Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds (‘pathway sensitivity’) and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability. Cells sense their surroundings and respond to soluble factors in the extracellular space. Extracellular factors frequently induce heterogeneous responses, thereby restricting the biological outcome to a fraction of the cell population. However, the question arises how such cell-to-cell variability can be controlled, because some cellular systems show a very homogenous response at a defined level of an extracellular stimulus. We derived an analytical framework to systematically characterize the cell-to-cell variability of intracellular signaling pathways which transduce external signals. We analyzed how heterogeneity arises from fluctuations in the total concentrations of signaling proteins because this is the main source of variability in eukaryotic systems. We find that signaling pathways can be highly variable or inherently invariant, depending on the kinetic parameters and the structural features of the cascade. Our results indicate that the cell-to-cell variability can be reduced by negative feedback in the cascade or by signaling crosstalk between parallel pathways. We precisely define the role of negative feedback loops in variability suppression, and show that different aspects of the dose-response curve can be controlled, depending on the feedback kinetics and site of action in the cascade. This work constitutes a first step towards a systematic understanding of cell-to-cell variability in signal transduction. External stimuli typically induce cellular responses by binding to cell surface receptors. Intracellular signaling networks transduce the signal, ultimately triggering gene expression responses in the nucleus. The basic building blocks of eukaryotic signaling networks are protein kinase cascades : The sigSignaling cascades can transduce information in different ways The dose-response curve of a signaling pathway relates the signaling activity to the amount of extracellular stimulus applied. The dose-response curve of signaling pathways is typically sigmoidal in shape and can be quantitatively described by the so-called Hill equation  manner see . The shmCell-to-cell variability is introduced into the models by assuming fluctuations in initial signaling protein expression levels. In contrast to previous studies on variability Our results show that generic five-step protein kinase cascades exhibit much stronger cell-to-cell variability than the yeast pheromone pathway, unless certain robustness requirements are fulfilled.Multi-step signaling cascades show a gradual dose-response behavior if the response of each individual cascade level is gradual as well The following ordinary differential equation describes the temporal evolution of the active kinase Each phosphorylation step is described as a reaction between the phosphorylated form  kinase  . The corThe steady state activity of each cascade level describing the activity upon long-term stimulation can be calculated by assuming that the kinase concentrations do not change over time  the variability cannot be reduced significantly by lowering the expression noise of certain signaling proteins; instead, a simultaneous noise reduction of all species would be required: The cell-to-cell variability can be quantified using the inter-quartile ratio (IQ ratio) which expresses the difference of cells with high and low signaling activities by dividing the third and first quartiles of the distribution (see Methods). Assuming realistic protein concentration fluctuations in Eq. 6, the IQ ratio only drops from 4.1 to 3.8 if the noise of 1 out of 9 signaling protein concentrations is eliminated, implying that the cell-to-cell variability remains essentially unchanged. (ii) the variability does not depend on the choice of the kinetic parameter values The variability principles derived from the analytical model (Eq. 1–7) were confirmed by explicit cell-to-cell variability simulations. To this end, each of the nine protein concentrations in the cascade was sampled from a log-normal distribution with a coefficient of variation that matches the experimentally observed variability of eukaryotic protein expression We investigated how the variabilities of the maximal pathway activation This protein concentration insensitivity can be explained as follows: strong negative feedback shifts the species The effects of strong negative feedback on the maximal pathway activation variability are less pronounced. Assuming log-normally distributed gene expression noise, the variance of The The We confirmed our findings concerning negative feedback regulation for more realistic feedback cascades with limited feedback strength Signaling cascades are often equipped with multiple negative feedback loops, some acting close to the receptor level, while others modulate the terminal cascade levels The steady state condition of the upstream feedback The steady state of We analyzed the downstream feedback model with limited feedback strength iability . Taken tiability .Our models predict that upstream negative feedback in the pheromone pathway may contribute to the invariant shmooing threshold, while downstream negative feedbacks may primarily ensure that all cells exhibit a similar maximal activation upon strong stimulation. One limitation of the negative feedback models is their shallow dose-response behavior which is inconsistent with the reported ultrasensitivity of the pheromone pathway The term ultrasensitivity describes signaling cascades with steep, sigmoidal dose-response curves that allow for all-or-none decision making. Ultrasensitive behavior has been reported for the yeast pheromone pathway, although the steepness of the dose-response curve differs between literature reports Two strategies exist to establish a very steep overall dose-response curve in a signaling cascade: Firstly, the all-or-none behavior may be primarily established at a single level, while the rest of the cascade shows gradual behavior (in isolation). Localized switching at the terminal cascade level has been reported for the yeast mating pathway Multi-step signaling cascades are capable of strong ultrasensitivity amplification, implying that a combination of multiple weak switches establishes a very steep overall dose-response curve. To simplify the mathematical analysis, we initially analyze a two-step signaling cascade with ultrasensitivity at each levelWe analyzed the overall dose-response curve relating the signaling output The activation resistances Numerical simulations were performed for a five-step signaling cascade, where each level was modeled using a Hill equation with n = 2 (similar to Eq. 14). The five-step signaling cascade exhibits very strong ultrasensitivity if low phosphatase activities are assumed for all cascade levels ; . The sde steps .The inherent invariance of the ultrasensitive system can be understood intuitively by considering an extreme case scenario, where each cascade level is a very steep switch : The cell-to-cell variability of a gradual cascade with realistic protein concentration fluctuations, quantified as the inter-quartile ratio (see Methods), only drops from 4.1 to 3.5 if a perfect correlation is introduced for a single kinase-phosphatase pair. This suggests that correlations in upstream signaling protein concentrations specifically promote the robustness of ultrasensitive systems.A way to increase the variability of the ultrasensitive cascade relative to seen in  , panel 2iability , panel 3Switch-like decision making may also be established if a single cascade level shows a very steep dose-response curve. Such a localized switch has been reported for the yeast mating pathway The switch-like dose-response at the terminal level . In this Section, we demonstrate that switching and invariance can be combined if the time window of variability suppression by negative feedback can be separated from the time window of switch-like decision making.de novo protein biosynthesis, thereby affecting signal transduction with a delay. Thus, a time window exists early after stimulation where the steepness of the dose-response curve is unaffected by transcriptional feedback. Yet, transcriptional feedback may promote robustness of the dose-response curve if it operates under basal conditions before stimulation as observed for the yeast mating pathway Feedback loops in mammalian signaling commonly involve transcriptional regulation, and the signaling cascades typically induce the expression of their own inhibitors We model basal transcriptional feedback by implementing a negative feedback loop in the single-switch cascade. The phosphatase at the second level is transcriptionally induced by the active terminal kinase   and alsoBasal signaling was implemented by assuming a low chronic level of the stimulus We performed cell-to-cell variability simulations of this system assuming a high Hill coefficient  . The bas . The sim , which i : TranscrThe invariance due to basal feedback is restricted to a certain range of activation resistances in the cascade, because increasing the expression of several phosphatases in the cascade shifts the system from complete switching of the whole cell population to heterogeneous switching of only a fraction of cells . In FiguRecent experimental evidence supports that Msg5, a basal transcriptional feedback regulator of the yeast mating pathway, suppresses the signaling variability upon pheromone stimulation Signaling networks commonly involve coherent feedforward loops where an upstream kinase controls a downstream target by two parallel pathways. Fus3, the yeast MAPK that mediates the shmooing response, is controlled by two pheromone-dependent pathways, both of which are required for full activation We model coherent feedforward regulation by extending the model of the five-step signaling cascade with a switch at the terminal level (Eqs. 16 and 17). The downstream signaling species tion of  . The effThe threshold In line with previous reports, we find that coherent feedforward regulation increases the ultrasensitivity of the dose-response curve, as the stimulus now enters with the exponent Explicit cell-to-cell variability simulations using Eq. 20 confirm that the threshold variability of the feedforward pathway  is less We conclude that joint regulation of Fus3 by Ste7 and Ptc1 may reduce the cell-to-cell variability of pheromone signaling. However, the simulated signaling thresholds still span a ∼50-fold range of stimulus concentrations , implyinA key question in biology is how cellular systems function robustly in face of internal and external fluctuations. We comprehensively characterized the determinants of cell-to-cell variability in protein kinase signaling cascades, and summarized our main findings in In this paper, we made a central simplifying assumption to study the behavior of protein kinase cascades: it was assumed that the individual levels of a protein kinase cascade function as isolated modules. Based on this assumption, we described the local dose-response behavior of each cascade level by Michaelis-Menten or Hill equations (Eqs. 3 and 14), and studied their behavior in tandem to gain insights into the global dose-response behavior of the five-step cascade. Depending on the protein concentrations and kinetic parameters in the cascade, the modularity assumption may be violated, and explicit simulations of all enzyme-substrate binding and dissociation events in the cascade may be necessary: strong sequestration of upstream kinases by highly abundant downstream substrates affects the phosphorylation state of the upstream kinase, thereby leading to retroactivity in the cascade Negative feedback is known to suppress the variability of biological systems and to reduce the steepness of signaling dose-response curves In this paper, we analyzed the steady state behavior of negative feedback circuits, but did not focus on their dynamical properties such as sustained oscillations One way to reduce noise in biological signaling systems is to correlate the expression fluctuations of antagonizing enzymes Gradual signaling systems transduce information quantitatively and faithfully report the stimulus concentration in the extracellular milieu. We therefore assumed that the maximal pathway activation and the pathway sensitivity of a gradual system should be invariant. However, recent experimental work revealed that the absolute signaling activities of a mammalian MAPK cascade pathway are highly variable, while the stimulus-induced fold-change in the signal is invariant between cells www.sympy.org). Numerical simulations were performed using the odeint function of the scipy.integrate package (www.scipy.org). The details of the model implementation process can be found in the figure captions and in the Supplemental All simulations were done in Python. Analytical solutions were obtained using the open-source python package SymPy . The IQ Ratio is a dimensionless number that reflects the fold difference between cells with high and low levels, while excluding (extreme) outliers. To further support our findings, we show in the Supplemental Figures S1, S2, S3, S4, S5, S6 that very similar results are obtained when using the coefficient of variation (CV = mean/standard deviation) as a measure of variability.Various methods were employed to characterize the cell-to-cell variability of the signaling dose-response curves. In vely see , and 6B.vely see , and 6C,Figure S1Cell-to-cell variability of a gradual kinase cascade quantified using the coefficient of variation. Concepts similar to (PDF)Click here for additional data file.Figure S2Cell-to-cell variability of gradual kinase cascades with negative feedback regulation quantified using the coefficient of variation. The concepts in panels A and B are similar to (PDF)Click here for additional data file.Figure S3Cell-to-cell variability of ultrasensitive kinase cascades with distributed switching quantified using the coefficient of variation. The concepts are similar to (PDF)Click here for additional data file.Figure S4Cell-to-cell variability of kinase cascades with ultrasensitive switching at a single step quantified using the coefficient of variation. The concepts are similar to (PDF)Click here for additional data file.Figure S5Cell-to-cell variability of ultrasensitive kinase cascades with basal transcriptional feedback quantified using the coefficient of variation. The concepts are similar to (PDF)Click here for additional data file.Figure S6Cell-to-cell variability of ultrasensitive kinase cascades with feedforward regulation quantified using the coefficient of variation. The concepts are similar to (PDF)Click here for additional data file.Figure S7Numerical stability analysis of the upstream feedback cascade with low activation resistances reveals that high feedback cooperativity is required for sustained oscillations. The eigenvalues of the Jacobi matrix at the steady state were calculated numerically for different feedback cooperativities (PDF)Click here for additional data file.Figure S8Numerical stability analysis of the upstream feedback cascade with high activation resistances reveals that sustained oscillations are not possible even for very strong feedback cooperativity. The concepts are similar to (PDF)Click here for additional data file.Protocol S1Implementation of model variants.(PDF)Click here for additional data file.Table S1Parameter values used for simulations. The index (PDF)Click here for additional data file.Text S1Analytical derivations and numerical analysis of steady state stability.(PDF)Click here for additional data file.


Rice is one of the most important model crop plants whose heterosis has been well-exploited in commercial hybrid seed production via a variety of types of male-sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and proteins related to cytoplasmic male sterility (CMS), photoperiod-sensitive male sterility, self-incompatibility, and other types of microspores deterioration have been characterized through genetics or proteomics. Especially the latter, offers us a powerful and high throughput approach to discern the novel proteins involving in male-sterile pathways which may help us to breed artificial male-sterile system. This represents an alternative tool to meet the critical challenge of further development of hybrid rice. In this paper, we reviewed the recent developments in our understanding of male sterility in rice hybrid production across gene, protein, and integrated network levels, and also, present a perspective on the engineering of male-sterile lines for hybrid rice production. Oryza sativa) has been used as a staple food for people over 10,000 years. Being high in carbohydrates, low in fat, and rich in protein, rice has become one of the major food supplies for the expanding world population. Today, over 50% of the global population and three quarters of the Asian population live on rice. However, facing the challenge of population explosion and reducing amount of cropland in the world, it is obvious that improve the yield of cereal crops like rice, wheat, and corn are the only way to solve this problem  system is derived from the common wild species Oryza. rufipogon) from Hainan Island, China with an early maturing indica variety called Lian-Tang-Zao. Hybrid rice varieties based on CMS-HL have been widely grown in China since the beginning of this century because it performs better agronomically and produces higher quality grain than those varieties based on other CMS systems  technology have been used to analysis the protein expression profile between the Yuetai A and Yuetai B. Results showed that proteins associate with energy production were reduction in the anther of HL-CMS rice  genetic background. With wide compatibility and good agronomic traits, PA64S has become the most widely used female parent for two-line hybrid rice breeding. However, the fertility transition of PA64S is controlled mainly by temperature rather than by day length: PA64S exhibits male sterility at temperatures higher than 23.5°C during the anther development, but it converts to male fertility when the temperature is ~21–23°C. Long-day (14 h) conditions can suppress the degree of sterility to fertility conversion under low temperatures (21–23°C), but short-day (12 h) conditions cannot restore male fertility under high temperatures , is the first spontaneous photoperiod-sensitive genic male-sterile (PGMS) mutant found in the japonica cultivar Nonken 58 (NK58) grown in Hubei Province, China in 1973. Large-scale application of two-line hybrid rice in agriculture was developed quickly with the discovery of NK58S Shi, . The ferpms3 located on chromosome 12 was the original mutation which converts Nongken 58 to become the PGMS rice NK58S. Recently, pms3 was cloned and shown to encode a long non-coding RNA (lncRNA) named LDMAR. A sufficient amount of LDMAR is required for male fertility under long-day conditions. A spontaneous G-C mutation causing a SNP between NK58 and NK58S, eventually brings about heritable increased methylation in the promoter region of LDMAR, which reduces the level of LDMAR expression. This then results in premature programmed cell death (PCD) in anther development under long days, and hence male sterility , the normal pathways have been deteriorated by missing some key factors. From this perspective, the study of GMS occupies a core position for comprehensively understanding male sterility, including both CMS and EGMS.The development of the male reproductive system of rice is a very complex process Figure  which inOsMADS3 and OsMADS58 which belong to C-class MADS box transcription factor family were identified to control archesporial specification and the start of anther development  mutant shows increased numbers of male and female sporocytes and disrupted anther wall layers. MSP1 which encodes a LRR receptor-like kinase (LRR-RLKs) might play the function of initiating anther wall development and controlling male and female sporogenesis  protein seems to be one of the earliest players in tapetal development  mutant has highly vacuolated and retarded PCD tapetum and degenerated microspores , were identified by chromatin immunoprecipitation-PCR and electrophoretic mobility shift assay (EMSA) as direct downstream targets of OsTDR protein. Recently, APOPTOSIS INHIBITOR5 (API5) and its two partners AIP1 and AIP2 were also found to bind the promoter of OsCP1 and increase its expression level. They regulate tapetal PCD through regulating the expression of OsCP1 . The GAMYB also acts downstream of MSP1-OsTDLIA which is supported by the fact that down-regulation of GAMYB was observed in msp1-4 anthers  which plays critical roles in rice growth, especially in pollen development. Interestingly, the attempt to over-express Ugp1 actually silenced Ugp1 and unexpectedly acquired a novel thermo-sensitive genetic male sterility rice. Pollen mother cells of Ugp1-silenced plants appeared normal before meiosis, but during meiosis normal callose deposition was disrupted. Consequently, the pollen mother cells began to degenerate at the early meiosis stage, eventually resulting in complete pollen collapse. This finding might give us a novel way to practical breeding of EGMS lines in rice using genes that are in the network of male reproductive development.Theoretically, we could acquire male-sterile plants by disturbing the expression of any genes that were identified to take part in anther or pollen development. To date, very few of them have been successfully applied in practical hybrid rice breeding. Chen et al.  used theEngineered nuclear male sterility controlled by a single gene is easy to handle from the angle of using current molecular techniques, but a problem with these nuclear MALE-STERILE transformants is that they segregate for male fertility or sterility and must be over planted and rogued by hand or sprayed with herbicides to remove male-fertile plants  sense the different photoperiod and alter their expression is also very interesting and awaits much deeper research.Taken together, concerted efforts from multiple angles would pave the road to rapid progress in understanding the complex regulatory networks and finally enable us to attain a holistic concept for the development of rice male reproductive system. All the data harvested in these studies will definitely help us to freely manipulate fertility in rice and other crop plants to facilitate hybrid breeding in the future.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.


Prescribing of antibiotics for upper respiratory tract infections (URTI) varies substantially in primary care.To describe and compare antibiotic and antipyretic/anti-inflammatory drugs use, URTI symptoms' resolution and occurrence of potentially-associated infections in patients seeking care from general practitioners (GPs) who exclusively prescribe conventional medications (GP-CM), regularly prescribe homeopathy within a mixed practice (GP-Mx), or are certified homeopathic GPs (GP-Ho).The EPI3 survey was a nationwide population-based study of a representative sample of 825 GPs and their patients in France (2007–2008). GP recruitment was stratified by self-declared homeopathic prescribing preferences. Adults and children with confirmed URTI were asked to participate in a standardized telephone interview at inclusion, one-, three- and twelve-month follow up. Study outcomes included medication consumption, URTI symptoms' resolution and potentially-associated infections (sinusitis or otitis media/externa) as reported by patients. Analyses included calibration to account for non-respondents and groups were compared using multivate analyses adjusting for baseline differences with a propensity score.518 adults and children with URTI (79.3% rhinopharyngitis) were included (36.9% response rate comparable between groups). As opposed to GP-CM patients, patients in the GP-Ho group showed significantly lower consumption of antibiotics (Odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.27–0.68) and antipyretic/anti-inflammatory drugs  with similar evolution in related symptoms . An excess of potentially-associated infections  was observed in the GP-Ho group . No difference was found between GP-CM and GP-Mx patients.Patients who chose to consult GPs certified in homeopathy used less antibiotics and antipyretic/anti-inflammatory drugs for URTI than those seen by GPs prescribing conventional medications. No difference was observed in patients consulting GPs within mixed-practice. A non-statistically significant excess was estimated through modelling for associated infections in the GP-Ho group and needs to be further studied. High antibiotic consumption is said to be associated with the emergence and dissemination of multi-resistant bacteria in the community While there is evidence that homeopathy has little effect on URTI or flu-like symptoms The objectives of this one-year population-based cohort study was to describe and compare antibiotic and antipyretic/anti-inflammatory drugs use, resolution of URTI symptoms and occurrence of potentially associated infections in patients who seek care for URTI from general practitioners (GPs) showing different prescribing preferences for homeopathy: strictly prescribers of conventional medications reluctant to prescribe homeopathic medicines (GP-CM), regular prescribers of homeopathic medicines in an otherwise conventional medical practice (“mixed prescribing” or GP-Mx), and certified homeopathic GPs (GP-Ho), who also prescribe conventional medications.th revision) The EPI3 survey was a nationwide survey of primary care practice conducted in a representative sample of GPs from across France and their patients between 2007 and 2008 At inclusion, GPs completed a medical questionnaire for each patient included in the cohort with the main reason diagnosis, a standardized history of respiratory diagnoses in the previous year and of respiratory symptoms in the current episode of URTI, up to five other diagnoses (comorbidities) and all drugs prescribed that day. Diagnoses were coded according to the ICD-9 classification by a trained research assistant. All consenting patients completed a self-administered questionnaire at inclusion, in the waiting room, collecting information on lifestyle and history of medical consultations and hospitalizations in the previous year. The follow-up telephone interview at one month included the inventory of URTI symptoms obtained via patients' self-assessment of changes in those symptoms from baseline . Interviews at one, three and twelve months spanned the patient's history since the previous interview with regard to the occurrence of infections associated with the URTI, defined as patients' self-report of a diagnosis (with or without treatment) of otitis (media or externa) and/or sinusitis, and any drug consumption . This calendar was used to aid patients' recall during the one-year follow-up. Drug consumption, whether prescribed or obtained over-the-counter or from the family pharmacy, was assessed using a standardized method named Progressive Assisted Backward Active Recall (PABAR) previously validated against medical prescriptions Four outcomes were evaluated as yes/no responses . ConsumpParticipants and nonparticipants in the cohort study were compared using information collected from all surveyed patients at baseline. Characteristics of patients not participating in the survey  were used to calibrate the final sample using a method known as the CALMAR procedure The three groups were compared for the four binary outcomes using the GP-CM group as the reference group using logistic regression analyses adjusted for baseline characteristics (propensity score) and the number of URTI symptoms at baseline  and the French National Medical Council (CNOM). In accordance with CNIL regulation, written consent was obtained from each participating adult patient and from one of the parents accompanying each participating child. Participating physicians received compensation fees for their participation but not patients.The general EPI3 health survey included 825 GPs and 8,559 participants . Of the latter, 1,402 children and adults fulfilled the specific inclusion criteria for the URTI cohort, of which 699 (49.9%) agreed to participate with 518 (36.9%) responding to all three follow-up interviews and therefore included in the analysis. Participants were slightly more often females (59.8%) compared to nonparticipants (54.2%), more likely to belong to the 50+ years age group , to have completed high school education (57.1% and 53.4%), and less likely to be a current smoker , all differences statistically significant . Of partWith regard to types of URTI at baseline, there were little differences between the three groups. The most commonly reported was rhinopharyngitis (73.9%), followed with bronchitis (28.0%), flu-like symptoms (12.7%), Strep-A negative viral angina (8.7%) and bronchiolitis (5.2%) . For symPrescribing preferences of physicians in the three groups were confirmed at baseline by their respective prescribing rates of homeopathic drugs, which were 0.6%, 9.4% and 61.3%, respectively, in the GP-CM, GP-Mx and GP-Ho groups . ConversFor resolution of the URTI symptoms at one-month follow-up, patients declared similar improvement in the three groups  with no statistically significant differences in multivariate analyses adjusting for baseline characteristics . On the other hand, there was a slightly higher rate of potentially associated infections (otitis media/externa and sinusitis) reported by patients in the GP-Ho group over twelve months follow-up (17.7%) compared to the GP-CM group (16.9%), a difference that was not statistically significant . Results on symptoms resolution and occurrence of potentially associated infections exhibited wide confidence intervals.This population-based prospective cohort study described and compared clinical management and evolution of patients consulting for URTI between three groups of physicians with different levels of prescribing preferences for homeopathy. At baseline, patients who chose to be seen by GP-Ho for URTI declared to have used half the amount of antibiotics and antipyretic/anti-inflammatory drugs compared to patients seen by conventional medicine practitioners. This lower consumption of conventional medications in the GP-Ho group was sustained over the 12-month follow-up. At the same time, no difference in the resolution of the URTI symptoms was observed between groups but confidence intervals were wide indicating lack of statistical power for that outcome. Similarly, the excess rate of potentially associated infections observed in the GP-Ho group, although non-statistically significant, cannot be ruled out. No difference was seen in patients from the GP-Mx group, which was comparable to the GP-CM group on all outcomes.Previous observational studies conducted in several countries have shown an antibiotic-sparing effect resulting from management by GPs using homeopathy without increase in complication rates of URTI The rise in bacterial resistance to antibiotics is widely recognized as a major threat to public health Our results could be explained in part by the different characteristics of patients seen by GPs who practice homeopathy and by the lower rate of fever, nasal obstruction and cough in the GP-Ho group at baseline compared to the two other groups. Adjustment by severity of URTI and other potential confounders did not alter the results but residual confounding cannot be excluded. As for our observation of a small non-statistically significant excess in the occurrence of potentially associated infections in the GP-Ho group, it may be due to chance or to a lack of protection against these infections. The latter instance cannot be ruled out as the study lacked statistical power to distinguish between the two interpretations.The participation rate in this URTI cohort study was only 36.9% of eligible patients, which is comparatively equivalent to what is seen in general health surveys where patients are asked to participate in a long follow-up Another potential limitation is related to the nature of URTI diagnoses that have not been validated against a disease management guideline. No such attempt was made to preserve the authenticity of primary care practice in real life. This is partially why diagnoses of bronchitis and bronchiolitis were included in this cohort as they may represent co-occurrences of URTI. The standardized collection of symptoms allowed a partial control for severity of URTI at inclusion. Two conditions, sinusitis and otitis, were studied as proxies for the occurrence of infections potentially associated to the URTI. Diagnoses were obtained from patients' self-declaration over the telephone and should not be interpreted strictly. It is not known whether they represent true complications or URTI and/or represent associated infections as a result of no antibiotic treatment. This should be studied, particularly in view of the apparent excess of infections observed in the GP-Ho group. However, the lack of diagnostic confirmation should not bias the comparison between the groups but may bias the results toward the null and thus reducing the statistical significance of the observation. In view of the different characteristics of patients in the GP-Ho group at inclusion, the lower frequency of symptoms reported that group might be explained by a lower threshold of these patients to consult a physician rather than a true difference in the diagnoses makeup of the group.Finally, results on resolution of URTI symptoms were underpowered to show non-inferiority between groups as illustrated by the wide confidence intervals. The estimates however were close to unity in both GP-Mx and GP-Ho groups, indicating similarity for self-declaration of symptoms resolution at one month between patients from both groups. Sample size was sufficient to show an Odds ratio superior to 1.22 (or inferior to 0.82) for the main outcomes. Strengths of this study included the length of follow-up and the quality of the data which combined medical and patient information collected from physicians and patients. Drug consumption was obtained from patients interviews using a validated approach In conclusion, this cohort study showed that patients with URTI who choose to consult homeopathy-certified GPs in primary care, had a lower consumption of antibiotics and antipyretic/anti-inflammatory drugs as compared to patients seen by physicians who use conventional medicine. This difference may be due to specific attributes of either physicians or patients but also interactions between the two. No difference was observed for patients consulting GPs with mixed prescribing habits. The non-significant excess of potentially associated infections in the GP-Ho group esteemed through modelling may be due to chance alone or driven by less use antibiotics. Further studies are needed to clarify this effect. Other large studies are needed to establish the long-term consequences of different prescribing practices in primary care.


Apart from cis-eQTLs that are typically easily identifiable using microarrays or RNA-sequencing, DeepSAGE also revealed many cis-eQTLs for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. We also identified and confirmed SNPs that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of messenger RNAs (mRNA). We then combined the power of RNA-sequencing with DeepSAGE by performing a meta-analysis of three datasets, leading to the identification of many more cis-eQTLs. Our results indicate that DeepSAGE data is useful for eQTL mapping of known and unknown transcripts, and for identifying SNPs that affect alternative polyadenylation. Because of the inherent differences between DeepSAGE and RNA-sequencing, our complementary, integrative approach leads to greater insight into the molecular consequences of many disease-associated variants.Many disease-associated variants affect gene expression levels  and expression profiling using next generation sequencing (NGS) technology is a powerful way to detect these eQTLs. We analyzed 94 total blood samples from healthy volunteers with DeepSAGE to gain specific insight into how genetic variants affect the expression of genes and lengths of 3′-untranslated regions (3′-UTRs). We detected previously unknown  Many genetic variants that are associated with diseases also affect gene expression levels. We used a next generation sequencing approach targeting 3′ transcript ends (DeepSAGE) to gain specific insight into how genetic variants affect the expression of genes and the usage and length of 3′-untranslated regions. We detected many associations for antisense and other non-coding transcripts, often in genomic regions containing retrotransposon-derived elements. Some of these variants are also associated with disease. We also identified and confirmed variants that affect the usage of alternative polyadenylation sites, thereby potentially influencing the stability of mRNAs. We conclude that DeepSAGE is useful for detecting eQTL effects on both known and unknown transcripts, and for identifying variants that affect alternative polyadenylation. Genome-wide association studies (GWAS) have associated genetic variants, such as single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), with numerous diseases and complex traits. However, the mechanisms through which genetic variants affect disease phenotypes or physical traits often remain unclear. To gain insight into these mechanisms, we have combined genotype data with gene expression data by conducting expression quantitative trait locus (eQTL) mapping. Previously, the level of gene expression was primarily assessed using oligonucleotide microarrays, which was a powerful method to profile the transcriptome 3′-CATG sequence of each transcript  from total blood samples from 94 healthy, unrelated individuals from the Netherlands Twin Register (NTR) and the Netherlands Study of Depression and Anxiety (NESDA) HBA1, HBA2, HBB) genes, we were left with sufficient sequencing depth to detect a total of 9,562 genes at a threshold of at least two tags per million.For cis-eQTL mapping, evaluating only those combinations of SNPs and tags that were located within a genomic distance of 250 kb, while using a Spearman rank correlation test  controlled at 0.05). We identified 540 unique cis-regulated tags.Once reads had been mapped, we quantified the expression levels of sequenced tags and performed cis-eQTLs . The list of detected eQTLs is given in To subsequently increase the statistical power of eQTL detection, we used principal component analysis (PCA) to correct for technical and known and unknown biological confounders. The first principal components (PC) generally capture a high percentage of the expression variation, and these PCs mostly reflect technical, physiological and environmental variability. Removing this variation allows for the detection of more eQTLs cis-eQTLs with cis-eQTLs that we had identified using the Affymetrix HG-U219 expression microarrays on the same 94 samples. In that analysis we detected cis-eQTLs for only 274 genes (FDR<0.05), only a third of what we identified using DeepSAGE.We then compared the DeepSAGE cis-eQTLs detected using Affymetrix microarrays nearly always reflected genes that are highly expressed in blood, whereas for DeepSAGE the detected cis-eQTL genes had expression levels that could be much lower  .It was therefore not a surprise that only 39% of the identified DeepSAGE rection) . Indeed,vice versa, we could significantly replicate 75% of the detected Affymetrix cis-eQTLs with the same allelic direction in the DeepSAGE data  to get the same number of significant cis-regulated genes as identified in the microarray analysis of the 94 samples. As such, these results clearly indicate that DeepSAGE has higher statistical power than microarrays.We estimated the reduction that could be made in the sample size of the sequencing-based dataset to get the same number of While most microarray platforms interrogate mainly the protein-coding part of the transcriptome, NGS-based expression profiling will detect the majority of all expressed transcripts. Indeed, we detected eQTLs for known, but non-protein coding, genes: 8 antisense genes and 31 lincRNAs .cis-eQTL effects on previously unknown transcripts. Of the 1,011 tags with a significant cis-eQTL effect, 230 did not map to known transcripts. Many of these tags map to retrotransposon-derived elements in the genome, which are known to be a source of novel exons cis-eQTLs that did not map to annotated genes mapped to 72 unique LINE, SINE and LTR elements in the genome , as published in the Catalog of Published Genome-Wide Association Studies. 104 of the 6,446 unique trait-associated SNPs were significant cis-eQTLs in our data , two tags antisense to the known transcripts, and two tags outside the annotated transcripts. These results indicate that several trait-associated SNPs affect the expression of previously unknown transcripts, adding functional relevance to SNPs and transcripts that are so far without annotation.We identified 13 trait-associated SNPs that did show a significant  dataset . This inPRSS3, we now saw that it also affects the expression of UBE2R2. There are many links between the ubiquitin system and bipolar disorder reported in the literature  contained tags that were regulated in opposite directions  structure and structure in the expression data) and performed the same analysis as above . Using the differential correlation significance threshold of 102≥0.8). We did not find any strong evidence for SNPs influencing the cleavage site and focused on cis-regulating SNPs located within polyadenylation signals. Considering the length and the motif of canonical and non-canonical polyadenylation signals cis-regulating SNP. We identified five SNPs that likely affect polyadenylation because there was a change in the polyadenylation signal  on the LD between the detected cis-eQTLs and the putative causative SNPs; by imputing to the 1000 genomes dataset we may have missed causative SNPs unique to the Dutch population; and the list of experimentally validated polyadenylation sites is not exhaustive, because their detection depends on the expression level and cell type analyzed.Since the eQTL SNPs are usually in strong LD with multiple SNPs, it is difficult to conclude whether a SNP is causal or which SNP is the likely causal variant. To identify the likely causal variant, we assessed whether any of these SNPs caused changes in polyadenylation site usage. A direct effect on alternative polyadenylation can be explained by a change in the polyadenylation site (corresponding to the cleavage site) or in the polyadenylation signal (a six-nucleotide motif located between 10–30 bases upstream of the cleavage site). We searched for likely causative SNPs in linkage disequilibrium with the polyA-QTL SNP .We have described the results from cis-eQTLs was much smaller than in the DeepSAGE data, indicating the higher power of DeepSAGE in eQTL mapping. Moreover, for 220 of the cis-eQTLs SNPs detected by DeepSAGE we did not detect a significant cis-eQTL in a much larger microarray-based study in 1,469 whole peripheral blood samples We performed eQTL mapping on the microarray expression data of the same samples and the number of detected cis-eQTLs detected in microarray data was higher in highly expressed genes, whereas for DeepSAGE the detected cis-eQTL genes had expression levels that could be much lower , detection of such an RNA molecule is direct proof that it is being expressed.We observed that the number of ch lower . This mecis-eQTLs in the microarray data with a consistent allelic direction  while the DeepSAGE study was on total blood, and some cis-eQTLs may be tissue-specific We subsequently conducted a cis-eQTLs, as it is able to quantify the expression of novel transcripts, and to detect alternative polyadenylation effects and alternative 3′-UTR selection. It is complementary to other sequencing-based approaches, as they each reveal slightly different regulatory effects of genetic variants. Different sequencing-based eQTL analyses generally deliver consistent results, allowing for meta-analyses across different technologies. Future eQTL mapping studies based on DeepSAGE and other next generation sequencing strategies, using larger cohorts and different techniques, will likely reveal a more comprehensive picture of how far genetic variation affects the expression of protein-coding genes and non-coding RNAs.We conclude that DeepSAGE technology is useful to determine The medical ethical committee of the VUMC, Amsterdam, the Netherlands, approved the collection and analysis of material blood, DNA and RNA from the 94 participants from the Netherlands Twin Registry (NTR) and the Netherlands Study of Depression and Anxiety (NESDA).We analyzed 21 bp DeepSAGE data from total blood RNA of 94 unrelated individuals who participated in NTR or NESDA. RNA was isolated using PaxGene tubes The NTR-NESDA data was imputed using Beagle v3.1.0, with HapMap2 release 24 as a reference.Tags from DeepSAGE sequencing were aligned to the NCBI build 37 reference genome using Bowtie v. 0.12.7 allowing for a maximum of 1 mismatch and a maximum of 2 possible alignments (-n 1 -k 1 -m 2 –best –strata options).The expression values were both quantified on an individual tag and transcript level. For the tag-wise analysis, the total number of occurrences of each unique tag in each sample was counted. We only included tags that were present in >90% of samples. Tags with SNPs in the CATG recognition sequence (according to dbSNPv135) and the transcripts containing those tags were removed before eQTL analysis, since these SNPs can affect the position of the SAGE tag in the transcript. For the transcript-wise analysis, the tag counts for tags overlapping the exons of a transcript by at least half of the tag length were summed.Coordinates of LINE, SINE, LTR elements were derived from UCSC's RepeatMasker track (update: 2009-04-24).To calculate the GC content per individual for DeepSAGE data, GC frequencies for all mapped tags were summed after excluding the twenty most abundant tags, since their high abundance would give biased estimates.Before eQTL mapping, transcript and tag expression values were quantile normalized. To perform cis-eQTL mapping, association of SNPs with the expression levels of tags or transcripts within a distance of 250 kb (as this is the average size of linkage regions) of the midpoint of the transcript or tag were tested with a non-parametric Spearman's rank correlation. Multiple testing correction was performed, controlling the false discovery rate (FDR) at 0.05. To determine the FDR, we created a null distribution by repeating the cis-eQTL analysis after permuting the sample labels 10 times cis-eQTL effects. To mitigate the effects of non-genetic sources of variability, we first log2 transformed the data and centered and scaled each tag, and subsequently applied PCA on the sample correlation matrix. We then used the first PCs as covariates, and re-did the non-parametric cis-eQTL mapping on the residual expression data  according to the position of the DeepSAGE tags with opposite directions, the position of LongSAGE tags from CGAP, and the position of reported and predicted polyadenylation sites from polyA_DB database. To calculate the coverage in proximal and distal regions in RNA-seq datasets, we created a coverage histogram from each .bam alignment file using coverageBed tool from BEDTools package (version 2.17.0) HPS1 and IRF5 was quantified by qRT-PCR, which was performed on a subset of RNA samples used for the DeepSAGE sequencing. cDNA was synthesized from 400 ng of total RNA using BioScript MMLV Reverse Transcriptase (Bioline) with 40 ng of random hexamer and oligo(dT)18 primers following manufacturer's instructions  for IRF5. Each measurement was performed in duplicates. PCR efficiency was determined using the LinRegPCR program Expression of short and long variants of cis-regulating the choice of polyadenylation site. To do this we used bi-allelic SNPs that pass QC from the 1000G European panel (v3.20101123) and took all SNPs that were in linkage disequilibrium with the query SNPs .We obtained all the proxy SNPs for all SNPs identified as cis-regulating SNPs in linkage disequilibrium, we kept only SNPs, which were located in the cis-regulated genes. The filtering was performed by intersecting .bed files containing SNPs coordinates and coordinates of cis-regulated genes from RefSeq database, using table browser tool in UCSC genome browser and the overlap intervals tool in Galaxy (version 1.0.0).From this list of AATAAA, ATTAAA, TATAAA, AGTAAA, AAGAAA, AATATA, AATACA, CATAAA, GATAA, AATGAA, TTTAAA, ACTAAA, and AATAGA. For every SNP located in a putative polyadenylation signal motif, the distance to validated and predicted polyadenylation sites from PolyA-DB was calculated. Only motifs within a distance of 30 nucleotides from a polyadenylation site were considered true polyadenylation signals. Newly formed polyadenylation signals were detected by changing the reference allele of the SNP with the alternative allele, followed by the same polyadenylation signal motif search using custom Perl scripts.Intersection of SNPs with validated and predicted polyadenylation sites was performed using annotation in the PolyA-DB database (PolyA_DB 1 and PolyA_SVM) on UCSC (table browser tool). Detection of SNPs within polyadenylation signals was performed by extracting the strand specific sequence five nucleotide upstream and downstream each SNP (using table browser tool in UCSC) and performing a motif search using custom Perl script. Canonical and non-canonical polyA motifs searched were cis-regulated genes where the SNP is located within a true polyadenylation signal, we retrieved the coverage of every SAGE tag upstream and downstream the putative affected polyadenylation site and calculated the ratio between proximal and distal tags for the different genotypes to confirm the expected effects of polyadenylation site formation or disruption.For the For the meta-analysis we combined DeepSAGE data with two published RNA-seq datasets. The first dataset was 37 bp paired-end RNA-sequencing data from HapMap individuals  and Argonne (using 46 bp reads).The second dataset was single-end RNA-sequencing data from HapMap individuals et al. paper used genotype data for some individuals that were not in the HapMap3 panel , we imputed these individuals using Beagle v3.1.0, with HapMap2 release 24 as a reference.Since the Montgomery Reads from single- and paired-end RNA-sequencing were mapped to the human genome NCBI build 37 (reference annotation from Ensembl GRCh37.65) using Tophat v. 1.3.3 To estimate expression levels in RNA-seq data, reads that overlapped with exons from known transcripts (GRCh37.65) were quantified using the coverageBed method from BEDTools suite s for transcript tr was calculated as a sum of expression values over all exons contained in this transcript:For transcript level quantification the read count tr,e by not less than half of read's length,e (% of exon length covered by the reads mapping to that exon).In case a read mapped to multiple transcripts, the read was counted for all transcripts, since the short reads are difficult to assign to a specific transcript. Multiplication by breadth of coverage was performed to help in distinguishing between different isoforms by assigning higher weight to exons fully covered by reads in contrast to alternative exons covered only partly.Because different methods have different capacity to identify alternative splicing events, we subsequently summarized our eQTL results to unique genes.Meta-analysis was conducted by using a weighted Z-method, weighing each of the datasets by the square root of the number of samples per dataset http://www.rucdr.org). NTR and NESDA samples were randomly assigned to plates with seven plates containing subjects from both studies to better inform array QC and study comparability. Gene expression data were required to pass standard Affymetrix QC metrics (Affymetrix expression console) before further analysis. Probe sets were removed when their mapping location was ambiguous or if their location intersected a polymorphic SNP  and 60 HapMap CEU individuals provided by Montgomery et al. .For RNA-seq data we used corresponding microarray datasets that were available for most of the individuals present in RNA-seq datasets. We used Illumina expression data provided by Stranger The same normalization procedure was performed as for the sequencing-based datasets: quantile normalization, and subsequent probe set centering to zero, z-score transformation, and scaling to a standard deviation of one.The newly generated DeepSAGE data for NTR-NESDA dataset is available in ArrayExpress under accession number E-MTAB-1181 (ENA: ERP001544).Figure S1Correlation of GC content with principal component 1 (PC1) eigenvector coefficients for all the three datasets. Pearson correlation coefficient and corresponding p-value are shown in the plot.(EPS)Click here for additional data file.Figure S29/L. Pearson correlation coefficients and corresponding p-values are shown in the plot.Blood cell counts in DeepSAGE data captured by the eigenvector coefficients on principal components PC7 (left) and PC11 (right). Experimentally determined blood cell counts at the time of RNA isolation were available for 36/94 samples. Blood cell counts are expressed as (number of cells)×10(EPS)Click here for additional data file.Figure S3cis-regulated genes is given after each filtering step.Replication of Affymetrix eQTLs in DeepSAGE dataset and DeepSAGE eQTLs in Affymetrix data. The numbers of unique (EPS)Click here for additional data file.Table S1List of detected eQTLs in tag-wise eQTL mapping.(XLS)Click here for additional data file.Table S2Trait-associated SNPs affecting the expression of DeepSAGE tags of 94 peripheral blood samples.(XLS)Click here for additional data file.Table S3List of candidate genes with alternative polyadenylation event detected using a permissive strategy.(XLS)Click here for additional data file.Table S4Replications between RNA-seq and DeepSAGE eQTLs.(XLS)Click here for additional data file.Table S5Replication of RNA-seq eQTLs in microarray-based datasets.(XLS)Click here for additional data file.Table S6List of detected eQTLs in the meta-analysis.(XLS)Click here for additional data file.Table S7Primer sequences for qPCR validation.(XLS)Click here for additional data file.Text S1Additional details on principal component analysis of DeepSAGE expression data.(DOC)Click here for additional data file.


Methylosinus trichosporium OB3b cultures based on reversed-phase HPLC fractionation used in combination with a previously reported resin extraction. HPLC eluent fractions of the resin extracted product were collected and characterized with UV-vis, FT-IR, and C-1s NEXAFS spectroscopy, as well as with elemental analysis and ESI-MS.The isolation of highly pure copper-free methanobactin is a prerequisite for the investigation of the biogeochemical functions of this chalkophore molecule produced by methane oxidizing bacteria. Here, we report a purification method for methanobactin from The results showed that numerous compounds other than methanobactin were present in the isolate obtained with resin extraction. Molar C/N ratios, mass spectrometry measurements, and UV-vis spectra indicated that methanobactin was only present in one of the HPLC fractions. On a mass basis, methanobactin carbon contributed only 32% to the total organic carbon isolated with resin extraction. Our spectroscopic results implied that besides methanobactin, the organic compounds in the resin extract comprised breakdown products of methanobactin as well as polysaccharide-like substances.Our results demonstrate that a purification step is indispensable in addition to resin extraction in order to obtain pure methanobactin. The proposed HPLC purification procedure is suitable for semi-preparative work and provides copper-free methanobactin. Aerobic methanotrophic microorganisms are Gram-negative bacteria that have the unique ability to use methane as their only source of carbon and energy . In a fiMethylosinus trichosporium OB3b, a small chromopeptide showing high affinity for copper that was named methanobactin ¯, thus confirming the presence of methanobactin in fraction 3  revealed a molar copper-to-methanobactin ratio of 0.0027. Methanobactin concentration was calculated from the carbon content of fraction 3. Consequently, methanobactin obtained with the combined resin extraction-HPLC procedure is virtually copper-free. To determine the stability of copper-free methanobactin after purification and storage, freeze-dried samples of fraction 3 were dissolved in deionized water and re-chromatographed using the HPLC procedure which was used for fractionation of resin extracts. Re-injection of copper-free methanobactin showed that a minor contribution of breakdown products was formed. An addition of CuClMethylosinus trichosporium OB3b culture medium revealed that methanobactin was present in the extract, but it contributed only 32% to the total isolated organic carbon of the extract. The extracted organic substances other than methanobactin were found to be peptide compounds as well as more polar compounds exhibiting primarily a polysaccharide-like character. We suggest based on our spectroscopic results that the peptide-like organic impurities represented mainly degradation products of methanobactin, whereas the polysaccharide-like material originated from exopolysaccharides or the decomposition of dead cells. The large variety of organic impurities detected with HPLC as well as the high quantity of these impurities present in the resin extract clearly demonstrated the need for a follow-up purification procedure in order to isolate pure methanobactin appropriate for experimental research on methanobactin. The presented HPLC fractionation using a C18 column was proven suitable for purification. We propose using a two-step procedure consisting of resin extraction followed by HPLC fractionation to isolate and purify methanobactin from Methylosinus trichosporium OB3b cultures. This procedure can be used for semi-preparative work. The isolated methanobactin obtained with this two-step procedure is virtually copper-free and thus well-suited for studies investigating copper binding properties of methanobactin as well as the role of methanobactin for copper acquisition of methanotrophs from organic and mineral sources.The analysis of resin extracts of harvested The authors declare that they have no competing interests.MLP developed the purification method, carried out all analysis, and wrote the manuscript as part of her PhD thesis. IC conducted NEXAFS analysis. KB helped to develop the HPLC purification method. IC, SK, and RK initiated and supervised the study, and were involved in planning experiments, interpreting data, and revising the manuscript for publication. All authors read and approved the final manuscript.Figure S1 Chromatograms of isolated methanobactin. HPLC elution spectra recorded at 390 nm and 280 nm are shown for isolated copper-free and copper-stabilized methanobactin.Click here for file


N-acetyltransferase type Ib) is an enzyme of clinical importance found in a wide variety of gram-negative pathogens. The AAC(6′)-Ib enzyme is of interest not only because of his ubiquity but also because of other characteristics, it presents significant microheterogeneity at the N-termini and the aac(6′)-Ib gene is often present in integrons, transposons, plasmids, genomic islands, and other genetic structures. Excluding the highly heterogeneous N-termini, there are 45 non-identical AAC(6′)-Ib related entries in the NCBI database, 32 of which have identical name in spite of not having identical amino acid sequence. While some variants conserved similar properties, others show dramatic differences in specificity, including the case of AAC(6′)-Ib-cr that mediates acetylation of ciprofloxacin representing a rare case where a resistance enzyme acquires the ability to utilize an antibiotic of a different class as substrate. Efforts to utilize antisense technologies to turn off expression of the gene or to identify enzymatic inhibitors to induce phenotypic conversion to susceptibility are under way.Enzymatic modification is a prevalent mechanism by which bacteria defeat the action of antibiotics. Aminoglycosides are often inactivated by aminoglycoside modifying enzymes encoded by genes present in the chromosome, plasmids, and other genetic elements. The AAC(6′)-Ib (aminoglycoside 6′- Aminoglycosides are bactericidal antibiotics that affect translation fidelity and, according to recent data, they may also stimulate the production of highly deleterious hydroxyl radicals  linked to amino sugars through glycosidic bonds. However, other compounds with different basic structures are also included within the aminoglycosides family, e.g., spectinomycin, an aminocyclitol not linked to amino sugars or compounds containing the aminocyclitol fortamine -Ib], which is of great clinical relevance and it is found in over 70% of AAC(6′)-I-producing gram-negative clinical isolates  belong to the GCN5-related N-acetyltransferase superfamily, also known as GNAT. This is a large group of enzymes that includes about 10,000 proteins from all kinds of organisms that share the property to catalyze the acetylation of a primary amine in numerous acceptor molecules using acetyl CoA as donor substrate -Ib gene versions found in different genetic environments and bacterial species. Some variants differing at the N-termini such as AAC(6′)-Ib3, AAC(6′)-Ib4, AAC(6′)-Ib6, and AAC(6′)-Ib7 have been compared and it was found that they have similar behavior -Ib, many of them identified by modifications in the name of the enzyme such as the addition of subscripts or a prime symbol superscript 1331 showed that the enzyme is homogeneously distributed in the cytoplasmic compartment -Ib was crystallized in complex with coenzyme A and also in complex with both coenzyme A and kanamycin. The structures were solved to 1.8 Å and 2.4 Å resolution, respectively -Ib enzyme encoded by TnTable aac(6′)-Ib genes are usually found as fully functional or deficient gene cassettes associated to class 1 integrons, insertion sequences such as IS26, and truncated or disrupted integrons -Ib gene cassettes can be found located adjacent to the 5′-conserved region, i.e., flanked by attI and attC, or internal to the variable portion containing attC loci at both ends -Ib, composed of a copy of *attI1 at the beginning of the structural gene and a regular attC downstream of it -Ib gene cassette-like structure, suggesting that it is nonfunctional or it excises at an extremely low efficiency -Ib genes with the capability to reach virtually all gram-negatives and other undetermined bacteria such as those that are still unculturable. The gene has also been found in plasmids harboring resistance genes of high importance such as the recently described ndm-1 , is becoming highly resistant to antibiotics including aminoglycosides -Ib -Ib -Ib consisted of identifying regions available for interaction with antisense oligonucleotides in a monocistronic in vitro synthesized mRNA by RNase H mapping in combination with computer prediction of its secondary structure -Ib was achieved by applying EGS technology, in which short antisense RNA molecules, known as external guide sequences, are used to elicit RNase P-mediated degradation of a target mRNA -Ib were transformed with recombinant clones specifying the appropriate RNA oligonucleotide sequences under an inducible promoter. The transformed derivatives were then cultured in the presence of amikacin under conditions of expression of the external guide sequences. The results showed that in a few cases the external guide sequences induced a reduction of the minimal inhibitory concentration of amikacin /DNA co-oligomers that were tested, LNA/DNA co-oligomers with certain configurations were found to be capable of eliciting RNase P-mediated cleavage of mRNA in vitro -Ib and it was found that growth was inhibited in the presence of amikacin, indicating that the compound may have induced RNase P-mediated inhibition of expression of the gene -Ib is still far from being a viable option to overcome aminoglycoside resistance in the clinical setting. Several problems remain to be solved like inducing penetration of the oligonucleotide analogs inside wild type cells in enough quantities to exert the biological activity or achieve enough inhibition levels in spite of the usual presence of multiple copies of the gene due to its inclusion in high copy number plasmids. Toward finding solutions to these problems, recent experiments suggest that LNA/DNA co-oligomers may be able to reach the cytoplasm of untreated cells at low efficiency (Traglia et al., An alternative approach that is being explored is silencing expression of the resistance gene. Early attempts at interfering with expression of 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.


Plasmodium falciparum asexual and sexual stages from gene expression measurements. These are modeled using constrained linear regression to characterize stage-specific expression profiles within mixed-stage populations. The resulting profiles were analyzed functionally by gene set enrichment analysis (GSEA), confirming differentially active pathways such as increased mitochondrial activity and lipid metabolism during sexual development. We validated model predictions both from microarrays and from quantitative RT-PCR (qRT-PCR) measurements, based on the expression of a small set of key transcriptional markers. This sufficient marker set was identified by backward selection from the whole genome as available from expression arrays, targeting one sentinel marker per stage. The model as learned can be applied to any new microarray or qRT-PCR transcriptional measurement. We illustrate its use in vitro in inferring changes in stage distribution following stress and drug treatment and in vivo in identifying immature and mature sexual stage carriers within patient cohorts. We believe this approach will be a valuable resource for staging lab and field samples alike and will have wide applicability in epidemiological studies of malaria transmission.In the current era of malaria eradication, reducing transmission is critical. Assessment of transmissibility requires tools that can accurately identify the various developmental stages of the malaria parasite, particularly those required for transmission . Here, we present a method for estimating relative amounts of  Plasmodium falciparum is transmitted through a mosquito vector and causes over half a million deaths per year. The microorganism cycles through asexual and sexual life cycle stages, and its successful transmission relies on cells in the sexual stage. These stages are, however, present only at low levels during infection; most infecting cells are asexually reproduced. It can be challenging to assign biomolecular activity to particular parasite life cycle stages from typical gene expression profiles, given the mixed stage composition of most samples. We developed a deconvolution model to identify components of Plasmodium transcriptional activity contributed by sexual and asexual life cycle stages, initially using samples of known composition. From these, we optimized a small set of stage-specific genes with highly informative expression patterns and trained an inference model to predict the stage composition of new samples. The model successfully inferred the parasite's transition from asexual to sexual development over time under laboratory conditions and identified a subset of patient samples harboring transmissible sexual stages. The system presented here can aid in epidemiological or laboratory perturbation in which stage composition is an important step in understanding and preventing malaria transmission.The human malaria parasite  Plasmodium falciparum transmissible parasite stage, or gametocyte, are the primary reservoir for malaria transmission, and thus proper surveillance of gametocyte carriers is critical to transmission reduction. Surveillance is difficult, however, because gametocytes comprise only a small fraction of the total body parasite load during active infection and are only observed in the bloodstream in their mature form, while developing stages are sequestered in tissues One of the tenets of the recently released Malaria Eradication Research Agenda  is the development of new diagnostics specifically addressing transmission reduction in vitroPlasmodium life cycle in vivo or in vitro, as even the most synchronized gametocyte induction protocols result in partially asynchronous and mixed gametocyte stages in vivo, as blood sampled during infection is likely to contain both gametocyte and asexual parasite populations, leading to a highly convolved transcriptional mixture.Gametocytes do execute substantially different transcriptional programs from asexual parasite stages, however, as has been well-studied Pfs25 and the putative immature gametocyte marker Pfs16. While these approaches enable sensitive detection of these transcripts, it is unclear how the detection of these transcripts - particularly Pfs16 - relates to actual gametocyte carriage P. falciparum genes lack introns and thus have identical sequences for both RNA and DNA. It is therefore worth identifying novel intron-containing markers for which exon-exon junction-spanning primers can be designed so that this approach can be used for in vivo gametocyte quantification.In addition to the need to dissect these signatures for analysis of microarray data, it is also of interest to develop a field-friendly approach for detecting and quantifying both immature  and mature (indication of infectiousness to mosquito vector) gametocyte stages. Transcriptional approaches such as RT-PCR, QT-NASBA and RT-LAMP have been developed Plasmodium transcripts genome-wide and subsequently identified intron-containing markers from across the full range of asexual and sexual development. In order to identify expression patterns specific to different gametocyte stages, particularly the immature stages, existing in vitro asexual and sexual developmental time course samples were re-analyzed to account for their mixed stage composition. We further developed a qRT-PCR assay based on these results and, applying the model in reverse, established an algorithm to estimate the amounts of immature and mature sexual and asexual stages in a patient sample based on the expression of a small set of stage-specific markers. This was inspired by related approaches that have been used successfully in dealing with mixed cancerous/non-cancerous tissue samples http://huttenhower.sph.harvard.edu/malaria2013; as a transmission-focused tool, this system can be applied in epidemiological settings, and as such will ideally support efforts directed toward reducing malaria prevalence worldwide.Our goal was thus to develop a new transcript-based gametocyte model that addressed these challenges. Using a deconvolution approach, we quantified the stage-specificity of P. falciparum intra-erythrocytic development into categories that reflect physiologically relevant distinctions during the course of natural infection in the human host. Specifically, we separated those phases found sequestered in tissues from those found in circulation, resulting in a total of five categories. These included two asexual categories, i) the circulating ring stage (termed “R”) and ii) the sequestering trophozoite and schizont stage (termed “T”). The three gametocyte categories were i) the young  and ii) intermediate  immature gametocytes that are absent from circulation and iii) the circulating mature stage V gametocyte (termed “MG”). For most analyses, we grouped YG and DG together as one category encompassing all immature gametocytes (termed “IG”).To identify expression patterns of individual stage categories while accounting for mixed stage composition, we began by using a labeled set of microarray data with asexual and gametocyte proportions assessed manually by microscopy P. falciparum gene's expression varied with respect to life cycle stage  or one  standard deviations further above mean than for any other stage. This process resulted in 637 stage-specific genes distributed across five stages: 154 (R), 34 (T), 229 (YG), 34 (DG) and 186 (MG), with the top fifteen individual stage-specific markers appearing in The model parameters P. falciparum, a pathway database was constructed using the gene annotations in PlasmoDB for the Gene Ontology g,sβ parameters z-scored across stages as a pre-ranked statistic for enrichment testing. The resulting stage-specific pathway enrichments included biological activities that are known to be associated with particular stages in the asexual cycle, such as host cell remodeling in ring stages (R) and host cell invasion and replication in the later stages of the asexual cycle (T). Our analysis supports earlier observations from the initial gametocyte transcriptome study and a more recent early gametocyte proteome Prior to selecting individual stage-specific sentinel genes from these data, stage-specific pathway activity was assessed genome-wide using Gene Set Enrichment Analysis (GSEA). Since the standard gene sets available for GSEA are somewhat sparse for π values for each gene based on a sequence comparison from 25 culture-adapted strains from Senegal π quantifies the average number of pairwise differences among a set of strains at a set of assayed SNPs. As a complementary measure for positive selection between populations, we also calculated Fst Interaction with the host immune system can result in a higher rate of single nucleotide polymorphisms (SNPs) in genes involved in these interactions. Since life cycle stages vary in their level of interaction with the host immune system, we were interested in whether any of our stage-specific gene sets varied in signatures of natural selection. As a measure for balancing selection within a parasite population, we determined the genetic diversity of individual genes within the three gametocyte stages and two asexual stages . For thiP. falciparum, we proceeded to identify a small subset of markers sufficient to recapitulate genome-wide resolution for stage prediction. To combine predictive accuracy with biological interpretability, we chose to identify the single markers representing each stage category while still minimizing prediction error. This resulted in a set of filtering criteria to obtain markers validated to be stage-specific and suitable for use in microarray and qRT-PCR analyses . It encodes skeletal-binding protein 1 (SBP1), a well-characterized component of parasite-induced membrane structures in the host red blood cell (RBC) termed Maurer's clefts PF10_0020 as the late asexual stage (T) marker. The gene encodes a protein that is predicted to be secreted into the host RBC g,s parameter, but also based on its independent gene expression profile from a comparative transcriptional analysis of three P. falciparum strains P. falciparum genes by the lowest standard deviation across life cycle stages PF11_0209 encodes a conserved protein of unknown function.To identify optimal markers for the better studied asexual R and T stages, we began with the stage-specific gene sets identified for each stage and first filtered based on presence of intron(s). We also required mass spectrometry evidence confirming the protein is expressed during asexual development g,s parameter) comparable to the currently used gametocyte marker Pfs25. We therefore decided to include Pfs25 as a MG marker to model stage distribution in microarray data, as the major criterion against using it for qRT-PCR (i.e. absence of an intron), does not apply to the analysis of chip-based data. For the remaining gametocyte stage categories in which highly expressed markers were successfully identified, we ranked gene lists based on frequency of retention during model predictive marker selection and selected PF14_0748 and PF14_0367 to represent young (YG) and developing (DG) gametocytes, respectively. PF14_0748 has previously been identified as an early gametocyte marker PF14_0367 has not been described previously.To select gametocyte markers, we began with the stage-specific gene sets identified for each stage category as above  and filtered first based on the presence of intron(s). Next, we retained only those genes in which mass spectrometry data again confirmed expression in the gametocyte development and absence in asexual development, providing evidence both for stage-specific activity and lack of evidence for non-specific stages. These criteria revealed a shortlist of gametocyte-specific candidates with high predictive accuracy, but in the case of the MG stage, no markers showed high and stage-specific expression levels  a previously published cohort of severe malaria patients from Blantyre, Malawi collected in 2009 A subset of the uncomplicated malaria patients from Senegal were also predicted to be gametocyte carriers  . As micrin vitro time course experiments for subsequent microarray analysis with a gametocyte-producing line of P. falciparum (3D7) in which parasites were grown in the presence of two experimental antimalarial compounds, Genz-666136 and Genz-644442. Genz-666136 is known to inhibit parasite dihydroorotate dehydrogenase (DHODH) enzyme, which catalyzes the rate-limiting step in de novo pyrimidine biosynthesis Plasmodium sexual differentiation in response to these compounds has not been previously explored.We concluded validation of the microarray model by using it to profile the effects of drug treatments on parasite stage distribution. This application is of particular interest as it was recently demonstrated that currently used anti-malarial treatments including artemesinin combination therapy (ACT) have limited efficacy against mature gametocytes in vitro gametocyte development In both the treated and untreated control samples, our model predicted an initial, subsequently decreasing fraction of the population to consist of young gametocytes . The preTreatment with both compounds, but to a greater extent with Genz-666136, resulted in an increasing proportion of transcriptional signature that could not be assigned to one of our stage categories  and mature (MG) gametocytes in combination, as PF14_0367 had a βg,s parameter similar to that of Pfs25 in mature gametocyte stages. We therefore cross-validated this 5-marker PCR set  and sequence-specific probes (distinguishing transcripts from non-specific background amplification). Following confirmation that our primer/probe sets selectively amplified cDNA and not genomic DNA or non-specific products, we validated the stage-specific expression using tage RNA  to confitage RNA . The asein vitro and tested their predictive-ability as a set in silico, we next compiled a larger set of in vitro samples with known distributions of parasite life cycle stages in order to tune a set of model parameters for stage prediction specifically using qRT-PCR-based expression measurements. For this purpose, we used the data from our in vitro validation on the 3D7 and F12 lines, in combination with additional in vitro samples generated from across a 48-hour period of asexual development, and a two-week period of gametocyte conversion. For the additional data points, we used a transgenic line in 3D7 background, termed 164/GFP, that expresses fluorescent protein under the gametocyte-specific PF10_0164 promoter g,sβ of each stage to our five markers' expression and performing cross-validation to evaluate the final prediction error rates per stage. Again, we see that the model accurately predicts the absence of sexual stages in F12 and low parasitemia cultures of 3D7 while predicting young gametocytes in the high parasitemia 3D7 cultures  microscopy-positive gametocyte carriers were qRT-PCR positive for one or both gametocyte markers, while only three of the 8 (38%) matched microscopy-negative individuals were positive for either gametocyte marker, with highest levels of expression observed in the microscopy-positive gametocyte carriers . This prIn comparison, our model provided additional detail, first predicting that most patients have a ring-dominated profile as expected in peripheral blood . A numbePlasmodium gametocytes. None of these existing tools have been appropriate for detection and quantification of the relevant range of parasite stages present during infection, however, due primarily to the lack of a sufficiently broad panel of stage-specific markers. Further, since malaria parasite populations exist as mixtures of the different phases of the life cycle, assays combining multiple markers require customized computational analysis methods for dealing with this complexity. We combined the development of such a bioinformatic deconvolution approach with panels of stage-specific, intron-containing markers appropriate both for microarray analysis and a newly developed qRT-PCR assay. This multi-marker platform enabled us not only to detect gametocyte carriers but primarily to infer the relative amounts of sexual and asexual stages within a sample. We provide an implementation of this platform for further development and application, particularly for refinement in field settings. This process can also be adapted bioinformatically by the exclusion or inclusion of markers to answer specific questions, such as determination of parasite sex ratios that are known to influence mosquito infectiousness Several highly sensitive single-marker molecular assays are currently used to detect P. falciparum gametocyte cycle using isolated and synchronous gametocyte stages will be required.Our deconvolution model provided the opportunity to define stage-specific gene sets and to characterize the biology of these stages' expression programs using tools such as GSEA, even in the absence of transcriptional data from pure stage populations. For example, our GSEA analysis confirms earlier studies that suggested increased mitochondrial and lipid metabolism during gametocyte development Pfs25detection from such data, which represent a potentially fruitful course of future computational investigation. Instead, by incorporating relative expression values of the markers, the model allowed us both to identify a subset of patients as gametocyte carriers and to additionally quantify sub-categories of immature and mature gametocyte fractions within the mixture of stages in the bloodstream.Transcriptional approaches have significantly increased the sensitivity of gametocyte detection in field-compatible assays in vitro growth experiments in the presence of drug. In the former, we found that both mean illness duration and hematocrit differed between inferred gametocyte carriers and non-carriers, in agreement with published data demonstrating that long illness duration and low hematocrit is linked to gametocyte carriage Following validation of our model on samples for which stage composition was known, we applied our model to two microarray data sets in which stage composition was unknown: (i) a cohort of uncomplicated malaria patients, and (ii) two in vitro experiments even on first application. When used initially in vivo for blood samples from a cohort of children with severe malaria in Malawi, the system successfully identified a subset of patients as immature and/or mature gametocyte carriers. Because immature gametocytes in particular are present in the body several days before the more mature forms emerge, our approach for detecting them could be used in further investigations into factors that influence gametocyte conversion in vivo.As the exon-exon junction spanning primer/probe sets for 5 markers designed here represent the first attempt at a multi-marker gametocyte-staged qRT-PCR assay, further modeling of PCR-specific measurement error and careful standardization of experimental protocol for this difficult task will both improve field inferences. Like the microarray expression model, however, this model successfully recapitulated the transition from asexual to sexual development across multiple The assay and algorithm framework presented here has potential for use in epidemiological studies such as those of asymptomatic carriers, who likely represent a major reservoir for malaria transmission. Multiple such studies are already ongoing and will yield additional samples to further optimize computational models of gametocyte differentiation. This is also true of data generated from other sensitive expression platforms such as glass-slide arrays or Nanostring. The inference process may thus have applications in better understanding the natural progression of malaria in the human host, by identifying gametocytes earlier in the course of infection and determining the impact of specific drug treatments on gametocyte development. By scaling to future population-level screens, the resulting information will help better characterize the epidemiology of gametocytemia based on malaria transmission intensity, geography, climate and season.The institutional review boards of the Harvard School of Public Health, Brigham and Women's Hospital, the University of Malawi College of Medicine, and the Ministry of Health in Senegal approved all or parts of this study. Consent was obtained from the patient or a child's guardian.Patients who enrolled in an ongoing severe malaria study Patients who enrolled in a study of uncomplicated malaria in Thies, a low malaria endemicity suburb of Dakar, Senegal in October 2008 during transmission season were included in this study. Patients who presented to Section de Lutte Antiparasitaire de Thies, with signs and symptoms of malaria were offered enrollment if they had microscopic confirmation of malaria infection and had mild symptoms. At the time of admission, a blood sample was taken from which 4.5 mL was transferred into Tri-Reagent BD (Molecular Research Center), shaken vigorously for 15 seconds, and frozen at −80°C for transcriptional analyses. General demographic data, history of illness and hematocrit were recorded. Samples were transported to HSPH in a liquid nitrogen dry shipper, thawed in a room temperature water bath and RNA isolated according to manufacturer's instructions (Molecular Research Center). Simultaneously, thin smears were collected and stored for later processing.P. falciparum was used to produce the mixed stage samples for model training and validation. This transgenic line, which aided in the quantification of gametocyte stages, produces stage-specific GFP expression under the PF10_0164 gene promoter, as described previously 2, 5% O2, 90% N2). Prior to induction, asexual parasite cultures were synchronized for two cycles with 5% D-sorbitol A transgenic line, 164/GFP, of a gametocyte-producing clone of the 3D7 strain of 50. Drug-treated and control parasites were harvested at 10, 20, 30, and 40 hours post-invasion and RNA was extracted (Qiagen).A 3D7 line was used to study the effect of drug perturbations on parasite growth. Culture conditions were performed as described above. Asexual parasite cultures were synchronized for three cycles with 5% D-sorbitol, and expanded to a parasitemia of 5–6%. Hematocrit was increased from 3 to 6% at the late schizont stage using fresh blood. Upon reinvasion drugs were added to the culture at a concentration of 5×ICin vitro samples, we used a combination of standard and fluorescence microscopy. Parasite stage distribution was monitored throughout the parasite synchronization and induction protocol using Wright's Giemsa stain applied to thin blood smears. Quantification of asexual rings and trophozoite stages, as well as developing and mature sexual stages was done directly by light microscopy. In order to quantify early stages of sexual development that are morphologically similar to asexual stages, we used a combination of live imaging and immunofluorescence microscopy. Live imaging was performed using the transgenic 164/GFP line. Parasites were analyzed using the FITC channel on an inverted epifluorescence microscope (Zeiss) and quantification was done of the proportion of GFP(+) parasites out of the total number of Hoechst (+) parasites. Immunofluorescence assays were performed with cell monolayers on glass slides, prepared as described previously In order to accurately quantify the stage distribution of parasites in our Plasmodium or human homologous sequences using PlasmoDB and NCBI Blast in order to eliminate the chances of non-specific amplification  assay for the quantification of five key gene transcripts. The assay includes primers and probes designed against the markers described in the RNA from mixed stage cultures was preserved and extracted and processed as previously described P. falciparum cDNA and genomic DNA . The possibility of non-specific amplification with host template was ruled out by performing qRT-PCR on cDNA and genomic DNA from a human whole blood sample from which GAPDH was successfully amplified (data not shown). Primer pair efficiencies were determined by calculating the slope of the crossing threshold (CT) values on 10-fold serial dilutions of mixed stage cDNA . The ability of primer pairs to discriminately amplify the cDNA product was determined by performing qRT-PCR on mixed stage Sensitivity of this assay was determined using 10-fold serial dilutions of mixed stage cDNA. Sensitivity was estimated by calculating the amount of parasites of each stage that were present in the sample at the limit of detection by qRT-PCR.in vitro drug perturbations) was assessed by Bioanalyzer (Agilent), and high quality RNA samples were labeled and hybridized to an oligonucleotide array (Affymetrix) custom-designed for the P. falciparum 3D7 genome, PlasmoFB, as published previously RNA  or five (for PCR data) markers ultimately selected as follows.Microarray data sets. For model inference in microarray data, missing values remaining in input microarrays were imputed per dataset using the row mean across all samples. For each set of two or more training and inference datasets obtained in different batches, values used in the modeling process were co-normalized using quantile normalization. Datasets were first merged, retaining only gene names common among both datasets and the resulting merged data was jointly quantile normalized.qRT-PCR data sets. Similarly for qRT-PCR expression values, raw values were delogged using the formula to bring the data into a range of log-scaled abundance counts comparable to microarray samples. Missing values were imputed to the lowest detectable limit on our qRT-PCR assay, which corresponds to a CT value of 50 or 3.23 in delogged expression space. Again considering sets of reference and inference data together, data were median normalized, such that all samples had the same median value .in silico marker selection based on greatest accuracy of stage inference in our labeled microarray training data. Iteratively, model inference was performed on the total labeled microarray time courses using the complete transcriptome. Each individual gene was removed, the model re-applied, and the marker inducing the smallest increase in inference error removed. These steps were repeated to a minimum of five markers, and the entire process repeated 15000 times. The resulting whole transcriptome rankings were averaged to assign a rank-average selection preference to retention of each Plasmodium gene as one criterion for our reduced marker set.To identify reduced marker sets appropriate for inference, we used the model's inference process to perform The model's accuracy in each labeled microarray and qPCR training set was evaluated by 10-fold bootstrap cross validation. For each fold, one third of conditions were selected for holdout with replacement to form a test set. The model was fit as above to the remaining training data and its accuracy in predicting new expression samples was calculated using a per stage root mean square error in the test set.Figure S1Performance of the 5 marker model on published microarray data sets. (A). Actual and (B) inferred stage distributions across five microarray time courses  with reference stage distributions determined by microscopy. Five markers were used to make these predictions (th–75th percentiles and 1.5× fences.dictions . (C). Bo(TIF)Click here for additional data file.Figure S2qRT-PCR assay optimization. (A) Efficiency of qRT-PCR reactions using 10-fold dilutions of mixed parasite cDNA. 4 to 6 dilutions were assessed for each primer, and efficiencies were in the acceptable range for all 5 primers (87–92%). R2-values were all greater than 0.96. Technical variation between replicates was very low: the average standard deviation between technical replicates was 0.243 and ranged between 0.01 and 1.543. (B) Stage-specificity of qRT-PCR markers. Using two clones of 3D7, F12 (gametocyte deficient) and wild type (gametocyte producer), we performed in vitro gametocyte inductions and collected parasite samples for microscopy and qRT-PCR at days −1, 0, 1, 5, 10 according to the Fivelman et al protocol PF11_0209, confirm stage-specificity of markers.(TIF)Click here for additional data file.Table S1Annotated gene list and metadata. Annotations of the 5160 genes in the P. falciparum transcriptome used in the analysis, including the frequency of selection in our subsampling and backward selection steps, presence of an intron, contribution of expression to stage, determination of stage specificity, product description and population genetic parameters of total SNP counts, diversity and divergence.(XLSX)Click here for additional data file.Table S2Complete GSEA results per stage. Results for each stage in our microarray model, wherein the per gene z-scored contributions of expression to that stage were ranked and were characterized for enrichment in functional pathways.(XLSX)Click here for additional data file.Table S3GSEA gene sets. Gold Standard Catalog of GO and Kegg pathways obtained from individual GO slims from PlasmoDB and the GO ontology integrated into the GO hierarchical structure.(XLSX)Click here for additional data file.Table S4Clinical parameter data for Senegal cohort. GraphPad Prism Version 6.0 was used to compare two groups (those inferred to have gametocytes and those not inferred to have gametocytes) for six continuous variables measured at admission: age, hematocrit, temperature, illness duration, height, and weight. A multiple t-test analysis was performed, analyzing each variable individually, and then using false discovery rate (Q = 0.25) to determine significance.(DOCX)Click here for additional data file.Table S5Additional qRT-PCR assay optimization data. Primers were specifically designed to cross exon-exon junctions, so as to reduce genomic DNA amplification, and were checked for homology against Plasmodium or human homologous sequences using PlasmoDB and NCBI Blast in order to eliminate the chances of non-specific amplification. Using our primer set with sequence-specific probes showed no cross-reactivity with genomic DNA or human templates. Our primer sets also greatly reduced the amount of genomic DNA amplification even using SYBR (CT>39 as compared with DNA-amplifying control marker at CT = 25), yet it was not zero.(DOCX)Click here for additional data file.Table S6Primer and probe sequences used in qRT-PCR. Sequences for the reverse and forward primers and minor groove-binding fluorescent probes used in the qRT-PCR assay.(DOCX)Click here for additional data file.