Six schools were randomised to receive the intervention and six acted as non-intervention controls. In intervention schools, TAs were trained to deliver an after-school programme for 15 weeks. The difference in mean accelerometer-assessed MVPA between intervention and control schools was assessed at follow-up (T1). The cost of programme delivery was estimated. Two schools did not deliver the intervention, meaning four intervention and six control schools were analysed at T1. There was no evidence for a difference in MVPA at T1 between intervention and control groups. Programme delivery cost was estimated at £2.06 per pupil per session. Existing provision in the 12 schools cost £5.91 per pupil per session. Action 3:30 was feasible to deliver and considerably cheaper than existing after-school provision. No difference in weekday MVPA was observed at T1 between the two groups, thus progression to a full trial is not warranted.Many children are not sufficiently physically active. We conducted a cluster-randomised feasibility trial of a revised after-school physical activity (PA) programme delivered by trained teaching assistants (TAs) to assess the potential evidence of promise for increasing moderate-to-vigorous physical activity (MVPA). Participants ( Physical activity is associated with lower levels of cardiometabolic risk factors , lower rThe school setting provides opportunities to recruit large numbers of children simultaneously, making it an opportune setting for interventions to increase children’s physical activity levels . SystemaThe time immediately after school is discretionary time for most children  and coulIn 2017 the UK government doubled the primary school PE and Sport Premium to £320 million and recommended that funds be used to make sustainable improvements to the quality of physical activity opportunities offered across the school day, including after school . A stateWe have previously developed a programme, called Action 3:30, which trained teaching assistants (TA) to deliver a physical activity club after-school. Action 3:30 was underpinned by Self-Determination Theory (SDT) . The evaThe aim of this study is to test the feasibility and potential for effectiveness of the revised version of Action 3:30. In this paper we report on the recruitment process, feasibility of conducting a definitive trial, potential efficacy of the trial and the likely cost of the intervention. Finally, we assess whether progression criteria for conducting a definitive trial were met (see Methods).The full methodology and study protocol are described in detail elsewhere  and outln = 8 schools) and North Somerset (n = 4 schools) in Southwest England were recruited to the study. The target population for this study were pupils in Year 4 and 5 (aged 8 to 10 years) at the time of the intervention. We aimed to recruit up to 30 children per school.Twelve state-funded primary schools from South Gloucestershire , and then during the final six intervention sessions (T1). To compare the activity levels of children who consented to join the study with the overall sample of eligible children there was an additional (pre-baseline) data collection prior to T0.Schools were randomly assigned to intervention (6 schools) or control (6 schools) arms after baseline data had been collected. Allocation was stratified by Local Authority and recruitment method: standard or enhanced. Random allocation and statistical analyses were conducted using Stata by an independent member of the Bristol Randomised Trial Collaboration who was blinded to school identity. Written parental informed consent was obtained for all participants for the T0 and T1 phases of the study. An additional, “opt-out” consent process was used for the pre-baseline survey phase of the project as outline in The Action 3:30 intervention has been described in detail elsewhere . BrieflyBased on findings from the outcome  and procIn order to compare those pupils who consented to join the study with all remaining eligible pupils, all children in the 12 study schools were asked to complete the Physical Activity Questionnaire for Older Children (PAQ-C)  prior toT0 was conducted when the target pupils were at the end of years 3 and 4, approximately 4 months before the start of the intervention. This was done to facilitate to enable the intervention schools to be notified of allocation after baseline data had been completed, while also allowing time for TAs to attend training. T1 was conducted in the last six weeks of the intervention period when the children were in years 4 and 5 respectively and therefore provided an indication of the levels of physical activity while the program was still running. Pupils wore ActiGraph wGT3X-BT accelerometers  for seven consecutive days to measure physical activity at both time points. Pupils were included in analysis if they provided ≥ 3 valid days (500 min) of data. Minutes of moderate-vigorous physical activity (MVPA) were estimated using the Evenson cut-point . Total p(not true for me) to four (very true for me). Three questionnaire items were used to assess each of four types of motivation  with the mean of all three items calculated for each type of motivation. Composite scores for autonomous (mean of intrinsic and identified) and controlled  motivation were then calculated. Autonomy, competence and relatedness needs satisfaction were each assessed using a 6-item Likert scale, ranging from one  to six . Parents were asked to complete a questionnaire to collect demographic data (T0 only), travel mode to and from school and pupils’ current participation in after-school clubs. Demographic information included ethnicity and parental education level.Pupils also completed a questionnaire assessing PA motivation and activity-based perceptions of autonomy, relatedness and competence needs satisfaction . PA motiHealth-related quality of life (HRQoL) was assessed using the KIDSCREEN-10  and ChilIntervention schools received £200 to buy equipment to deliver the club and were reimbursed to cover the cost of two hours per week for two TAs to deliver the club. As recompense for the additional work involved in facilitating data collection control schools received a one-off payment of £300. Pupils received a small gift at each data collection in recognition of their contribution to the study.Summary statistics were presented to describe control and intervention arms at T0 and T1 for demographics (T0 only), accelerometer, psychosocial and health-related quality of life variables. Data provision rates for accelerometer  and questionnaire data  were recorded at T0 and T1.To estimate the potential intervention effect, an intention-to-treat (ITT) analysis using a multivariable mixed effects linear regression that took account of the clustering of pupils in schools was conducted with accelerometer-assessed mean minutes of weekday MVPA at T1 as the candidate primary outcome in a definitive trial. The model included covariates for intervention arm, local authority, recruitment strategy and mean minutes of weekday MVPA at baseline (T0) and accommodated variation in outcome between schools using a random effect. The difference in mean weekday MVPA was presented with its 95% confidence interval. This analysis was repeated with stratification by sex and for the other accelerometer-derived secondary outcomes.We conducted a post hoc comparison of control and intervention schools in the amount of physical activity during the after-school period (3 to 5 p.m.) using an adaptation of the above statistical model to estimate the difference in mean activity with 95% confidence interval. For intervention schools we calculated for each pupil the difference in their physical activity during the after-school period between Action 3:30 club days versus other school days, presenting the mean of these differences with its 95% confidence interval.A resource checklist adapted from previous studies ,25 was u(a)25% of schools that are approached agree to join the study(b)25% of eligible Year 4/5 pupils express an interest in the study by returning consent forms.(c)At least 40% of participants expressing an interest in the study are girlsd)At least 50% of the participants in the intervention arm attend 50% of the sessions(e)At T1, at least a small benefit for weekday MVPA is observed for boys & girls, comparing intervention to control schools, and the upper bound of the 95% CI for each difference exceeds a 10-minute benefit for the intervention group.The following five criteria were agreed with the funding agency as progression criteria to full-trial: A total of 44% of schools that were approached agreed to take part in the study, meeting progression criteria a. Across the 12 schools, 1139 pupils were eligible to participate, and 1125 pupils completed the PAQ-C survey (98.77%) with only 14 pupils’ (1.23%) parents completing opt-out forms to exclude their child from the measures. In four schools, pupils were also asked to wear an accelerometer for seven days. The percentage of pupils recruited was similar when pupils were asked to wear an accelerometer for 7 days and complete the PAQ-C survey (99.01%) compared to when asked to only complete the survey (98.19%) suggesting this measure and consent process was acceptable to pupils and their parents.A total of 459 pupils returned parental consent forms (41.39% of eligible), 228 (48.66%) of whom were girls, meeting progression criteria b and c. The number and percentage of pupils recruited was similar for standard  and enhanced  recruitment methods. There was no meaningful difference in the PAQ-C scores between standard recruitment  and enhanced recruitment  or the proportion of girls recruited between standard recruitment (51.32%) or enhanced recruitment (48.07%) . Thus, tTo determine the effectiveness of recruiting low active children, accelerometer and PAQ-C data were used to compare physical activity levels of those who did and did not consent to participate in the intervention. In the four schools with pre-baseline accelerometer data, mean weekday minutes of accelerometer-measured MVPA was 63.23 (SD 19.69) versus 66.75 (SD 18.77) for non-consenting versus consenting participants respectively. Across all 12 schools, PAQ-C scores were 3.13 (SD 0.71) versus 3.21 (SD 0.68) respectively for those who did and did not consent to participate. Minimal differences between groups suggested that the study appealed to children across the physical activity spectrum and that the study was able to recruit high and low-active children within schools.A total of 335 pupils provided data at T0 with 165 pupils at schools randomly allocated to the control group and 170 at schools randomly allocated to the intervention group see . FollowiAfter adjustment for local authority, recruitment strategy and baseline MVPA, mean weekday MVPA at T1 was very similar between control and intervention groups, overall and for boys and girls separately .Exploration of psychosocial and BMI variables also found no meaningful difference between trial arms at T1 in the adjusted models .The number of active travel days from school and number of after-school clubs attended (excluding Action 3:30) was slightly lower in the intervention group versus control (2.21 vs. 2.63 and 2.31 vs. 2.49 respectively), however 95% confidence intervals spanning zero suggest the differences are not meaningful. There were no differences in the psychosocial variables.The total cost of the Action 3:30 intervention across the four intervention schools, including training, preparation and delivery costs was £7422.15, with an average cost per school of £1855.55 (based on 2017–2018 prices) . DeliverKIDSCREEN-10 and CHU9D responses were analysed to explore health-related quality of life and are presented in Data from key contacts in each school showed that on average, intervention schools provided more after-school physical activity clubs per week at baseline (T0) and at T1 when compared to control schools. In addition, delivery of after-school clubs cost more in intervention schools compared to control schools at T0 and T1 see . The totThe aim of this study was to explore the feasibility and potential efficacy of a revised teaching assistant-led intervention designed to increase levels of weekday MVPA in boys and girls. Five criteria were agreed with the funder for progressing to a definitive trial, as listed in the Methods. Criteria a–d were met; 44% of schools approached joined the study, 43% of eligible pupils expressed an interesting in participating, of which 49% were girls and 70% of pupils attended at least half of the 30 sessions, with attendance being similar between boys and girls. The fifth progression criterion (e) focused on the evidence of promise for an increase in weekday MVPA among boys and girls in the intervention group compared to the control group at follow-up. Our data showed that the pupils in intervention and control groups engaged in similar levels of weekday MVPA at the end of the intervention, suggesting that Action 3:30 replaced existing PA rather than providing additional PA. These findings indicate that the Action 3:30 intervention is feasible to deliver, however there was no evidence that it increased overall MVPA levels in the intervention group.Exploratory, post-hoc examination of PA levels in the window immediately after school (3–5 p.m.) revealed that the intervention was effective at increasing MVPA straight after school on days when children attended the Action 3:30 club . A diffeWe have shown in this study and in our previous work  that traPrimary schools often choose to spend their PE and Sport Premium funding on paying for extra-curricular physical activity provision . Our finOur economic evaluation also revealed that intervention schools provided slightly more and spent more money on after-school physical activity clubs than control schools in this study. The results from our process evaluation (under review) also found that some pupils did not attend all the Action 3:30 sessions because they chose to do other clubs or activities instead. Together this evidence may suggest that because intervention schools already provided a variety of after-school provision, pupils were likely to switch between clubs, which has been similarly found in other PA interventions ,31. TherMany previous PA interventions in children have not reported the representativeness of participants . An objeMany studies have shown that girls are less active than boys ,37. TherIn this study we found that there was no evidence that any of the motivation or health related quality of life variables of the participants in the intervention group were different to the control group at the end of the study. As there was also no evidence of an overall impact on physical activity it is unclear if this a function of the measurement tools, i.e. they were unable to detect differences or merely attributable to the overall lack of impact. It is no possible to untangle these issues without first detecting an intervention effect. As such, there is a need for further examination of the utility of these measures in childhood studies.A major strength of this study was the rigorous design, with clear research questions that mapped onto five progression criteria. Both low and high deprived schools were recruited, and pupil recruitment rates were relatively high. An opt-out phase was used to assess representativeness of the study population and a re-enrolment point was introduced half-way through the intervention, both of which increased external validity of the study. Data provision rates were high at baseline and follow-up for all measures and an objective measure of physical activity (accelerometers worn for seven days) was used. The main limitation of the study was that two schools were unable to deliver the intervention, one of which did not provide a reason for withdrawal. Generalisability is also limited due to the small number of schools involved, however this is inherent in the design of a feasibility trial.While feasible to deliver, no evidence was found that the Action 3:30R intervention increased physical activity compared with a control group, potentially because pupils were swapping one club for another instead of adding new activities. The revised programme appealed to and engaged children across the physical activity spectrum, including girls, and was cheaper to deliver than existing provision with the added benefit of developing staff—something which aligns with current school objectives. Providing activities in the after-school period is important for helping children to meet the physical activity recommendations . Work fr


Various studies have demonstrated that the two leaflets of cellular membranes interact, potentially through so-called interdigitation between the fatty acyl groups. While the molecular mechanism underlying interleaflet coupling remains to be fully understood, recent results suggest interactions between the very-long-chain sphingolipids in the outer leaflet, and phosphatidylserine PS18:0/18:1 in the inner leaflet, and an important role for cholesterol for these interactions. Here we review the evidence that cross-linking of sphingolipids may result in clustering of phosphatidylserine and transfer of signals to the cytosol. Although much remains to be uncovered, the molecular properties and abundance of PS 18:0/18:1 suggest a unique role for this lipid. There are several lines of evidence for interactions between the two membrane leaflets in cells. In this review the authors discuss the transmembrane coupling of lipids, the involvement of phosphatidyl serine species PS 18:0/18:1, and their importance for various cellular processes. Glycerophospholipids are the main constituents of cell membranes. Their amphiphilic character is due to the hydrophilic head and the hydrophobic fatty acyl tails. Generally referred to as phospholipids, glycerophospholipids can form a bilayer. Cellular membranes are such bilayers, where the hydrophobic tails of the phospholipids are located in the middle of the membrane with the hydrophilic head groups facing the hydrophilic surroundings on both sides. Most phospholipids contain ester-bound fatty acyl groups  with 16 or 18 carbon atoms and zero, one or two double bonds in cis configuration. However, other fatty acyl groups like C20:4 (arachidonic acid) and C22:6  are also common in the sn-2 position. In this review much of the emphasis will be put on phosphatidylserine (PS), which is a phospholipid with serine bound via a phosphodiesther linkage to glycerol . The reason for this bimodal distribution is not known. For the purpose of the present discussion it should be noted that C16 and C18 hydrophobic chains are able to reach approximately to the middle of the membrane bilayer, whereas C24 chains are so long that they should be able to reach far into the opposite layer , a phospholipid with a choline headgroup, are present in the outer leaflet, whereas most of the other plasma membrane phospholipids, such as phosphatidylserine (PS), phosphatidylethanolamine (PE) and phosphatidylinositol (PI), are present in the cytosolic leaflet. It should be noted that cholesterol constitutes 30–40% of all lipids in the plasma membrane. The distribution of cholesterol between the two leaflets have been heavily discussed for many years, but recent data indicate that it is close to evenly distributed in the two leaflets. Although a lot of information about the intracellular distribution of lipids is availableins Fig. . Here we17. Such domains are enriched in cholesterol and glycosphingolipids which as mentioned above often have long fatty acyl chains, thus facilitating interactions with the inner leaflet. The binding of multivalent bacterial toxins to glycosphingolipid receptors on host cells results in intracellular signalling23. This raises the question of how binding of a protein toxin to lipids in the extracellular leaflet of the plasma membrane could result in signalling on the other side of the membrane. One possibility is that toxin-induced signalling is due to cross-linking and clustering of glycolipids.Membrane domains with a high lipid order, including lipid rafts (often referred to as membrane rafts) have been reported to show interactions between the two membrane leaflets and to be important for transmembrane signalling, endocytosis and intracellular transport27. Also, the SV40 virus multivalently binds to GM128, and there are additionally several toxins and viruses that require glycosphingolipids to interact with cells and induce signalling29.The bacterially produced Shiga toxin (Stx) and cholera toxin, so called AB5 toxins, contain one enzymatically active A-chain and five B-chains binding to glycosphingolipids on the host cell’s outer membrane. Stx has three binding sites for globotriaosylceramides (Gb3) on each B-chain, but it is not known how many of these binding sites that normally are occupied when Shiga toxin is bound to cells. In contrast, the five B-chains of cholera toxin each bind one monosialotetrahexosylganglioside (GM1); for reviews see32. Several possibilities exist for how this might occur. (i) An unknown transmembrane protein could be interacting with the toxins and be involved in this signalling, somewhat similar to what is known for many protein receptors in the plasma membrane. However, no such protein has yet been identified for Stx binding to Gb3 at the cell surface. (ii) The sphingolipids often contain a high fraction of very-long-chain N-amidated fatty acyl groups, e.g. with 22–24 carbon atoms, such that they theoretically should be able to penetrate almost halfway into the inner leaflet. The toxin-induced binding and clustering of these very-long-chain sphingolipids in the outer leaflet by interleaflet coupling could therefore cluster lipids such as PS or phosphorylated phosphatidylinositols (PIPs)34 in the inner leaflet. The accumulation of PS or PIPs would in turn provide an increased avidity for binding of cytosolic proteins and thus possibly initiate a signalling cascade. (iii) The multivalent binding of these toxins to the cell by cross-linking of lipids could change the conformation of a transmembrane protein, and create Ca-channels resulting in intracellular signalling and downstream effects. In such a scenario, transbilayer coupling may be required.Consistent with the idea that toxin-induced signalling is due to cross-linking of glycosphingolipids on the plasma membrane, cross-linking of these lipids with antibodies or lectins results in a similar signalling37. Furthermore, GM1 is able to interact with membrane proteins38. Thus, we will focus on Shiga toxin binding to its lipid receptor Gb3 to provide a basis for discussing interleaflet coupling.Cholera toxin might trigger intracellular signalling via multiple pathways, since it can also bind to surface glycoproteins8. The length of the N-amidated fatty acyl groups of Gb3 has been suggested to play a role not only in the binding to Gb325, but also for transport of Stx to the Golgi apparatus and for the Stx toxicity on cells42. Thus, there may be a communication between Gb3 in the outer leaflet and the sorting machinery at the cytosolic side of the plasma membrane, e.g., due to interleaflet coupling between Gb3 and lipids in the inner leaflet.Gb3, the lipid receptor for Stx, consists of a mixture of species with different N-amidated fatty acyl groups containing mainly between 16 and 26 carbon atoms8. It should be noted that the dominant Gb3 species (C24:1) in HEp-2 cells is the only major Gb3 species with a double bond43. Since C24:1  has its double bond between carbon atoms 15 and 16, the double bond of Gb3 d18:1/24:1 will be almost exactly at the centre of the membrane bilayer. Why do biological membranes contain such high amounts of very-long-chain sphingolipids, including one with a double bond placed close to the middle of the bilayer? We speculate that the bimodal distribution of sphingolipids species  in cells is important to have such lipids reaching both to the centre of the double layer and far into the other leaflet, thereby creating flexibility for interactions of different strengths between sphingolipids in one leaflet with lipids in the other leaflet.Theoretically, a high fraction of the Gb3 species should be able to reach approximately halfway into the inner leaflet of the plasma membrane and thus contribute to interleaflet coupling44. Although DRMs as a tool to study lipid rafts is under debate45, the lipid composition of DRMs may to some extent reflect the composition in lipid rafts. Different studies, including studies using DRMs, indicate that Gb3 present in high lipid-order membrane fractions such as lipid rafts are involved in retrograde transport  and toxicity of Stx following transport of the A-chain to cytosol41. In particular, nanodomains containing cholesterol were reported to be important for the Stx induced intracellular signalling18 since addition of filipin (which binds to cholesterol) had an inhibitory effect, and extraction of cholesterol with methyl-β-cyclodextrin reduced Golgi transport of Stx46. One can, however, not exclude that such treatments have unspecific effects.Gb3 is found both in detergent resistant membranes (DRMs) and non-DRM membrane fractions of many cell lines48. Those interactions are often investigated using model membranes lacking the complexity and asymmetry of biological membranes. To our knowledge the first study where quantification of many lipid species of biological membranes was followed up with molecular simulation, was the investigation of exosomes released from PC-3 cells11. Exosomes are small extracellular vesicles released from cells upon fusion of multivesicular bodies (late endosomes) with the plasma membrane. Mass spectrometry (MS) analyses of PC-3 cells and exosomes released from these cells, revealed that cholesterol—the very-long-chain SM species (mainly SM d18:1/24:0 and SM d18:1/24:1) and PS 18:0/18:1 (which is the main PS species making up ~40% of PS species in these exosomes)—were enriched to a very similar extent between cells and exosomes. Furthermore, if one compensates for the different surface areas of the inner and outer leaflet of exosomes with a diameter of 70 nm, PS constitutes approximately half of the phospholipids of the inner leaflet, and PS 18:0/18:1 in the inner leaflet could theoretically cover ~80% of the area covered by the very-long-chain sphingolipids (mainly SM) in the outer leaflet11. These data lead to the idea that these lipids might be sorted together due to interactions (“hand-shaking”) between the very-long-chain SM in the outer leaflet and the PS 18:0/18:1 in the inner leaflet11.While many proteins span the membrane bilayer and are important for its stability, we will focus on interleaflet coupling due to the less well understood interactions between lipids in the two leaflets49. Furthermore, a recent study of exosomes purified from urine of 15 prostate cancer patients and 13 healthy volunteers50 revealed a lipid composition different from that observed in PC-3 cells, but PS 18:0/18:1 in the inner leaflet could still theoretically occupy 78 ± 18% (mean ± SD) and 66 ± 22% of the area covered by the very-long-chain sphingolipids in the outer leaflet in patients and controls, respectively50. It should be noted that the cholesterol content has been reported to be above 40% of total lipids in several exosome preparations45. In the studies discussed above the cholesterol content was 44% of total lipids in exosomes released from PC-3 cells11 and 63% of total lipids in the urinary exosomes50. The last number is close to the maximum solubility reported for cholesterol in model membranes51.A follow up study performed with PC-3 cells treated with the ether lipid precursor hexadecylglycerol resulted in major changes in the cell lipids, including large increases in the ether lipids. The exosomes released from these cells still had a similar ratio of PS 18:0/18:1 to the very-long-chain SM as in exosomes isolated from untreated cells11. Later, a simulation study using 14 asymmetric lipid membranes52, demonstrated that in asymmetric models the very-long-chain N-amidated fatty acyl groups of SM were able to penetrate deeply into the inner leaflet. Interestingly, the conformational order of these amide-linked acyl chains was found to increase as they penetrated into the opposing leaflet, revealing that the acyl chains interact with lipids in the inner leaflet. Furthermore, cholesterol was shown to modulate the effect of SM interdigitation by influencing the conformational order of lipid chains in the inner leaflet52. The strongest interdigitation was observed between SM d18:0/24:0 and PS 18:0/18:1 . Notably, the interaction between these two species was the only one that increased in the presence of cholesterol. The strongest interdigitation was observed when there was a little more cholesterol in the SM containing leaflet than in the inner leaflet52.As mentioned above, initial molecular dynamic simulation studies indicated a hand-shaking between the very-long-chain (C24) SM species and PS 18:0/18:145, and there is a high enrichment of PS, SM and cholesterol from the mother cell to both the HIV particles53 and the DRMs55. Moreover, PS 18:0/18:1 was the dominant PS in these preparations (PS 36:1 quantified in the HIV particles)53. Assuming that HIV particles have a diameter of 120 nm53, the PS 36:1 in the inner leaflet could theoretically cover ~60% of the very-long-chain sphingolipids in the outer leaflet, indicating that there could be a similar hand-shaking between the very-long-chain sphingolipids and PS 18:0/18:1 also in HIV particles45. It has for many years been stated that DRMs are highly enriched in saturated phospholipids. This may be due to the fact that DRMs used in an early and detailed study of their lipid composition were reported to be enriched in saturated phospholipids, the caveat that “a phospholipid was deemed saturated if containing no more than one double bond”55 might often be overlooked. It should also be noted that recycling endosomes have been shown to be enriched in SM, PS and cholesterol, but lipid species were not reported in that study56.The lipidomic analyses and the molecular dynamic simulation studies discussed above suggest a co-sorting and hand-shaking between the sphingolipids in the outer leaflet and PS 18:0/18:1 in the inner leaflet of exosomes. The fact that PS 18:0/18:1 is a major lipid species in exosomes excreted from many cell types led us to propose the model shown in Fig. 57 are discussed below. One may wonder why C18:1  in PS seems to be important for such interactions. Is this due to the fact that this double bound is located approximately in the middle of the inner leaflet, and close to the end of the very-long-chain N-amidated fatty acyl group penetrating from the other leaflet? Is the position of this double bound also important for cholesterol induced packing of cell membranes? It should be noted that cells contain much more PS 18:0/18:1 than e.g. PS 16:0/18:1 or PS 18:0/16:1  after gold immune-labelling of this probe. They showed that PS on the cytosolic side of the plasma membrane forms clusters with a diameter of ~11 nm. PS clusters were also observed at the trans-Golgi network and at endocytic organelles. Importantly, PS clusters were observed at 60–80 nm vesicular profiles with the morphology of caveolae, which are known to be enriched in sphingolipids and cholesterol and can be regarded as raft-like structures59. Very recently another study showed that PS in fact dictates the assembly and dynamics of caveolae, indicating that membrane leaflet coupling is a prerequisite for their formation60. Grinstein and colleagues also showed that polarisation of PS into the bud of Saccharomyces cerevisiae was required for proper Cdc42 localisation and for development of cell polarity61. However, whether this polarisation is associated with membrane leaflet coupling has to our knowledge not been investigated.To our knowledge, a study published in 2011 was the first to indicate a possible interaction between sphingolipids in the outer leaflet and PS in the inner leaflet. The groups of Grinstein and Parton demonstrated topologically distinct cellular pools of PS62. More recently the same group  reported clustering and interaction between analogues of GPI-AP in the outer leaflet and PS in the inner leaflet of the Chinese hamster ovary (CHO) cell line PSA3, which carries a mutation in the PS synthase 1 gene63. Most of the reported experiments were performed using synthetic fluorescent GPI-AP analogues carrying glucosamine inositol linked phosphatidic acid (PA) with a fluorescent label linked to glucosamine, i.e. without any protein part. The clustering of the GPI analogue was reduced when cholesterol was extracted with methyl-β-cyclodextrin or by disrupting actin activity. Based on these data the authors concluded that both cholesterol and actin were important for this clustering63. In such experiments, it is however difficult to exclude that removal of cholesterol induces secondary effects that might be affecting the clustering. The clustering was observed when the lipid part of the GPI analogue contained two saturated long-chain fatty acyl chains (16:0/16:0 and 18:0/18:0 were used), but not when it contained two unsaturated (18:1/18:1) or short chains (8:0/8:0). Thus, the clustering was observed with GPI-AP analogues having fatty acyl chains close to those found in mature endogenous GPI-APs which contains an alkyl chain in the sn-1 position64. Moreover, Raghupathy et al.63 showed that PS in the inner leaflet was required for this clustering, whereas PIP2 was shown not to be involved.The Mayor group has shown that glycosylphosphatidylinositol (GPI)-anchored proteins (GPI-AP) could form nanoscale domains in living cell membranes63. There are some similarities between those observations and some earlier reports that cross-linking of GPI-AP resulted in a cholesterol-dependent intracellular signalling65.By using a protein construct that connect PS to actin they could recruit endogenous GPI-APs in the outer leaflet, thus indicating that actin filaments at the inner leaflet have the capacity to link to PS and immobilise and stabilise local lipid-ordered domains in the outer leaflet of the plasma membrane63 they compared the results obtained using PS with two saturated fatty acyl groups with those obtained with PS 18:1/18:1. Atomistic molecular dynamic simulation studies with asymmetric bilayers  showed the strongest transbilayer interdigitation obtained with the GPI analogue carrying two fatty acyl groups with C16:0 and with PS 18:0/18:0; the interdigitation was much less when exchanging PS 18:0/18:0 with PS 18:1/18:1 or PS 12:0/12:0. Furthermore, Raghupathy et al.63 studied the effect of different PS species on the GPI-AP clustering; various PS species were added to the medium, and shown to be incorporated into the inner leaflet of the plasma membrane. Also when measuring the clustering of endogenous GPI-AP on the cell surface by adding PS species in the presence of a PLA2 inhibitor, PS 18:0/18:0 was much more effective than PS 18:1/18:1 or PS 12:0/12:063. For these clustering experiments they also added PS 18:0/18:1 as they found PS 36:1 to be the dominant PS species in these cells. Since PS 18:0/18:1 gave a similar effect on clustering of GPI-AP as PS 18:0/18:0, it was concluded that long-chain PS species were needed to obtain this clustering63. As discussed further below, none of the 9 PS species reported to be present in PSA3 cells contained two saturated FAs P2, PC 18:0/18:1, PE 18:0/18:1 and PA 18:0/18:1. Thus, this data represent another example of the importance of PS 18:0/18:1, and indicates a role for interactions between PS 18:0/18:1 and cholesterol57. A summary of data obtained for PS 18:0/18:1 with those obtained for other PS species is given in Table These authors also used giant unilamellar liposomes (GUVs) to study phase separation of cholesterol and PS67. It was proposed that cholesterol associates with PS to form clusters where the headgroups of PS are sufficiently separated to limit the curvature otherwise caused by the negative charges of PS67. Also, addition of PS 10:0/10:0 was sufficient to increase endocytosis, and removal of cholesterol with methyl-β-cyclodextrin resulted in PS-rich tubules emanating from the plasma membrane67.It was also recently reported that interactions between PS and cholesterol is important for control of membrane curvature63, one may wonder if PS clustering is important to increase interactions between PS and several cytosolic PS-binding proteins. It should be kept in mind that translocation of proteins from the cytosol to the membrane may depend on the PS density in the binding area, such that it is not only the binding affinity between one PS molecule and the PS-binding protein that is important, but the high avidity created by the accumulated strength of interactions occurring close to each other in the membrane. Also the observation that HeLa cells have a significant fraction of PS with limited mobility, and that cortical actin contributes to the confinement of PS in the plasma membrane68 suggests that “immobilisation” of a fraction of PS could be due to binding of other cytosolic proteins as well. Although several PS-binding proteins are known69, more information is needed about this type of proteins and their interaction with PS in order to evaluate their importance for intracellular signalling. A search for proteins (SMART-EMBL database) with pleckstrin homology (PH) domains revealed 259 verified PH-containing proteins and 696 proteins containing the predicted sequences of PH domains thus being candidates for binding to PS. It is not our goal to discuss in detail what is known about PS-binding proteins, but Table As several lines of evidence support that PS can be clustered in the inner leaflet of the plasma membrane58, and PS is also exposed to the cytosol during transport from the trans-Golgi to the plasma membrane, as well as in endosomes and lysosomes70. The flipping of PS to the cytosolic leaflet of trans-Golgi has been shown to be due to Arl1 activation of the flippase Drs2 in the trans-Golgi network, and has recently been reviewed71. It should be noted that sphingolipids and cholesterol are selectively enriched in membranes from trans-Golgi to the plasma membrane73, and it has been proposed that rafts containing cholesterol and sphingolipids are important for sorting of proteins from the trans-Golgi to the plasma membrane74. Thus, flipping of PS to the cytosolic side of the trans-Golgi71 and interleaflet coupling may be important for sorting of at least of some of these proteins.Our discussion so far has to a large extent focused on the structure of the plasma membrane, but several of the issues discussed are also relevant for other cell membranes. PS was found in the luminal side of ER, the Golgi complex and mitochondria13, flippases etc.76, and PS-probes77. The formation and some functional aspects of PS have recently been reviewed78.For more information about the asymmetry of PS, the importance of flippases, floppases and scramblases as well as other probes for intracellular detection of PS, we refer to the following articles: asymmetry79. The headgroup of PS is not metabolised as fast as PIPs, but on the other hand PS is often present in much higher amounts than the PIPs.Discussions in the literature about interactions between protein and lipids in the plasma membrane and endosomal vesicles have until now mainly been related to interactions with PIPs. The rapid metabolism of PIPs and their importance for the function of Rab proteins make them suitable for regulating intracellular transport80, EHD1 is important for transport through recycling endosomes to the Golgi apparatus, and it has been suggested that EHD1 not only binds to PA but also to PS81. The flippase ATP9A, which is colocalized with PS in early and recycling endosomes, plays a crucial role for recycling from endosomes to the plasma membrane82. Thus, it is clear that PS can play a role for intracellular transport.There are examples of PS-binding proteins being important for intracellular transport, e.g., that evectin-2 is important for retrograde transport of cholera toxin from endosomes to the Golgi apparatus52. Although a significant interdigitation has been observed in the symmetric models, a considerably stronger interdigitation was observed when using asymmetric models52, thus stressing the importance of using asymmetric membrane models, i.e. models that are more similar to biological membranes13. For more information about simulation studies we refer to recent reviews discussing cholesterol and sphingolipids in raft-like membranes83, the role of charged lipids in membrane structures84, and to two reviews discussing different theories of leaflet coupling and various methods used to study such interactions85. Of special interest for the present discussion is that these reviews all stress the importance of using asymmetric models and the ability of the very-long-chain sphingolipids to cross the membrane midplane.Recently molecular dynamic simulation has proven to be a useful tool to study membrane structures. Although we focused in this review on interleaflet interactions between the very-long-chain sphingolipids and PS 18:0/18:1, it is important to be aware of that interleaflet coupling is found in almost all membrane models. Thus, although we reported the highest interdigitation in asymmetric models with SM d18:1/24:0 and PS 18:0/18:1 in the presence of cholesterol, it should be noticed that interdigitation was measured both when using the shorter SM species with N-amidated C16:0, and with other lipids species and headgroups than PS 18:0/18:185 there are also discussions about the possible contribution of interleaflet coupling due to membrane curvature, line tension, electrostatic interactions and cholesterol flip-flop between membrane leaflets. It has been suggested that the so-called cholesterol flip-flop may give important contributions to the leaflet coupling, but it is argued in the review by Nickels et al.85 that the flip-flop contributes more than a magnitude less than that due to the acyl chain interactions.In the two last-mentioned reviews86 measured diffusion (using fluorescent correlation spectroscopy) in each membrane leaflet of vesicles containing various SM species in the outer leaflet and various PC species in the inner leaflet. They observed the largest reduction of diffusion in the inner leaflet when using SM C24:0 in the outer one, indicating that this very-long-chain species gave the strongest coupling between the leaflets. The largest reduction of diffusion in the outer leaflet was obtained when the inner leaflet contained PC with one saturated fatty acyl group  and one unsaturated fatty acyl group (C18:1)86. They also postulated that van der Waals interactions between the terminal portions of acyl chains from the two leaflets meeting near the midplane is likely to contribute to chain interdigitation effects. Such effects may thus be important for bilayer coupling between membrane areas containing mainly C16 or C18 fatty acyl groups. Later, the same group, using vesicles containing 37% cholesterol and asymmetric membranes with an SM-rich outer leaflet demonstrated the formation of ordered domains in the inner leaflet containing only PC with two unsaturated fatty acyl groups (PC 18:1/18:1)87. Thus, such interactions were demonstrated even with two unsaturated fatty acyl groups in the glycerophospholipids in the inner leaflet, i.e., at conditions expected to be less optimal for such interactions.Also several interesting studies have been performed using vesicles with an asymmetric membrane. Thus, Chiantia and London88.The knowledge of making asymmetric liposomes is relatively new and one should expect more studies using such liposomes in the future. It would be useful if one could develop an asymmetric vesicle system with cholesterol, with defined species of both a glycosphingolipid such as Gb3 in the outer leaflet and PS in the inner leaflet and include a reporter kinase to determine whether crosslinking of Gb3 could induce a transmembrane signalling similarly to what was reported by Iwabuchi et al.66. Even fatty acyl groups much shorter than those found in biological membranes are often used in such models. Thus, a critical discussion of the biological significance of the data obtained in such studies should be included, but are most often lacking. Although the use of giant plasma membrane vesicles have several advantages compared to liposomes regarding similarity with cellular membranes66, it should be noted that these large membrane vesicles have been reported to lose their PS asymmetry89, a change that might affect membrane properties, including interdigitation.Despite that asymmetric membrane models are more similar to biological membranes, most studies are still performed with symmetric models containing only one or a few lipid species. Moreover, the lipid species used in such models are often present in very low amounts in cells. These models typically contain identical saturated, monounsaturated or diunsaturated fatty acyl groups, whereas mammalian cells contain high levels of phospholipid species with one saturated and one unsaturated fatty acyl groupHere we have summarised recent studies which demonstrate coupling between the two leaflets in cellular membranes with a special focus on the importance of the ability of PS 18:0/18:1 present in the inner leaflet of the plasma membrane to interact with very-long-chain sphingolipids in the outer leaflet in a cholesterol dependant manner. The role of coupling between the two membrane leaflets for association with cytosolic proteins and intracellular signalling is discussed. Membrane domains referred to as lipid rafts are often described to be enriched in phospholipid species with two saturated fatty acyl groups. However, such saturated species normally constitute a very small fraction of cellular lipids, and the view that lipid rafts mainly contain phospholipids with two saturated fatty acyl groups should therefore be reconsidered. PS 18:0/18:1 is the major PS species in many cell types and the only species where addition of cholesterol in molecular dynamic simulation studies revealed increased interleaflet coupling with sphingolipids. Although more has to be learnt, the molecular properties and abundance of PS 18:0/18:1 suggest a unique role for this lipid species in cell membranes. Some ideas for future studies in this field are included in Box Box 1 Recommendations for future studiesQuantification of hundreds of lipid species can now be performed in one sample using MS analyses. It is important that such quantitative data are made available for everybody, as it may take several years before we are able to interpret these data.The methods used for sample preparation and for lipid analyses need to be better documented and validated such that scientists can trust that the data are reproducible.People should carefully evaluate if their lipid data indicate the presence of impurities in their samples. For example, large amounts of triacylglycerols or cholesteryl esters in preparations of cellular membranes indicate the presence of lipid droplets.When studying the effect of adding lipids to cells it is important that not only the amount and lipid class is given, but the composition of lipid species.New studies should clarify if plasma membranes and cells in general contain as much PS 18:0/18:1 and as little phospholipids with two saturated fatty acyl groups as discussed in this review.Future studies should address whether the importance of PS 18:0/18:1 is due to that the double bond of C18:1 is in the middle of the membrane leaflet, and if that provides optimal interaction with cholesterol.New studies should aim at clarifying if interdigitation between sphingolipids and lipids in the other leaflet is stronger with PS than with other lipids. If yes, why?One would like to know if the large amount of sphingolipids with N-amidated C24:1 is due to that the double bond is just in the middle of the lipid bilayer and why that is important.To make synthetic lipid layers as similar to biological membranes as possible—including for molecular dynamic simulation studies—it is important to use asymmetric membranes and lipid species known to be present in high amounts in the membrane to be mimicked.


Diabetes mellitus is a life threatening disease and scientists are doing their best to find a cost effective and permanent treatment of this malady. The recent trend is to control the disease by target base inhibiting of enzymes or proteins. Secreted frizzled-related protein 4 (SFRP4) is found to cause five times more risk of diabetes when expressed above average levels. This study was therefore designed to analyze the SFRP4 and to find its potential inhibitors. SFRP4 was analyzed by bio-informatics tools of sequence tool and structure tool. A total of three potential inhibitors of SFRP4 were found, namely cyclothiazide, clopamide and perindopril. These inhibitors showed significant interactions with SFRP4 as compared to other inhibitors as well as control (acetohexamide). The findings suggest the possible treatment of diabetes mellitus type 2 by inhibiting the SFRP4 using the inhibitors cyclothiazide, clopamide and perindopril. This leads to polyuria, polydipsia and polyphagia. Chronic hyperglycemia causes damage to the kidneys, nerves, eyes, heart and blood vessels [i.e., type 1 and type 2. In type 1 diabetes, the body fails to produce insulin, while type 2 diabetes is caused by insulin resistance. It is also due to altered expression of several genes and their products [Diabetes mellitus (DM) is a leading cause of morbidity and mortality throughout the world. The number of patients is expected to rise to 366 million in 2030 as compared to 171 million in 2000. The prevalence of disease in patients living in city areas of developing countries suffering from diabetes mellitus will become double in 2030 as compared to year 2000 [products ,5.2+ channel expression. Furthermore, the inhibition of exocytosis by SFRP4 was not influenced by co-treatment with canonical or non-canonical Wnt proteins [Secreted frizzled related protein 4 (SFRP4) is a member of the SFRP family. SFRPs act as modulators of the Wnt signaling pathway. A large number of diabetes-associated factors are studied in the Wnt signaling pathway ,7,8,9,10proteins ,12. Indiproteins  so the iThe use of structure-based drug design is a promising technique to discover novel inhibitors/molecules and is becoming a famous practice in modern drug discovery. The drug is an organic small molecule that is used to activate or inhibit the function of different biomolecules like proteins. This in turn results in a therapeutic benefit to the patient. Drug design involves the construction of small molecules that are complementary in shape and charge to the target with which they interact. Drug design frequently relies on computer modeling techniques . This woThe construction of 3D structure for SFRP4 protein is reported here A. The moSFRP4 was found to be folded into two independent domains: the N-terminus containing a secretion signal peptide and a cysteine-rich domain. The active sites of SFRP4 were not clearly mentioned in the literature, so the active sites for binding of ligand were determined by using Pocket finder. According to the results, site 1 contains the amino acids glutamine 60, tyrosine 61, glutamine 63, leucine 64, leucine 111, methionine 112, tyrosine 115, histidine 117, tryptophan 119 . Hence tProtein-protein interactions have a very key role in structure-based drug design. We analysed the interactions of some inhibitors and the antidiabetic drug acetohexamide as a control, with insulin suppressor protein Secreted Frizzled-Related Protein 4 and observed that these compounds can inhibit the protein activity by binding to its active site. SFRP4 protein was docked with twelve inhibitors, and three of them were properly bound to the desired site of the protein: cyclothiazide A, clopamThe energy values obtained are shown in The ligand was selected with best dock score and low bound energy. The ligand clopamide pose 2 had affinity  and maximum hydrogen bonds with , cyclothiazide with pose 6 had affinity  and maximum hydrogen bonds with  and perindopril with pose had affinity  and maximum hydrogen bonds with  as shown in Thus it can be inferred from the results that diabetes mellitus is a metabolic disorder caused by the deficiency of insulin. In Type 1 diabetes, insulin deficiency occurs due to the destruction of the β-cells of the pancreas. Type 2 diabetes involves decreased insulin secretion or insulin resistance . Insulin2+ channels [Type 2 diabetes mellitus is caused by decreased insulin secretion. It is reported that people having above normal levels of SFRP4 produce less insulin, which results in decreased carbohydrate metabolism . It has channels . Thus itchannels . Recent channels .Thus there is a need to stabilize the expression of SFRP4. This can be done by using different inhibitors that successfully downregulate the expression of SFRP4. Different inhibitors could be used against SFRP4 like cyclothiazide . AcetoheThe activated SFRP4 reduces the secretion of insulin if individuals have above average levels of protein and are five times more likely to experience diabetes . The act50 values, docking score and binding interactions, so these compounds cyclothiazide, clopamide and perindopril may be potent drug candidates and their potency against SFRP4 may be increased.The docking analysis resulted in the detection of important inhibitors that are compatible with the binding site of the targeted protein. As a result of this study we can conclude that the inhibitors computationally studied here have shown good relationships between ICAlong with these finding we should raise our national standards of care for people with DM. People should be educated about the fall-out of this malicious disease by educational programmes and media campaigns since prevention is better than cure in this disease. Routine check-ups by doctors are necessary and the first step to control this disease while specific diets and nutritional cures are the next. Even when this disease is developed, it may be cured if properly treated.SFRP4 protein sequence (Accession No. CAG46532.1) was retrieved from NCBI database . The proThe active sites of SFRP4 were found by using Pocket-Finder ,28. Pockin-silico approach we have found some potential inhibitors against SFRP4, which in the future may pave the way for diabetes mellitus cures by decreasing the expression of SFRP4 and increasing insulin production.Diabetes mellitus is a life threating disease globally and in Pakistan as well as in rest of world, its prevalence is increasing at an alarming rate. A person suffering from heart disease, lung disorder and hepatic liver infection may also show high levels of blood glucose. Due to this drawback, treating high blood sugar alone is not a guarantee of curing diabetes. The levels of SFRP4 are found to be high in this disease, which results in less insulin production, leading to decreased carbohydrate metabolism. In this study by using the


Dorsoventrality in leaves has been shown to depend on the pre-patterned expression of KANADI and HD-ZIPIII genes within the plant shoot apical meristem (SAM). However, it has also been proposed that asymmetric auxin levels within initiating leaves help establish leaf polarity, based in part on observations of the DII auxin sensor. By analyzing and quantifying the expression of the R2D2 auxin sensor, we find that there is no obvious asymmetry in auxin levels during Arabidopsis leaf development. We further show that the mDII control sensor also exhibits an asymmetry in expression in developing leaf primordia early on, while it becomes more symmetric at a later developmental stage as reported previously. Together with other recent findings, our results argue against the importance of auxin asymmetry in establishing leaf polarity. The mature leaves of seed plants are usually flat with distinct cell types making up their dorsal (upper) and ventral (lower) tissues, which are derived from the adaxial (side closest to meristem) and abaxial (side farthest from meristem) primordium tissues. A fundamental question in plant development is how this tissue patterning is first specified. Recently, we reported evidence in support of a previously proposed hypothesis that dorsoventral leaf patterning is derived from a pre-pattern present in the shoot apical meristem (SAM), where leaves arise . We founpREV::REV-2xYPet and pKAN1::KAN1-2xGFP in similarly staged primordia we found that REV expression can coincide with PIN1 at later stages when it expands peripherally but that KAN1 expression is invariably complementary to cells with high PIN1 expression   . We thenfiji.sc)  (Materiafiji.sc) . Accordifiji.sc) . Similarfiji.sc) , althougfiji.sc) . Given tfiji.sc) . All togfiji.sc) . HoweverAs our results using the R2D2 auxin sensor indicate a different cellular auxin sensing pattern compared to that reported previously using the DII marker , we deciThe finding that both the mDII and DII markers, when driven by the 35S promoter, are expressed in an asymmetric pattern during leaf initiation suggests that one reason our conclusions may differ from those reported earlier  is that WOX1 or PRS gene expression , the adaxially expressed HD-ZIPIII genes restrict auxin response, thereby limiting WOX1 and PRS expression to the middle domain whether the level of auxin in adaxial cells is high or not  were obtained from Dr. Teva Vernoux (p35S::mDII-VENUS transgenes (Columbia ecotype) was also obtained from NASC  for analysis. R2D2 reporter (pRPS5::DII-3xVN-7 and pRPS5::mDII-tdTOMATO-N7) line carrying pPIN1::PIN1-GFP transgene (Landsberg ecotype) has been described previously (Seeds of the plants expressing  Vernoux . An indeeviously . Seeds wSeedlings aged 3DAS (days after stratification) and 4DAS were dissected as described previously . For imaSeedlings were imaged live, on a Leica TCS-SP5 upright confocal laser-scanning microscope with hybrid detectors (HyDs) using a 25X water objective (N.A 0.95). VENUS was imaged using argon laser (excitation wavelength 514 nm) while tdTomato was imaged using a white light laser (excitation wavelength 561 nm). Z-stacks were acquired in a 512 × 512 pixel format, with a resolution of 12-bit depth, section spacing of 1 μm and line averaging 2.https://fiji.sc) in two ways.Ratio-metric calculations for R2D2 auxin sensor were performed using ImageJ . A binary mask was generated from mDII channel and was applied to both DII and mDII channel. The resultant DII channel was divided by the resultant mDII channel. Since the values after division were lower than 1, the ratio image was generated as a 32-bit-float and exported as tiff series. PIN1 channel was also exported in 32-bit to visualize the ratio along with PIN1 expression in IMARIS (version 9.2.1) (see below).1.For DII/mDII calculations (negative readout of auxin sensing), DII signal was normalized against mDII (such that max intensitymDII = max intensityDII ). The mDII-tdTOM channel was divided by the DII-3XV channel. The calculated image was duplicated and then segmented using intensity threshold- based segmentation to create a binary mask with pixel value inside the nuclei one and outside the nuclei to zero. Masking was done to set all the values outside the nuclei uniformly zero. This mask was then multiplied to the original ratio calculated image to set the intensity values in cells with low auxin close to background (zero). The result appeared in a new window and was exported as a tiff series , The DII signal was normalized against the mDII signal (such that f series .Both of these protocols are specific for images captured with a resolution of 12-bit.The tiff series were then opened in IMARIS 9.2.1 (bit-plane) and processed further. Optical sections  were reconstructed using oblique slicer in IMARIS. If the orientation of the first two leaves was different to each other with respect to the meristem, different oblique slicers were used. This was done to position the slicers parallel to the leaf proximo-distal axis and perpendicular to the dorso-ventral axis to generate precise optical sections. The optical sections are 2 μm in thickness.DII/mDII ratio quantifications were performed on the leaves of seedlings aged 4DAS. During imaging, seedlings were positioned and oriented such that the imaging plane was perpendicular to the first two leaves along the medio-lateral axis. This orientation allowed a clear demarcation between adaxial and abaxial side based on PIN1-GFP expression in the vasculature. 3D volumes were cropped in Fiji along PIN1-GFP expressing cells in the vasculature as shown in Figure (3A and B). Median optical slice for each leaf was chosen to draw the Region Of Interest (ROI) to crop adaxial or abaxial volumes of the leaves. Before cropping, all the slices were visualized to ensure the chosen ROI covered all the cells in adaxial or abaxial side.psf Python library (https://www.lfd.uci.edu/~gohlke/code/psf.py.html) for generating point-spread functions, and the Richardson-Lucy iterative algorithm (Scikit-Image (v. 0.14) library  nuclei differently than others during the segmentation. All cropped images were deconvolved using the lgorithm  in the S library  for rest library  from the library . All micjI of each voxel j was set to the maximum intensity of the nuclear channels at that voxelIn order to optimise information for segmenting nuclei, the nuclear signals were merged together so that the intensity value Before segmentation, the images were thresholded using Otsu’s method . Median ImageJ plugin Costanza , denoted pyCostanza, which was further improved for maintainability and flexibility. Both the ImageJ plugin and pyCostanza are freely available via the Sainsbury Laboratory gitlab repository   .pyCostanza performs marker-less object-identification by identifying gradient attractors in the input image. That is, for every voxel within a given  mask of the image, the algorithm identifies the neighbouring voxel that maximises the value ofneigh - Icurrent)/(xneigh - xcurrent)ΔI/Δx =  after all voxels have been visited correspond to initial approximations of the objects in the image. An attractor is defined as the voxel with the highest intensity within the corresponding basin-of-attractor.where x denotes the spatial position (unit length) of the current of neighbouring voxel, and The initial segmentation is improved upon by a number of merging or removal algorithms, functioning as follows:Merge, Distance: Attractors within a given (Euclidean) distance of each other are merged together.Merge, Depth: Neighbouring domains are merged together if the intensity depth of a domain is less than a given threshold. The intensity depth is defined as the difference between the maximal intensity value in either region, and the minimal intensity value in the boundary between the domains.Merge, Small2Closest: Domains under a certain size (volume) are merged with the closest domain of a volume bigger than the given threshold. A maximal upper limit for whether domains shall be merged or not can be set.Remove, Size: Domains are removed based on a volume threshold.Remove, Intensity: Domains are removed based on the (mean) intensity within the domain.pyCostanza also includes functions for performing morphological erosion, dilation, opening and closing operations on the labels.Scikit-Image function regionprops. Specific attributes extracted were label centroids, mean DII intensity, and the mean mDII intensity. The intensity ratio, taken as the DII signal relative to the mDII signal, was calculated from these values. For identification of spatial distribution patterns, label centroid coordinates were downprojected in the XY-plane of the input image such that the intensity value of the voxels correspond to the signal ratio :=max(mDII) for voxels with a non-background label, resulting in ratios within . The corresponding data per labelled object were extracted from the image using the al ratio . All nucSeedlings aged 3DAS were dissected imaged and treated with approximately 10 μL of 5 mM NAA  solution in water (0.5M stock in 1M KOH) for 60 min and imaged again with same settings as prior to treatment.https://www.ebi.ac.uk/biostudies/studies/S-BSST223). All scripts and software for nuclear segmentation and signal quantification are available via the Sainsbury Laboratory gitlab server   . In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by Christian Hardtke as the Senior Editor, Dominique Bergmann as the Reviewing Editor and two reviewers. The reviewers have opted to remain anonymous.Thank you for submitting your article "The establishment of dorsoventrality in leaves does not depend on an asymmetry in auxin distribution" for consideration by eLife paper, but the KAN/REV story was not thought to be advanced enough to be suitable for this. On the other hand, the auxin distribution section was a more developed story. Our conclusion is to reroute this into the eLife "short report" format, and so we ask that you submit a revision addressing the reviewers' concerns in that form.This has been a complicated paper to review (as you likely know) and the reviewers and I had a number of discussions about how to proceed. The consensus was that there were two, not totally integrated stories here, one on KAN/REV and a second on auxin distribution. The "Advance" format is really designed to build on a recent A revision focused on the auxin distribution story needs to address the following points:1) More precise, quantitative analysis on a large number of samples is needed. Specifically,- Need essential information on the number of plants that were imaged in each experiment at each time point, how many plants showed the patterns shown in the figures, how many plants were discarded, because they suffered growth arrest etc.- You make the point that the 35S promoter may be highly variable in different transgenic lines and provide two examples with two very different reporter proteins in Figure 4. Since the reporters inevitably will have a massive influence on protein accumulation and hence the measured entity, this experiment does not support the claim made by the authors. It is essential to analyze multiple T1 lines of a single reporter to assess the variability of the promoter without confounding effects by the reporter. Ideally compare variation between 35S and other commonly used promoters, such as Ubiquitin10 or RPS5.2) The methods need to be described in more detail. Specifically,- The critical experiments on auxin signaling are poorly described. One example is found in the Results and Discussion section "We first focused on incipient stages where PIN1 convergence pattern is obvious but the expression patterns of REV and KAN are being defined, by looking at later forming primordia . We found no asymmetries in auxin levels in the future adaxial and abaxial cells .- I´d like to point out that the reporters used do not measure auxin levels but are proxies for the cellular sensing of auxin. This needs to be corrected throughout the manuscript.- The color coding of Figure 2 is not explained and simply labeled R2D2 ratio. Does signal suggest absence of auxin sensing or the converse?- Again, it is quite difficult to know what one is looking at – where is the centre of the SAM, where are the first visible organs?- You claim that there is no asymmetry in R2D2 signal at the early primordia stage and provide longitudinal optical sections to support this claim. However, depending on which cells you define as being the primordium, there are substantial differences in signal intensities. Most notably, the "outermost cell" that is PIN1-GFP positive consistently appears to have much less R2D2 signal . You need to be much more careful in interpreting, presenting and describing their data.- What does "mDII/DII (auxin)" in Figure 3 mean? Is it different from "mDII/DII (R2D2)" in Figure 3—figure supplement 1, or Figure 3—figure supplement 2 (R2D2 ratio), or Figure 3—figure supplement 5 (R2D2 auxin sensor), or Figure 2 (R2D2 ratio)?3) The discussion of discrepancies between this report, previous reports (and now the new bioRxiv report from Jiao) need a more nuanced discussion.- While you focus here on auxin distribution mainly, Guan et al. describe a much more complex scenario, where auxin concentrations, the level of several ARFs and the final auxin signaling output level (DR5) do not clearly overlap. This view is not profoundly challenged by your work.- You argue that previous work by Guan et al., led to erroneous conclusions, because the DII line used shows an auxin independent, asymmetric expression possibly due to the 35S promoter. Proof for this is that another line expressing the auxin insensitive mDII marker shows the same asymmetry. This is in sharp contrast with Guan et al., (2017) or Wang et al., (2014) who did not find this asymmetry for the mDII line .Are these just different interpretations of the same result ? If not, what is the cause of this discrepancy ? The patterns of the different markers used also seem highly dynamic and might even differ between plants at more less the same stage. Could that play a role? Looking at Figure 3G for example, one might conclude that the level of auxin is slightly higher at the adaxial side than at the abaxial side. The same for supplementary Figure 30. If true there might indeed be a (transient) gradient, in contrast to what is concluded. At a slightly later stage, auxin concentrations are higher in the internal (pro) vascular tissues, which is suggested by both R2D2 and DII . Guan et al., also find this difference between the adaxial side and internal tissues. This implies that there might be less difference between the two markers (and the two manuscripts) than suggested. A revision focused on the auxin distribution story needs to address the following points:1) More precise, quantitative analysis on a large number of samples is needed. Specifically,- Need essential information on the number of plants that were imaged in each experiment at each time point, how many plants showed the patterns shown in the figures, how many plants were discarded, because they suffered growth arrest etc.We have now performed ratiometric analysis of R2D2 on more seedlings than reported in the earlier version and clearly provide the information on number of plants and the leaves imaged in the main text and figure legends. Plants that suffered growth arrest, or damage while dissecting and imaging were not considered.- You make the point that the 35S promoter may be highly variable in different transgenic lines and provide two examples with two very different reporter proteins in Figure 4. Since the reporters inevitably will have a massive influence on protein accumulation and hence the measured entity, this experiment does not support the claim made by the authors. It is essential to analyze multiple T1 lines of a single reporter to assess the variability of the promoter without confounding effects by the reporter. Ideally compare variation between 35S and other commonly used promoters, such as Ubiquitin10 or RPS5.eLife reviewing editor suggested an alternative which is to do time-lapse imaging of one line to demonstrate how the expression pattern changes from asymmetric to uniform. We now include such a time-lapse experiment on 3DAS seedlings expressing p35S::mDII-V showing a lower mDII signal intensity on the abaxial side of the leaves at 0h but then increased signal intensity within 12 hours .Rather than assessing many new T1 lines, the 2) The methods need to be described in more detail. Specifically.- The critical experiments on auxin signaling are poorly described. One example is found in the Results and Discussion section "We first focused on incipient stages where PIN1 convergence pattern is obvious, but the expression patterns of REV and KAN are being defined, by looking at later forming primordia . We found no asymmetries in auxin levels in the future adaxial and abaxial cells .We have now improved this text (Results and Discussion section).- I´d like to point out that the reporters used do not measure auxin levels but are proxies for the cellular sensing of auxin. This needs to be corrected throughout the manuscript.This has been addressed in the beginning of Results and Discussion section. We have replaced the word ‘auxin levels’ with auxin sensing’ throughout the text.- The color coding of Figure 2 is not explained and simply labeled R2D2 ratio. Does signal suggest absence of auxin sensing or the converse?We have now provided the colour stamp indicating dynamic range of the intensity based LUT used in the figures and provided a better description of what the ratios imply (DII/mDII or mDII/DII) in the text (Results and Discussion section).- Again, it is quite difficult to know what one is looking at – where is the centre of the SAM, where are the first visible organs?We have improved the annotations in all the figures. For example- we have now labelled the meristem with M in all the figures, labelled the position of first two leaves that were removed to expose leaves 3, 4, 5  in Figure 1 and also indicated where the first two leaves are in Figure 2.- You claim that there is no asymmetry in R2D2 signal at the early primordia stage and provide longitudinal optical sections to support this claim. However, depending on which cells you define as being the primordium, there are substantial differences in signal intensities. Most notably, the "outermost cell" that is PIN1-GFP positive consistently appears to have much less R2D2 signal . You need to be much more careful in interpreting, presenting and describing their data.A major addition to this version of the manuscript is the quantitative comparison of DII/mDII ratio intensities in the adaxial and abaxial halves of young leaf primordia . We did this because we found that DII/mDII ratios appear highly variable and that depending on the optical section examined, different patterns could be perceived from visual examination . Hence, we used the PIN1-GFP marker as a dividing line to split R2D2 imaging data from whole leaf primordia into adaxial and abaxial halves. We then segmented the mDII signal to demarcate nuclei, calculated the DII/mDII ratios within these nuclei and averaged these results for each nucleus. This analysis thus encompasses all nuclei detected throughout the 3D volume of each leaf. Our results are provided in Figure 3 and clearly show that there is little difference in the distribution of DII/mDII ratio intensities between the two halves. If anything, we find a slightly larger proportion of abaxial cells with high DII/mDII ratios (low auxin sensing) compared to adaxial cells.- What does "mDII/DII (auxin)" in Figure 3 mean? Is it different from "mDII/DII (R2D2)" in Figure 3—figure supplement 1, or Figure 3—figure supplement 2 (R2D2 ratio), or Figure 3—figure supplement 5 (R2D2 auxin sensor), or Figure 2 (R2D2 ratio)?We have now made the descriptions clearer. mDII/DII (inverse plotted ratio of intensities of DII and mDII) is an indicator of cellular auxin perception with high intensities corresponding to high auxin sensing (please see above).3) The discussion of discrepancies between this report, previous reports (and now the new bioRxiv report from Jiao) need a more nuanced discussion.- While you focus here on auxin distribution mainly, Guan et al. describe a much more complex scenario, where auxin concentrations, the level of several ARFs and the final auxin signaling output level (DR5) do not clearly overlap. This view is not profoundly challenged by your work.A central message from Guan et al., is that low levels of auxin limit WOX/PRS expression in adaxial tissues while ARF2/3/4 repress these genes abaxially – hence they are expressed in the middle domain. In this study, we show that cellular auxin sensing is not lower in adaxial tissues compared to abaxial tissues – instead they are similar. Nevertheless, it could still be argued that low auxin levels may limit WOX/PRS expression in adaxial tissues, as Guan et al., propose. Are WOX1 and PRS normally expressed only in cells with higher cellular auxin sensing than adaxial cells? In this study, we find high levels of auxin sensing in cells that also express high levels of PIN1-GFP (provascular cells). Is this where WOX1 and PRS are expressed? At least for PRS, the answer is no – data from Caggiano et al., show that although both PIN1 and PRS are expressed in the middle domain, PRS is not restricted to those cells expressing high levels of PIN1-GFP in the centre of the leaf (pro-vasculature). Hence PRS expression does not only occur in cells with obviously higher levels of auxin sensing compared to adaxial cells. Perhaps more convincingly, in Caggiano et al., it was also shown that if auxin is applied exogenously, WOX/PRS do not become expressed ectopically. Overall then, the data in both this study and Caggiano et al., argue against a role for auxin being a limiting factor for defining WOX/PRS expression in the middle domain. Instead, these data are consistent with the hypothesis proposed in Caggiano et al., which is that WOX/PRS expression is actively repressed in adaxial tissues by the HD-ZIPIII proteins. We do not argue against Guan et al. in terms of what happens in abaxial tissues.These points are now better described in the Discussion section.- You argue that previous work by Guan et al., led to erroneous conclusions, because the DII line used shows an auxin independent, asymmetric expression possibly due to the 35S promoter. Proof for this is that another line expressing the auxin insensitive mDII marker shows the same asymmetry. This is in sharp contrast with Guan et al., (2017) or Wang et al., (2014) who did not find this asymmetry for the mDII line .Asymmetric mDII expression is only observed in very young leaf primordia at 3DAS. Previous studies such as Guan et al., (2017) or Wang et al., (2014) or even the new preprint from Jiao at BioRxiv do not present mDII data in leaf primordia at this early stage, only older stages. This is discussed in detail in our text, with the conclusion being that the data they report is not sufficient to justify their conclusion or properly compare to our data (Results and Discussion section).Are these just different interpretations of the same result ? If not, what is the cause of this discrepancy ? The patterns of the different markers used also seem highly dynamic and might even differ between plants at more less the same stage. Could that play a role? Looking at Figure 3G for example, one might conclude that the level of auxin is slightly higher at the adaxial side than at the abaxial side. The same for supplementary Figure 30. If true there might indeed be a (transient) gradient, in contrast to what is concluded. At a slightly later stage, auxin concentrations are higher in the internal (pro) vascular tissues, which is suggested by both R2D2 and DII . Guan et al., also find this difference between the adaxial side and internal tissues. This implies that there might be less difference between the two markers (and the two manuscripts) than suggested.As the reviewer suggests, a striking feature of R2D2 ratios in young leaf primordia is the variation in pattern. Not only between leaves but even within adaxial and abaxial tissues of the same leaf . Hence being restricted to a limited number of individual sections for analysis easily leads to bias. To eliminate bias in our analysis we therefore undertook a quantitative comparison of DII/mDII ratio intensities in the different sides of the leaf. This involved assessing all detectable nuclei within the adaxial and abaxial tissues of twenty leaves, using PIN1-GFP to mark the boundary between the tissue types. Our quantitative and comprehensive analysis clearly indicates no dramatic differences in R2D2 ratios between adaxial and abaxial nuclei. We conclude that for a case such as this, taking a quantitative and unbiased approach to the cells chosen for analysis is absolutely critical for making a fair conclusion (See Results section).


ObjectiveThe aim of this study was to observe the weight change in a patient with type 2 diabetes initiated on insulin therapy and the relation of weight gain with caloric intake.MethodsThis retrospective longitudinal follow-up study was conducted at the Baqai Institute of Diabetology and Endocrinology (BIDE), a tertiary care hospital of Karachi, Pakistan. Records of 917 patients attending the tertiary care diabetic clinic were retrieved from January 2009 to May 2016 from the Hospital Management System (HMS). Subjects were divided into two groups: group A consisted of subjects on oral hypoglycemic agents (OHA), while group B consisted of subjects on insulin therapy with OHA. Change in weight, change in HbA1c, and change in calories intake were calculated by examining data at baseline and end-line visit of the study.ResultsGroup B showed significantly higher weight gain than group A (48.3% vs 24.8%). Insulin therapy with OHA (OR (95% CI = 1.78(1.05-3.02)), increased caloric intake (OR [95% CI = 1.98(1.093.60)]) and decreased HbA1c (OR [95% CI = 0.44(0.24-0.79)]) were the only factors identified as significant predictors of weight gain.ConclusionIt is concluded that type 2 diabetic subjects, especially on insulin treatment, gain weight due to increase or unadvised caloric intake. Long-term multicenter studies are needed to ascertain the findings of this study. Diabetes mellitus is a chronic metabolic disease characterized by either insulin deficiency or resistance of body to its action or both. The International Diabetes Federation (IDF) Atlas 2017 reports a prevalence of 8.8% around the globe . Weight Pharmacological agents used in the treatment of diabetes directly contribute to weight gain through their glucose-lowering mechanisms -11. TreaIn this study, we tested the hypothesis that initiation of insulin therapy would be associated with an increase in weight gain as compared to oral hypoglycemic agents secondary to increased appetite and caloric intake.This retrospective longitudinal follow-up study was conducted at the Baqai Institute of Diabetology and Endocrinology (BIDE), a tertiary care hospital of Baqai Medical University, Karachi, Pakistan from the duration of January 2009 to May 2016. Ethical approval for the study was obtained from the institutional review board (IRB) of BIDE.A total of 917 cases were included in this study on the basis of inclusion criteria, that is, 1) age of 18 years and above, 2) being diagnosed with type 2 diabetes, 3) at least two visits during follow-up and the first and last visit included visit to dietician, and 4) having complete medical record in health management system (HMS) software of the institute. Subjects with other than type 2 diabetes and incomplete data records were excluded from the study. The records of subjects under study included demographical, anthropometrical, clinical, biochemical and dietary parameters. All subjects were given standard activity advice by the physician during follow-up.The selected subjects were divided into two groups. Group A consisted of subjects on oral hypoglycemic agents (OHA), while group B consisted of subjects on insulin therapy along with OHA. Subjects who were on insulin therapy were mostly treated by free mixed insulin followed by basal human, premixed insulin, and analogs.2). Change in weight, change in HbA1c, and change in calories intake were calculated by examining data at baseline and end-line visit of the study. The change in weight was categorized into three groups as decrease or no weight change , slight weight gain (BMI difference ≥0.1 to ≤1) and substantial weight gain (BMI difference ≥1.01). Change in calories was categorized as same or decreased caloric intake (if the difference was zero or negative) and increased caloric intake (if the difference was positive). Likewise, the change in HbA1c was categorized as decreased or no change in HbA1c (if the difference was zero or negative) and increased HbA1c level (if the difference was positive).Caloric intake was calculated by American dietician food exchange list. Body mass index (BMI) was calculated as weight in kilograms divided by height in metre squared  20 software program was used for data analysis. The chi-square test was applied to compare differences among the groups. Logistic regression was used to investigate the associations of the binary dependent variable “weight change” with continuous or categorical independent variables. Using backward selection, variables that remained significant were retained in the final model. Out of 917 type 2 diabetic subjects, 463 were males and 454 were females. Characteristics of subjects at the baseline visit are shown in Table Group B showed significantly higher weight gain than group A 48.3% vs 24.8%). It was also observed that in group B, 59.7% subjects had increased caloric intake whereas in group A, 54.1% subjects had increase caloric intake. This difference was not statistically significant (Table 4.8%. It In Table P > 0.05).When comparing weight change with change in the HbA1c level in the treatment groups, it was noted that, in group A, 60% of subjects maintained their HbA1c level and gained weight over the follow-up period, whereas 25% subjects got weight gain with elevated HbA1c level .However, in group B, the majority of subjects who maintained their HbA1c level had significantly gained their weight . Multivariate analysis demonstrated that caloric intake, use of insulin with oral hypoglycemic, and decreased HbA1c level remained associated with weight gain.In Table Insulin therapy initiation has long been delayed in most of the type 2 diabetics because of its weight-gaining potential, especially for those who are already obese. It gives an upper edge to the oral hypoglycemic in obese diabetics because these drugs are called weight neutral most of the times. The results of this study opened a new gate to researchers and fact seekers regarding this long-standing relationship between weight gain and insulin.In this study, the finding of 48.3% subjects on insulin falling in the category of significant weight gain is in agreement with other previous studies, but the new variable of increased caloric intake in significant weight gain individuals has never been tested before in such a large sample size -13. AnotIn most of the patients of both groups with significant weight gain, one similar finding was a significant increase in caloric intake which is not a common variable tested in most of the earlier studies with sufficient sample size.This finding greatly emphasizes the importance of individualized diet plans with caloric counting and diabetic education in patients on insulin specially and oral hypoglycemic generally. Structured intensive diabetes education programs (SIDEPs) can motivate and empower patients to take control of their disease and have been associated with improved glycemic control, better weight management, and better adherence on insulin. Ko et al. proved the point in their study on type 2 diabetics who underwent an in-patient SIDEP versus hospitalized patients aiming for glycemic control without intensive education, the group receiving intensive education had significantly improved HbA1c levels, less frequent subsequent hospitalizations, and improved adherence to self-care behavior, the most challenging part of this education programme is deciding a food plan acceptable to both . NutritiWorsening of glycemic control in patients with stable or decreased weight as evident by worsened HbA1c levels in those groups is not a finding which is in consensus with most of the earlier studies stating that weight loss causes improved glycemic control, but this finding may be related to suboptimal insulin or oral regimen causing suboptimal glycemic control with less weight gain or even weight loss, which is a proven fact by some studies -29.Multivariate analysis showing that the increase in caloric intake and decreased HbA1c level remains associated with weight gain in addition to the use of insulin, which is again proving our point in the study. For people with diabetes on insulin therapy, education on how to use carbohydrate counting and in some cases fat and protein gram estimation to determine mealtime insulin dosing can improve glycemic control and prevent excessive weight gain. A simple and effective approach to hypoglycemia and weight management emphasizing portion control and healthy food choices may be helpful if standard MNT approach is not possible .Limitations and strengthThis study is of great significance because it is one of the very few studies known to discuss the role of caloric intake in a well-known side effect of insulin therapy. And it can help in controlling the weight of the patients on insulin therapy thus preventing the initiation of a vicious cycle of weight gain and then uncontrolled diabetes.This study has a few limitations. First, this was a retrospective study, and the influence of unmeasured confounders on the results such as family history of obesity and physical activity of the patients is unknown.Unfortunately, our groups did not include patients on the newer treatments in sufficient numbers suitable for analysis, for example, glucagon-like peptide-1 receptor agonists and SGLT2 blockers. We also did not sub-divide the groups on the basis of the type of insulin and the type of oral agents used.Another limitation of this study is the difference in the number of subjects in groups A and B. As the follow-up of patients on oral hypoglycemic agents is usually long, hence we could not find a large number of patients on oral agents with at least two visits to dietician in a time span.It is concluded that type 2 diabetic subjects, especially on insulin treatment, gain weight due to unadvised and increased caloric intake. This finding emphasizes the importance of individualized diet plans with caloric counting and diabetic education in patients on insulin specially and oral hypoglycemic generally. This study further requires long-term follow-up with the dietician and its implementation in various other hospitals.