence for a mechanism of microRNAs-pathway generic regulation. This regulation is tightly associated with clinical phenotype. The presented approach may catalyze targeted treatment through exposure of hidden regulatory mechanisms and a systems-medicine view of clinical observation. The role of miRNAs as key regulatory molecules that control a wide variety of fundamental cellular processes, such as proliferation, death, differentiation, motility, invasiveness, etc., has been demonstrated , 2. MiRN in vivo . MiRNAs’ in vivo . A possi in vivo . For exa in vivo . Other e [et al.  demonstr [et al. , 15. Mul [et al. . The Pte [et al.  while th [et al. . These iTo answer this question, we analyzed a comprehensive collection of 357 pathways (137 NCI/Nature curated pathways and 220 pathways imported from the Biocarta database) from the National Cancer Institute's Pathway Interaction Database (PID) , which tAs mentioned above, a critical role for miRNAs in cancer has been established. Further, multiple targeting of mRNAs, by a single miRNA, has also been universally demonstrated; the transition from multiple, disengaged, mRNA targets, to an hypothesis about miRNA and functional pathways has been raised, but has not yet been demonstrated. Using computational methods, we reveal significant associations between known curated pathways and specific miRNAs. We also show that this association is unique only to pathways. It is absent from otherwise dissociated random gene groups. Furthermore, and most important from a translational point of view, we show how these miRNA-Pathway associations are correlated with disease phenotypes, demonstrated in multiple datasets from ten different cancer types.p-value has been associated with every pathway and it's indicated miRNA. This p-value has been obtained using enrichment considerations, utilizing the hyper-geometric function (see Methods). All of the 357 pathways presented a significant FDR-adjusted p-value as can be seen in Figure p-values were calculated using the procedure introduced by Storey, 2002 [p-values received by the hyper-geometric function.We analyzed 357 curated pathways from the PID database  with oveey, 2002  for the Instead of setting the error rate at a particular level and estimating the rejection region, Storey et al. have proposed to fix the rejection region and estimate the error rate. This approach allows a more straightforward analysis of the problem.Under multiple hypotheses testing, FDR procedures are designed to control the expected proportion of “false discoveries”.To determine the strength and accuracy of the overall performance of the miRNA-pathway association using bootstrapping, we scrambled the data and randomly build 357 new pathways with a random size between 4–50 genes .p-value of 3.35e-037) to defined cellular networks and is not observed in a non-biologic random network. In addition, this analysis revealed that there are 4 microRNA  that are associated with 34% of the pathways  status. Estrogens are important regulators of growth and differentiation in the normal mammary gland and are also important in the development and progression of breast carcinoma. Estrogens regulate gene expression via ER, however the details of estrogen effect on downstream gene targets, the role of cofactors, and cross-talk between other signaling pathways are far from fully understood –29. The “GATA3 Participate in Activating the Th2 Cytokine Genes Expression” demonstrated significantly higher activity levels in ER+ patients compared to ER- patients, across all three datasets, as can be seen from Figure Using the same procedure we previously describe , 33, we p-value of 3×10−5. By separately obtaining the correlation of the miRNA and the pathway in the two clinical groups - ER+ and ER-, we found a consistent negative correlation between the miRNA and the pathway in the ER- group of patients, in all three datasets; and, we found (close to) zero correlation between the miRNA and the GATA3 pathway in the ER+ group of patients, as can be seen in Figure Gata3 has previously shown to over-express in ER+ patients, and it has also suggested to regulate genes critical to the hormone-responsive breast cancer phenotype –36. HoweWhen we observe the set of pathway genes, predicted to be targeted by hsa-miR-532, we see the behavior presented in Figure In conclusion, these findings point to possible control mechanisms, involved with regulation in ER- breast cancer patients. As demonstrated, these results are consistent and robust in their presentation of the GATA3 pathway and its connection to hsa-miR-532. Further, the finding that this association is absent from ER+ tumors, suggests a possible intervention mechanism and calls for further study of a possibility of realigning regulation in breast cancer, from the harder-to-treat ER- phenotype, to the improved prognosis of ER+ phenotype.The hypothesis of whole pathway regulation by miRNAs, through the multiple targeting of mRNAs of the same cellular pathway, mandates validation beyond the originating datasets. To validate whether the phenomenon may be associated with phenotype, we applied the same pipeline described above to the most comprehensive currently obtainable dataset. As the requirements for this dataset include miRNA and mRNA expression levels, as well as well documented phenotypic affiliation, we were able to combine information from patients in studies of unrelated nine different types of cancer. These data sets were downloaded from TCGA: ovarian, liver, melanoma, kidney, thyroid, leukemia, stomach adenocarcinoma, bladder urothelial carcinoma and head and neck squamous cell carcinoma. For each of these tumor types we downloaded, from TCGA, gene expression information to test different phenotypes. The following list of phenotypes is the ones tested and are available in TCGA database: survival, breslaw depth value, stage and morphology. Iterating over pathways as described above, we identified, for every one of the datasets, a single pathway that stratified the patients. Then, for each of these pathways, through the enrichment method described above, we identified a single microRNA that targets the identified pathway in a specific manner , six of them potentially targeted by hsa-miR-532 (X = 6). hsa-miR-532 was predicted to target 124 genes within the dataset (K = 124). Given that the total number of genes in the dataset is 1460 the resulted p-value for targeting Gata3 signaling pathway by hsa-miR-532 is 3 × 10−5.For example: Gata3 signaling pathway comprises 13 genes : Breast cancer dataset and all datasets used for validation on different types of cancer were obtained from the TCGA database, available at Gene Expression Omnibus (GEO): Two additional datasets were used in the breast cancer analysis GSE22220  and GSE1miRWalk  is a comTow sample Student's T-Test was performed in order to estimate the pathways significance in stratifying phenotypes. Survival analysis was performed using Kaplan-Meier through clinical data to determine the power of a pathway in survival stratification.p-value for all 357 microRNA-pathway pairs using hyper-geometric function. We iteratively repeated this analysis 1000 times. Figure To determine the strength and accuracy of the overall performance of the miRNA-pathway association using bootstrapping, we scrambled the data and randomly build 357 new pathways with a random size between 4–50 genes . We then performed (as described) a prediction of microRNA binding sites in order to choose the most significant microRNA that associates with each random pathway and calculated Networks generation performed using MATLAB R2010a and networks visualization created using Cytoscape , 58. Eve


ReDuNing injection (RDN) is a patented traditional Chinese medicine, and the components of it were proven to have antiviral and important anti-inflammatory activities. Several reports showed that RDN had potential effects in the treatment of influenza and pneumonia. Though there were several experimental reports about RDN, the experimental results were not enough and complete due to that it was difficult to predict and verify the effect of RDN for a large number of human diseases. Here we employed multiscale model by integrating molecular docking, network pharmacology and the clinical symptoms information of diseases and explored the interaction mechanism of RDN on human diseases. Meanwhile, we analyzed the relation among the drug molecules, target proteins, biological pathways, human diseases and the clinical symptoms about it. Then we predicted potential active ingredients of RDN, the potential target proteins, the key pathways and related diseases. These attempts may offer several new insights to understand the pharmacological properties of RDN and provide benefit for its new clinical applications and research. Traditional Chinese medicine (TCM), which mainly consists of herbal medicines, has been practiced over thousands of years. Today it is still regarded as an important source for drug discovery and is growing in popularity worldwide. Therefore understanding the action mechanism of TCM is significant for the treatment of disease. But it is difficult to study the action mechanism of TCM using routine methods due to that herbal formulae are mixtures of lots of chemical ingredients and most of ingredients may interact with multiple targetset al developed kernel-based methods to integrate drug related omics data sourceset al to group the biological function related drugs, diseases and genesWith the advent of big data era, our thinking, technology and methodology are being transformed. Along with the development and application of the Internet information technology, the information of drug molecules, target proteins, biological pathways, diseases, clinical data of diagnosis and treatments have been accumulated dramatically, which generates big data in medical field. The research target was shifted from the “causality inference” to the “correlativity analysis” graduallyArtemisia annua L., Gardenia jasminoides E. and Lonicera japonica T. The components have been proven that they had potential antiviral, significant anti-inflammatory and immunomodulatory activitiesReDuNing injection (RDN) is a patented TCM, which contains the effective components extracted from three herbset al. evaluated the protective effects of RDN on lipopolysaccharide-induced acute lung injury in rats and its underlying mechanisms of actionet al. assessed the efficacy and safety of RDN for fever, rash and ulcers in children with mild hand, foot, and mouth disease, and no adverse reactions were observedet al. developed a method for screening and analyzing the potential bioactive components from RDN using macrophage cell extraction and ultra-high performance liquid chromatography coupled with mass spectrometryet al. investigated the effect of RDN in the clinic for upper respiratory tract infections by using molecular docking, network analysis and cell-based assayset al. developed and validated a method for screening and determining the concentration of seven antioxidants of RDNet al. studied the effect of RDN for influenza diseases using a novel systems pharmacology-based strategyRecently, several experimental studies related to the RDN were reported. For example, Tang In many ways, drug development is the ultimate multiscale optimizationQinghaosu, its derivatives were a group of drugs against Plasmodium falciparum malariaArtemisia annua, which was a kind of herb in Chinese traditional medicine. UNPD149880 is luteolin, it is a plant flavonoid and pharmacologically active agent that had been isolated from the flowers of Lonicera japonicaSalvia tomentosa in mint family LamiaceaeWe constructed the drug-target network  based on2 and NO production in experimentsThe higher degree of nodes meant highly connected molecules which may play important roles in the drug-target interaction network. RDN15 was a new molecule, which had not the CAS Number, and there were other new molecule that was named by RDN plus number, such as RDNB23. The high degree ingredients of RDN suggested that they could interact with multiple targets and they could have effect on the treatment of multiple diseases. For example, the artemisinin, luteolin and quercetin were proven that they could inhibit LPS-induced PGETo find the important relations between target protein and the important pathway further, we constructed the target-pathway network  based onFrom Drug-pathway interactions is very significant not only for understanding the various drug response and molecular interaction process, but also for the development of Chinese traditional medicine and the therapy of human diseases. However, many drugs play a role by affecting all kinds of pathways in the body, such as metabolism pathway or signal transduction pathway. At the same time the effect of drugs to the pathway could affect the diseases that related with the pathways. Building a drug-pathway network would be a direct method to find the relations between the drugs and pathways.The drug-pathway network  was consThe parameters of the drug-pathway network were shown in The pathways had often related with some diseases, while the drugs interacted with the pathways, they would affect the related diseases. In order to find the diseases that could be related with the pathways, we constructed the pathway-disease network based on all of pathways to find several key diseases to study further. The pathway-disease network was shown in Then we analyzed the category of all of the related diseases. The results were listed in In the post-genomic era, the elucidation of the relationship between the diseases and their clinical phenotypes  is vital for medical research. An important available resource is symptom, the highest level clinical phenotype, which had often been overlooked in researchet al. explored new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithmsZHENG is the key concept and the basic unit in TCM theory, it has been used for thousands of years in ChinaIn order to clarify the possible interaction mechanism of RDN, the pathways, target proteins and the possible active ingredients of RDN were analyzed. Here Tuberculosis and influenza were selected as examples to explore the interacted mechanism.Mycobacterium tuberculosis, it typically attacks the lungs, but can also affect other parts of the bodyTuberculosis is a widespread infectious disease caused by various strains of mycobacteria, usually et al. had studied the anti-inflammatory effects of RDN on lipopolysaccharide-induced acute lung injury of ratsTang Influenza is an infectious disease of birds and mammals caused by RNA viruses of the family Orthomyxoviridae, the influenza viruses. Although it is often confused with the common cold, influenza is a severe diseaseet al. investigated the protective effect of RDN against influenzaet al. suggested that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interactTang The compounds in CMAP reference database were tested only in three human cancer cell lines and not every compound was tested in all cell lines. To further evaluate the quality and usability of our predicted results, we used the gene expression profiles of MCF-7 cells treated with luteolin in CMAP as a query to search the CMAP database. If our data are in good quality and the CMAP approach works, we should find the drug that has an effect on MCF-7 cells similar to that of luteolin. The drug might be performing activities based on same or similar targets, pathways and mechanisms and has similar activities. The query results were listed in Though Traditional Chinese medicines had exhibited pharmacologic effects through a large number of clinical practices over thousand years, there were many unclear underlying mechanisms. Multiscale computer modeling provided an effective way to help us to explore the underlying mechanisms of TCM. Here we explored the interaction mechanism of RDN and predicted possible target proteins by employing multiscale computer modeling integrating molecular docking, network pharmacology and the clinical symptoms information of diseases. We constructed the drug-target network, target-pathway network, drug-pathway network and pathway-disease network and found several potential active molecules in RDN and several important interaction pathways. Meanwhile, we analyzed the relations among the drug ingredients, targets, pathways, diseases and clinical symptoms and explored the possible drug interaction mechanism of RDN. For example, we predicted the possible interaction mechanism of RDN ingredients for tuberculosis and influenza. The results of our work attempt to offer new insights to understand the pharmacological properties of RDN and provide benefit for its new clinical applications and research.The 3D structures of 90 ingredients from RDN were collected from UNPD databaseTo keep the accuracy of predicted results and integrity of data, we selected the molecules which docking scores were higher than the score of original ligand in the complex structure and higher than 6.0, then the target proteins which satisfying the following criteria were selected to construct the drug-target network: (a) The source organism is human. (b) The protein has a KEGG annotation of pathway in KEGG databaseTo find the potential active molecules and target protein, the drug-target network  was consMany drugs play a role by affecting the related pathways or affecting the metabolisms in the body. Moreover, the state of disease is often the results of a series of variations in our body such as the variations in pathways, instead of the anomaly of drug-target interactionet al. used large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactionshttp://omim.org/) and MedlinePlus database ( http://www.nlm.nih.gov/medlineplus/). Then we selected the diseases associated with ZHENG and classified them into Cold ZHENG and Hot ZHENG according to the ZHENG-related disease and symptom data sets in the worket al. We expected to find the underlying molecular interactions of the symptoms and ZHENG. Finally, we predicted the possible RDN molecular mechanisms of some diseases, and our results were proven by several experimental reports, which showed that the methods we used were reliable.The abnormal of every pathway could result to one or more diseases, and every disease is related to several clinical symptoms. So symptoms play important role in modern medical diagnosis and disease classification, patients with different disease would often present different symptoms. Understanding the underlying molecular mechanisms of symptoms could help us treat disease efficiently. Several symptoms such as depression, pain, and high blood pressure have been discussed previouslyhttp://www.broadinstitute.org/cmap/) reference database (Build 02), which contains more than 7,000 expression profiles mainly from three cell lines  treated with 1,309 compoundsTo further evaluate the quality of the predicted results, we used the gene expression profiles of luteolin as queries to search the “Connectivity Map” .


The small brown planthopper (SBPH) is one of the destructive pests of rice. Although different biochemical pathways that are involved in rice responding to planthopper infestation have been documented, it is unclear which individual metabolic pathways are responsive to planthopper infestation. In this study, an omics-based comparative transcriptional profiling of two contrasting rice genotypes, an SBPH-resistant and an SBPH-susceptible rice line, was assessed for rice individual metabolic pathways responsive to SBPH infestation. When exposed to SBPH, 166 metabolic pathways were differentially regulated; of these, more than one-third of metabolic pathways displayed similar change patterns between these two contrasting rice genotypes; the difference of change pattern between these two contrasting rice genotypes mostly lies in biosynthetic pathways and the obvious difference of change pattern lies in energy metabolism pathways. Combining the Pathway Tools Omics Viewer with the web tool Venn, 21 and 6 metabolic pathways which potentially associated with SBPH resistance and susceptibility, respectively were identified. This study presents an omics-based comparative transcriptional profiling of SBPH-resistant and SBPH-susceptible rice plants during early infestation by SBPH, which will be very informative in studying rice-insect interaction. The results will provide insight into how rice plants respond to early infestation by SBPH from the biochemical pathways perspective. Oryza sativa L.) is an important food crop and a source of calories for billions of people [Nilaparvata lugens Stål), the small brown planthopper  and the white-backed planthopper  are three major planthoppers attacking rice, causing significant loss of rice yield [Rice  was ultimately employed to visualize the expression patterns and further identify the individual pathways that are responsive to SBPH feeding in rice.In attempt to gain insight into the molecular mechanisms of rice resistance to SBPH, in this study, two contrasting rice genotypes, an SBPH-resistant introgression line Pf9279-4; R) and its SBPH-susceptible parent , were assessed for rice plant responses to SBPH attack at the molecular level. Previously, two-dimensional fluorescence difference gel electrophoresis analysis of these two contrasting rice genotypes that sampled at different time-points after SBPH infestation showed that the differential protein expression between these two contrasting rice genotypes were obvious within 6 h of infestation by SBPH. We therefore examined the comparative transcriptional profiling of these two contrasting rice genotypes which infested with SBPH for 0 h and 6 h by using Affymetrix GeneChip analysis. Although different biochemical pathways including cell defense, cellular transport, metabolism, signal transduction, macromolecular degradation and biogenesis, cellular communication and plant defenses have been documented 9-4; R an,11,12, ihttp://www.gramene.org), provides visual analysis of whole-organism datasets and maps gene expression data sets to the metabolic pathways [http://pathway.iplantcollaborative.org/). In the present study, to understand the global gene expression profiles, the transcriptomic data was uploaded to visualize expression patterns by using the Pathway Tools Omics Viewer. To identify the SBPH resistance and susceptibility-related metabolic pathways, by using Pathway Tools Omics Viewer, the individual metabolic pathways that were differentially regulated were displayed with omics data painted onto the pathway; by combining Pathway Tools Omics Viewer with the web tool Venn (http://bioinfogp.cnb.csic.es/), 21 and 6 metabolic pathways potentially related to SBPH resistance and susceptibility in rice were identified, respectively. This study presents an omics-based comparative transcriptional profiling of SBPH-resistant and SBPH-susceptible rice plants during early infestation by SBPH and aims to identify individual pathways that are responsive to early infestation by SBPH in rice. The identified biochemical pathways will provide insight into how rice plants respond to early infestation by SBPH and the results will help us understand the general defense responses of rice plants to insect infestation from the perspective of biochemical pathways. The Pathway Tools Omics Viewer, an integrated tool in the Gramene “Cyc” Pathways databases  and its SBPH-susceptible parent (02428) that had been fed upon by SBPH for 0 and 6 h was thusly examined via Affymetrix GeneChip analysis. To analyze the molecular mechanism underlying rice plant SBPH resistance, the expression levels of all rice genes in the resistant and susceptible rice plants that had been fed upon by SBPH for 0 and 6 h were examined and the comparison between before and after infestation was made and named as S6_S0 and R6_R0 in the resistant and susceptible rice plants, respectively. To gain an understanding of the basis of defense, comparisons of gene expression were also made between resistant and susceptible rice plants and named as R0_S0 and R6_S6 before and after infestation, respectively. The microarray experiments identified a total of 57,381 probe sets expressed in the four comparisons. In the comparison of rice plants that had been fed upon by SBPH for 0 and 6 h, a total of 2730 differentially expressed genes were identified in the resistant rice plants (R6_R0), while a total of 3024 differentially expressed genes were identified in the susceptible rice plants (S6_S0). In the resistant and susceptible comparison, a total of 2299 differentially expressed genes were identified when the rice plants had been fed upon by SBPH for 0 h (R0_S0), while a total of 2891 differentially expressed genes were identified when the rice plants had been fed upon by SBPH for 6 h (R6_S6). other biosynthesis differentially altered only in the R6_R0; respiration differentially altered in the R6_S6 and S6_S0 as well as R0_S0; inorganic nutrients metabolism differentially altered in both R6_R0 and R0_S0; C1 compounds utilization and assimilation (utilization and assimilation of compounds containing one carbon) differentially altered in the R6_S6 and R6_R0 as well as R0_S0. In the R0_S0, in addition to other biosynthesis, energy metabolism pathways including pentose phosphate pathways and respiration, degradation pathways including hormones degradation, secondary metabolites degradation, carbohydrates degradation and degradation/utilization/assimilation-other were not differentially altered in all four comparisons. The remaining metabolic pathways as follows were differentially altered in all four comparisons. Biosynthetic pathways include nucleosides and nucleotides biosynthesis; cofactors, prosthetic groups, electron carriers biosynthesis; carbohydratesbiosynthesis; secondary metabolites biosynthesis; fatty acids and lipids biosynthesis; amino acids biosynthesis; amines and polyamines biosynthesis; hormones biosynthesis and cell structures biosynthesis. Energy metabolism pathways include fermentation; glycolysis; photosynthesis and TCA cycle (tricarboxylicacidcycle acid cycle). Degradation pathways include amino acids degradation; alcohols degradation; detoxification; carbohydrates degradation; fatty acid and lipids degradation as well as nucleosides and nucleotides degradation.As shown in The Pathway Tools Omics Viewer provides the capability to display the output as an animation for time-series experiments but not for single experiment time step. As shown in To gain an understanding of the difference in the basis of defense in response to SBPH infestation, the R6_S6 and R0_S0 were also taken as the combination for an animation and marked the difference between R6_S6 and R0_S0 and then shown in one picture. As shown in To further explore the defense responses of rice plants to SBPH attack in more detail, the individual metabolic pathways were afterward screened by Pathway Tools Omics Viewer. Besides the capabilities for overlaying transcriptomic data onto the overview as a whole, the Pathway Tools Omics Viewer can also generate a table of magnified views of all pathways with omics data painted onto the pathway . Therefohttp://bioinfogp.cnb.csic.es/), can calculate the intersection(s) of lists of elements and generate an output displaying which elements are in each intersection or are unique to a certain list [With the help of the Pathway Tools Omics Viewer, the interpretation of large-scale biological data sets can be analyzed and derived from transcriptome profiling experiments. For example, hypotheses about how the phenotypic changes which resulted from different kinds of factors such as stresses based on metabolic explanations can be generated ,18. Meanain list . Therefoain list ,21,22. SClass I: in response to SBPH infestation, the metabolic pathway was differentially regulated in the resistant rice plants (R6_R0) but not in the susceptible rice plants (S6_S0); meanwhile, the basis of defense showed difference between resistant and susceptible rice plants after attack by SBPH (R6_S6) but not before attack by SBPH (R0_S0). Class II: in response to SBPH infestation, the metabolic pathway was differentially regulated in the resistant rice plants (R6_R0) but not in the susceptible rice plants (S6_S0); meanwhile, the basis of defense showed difference between resistant and susceptible rice plants before attack by SBPH (R0_S0) and after attack by SBPH (R6_S6).Class III: in response to SBPH infestation, the metabolic pathway was differentially regulated in both resistant rice plants (R6_R0) and susceptible rice plants (S6_S0) and showed opposing differential changes in S6_S0 and R6_R0; meanwhile, the basis of defense showed difference between resistant and susceptible rice plants after attack by SBPH (R6_S6) and or not before attack by SBPH (R0_S0).As shown in http://pathway.iplantcollaborative.org/), the network of the metabolic pathways potentially related to SBPH resistance in the SBPH-resistant rice plant was subsequently constructed. As shown in Based on the classification hierarchy for individual metabolic pathways from the rice pathway database  but not in the resistant rice plants (R6_R0); meanwhile, the basis of defense showed difference between resistant and susceptible rice plants after attack by SBPH (R6_S6) but not before attack by SBPH (R0_S0). Class II: in response to SBPH infestation, the metabolic pathway was differentially regulated in the susceptible rice plants (S6_S0) but not in the resistant rice plants (R6_R0); meanwhile, the basis of defense showed a difference between resistant and susceptible rice plants before attack by SBPH (R0_S0) and after attack by SBPH (R6_S6).Class III: in response to SBPH infestation, the metabolic pathway was differentially regulated in both susceptible rice plants (S6_S0) and resistant rice plants (R6_R0) and showed opposing differential changes in S6_S0 and R6_R0; meanwhile, the basis of defense showed a difference between susceptible and resistant rice plants after attack by SBPH (R6_S6) and or not before attack by SBPH (R0_S0). According to the classification of the metabolic pathways potentially related to SBPH susceptibility in rice, as shown in The result of the identification of metabolic pathways potentially related to SBPH resistance and susceptibility in rice showed that several kinds of amino acids were found to be correlated with SBPH resistance or susceptibility in rice, therefore, the free amino acid levels were measured. As shown in the In this study, we visualized the rice transcriptomic data of microarray expression profiling by using Pathway Tools Omics Viewer. By using the capabilities of Pathway Tools Omics Viewer for overlaying transcriptomic data onto the overview as a whole and animation of the comparative transcriptome analysis, our data suggested that the difference of change pattern between these two contrasting rice genotypes mostly lies in biosynthetic pathways when exposed to SBPH; while the obvious difference of change pattern between these two contrasting rice genotypes lies in energy metabolism pathways . By usinThe plant epidermis is considered to have an important role in defense against insect attack . ObservaNicotiana tabacum cv. Xanthinc) plants over-expressing a small GTP-binding protein, which resulted in increased rates of JA production [It is reported that some phloem feeding insects induce the SA pathway while suppressing the plant’s JA response . In respoduction . In thisBetanidin is synthesized from tyrosine and is water-soluble nitrogen-containing pigment . Pigmenthttp://pathway.iplantcollaborative.org); in the resistant rice plants, isoleucine level which was the second most abundant before attack showed the third significant decreased after attack, meaning less glutamate used in synthesis of isoleucine. Collectively, the increased N-rich glutamate and arginine in the resistant rice plants means that the N-rich soluble molecules accumulated. The accumulated N-rich soluble molecules have been considered as a means of nitrogen (N) and the carbon (C) store after wounding, which helps to prevent N losses during the wounding response and could work as a source for new growth after the wound recovery phase [2) [2 can be fixed [Amino acids and amino acid derivatives have been considered to have an important role in defense response . Glutamary phase ,50. Addihase [2) ,52 and bbe fixed .Hyaloperonospora. arabidopsidis, presumably by altering the pathogen’s ability to grow under that condition; mutations in threonine biosynthesis gene displayed increased resistance to H. arabidopsidis and increased accumulation of threonine [Threonine has been conferred as the amino acid that resistant to hreonine . Similarhreonine . There ahreonine . In respOverall, more than one third of the 166 differentially regulated metabolic pathways displayed similar change pattern in both two contrasting rice genotypes when exposed to SBPH. Twenty-one differentially regulated metabolic pathways were associated with SBPH resistance in rice. Briefly, SBPH infestation modulated phytohormones such as SA, JA, auxin and cytokinin; particularly, SA and JA showed antagonistic phenomena; inactivation of auxin enhanced significantly and showed the strongest response, meaning a shift from growth and development to defense. Following SBPH infestation, the major diterpenoid phytoalexin momilactone A metabolism enhanced significantly and showed the second strongest response; the N-rich glutamate and arginine levels increased and were the most abundant in resistant rice plants, together with the down-regulation of mixed acid fermentation and up-regulation of the C1 compounds utilization and assimilation, suggesting a N and C store to prevent N and C losses and work as a source for new growth after the phase of wound recovery. In summary, SBPH infestation caused changes in the primary and secondary metabolism; rice plants protect themselves against SBPH at several levels. Of these, one is the well-organized cuticle layers as first barrier encountered by SBPH. The second is the biosynthesis of inducible chemical defences such as the major diterpenoid phytoalexin momilactone A. The third is a shift from growth and development to defense, enhancing the capacity of rice plants to main high fitness in the face of enemies. The fourth is the modulation of N and C store and work as a source for new growth after the phase of wound recovery. In infested resistant rice plants, metabolic processes presumably not only protect them against SBPH but also enhance the capacity of rice plants to repair the tissue that injured by SBPH attack.O. sativa (cv. 02428), the SBPH-resistant and SBPH-susceptible rice line, respectively, were selected for this study. Pf9279-4 is an introgression line derived from the asymmetric somatic hybridization between Oryzaofficinalis and O. sativa (cv. 02428). Pf9279-4 bears a major QTL Qsbph3d and two minor QTLs Qsbph7a and Qsbph12b [Two rice genotypes, Pf9279-4 and Qsbph12b . The SBPQsbph12b . The nonThe resistant and susceptible rice plants were planted in pots (50 cm in length and 30 cm in width). When the seedlings were at the fourth leaf stage, weak plants were removed from the pots. In the insect-proof in greenhouse, second to fourth instar nymphs of the mixture of non-viruliferous and viruliferous SBPH population were introduced at a density of ten insects per seedling . At each® Expression Analysis Technical Manual. Three biological replications were performed. Expression levels were compared with those in Affymetrix® Microarray Suite version 5.0 . Significantly differentially expressed genes were screened in the Significant Analysis of Microarray software  [q-value of <0.05 in the SAM output result. Checks of annotations from Affymetrix for significant differentially expressed genes were performed by using Blastx program in NCBI public database [http://rice.tigr.org) [According to the results of our previously comparative two-dimensional fluorescence difference gel electrophoresis analysis, significant differences in protein expression level between these two contrasting rice genotypes that sampled at different times after SBPH infestation were obvious within 6 h of infestation. Therefore, the two outmost layers of leaf sheaths of these two contrasting rice genotypes that had been fed upon by SBPH for 6 h and controls were used for microarray experiments. Beijing Biochip Co., Ltd. carried out the microarray experiments and data analysis. RNA purification, cDNA probe preparation, target hybridization, washing, staining, scanning, image analysis and the initial data processing were performed according to Affymetrix GeneChipCA, USA) . Signifidatabase  and perfigr.org) ; correcthttp://www.plexdb.org) or by using the tool Omics Validator at the Gramene “Cyc” Pathways databases (http://pathway.iplantcollaborative.org/). For single experiment time step, listing all differentially expressed gene in a microsoft excel worksheet. The table in the microsoft excel worksheet contains 2 columns, the first row contains column header following TIGR locus id and the relative or absolute expression level of each differentially expressed gene, one gene per row. In the Pathway Tools Omics Viewer, for column numbering purposes, the first column, which contains the TIGR locus id, is column number 0 and the first potential data column is column number 1 [microsoft excel worksheet, the first row contains column header following TIGR locus id and column numbers , one gene per row. The microarray expression data is then saved as a tab-delimited data file and stored on the local computer [The Pathway Tools Omics Viewer maps the expression values is in Gene ID-dependent manner . Therefonumber 1 . Therefocomputer . http://pathway.iplantcollaborative.org). It is worth noting that the rice pathway database should be first selected when the rice transcriptomic data was visualized [http://pathway.iplantcollaborative.org), click on the “change” link. In the dialog that pops up, select the Oryza sativa Japonica Group, then click OK to exit the dialog. The Pathway Tools Omics Viewer is next selected. Upload the transcriptomic data that validated as noted above. In the column number fields, enter 1 for single experiment time step and enter a list of column numbers (with each column number corresponding to a single timepoint) for animated time series. In the Color Scheme, “three color display with specified threshold” was selected and the specified threshold was entered in the specified threshold field. The other fields should be default selection. Then click the “Submit ”at the bottom of the Pathway Tools Omics Viewer webpage. When the Oryza sativa Japonicagroup metabolic map was generated, a metabolite can be identified by moving the mouse over the metabolite icon; the metabolite page or a related pathway page can be navigated by clicking on the metabolite icon [This section was performed as follows according to the guidelines at Pathway Tools Omics Viewer webpage  and susceptible rice plants (S6_S0) respectively was taken as one combination for animated time series; while the individual metabolic pathways that were differentially regulated between resistant and susceptible rice plants before (R0_S0) and after infestation (R6_S6) respectively was taken as another combination for animated time series. Then mark the differentially regulated individual metabolic pathways between R6_S6 and R0_S0 or between R6_R0 and S6_S0 with green box. Although a pathway in the metabolic map generated by pathway tools can be navigated, in most cases, e.g., comparative transcriptome analysis of interest, it is more ideal and direct to get clear understanding of the differentially regulated metabolic pathways in more details if the differentially expressed genes and its differential expression values can be mapped to the individual metabolic pathway. Notably, the Pathway Tools Omics Viewer provides an alternative display to generate a table containing all individual metabolic pathways . Therefohttp://bioinfogp.cnb.csic.es/) was next employed. The differentially regulated individual metabolic pathways in the four comparisons that identified by the Pathway Tools Omics Viewer were represented in a Venn diagram. In the Venn diagram webpage, click the numbers in the Venn diagram to see the results and copy each result of 15 categories to a microsoft excel worksheet. The table in the microsoft excel worksheet contains five columns, the first row contains column header following metabolic pathway and four comparisons, one metabolic pathway per row. According to the result of the individual metabolic pathway that was classified into three groups mentioned as above, mark the individual metabolic pathway with up or down or up/down as well as higher or lower or higher/lower. By searching the metabolic pathway in the Gramene database or click the link of the metabolic pathway in the result that displayed as a table containing individual metabolic pathway, download the classification hierarchy for individual metabolic pathways from the rice pathway database (http://pathway.iplantcollaborative.org/).To identify the most relevant metabolic pathways associated with SBPH resistance and susceptibility in rice, the web tool Venn  was determined by using the automatic amino acid analyzer L-8900 .The result of the identification of the metabolic pathway associated with resistance and susceptibility to SBPH showed that several kinds of the amino acids were correlated with resistance and susceptibility to SBPH, therefore, the concentrations of the free amino acids were then determined as described by Lei . Briefly


To boost food production for a rapidly growing global population, crop yields must significantly increase. One of the avenues being recently explored is the improvement of photosynthetic capacity by installing the C4 photosynthetic pathway into C3 crops like rice to drastically increase their yield. Crops with an enhanced photosynthetic mechanism would better utilize the solar radiation that can be translated into yield. This subsequently will help in producing more grain yield, reduce water loss and increase nitrogen use efficiency especially in hot and dry environments. This review provides a summary of the factors that need to be modified in rice so that the C4 pathway can be introduced successfully. It also discusses the differences between the C3 and C4 photosynthetic pathways in terms of anatomy, biochemistry and genetics.The online version of this article (doi:10.1186/1939-8433-6-28) contains supplementary material, which is available to authorized users. Escalating population means more demand for food, water and land at a time when the natural resource base for agriculture is being degraded because large areas of farmland are being diverted from food production to industrialization and bio-fuel production. An unpredictable climate change is threatening to further reduce agriculturally viable land due to more instances of drought and flood (http://www.fao.org/docrep/017/aq191e/aq191e.pdf). As a growing population and global climate change place increasing pressure on the world’s food supply, it is essential that we continue to improve crop performance in terms of grain productivity to keep pace with population growth. The increase in crop productivity conferred by the plant types created during green revolution period supported the population boom following the two world wars. Since then, despite the use of improved varieties and advanced technologies, the yield potential of present day rice cultivars has improved just a little indicating that these varieties have hit a yield ceiling . As the name indicates, Rubisco also catalyzes oxidation of ribulose-1, 5-bisphosphate (RuBP) in a wasteful process known as photorespiration which can incur a loss of upto 25% of previously fixed carbon  pathways. C4 photosynthesis has evolved more than 66 times independently . This review provides an update on the requirements to develop C4 rice and progress made in the field of genetic engineering. Based on the study of the evolution of C4 from C3 species and the associated changes, the following modifications are essential to establish a functional C4 photosynthetic pathway in rice.C4 plants are potentially more productive at higher temperatures typically experienced by rice. To take advantage of this more efficient photosynthetic system at a time when the population and food prices are soaring, there are efforts towards inserting the C4 mechanism such as that found in maize into rice  genes in a cell specific manner by using C4 gene promoters such as phosphoenol pyruvate carboxylase (PEPC) of Zea mays for MC specific expression and phosphoenol pyruvate carboxykinase (PCK) promoter of Zoysia japonica for BSC specific expression in rice leaves  within the leaf  gene family members encode nuclear transcription factors that have been implicated to regulate chloroplast development in Arabidopsis, Zea mays, and the moss Physcomitrella patens  and its homologue ZmGLK1 transcripts accumulate primarily in BS and M cells, respectively, suggesting a specific role for each gene regulating the dimorphic chloroplast differentiation  have more than 7 veins per mm  gene produced curled leaves in Arabidopsis  . The oxaloacetate is then converted to malate or aspartate and is transported to BSCs where it is decarboxylated and the CO2 is released. This CO2 is refixed by Rubisco and all the subsequent activities of Calvin cycle take place in chloroplast of BSCs . Within the Calvin cycle, the two PGA molecules form an energy rich molecule of sugar (triose phosphate) and regenerates RuBP for the next cycle. At current atmospheric CO2 concentrations (ca. 400 ppm) Rubisco also catalyses a reaction between RuBP and O2 resulting in one molecule each of 2-phosphoglycolate and 3-PGA  - a five carbon compound, catalyzed by an enzyme ribulose-1,5-bisphosphate carboxylase oxygenase  reacts with CO2 in the intercellular spaces, thus preventing loss of energy through photorespiration. The elimination of photorespiration by C4 plants is evidenced by their very low CO2 compensation point which is nearly zero and constantly high carboxylation efficiency (CE) without responding to the changes in O2 concentrations  Royle., four enzymes  involved in the pathway were overproduced in the transgenic rice leaves , dicarboxylate transporter1 (DiT1), dicarboxylate transporter2 (DiT2), PEP/phosphate transporter (PPT1), mesophyll envelope protein (MEP) and triose-phosphate phosphate translocator (TPT) that were recently identified through proteomics of maize BS and MS cells (Friso et al. To engineer the photosynthetic pathway from C3 to C4 within two decades, which took million of years in nature, C4 rice consortium began the simultaneous gene discovery and engineering of already known genes into rice aiming to form C4 rice with Kranz type anatomy. C4 genes such as The C4 photosynthetic pathway has evolved more than 66 times in different species, suggesting that C3 plants may be in some way preconditioned to C4 photosynthesis. Developing crop plants with enhanced photosynthesis will improve crop yield and make efficient use of resources in a sustainable manner. The C4 rice consortium is striving to install a maize-like photosynthetic mechanism in rice to break its yield barrier and to breed a new generation of “climate-ready” rice which will yield more even under the situations of increasing temperature and decreasing water availability. This initiative demands a holistic approach of understanding the regulatory networks and the complex mechanism underlying the C4 photosynthetic pathway and their systematic introduction into the rice genome.