The toxicity of anti-cancer chemotherapeutic agents can be reduced by associating these compounds, such as the anti-proliferative agent paclitaxel, with a cholesterol-rich nanoemulsion (LDE) that mimics the lipid composition of low-density lipoprotein (LDL). When injected into circulation, the LDE concentrates the carried drugs in neoplastic tissues and atherosclerotic lesions. In rabbits, atherosclerotic lesion size was reduced by 65% following LDE-paclitaxel treatment. The current study aimed to test the effectiveness of LDE-paclitaxel on inpatients with aortic atherosclerosis.2 body surface area dose of LDE-paclitaxel  in patients with aortic atherosclerosis who were aged between 69 and 86 yrs. A control group of 9 untreated patients with aortic atherosclerosis (72-83 yrs) was also observed.This study tested a 175 mg/mThe LDE-paclitaxel treatment elicited no important clinical or laboratory toxicities. Images were acquired via multiple detector computer tomography angiography (64-slice scanner) before treatment and at 1-2 months after treatment. The images showed that the mean plaque volume in the aortic artery wall was reduced in 4 of the 8 patients, while in 3 patients it remained unchanged and in one patient it increased. In the control group, images were acquired twice with an interval of 6-8 months. None of the patients in this group exhibited a reduction in plaque volume; in contrast, the plaque volume increased in three patients and remained stable in four patients. During the study period, one death unrelated to the treatment occurred in the LDE-paclitaxel group and one death occurred in the control group.Treatment with LDE-paclitaxel was tolerated by patients with cardiovascular disease and showed the potential to reduce atherosclerotic lesion size. Severe atherosclerotic aortic disease is a life-threatening condition that markedly increases the risks of thromboembolic accidents such as stroke, acute myocardial infarction and obstruction of peripheral vessels, especially in elderly subjects with large, complex lesions The most potent anti-proliferative drugs currently available are chemotherapeutic agents used for cancer treatment. However, the systemic use of these drugs at high doses for the treatment of atherosclerotic cardiovascular diseases When tested in mice by measuring classical pharmacological parameters, the toxicity of paclitaxel was markedly reduced upon association of the drug with LDE particles. The LD50 of LDE-paclitaxel was tenfold higher than that of a commercial preparation that uses Cremophor EL as a drug delivery vehicle LDE-paclitaxel treatment of rabbits induced to exhibit atherosclerosis via high cholesterol intake resulted in a 65% reduction in lesion size The low toxicity of LDE-paclitaxel shown in cancer patients The study group included consecutive patients referred for computerized tomography of the aorta at the Cardiovascular Computed Tomography Section of Dante Pazzanese Cardiology Institute in the city of São Paulo and at the Diagnosis Center of the Paraná Hospital in the city of Maringá. The recruited patients were on clinical follow-up due to aortic disease. The inclusion was a diagnosis of severe aortic atherosclerosis (plaque thickness >4 mm) by multidetector computed tomography (MDCT) angiography. Ten volunteers were enrolled in the study for treatment with LDE-paclitaxel. Seven of the volunteers were male and three were female. They ranged in age from 69 to 86 years and all showed aortic dilations and ulcers on angiography. All had systemic arterial hypertension, 7 had type 2 diabetes mellitus, 3 had dyslipidemia and 3 were ex-smokers. All were using statins.A control group (without LDE-paclitaxel treatment) of nine consecutive volunteer patients composed of 8 men and one woman aged 72-84 years with severe aortic atherosclerosis diagnosed by MDCT angiography was also studied. All had systemic arterial hypertension and dyslipidemia, 4 had type 2 diabetes mellitus and 5 were ex-smokers. All were using statins.Patients with renal dysfunction defined as creatinine clearance <60 mL/min, or with aortic dissection were excluded from both groups. The study protocol was approved by the medical ethics committees of the Dante Pazzanese Cardiology Institute and Paraná Hospital and all participants provided informed written consent.LDE particles containing paclitaxel oleate were prepared by high-pressure homogenization from a pre-emulsion obtained by ultrasonic irradiation until complete drug dissolution, according to previously described methods 2 LDE-paclitaxel dissolved in a 0.9% saline solution to a total volume of 250 ml. The patients were clinically monitored for adverse reactions during the infusion and for 1 hour after the procedure. The treatment was administered every 3 weeks for a total of 6 cycles. Clinical and laboratory evaluations were performed 2 weeks after each cycle and the patients could contact the researchers anytime to report symptoms. Toxicity ratings based on NCI Common Toxicity Criteria were assessed before each treatment cycle and treatment was delayed or suspended if a patient experienced serious adverse reactions or if significant laboratory changes were detected. The evaluated laboratory parameters included red and white blood cell counts, platelet counts, electrolytes, cholesterol, triglycerides, apolipoproteins A-I and B, C-reactive protein, liver and renal function and coagulation tests.Administration of the treatment commenced at approximately 10:00 a.m. The patients were infused via the brachial vein over an approximate 90-min time course with 175 mg/mIn the group of patients treated with LDE-paclitaxel, MDCT angiography was performed before treatment initiation and again 1-2 months after the last treatment cycle. In the control group of patients, MDCT angiography was performed twice with an interval of 6-8 months.Images were acquired using a 64-slice MDCT scanner , which is a 64x0.5 mm collimation scanner with a gantry rotation speed of 400 ms/rotation. Scanning was performed at 100 kV and 400-500. Depending on each patient’s body mass index (BMI), the table feed was 6.4 mm/gantry rotation with a beam pitch of 0.2. For thoracic aorta scans, a retrospective electrocardiogram (ECG)-gated protocol with dose modulation was adopted; abdominal aorta scans were performed using a non-gated protocol. The patients received 100 ml intravenous iodinated contrast (iopamidol 370 mg/dl) or a maximum dose of 2 ml/kg.Atherosclerotic plaques were quantified along the aorta by planimetry performed on 1-mm-thick axial images from pre- and post-treatment scans, such that an exact comparison of the plaque measurements before and after treatment could be performed. Plaque volume was independently quantified by two trained imaging technicians who were blinded to whether the images were taken pre- or post-treatment. Variations in the measurements of plaque volume between pre- and post-treatment were considered accurate when <3%.Bland-Altman analysis was performed using Prism 6 GraphPad software  to verify that agreement existed in the plaque volume measurements taken by the two observers. The differences between the pre- and post-treatment values of plaque volume were compared using a paired Student’s t test. Results were considered significant at the <0.05 level.th) treatment cycle.A total of 59 treatment cycles were performed and analyzed with respect to toxicity parameters. Eight patients completed the 6 scheduled treatment cycles without showing appreciable clinical or laboratory toxicity. A 78-year-old male patient experienced sudden death after the fifth treatment cycle. The patient had high cardiovascular risk and was found in his home under cardiopulmonary arrest and transported to the nearest hospital where his death was registered. Necropsy was not performed due to family refusal. This patient had not shown any clinical or laboratory toxicities at any point during the treatment period, and his death was probably unrelated to the intervention. Another patient did not return for the post-treatment angiography within the pre-established period of 1-2 months after the last .p<0.05).In Aortic atheroma is a manifestation of systemic atherosclerosis in which the presence of aortic plaques is associated with the presence of carotid artery or coronary artery plaques. Thus, the identification of aortic atheromas is a strong predictive risk factor for major cardiovascular events, such as stroke or acute myocardial infarction 2 body surface area paclitaxel dose was tested in the patients with aortic atheroma, which is equal to that used in cancer chemotherapy schemes. Paclitaxel is a major drug in cancer chemotherapy and is also used in pharmacological stents to inhibit restenosis. The mechanism of action for the drug is based on the ability of taxanes to increase tubulin polymerization into stable microtubules. It also promotes microtubule stabilization, thus preventing depolymerization. The overall effect is inhibition of the mitotic apparatus formation and arrest of mitosis in the G2/M phase of the cell cycle A 175 mg/mThe toxicity of commercially available paclitaxel, in which Cremophor EL is used as a drug delivery vehicle, is cumulative. The main toxicities are hematological, with neutropenia being the most prominent toxicity of the drug. Neurotoxicity, arrhythmias and alopecia are other frequent toxicities elicited by paclitaxel, together with major hypersensitivity reactions related to Cremophor EL.The fact that the toxicity of commercial paclitaxel is cumulative and that paclitaxel-associated LDE particles were administered here at a dose roughly twice that used in the study reported by Margolis et al. In a previous study, treatment of rabbits harboring atherosclerotic lesions induced by high cholesterol consumption with LDE-paclitaxel markedly decreased macrophage and smooth muscle cell invasion of the intima, as well as the presence of apoptotic cells The large caliber and thickness of the aortic vessel facilitates the quantification of lesion volumes using MDCT, which is non-invasive. The reliability of the quantitative analyses that were independently made by the two blinded imaging technicians was confirmed by the <3% inter-observer variability. Although the study was limited by the sample containing only eight patients, it is noteworthy that four of the patients showed a regression in aortic plaque volume, whereas none of the patients in the non-treated control group showed a regression in lesion size. Additionally, although there was no significant reduction in plaque volume in the LDE-paclitaxel group after treatment, in the control group, probably due to the small number of patients, there was an increase in plaque volume, suggesting disease progression in these patients.With respect to the data on plasma lipid and apolipoprotein concentrations collected during the study, the lack of observed effect following the LDE-paclitaxel treatment was expected because paclitaxel does not interfere with plasma lipid metabolism. Indeed, the natural history of aortic atheroma is not always progressive; rather, a slow progression in plaque thickness is the most common clinical course This pilot study showed that treatment with high-dose LDE-paclitaxel had low enough toxicity to permit its use in patients with cardiovascular disease. Although not statistically significant, there was an average 18% reduction in plaque volume in four out of the eight participants, which is a promising finding. This result was especially noteworthy in view of the short 18-week treatment period and when considering that plaque reduction did not occur in any of the control group patients. In contrast, statistically significant disease progression was observed in the non-treated control patients. The results of the current study are supportive for future trials aiming to confirm the efficacy of this novel treatment in a large number of participants.Shiozaki AA researched the study design, wrote the manuscript and executed the work. Senra T and Paladino-Filho AT executed the work, interpreted the data and performed case selection. Morikawa AT and Dus DF. Pinto IM performed data interpretation and case selection. Maranhão RC researched the study design and helped writing the manuscript.


Supplemental Digital Content is available in the text. Reduced antiplatelet drug efficacy occurs in conditions of increased platelet turnover, associated with increased proportions of drug-free, that is, uninhibited, platelets. Here, we detail mechanisms by which drug-free platelets promote platelet aggregation in the face of standard antiplatelet therapy.12 receptor blocker prasugrel active metabolite, or aspirin plus prasugrel active metabolite. Different proportions of uninhibited platelets were then introduced. Light transmission aggregometry analysis demonstrated clear positive associations between proportions of drug-free platelets and percentage platelet aggregation in response to a range of platelet agonists. Using differential platelet labeling coupled with advanced flow cytometry and confocal imaging we found aggregates formed in mixtures of aspirin-inhibited platelets together with drug-free platelets were characterized by intermingled platelet populations. This distribution is in accordance with the ability of drug-free platelets to generate thromboxane A2 and so drive secondary platelet activation. Conversely, aggregates formed in mixtures of prasugrel active metabolite–inhibited or aspirin plus prasugrel active metabolite–inhibited platelets together with drug-free platelets were characterized by distinct cores of drug-free platelets. This distribution is consistent with the ability of drug-free platelets to respond to the secondary activator ADP.To model standard antiplatelet therapy, platelets were treated in vitro with aspirin, the P2YThese experiments are the first to image the interactions of inhibited and uninhibited platelets in the formation of platelet aggregates. They demonstrate that a general population of platelets can contain subpopulations that respond strikingly differently to overall stimulation of the population and so act as the seed for platelet aggregation. Despite these therapeutic approaches, subsequent thrombotic events still occur and research has focused on ways to quantify individual patient risk. As a result, the concepts of antiplatelet drug resistance and high on-treatment platelet reactivity have developed.11 Through this research, it has become apparent that patients who have conditions associated with increased platelet turnover also have particularly elevated risks.15 Collectively, these observations lead to the idea that increased risk while on antiplatelet therapy may be explained by the relationship between the pharmacokinetic and pharmacodynamic properties of aspirin and P2Y12 inhibitors, and the dynamic changes associated with turnover of the platelet population.Antiplatelet drugs are a cornerstone of preventive therapy for patients at risk of atherothrombotic events. Typically, these patients take low-dose aspirin (≈75–100 mg) once a day. Patients requiring further protection, such as following acute coronary syndrome with ST-segment–elevation or percutaneous coronary intervention, take a P2Y2081See accompanying editorial on page 16 It is then rapidly metabolized by esterases in the gut, liver, and circulation and has a circulating half-life of 13 to 19 minutes. Overall, this means that little active aspirin remains in the body 2 to 3 hours after consumption. However, as aspirin irreversibly acetylates and blocks the cyclooxygenase (COX)-1 enzyme in platelets, and as anucleated platelets are limited in their ability to generate new COX-1, the inhibitory effect of aspirin on platelets persists for their entire life span of 7 to 12 days.17 The thienopyridine P2Y12 blockers, clopidogrel and prasugrel, are prodrugs with complex pharmacokinetics requiring metabolism to thiolactone active metabolites that are irreversible receptor antagonists of platelet P2Y12 receptors.20 Like aspirin, these active metabolites are short lived in the circulation but produce long-lasting effects because of irreversible inhibition of their target. Given these pharmacokinetic and pharmacodynamic profiles of aspirin and clopidogrel/prasugrel, we can reason that platelets that enter the circulation after the drugs have been cleared will not be inhibited and that the proportion of circulating drug-free platelets will increase in proportion to the rate of platelet turnover. Elevated rates of platelet production are associated with many conditions/procedures which feature increased thrombotic risk including patients undergoing coronary artery bypass surgery21 or those with diabetes mellitus,27 chronic kidney disease,29 metabolic syndrome,30 and essential thrombocythemia.32 In these patient groups, increased circulating subpopulations of uninhibited platelets may be of crucial importance in providing the seed for the formation of occlusive platelet thrombi. Drug-free platelets could also drive the changes noted in ex vivo tests of platelet reactivity.Aspirin is rapidly absorbed from the stomach and small intestine reaching a plasma peak around 1 hour later.In the in vitro study reported here, we have assessed the impact of drug-free platelet subpopulations on overall platelet responses to antiplatelet therapy by modeling the entry of drug-free platelets into the circulation and used confocal microscopy and flow cytometric imaging to provide unique images of the interactions of inhibited and drug-free platelet subpopulations in the formation of platelet aggregates. These studies provide insight into potential mechanisms underlying the disease-associated incidence of thrombotic events and illustrate important concepts relevant to ideas of antiplatelet drug resistance.Materials and Methods are available in the online-only Data Supplement.2 (TXB2) as a measure of the formation of the COX product TxA2 demonstrated a linear increase of TXB2 with increasing proportions of drug-free platelets (R2=0.8867), ranging from 1.6±0.4 ng/mL TXB2 when 0% platelets were aspirin-free to 1220±126 ng/mL when 100% platelets were aspirin-free  having previously established the effectiveness of antiplatelet drugs (Table I in the online-only Data Supplement). Increasing proportions of drug-free platelets were added to inhibited platelet populations, and the net aggregatory responses were recorded Figure Ai. In ge12 receptor ligand ADP there was a linear increase in aggregation that followed the addition of drug-free platelets to platelets inhibited with either PAM or aspirin+PAM . Aspirin alone had little effect on aggregation induced by ADP and so responses of aspirin-inhibited platelets were minimally affected by the addition of drug-free platelets  VI agonist collagen-related peptide, which were returned to full responses by the addition of 40% or 60% to 80% drug-free platelets, respectively. The combination of aspirin+PAM required 80% drug-free platelets for the aggregation to collagen to return to a full response and 60% for collagen-related peptide .Similar to the first series of experiments (Table I in the online-only Data Supplement), responses to ristocetin were unaffected by aspirin Figure Ai, PAM Aii, or a8 platelets.In inhibited platelet populations, lumiaggregometry confirmed that the increases in platelet aggregations in response to AA or ADP following addition of increasing proportions of drug-free platelets correlated with matched increases in platelet release of ATP Figure B. For exIn order to mimic physiologically more relevant conditions, aggregation was also stimulated by the combination of subthreshold concentrations of the platelet agonists ADP, collagen, thrombin receptor activator peptide-6, and U46619. Aggregation in an inhibited platelet population was returned to a full response by the inclusion of 40% drug-free platelets in the presence of aspirin, 60% drug-free platelets in the presence of PAM, and 80% drug-free platelets in the presence of aspirin+PAM .To elucidate the contribution of drug-free platelets to platelet aggregation, the morphologies of aggregates containing mixed platelet subpopulations were characterized by confocal imaging. Platelet aggregates comprising aspirin-inhibited platelets with a 20% subpopulation of drug-free platelets were characterized by intermingling of platelet populations  or corresponding vehicle before aggregation to ADP. Analysis of confocal images indicated no difference in the ratio of PAM-inhibited to drug-free platelet volume in formed aggregates in the absence (1.15±0.35) or presence of aspirin , indicating the association of PAM-inhibited platelets to the core was not an artifact of the imaging procedure , indicating that GP IIb/IIIa, but not TXA2, is involved in the recruitment of PAM-inhibited platelets to the drug-free platelet core.To further elucidate the underlying mechanism causing the recruitment of PAM-treated platelets into the drug-free platelet core, fibrinogen-binding studies were performed. The drug-free core was associated with the highest proportion of fibrinogen per volume of drug-free platelets (0.52±0.10) followed by the PAM-treated shell (0.17±0.03) and the control platelet sample  plus AA (1 mmol/L). More recent studies have associated reduced effectiveness of aspirin in vivo to increased platelet turnover as defined by the proportion of reticulated platelets in the circulation.33 Similar analyses have indicated that increased proportions of reticulated platelets are associated with reduced effectiveness of clopidogrel in both rats34 and humans,35 in humans receiving DAPT of aspirin plus clopidogrel,37 and most recently in humans receiving DAPT of aspirin plus prasugrel.38 Studies of the duration of drug action after treatment withdrawal also indicate that the return of aggregatory responses is commensurate with the time for replenishment of circulating platelets.39 In the first part of our studies, we modeled these effects in LTA using a panel of platelet agonists40 and rising proportions of drug-free platelets against a background of standard antiplatelet therapy, aspirin and aspirin plus prasugrel (by the use of PAM). We also studied PAM alone and provided aggregation curves from serially diluted platelet-rich plasma to assist in interpretation of data. Considering first the agonists most sensitive for testing the effects of aspirin, ie, AA, and PAM, ie, ADP, we noted that whereas addition of 30% of drug-free platelets could return full responses to AA in the presence of aspirin, 80% of drug-free platelets were required to return full responses to ADP in the presence of PAM or aspirin+PAM. Aspirin and P2Y12 receptor blockers have different targets on the platelet and inhibit with different functional modalities. Aspirin inhibits platelet COX-1 and so blocks the production of TXA2 by platelets. This does not stop aspirin-inhibited platelets responding to TXA2 produced by aspirin-free platelets, and so the hypothesis has developed that a minority of aspirin-free platelets are capable of generating sufficient TXA2 to support a full aggregatory response.41 While the in vivo proportion of aspirin uninhibited platelets required to provide a full response has been suggested to be as low as 5% or less,41 the response of a population of platelets to a particular concentration of TXA2 will depend on their existing state of activation so there is unlikely to be a particular proportion relevant in all conditions; although clearly even a small subset of platelets can produce enough TXA2 to aggregate a larger population.Others have previously reported that in tests of platelet reactivity conducted in vitro a relatively small population of aspirin naïve platelets can support full platelet aggregation, subject to the stimulus being applied. For instance, di Minno et al12 receptors, inhibits the ability of a platelet to respond to ADP released by other activated platelets. Unlike the effects of aspirin, the blockade of one platelet’s P2Y12 receptors cannot be compensated for by lack of blockade on another platelet; hence, we found a linear relationship between drug-free platelets and the response to ADP. For the other platelet agonists, we noted composite responses as expected from their relative dependencies on the TXA2 and P2Y12 receptor pathways. For example, collagen and collagen-related peptide activate both pathways; U46619 is sensitive to blockade of P2Y12 receptors, because TXA2 receptor activation leads to ADP release, but not to the effects of aspirin. Consistent with this, AA stimulates the generation of TXA2 and so is sensitive both to aspirin, by inhibition of COX, and to PAM, because of inhibition of TXA2 receptor amplification.44 Overall, in the presence of aspirin+PAM, which models the effects of DAPT using aspirin and a thienopyridine, all agonists demonstrated increasing responses with increasing proportions of drug-free platelets. The exception was ristocetin, which we included as a control agent, because it causes platelet agglutination that does not require active platelet involvement.45PAM, by blocking P2Y2 throughout the platelet mixture. In this study, we have not examined the roles of COX-1 and COX-2 isoforms because we have used mature platelet populations. However, in vivo newly formed platelets may express COX-2, which could provide a further level of complexity in the responsiveness to aspirin as could non-COX effects of aspirin.46 Conversely, platelets with blocked P2Y12 receptors cannot respond to the effects of ADP, do not become activated by ADP released as part of platelet amplification pathways, and so do not become part of platelet aggregate cores. It is interesting to contemplate that platelets with their P2Y12 receptors blocked are entirely mixed with drug-free platelets in our system, yet after addition of a platelet activator the drug-free platelets bind to each other so assembling an activated core from the larger mixed platelet population.Our confocal and flow cytometric imaging studies permitted a unique and deeper exploration of the interactions between inhibited and drug-free platelet populations. Notably, drug-free platelets were distributed throughout aggregates of aspirin-inhibited platelets, whereas drug-free platelets formed clear cores within PAM- or aspirin+PAM-inhibited platelet aggregates. We conclude that the intermingled pattern of drug-free and aspirin-inhibited platelets is consistent with the drug-free subset driving the aggregatory response by producing TXA26 For completeness, we also characterized a range of combinations between 10% drug-free and 80% drug-free platelets. These studies demonstrated in PAM-inhibited or aspirin+PAM-inhibited platelets that the size of the drug-free platelet aggregate core grew with the proportion of drug-free platelets, whereas inhibited platelets were only ever associated with the outer portion. Furthermore, we found that addition of aspirin did not reduce the association of inhibited platelets, indicating that this was not dependent on the formation of TXA2. However, the recruitment of P2Y12-inhibited platelets to the forming aggregate was the result of active aggregation because these inhibited platelets bound fibrinogen more than unstimulated control platelets, and their association to the drug-free core was reduced by the GP IIb/IIIa blocker abciximab.Our imaging studies primarily concentrated on the effects of a population of 20% drug-free platelets. This ratio is what could be expected under a once-daily drug regimen in conditions, such as diabetes mellitus, in which circulating platelet lifetimes are ≈5 days.2, we demonstrate for the first time a mechanism by which this is achieved; aspirin-free platelets are distributed throughout aspirin-inhibited platelet aggregates allowing generated TXA2 to activate a larger proportion of platelets , the Wellcome Trust (101604/Z/13/Z), and the William Harvey Research Foundation. T.D. Warner has received research grant funding and consultancy fees from Astra Zeneca.None.


Pre-eclampsia is a common pregnancy disorder that is a major cause for maternal and perinatal mortality and morbidity. Variants predisposing to pre-eclampsia might be under negative evolutionary selection that is likely to keep their population frequencies low. We exome sequenced samples from a hundred Finnish pre-eclamptic women in pools of ten to screen for low-frequency, large-effect risk variants for pre-eclampsia. After filtering and additional genotyping steps, we selected 28 low-frequency missense, nonsense and splice site variants that were enriched in the pre-eclampsia pools compared to reference data, and genotyped the variants in 1353 pre-eclamptic and 699 non-pre-eclamptic women to test the association of them with pre-eclampsia and quantitative traits relevant for the disease. Genotypes from the SISu project (n = 6118 exome sequenced Finnish samples) were included in the binary trait association analysis as a population reference to increase statistical power. In these analyses, none of the variants tested reached genome-wide significance. In conclusion, the genetic risk for pre-eclampsia is likely complex even in a population isolate like Finland, and larger sample sizes will be necessary to detect risk variants. Pre-eclampsia is a common and complex vascular pregnancy disorder. It is characterized by hypertension and proteinuria, and often involves impaired placental development14Genetic factors contribute to pre-eclampsia susceptibility. Heritability estimates for pre-eclampsia range between 0.54 and 0.68, consisting of both maternal and fetal contribution610111213151618Because pre-eclampsia is a major cause for both maternal and fetal mortality, preterm birth and fetal growth restriction320th century  case-control cohort that was collected from five Finnish university hospitals during 2008 to 2011The first individually Sequenom genotyped sample consisted of 180 pre-eclamptic and 180 healthy pregnant women, including the 100 pre-eclamptic cases originally exome sequenced, and additional study subjects from the FINNPEC cohort. The exclusion criteria for the controls were any pregnancy complication in the current pregnancy, pre-eclampsia in any pregnancy, chronic hypertension and pregestational diabetes mellitus.The second individually genotyped sample set included 1353 pre-eclamptic and 699 non-pre-eclamptic women. These also include all the samples from the first Sequenom genotyping, all of which except the family samples were re-genotyped in the second run. All the women, except for the ten family cohort members, were selected from the FINNPEC cohort. The inclusion criterion for the cases was pre-eclampsia, and for the controls non-pre-eclamptic pregnancy. The exclusion criteria for the controls were pre-eclampsia in any pregnancy, small-for-gestational age (SGA) infant or placental insufficiency, gestational hypertension, chronic hypertension and placental ablation.All study participants have provided a written informed consent. The study protocols have been approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa, and the methods were carried out in accordance with the approved guidelines.Variant data from a hundred Swedish scoliosis caseshttp://www.sisuproject.fi/) and at the time of the analysis contained exome sequence data from over 6100 Finns. The data set was utilized as a reference unselected for the pre-eclampsia phenotype as this information was not available for the sequenced individuals.The Sequencing Initiative Suomi (SISu 3.0) data set was utilised in the Sequenom phase 2 association analysis as population specific reference data to increase the statistical power. The SISu database is available online . By using three reference data sets we were able to obtain comprehensive picture of the variant frequencies in the Finnish and European populations, and also to estimate relevance of those variants that were absent in the population-specific reference data SISu. The variants with pre-eclampsia_MAF/reference_MAF ratio ≥1.5 in all the available comparisons were selected. In addition, five variants that were close to the cut-off or had high ratio in one or two of the comparisons were included. The following procedures were also utilised. Variation located in the genes listed in the papers by Fuentes Fajardo Two rounds of Sequenom genotyping were performed. The purpose of the first round was to verify the presence and enrichment of the selected variants individually in the original exome-sequenced samples, whereas the second round was conducted to test the association of the variants with pre-eclampsia.2 = 1 and D′ = 1 in the 1000 Genomes FIN and CEU populations, and rs12462506 tagged rs3745601 with r2 = 0.96 and D′ = 1 in the 1000 Genomes FIN population. The rest of the SNPs were left out, because of issues in assay design. In total, 48 variants were genotyped. The first, the second and the third iplexes contained 24, 17 and 7 variants, respectively. For the second genotyping run, 28 variants were selected for genotyping in two iplexes. In addition, rs6025 in F5 was included to the panel to investigate if the previously found association between the variant and pre-eclampsia1516Of the 59 variants selected based on the exome sequencing, 46 were directly fitted to three Sequenom iplexes. In addition, two of the variants were captured through tagging SNPs. Rs117741116 tagged rs139702277 with rAssociation of the variants with pre-eclampsia was evaluated by chi-square test in the PLINK software v1.07A standard linear regression model in the PLINK software was applied for testing association of the genetic variants with quantitative traits relevant for pre-eclampsia in the FINNPEC cohort. The quantitative traits utilized in the association testing included the highest systolic blood pressure, the highest diastolic blood pressure, proteinuria and relative birth weight of the baby. An additive model was applied in the analyses.http://pngu.mgh.harvard.edu/~purcell/gpc/cc2.html). With a pre-eclampsia prevalence of 0.05 and a risk allele frequency of 0.05, our cohort of 1353 pre-eclamptics and ~6800 controls (the non-pre-eclamptic FINNPEC controls and the SISu (3.0) data unselected for the pre-eclampsia phenotype combined), was estimated to be sufficient to detect an effect size of 1.65 for risk heterozygote and 3 for risk homozygote with power of 0.80 when α < 5 × 10−8. Under the aforementioned parameters, for the variants with a risk allele frequency of 0.01, we could detect effect sizes of 2.52 and 5 for the risk heterozygote and homozygote, respectively.Statistical power was calculated with Genetic Power Calculator using the “case-control for discrete traits” moduleClinical characteristics of the study participants are presented in The sequencing provider delivered a total number of 299 to 482 million reads in each sample pool. Over 98% of the reads could be mapped to the reference genome hg19. Per pool on average, 90% and 80% of the SureSelect target regions were covered by a depth of at least 30x and 60x, respectively, and the average depth of the target regions per pool was between 95x and 277x, the average of all pools being 220x. Eight of the ten pools had average coverage over 200x in the enrichment regions. The total number of variants called was 2,308,376. The main filtering strategy applied to these variants is illustrated in The first round of Sequenom genotyping was carried out in a sample set of 180 cases and 180 controls including the 100 exome sequenced samples. The purpose of this step was to verify the presence of the variants in the original samples and the MAF difference between pre-eclamptic women and general population. All assays had a success rate >95%. Of the 48 variants that were genotyped, three were monomorphic. After excluding these variants, correlation coefficient between the MAF estimates from the exome sequencing and the MAFs obtained from the Sequenom genotyping for the exome sequenced samples was 0.94, showing that we were able to estimate MAFs in the exome sequencing fairly accurately. Four of the variants were not in HWE in the controls. Twenty-eight variants that had OR ≤0.65 or ≥1.3 were selected for the second round of Sequenom genotyping in a larger case-control cohort. The results of the first Sequenom round are shown in NRTN; p-value = 0.0314, OR (95% CI) = 0.69 (0.48–0.97)), but the variant did not pass differential missingness test between the cases and controls. None of the association tests reached genome-wide significance. Full results are shown in In the second round of genotyping all the variants except failed rs6681 were successfully genotyped. After excluding 17 cases and 5 controls with more than 2 failed genotypes, the genotyping rate for the variants in the remaining individuals was over 96%. Concordance rate of the genotypes in the 350 individuals included in both Sequenom runs was 0.9992. Three individuals with at least one discordant genotype between the runs were excluded. Association of the variants with pre-eclampsia was first tested in the Sequenom genotyped FINNPEC case-control sample set. Three variants deviated from HWE in the FINNPEC controls and were omitted. In the association analysis, one of the genotyped variants was nominally associated with pre-eclampsia  data, and tested association of the genotyped variants with pre-eclampsia in this combined data set. Three of the variants deviated from HWE in the controls in these data, and were excluded from the analysis. Furthermore, the X chromosomal variants were excluded as the SISu data contain both males and females. In the association analysis in the merged data, we detected nominal association of four variants . None ofF5 gene and has previously been connected to pre-eclampsia1516The variant rs6025, which is located in the TP53BP1 showed nominal association  with the highest systolic blood pressure, and rs61747120/T in ZFR2 with proteinuria , both in the pre-eclamptic women. In addition to those two SNPs, there were several other variants associated with the highest systolic or diastolic blood pressure, relative birth weight of the baby or proteinuria in the pre-eclamptic patients, the non-pre-eclamptic controls or the whole case-control sample set  and relative birth weight of the baby . However, none of the SNPs were associated with the quantitative traits at the genome-wide significance level.Assuming an additive model of genetic inheritance, we investigated the association between the genotyped SNPs and quantitative clinical characteristics of pre-eclampsia . Among tmple set . Rs22915In this first attempt to screen exome-wide for low-frequency, moderate or large-effect risk variants for pre-eclampsia in a Finnish founder population, we did not find any genome-wide significantly associated risk variants. Whereas many complex diseases such as type 2 diabetes or cardiovascular diseases often have onset at midlife or later, pre-eclampsia affects women at their reproductive years, or if the phenotype of offspring is considered, already during the fetal period. Consequently, the disease decreases reproductive success, and there is a reason to hypothesise that pre-eclampsia risk variants are under negative evolutionary selection, which would keep their population frequencies low. A couple of GWA studies have assessed the role of common variation in pre-eclampsia susceptibility18The DNA samples were exome sequenced in pools of ten in order to maximise the number of sequenced samples while maintaining the sequencing costs low. With this strategy we were able to perform cost-effective exome-wide screen for pre-eclampsia risk variants, but the approach also has several limitations, such as missing information on individual genotypes and on exact MAF of each variant in the study sample. We were able to estimate MAFs from the pooled exome-sequenced samples fairly accurately: correlation coefficient between our estimates and the 1000 Genomes European data was 0.972 when the sequencing depth was at least 30x per pool. We however acknowledge that variants with biased MAF estimates were prone to be selected for the Sequenom genotyping, whereas some truly enriched variants could have been missed. In the association analysis we utilized the large SISu data as a population specific reference to increase statistical power of the analysis. Both the FINNPEC data and the data sets contributing to the SISu project have been collected to represent the Finnish population. However, population substructure within Finland39There are several possible explanations for not identifying pre-eclampsia risk variants in this study. Our screening did not cover non-coding regions, and also structural variation was omitted from the analysis. Furthermore, variants in low-coverage regions could have been missed. At the time of the exome sequencing data analysis robust variant callers with ploidy setting were not available. Utilizing a caller assuming a diploid genome might have caused us to miss some candidate variants. Some risk variants may have been lost due to choices made in the filtering. As shown previously, variant annotations are much dependent on a transcript set and annotation software utilized, and often there are several plausible annotations for a single variantTP53BP1, rs61747120 in ZFR2, rs113926353 in ANO9, and rs142394560 in TMTC1), or with proteinuria and relative birth weight of the baby in the pre-eclamptic cases (rs2291516 in RGL3). These variants or genes have not previously been linked to pre-eclampsia or any other pregnancy disorders. TP53BP1 encodes a protein involved in DNA damage response and cell cycle regulationTP53BP1 have shown a suggestive association with blood pressure4548TP53BP1, TMTC1 and RGL3ANO9 most abundantly in skin and digestive tract and ZFR2 in adrenal gland, cerebral cortex and testis ( http://www.proteinatlas.org/). A larger sample size would have been needed to state anything conclusive about the role of the variants in these genes in the risk of pre-eclampsia.Although we could not detect any robust risk variant for pre-eclampsia in this study, we mention here five missense variants nominally associated either with the disease phenotype .


The sex ratio of the local population influences mating-related behaviours in many species. Recent experiments show that male-biased sex ratios increase the amount of financial resources men will invest in potential mates, suggesting that sex ratios influence allocation of mating effort in humans. To investigate this issue further, we tested for effects of cues to the sex ratio of the local population on the motivational salience of attractiveness in own-sex and opposite-sex faces. We did this using an effort-based key-press task, in which the motivational salience of facial attractiveness was assessed in samples of faces in which the ratio of male to female images was manipulated. The motivational salience of attractive opposite-sex, but not own-sex, faces was greater in the own-sex-biased (high competition for mates) than in the opposite-sex-biased (low competition for mates) condition. Moreover, this effect was not modulated by participant sex. These results present new evidence that sex ratio influences human mating-related behaviours. They also present the first evidence that the perceived sex ratio of the local population may modulate allocation of mating effort in women, as well as men. For example, in many non-human species, greater selectivity is evident in females’ mate preferences when the local population's sex ratio is male biased than when it is female biased (reviewed in ). Additifinancial resources men will invest in potential mates, it is not known whether this pattern of results also occurs for other measures of mating effort.More recent work suggests that the sex ratio of the local population may also influence how men allocate mating effort . For exaIn light of the above, we investigated the effects of manipulating cues to the sex ratio of the local population on the motivational salience of attractiveness in own-sex and opposite-sex faces. We measured the motivational salience of attractive faces using a standard key-press (i.e. effort-based) task in which participants can control the viewing duration for faces ,7. Given2.(a)α > 0.96), and men's and women's ratings were highly correlated for both male and female faces (both r > 0.97).In an initial pilot study, 100 heterosexual men and 100 heterosexual women  rated the attractiveness of 50 young white men's faces . A different group of 100 heterosexual men and 100 heterosexual women  rated the attractiveness of 50 young white women's faces . All faces had direct gaze and neutral expressions and the photographs were taken under standardized conditions. The order in which the faces were presented for rating was fully randomized and ratings were made using 1 (much less attractive than average) to 7 (much more attractive than average) scales. Male and female faces were presented in separate blocks of trials. Inter-rater agreement for ratings of the male and female faces was high  and the eight most attractive female faces . These 16 images are referred to hereon as the high attractiveness targets. The average attractiveness ratings were also used to identify the eight least attractive male faces  and the eight least attractive female faces  in the two sets. These 16 images are referred to hereon as the low attractiveness targets. The high and low attractiveness targets were used to assess the motivational salience of relatively attractive and relatively unattractive faces in the motivational salience test. We also used these ratings to identify the 16 male faces  and 16 female faces  around the median in the male and female face sets. These 32 images are referred to hereon as the filler faces and were used to manipulate the sex ratio of the sample of images presented in the motivational salience test.The average attractiveness ratings of the individual faces in the two sets were used to identify the eight most attractive male faces ((b)stumbleupon.com), participated remotely (i.e. not in the presence of an experimenter) and received no compensation for participating. The website where the experiment was run required that participants register with a unique username prior to participation. No participants took part in both the pilot study and main experiment. Each participant completed a ‘pay-per-view’ key-press task, similar to those that have been used in previous studies to assess the motivational salience of attractive faces [Two hundred and ninety-one heterosexual men and 292 heterosexual women  participated in the main online experiment. Participants were recruited by following links on social bookmarking websites  was 4 s. Participants were told that the key-press task would last for a total of 3 min in order to discourage responses aimed at changing the length of engagement with the task. However, in reality, the total length of the key-press task was dependent on participants’ responses. All participants key-pressed at least once during the experiment. All participants completed a brief training task designed to familiarize them with the key-press procedure prior to beginning the experiment. Faces were not presented in this training task.Participants were randomly allocated to one of three versions of the key-press task: a male-biased sex ratio version, a female-biased sex ratio version or an unbiased sex ratio version. The 16 high attractiveness targets and 16 low attractiveness targets were presented in each version of the task. In addition to these target faces, however, the male-biased sex ratio version also included the 16 male filler faces, the female-biased sex ratio version also included the 16 female filler faces, and the unbiased sex ratio version also included the eight male and eight female filler faces around the median attractiveness rating for the male and female face sets. In each version of the task, all faces were presented in a single block of trials in which trial order was fully randomized. Random allocation of participants to versions was done separately for male and female participants to ensure task version was not confounded with participant sex.(c)target faces, separately for each participant. Following previous work [attractiveness motivation score for male faces  by subtracting their mean key-press score for the male low attractiveness targets from their mean key-press score for the male high attractiveness targets. A corresponding attractiveness motivation score for female faces was also calculated for each participant. One-sample t-tests showed that both men's and women's attractiveness motivation scores were significantly greater than chance (i.e. 0) for both male and female faces .First, we calculated the key-press score for each of the ous work ,7, key-p3.sex of target face (opposite-sex and own-sex) as a within subject factor and task version  and participant sex  as between subjects factors. Note that sex of target face and task version are both coded relative to each participant's sex, allowing us to directly test whether the predicted effects involving sex of target face and task version are significantly different for male and female participants. The data set is available as the electronic supplementary material.Attractiveness motivation scores were analysed using a mixed-design ANOVA with sex of target face  than own-sex faces . The interaction between sex of target face and participant sex was also significant  than female participants .Our analysis revealed a significant main effect of sex of target face and task version was also significant , but not own-sex faces (p = 0.65). Additionally, independent samples t-tests showed that attractiveness motivation scores for opposite-sex faces differed significantly between the own-sex- and opposite-sex-biased task versions (p = 0.012), but not between the own-sex-biased and -unbiased task versions (p = 0.28) or between the opposite-sex-biased and -unbiased task versions (p = 0.11). Repeating these comparisons for own-sex faces revealed no significant differences .To interpret the two-way interaction between 4.Consistent with previous research ,7, attraAlthough recent work found that own-sex-biased sex ratios increased the amount of financial resources men invested in potential mates, sex ratios did not influence how women allocated financial resources . By contPrevious research on the motivational salience of facial attractiveness has emphasized how much effort individuals allocate, on average, to attractive and unattractive faces ,7. By co


Members of the NAC transcription factor family in barley appear to be highly involved in initiation and progression of leaf senescence via regulation of target genes having palindromic NAC-binding sequences in their promoters. The senescence process of plants is important for the completion of their life cycle, particularly for crop plants, it is essential for efficient nutrient remobilization during seed filling. It is a highly regulated process, and in order to address the regulatory aspect, the role of genes in the NAC transcription factor family during senescence of barley flag leaves was studied. Several members of the NAC transcription factor gene family were up-regulated during senescence in a microarray experiment, together with a large range of senescence-associated genes, reflecting the coordinated activation of degradation processes in senescing barley leaf tissues. This picture was confirmed in a detailed quantitative reverse transcription–PCR (qRT–PCR) experiment, which also showed distinct gene expression patterns for different members of the NAC gene family, suggesting a group of ~15 out of the 47 studied NAC genes to be important for signalling processes and for the execution of degradation processes during leaf senescence in barley. Seven models for DNA-binding motifs for NAC transcription factors were designed based on published motifs, and available promoter sequences of barley genes were screened for the motifs. Genes up-regulated during senescence showed a significant over-representation of the motifs, suggesting regulation by the NAC transcription factors. Furthermore, co-regulation studies showed that genes possessing the motifs in the promoter in general were highly co-expressed with members of the NAC gene family. In conclusion, a list of up to 15 NAC genes from barley that are strong candidates for being regulatory factors of importance for senescence and biotic stress-related traits affecting the productivity of cereal crop plants has been generated. Furthermore, a list of 71 senescence-associated genes that are potential target genes for these NAC transcription factors is presented. Plant nutrients are to a very large extent taken up and stored temporarily in the vegetative parts of cereal crop plants, either as structural components or as part of compounds in metabolic fluxes, until the seed filling and maturation stage, where they are remobilized to varying extents and translocated to the developing seeds. This developmental process is of the utmost importance for the productivity of crop plants, and a crucial part of it is the terminal process described as leaf senescence, in which a highly regulated degradation of the components of leaf tissues takes place  has been intensively studied over the last two decades (e.g. Hordeum vulgare) were studied in order to substantiate the understanding of the senescence process in a typical cereal plant. It is demonstrated that the generic senescence programme, studied mainly in Arabidopsis  were grown in the greenhouse in pots containing a 50:50 peat–perlite mixture. A standard nutrient solution was supplied via the irrigation system. Artificial illumination was used for supplementation of sunlight and for ensuring a day/night cycle of 16/8h. Sampling of leaves was done between 12:00h and 14:00h. Non-senescing flag leaves were harvested ~5 d before anthesis, medium-senescing leaves at ~15 d post-anthesis, and senescing leaves at ~30 d post-anthesis, selecting leaves with ~50% green leaf area. Frozen homogenized leaf samples were used for RNA isolation as described previously  was used for the microarray experiments. Sample quality control, sample labelling, hybridization, scanning, and data extraction were performed by imaGenes , according to the complete integrated Agilent Workflow Solution certified by Agilent . In brief, the isolated RNA was reversed transcribed into cDNA, followed by an in vitro transcription reaction during which Cy3 labels were incorporated into cRNA, which was subsequently hybridized to the microarray chip in single channel hybridizations. Data acquisition was performed with an Agilent DNA microarray scanner with Feature Extraction Software. The quality filtering and statistical analysis of the raw data obtained was performed with the Agi4×44PreProcess  Several target sequences, defined by the same primary accession number, were represented by from two to several probes in the chip. (ii) Since many of the target sequences were partial expressed sequence tags (ESTs), several of them in fact represent the same gene sequence. To cope with the redundancy, only one probe for each target sequence was used in the analysis, selected via the following procedure: the probe with the highest or second highest signal closest to the 3′ end of the target sequence was selected as a representative probe. The distal 3′ probes were favoured since the labelling of cRNA for the chip hybridization was performed from the poly(A) tail. Furthermore, the redundancy was alleviated by making blastN searches of the Agilent Chip probes against recently released full-length cDNA sequences of barley (http://mapman.gabipd.org (last accessed 12 February 2014), which also formed the basis for the MapMan categorization of the target genes into functional bins, supplemented by manual annotation of a limited number of genes. The MapMan bins , actin (AY145451), α-tubulin (X99623), EF1-α (Z50789), G6PDH (AM398980), HP68 protein (AK251800), HSP70 (AK248694), and Splicing factor 2 (AK249101)  gene also showed high stability and was included in the presentation of the data as a control gene.Procedures for RNA isolation, cDNA synthesis, primer design, primer efficiency testing, and quantitative real-time PCR were performed as described previously (software  and the software  were useK249101) ] were teK249101) . Based oin silico using the resources from http://coexpression.psc.riken.jp/barley/search.pl, last accessed 12 February 2014  was used (Putative barley promoter sequences (1000 bases upstream of ATG) and their corresponding coding sequences (CDS) were obtained from ary 2014 . For dethttp://www.ebi.ac.uk/arrayexpress/, last accessed 12 February 2014; accession no. E-MTAB-2133). The data were analysed using the Agi4×44PreProcess and Limma R-packages. Herein, mainly the data from the contrast between non-senescing and strongly senescing samples will be presented and discussed. In a standard hierarchical clustering analysis of the samples, one of the senescing samples, Sen3, appeared as an outlier with considerably lower signal values than the other two senescence samples. Accordingly, the Sen3 sample was omitted in the final data analysis, which, however, only marginally affected the ordering of differentially expressed genes.In order to study the general changes in gene expression taking place during senescence of the barley flag leaf, a microarray experiment was performed on three stages of senescence of the flag leaf of greenhouse-grown barley plants (cv. Golden promise). Sampling was done from young, just fully developed barley flag leaves (~5 d before anthesis); from medium-senescing flag leaves (~15 d post-anthesis); and from strongly senescing flag leaves . Three samples, each comprising 3–5 leaves, were used for the first and last harvest time points, and two samples for the medium stage. This provided RNA samples for, in total, eight hybridizations distributed over two Agilent 4×44 Barley Microarray chips. The hybridizations were performed by imaGenes , from whom the extracted raw data were received. These raw data are available from ArrayExpress (Supplementary Table S1 available at JXB online). According to the adjusted P-values of the Limma tests for differential expression, a large number of genes appeared as differentially expressed in the contrast between strongly senescing and non-senescing flag leaves, presumably reflecting the immense physiological changes taking place in the leaves during senescence. Since a decision on a precise threshold between significant and non-significant differential gene expression in studies like this is difficult . The Agilent barley chip contained a high number of redundant sequences, which were handled according to the approach described in the Materials and methods. This resulted in a list of 3867 non-redundant differentially expressed genes that were subject to detailed analysis.During pre-processing of the data in the Agi4×44PreProcess software, background correction was performed with the method ‘half’, and normalization across the eight hybridizations with the method ‘quantile’. Low quality features, in particular those with signals close to or below background levels, were filtered out, according to default settings of the Agi4×44PreProcess software. The pre-processing resulted in 24 646 features with acceptable signal values. The statistical analysis of these filtered data using the Limma software resulted in ordered lists of gene probes according to the probability of their differential expression across the different contrasts in the experiment  and tetrapyrrole biosynthesis (19)], which showed predominantly down-regulation of genes. A number of the up-regulated genes in the photosynthesis category are related to the photorespiration process, and their encoded functions in the context of senescence might be related to the turnover of glycine also originating from sources other than the photorespiration cycle  metabolism, of a general up-regulation of the TCA cycle of the mitochondria.In accordance with the presumed translocation of compounds and mineral nutrients from senescing leaves, a number of transporter gene transcripts were up-regulated during senescence, mainly comprising transporters for amino acids, peptides, and sugars, but there was also a clear indication for up-regulation of transporters involved in transport of minerals.Supplementary Fig. S1 available at JXB online), was analysed for sub-bins that in particular represented this up-regulation, and the seven sub-bins shown in Top-bin 26, with miscellaneous functions, which showed an overall bias towards up-regulated genes in senescing leaves  clusters for k-means clustering, seven clusters were chosen as an optimal cluster number that divided the genes into distinct groups. Supplementary Fig. S2B available at JXB online. The difference in expression level between sample 9 and 1 from the qRT–PCR experiment was used as the senescence-associated expression that could be compared with the microarray expression results.In order to study in more detail the regulation patterns of SAGs and the NAC genes during the progress of senescence, 44 SAGs were selected for detailed qRT–PCR studies across the different functional categories shown in the MapMan ‘senescence pathway’ in software . FollowiPaO, ACO, or ATG8A, was not reflected in the expression profile of this cluster, apart from a slight transient up-regulation at the mid-stages. The NAC genes HvNAC003, -7, and -10 were included in cluster 6, and the apparent up-regulation in the microarray of these three NAC genes was hence not confirmed.Since mostly up-regulated SAGs were selected, most of the clusters in ICL), which confirmed the strong up-regulation also observed in the microarray experiment. None of the other selected SAGs had a similar expression pattern and hence the ICL gene was the only gene in cluster 1. In the microarray experiment, the ICL probe represented by far the most strongly up-regulated gene on the chip, with a log2-fold change of 11.6. Cluster 2 also showed strong, ~8-fold (log2) up-regulation of three SAGs, encoding nuclease I, saccharopine dehydrogenase, and a xyloglucan endo-transglycosylases/hydrolase (XTH). Again, this confirmed the strong up-regulation also observed in the microarray data of these genes. Cluster 2 also showed the strong up-regulation of one NAC gene, HvNAC026, encoding a close homologue of AtXND1, a putative negative regulator of xylem development in A. thaliana  belonged to the NAC-d group  belong to the same phylogenetic group, the NAC-a group  with high affinity for binding to NAC domains. They all contain a CGT core sequence with varying number of surrounding consensus nucleotides, which is thought to facilitate binding of the NAC transcription factors of promoter elements (see Supplementary Table S4 available at JXB online for details) were designed based on the information in http://meme.nbcr.net/meme/cgi-bin/fimo.cgi (last accessed 12 February 2014) against available promoter sequences of genes represented in the Agilent barley chip. The Agilent chip probe sequences were assigned to promoter sequences via blastN searches against the downloaded CDS sequences. This process presumably again reflected the sequence redundancy in the Agilent chip, inasmuch as only 12 643 unique promoter sequences were found for the 24 646 filtered probes from the analysis of the microarray experiment. However, the relatively low number might also reflect the provisional status of the gene models in the current barley genome sequences.In order to investigate the possible regulation of SAGs by NAC transcription factors in barley, seven model NACBS motifs  and distributions in the co-expression lists for the 32 NAC genes were obtained as illustrated in Supplementary Fig. S3 available at JXB online. A number of NAC genes clearly showed a higher degree of general co-expression with the 71 genes having the NACBS-8 in the promoter than with up-regulated genes without any NACBS motif in the promoter. This was particularly prominent for HvNAC008, -13, -23, and -44 that were all located in clusters in HvNAC013 and -23 (both with high co-regulation) and for HvNAC011 (low co-regulation) are shown in HvNAC013) themselves contain NACBSs in their promoter, and may, hence, be part of gene activation cascades. In conclusion, the co-expression data support the notion that the occurrence of NAC-binding sites in the promoters of SAGs is of importance for the up-regulation of these genes during senescence in barley.In order to validate the results on putative NACBSs amongst the up-regulated genes during senescence, the overall gene co-expression with NAC genes was investigated in the barley co-expression database available at ary 2014  were dowWhen the microarray experiment described in this work was conceived, the barley Agilent 4×44 Barley Microarray was the platform that had the best representation of the barley gene transcriptome. During the course of the experiment and the analysis of the results, new data sources for barley genomics information have become available, in particular the release of 24 000 full-length barley cDNA sequences , which pArabidopsis have been performed  showed clear up-regulation. In contrast, three probes showed strong down-regulation of the chloroplastic GS2 gene. There was no indication of up-regulation of the mitochondrial TCA cycle for the generation of NADP(H) reducing agents or for anaplerotic purposes. The possible needs for these agents for the cells were presumably met via the massive up-regulation of lipid and protein degradation processes. Mitochondrial functions that appeared to be up-regulated were mainly related to the electron transport chain, reflecting possible needs for ATP generation or for channelling of energy into dissipating processes, for example via the alternative oxidase.The inter-related up-regulation of amino acid and lipid metabolism seems central to the senescence process, and here peroxisomal processes appear highly involved. In the end, this complex of up-regulated processes is believed to work as a way of rescuing nitrogen released during protein degradation for translocation in the form of amino acids to other plant parts, in particular to the developing grain  A group of genes with a relatively early up-regulation that tend to level off towards the later stages of senescence, exemplified by the closely related NAC-a genes HvNAC005, -23, -27, -29, and -30. (3) Genes up-regulated during the late burst of degradation processes in the leaf tissues. This is exemplified by the NAC-d genes HvNAC013, -22, and -25. Further studies are needed in order to fine-tune this classification. However, a preliminary hypothesis would be that the group 2 genes, with their early induction, are involved in signalling processes, inasmuch as this group of genes seems to be associated with abscisic acid (ABA) processes. Most of them are up-regulated following ABA treatment  with varying span widths and with varying weights on nucleotides in surrounding sequences based on the suggested NACBSs in One of the ways to study the direct regulation of target genes of transcription factors is to screen for the putative binding sites in promoter sequences of possible target genes. Thus, Supplementary Table S4 available at JXB online) constitute a list of possible target genes, for which further results from testing of direct interaction between promoter elements and NAC transcription factors are pending.In order to substantiate the role of the putative NACBS for regulation by NAC transcription factors, the general co-expression of SAGs having an NACBS in their promoter sequence with the NAC genes was investigated. The analysis clearly showed that 71 up-regulated SAGs that had an NACBS in the promoter, compared with those that did not, exhibited strong general co-expression with senescence-associated NAC genes. Hence, not only during senescence, but in general, the occurrence of the NACBSs seems to favour co-expression of SAGs with some of the NAC transcription factor genes, which implies the importance of the NACBSs for the regulation of target genes by the NAC transcription factors. The 71 up-regulated SAGs (Arabidopsis senescence processes and the results from barley presented here, the visual senescence pathway map and expression data are likely to be representative for other cereals as well. Furthermore, additional evidence of the importance of not only single NAC transcription factors but multiple members of the family for a concerted regulation of the complex senescence processes was provided. A list of up to15 barley NAC genes that are particularly interesting for this regulation process is presented. Finally, a list of 71 novel putative target genes of the NAC transcription factors is presented.In conclusion, considering the large degree of conservation observed between the well-characterized JXB online.Supplementary data are available at Figure S1. Distribution of 3867 differentially expressed probes from the Agilent microarray experiment across the 35 top-bins of the MapMan annotations.Figure S2. Correlation of gene expression levels in the Agilent microarray experiment and in qRT–PCR experiments.Figure S3. Distributions in co-expression lists for 32 NAC genes.Table S1. Result table after Limma analysis of microarray data.Table S2. Annotations of genes in the MapMan top-bins of Supplementary Fig. S1.Table S3. Annotations of genes in MapMan pathway of Supplementary Fig. 1.Table S4. Letter probability matrices used for FIMO analysis.Table S5. List of 71 putative target genes of NAC transcription factors.Table S6. List of primers used in qRT–PCR experiments


To compare the pre-procedural anxiety and depression levels of patients undergoing chorion villus sampling (CVS) and amniocentesis (AC).Patients referred to our department for fetal karyotype analysis with a positive first or second trimester screening test for aneuploidy between January 2013 to June 2015 were included. CVS and AC procedures were performed in patients with gestation periods of between 11-14 and 16-20 weeks, respectively. Anxiety was evaluated using the Spielberger State-Trait Anxiety Inventory (STAI), and depression was assessed using the Beck Depression Inventory II (BDI-II).A total of 1,400 patients were included. Compared to first trimester controls, patients undergoing CVS had significantly higher STAI-state and BDI-II results. Likewise, patients undergoing AC had higher STAI-state and BDI-II scores than controls in the second trimester. In terms of STAI-trait results, no difference was found between the groups. Our results also showed that, compared to AC group, patients undergoing CVS had similar STAI-state, STAI-trait and but higher BDI-II scores.We conclude that evaluating the stress and depression levels of these patients should be one of the routine procedures in pregnancy follow-up. Pregnancy itself is one of the most stressful and depressing periods in a woman’slife.The procedures used for fetal genetic analysis are divided according to the gestational time and the indicated tissues.5Although the relations between maternal stress, anxiety and perinatal handicaps such as early labor onset, low birth weight, and poor fetal and child development have been reported, still very little attention is being paid among obstetricians on the detection, significance and treatment of anxiety and depression, particularly in daily routine practice.8This issue also relates to all professionals who perform invasive procedures such as CVS or AC. Generally, a successful and uncomplicated invasive procedure is the main goal for the obstetrician. Very few concern themselves with the psychological state of the patient. Nevertheless, patients suffering from severe anxiety and/or depression may need psychiatric support.We performed this study in order to compare the pre-procedural anxiety and depression levels of patients undergoing CVS or AC.The protocol of this study was approved by the ethics committee for human studies at our university (number: 04.09.2012/257), and informed consent was obtained from all participants before the study was conducted between January 2012 and June 2015.All patients referred to our perinatology department for fetal karyotype analysis with a positive first or second trimester screening test for aneuploidy were recruited for this case-control study. CVS and AC procedures were performed in patients with gestation periods of between 11-14 and 16-20 weeks, respectively. A complete fetal sonographic examination was then performed to document other structural anomalies, and patients with a fetal anomaly and/or multiple pregnancies were excluded.Anxiety was measured prior to the procedure using the STAI, and depressive symptoms were assessed using the BDI-II, again before the procedure. Twenty items were considered in assessing the trait measure, which reflects more stable characteristics – an anxious personality, for example. The other 20 items assess the state measure, which reflects transient characteristics such as anxiety disorders. The STAI questionnaire rates responses on a four-point intensity scale ranging from ‘not at all’ to ‘very much’. The range of scores is 20-80. Higher scores are positively correlated with higher levels of anxiety.11The BDI-II is a 21-item self-reporting questionnaire that assesses the severity of depression. Individuals are asked to rate themselves on a spectrum of 0-3  with a score range of 0-63. The cut-offs are 0-13, minimal depression; 14-19, mild depression; 20-28, moderate depression; and 29-63, severe depression. The total score is the sum of all the items. The Turkish version of the BDI-II used in this study has been validated in Turkish populations.13All patients referred for first or second trimester screening tests for aneuploidy were informed about the study and invited to complete the above inventories to be included in the control group.Continuous variables are presented as mean±standard deviation, while categorical variables are given as percentages. The Kolmogorov–Smirnov test was used to detect normal distribution. Given the non-parametric distribution of the groups, statistical analysis of the mean ranks between the groups was made using the Mann-Whitney U test. Categorical data was analysed by Pearson Chi-square test and Chi-square for trend. In addition Spearman correlation analysis was used to assess correlation among variables. Analyses were performed with PASW 18  software and two-tailed P value less than 0.05 was considered statistically significant.A total of 1,400 patients were included in the study. The numbers of patients undergoing CVS and AC procedures were 220 and 480, respectively. The control group consisted of 220 patients in the first trimester (controls for the CVS group) and 480 patients in the second trimester (controls for the AC group).The characteristics of the groups are shown in The comparison of STAI and BDI-II scores between groups are sown in Finally, no significant effects of maternal age, occupational status, a healthy child from a previous pregnancy, level of education and place of residence were determined on STAI-state, STAI-trait and BDI-II scores.In this prospective study, we surveyed pregnant women undergoing a prenatal invasive procedure (CVS or AC) after an abnormal first or second trimester screening test for aneuploidy, and compared them to controls. We found that both procedures were associated with higher scores for state anxiety and depression. Moreover, our results revealed that patients undergoing CVS had higher scores for depression than patients undergoing AC.Comparison of invasive prenatal diagnosis techniques (CVS and AC) and invasive fetal therapy was performed as part of a previous study designed to examine the psychosocial impacts of these methods.Women are at an increased risk of developing anxiety and depressive disorders during the perinatal period.Antenatal depression, as well as both state and personality trait anxiety, have been shown to be some of the strongest risk factors associated with postpartum depression.Perinatal diagnostic technologies are currently at their highest ever level. We feel that a similar level of attention should be given to the evaluation, and if necessary treatment, of stress and depression in patients undergoing invasive prenatal procedures. Such measures would serve to lessen the potential associated negative influences on perinatal outcomes.As this study aimed at evaluating the pre-procedural anxiety and depression levels of the patients about to undergo CVS or AC, this was the largest of its type. The case-control design depending on the type of self-reporting questionnaire is hence a possible limitation of our study. Patient referral to a psychiatry clinic would also provide more valuable data on anxiety and depression levels, particularly for those patients with higher scores. Finally, revealing the long-term perinatal effects of high anxiety and depression levels would probably disclose more conclusive evidence.


MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify primary miRNAs and precursor miRNAs (pre-miRNAs), very few provide the functionality of locating mature miRNAs within plant pre-miRNAs. This manuscript introduces a novel algorithm for predicting miRNAs named miRLocator, which isbased on machine learning techniques and sequence and structural features extracted from miRNA:miRNA* duplexes. To address the class imbalance problem , the prediction models in miRLocator were optimized by considering critical (and often ignored) factors that can markedly affect the prediction accuracy of mature miRNAs, including the machine learning algorithm and the ratio between training positive and negative samples. Ten-fold cross-validation on 5854 experimentally validated miRNAs from 19 plant species showed that miRLocator performed better than the state-of-art miRNA predictor miRdup in locating mature miRNAs within plant pre-miRNAs. miRLocator will aid researchers interested in discovering miRNAs from model and non-model plant species. MicroRNAs (miRNAs) are small (~22 nucleotides), non-coding RNA molecules with important regulatory roles in gene expression. In plants, mature miRNAs are mostly generated from the cleavage of hairpin-structured miRNA precursors (pre-miRNAs) derived from long primary transcripts (pri-miRNAs). MiRNAs A number of computational approaches called miRNA predictors have been developed over the past few years, but most of these actually predict pri-miRNAs and/or pre-miRNAs . The preThe rule-based approaches identify mature miRNAs based on several criteria determined through an analysis of known miRNAs, such as at most four unpaired nucleotides in putative miRNA duplexes and at least 70% sequence similarity with known miRNAs. This approach has inherent limitations for the identification of novel miRNAs with characteristics that differ from those of known miRNAs.ML is a branch of artificial intelligence that applies various mathematical algorithms to improve the performance of prediction systems for the discovery of knowledge from novel datasets. ML was recently applied to the identification of mature miRNAs through the integration of thousands of sequence and structural properties extracted from miRNA sequences and/or miRNA duplexes. Notable ML-based miRNA predictors include miRdup, miRduplFirst, the majority of these ML-based miRNA predictors were designed to identify animal miRNAs,10,12,14https://github.com/cma2015/miRLocator.In this manuscript, we present an ML-based system named miRLocator forthe computational prediction of mature miRNAs within plant pre-miRNAs. miRLocator was constructed based on 440 sequence and structural features extracted from miRNA duplexes. We explored the effect of class imbalance on the prediction performance of miRNA predictors and optimized the prediction models in miRLocator with an appropriate machine learning algorithm (random forest) and RPNS(1:5). A ten-fold cross-validation evaluation using 5854 experimentally validated miRNAs from 19 plant species demonstrated that the prediction performance of miRLocator was comparable to or better than that achieved with the state-of-art miRNA predictor miRdup. miRLocator was implemented in Python and provides functions for ML-based training and prediction processes using user-specified datasets. miRLocator is available at The general process for the training and testing of ML-based miRNA predictors is shown in The training procedure involves the following steps :Obtain a set of experimentally validated miRNAs and their corresponding pre-miRNA sequences.http://www.tbi.univie.ac.at/RNA; Version 2.1.8).Fold the secondary structure of each pre-miRNA sequence using the RNAfold program in the ViennaRNA package . The RF-based Gini importance calculates the mean decreases in impurity at each node of the trees obtained after the removal of the feature of interest from the feature matrix[Encode positive and negative samples with sequence and structural properties extracted from miRNA duplexes. The importance of each feature in distinguishing positive and negative samples can be measured through the random forest (RF)-based feature ranking algorithm (Gini importance) using the scikit-learn Python package , which is a collection of mature miRNAs and their corresponding pre-miRNAs generated through experimental and computational approaches. A total of 7009 experimentally verified plant miRNAs and their pre-miRNA sequences were automatically downloaded from the miRBase database using the miRdup software  with the command "java -jar miRdup.jar -k Viridiplantae". To effectively evaluate the performance of miRNA predictors trained with miRNAs from individual species, we excluded 37 species annotated with less than 100 experimentally verified miRNAs. This filtration resulted in 5854 experimentally verified miRNAs generated from 4626 pre-miRNAs of 19 plant species  and twelve dicotyledonous plant species within the Brassicaceae, Fabaceae(Glycine max and Medicagotruncatula), Rosaceae, Vitaceae (Vitisvinifera), Salicaceae(Populustrichocarpa), Solanaceae(Nicotianatabacum and Solanumtuberosum) and Malvaceae (Gossypiumraimondii) families. Functional miRNAs generated from both the 5' and 3' arms of the pre-miRNAs were observed in almost all of the studied plant species with the exception of M.domestica, N.tabacum and G. raimondii ranged from 0 (pij = 1 for some j) to log2(n) (pij = 1/n for each j).Positive and negative samples were encoded with a numerical vector of length 440 that includedthe sequence and structural properties of the miRNA duplexes and their combinations.A total The remaining 191 features consisted of different combinations of the sequence and structural properties extracted from the miRNA duplexes . For example, ‘A’in the ‘triplets’ list represents an unpaired A with two paired neighboring nucleotides in the miRNA duplex.C and various kernel function parameters, such as gamma (γ) for the RBF kernel. The NB is another supervised ML algorithm based on the application of Bayes' theorem, which assumes that the likelihood of the features is Gaussian. The kNN is a type of instance-based ML algorithm that classified a sample based on the majority of the k-nearest neighbors. All of these ML algorithms were implemented with functions in the "scikit" library written in the Python programming language.Many ML algorithms have been developed to construct binary classifiers in the fields of bioinformatics and computational biology .In this Cross-validation is a commonly used approach to evaluate the binary prediction performance of ML systems. For the ten-fold cross-validation algorithm, positive and negative samples are randomly divided into ten subsets of approximately equal sizes. For each round of cross-validation, nine of these ten subsets are selected to form the training datasets used to train the ML systems, and the tenth subset is used as the testing dataset to evaluate the prediction performance of the trained ML system throughreceiver operating characteristic (ROC) curve analysis. The ROC curve reflects the variability of the true positive rate (sensitivity) against the false positive rate (1-specificity) at all possible thresholds. The value of the area under the curve (AUC) can range from 0 to 1, with a higher AUC value indicating that the classifier has a higher discriminative capability to differentiate positive samples from negative samples. After all of the subsets are used as the testing dataset, the mean value of the ten AUCs is considered the final performance of ML systems.p on the pre-miRNA hairpin, the summed prediction score was calculated asBecause the location and length of the mature miRNAs are unseen by the miRNA predictors during the testing procedure, all of the possible miRNA duplexes with candidate miRNAs ranging from 16nt to 30nt were generated for each tested pre-miRNA sequence and scored using the trained miRNA predictor. We applied a post-processing approach to determine the length and location of the predicted miRNAs by considering the score of all of the candidate miRNAs. For a given position ppred) was identified as the position at which S(p) reaches the maximal value. The length of the predicted miRNA was determined as the length that yields the maximal value of score. miRLocator outputs the predicted miRNA and its corresponding passenger strand for the tested pre-miRNA sequence.The start position of the predicted miRNA(d(0 ≤ d ≤ 10nt). In this manuscript, the resolution d represents the start (end) of the predicted miRNAs located within d nucleotides of the true start (end) position.The accuracy of different ML prediction systems in predicting mature miRNAs within pre-miRNAs was evaluated by comparing the start and end positions of the predicted miRNAs with those of the true miRNAs.The cumuN  pseudo miRNA duplexes derived from randomly selected segments within the pre-miRNA hairpins. We then encoded each sample with 440 numeric features  values for RF, SVM, NB, kNN and DT were 0.917, 0.897, 0.724, 0.853 and 0.759, respectively , Fig 3A.The contribution of each feature to the classification of positive and negative samples was measured using the RF-based Gini importance algorithm see  section.Feature ranking was repeated for different RPNSs. Some features, such as bpNum, mlBulge and numbp_Win4,6, and 8, were ranked at the top with different RPNSs, whereas other features, such as BulgeAtEnd_u1, Persent_10mer, and U), were ranked differently with different RPNSs . BecauseOverall, RF and SVM are two of the most powerful ML algorithms for the identification of mature miRNAs. Considering the computational time required for optimizing parameters in SVM, the RF algorithm and all 440 features were applied in miRLocator to construct the prediction model formature miRNAs.We then evaluated the performance of miRLocator and previous miRNA predictors in locating mature miRNAs within pre-miRNA sequences from plants. A given set of pre-miRNAs was divided into ten subsets, nine of which were selected to constructpositive and negative sample sets for trainingthe miRNA predictors, whereas the tenth subset was used to evaluate the performance of the trained miRNA predictors. The prediction accuracy of the miRNA predictors was calculated by comparing the start (end) positions of the predicted miRNAs with the start (end) of the true miRNAs.To ensure a fair comparison, different miRNA predictors should be trained with the same training dataset and assessed with the same testing dataset. However, to the best of our knowledge, only the recently published miRNA predictor miRdup provides functions to implement the ML-based training and testing procedures using user-provided datasets. By combining lineage-specific features with the RF algorithm, miRdup achieves a superior prediction accuracy relative to that obtained with other miRNA predictors, including MatureBayes, miRAlign, MaturePred and ProMiR.d = 0) the start and end positions for 25.5% and 25.0% of the true positions, respectively. In contrast, miRdup made perfect predictions of start and end positions in 20.8% and 14.7% of the cases, respectively. The prediction performance of miRLocator was superior or comparable to that of miRdup at all of the other resolutions analyzed. There are two possible reasons for this result. First, miRLocator characterized miRNAs with more sequence and structural features, some of which  were applied for the first time to the prediction of mature miRNAs. Second, miRLocator applied an improved post-processing approach to locate mature miRNAs within the pre-miRNAs by considering the prediction score of all possible miRNA duplexes. In contrast, miRdup focused on the possible miRNA duplexes with prediction scores higher than 0.99, leading to the failure to accurately locate the start and end positions of the mature miRNAs within a proportion of pre-miRNA sequences [equences .d = 0) produced by miRLocator increased from 25.5% to 33.5%, and the perfect prediction of the end positions obtained by miRLocator increased from 25.0% to 32.1%   and their corresponding pre-miRNA sequences. We then evaluated the performances of miRLocator and miRdup with these selected miRNAs and calculated the cumulative frequency of the correctly predicted start and end positions of the miRNAs at different resolutions using miRLocator and miRdup. This process was repeated 20 times. We subsequently drew abox plot of the cumulative frequencies at are solution of d = 5 for the different sample sizes. With increases in the sample size, the prediction accuracy of miRLocator increased from 0.61 to 0.73 and became relatively stable once the number of positive training samples reached 400  .To elimiched 400 . In contched 400 . These rTo confirm these results, we assessed the performances of miRdup and miRLocator with a limited number of experimentally validated miRNAs from individual species. As expected, the prediction performance of miRLocator was comparable to or better than that of miRdup in all tested situations .http://www.tbi.univie.ac.at/RNA), Python (v2.7.6) programming language and Python-related libraries, including sklearn , NumPy  and SciPy . The source codes of miRLocatorcan be downloaded from https://github.com/cma2015/miRLocator. The RF-based miRNA prediction models in miRLocator were implemented using the "RandomForestClassifier" function in the "sklearn" library and can be automatically trained with user-specified miRNA datasets. For a given pre-miRNA sequence, miRLocator first scores all of the miRNA candidates with sequence lengths ranging from 16nt to 30nt and then outputs the most probable miRNA and its corresponding passenger strand as the final prediction result.miRLocator was implemented as a Python program running on a Linux/Unix platform that requires pre-installation of the ViennaRNA package  to predict mature miRNAs within plant pre-miRNAs was developed. We then demonstrated the importance of the ML algorithm, the sample size and the ratio between training positive and negative samples (RPNS) to address the class imbalance problem observed in the development of ML-based miRNA predictors with high prediction performance. We also determined the appropriate ML algorithm (RF) and RPNS (1:5) for the prediction of mature miRNAs. miRLocator can be used to more accurately locate mature miRNAs within plant pre-miRNA sequences compared with the state-of-art miRNA predictor miRdup. Combined with pre-miRNA predictors,20, miRLS1 FigNumbp_Win4 and numbp_Win8 denote the average number of base pairs in a sliding window of 6nt(a) and 8nt(b), respectively.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLS)Click here for additional data file.