Plasmodium falciparum and the acquisition of immunity in children at the extreme ends of the over-dispersed distribution of the incidence of clinical malaria.Epidemiological studies indicate that some children experience many more episodes of clinical malaria than their age mates in a given location. Whether this is as a result of the micro-heterogeneity of malaria transmission with some children effectively getting more exposure to infectious mosquitoes than others, or reflects a failure in the acquisition of immunity needs to be elucidated. Here, we investigated the determinants of increased susceptibility to clinical malaria by comparing the intensity of exposure to n = 35), remained malaria-free (n = 12) or had a single episode (n = 26). We quantified antibodies against seven Plasmodium falciparum merozoite antigens in plasma obtained at six cross-sectional surveys spanning these five years. We analyzed the antibody responses to identify temporal dynamics that associate with disease susceptibility.The study was nested within a larger cohort in an area where the intensity of malaria transmission was low. We identified children who over a five-year period experienced 5 to 16 clinical malaria episodes  and had a significantly higher malaria exposure index, than those in the malaria-free or single episode groups . In contrast, the five-year temporal dynamics of anti-merozoite antibodies were similar in the three groups. Importantly in all groups, antibody levels were below the threshold concentrations previously observed to be correlated with protective immunity.Children experiencing multiple episodes of malaria were more likely to be parasite positive by microscopy at cross-sectional surveys  contains supplementary material, which is available to authorized users. Plasmodium falciparum malaria in malaria-endemic areas has long been recognized as a common feature of the epidemiology of malaria [Heterogeneity in the risk of  malaria . Recentl malaria , Uganda  malaria  and Keny malaria ,5 as wel malaria . In Sene malaria . Analysi malaria .P. falciparum antigens is unknown.The factors underlying the heterogeneous epidemiology of malaria are not fully understood. The heterogeneity has been partly attributed to differences in: human genetic  and behaHere, we describe the temporal dynamics of anti-merozoite antibodies in children who were part of the Kenyan cohort described above  and diffThe study was conducted within a longitudinally monitored population in Ngerenya, located within Kilifi District at the Kenyan coast ,12. ThisThe malaria exposure index estimates a distance-weighted local prevalence of malaria infection within a kilometer radius around an index child . In esseP. falciparum in whole blood samples collected at the cross-sectional surveys has been described previously [The detection of eviously . BrieflyP. falciparum msp2 gene was performed as previously described [msp2 domain, followed by nested reactions with fluorescently labeled primers targeting the FC27 and IC-1/3D7allelic types of msp2. Fragment sizes were determined by capillary electrophoresis and analyzed using GeneMapper software (Applied Biosystems).Genomic DNA was extracted from packed erythrocytes using QiaAmp Blood Mini kit . Genotyping of escribed . BrieflyEscherichia coli. The 19-kilodalton fragment of merozoite surface protein (MSP) one-1 (MSP-119) [P. falciparum reticulocyte-binding homologue 2 (PfRh2) [msp2 allelic family) and MSP-2_CH150/9  [Pichia pastoris as 6xHis-fusion proteins [PfRh2 and MSP-119, all antigens were expressed as full-length proteins.Five recombinant antigens representing four vaccine candidate antigens were expressed in MSP-119) , P. falc family)  were exp family) . Apical proteins  and geneP. falciparum unexposed donors residing in the United Kingdom, were included in each plate to allow for the determination of seropositivity cut-offs. The seropositivity cut-off was determined as the mean fluorescent intensity (MFI) of the negative control plus two standard deviations.Plasma immunoglobulin G (IgG) to the recombinant antigens was measured using a previously described multiplex bead-based assay . Seriall19, MSP-2, MSP-3_3D7 and AMA-1 antigens were 59, 19, 16 and 55 AUs, respectively [PfPR2–10 of 44% [PfPR2–10 of 49% [fPR2–10 is a measure of malaria transmission intensity at a population level [Data analysis was performed using STATA 11.2. Antibody titers measured in this study were compared to threshold antibody concentrations. These are antibody concentrations against individual antigens measured that appeared to be associated with protection against clinical episodes of malaria in two independent cohort studies ,15, and ectively ,15. Anti0 of 44%  and a co0 of 49% . The PfPon level -28.Differences in rates of change in antibody titers over the study period were tested using a multilevel mixed-effects linear regression model that accounts for inherent correlations between repeated measurements done on the same subject . In thisP. falciparum transmission was highest  and were classified into the malaria-free group  were excluded from the present analysis so as to allow for a comparison of antibody responses in children at the extreme ends of the over-dispersed clinical malaria incidence distribution , antibody titers to all antigens were comparable among the three study groups. Univariate analysis showed evidence for a significantly higher rate of increase in antibody titers to AMA-1_FVO and AMA_3D7 over the five-year period in the multiple-episodes compared to the single-episode group of children Table . After aPlasmodium falciparum merozoite antigens were generally similar in all groups of children. Of note, antibody levels in all groups of children were lower than those previously shown to correlate with protection against clinical malaria. These data suggest that the differences in susceptibility to clinical malaria among children in this context are attributable to differences in exposure to infectious mosquitoes rather than to a failure to acquire immunity, as reflected by anti-merozoite antibodies.This is, to our knowledge, the first attempt to investigate the underlying immunological determinants of the over-dispersion of clinical episodes of malaria in a low transmission intensity setting. Our study showed that over a five-year period, children who were susceptible to multiple clinical attacks of malaria had higher indices of exposure to infectious mosquitoes than those with single or no episodes. In contrast, the temporal dynamics of antibody responses to msp2 genotypes) at the cross-sectional surveys is a strong indication that children in the multiple episodes groups are indeed more exposed to the parasite. In the absence of clear differences in the acquisition of immunity (as estimated by anti-merozoite antibodies), our data suggest that the differences in disease susceptibility were driven by the observed differences in the intensity of exposure to the parasite.The malaria exposure index  estimateP. falciparum exposure (as estimated by PfPR) in the three groups of children studied here did not exceed 30% at any of the cross-sectional surveys over five years. This is relatively low compared to the 40% cutoff above which a population is considered to be under high malaria transmission [Malaria transmission intensity was low in the study area. smission , and is smission ,15. It mOverall, with the exception of AMA-1-FVO, the rate of change of antibody titers over time did not differ significantly between the three groups. The absence of any significant differences in the temporal change in antibody titers between the groups could be attributable to the young age of the study subjects, small sample size or the antigens studied. It is possible that the young age of the study subjects precludes any potential differences in rates of buildup of antibodies among the study groups. This is plausible considering that, given uniform parasite exposure, the buildup of antibody titers is slower in children than in adults , implyinP. falciparum infections. Considering that differentiation of MBCs into SLPCs peaks six to eight days after re-exposure to antigen [msp2 genotype to the infection could not be determined using capillary sequencing as employed here, interpretation on disease causation and/or immunity should be cautious.Generally, we observed allele-specific boosting of anti-MSP-2 antibodies, which has also been reported in relation to MSP-2  and AMA- antigen , antibod antigen . The incThe observed difference in the rates of change of antibody titers to AMA-1-FVO, but not to the other antigens in the three study groups, may be a reflection of antigen-specific differences in human immune responses to different antigens. Antibody longevity  and affiWe observed that children in the multiple-episodes group had more genetically-diverse infections compared to children in the single-episode and malaria-free groups. This finding is consistent with previous observations that genetically-diverse infections in children are associated with an increased risk of malaria ,41,42. WP. falciparum exposure on antibody titers in the children studied here [In our study, other than at cross-sectional surveys, children were only tested for malaria parasites when they were symptomatic. Thus, it is possible that some asymptomatic infections were undetected, but that would apply equally to all groups and thus is unlikely to introduce bias. Another limitation of the study was the availability of antibody measurements only at the cross-sectional surveys and not when children were symptomatic. Thus, we could not compare antibody levels at the point of symptomatic infections in the multiple- and single-episode groups. We were also unable to account for the possible effects of HIV, malnutrition and prenatal ied here .In summary, our data shows that in the context of a low malaria transmission setting, multiple episodes of clinical malaria are more likely the consequence of increased exposure rather than failure to acquire immunity. We hypothesize that intensive exposure induces antibodies at protective concentrations, while little to modest exposure may manifest as multiple clinical infections with low levels of antibodies. Future studies on the determinants of increased susceptibility to clinical malaria in areas with high malaria transmission intensity will complement the data presented here and help to define the tipping point in malaria transmission intensity where exposure translates into protective immunity as opposed to increased susceptibility to disease. Our data have implications for interventions including vaccines that effectively lower but do not completely abolish malaria transmission intensity. These interventions may reduce malaria transmission intensity below the threshold necessary to induce protective immunity and thus drive increased susceptibility to clinical malaria.


Prediction models for old-age mortality have generally relied upon conventional markers such as plasma-based factors and biophysiological characteristics. However, it is unknown whether the existing markers are able to provide the most relevant information in terms of old-age survival or whether predictions could be improved through the integration of whole-genome expression profiles.We assessed the predictive abilities of survival models containing only conventional markers, only gene expression data or both types of data together in a Vitality 90+ study cohort consisting of n = 151 nonagenarians. The all-cause death rate was 32.5% , and the median follow-up time was 2.55 years.C-index boosting algorithm, were used to test the genomic data. The Ridge regression model incorporating both the conventional markers and transcripts outperformed the other models. The multivariate Cox regression model was used to adjust for the conventional mortality prediction markers, i.e., the body mass index, frailty index and cell-free DNA level, revealing that 331 transcripts were independently associated with survival. The final mortality-predicting transcriptomic signature derived from the Ridge regression model was mapped to a network that identified nuclear factor kappa beta (NF-κB) as a central node.Three different feature selection models, the penalized Lasso and Ridge regressions and the Together with the loss of physiological reserves, the transcriptomic predictors centered around NF-κB underscored the role of immunoinflammatory signaling, the control of the DNA damage response and cell cycle, and mitochondrial functions as the key determinants of old-age mortality. Human longevity has proven to be a complex trait, and the factors enabling survival to old age are diverse. A great deal of variation also exists in the state of health in which old age is attained; some individuals age with good cognitive and physical health, whereas others suffer from multimorbidity and disabilities in daily functioning. Nevertheless, a variety of biomarkers, such as immunoinflammatory factors, endocrine mediators and indicators of functional capabilities and frailty, have been reproducibly demonstrated to be predictive of old-age survival in different populations -3. In veHowever, only certain immunoinflammatory functions can be captured by assessing circulating inflammatory markers and determining the numbers of distinct leukocyte subsets. It is also unclear whether these parameters yield complete pictures of the biological processes that are crucial in old-age mortality. Indeed, the predictions of outcomes in patients with certain types of cancer have recently been shown to benefit from the incorporation of gene expression profiles into traditional clinical cancer risk scores ,10. In oTo address these issues, we sought to systematically determine the predictive performances of a wide array of conventional markers, whole-genome transcriptomic data and the combination of these data with regard to all-cause mortality. We observed that the Ridge regression model, containing the body mass index (BMI) and frailty index  together with nine transcripts related to immunoinflammatory processes, cell cycle control and mitochondrial functions yielded the best-performing final signature model in terms of discriminative power and goodness-of-fit. The network analysis of the mortality-associated transcripts revealed that their actions were largely mediated through nuclear factor kappa beta (NF-κB) signaling. Thus, in addition to demonstrating the usefulness of combining transcriptomic data with conventional markers in the assessment of late-life survival, our results provide novel insights into the transcriptomic landscape preceding all-cause mortality in old age.The study population consisted of n = 151 nonagenarians (n = 106 women and n = 45 men) participating in the Vitality 90+ Study, which is an ongoing study of individuals aged 90 years and older who reside in the city of Tampere, Finland. The individuals in the current study population were born in 1920 and were recruited and characterized as in the previous Vitality 90+ study cohort . A home-, Austin, TX, USA). Following RNA extraction  and amplification , the RNA was hybridized to a HumanHT-12 v4 Expression BeadChip  and the chips were scanned using Beadscan . The qualities of the biotinylated complementary RNA products were assessed with the Agilent 2100 Bioanalyzer . The validation of the microarray expression data through qPCR was performed as previously described [http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE40366.The protocols for the leukocyte separation, RNA isolation and microarray analysis have been previously described ,15. Brieescribed . The michttp://chipster.csc.fi/) [The preprocessing of the microarray data was performed using the Chipster v2.8 software (csc.fi/) . A box pAlu repeat cf-DNA plasma mitochondrial copy numbers, and CRP, IL-6, and IL-10 levels have been previously described [The methods used to measure the plasma cf-DNA levels, unmethylated cf-DNA levels, escribed . The tecescribed . BrieflyThe techniques used to assess the anthropometric characteristics and functional performance, i.e., the Barthel index, handgrip and Mini-Mental State Examination (MMSE), have been previously described ,18. The The characteristics of the study population are presented in Table C-index boosting algorithm; all the significant 331 transcripts were included in the models without adjusting for multiple testing (please see the next paragraph for the model characteristics).For high-dimensional predictors, such as whole-genome transcriptomic data, the traditional Cox regression model cannot be directly applied. As a general rule of thumb, the Cox model should be used only when there are a minimum of 10 events per predictor variable (EPV), or at least 5–9 EPV under certain circumstances . Thus, wC-index boosting algorithm, which has been presented as an alternative means for the derivation of marker (gene) combinations via a gradient boosting framework and the direct optimization of the C-index [Using the 331 mortality-associated transcripts, we proceeded to test and utilize three different dimension reduction methods for feature selection. The Ridge regression model  shrinks  C-index .R2 measure based on the Brier score Brier score (iRBS) were calculated , which is a measure of the separation of two survival distributions [C-index is a performance characteristic for survival models, and it represents the fraction of all pairs of subjects whose predictions exhibit correct orders over the pairs that are able be ordered. The C-index estimates the probability that the order of the predictions of a pair of comparable subjects is consistent with their observed survival data.We began the model selection process by evaluating the predictive performance of each model and the variable combinations through cross-validation, for which the data were split into training and test sets, and the differences in the deviance and an ibutions . The C-iThe cut-off point for the absolute values of the coefficients was 0.0365 for the best-performing prediction model . All predictors showing a regression coefficient above the cut-off point were fitted to a multivariate Cox regression model using a stepwise selection method. The Cox model assumes proportional hazards, i.e., a log-linear relationship between the hazard rates and the independent parameters in the model during the follow-up period. The violation of the proportionality assumption during the follow-up period was assessed by extending the Cox model to incorporate time-dependent covariates representing the interactions between each of the independent parameters and the parametric function of the follow-up time. We also calculated the scaled Schoenfeld residuals for each independent parameter. Testing time-dependent covariates is equivalent to testing for a non-zero slope in a generalized linear regression of scaled Schoenfeld residuals as a function of time. A non-zero slope is an indication of a violation of the proportional hazard assumption. Based on the global test, no evidence of a statistically significant dependence of mortality on time was observed (p = 0.11). All Cox regression models were performed using the Stata software .The distributions of all examined variables  are presented in Table Cell death and Survival, Inflammatory Response and Cellular Function and Maintenance functions  of the variable combinations, beginning with the model containing only the conventional markers and adding the Ridge regression-identified transcripts one-by-one , NME/NM23 nucleoside diphosphate kinase 4 (NME4) and growth arrest and DNA-damage-inducible beta (GADD45B) and low expression levels of myelin basic protein (MBP), SH2 domain containing 1B (SH2D1B), checkpoint kinase 2 (CHEK2), leucine-rich repeats and calponin homology domain containing 3 (LRCH3), transmembrane protein 70 (TMEM70) and vitamin K epoxide reductase complex, subunit 1 (VKORC1) together with a low BMI and increased frailty were the most predictive of mortality  and discriminative power (Harrell’s LTA (high expression) and SH2D1B, MBP and LRCH3 (low expression) were ascribed to immunoinflammatory processes. Specifically, LT-α (the protein product of LTA), which is a member of the TNF superfamily that plays pivotal roles in the function and development of the immune system, has been regarded as a central player in various inflammatory conditions [2+ influx; the ablation of Golli-MBP leads to T cell hyperproliferation in the first phase but may subsequently trigger T cell anergy [The prediction of mortality in very elderly individuals has traditionally relied upon markers reflecting immunoinflammatory and endocrine functions and parameters involving physiological capabilities. In this study, we demonstrated that integrating gene expression data into a model containing these traditional predictors results in the improved prediction of old-age mortality in terms of the discriminative power and goodness-of-fit of the model. However, among the tested feature selection methods, only the Ridge regression model performed satisfactorily in the generalizability assessment; therefore, it was selected as the preferred method for survival-signature modeling. In addition to providing a means of avoiding overfitting, obtaining a parsimonious variable set through the penalized Ridge regression was necessary to assess the relative strengths of the conventional markers and the transcripts in the final model. Among the conventional markers, both the BMI and the frailty index remained in the final model, indicating that the concomitant loss of physiological reserves in multiple homeostatic systems is detrimental to survival. However, the cf-DNA level, which appears to be the best plasma-based mortality predictor according to current data as well as our earlier Vitality 90+ cohort , was repnditions . The adanditions . For MBPl anergy . The funl anergy . InteresGADD45B (elevated), CHEK2 (decreased), TMEM70 (decreased) and NME4 (elevated) demonstrated that the control of the DNA damage response, apoptosis and cellular maintenance, including mitochondrial functions, were likewise essential to mortality. In addition to serving as a crucial regulator in of immune cell differentiation and cytokine production, GADD45β is upregulated under conditions of growth arrest due to cellular (genotoxic) stress and DNA damage [Cell Death and Survival and Cellular Function and Maintenance functions, further recapitulates the relevance of these processes in old-age mortality. Interestingly, the final signature also incorporated VKORC1 (decreased), which is an enzyme that aids in the maintenance of hemostasis through the conversion of vitamin K to its active from. However, the significance of VKORC1 expression in immune cells in relation to mortality is ambiguous. One plausible link could be the role of vitamin K as a cofactor in posttranslational protein modification, leading to the production of γ-carboxyglutamate/vitamin K-dependent (VKD) proteins. Indeed, the VKD protein GAS6 has been demonstrated to play a role in leukocyte migration and proliferation, phagocytosis and apoptosis [The final signature transcripts A damage . The celA damage . InteresA damage . Both NMA damage , whereasA damage . In addipoptosis . Alternapoptosis .CORO1A, IQGAP1, AURKB, TERF2IP and CBX5, which play roles in processes such as T-cell mediated immunity, mitochondrial apoptosis, mitosis and chromatin maintenance. However, a between-study comparison of the survival-associated transcripts  exhibited Z-scores of the same irectionality. This discrepancy could be at least partially due to the differences in the cell types used in these investigations, which were in vivo blood mononuclear cells in our study but were cultured and transformed B cells in that of Kerber et al. [In addition to the present study, one previous study performed a penalized regression analysis (Lasso) to predict age-associated mortality using transcriptomic data from cultured lymphoblastoid cell lines . Despiter et al. .MTERF, ACADM and TFB2M) and the regulation of the cell cycle and mitosis . Four age-associated transcripts in Module F  were also observed in our dataset  were both mortality-associated and age-regulated. A similar phenomenon was observed in the comparison of our mortality-associated transcripts with previously reported age-regulated transcripts [GRAP was associated with an increased risk of mortality  were present among the 331 mortality-associated transcripts. Thus, we hypothesize that the significance of mTOR-mediated cellular regulation subsides in the later phase of life, once an individual has already reached very old age. Overall, our results suggest that while the majority of the decisive processes are likely to be common in ageing and mortality, the actual driver genes underlying these phenomena may differ.Overlaying the mortality-associated pathways Table  with thenscripts ,14. FurtThe major limitations of the current study are the lack of an external validation cohort, which would be ideal for assessing the universality of the transcriptomic predictors, and the small sample size. In addition, because all subjects were homogeneous in terms of age (90 years), we were unable to determine whether the identified predictors perform similarly in individuals of other (old) ages.Taken together, our systematical characterization of the determinants of old-age mortality underscores the joint impact of the decline in physiological reserves, the fidelity of immunoinflammatory processes and the control of the DNA damage response, cell cycle and mitochondrial functions. In addition, our findings corroborate the proposed roles of NF-κB in the aging process and aging-related degeneration , and indR2 measure based on the Brier score; IPA: Ingenuity Pathway Analysis; NF-κB: Nuclear factor kappa beta.BMI: Body mass index; cf-DNA: cell-free DNA; iRBS: The authors declare that they have no competing interests.JJ collected the samples, designed and performed the laboratory analyses, processed the microarray data and wrote the manuscript. JR performed the statistical and bioinformatic data analyses and wrote the manuscript. SM collected the samples and performed the laboratory analyses. MJ was responsible for the Vitality 90+ study design and cohort recruitment. AH was responsible for the Vitality 90+ study design and cohort recruitment. MH supervised the work and provided the facilities and reagents. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/7/54/prepubAdditional methods.Click here for fileA table listing the 478 transcripts whose expression level were associated with survival in the Cox univariate model.Click here for fileTable listing the 331 transcripts whose expression level remained as mortality predictors in the Cox multivariate model after adjusting for BMI, frailty index and cf-DNA level.Click here for filePredictive accuracies based on the differences in deviance. The conventional markers-only model and the six prediction methods (50 splits into training and test datasets for each) were compared with the null model containing no covariates . A low value for the difference in deviance corresponds to a good predictive performance. The Lasso1, Ridge1 and BoostC1 models contain only the transcriptomic data, whereas the Lasso2, Ridge2 and BoostC2 models contain the transcriptomic data and the conventional predictors.Click here for filePredictive accuracies based on the iRBS. The conventional marker-only model and the six prediction methods (50 splits into training and test data sets for each) were compared to the null model with no covariate . A large value for the difference in iRBS corresponds to a good prediction performance. The Lasso1, Ridge1 and BoostC1 models contain only the transcriptomic data, whereas the Lasso2, Ridge2 and BoostC2 models contain the transcriptomic data and the conventional predictors.Click here for fileThe IPA-generated network based on the final signature transcripts. This network demonstrates that NF-κB is a prominent mediator of the molecular interconnections. The molecules incorporated into the final signature are shown in enlarged bold font, and the connective molecules are shown in regular font. Green color indicates that low expression level of the transcript predicts mortality, whereas red indicates that high expression level of the transcript predicts mortality.Click here for fileDisplaying the stepwise assessment of the variable combinations for the final Cox regression model.Click here for fileDisplaying the results of the Ridge regression model performed with the conventional predictors and transcriptomic data (the combined model).Click here for file


Our results indicate that both the heterogeneity and the plasticity of the transformed cell population are enhanced by exposure to hypoxia, resulting in a shift towards a more stem-like population with increased EMT features. Our findings are further reinforced by gene expression analyses demonstrating the upregulation of EMT-related genes, as well as genes associated with therapy resistance, in hypoxic cells compared to normoxic counterparts. In conclusion, we demonstrate that mathematical modelling can be used to simulate the role of hypoxia as a key contributor to the plasticity and heterogeneity of transformed human mammary epithelial cells.Hypoxia, or oxygen deficiency, is known to be associated with breast tumour progression, resistance to conventional therapies and poor clinical prognosis. The epithelial-mesenchymal transition (EMT) is a process that confers invasive and migratory capabilities as well as stem cell properties to carcinoma cells thus promoting metastatic progression. In this work, we examined the impact of hypoxia on EMT-associated cancer stem cell (CSC) properties, by culturing transformed human mammary epithelial cells under normoxic and hypoxic conditions, and applying  The tumour microenvironment has long been recognized as a key factor driving the tumour growth and metastasis underpinning breast cancer progression3—contain hypoxic regions as a result of the increased oxygen diffusion distances and the disordered and inadequate tumour vasculature, adding biochemical and metabolic stresses to the tumour cellsA key feature of the tumour microenvironment is the presence of hypoxiaHypoxia impacts tumour growth in multiple respects e.g. by stimulating recruitment of inflammatory cell types and endothelial cells, enhancing angiogenesis, promoting immune suppression, exacerbating inflammation and supporting metabolic reprogramming2257et al.Multiple lines of evidence suggest that the tumour microenvironment plays a key role in the induction of EMT, as reviewed in Talbot loCD24hi non-CSCs can spontaneously dedifferentiate into CD44hiCD24lo CSCs under certain conditions16181920The cancer stem cell (CSC) hypothesis postulates the existence of a subpopulation of cancer cells with a high capacity for self-renewal and the ability to regenerate the heterogeneity of the parental tumour, compared to differentiated non-CSC counterpartsin situ , suggesting the acquisition of cellular attributes reminiscent of a “stem-like” stateDedifferentiation of differentiated cells has been reported to occur in hypoxic regions at higher frequencies compared to normoxic regions within the same tumourshi/CD24lo subpopulation, known to be enriched for breast CSCsWe recently identified the ganglioside GD2, a cell surface glycosphingolipid, as a breast CSC markerThe development of the phenotypic and functional heterogeneity, observed within tumours, is thought to be driven by selection pressures imposed by ever-changing microenvironmental conditionsin vivoThe clinical implications of hypoxia for cancer therapy are also of great importance, most evidently in the context of radiation and chemotherapy. It has been established that resistance to radiation is conferred by hypoxia, and the mechanism by which this is achieved is well understoodin vitro experiments and in silico mathematical modelling. The effects of hypoxia were analysed through five primary in vitro methods, including cell morphology, metabolism- and EMT-related gene expression, GD2-mediated fractionation of CSCs, cell growth, and mammosphere assays as an indicator of CSC self-renewal/plasticity. We also show that the experimental results can be predicted theoretically through ordinary differential equation-based mathematical modelling and stochastic simulations.In this work, we studied the effects of hypoxia on Ras-transformed human mammary epithelial (HMLER) cells via et al.The methods used in this work for the analysis of the obtained gene expression signatures were as described in Zarkoob We applied a two-compartment model , consistParameters were fit using a computational brute-force search through the parameter space over all possible combinations of parameters, within a fixed gridspace. The parameter sets which provided the closest solutions to the model within a given tolerance were taken as possible parameter sets. Those parameters, presented within this work, represent the average of the given parameter over all such sets. For completeness, we also present the standard deviation of these values over the determined space of solutions. The model was able to fit well to the experimental data, and captured the growth of cancer cells at both low and high cell number limits as well as the dynamics of cell growth.in silico mammosphere formation rate, as simulated by the stochastic model.A stochastic implementation of the mathematical model, using the cellular reactions described in 2; 5% CO2) using a hypoxic chamber . Mammosphere formation was assayed by seeding 3000 cells (per well) of 96-well ultra-low attachment plates (Costar) in mammosphere-formation mediaHMLER cells were grown as previously describedin vitro transcription were removed through cDNA filter cartridges (part of the amplification kit). In vitro transcription was performed and biotinylated cRNA was synthesized by 14-hour amplification with dNTP mix containing biotin-dUTP and T7 RNA polymerase. Amplified cRNA was subsequently purified and the concentration measured by a NanoDrop ND-1000 Spectrophotometer . An aliquot of 750 nanograms of amplified products was loaded onto Illumina Sentrix Beadchip Array hum HT-12.v4 arrays, hybridized at 58 °C in an Illumina Hybridization Oven  for 17 hours, washed and incubated with streptavidin-Cy3 to detect biotin-labelled cRNA on the arrays. Arrays were dried and scanned with BeadArray Reader . Data were analysed using GenomeStudio software . Clustering and pathway analysis were performed with GenomeStudio and Ingenuity Pathway Analysis  software respectively.Three hundred nanograms of Total RNA were amplified and purified using Illumina TotalPrep RNA Amplification Kit  according to the manufacturer’s instructions. Briefly, first strand cDNA was synthesized by incubating RNA with T7 oligo(dT) primer and reverse transcriptase mix at 42 °C for 2 hours. RNase H and DNA polymerase master mix were immediately added to the reaction mixture following reverse transcription, and were incubated for 2 hours at 16 °C to synthesize second strand cDNA. RNA, primers, enzymes and salts that would inhibit 2, 5% CO2) (hereafter termed HR-N) or hypoxic  conditions. Initially, we sought to establish a timecourse of morphological and gene expression changes, related to EMT induction following exposure to hypoxia. While we expected to observe a rapid transition to a mesenchymal phenotype within 24 hours34In order to profile hypoxia-induced changes in EMT-related gene expression and CSC properties, we cultured Ras-transformed immortalized human mammary epithelial cells (HMLER) under normoxic  and the Methods section. Notably, a number of comparisons did show statistical significance. As expected, it was shown that the upregulated genes in HR-H6 and HR-H8 showed an overlap that was statistically significant, and considerably more significant than could be due to chance alone . The two-sided Fisher exact test was performed on gene signatures to determine the degree of statistical significance of overlapping genes present in multiple signatures, as described in Zarkoob ce alone .per se is initiated before day 6 and precedes the activation of the EMT program and the establishment of the hallmarks of EMT. Additionally, it should be noted that when comparing the gene signatures for downregulated genes between HR-H6 and HR-SN and also between HR-H8 and HR-SN, both comparisons were statistically significant , suggesting that there is a greater degree of similarity between the upregulated gene signatures of HR-H8 cells and HR-SN cells that is not apparent at the earlier timepoint (HR-H6). Collectively, these results indicate that the genes upregulated during the hypoxic response of HMLER cells, at 6 days post-hypoxia induction (HR-H6), are, in fact, distinct from the genes underpinning the established mesenchymal phenotype of the HR-SN cells. Given that the upregulated genes of HR-H8 cells show significant overlap with those of HR-SN cells, this, in turn, suggests that the dynamic process of hypoxia-induced EMT unfolds between days 6 and 8 post-hypoxia exposure in the HMLER model. Moreover, our data indicate that the hypoxic response nificant .BRCA1  has been shown to correlate with an insensitivity to the chemotherapeutic agent doxorubicinBRCA1 expression was downregulated following exposure to hypoxia and Snail-induced EMT. Additionally, we noted the upregulation of Twist1, in HR-H8 and HR-SN cells compared to HR-N cells, which has been shown to increase the resistance to microtubule-disrupting anticancer drugs, such as taxanesMUC1 (Mucin 1), a gene encoding an oncoprotein that is aberrantly overexpressed in human breast carcinoma and blocks irradiation- and hypoxia-induced cell death39et al.Of note, other effects of hypoxia on gene expression, observed in this model, represent differences in genes known to be associated with therapeutic response. Indeed, it is known that microenvironmental factors affect the efficacy of conventional therapeutic regimens, and that cells that have undergone EMT and CSCs are resistant to standard therapies such as radiation and chemotherapy. The downregulation of the gene The hypoxia-derived gene signatures were profiled for association with existing datasets through Ingenuity Pathway Analysis (QIAGEN), gene ontology classification (DAVID)We next used GSEA to identify functional pathways and datasets altered by hypoxic exposure  by compa44in vitro experimentation, we nevertheless observed that CYBA and IDH1 increase transiently in HR-H6 cells but decrease to a slightly elevated level above normoxia in HR-H8 cells, while GPX4 increases continually, up to 8 days of hypoxia. Moreover, we note that these genes are not significantly differentially expressed in HR-SN cells, as compared to control counterparts, again suggesting that changes in ROS metabolism are changes conferred uniquely by hypoxia, not manifested in non-hypoxia/Snail-induced EMT.Additionally, our microarray analyses support an increase in CSC-associated genes following exposure to hypoxia. First, we determined that TWIST1, a gene known for promoting a CSC phenotype in breast cancer12We recently described the ganglioside GD2 as a novel breast CSC-specific cell surface marker2), as illustrated in In comparison, the change in the proportion of GD2(+) cells, as a function of time, was also obtained under hypoxic conditions  and mammosphere formation analysis (using stochastic simulations) to the corresponding experimental data, as described in the Methods section. This comparison reveals that the only parameter, which significantly varies when the microenvironment changes from normoxia to hypoxia, is the rate of dedifferentiation, i.e. the rate of conversion of non-CSCs to CSCs, which is representative of the observed increase in cell plasticity following exposure to hypoxia . It appeIn this study, we used a combination of CSC assays, gene expression analyses, and mathematical modelling to study the phenotypic plasticity underlying the conversion from a differentiated to a stem-cell phenotype under both normoxic and hypoxic conditions.A primary result of this work describes the adaptation of transformed human mammary epithelial cells to growth under hypoxic conditions. Thus, we showed that hypoxic cells undergo a biphasic adaptation involving both gene expression changes as well as phenotypic alterations. Unlike HR-H8 cells exposed to hypoxia for 8 days, HR-H6 cells did not show as strong a statistical correlation with cells that have undergone a Snail-driven EMT. This suggests the occurrence of important changes, between days 6 and 8 of the hypoxia timecourse, which culminate in the acquisition of an EMT-like phenotype. More specifically, for those genes that must be downregulated to permit the cell to undergo an EMT-like transition, the repression occurs largely within the first 6 days, via a similar mechanism as within HR-SN cells. However, the analogous process for the genes which must be upregulated to reach an EMT-like state is different in the sense that the induced hypoxic response may involve a separate or delayed pathway of gene activation to reach a mesenchymal state. By the eighth day of exposure to hypoxia, the upregulated gene expression signature of HR-H8 cells more strongly matches the upregulated gene signature of HR-SN cells, suggesting that, at this point, a more complete EMT-like process is underway.To further analyse the differences between the mechanisms governing these phenotypic transitions in response to a hypoxia timecourse, gene set functional analyses were carried out. These showed that, as expected, the primary drivers of the hypoxic phenotype were the HIF1α-related transcription factors and TNF1α. Moreover, GSEA reveals a switch from expression of TCA cycle genes to the expression of genes involved in the glycolytic pathway, in hypoxic cells at day 6, suggesting that this metabolic reprogramming occurs within 6 days during the transition from a normoxic to a stable hypoxic phenotype.A second aspect of this study analysed the change in the cell surface expression of GD2, a glycosphingolipid and purported CSC biomarker, in HMLER cells exposed to hypoxia. Thus, we profiled the temporal dynamics of the transition from a GD2(−) population to a GD2(+) population, presumably comprised of a higher proportion of CSCs under the hypoxic regime. Moreover, the mammosphere formation assay was used to quantify the proportion of CSCs via a direct assay of their self-renewal and growth under ultra-low attachment conditions. The corresponding results must be interpreted accounting for stochastic variation in behaviour due to the small numbers of mammosphere-forming cells involved (HMLER cells have a lower ability to generate mammospheres compared to e.g. HMLER-Twist or HMLER-Snail cells). These two experiments, with results reconciled through mathematical modelling accounting for both deterministic and stochastic observed behaviour in a two-compartment model, show that the transition from normoxia to hypoxia induces a definite switch in the proportion of CSCs, with a higher proportion of CSCs emerging after hypoxia. Moreover, a quantitative parametric analysis of the mathematical model reveals that the nature of this differing phenotype between normoxia and hypoxia is due to a higher rate of dedifferentiation of non-CSCs to CSCs.et al.in vivo lesion, where biological factors induce great microenvironmental variability.Importantly, this study highlights the fact that the phenotype of a tumour, in terms of the proportions of non-CSC/CSC (or differentiated vs. dedifferentiated) subtypes of cells comprising the lesion, will depend significantly on the microenvironmental parameters in the immediate vicinity of the tumour cells and the variability of these parameters within the tumour as a whole (e.g. tumour core vs tumour margin). That is, we have shown, for the studied cell line in particular, that oxygen conditions significantly impact gene expression and consequently modify the degree of cellular plasticity. For instance, model fitting during the induction of hypoxia necessitated the use of distinct parameter sets for early and late timepoints of exposure to hypoxia, representing a deterministic shift in behaviour. Thus, as an addendum to the result of Gupta How to cite this article: Dhawan, A. et al. Mathematical modelling of phenotypic plasticity and conversion to a stem-cell state under hypoxia. Sci. Rep. 6, 18074; doi: 10.1038/srep18074 (2016).


In this study, we investigated differentially expressed microRNAs (miRNAs or miRs) and their functions in metastatic melanoma using next-generation sequencing technology. The GSE36236 data set was downloaded from the Gene Expression Omnibus (GEO) database and 4 primary cutaneous melanoma samples (used as controls) and 3 metastatic melanoma samples were selected from 31 samples for further analysis. Firstly, the differentially expressed miRNAs were screened by limma package in R language. Secondly, the target genes of the miRNAs were retrieved with TargetScanHuman 6.2, and the interactions among these genes were identified by String and an interaction network was established. Finally, functional and pathway analyses were performed for the genes in the network using Expression Analysis Systematic Explorer (EASE). A total of 4 differentially expressed miRNAs  were obtained between the metastatic melanoma and primary cutaneous melanoma samples. We predicted 101 high-confidence target genes of hsa-miR-27 and obtained a network with 41 interactions. Finally, functional and pathway analyses revealed that the genes in the network were significantly enriched at the transcriptional level. Differentially expressed miRNAs were identified in the metastatic melanoma compared with the primary cutaneous melanoma samples and the target genes of hsa-miR-27 were found to be significantly enriched at the transcriptional level. The results presented in our study may prove helpful in the diagnosis and treatment of metastatic melanoma. The incidence of metastatic melanoma and mortality rate has continually increased over the past decades ,2. The iet al demonstrated that miRNA-155 acts as a suppressor of the cytokine signaling 1 gene that is a tumor suppressor in breast cancer  are associated with the metastasis of colon cancer, breast cancer and lung cancer cells –7. Over t cancer . HoweverGiven the detrimental effects of metastatic tumors and the growing development of sequencing technology, small RNA sequencing was adopted in our study to identify the differentially expressed miRNAs in metastatic melanoma compared with primary cutaneous melanoma samples. The target genes were predicted, and functional and pathway analyses were performed to reveal their roles in the progression of metastasis. Our findings may provide new insight into the diagnosis and treatment of metastatic tumors.Homo sapiens). Deep sequencing was performed on various tissues and sequencing data were obtained. We transformed the data from SRA format to FASTQ format, as previously described  database. A total of 7 melanoma samples were selected, including 4 primary cutaneous melanoma samples (used as controls) and 3 metastatic melanoma samples. Raw data were obtained in SRA format, as previously described . The plaescribed  using thescribed . Sequencescribed , allowinescribed . The expescribed , which hescribed .After the expression levels were determined, the differentially expressed miRNAs were screened out between the metastatic melanoma and primary cutaneous melanoma (controls) samples with limma package in R language ; a p-valet al   (p<0.05)After the raw data were transformed into FASTQ format, the sequence read length was 36 bp. The number of original reads, miRNA alignments and miRNA types for each sample are listed in A total of 4 differentially expressed miRNAs were identified in the metastatic melanoma samples as compared with the primary cutaneous melanoma (control) samples according to the criteria (p<0.05 and |logFC|>1). These 4 miRNAs were hsa-miR-146, hsa-miR-27, hsa-miR-877 and hsa-miR-186. The information for these 4 miRNAs is presented in A total of 101 target genes with high confidence were found for hsa-miR-27 (data not shown). A total of 41 interactions (data not shown) were disclosed and the network was then constructed .A total of 4 significant functional terms and 1 pathway were identified  for the We identified 4 differentially expressed miRNAs by next-generation sequencing technology. In order to determine the functions of these miRNAs, the target genes of hsa-miR-27 were predicted and the interaction network of the genes was constructed. Finally, the functions of and pathways associated with these genes were analyzed in order to further elucidate the mechanisms responsible for metastatic melanoma.et al found the overexpression of hsa-miR-146 in lung cancer (et al demonstrated that the expression of miR-146b-5p inversely correlated with glioma invasiveness in the brain (et al indicated that miR-27a is overexpressed in breast cancer cells, and that cell proliferation is decreased by the inhibition of this miRNA using antisense molecules in MDA-MB-231 cells (et al reported that in SKBr3 breast cancer cells, the inhibitor of histone deacetylases, LAQ824, rapidly decreased miR-27a levels (et al indicated that hsa-miR-186 was differentially expressed in paired primary and metastatic cancers (et al demonstrated that hsa-miR-186 combined with other miRNAs was able to distinguish melanoma patients from healthy individuals (The 4 differentially expressed miRNAs were hsa-miR-146, hsa-miR-27, hsa-miR-877 and hsa-miR-186. Rabinowits g cancer . Katakowhe brain . The stu31 cells . Scott ea levels  and actsa levels . Guttilla levels , a putata levels . In a pra levels . Baffa e cancers . Leidingividuals . Therefoet al found that the high expression of GATA2 is associated with metastatic progression and biochemical recurrence in prostate cancer through the regulation of key androgen-regulated genes (To further elucidate the regulatory mechanisms, the target genes of the 4 miRNAs were predicted by TargetScan. A total of 101 target genes were revealed for hsa-miR-27. Functional enrichment analysis revealed that the most significantly enriched term was transcription, indicating that a number of gene expressions are regulated, which may lead to the acquisition of metastatic tumor cells. In addition, several target genes have been disclosed and are worthy of further investigation. ING5 is predicted to be one of the target genes which is linked to tumorigenesis in human head and neck carcinoma . GATA2 ied genes . ERG is ed genes ,42.Overall, next-generation sequencing technology was adopted in this study to identify the differentially expressed miRNAs in metastatic melanoma as compared with primary cutaneous melanoma samples. Moreover, the functions and pathways of target genes were revealed, which may provide insight into the regulatory mechanism. The data presented in our study may prove beneficial in the diagnosis and treatment of metastatic melanoma and may aid in the development of novel clinical applications using miRNAs. However, further sutdies are required to confirm our results.


Dehydration has been shown to decrease sports performance. However, the exact cause of the decreased performance due to dehydration is still unclear. PURPOSE: To compare sport-specific reaction time values between men and women and at different quartiles after a dehydrating protocol to approximately 2% body mass loss.Ten women and eleven men between the ages of eighteen and thirty-five volunteered to participate in the study . Subjects reported to the lab in a fasted and normally hydrated state and completed a two-minute, multi-directional sport-specific reaction time test. Subjects then ran on a treadmill at 80% estimated max HR for 30 minutes, followed by multiple 15 minute sessions in a dry sauna at approximately 150 degrees F. After reaching a 2% (+/- 0.4%) reduction in dry body weight subjects completed the same procedures as pre-dehydration. Reaction times were separated into quartiles  and averaged to examine the data within each test. Consent to publish the results was obtained from all participants.The average total (Q1-Q4) reaction time for men and women combined after dehydration (1375 +/- 210 milliseconds (ms)) was significantly higher than before dehydration . The average total reaction time for women after dehydration (1366 +/- 400 ms) was significantly higher than before dehydration . However, men did not demonstrate a significant change in reaction time from pre (1305 +/- 300 ms) to post-dehydration . When quartiles were compared, the average reaction time for women was significantly higher in the third quartile of post-dehydration  and the fourth quartile of post-dehydration  than the first quartile of pre-dehydration (1272 +/- 198 ms). Regarding men, the average reaction time was significantly higher in the first quartile of post-dehydration (1427 +/- 220 ms) than the first quartile of pre-dehydration , the second quartile of pre-dehydration , the third quartile of pre-dehydration , and the fourth quartile of pre-dehydration . Additionally, the average reaction time was significantly higher in the second quartile of post-dehydration (1353 +/- 211 ms) than the first quartile of pre-dehydration .Dehydration appears to affect the sport-specific, total body reaction time performance of athletic men and women differently. Overall, average reaction time was significantly greater after dehydration for the combined group of men and women; however, women’s average reaction time was significantly greater after dehydration, and men’s reaction time trended upward, but did not reach significance. When divided into quartiles, the data suggest that women slowed down  within each test and between the pre and posttests, though significance was only observed when comparing the beginning of pre-dehydration and the end of post-dehydration. Regarding quartiles with men, the highest time point was the beginning of the post-dehydration test, and their subsequent reaction times trended lower from quartile to quartile, suggesting improvement. Future studies could include more subjects or a longer test in order to elucidate the discrepancy of these data. Nevertheless, this study suggests that sex differences exist regarding effects of dehydration, such that women’s reaction time performance was significantly affected, whereas men’s performance did not change significantly, due to a possible recovery of performance during the test.


Light is one of the most important environmental factors affecting flavonoid biosynthesis in plants. The absolute dependency of light to the plant development has driven evolvement of sophisticated mechanisms to sense and transduce multiple aspects of the light signal. Light effects can be categorized in photoperiod (duration), intensity (quantity), direction and quality (wavelength) including UV-light. Recently, new information has been achieved on the regulation of light-controlled flavonoid biosynthesis in fruits, in which flavonoids have a major contribution on quality. This review focuses on the effects of the different light conditions on the control of flavonoid biosynthesis in fruit producing plants. An overview of the currently known mechanisms of the light-controlled flavonoid accumulation is provided. R2R3 MYB transcription factors are known to regulate by differential expression the biosynthesis of distinct flavonoids in response to specific light wavelengths. Despite recent advances, many gaps remain to be understood in the mechanisms of the transduction pathway of light-controlled flavonoid biosynthesis. A better knowledge on these regulatory mechanisms is likely to be useful for breeding programs aiming to modify fruit flavonoid pattern. Vitis vinifera), flavonoid composition has effects on taste and quality of wine as well as conservation. Over 10,000 naturally occurring flavonoids have been described so far  and pear (Pyrus communis)  and WD40-repeat proteins , blueberry and bilberry (Vaccinium spp.), the plant hormone abscisic acid (ABA) seems to have a regulatory role both in ripening and initiation of anthocyanin biosynthesis  causes conversation of Pr to biologically active Pfr form and far-red light (730 nm) the conversation back to Pr form. Pfr can also be degraded in the proteasome after ubiquitination if not back-reverted to Pr. Under white light containing both red and far-red wavelengths, photoequilibrium is established after few minutes. The different forms allow phytochrome to function as a biological switch, turning responses on and off enabling the detection of circadian rhythms and seasonal changes in light conditions  is the only ferredoxin-dependent bilin reductase (FDBR) producing the phytochromobilin for phytochromes. It has been shown that mutations in ns Reed, .Cryptochromes are flavin-containing photoreceptors for blue, green and UV-A light perception. Cryptochromes are involved in sensing circadian rhythms and regulation of many developmental and adaptive processes including biosynthesis of secondary metabolites, such as flavonoids , fruit bagging treatments have been shown to inhibit accumulation of anthocyanins as well as expression of anthocyanin biosynthetic genes chalcone synthase (LcCHS), chalcone isomerase (LcCHI), flavanone 3-hydroxylase (LcF3H), dihydroflavonol 4-reductase (LcDFR), anthocyanidin synthase (LcANS), and UDP-glucose: flavonoid 3-O-glucosyltransferase (LcUFGT) that were again up-regulated accompanied by elevated anthocyanin accumulation after debagging and exposure the fruits to sunlight , DFR, O-methyltransferase (OMT) as well as UFGT. Exposure to light was shown to increase anthocyanin concentrations in grape berry skin regardless of ambient temperature , has been reported by light exposure in different cultivars such as Shiraz  and MdUFGT  fruit, anthocyanin accumulation is unaffected by light  as well as those specifically responsible for anthocyanin , flavonol , and PA  biosynthesis , high light induced expression of positive regulator FvMYB10 but not FvMYB1, a repressor of anthocyanin biosynthesis  that is needed for light-mediated induction of CHS. Since then, LRUs as well as other light-responsive elements have been reported not only in promoters of flavonoid structural genes, such as MdDFR and MdUFGT in apple  fruit  that is present in the promoters of the structural target flavonoid genes. The MRE has been found to be necessary for light-induced expression of the structural flavonoid genes, such as CHS , a bZIP transcription factor promoting photomorphogenesis , gene encoding MYB transcription factor, in tomato hp mutant positively affects fruit flavonoid content  at 5°C compared to the control fruits  and part of UV-B (280–320 nm) while most of the UV-B and all UV-C (<280 nm) radiation is absorbed by the ozone layer. Over the last decades, depletion in ozone layer has increased the level of solar UV-B radiation reaching the earth and now approximately 0.5% of total solar light radiation accounts from UV-B  damaging DNA, proteins and photosynthetic apparatus in plants, but these effects are dose and phenotype dependent  was responsive to UV-B. Of the related transcription factors , only VvMYB12 was found to be responsive to UV-B. In the same study, other candidate genes associated with low and high UV-B fluence responses  showed variable results.Liu et al.  reportedArabidopsis COP1 has been shown to act as a positive regulator by interacting with UVR8 and promote through an unknown mechanism the expression of HY5  of supplemental UV-B radiation, under controlled conditions, in order to study the effect on grape traits including flavonoid profile. The contents of anthocyanins and flavonols were enhanced by UV-B in grape berry skin, and qualitative differences were also detected in the flavonol profiles compared to untreated fruits  and Chinese sand pear  exposed to white light supplemented with UV-light was after 72 h in accordance with enhanced transcript levels of flavonoid pathway genes such as PpC4H, Pp4CL, PpF3H, PpDFR, PpCHI, PpPAL, PpCHS, PpLDOX) were consistent with the detected metabolite levels  on flavonoid contents and antioxidant activity in post-harvest blueberries (V. corymbosum). The results indicated substantial increase in flavonol and anthocyanidin glycosides and antioxidant activities instantly after the treatments. However, the contents decreased to the same level with the untreated control berries after 19 h from the treatments. In strawberry, the effect of 4.1 kJ m−2 UV-C radiation on anthocyanin content, antioxidant activity, and overall quality was studied in post-harvest berries at the large green maturity stage  showed contradictory results in the flavonoid levels between different latitudes  cultivars. However, these field experiments were performed in long day conditions with the differences in day length varying from about 16 h to 24 h during the growth period.Increased levels of flavonoids, especially anthocyanins under longer photoperiod have been detected in different plant species (Camm et al., Arabidopsis (Lau and Deng, Increasing evidence has been gathered to support the idea that ubiquitination-mediated targeted protein degradation is involved in controlling day length perception in plants in response to photoperiod. Ubiquitin-mediated protein degradation by COP1 has been shown to be important element in stabilization of circadian clock components in In conclusion, flavonoid composition in fruits is strongly affected by surrounding light conditions. In general, higher solar radiation tends to increase flavonoid content in fruits. Specific wavelengths can also alter the profile of flavonoids in fruit tissues. However, variation in response can be high between and even within species. Interaction of specific light conditions with other environmental factors can also change the response markedly. Although biosynthesis of flavonoids has been extensively studied, only recently the underlying molecular mechanisms of light-controlled flavonoid biosynthesis have begun to be revealed. Some of the mechanisms shown in model plants, such as COP1-mediated signaling pathways, have recently also been found in fruit tissues. Understanding the regulation of flavonoid biosynthetic pathway as well as involved light signaling machinery in fruit producing species is important to generate and select fruits enriched with flavonoid compounds to obtain desirable dietary and health-beneficial properties. The same compounds can also affect to self-life of the fruits. Moreover, several recent studies show that pre- and post-harvest treatments with selected light conditions have potential for commercial applications.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Systematic reviews can include cluster-randomised controlled trials (C-RCTs), which require different analysis compared with standard individual-randomised controlled trials. However, it is not known whether review authors follow the methodological and reporting guidance when including these trials. The aim of this study was to assess the methodological and reporting practice of Cochrane reviews that included C-RCTs against criteria developed from existing guidance.nd December 2013), and the 50 most recent reviews that included C-RCTs were retrieved. Each review was then assessed using the criteria.Criteria were developed, based on methodological literature and personal experience supervising review production and quality. Criteria were grouped into four themes: identifying, reporting, assessing risk of bias, and analysing C-RCTs. The Cochrane Database of Systematic Reviews was searched  of the 33 reviews reported the method of cluster adjustment for their included C-RCTs. For assessing risk of bias, only one review assessed all five C-RCT-specific risk-of-bias criteria. For analysing C-RCTs, of the 27 reviews that presented unadjusted data, only nine (33%) provided a warning that confidence intervals may be artificially narrow. Of the 34 reviews that reported data from unadjusted C-RCTs, only 13 (38%) excluded the unadjusted results from the meta-analyses.The methodological and reporting practices in Cochrane reviews incorporating C-RCTs could be greatly improved, particularly with regard to analyses. Criteria developed as part of the current study could be used by review authors or editors to identify errors and improve the quality of published systematic reviews incorporating C-RCTs. Systematic reviews summarise existing studies of interventions for a particular disease. Cochrane reviews are high-quality systematic reviews of primary research in human health care and health policy, and are conducted using standard methods by review groups within Cochrane . RandomiIn an I-RCT, individual participants are randomly allocated to intervention groups. However, sometimes it is impractical, even impossible, to randomise individuals but it may be feasible to randomise clusters of individuals  to intervention groups . TherefoThe Cochrane Handbook  and otheet al. [This report introduces assessment criteria that were developed based on the published guidance, and which were used to examine the methodological and reporting quality of Cochrane reviews that include C-RCTs. Specifically, this study assesses whether the following details are considered and/or reported in systematic reviews: C-RCTs are identified throughout the review; general cluster information is reported; risk of bias is assessed appropriately; and analyses are carried out correctly. The study also ascertained how often reviews incorrectly analyse C-RCTs in the same way as I-RCTs. This research updates and extends the previously published review by Laopaiboon et al. , which eCriteria were developed to assess the quality of each review that included C-RCTs. The criteria were categorised under four themes: identifying C-RCTs, reporting C-RCTs, assessing risk of bias of C-RCTs, and analysing C-RCTs. The criteria were based on recommendations for review authors given in the Cochrane Handbook  and methThe 50 most recent Cochrane intervention reviews (new or updated) that reported the term “cluster” and/or “community” in the review (or references of included studies) and that included at least one C-RCT were eligible for inclusion in the current study. The presence of at least one C-RCT in the review was verified by obtaining the original trial articles for which the term “cluster” and “community” was used in the review. Review protocols and withdrawn reviews were excluded. Fifty reviews were considered sufficient to obtain a picture of current practice while keeping the study manageable. Only Cochrane Reviews were included because explicit guidance is available and promoted to review authors in the Cochrane handbook ; therefoReviews were sought via the Cochrane Database of Systematic Reviews  using the search terms “cluster” OR “community”.The full text was obtained for each review identified by the search. One author (MR) assessed the eligibility of reviews using a pre-prepared form. If eligibility was unclear, the review authors were contacted for more information.One author (MR) extracted data from both the reviews and their included C-RCTs using two pre-piloted forms . A second author (SD) independently extracted data for a sample of 10 reviews (20%); any differences in opinion between the two authors were resolved through discussion. The full trial report was retrieved for every C-RCT trial that was labelled as “community” or “cluster” in the review, and trials with “cluster” or “community” in the title. The original trial reports for each C-RCT were obtained by using the University of Liverpool electronic library and through inter-library loans. Data were extracted from the C-RCTs in order to assess the reviews using the criteria .Information was extracted regarding review characteristics, and the C-RCT details . See The characteristics of each review and C-RCTs were tabulated. Each review was assessed by MR against the proposed criteria using pre-piloted assessment forms. It was possible for one review to meet a criterion for some included C-RCTs but not meet the same criterion for other C-RCTs. For example for criterion 8, unit of randomisation may be reported for some C-RCTs but not for others. Therefore, the percentage of reviews meeting each criterion for all included C-RCTs was calculated and presented. For criteria 12, 18, 19–26, it was possible for a particular review to meet the criteria for one outcome but not for other outcomes; for these criteria, a review was judged to satisfy the criterion if the review satisfied the criterion for every outcome in the review. Reviews for which a criterion was not applicable were excluded from the calculation. The assessment result for each criterion for each review were tabulated.A total of 276 reviews were assessed for eligibility, and 50 reviews were included . S1 TablA total of 201 C-RCTs were included in the included reviews, and references for these C-RCTs are provided in Forty-five reviews (90%) included both C-RCTs and I-RCTs; five reviews (10%) included C-RCTs alone. Forty-six reviews (92%) included C-RCTs that randomised groups of individuals to interventions, and four reviews (8%) included C-RCTs that randomised individuals to interventions, with each individual considered to be a cluster of observations .The included reviews varied considerably in terms of disease area . Fig 1 sInterventions compared included: non-pharmacological (21 reviews); drugs (six reviews); screening (five reviews); training/education (four reviews); counselling/advice (four reviews); behavioural (two reviews); techniques (two reviews); psychosocial (one review); vaccination (one review); drugs/psychosocial/non-pharmacological (one review), drugs/non-pharmacological (one review), drugs/psychosocial (one review), drugs/psychosocial/behavioural (one review). A variety of outcomes were included in the reviews .About half of the abstracts of the reviews reported C-RCTs , and a little more than half were explicit that C-RCTs were eligible for inclusion . Most reviews described methods for analysis of C-RCTs . In results, 68% of reviews (34 reviews) identified C-RCTs in the description of the included studies (the “Description of studies” text), and most reviews  identified C-RCTs in the detailed “Characteristics of included studies” table. Only 24 reviews (48%) referred to C-RCTs in the risk of bias results tables, and fewer still  labelled C-RCTs in the efficacy results .One review was excluded from this analysis because none of the included C-RCT reports could be obtained. Of the remaining 49 reviews, systematic reporting varied across the criteria: 44 (90%) reported the unit of randomisation; 34 (69%) reported the study design; 22 (48%) reported the average cluster size; 29 (59%) reported whether the data were cluster-adjusted; eight (24%) reported the analysis method; and seven (30%) reported the ICC for each outcome of interest in each C-RCT.The percentages of reviews that reported these characteristics for at least one of the included C-RCTs  assessed the risk of baseline imbalances, whereas only one review (2%) assessed the risk of non-comparability with RCTs. The assessment of comparability between I-RCTs and C-RCTs was not applicable to five reviews that did not include I-RCTs. A small percentage of reviews assessed the risk of recruitment bias (10%), the risk of loss of clusters and individuals (24%), and the risk of incorrect analysis (26%).Only one review assessed all risk of bias criteria.Only 26% of reviews (13 reviews) stated whether all C-RCT results were adjusted or unadjusted in the forest plots, text or tables.The analysis of unadjusted data from C-RCTs was poor. Of the 27 reviews that presented unadjusted data, only nine (33%) provided a warning to say that CIs may be artificially narrow. Of the 34 reviews that reported unadjusted data, only 13 (38%) excluded the unadjusted results from the meta-analyses.Of 27 reviews that included trials with unadjusted data that did not state that it would not be possible to adjust data themselves, six (22%) adjusted unadjusted cluster data using the average cluster size and ICC as reported in the C-RCT trial reports. In the case of 10 reviews that estimated an ICC  to adjust cluster data, five (50%) carried out sensitivity analyses to investigate the effect of estimating ICC.A total of 31 reviews included trials that adjusted for clustering, of which 21 (68%) correctly extracted adjusted data.Of the 40 reviews that included both C-RCTs and I-RCTs in the same meta-analysis, only five reviews (13%) subgrouped the data according to study design to enable correct interpretation or investigation of heterogeneity. Furthermore, of 22 reviews that included C-RCTs with different units of randomisation in the same meta-analysis, no review subgrouped the data according to unit of randomisation.In the current study, assessment criteria were devised and used to evaluate how well Cochrane reviews that included C-RCTs were carried out in practice. Findings showed that reviews could be substantially improved in terms of methodology and reporting.The results of this study suggested that there is room for improvement in the identifying of C-RCTs for each of the assessed criteria. Perhaps the most concerning findings were that only 56% of reviews specified that C-RCTs were eligible for inclusion in the eligibility criteria (28 reviews), and that eight reviews did not describe any methods specific to C-RCTs (16%).In order to ensure that readers of Cochrane review understand the study design of included C-RCTs and are able to interpret results correctly, review authors ought to report several details about the C-RCT. The results of the current study suggested that Cochrane review authors are unaware of the importance of reporting these details about included C-RCTs; reporting standards were low across all criteria, with the exception of “unit of randomisation”. Only 59% of the reviews reported whether C-RCT results were adjusted for clustering, and therefore readers of these reviews would be unable to assess whether the data had been analysed correctly.The five additional risk-of-bias criteria for C-RCTs are listed in the Cochrane Handbook . Review The importance of performing appropriate analyses for C-RCTs is crucial because errors in the analyses can affect results and influence healthcare decisions. Only 26% of included reviews stated whether C-RCT results were adjusted, and only 38% of the reviews excluded unadjusted results from the meta-analyses. When unadjusted results are presented without indicating that no adjustments for clustering have been performed, CIs are too narrow and these incorrect results may influence conclusions drawn from the review. When confidence intervals are too narrow for C-RCTs, the overall pooled effect is more precise than it should be and this may lead to incorrect conclusions (e.g. a significant difference may be found where none exists). Furthermore, if confidence intervals are too narrow for C-RCTs, this also implies that inverse variance weights for these trials are overestimated, and thus the C-RCTs obtain more weight in the meta-analysis than they should. This may cause bias in favour of the results generated by C-RCTs.n = 92); this corroborates with the findings of this review , suggesting that very few authors perform risk-of-bias assessments that are specific to C-RCTs. Issues with incorrect analyses were also highlighted in the review of Cochrane Infectious Diseases Group reviews; for example, it was found that 57% of reviews (n = 92) combined non-adjusted C-RCTs with I-RCTs in the meta-analysis. [We believe that the current findings indicate that review authors do not understand or are not aware of appropriate methods and reporting in reviews including C-RCTs. The findings corroborate with research conducted in 2003; a study of 25 meta-analyses involving C-RCTs found that half (13/25) used inappropriate analysis methods that ignored the effect of clustering (i.e. review authors treated the included C-RCTs as though they were I-RCTs), and pooled these invalid results in meta-analyses . In the The main limitation of this research is that only Cochrane reviews were included. Yet, the criteria are certainly applicable to other types of reviews, such as health technology assessment reviews and reviews published in journals. However, the results in the current study may not be generalisable to other types of reviews because guidance regarding the inclusion of C-RCTs is given in the Cochrane handbook and extra support is provided by Cochrane editorial teams. As further work, this project could be extended to assess the inclusion of C-RCTs in other types of reviews.A second limitation of the current study is that only reviews that used the term “cluster” or “community” were included; therefore, any reviews that included C-RCTs but did not use these specific terms would not have been included. Identifying such reviews would involve obtaining the published article for each included trial in each review and would not be feasible. Such reviews may have been of poorer quality; therefore, the results reported in this article may be an underestimation of the methodological and reporting deficiencies identified in this study.A further limitation of this study is the search date; the last search was carried out in November 2013 and the included reviews were published between June and December 2013. Although, many Cochrane reviews have been published since this date, they are unlikely to differ greatly from those included in the current study because, to the authors’ knowledge, no updated or new guidance has been published or presented at conferences.In some respects, the quality of a review that includes C-RCTs is limited by the methodological and reporting quality of the included C-RCTs. For example, if a study does not adjust data for clustering, and does not report an ICC and average cluster size, it is difficult for review authors to obtain an estimate of treatment effect which takes the effects of clustering into consideration. The current study highlights that flaws exist in published C-RCTS, with many of the included C-RCTs not reporting key information, and not adjusting analyses for clustering. This could be due to trial authors not following the Consort statement for C-RCTs , or statIt is clear that a consolidated and comprehensive set of guidelines is needed that address the different aspects of including C-RCTs in systematic reviews to help improve the quality of published reviews including C-RCTs. An extension to existing systematic review and meta-analysis guidelines, such as the PRISMA statement , would mS1 File(DOCX)Click here for additional data file.S2 File(DOC)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


South Africa has the highest reported rates of multi-drug resistant TB in Africa, typified by poor treatment outcomes, attributable mainly to high default and death rates. Concomitant HIV has become the strongest predictor of death among MDR-TB patients, while anti-retroviral therapy (ART) has dramatically reduced mortality. TB Case fatality rate (CFR) is an indicator that specifically reports on deaths due to TB.The aim of this paper was to investigate causes of death amongst MDR-TB patients, the contribution of conditions other than TB to deaths, and to determine if causes differ between HIV-uninfected patients, HIV-infected patients receiving ART and those without ART.We carried out a retrospective review of data captured from the register of the MDR-TB programme of the North West Province, South Africa. We included 671 patients treated between 2000–2008; 59% of the cohort was HIV-infected and 33% had received ART during MDR treatment. The register contained data on treatment outcomes and causes of death.Treatment outcomes between HIV-uninfected cases, HIV-infected cases receiving ART and HIV-infected without ART differed significantly (p<0.000). The cohort death rate was 24%, 13% for HIV-uninfected cases and 31% for HIV-infected cases. TB caused most of the deaths, resulting in a cohort CFR of 15%, 9% for HIV-uninfected cases and 20% for HIV-infected cases. Cohort mortality rate due to other conditions was 2%. AIDS-conditions rather than TB caused significantly more deaths among HIV-infected cases receiving ART than those not (p = 0.02).The deaths among HIV-infected individuals contribute substantially to the high death rate. ART co-therapy protected HIV-infected cases from death due to TB and AIDS-conditions. Mechanisms need to be in place to ensure that HIV-infected individuals are retained in care upon completion of their MDR-TB treatment. Globally there were an estimated 250 000 cases of multi-drug resistant (MDR) tuberculosis (TB) in 2009 with SouEven though it is recognised that TB disease itself is responsible for a large proportion of deaths among patients treated for TB, data on the relative contribution of TB as compared to other causes is unknown. Information on TB as a specific cause of death  for a country can only be gleaned on a population-wide level from death notification records, but as such cannot be linked to specific TB cohorts or sub-groups treated during a specific year. The indicator of TB Case Fatality is seldom reported on its own for surveillance and TB impact measurements.,4 This sWe were particularly interested in deaths due directly to TB, and the relative contribution of AIDS-related sequelae on the additional deaths among the HIV-infected cases. Little is known about the specific mechanisms of continued high deaths rates of HIV-patients on ART  while feThe aim of this study was to investigate causes of death amongst a large cohort of MDR-TB patients, the relative contribution of conditions other than TB to death, and to determine how causes of death differ among HIV-infected patients depending on the provision and timing of ART. In addition, we used data on post-treatment deaths to determine the long-term survival of the cohort.We conducted a retrospective review of data routinely collected in a MDR-TB register kept by the NWP (this being the only province to keep a register at this time), consisting of data on causes of death, HIV status and ART usage. We included all patients with culture-confirmed MDR-TB treated between 2000–2008. Causes of death data were as per verbal autopsy.From 2003 all TB patients in NWP had access to HIV Voluntary Counselling and Testing (VCT) and were eligible for ART when CD4 cell count <450. Since 2006 ART eligibility criteria w changed to offer ART to MDR-TB patients regardless of CD4 cell count. Patients not yet on ART at onset of MDR treatment were not automatically initiated on ART, due to concerns around pill burden, overlapping toxicities and IRIS. ART initiation was usually delayed for four months (or at the end of the intensive phase).The TB-specific CFR was calculated as the number of deaths specifically due to the TB disease, divided by the cohort size. This differed from the death rate which is the overall number of all deaths of patients that died during treatment divided by the cohort size. The sum of the CFR and a mortality rate calculated from deaths due to other causes equals the death rate. Data elements captured in the register were: HIV-coinfection, ART usage, and treatment outcomes. Patients who had received no ART during treatment and who were referred for ART once MDR-TB treatment had been completed were defined in our analyses as “No ART”. Patients already on ART at MDR-TB treatment initiation were defined as “ART during treatment”. A small number of patients were started on ART at the end of the intensive phase of treatment, and included in the latter group. For treatment outcome analysis we combined patients with unknown outcome with default. The categories still on treatment and treatment stopped due to side effects with failure.The NWP started treating MDR-TB patients under DOTS-Plus  in 2000,Patients returned to the hospital at six-monthly intervals for wellness care. Post-treatment follow-up was censored at the end of 2010. The post-treatment follow-up period ranged between two years (patients who started in 2008) and ten years for patients who started in 2000. Patients were asked about recurrent TB, and if available the sensitivity pattern was acquired. Patients not attending the pre-scheduled follow up visit were fastidiously traced, and if found to have died, cause of death information was collected where available. Since 2008 the province has used an electronic DR-Register for DR-surveillance.This was a retrospective review of information previously collected in the provincial registers during routine practise. No patient identifiers were entered in the register; data were abstracted by patient record number.Between 2002–2008 711 MDR-TB patients were treated at the NWP MDR-TB hospital, of which 671 had a known HIV result, and of whom 58.6% (393/671) tested HIV-infected . In thisIn this cohort of 671 patients 158 deaths occurred during treatment , with onCryptococcus meningitis, lactic acidosis, Kaposi sarcoma, malaria, Pneumocystis pneumonia and a pulmonary embolus in one patient. No deaths due to IRIS occurred.Although 393 individuals were HIV-infected and thus eligible for ART  only 92/An additional 41 patients died during the post-treatment follow-up period , therebyThis study on the causes of death among MDR-TB patients spans a period of nine years in a setting with a high TB/HIV-coinfection burden. The MDR-TB programme in the NWP of SA ran a MDR-TB programme with close follow up of patients while on MDR-TB treatment as well as post treatment, and tracing of patients if visits were not attended. During the study period the overall successful treatment rate was 65%, which are higher than the average rate in South Africa or globally.  As such Globally there is a paucity of data looking at causes of death among MDR-TB patients, irrespective of HIV status, both during and after treatment. This excellent data on causes of death enabled us to calculate TB-specific case fatality rates. Accurate estimates of TB specific mortality are crucial for the proper evaluation of TB control programmes. Whereas the overall death rate was 24%, the TB-specific case fatality rate was much lower at 16%. As expected, the case fatality rate was lower for the HIV-uninfected sub-group (9%), but still higher than a reported rate of 5% for drug sensitive TB [The quality of the evidence on the impact of ART among HIV-infected MDR-TB patients is weak, ,27 as isThe main limitations of this study are related to the retrospective nature of the data collected under routine TB control programme settings. There was no CD4 cell count information in the register, which results would have enabled us to further investigate the association between CD4 cell count and death. Furthermore we had no data on time to death or on time from diagnosis to treatment start which would have allowed us to stratify our analysis by patients who died early on in treatment vs. those who died later on in treatment. We also had no access to patient demographic and other clinical data, which could have explained some of our findings. Similarly, genotyping of the MDR isolate and the recurrent isolate was not done, which may have provided information on relapse or a new infection. Generalisation of our findings were compounded by the fact that there was considerable variation between other cohort sizes and settings. ,22,24,25Future research should aim to identify patients at risk of clinical deterioration as early as possible during treatment and to intervene appropriately to prevent death. Furthermore, the mechanisms behind patients dying early on in treatment opposed to those who die later on in treatment need to be elucidated, as it is unknown if severe disease at onset of treatment leads to early death as compared to patients who die later on during treatment. Furthermore, due to recurrent TB and its impact on long term death rates, the optimal duration of follow up after MDR treatment need to be determined, and how this follow up can be done in the most cost effective manner.Findings on death after successful outcome also needs in depth exploration and related recommendations for prevention strategies devised.