Drosophila lipoprotein LTP in conveying information about dietary lipid composition to the brain to regulate Insulin signaling. When yeast lipids are present in the diet, free calcium levels rise in Blood Brain Barrier glial cells. This induces transport of LTP across the Blood Brain Barrier by two LDL receptor-related proteins: LRP1 and Megalin. LTP accumulates on specific neurons that connect to cells that produce Insulin-like peptides, and induces their release into the circulation. This increases systemic Insulin signaling and the rate of larval development on yeast-containing food compared with a plant-based food of similar nutritional content.The Insulin signaling pathway couples growth, development and lifespan to nutritional conditions. Here, we demonstrate a function for the DOI:http://dx.doi.org/10.7554/eLife.02862.001 How does an animal sense if it is well nourished or not, and then regulate its metabolism appropriately? This process largely relies on the animal's body deciphering signals that that are transmitted between different organs in the form of molecules and hormones. Many animals—ranging from insects to mammals (including humans)—also use their brains to sense and decipher these nutritional signals.Drosophila, this hormone is produced by cells within different tissues, including the insect’s brain.A signaling pathway involving the hormone insulin controls how various different animals grow and develop—and how long they will live—based on these animals' food intake. Insulin is produced in mammals by an organ called the pancreas. But in the fruit fly The fruit fly is used to study many biological processes because it is easy to work with in a laboratory. Insulin-producing cells make and release insulin-like molecules into the insect's hemolymph (a blood-like fluid) in response to sugar and to other nutrients . The fat body produces lipophorin, a protein which carries fat molecules in the hemolymph, and which is known to be able to move from the hemolymph to the brain and accumulate within the brain. The fat body also produces lipid transfer protein (or LTP), which transfers fats absorbed or made within the insect's gut onto lipophorin, and can also unload fat molecules to other insect cells. If LTP can also enter the brain, and what it might do there, was unclear.Brankatschk et al.now discover that LTP can cross the ‘blood brain barrier’ in fruit fly larvae and can accumulate over time on their insulin-producing cells and the neurons in direct contact with these cells. This accumulation depends on the flies’ diet: flies fed a diet made from yeast cells accumulated LTP on these neurons, while those fed only on sugar and proteins did not.Furthermore Brankatschk et al. found that when they switched flies from a yeast-based to a plant-based diet, the larvae grew more slowly and the flies lived longer. Both of the diets contained abundant calories and nutrients, but contained slightly different kinds of fat molecules. The fly larvae on the plant-based diet also accumulated less LTP on their insulin-pathway neurons, and insulin signaling was reduced.Branskatschk et al. also found that fat molecules from the yeast-based diet activated the cells of the blood brain barrier, and that this encouraged LTP to be transported the brain. Blocking LTP from crossing the blood brain barrier reduced insulin signaling, slowed the growth of the fly larvae, and extended the lifespan of the flies.These findings of Brankatschk et al. thus reveal that fat-containing molecules carry information about specific nutrients to the brain. The extent to which these mechanisms operate in other animals—such as mammals—remains to be explored.DOI:http://dx.doi.org/10.7554/eLife.02862.002 Drosophila forms a tight barrier to passive transport, and is formed by highly conserved molecular components  that are expressed in different tissues , the major hemolymph lipid carrier, and Lipid Transfer Particle (LTP). LTP transfers lipids from the intestine to LPP. These lipids include fatty acids from food, as well as from endogenous synthesis in the intestine  . SystemiWe next examined the role of lipids and proteins in promoting neuronal LTP accumulation and systemic IIS. We compared larvae fed on YF, with those transferred at late second instar to a lipid-depleted food (LDF), which contains a yeast autolysate that has been chloroform-extracted to remove lipid. LDF contains proteins and sugars, and has a similar caloric content to YF. However, larvae arrest on LDF because of the absence of sterols , and sysTo address the sterol-dependence of neuronal LTP accumulation, we supplemented LDF with 10 µM cholesterol (LDSF). Larvae transferred from YF to LDSF grow slowly and give rise to viable adults of reduced size . ConsistWe wondered whether lipids in general were required for LTP accumulation on DRNs. Interestingly however, experiments with plant food (PF) suggest that bulk lipid is not sufficient. PF contains no yeast and is based entirely on plant materials. It has the same caloric content as YF, and slightly more lipid with a different fatty acid composition . Strikindilp2-GAL4) or in all neurons (under the control of rab3-GAL4) and compared GFP fluorescence in brains from YF and PF-reared larvae. While the activity of most neurons is not affected by feeding with these different foods  under the control of the IPCs . Dilp2 sthe IPCs . They methe IPCs . Thus, iHow does the activity of IMPL2-positive neurons influence that of IPCs? To address this question, we inhibited synaptic release by knocking down Rabs 3 and 27—two Rabs that both contribute to synaptic vesicle release . To do tltp knock-down in the fat body causes larval arrest in the second or third instar  reveals a marked reduction in GFP fluorescence in BBB glia relative to other glia. We confirmed this result by specifically driving the reporter in BBB glia using moody-GAL4 . However we were surprised to observe dramatically higher levels of free intracellular calcium in BBB glia when larvae are fed YF compared to PF. Driving the free intracellular calcium sensor GCaMP using ody-GAL4 .10.7554/moody-GAL4 in PF-reared larvae and stained their brains for LTP and Dilp2. Strikingly, while LTP is undetectable on DRNs of wild type larvae reared on PF, it accumulates to high levels on these neurons when Ca++ influx is induced by TRPA1 expression in BBB cells  does not depend on lipoproteins to supply it with bulk sterols , placing it upstream of a SV40 polyadenylation sequence and a mini-white gene.A DNA fragment containing 5.4 kbp 5′ to the lipophorin was fused to a HA-epitope triplet and cloned 5′ to an mcherry cDNA. This was fused at its 3′ end to a CD8 transmembrane domain sequence that was flanked at the 5′ end with the MYC-epitope and the 3′ end with a V5 epitope. The construct was cloned into pCM43 vector containing a 5xUAS-hsp70 promoter element followed by a multiple cloning site, a SV40 element and mini-white gene.An 840 bp fragment containing the predicted signal sequence of 16B12 1:1500 , anti-Elav7E8A10 1:1500 , anti-LTP 1:1000 (8D12 1:1000 (DSHB), anti-LpR1Sac8 and anti-LpR2Sac6 (1:500), anti-LRP1 and anti-LRP2 (1:500)  , anti-Di (1:500) .If not stated otherwise, larval sections were performed in ice cold PBS, samples homogenized in 1% Triton X-100 lyses buffer and proteins measured with BCA protein standard Kit . Resultant SDS-PAGE blots were probed with anti-AKT1 1:2000 , anti-ApoLII 1:4000 , anti-Cvlrp1RNAi and UAS:lrp2RNAi lines are from Vienna Drosophila RNAi Center, and oregonRC, repo-Gal4, dilp2-Gal4, dilp2, UAS:gfpRNAi, UAS:GCaMP, UAS:trpA1 lines are from Bloomington Stock center. Published are UAS:ltpRNAi, ltp , impl2-Gal4 (gift from E Hafen). UAS:cherry and rab3-Gal4 were generated by transforming VK37 or VK33 flies  generated by targeted YFPMYC integration into the endogenous locus (publication in preparation).If not stated otherwise, flies were kept at 25°C on either PF or YF under a 12hr light/12hr dark cycle. UAS:NAi, ltp , imp-l2--l2-Gal4 , lpp-Gallpp-Gal4 , lpr1, lr1, lpr2 , lrp1, lp1, lrp2 , moody-G33 flies  with our2(2)H2O, 0.5 MgSO4(6)H2O, 6.3 ml propionic acid, 1.5 g nipagen.Glucose food (GF) per liter: 10 g agar, 2 g glucose, phosphate-buffered saline buffer (PBS); lipid depleted food  per liter: 10 g agar, 100 g glucose, 100 g chloroform extracted yeast extract, PBS; yeast extract food supplemented with Ergosterol (LDS) per liter: 10 g agar, 100 g glucose, 100 g chloroform extracted yeast extract, 10 g Ergosterol (from a 5 mM EtOH solution), PBS; plant food  per liter: 10 g agar, 38 g peptone (soy), 80 g cornmeal, 80 g malt, 2 ml cold pressed sun-flower oil, 22 g treacle, 6.3 ml propionic acid, 1.5 g nipagen; yeast food  per liter: 10 g agar, 80 g yeast (brewers), 20 g yeast extract, 20 g peptone (soy), 30 g sucrose, glucose 60 g, 0.5 g CaCl40 g PF or YF were homogenized in 100 ml Chloroform. The resulting homogenates were centrifuged and the lower organic phase was removed and evaporated. The residues were re-suspended in 2 ml Chloroform and 30 µl of these solutions were applied to lipid-depleted food.If not stated otherwise, embryos were collected for either 1 hr see  or 4 hr We quantified numbers of stained cells per brain from larval CNS confocal stacks comprising the entire brain lobes but excluding the ventral ganglion. Sections were spaced 1.5 μm in Z. Within each experiment, brains were stained and recorded in parallel using the same confocal settings.Unless otherwise stated, embryos were collected on apple-juice-agar plates for 4 hr at 25°C and transferred to the stated diet. Newly formed pupae were counted on a daily basis. Newly hatched female flies were collected in 2–3 hr intervals at 25°C, 3 flies were placed into one vial and weighed. For lifespan experiments, females and males were kept together (2:1 ratio) at 25°C and flipped every 2 days. Deceased female animals were counted daily. review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and 3 reviewers, one of whom, Mani Ramaswami, is a member of our Board of Reviewing Editors.Thank you for sending your work entitled “Delivery of circulating lipoproteins to specific neurons in the The Reviewing editor and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.All the reviewers appreciated the significance, clarity and interest of the findings described in your manuscript, which link metabolism and nutrition to neuroendocrine signaling and functions via a specifically identified molecular, cell biological and neural circuitry However, a consensus from the discussion is that a few additional experiments will be necessary to support some of the key conclusions in the current paper. These, are summarized below and elaborated in the specific reviewer comments that are included with this decision letter.We would be enthusiastic about a revised manuscript in which you:a) Provide additional evidence to address the issue of how and why DRN neurons and IPC neurons functions are connected. In current form, there is not enough data to support a conclusion that DRNs induce IPCs to release insulin, and the role and mechanism of LTP accumulation on DRNs is unclear.b) Try to better identify the primary nutrient signal (lipid) that stimulates LTP signaling. Some thoughtful experimental effort towards this end will be very valuable and useful, even if these prove (unfortunately) to be unsuccessful.c) Strengthen the existing data using additional measures of insulin signaling, beyond Foxo localization.Further detail is provided below:Reviewer #1:The manuscript describes a very nice series of experiments that outline the role and mechanism by which circulating lipoprotein may be transported across the BBB and regulate neurons that control the release of insulin-like peptides. LTP accumulates on a small number of neurons in the brain of which localization to a subset (the DRNs) is most tightly correlated with stimulation of insulin signaling.The experiments are very well done and the data well presented. However, there are a few issues that should be addressed before publication.1) Additional experiments to demonstrate or more strongly argue a causal role for DRN cells and for LTP accumulation on these neurons in promoting ILP release will greatly strengthen an important conclusion of this paper.2) A model figure indicating the proposed pathway from nutrient signal to LTP and finally to ILP release and FOXO signaling will greatly help communicate the core model. At this point, how nutrient signal is sensed by LTP and alters the effectiveness of its transport and localization is not clear.3) It is not obvious why DRN neurons accumulate ILPs. Is this simply an observation or is it potentially significant in a way that is consistent with LTP accumulation on these cells? Some clarification on the authors' thinking here will be useful.4) The abbreviations and acronyms used are sometimes very distracting and occasionally make things difficult for the reader. It will be useful for the authors to either define the acronym more than once as is conventional, or take some other measures to make this easier on the non-expert reader.Reviewer #2:In a series of logically designed and carefully controlled experiments, Brankatschk and colleagues show that fat body-derived LTP is transported across the blood brain barrier to reach a subset of neurons. These include the insulin-producing cells regulating growth and sugar metabolism and the adjacent Imp-L2 producing cells (referred to as DRNS). The presence of LTP in neurons is regulated by nutrients and seems to modulate systemic insulin signalling, at least based on the differential subcellular localization of FOXO in the FB. This is an exciting manuscript potentially showing a novel role for a lipoprotein (LTP) in conveying nutritional information from the fly adipose tissue to the brain. I only have two major issues:1) The involvement of the DRNS and IPCs in the described process needs to be clarified, especially with regard to signal directionality between these two neuronal populations. The authors use the correlation between LTP reduction/absence from DRNs and low systemic insulin signalling observed in their experiments  to imply and sometimes conclude that LTP in DRNs is required for IPC insulin release and high insulin signalling. Based on all the data provided, would it not be equally conceivable that LTP is transported across the blood brain barrier directly to the IPCs, and the presence/absence of LTPs in DRNs is merely a correlate of insulin release from the IPCS? As the authors state, DRNS recruit insulin from IPCs using Imp-L2, so this alternative explanation seems more likely. The issue is further complicated by the fact that 1) there are other Imp-L2-expressing neurons in the VNC, including other insulin-producing neurons and 2) to my knowledge , and contrary to what the authors state, direct contact between the DRNS and IPCs has not been shown.One experiment that would begin to clarify the involvement of all these neuronal populations is to test whether expression of LTP in either DRNs  or IPCs  can rescue systemic insulin levels , increased Ilp2 production and/or release in lipid-poor conditions?2) All the statements about systemic insulin signalling are based on the subcellular localisation of FOXO in the fat body. This is a very indirect and sometimes unclear readout, especially because LTP is produced by the FB so it could affect FOXO localisation intracellularly or in an autocrine manner. Are Ilp2 levels in IPCs or Ilp2 release affected by any of the described manipulations?A less substantive concern is that LTP is also present in a couple of glial cells. Is that LTP also FB-derived (i.e. gone when LTP is depleted from the FB?). Otherwise it could provide a local source of CNS LTP.Reviewer #3:LTP (Lipid Transfer Particle) transfers lipids including fatty acids from food and lipids produced within the intestines from the intestines to a protein known as Lipophorin (LPP). Additionally LTP can aid in the transport of LPP lipids into other cells. LPP is able to cross the blood brain barrier (BBB) and accumulates within the brain. The authors link the function of LTP in the brain to nutrient sensation and systemic insulin signaling. They find a requirement for lipoproteins in mediating LTP recruitment to DRNs and that LTP function at DRNs is necessary for normally high levels of Insulin signaling. This is done by crossing the blood brain barrier. The failure to recruit LTP to these cells results in arrested development and growth as well as decreased Insulin signaling.The authors do a very nice job showing LTP expression in distinct neurons within the brain and documenting its colocalization with specific DRNs. This paper describes an interesting link between larval diet and accumulation of LTP at these DRNs as well as being able to show that a loss of both Lrp1 and Lrp2 causes a loss of LTP within DRNs. The paper advances the current understanding of how lipid sensing is transmitted from the fat body to the brain using LRP1/2 to transport LTP across the BBB.This paper could be strengthened by better showing some of the raw data. For example, being able to see stacks of brain sections in which all of the DRNs or LTP positive cells are in view simultaneously will support the cartoon depictions in Also, FOXO nuclear/cytoplasmic localization in the fat body is pretty much all the authors use in the entire manuscript as an output of IIS within the animal. Since Insulin signaling is central to the conclusions of this manuscript, it will help to show altered insulin signaling in an additional way .A central question that remains to be resolved is which lipid/s within the yeast containing food is essential for proper LTP localization. The negative nature of the plant lipids can be a clue. The authors need to supplement with combinations of lipids to the media to determine the necessary lipids for this process.Finally, does the activation of the Insulin pathway in the ltp-RNAi background rescue the growth/life span phenotype? a) Provide additional evidence to address the issue of how and why DRN neurons and IPC neurons functions are connected. In current form, there is not enough data to support a conclusion that DRNs induce IPCs to release insulin, and the role and mechanism of LTP accumulation on DRNs is unclear.The effect of DRN’s on IPC neurons:We have traced the projections of IPCs and DRNs and show that they come into very close contact in the neuropil. We have also blocked synaptic vesicle release in IMPL2-expressing neurons and shown that this slows larval growth and reduces systemic IIS as shown by lowered phosphoAKT in the fat body . Taken tThe role/mechanism of LTP accumulation on DRNs:We include new experiments showing that blocking BBB transport of LTP to DRNs reduces systemic IIS by inhibiting Dilp release by IPCs. Knock-down of the LTP transporters LRP1 and LRP2 in the BBB reduces circulating levels of Dilp2 in the hemolymph and reduces levels of phosphoAKT in the fat body (in addition to causing nuclear accumulation of Foxo). This is strong evidence that accumulation of LTP on DRNs promotes Dilp release by IPCs .Our previous work showed that LRP1,2-mediated LTP transport to DRNs was required for high level systemic IIS on yeast food. We now provide the complementary result: forcing LTP accumulation on DRNs is sufficient to activate systemic IIS even when larvae are fed with plant food. We were able to do these experiments because we made the novel discovery that yeast food, but not plant food, increases cytoplasmic free calcium in blood brain barrier glial cells. Strikingly, ectopically inducing calcium influx by over-expressing the TRPA1 calcium channel in BBB glia causes LTP to accumulate specifically on DRNs even when larvae are fed with plant food. Finally, calcium influx into BBB cells is sufficient to activate systemic IIS (as reflected by phosphorylation of AKT in the fat body) even when larvae are fed with plant food. This data is now included in . These eb) Try to better identify the primary nutrient signal (lipid) that stimulates LTP signaling. Some thoughtful experimental effort towards this end will be very valuable and useful, even if these prove (unfortunately) to be unsuccessful.The first step towards this goal was to show directly that the nutrient is a lipid that is present in yeast but not plant food. To do this, we prepared chloroform extracts of lipids from yeast food and from plant food and compared their ability activate IPCs (as reported by the Ca++ sensor GCaMP) and to promote larval growth. Indeed, yeast lipids activate Ca++ signaling in IPCs and speed larval growth whereas plant lipids do not. This data is now included in . We havec) Strengthen the existing data using additional measures of insulin signaling, beyond Foxo localization.We have now added several different readouts:1) A calcium sensor to monitor activity of IPCs2) Western blots of hemolymph to detect circulating Dilp23) Western blots to detect phospho-Akt in the fat bodyGFP4) Membrane association of PHUsing these tools, we now clearly show that yeast food, but not plant food, activates systemic IIS by activating IPCs and promoting Dilp release.Specifically:1) The calcium sensor is active in IPCs of larvae fed yeast food, but not plant food .2) Circulating Dilp2 levels are high in animals fed yeast food and almost undetectable in animals fed plant food .3) Yeast food increases Akt1 phosphorylation in the fat body .GFP in the salivary gland (4) Yeast food increases membrane association of PHry gland .Reviewer #1:The manuscript describes a very nice series of experiments that outline the role and mechanism by which circulating lipoprotein may be transported across the BBB and regulate neurons that control the release of insulin-like peptides. LTP accumulates on a small number of neurons in the brain of which localization to a subset (the DRNs) is most tightly correlated with stimulation of insulin signaling.The experiments are very well done and the data well presented. However, there are a few issues that should be addressed before publication.1) Additional experiments to demonstrate or more strongly argue a causal role for DRN cells and for LTP accumulation on these neurons in promoting ILP release will greatly strengthen an important conclusion of this paper.See response to consensus comment (a).2) A model figure indicating the proposed pathway from nutrient signal to LTP and finally to ILP release and FOXO signaling will greatly help communicate the core model. At this point, how nutrient signal is sensed by LTP and alters the effectiveness of its transport and localization is not clear.3) It is not obvious why DRN neurons accumulate ILPs. Is this simply an observation or is it potentially significant in a way that is consistent with LTP accumulation on these cells? Some clarification on the authors' thinking here will be useful.While our data suggest that the DRNs are regulating Dilp release by IPCs, the fact that IPC-derived Dilps are found in DRNs seems to suggest that signals may pass in both directions. We now mention this in the Discussion. In mammals, insulin stimulates the activity of lipoprotein lipase, which mobilizes fatty acids from lipoproteins. Thus, one intriguing speculation is that lipids from LTP might be more effectively mobilized to DRNs when these cells receive Dilps from IPCs. Of course the IPCs likely integrate signals about many different nutrients, not only yeast lipids, to regulate Dilp release. Perhaps LTP signals to DRNs more effectively if these cells are sensitized by the Dilps derived from IPCs?4) The abbreviations and acronyms used are sometimes very distracting and occasionally make things difficult for the reader. It will be useful for the authors to either define the acronym more than once as is conventional, or take some other measures to make this easier on the non-expert reader.We now spell out fat body rather than abbreviating it as FB.Reviewer #2:1) The involvement of the DRNS and IPCs in the described process needs to be clarified, especially with regard to signal directionality between these two neuronal populations. The authors use the correlation between LTP reduction/absence from DRNs and low systemic insulin signalling observed in their experiments  to imply and sometimes conclude that LTP in DRNs is required for IPC insulin release and high insulin signalling. Based on all the data provided, would it not be equally conceivable that LTP is transported across the blood brain barrier directly to the IPCs, and the presence/absence of LTPs in DRNs is merely a correlate of insulin release from the IPCS? As the authors state, DRNS recruit insulin from IPCs using Imp-L2, so this alternative explanation seems more likely.In the new manuscript, we provide new data to support the idea that information does flow from ImpL2-producing neurons to IPCs. We would argue that LTP on DRNs is unlikely to derive from IPCs because we only rarely observe LTP on one cell of the IPC cluster under conditions where it is reproducibly found on all DRNs. If IPCs were the source of LTP on the DRNs, we might expect it to accumulate there at higher levels (like Dilps do). Of course it is hard to rule out that LTP travels so fast through the IPCs that it can’t be observed there in the steady state, but it makes the possibility seem less likely.The issue is further complicated by the fact that 1) there are other Imp-L2-expressing neurons in the VNC, including other insulin-producing neurons and 2) to my knowledge , and contrary to what the authors state, direct contact between the DRNS and IPCs has not been shown.We have now demonstrated that projections from IPCs and ImpL2-expressing neurons do appear to contact each other; see response to consensus reviewer comment (a).One experiment that would begin to clarify the involvement of all these neuronal populations is to test whether expression of LTP in either DRNs  or IPCs  can rescue systemic insulin levels , increased Ilp2 production and/or release in lipid-poor conditions?This experiment might distinguish these possibilities if the apolipoprotein moiety of LTP, rather than a lipid provided by LTP, were responsible for its influence on IIS. But we strongly suspect that the effects are due to a nutritional lipid that is carried by LTP into the brain, so expressing LTP in neurons would not recapitulate this effect. There are also several technical difficulties in performing this experiment. LTP cannot be secreted by cells that do not express microsomal triglyceride transfer protein and it is unclear whether neurons do so. Furthermore, there are no full length cDNAs available for LTP  and to piece one together and make a transgenic to express it would take longer than the time frame of this revision.2) All the statements about systemic insulin signalling are based on the subcellular localisation of FOXO in the fat body. This is a very indirect and sometimes unclear readout, especially because LTP is produced by the FB so it could affect FOXO localisation intracellularly or in an autocrine manner. Are Ilp2 levels in IPCs or Ilp2 release affected by any of the described manipulations?We have now examined hemolymph levels of Dilp2 and shown that they are high in wild type animals on yeast food and low on plant food. Levels of circulating Dilp2 are also reduced by blocking LTP transport across the BBB using LRP1,2 knock-down. See also response to consensus reviewer comment (c).A less substantive concern is that LTP is also present in a couple of glial cells. Is that LTP also FB-derived (i.e. gone when LTP is depleted from the FB?). Otherwise it could provide a local source of CNS LTP.ltp has been knocked down in the fat body .We have now added several different readouts:1) A calcium sensor to monitor activity of IPCs.2) Western blots of hemolymph to detect circulating Dilp2.GFP.3) Western blots to detect phospho-Akt in the fat body membrane association of PHA central question that remains to be resolved is which lipid/s within the yeast containing food is essential for proper LTP localization. The negative nature of the plant lipids can be a clue. The authors need to supplement with combinations of lipids to the media to determine the necessary lipids for this process.The first step towards this goal was to show directly that the nutrient is a lipid that is present in yeast but not plant food. To do this, we prepared chloroform extracts of lipids from yeast food and from plant food and compared their ability activate IPCs (as reported by the Ca++ sensor GCaMP) and to promote larval growth. Indeed, yeast lipids activate Ca++ signaling in IPCs and speed larval growth whereas plant lipids do not. This data is now included in . We haveFinally, does the activation of the Insulin pathway in the ltp-RNAi background rescue the growth/life span phenotype?ltp knock-down prevents loading of sterols onto Lipophorin into the intestine, thereby causing sterol depletion throughout the larva . Sterol depletion reversibly arrests larval development by a mechanism that doesn’t seem to involve insulin signaling . This was why we needed to make the more specific manipulation of blocking BBB transport of LTP to study its role on DRNs.We did not attempt this experiment because systemic loss of LTP has pleiotropic effects. In addition to reducing insulin signaling via its effects in the brain,


Functional MRI analyses commonly rely on the assumption that the temporal dynamics of hemodynamic response functions (HRFs) are independent of the amplitude of the neural signals that give rise to them. The validity of this assumption is particularly important for techniques that use fMRI to resolve sub-second timing distinctions between responses, in order to make inferences about the ordering of neural processes. Whether or not the detailed shape of the HRF is independent of neural response amplitude remains an open question, however. We performed experiments in which we measured responses in primary visual cortex (V1) to large, contrast-reversing checkerboards at a range of contrast levels, which should produce varying amounts of neural activity. Ten subjects (ages 22–52) were studied in each of two experiments using 3 Tesla scanners. We used rapid, 250 ms, temporal sampling  and both short and long inter-stimulus interval (ISI) stimulus presentations. We tested for a systematic relationship between the onset of the HRF and its amplitude across conditions, and found a strong negative correlation between the two measures when stimuli were separated in time . Thus, stimuli that produce larger neural responses, as indexed by HRF amplitude, also produced HRFs with shorter onsets. The relationship between amplitude and latency was strongest in voxels with lowest mean-normalized variance . The onset differences observed in the longer-ISI experiments are likely attributable to mechanisms of neurovascular coupling, since they are substantially larger than reported differences in the onset of action potentials in V1 as a function of response amplitude. Most analyses of neuroimaging data assume a fixed shape for the measured response. In BOLD fMRI, for example, standard analyses measure neural activity by regressing data onto a cannonical hemodynamic response function (HRF); larger responses are assumed to be scaled copies of smaller ones. While scaling holds at least roughly  fMRI at 3 Tesla, and an event-related (ER) experimental design to allow detailed, accurate characterization of the hemodynamic response. Onset latency has previously been shown to vary across voxels, due to hemodynamic factors  experiment was used to measure HRFs from neural activity produced by two stimulus contrasts; the HRFs were not affected by temporal overlap or the deconvolution necessary to produce event-triggered average responses. A short-ISI experiment allowed us to collect responses to four different contrast levels while keeping the total scan time approximately equal to the long-ISI experiment. Experimental design and stimuli are illustrated in Figure Sixteen experienced observers  participated in this study after providing written informed consent. Seven participants were aware of the experimental aims; three are authors. Four subjects  participated in both the short- and the long-ISI experiments. An additional six subjects  participated in the long-ISI experiment but not the short-ISI experiment; six other subjects  participated in the short- but not long-ISI experiment. All participants had normal or corrected-to-normal visual acuity. Participation was voluntary and in compliance with the University of Minnesota Institutional Review Board guidelines.2. The monitor was mounted to the back wall of the scanning suite; subjects viewed the monitor through a mirror mounted to the top of the head coil. Stimuli for the remaining six subjects in each experiment were presented on projection systems composed of a Sony projector fit with a custom lens made by Navitar, projecting the stimulus onto a screen behind the subjects' heads in the bore of the scanner. Two of the last six long-ISI and four of the last six short-ISI subjects were scanned in the Siemens Trio system, where the mean luminance of the projector was 120 cd/m2. Four of the last six subjects in the long-ISI and two of the six subjects in the short-ISI experiment were scanned in a Siemens Skyra system (details below) where the mean luminance of the projector was 640 cd/m2.Visual stimuli were created using Matlab and the Psychophysics Toolbox extensions  localization and two types of ER scan to collect timing data (short and long ISI). To elicit the HRF, all three types of scans employed checkerboard stimuli that reversed at a rate of 4 Hz extending across 12° of visual angle in the horizontal direction, and 9° in the vertical direction for the LCD display, 24° by 18° for the projection systems. All three scans also used a rapid serial visual presentation (RSVP) task to encourage participants to maintain fixation at the center of the screen. Letters were presented at the center of the screen at a rate of 4 Hz and subjects were instructed to press a button every time they saw the letter “x,” which occurred with a 10% probability.In both experiments, block-design scans were used to localize the cortical region corresponding to the retinotopic location of the checkerboard Figure . Each 24Experiment 1 was performed to measure the characteristics of the hemodynamic response stimulated from rest, or from a nearly relaxed state. Accordingly, we used an ISI of 15.5 s. Because the long-ISI ER design requires considerable time per trial, we limited this experiment to two contrasts . Trials contained a stimulus for 500 ms, followed by 15.5 s of rest. Each scan contained 24 events . Each subject participated in a single scanning session containing 4–6 long-ISI ER scans and 1 block-design localizer.Experiment 2 used a shorter ISI (1 s), in order to collect the maximal number of stimulus responses with minimal subject fatigue. We measured responses to 2.5, 5, 25, and 90% contrast for 4–8 scans. Each trial contained a 500 ms stimulus followed by a blank screen for 500 ms (ISI of 1 s). Each scan contained 248 events  for a total of 1008 TRs in 4 min, 12 s. Each subject participated in a single scanning session containing 1 block-design localizer scan and five or six short-ISI scans.In both short- and long-ISI experiments, the order of conditions within a scan was counterbalanced using an m-sequence, an ordering of conditions that has optimal statistical power for measuring HRF shape  and a different stimulus geometry (four circular patches of contrast-reversing gratings). Results therefor are not directly comparable to Experiments 1 and 2, but are nonetheless informative because they are representative of many common experimental designs. Full details of the data acquisition are published in Schumacher et al. (2*-weighted images (TE = 20 ms) were acquired with 2 mm isotropic resolution on a 7 Tesla Siemens scanner while subjects viewed stimuli presented at 5, 10, 30 and 90% contrast. The stimuli were presented in a random ER design, with an average ISI of 4.5 s; each ISI was drawn randomly from the set . Twelve repetitions of each of the four contrasts were included in each of 5–8 ER scans per scanning session (depending on the subject's stamina); separate block-design localizers during the same scanning session (3 4-min scans) were used to define regions of interest in primary visual cortex for analysis. Data pre-processing and GLM analysis were as described below for Experiments 1 and 2.To further validate the findings of Experiments 1 and 2, we re-analyzed a medium ISI (about 4.5 s) experiment run at 7 Tesla for different purposes . Six of the long-ISI datasets and eight of the short-ISI datasets were acquired on a Siemens Trio system with a 12-channel receive-only head RF coil (body transmit); four of the long-ISI datasets and two of the short-ISI datasets were acquired on a Siemens Skyra system with a 16-channel receive-only head RF coil (body transmit). The two different scanners were used for scheduling convenience, and acquisition parameters were matched to the fullest extent possible. Post-hoc analyses (not shown) verified that image signal-to-noise ratio, BOLD contrast magnitude, and final results (latency as a function of response amplitude) were not significantly different in the datasets acquired on different scanners. Slices were oriented perpendicular to the calcarine sulcus in the occipital cortex. The field of view was 172 × 144 with a matrix size of 64 × 48 voxels , yielding a resolution of 2.7 × 3 × 2.7 mm with four slices total. Flip angle was 40° . Motion compensation was performed on the data from each scan using the MCFLIRT tool provided with FSL (http://fsl.fmrib.ox.ac.uk/fsl). Motion-compensated data were then distortion-compensated using a field map that was acquired during the functional imaging scanning session and the FUGUE toolbox distributed with FSL.All MR images were acquired at 3 Tesla using a gradient echo EPI sequence  was identified using standard retinotopic mapping techniques  and the top 20% were discarded from the ROI in order to minimize the contribution of large pial vessels to the signal , and full-width-at-half-maximum (FWHM). These parameters are commonly used to describe the temporal characteristics of the HRF  for which the fit did not explain at least 80% of the variance were excluded from further analysis . We then estimated onset latency for each V1 average HRF as the time the model HRF first reached 10% of its peak value. To corroborate the trends found at this estimate, we also calculated onset at 50% of the peak response. The same pattern of latencies was observed for both 10 and 50% of the peak value, so all subsequent analyses were performed and reported only for the latencies at 10% of the peak Figure . Time toOnset latencies, peak latencies, and FWHM were compared to HRF amplitudes using a linear model to quantify trends. To minimize outlier influence, we used robust regression with an iterative reweighting of the least squares with the bisquare weighting function  = 3.8, p < 0.03].In Experiment 1, which used long ISIs, higher contrast stimuli produced larger BOLD responses that arose earlier in time. The larger BOLD response for the higher contrast stimuli arose presumably because higher contrast stimuli produce larger responses in V1 neurons. Onset latencies were computed from HRFs fit to the average signal from active voxels in V1 for each subject and contrast condition. These latencies were reliably shorter for the high contrast stimulus condition than for the low contrast condition [Figure We repeated our analysis on a second timing parameter, TTP, to compare our results to prior findings, which have observed a TTP that is constant across stimulus contrasts  ISI's, no reliable relationship was found between amplitude of neural activity as indexed by the BOLD response, and HRF timing or shape, though trends were in the same direction as in Experiment 1 Figures , 5. HoweTR = 1.5 s instead of TR = 0.25 s; additionally, stimuli were circular patches of sinusoidal grating instead of full-field gratings. However, data were analyzed in the same way as for Experiments 1 and 2, and showed a reliable amplitude-dependent onset difference, with a slope of −0.83. These results should be interpreted with caution because of the changes in field strength and TR from data acquired in Experiments 1 and 2.Data acquired with an intermediate ISI  produced results similar to Experiment 1 Figure . These dOur results demonstrate that the timing of the BOLD fMRI signal is dependent upon the amplitude of the neural activity that produces it. When stimuli are reasonably well separated in time, signals produced by stronger neural activity begin earlier in time. The effect of amplitude on timing was large; weak signals were often delayed relative to strong ones by more than a second. This effect was statistically reliable for long (15.5 s) and intermediate (4.5 s) ISIs, but for very short (1 s) ISIs trends in the same direction were only reliable in the lowest variance voxels .Our results are also consistent with two sets of prior measurements, neither of which commented upon observable  decreased BOLD onset latencies for larger neural responses. Grinband et al.  measuredThus, BOLD responses produced by larger amounts of neural activity arise more rapidly. While the present data do not identify the causes of this effect, our results do rule out several possibilities. First, the effects measured here are too large to be accounted for by changes in neural latency that arise as a function of response strength. Neural responses measured with single unit recording show changes in response onset times of less than 100 ms as response strength varies over a large range  can expect that larger neural responses will have more rapid BOLD signals.Why did Experiment 2 show mainly unreliable trends? One possibility is that the BOLD signal change was weaker; mean amplitudes in Experiments 1 and 3 were over 0.5% signal change, but were much lower in Experiment 2. This could indicate either that the amplitude-onset relationship is weaker for lower amplitudes, or that Experiment 2 simply lacked the statistical power to detect the relationship due to low SNR. Another, perhaps more interesting possibility, is that the amplitude-onset relationship depends upon hemodynamics starting at or near resting state levels. Note, however, that Experiment 3 shows that amplitude-dependent onset differences can be evident even when hemodynamic responses overlap significantly. Glover , has shoOur data are inconclusive about whether the shift in HRF onset is due to either a shift of the entire response earlier in time or just a shift of the onset. The former model would predict a shift in the peak latency in addition to the shift in onset, while the latter would predict an increase in FWHM. Neither of these was seen with statistical significance, though there were numerical trends for both.One concern in any study aiming to characterize the speed of the HRF is the determination of onset time, i.e., the departure of signal from the baseline response. The use of rapid sampling in the 3T data is one strength of the present study for obtaining reliable estimates of the timing properties of the contrast evoked HRF. Undersampling is known to misrepresent the fine timing characteristics of the HRF by introducing up to a 50 ms onset lag, over-representing the large vascular response to the stimulus, and creating up to a 100 ms shift in the peak time (Dilharreguy et al., priori reason to doubt that this would be the case, however. Whether neural activity in other parts of the brain shows the relationship also remains to be tested. The timing of HRFs from subcortical visual regions are already known to differ from each other and from those in cortex (Lau et al., The generality of these results remains to be fully explored. While similar, but uncommented upon, results have already been reported from other labs (Wan et al., BOLD onset differences between low and high response amplitude neural responses have three important implications for the analysis of fMRI data. First, and perhaps most obviously, our results bear upon studies that attempt to use fMRI to make inferences about the relative timing of various neural processes, even within the same cortical region: processes that generate only low amplitude neural and fMRI responses will likely have their onsets systematically underestimated compared to processes that generate larger responses.Second, timing differences will lead to systematic error in estimation of response amplitudes if analyses assume a fixed HRF (McClure et al., Finally, our results also have implications for some modeling of causal relationships between regions of neural activity. Timing is one factor that can be used in such analyses, since causes must arise before effects. Our results suggest that larger neural effects generate fMRI responses that arise generally earlier than weaker effects; unless care is taken to remove this amplitude confound, analyses may spuriously infer that larger effects are causes of smaller ones.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods. MicroRNAs (miRNAs) are small non-coding RNA ∼22 nt) involved in the post-transcriptional regulation of gene expression. miRNAs promote the degradation or inhibit the expression of messenger RNA by binding to specific sequences generally located in the 3′ UTR of their target . Therefo2 nt invoin silico analysis of pathways based on the known or inferred miRNA target genes. In human, for example, miRNA signatures of different diseases such as cancer  is a comprehensive manually curated database of mammalian biology and medicinal chemistry data. For human, 2247 targets are associated to 699 miRNAs.Three resources were used to identify miRNA targets. mirTarBase  version–14 is anTwo pathway databases were used in the frame of this study. The KEGG.db package  providesN the number of elements in the universe under focus, R the number of element in the query, n the number of elements in the reference and r the overlap between the query and the reference. Correction for multiple testing was done using Benjamini–Hochberg method  , one of All of the following analyses were based on considering pathways as lists of protein coding genes. Thus, one important step common to all the strategies is to first identify the target genes of miRNAs. Several resources are available to perform this step . Again, in order to compare the strategies as such, we deliberately focused on one of these resources: mirTarBase . AD-up aThe most straightforward and widely used strategy to identify pathways associated to a list of miRNAs is to perform an enrichment analysis of the miRNA target genes Figure  e.g. 33,4,6–10),6–104,6–According to mirTarBase, 70 genes are targeted by at least one of the 16 AD-up miRNAs. These target genes are significantly enriched (FDR < 0.05) in genes of 38 KEGG pathways (Supplementary Table S1—AD-up S1 pathways). Similarly, the 99 AD-down miRNAs led to the identification of 762 target genes (Supplementary Table S1—AD-down targets) significantly enriched in genes of 73 KEGG pathways (Supplementary Table S1—AD-down S1 pathways). Intriguingly, 37 pathways are common to the two lists of miRNAs even if, by definition, no miRNA belongs to both lists. This observation raised doubts about the validity of the approach and the results it generates.In order to assess the specificity of this strategy, the same workflow was applied to 1000 random selections of 16 (RAND-16) and 99 (RAND-99) miRNAs, lists of lengths equal to the AD-up and AD-down miRNAs respectively. On average 120 genes (SD = 46) are targeted by RAND-16 and 555 (SD = 78) by RAND-99 miRNAs. Among the 229 KEGG pathways, 14 are enriched (FDR < 0.05) in at least 90% of RAND-16 targets and 64 KEGG pathways are enriched in at least 10% of RAND-16 targets. These 64 pathways include 37 of the previously identified 38 AD-up S1 pathways (97%). Similarly, 51 KEGG pathways are enriched in at least 90% of RAND-99 targets and 78 pathways are enriched in at least 10% of RAND-99 targets, including 72 of the 73 AD-down S1 pathways (99%). This result shows that this broadly used strategy in miRNA-related literature is biased and leads to highly unspecific outcomes, i.e. the same pathways are typically identified whatever the initial input list of miRNAs.The results with random selections of mirRNAs indicate that the whole list of miRNA targets is biased for a subset of biological functions. In order to test this hypothesis, an enrichment analysis was performed starting from the whole list of 1324 genes targeted by at least one miRNA in mirTarBase. 73 KEGG pathways are significantly enriched (FDR < 0.05) with these 1324 miRNA target genes and they mainly relate to cancer and cell cycle (Supplementary Table S2). Table In order to check if the bias is only related to mirTarBase and KEGG pathways, the same approach was applied using other resources for miRNA target identification and biological pathways. Many KEGG pathways are also enriched with two other lists of miRNA targets provided by targetScan –14 and bIn his review, Satoh  describeOther similar studies related to miRNA signatures use this strategy to identify impacted pathways. For instance one identified miRNAs differentially expressed during human brain development , anotherMany pathways identified by the approach described above, whatever the databases used, are related to cancer which suggests a knowledge bias in this field for microRNA compared to protein coding genes. To test this hypothesis, the relative representations of miRNA and protein coding genes in cancer-related scientific literature were compared using text-mining approach (Supplementary Information). The analysis confirms a significant bias in current knowledge related to the role of miRNA in diseases toward neoplastic and cancer-related processes. When performing analysis, such as pathway identification in this case, the method used should not be influenced by any bias in the knowledge representation.In the first strategy, all the genes belonging to each pathway were taken into account to compute the significance of the overlap with the list of targets of the miRNA signature. However, many of these genes are not known to be targeted by a miRNA and we previously showed that miRNA targets are significantly enriched with genes from some pathways, mainly related to cancer. Thus, these pathways have more chances to be selected if this imbalance is not taken into account. The most straightforward way to handle this bias is to tailor pathways to only keep genes targeted by at least one miRNA, using the same resource to identify targets of the list of miRNAs of interest Figure . This neWhen applying this strategy, 5 KEGG pathways (AD-up S2 pathways) are enriched in AD-up targets (FDR < 0.05) and 3 KEGG pathways (AD-down S2 pathways) show enrichment for AD-down targets. To assess the specificity of this strategy, the same workflow was applied on RAND-16 and RAND-99 miRNAs. 12 KEGG pathways are enriched in at least 10% of RAND-16 targets, including all the 5 AD-up S2 pathways. Similarly, 18 KEGG pathways are enriched in at least 10% of RAND-99 targets, including all the three AD-down S2 pathways (Supplementary Table S1). These results show that this alternative strategy, based on the filtering of pathway resources for miRNA target genes, is also not specific even if fewer pathways are finally selected compared to the first strategy.The low specificity of this approach could be related to the identification of genes targeted by miRNAs of interest. Indeed, since the genes on which the enrichment analysis is performed are selected indirectly, the number of miRNAs targeting each gene may affect the probability to include them in the enrichment analysis, with genes targeted by many miRNAs having more chances to be selected in the first step of the strategy. To test this hypothesis an enrichment analysis was performed on the whole list of targets in mirTarBase but also on the 1000, 500, 100 and 50 most commonly targeted genes. By design, no pathway is significantly enriched with the whole list of 1324 miRNA target genes and only one is enriched with the top 1000 most targeted genes. In contrast, 20, 12 and 18 KEGG pathways were significantly enriched (FDR < 0.05) respectively with the top 500, 100 and 50 targeted genes, most of these pathways being related to cancer biology (Supplementary Table S2). This result shows that genes having more chances to be selected in the first step of this approach are already significantly enriched in some pathways, thereby unbalancing the second step of the strategy.Again, to check the dependence of this result toward the tools and databases, the same workflow was applied using alternative resources. The same kind of bias is observed when using other sources for miRNA target identification and when performing the enrichment analysis on the MetaBase pathways Figure . This reIn order to avoid bias related to the previously described indirect identification of the genes to be included in the enrichment analysis, a third strategy was set-up Figure . First, After applying this strategy to the AD use case, not a single pathway was found to be significantly enriched in the 16 AD-up miRNAs whereas 81 pathways were significantly enriched (FDR < 0.05) in the 99 AD-down miRNAs (AD-down S3 pathways) (Supplementary Table S1). Again, to assess whether this new strategy leads to an improvement in specificity, this methodology was applied to 1000 random selections of 16 or 99 miRNAs. No single pathway was selected more than three times on the 1000 trials (FDR < 0.05), clearly supporting the specificity of this approach.Again, different tools to identify miRNA targets were compared in order to assess their impact on pathways associated to the miRNA signature. Beside a strong overlap between the different tools, a significant number of pathways are identified only when using only one of them Figure . Also, tNevertheless, the number of pathways significantly enriched with AD-down miRNAs is surprisingly large (81 KEGG and 631 Metabase pathways) considering the number of miRNAs (99). This result indicates that these pathways share many miRNAs.To test this hypothesis, the number of KEGG pathways associated to each miRNA was compared to the number of pathways containing each original protein coding gene. As shown in Figure The structure of this relative overlap was also compared applying a hierarchical clustering on the Jaccard index computed for each pathway pair at the level of protein coding genes  of the 81 pathways significantly enriched in the 99 miRNAs under expressed in AD brains belong to the large cluster of 70 pathways (Supplementary Table S1). It is therefore difficult to identify which of those are relevant to the disease.Nevertheless, 11 pathways which are significantly enriched in the 99 AD-down miRNAs belong to small clusters (≤2 pathways). These 11 pathways are more probably specific to the study under focus: miRNA regulations involved in Alzheimer's disease. Strengthening this hypothesis, the Alzheimer's disease KEGG pathway belongs to this list of 11 pathways. The FDR of the overlap between this pathway and the 99 AD-down miRNAs is 0.007. This pathway was not identified by the two previous strategies.The same strategy was applied on various additional lists of miRNA identified by former studies as being differentially expressed during the development of human brain , in periin silico strategy to infer pathways that are under the regulation of a miRNA signature is not specific and leads to the systematic identification of highly related biological processes, namely cell cycle and cancer biology. In this indirect approach, targets of the miRNAs are compared to pathways of protein coding genes. Many published results related to the functional interpretation of differentially expressed miRNAs are based on this biased indirect analysis (e.g. (Here we show that the most commonly applied is (e.g. ,10,18,19is (e.g. , should Two alternatives are also explored to avoid this bias. One is highly related to the previous strategy with the exception that protein coding genes taken into account in the pathways are limited to known targets of miRNAs. We showed that this other indirect strategy is also biased because of the knowledge about miRNA biology which is highly related to cancer. As recently discussed by Mørk , this biThe last strategy we explored is the reverse of the first two. Pathways of protein coding genes are converted into lists of miRNAs targeting at least one gene of the original pathway, avoiding overcounting the miRNAs targeting multiple genes belonging to the same pathway. Then, the enrichment analysis is directly performed with the miRNA signature under focus. Recently, we applied this approach to miRNA signatures of various mouse models of epilepsy  and we pMore generally, enrichment in protein coding gene pathways should be performed with care when not directly starting from genes but, for example, from their upstream regulators such as miRNAs, transcription factors or protein kinases. In such case, the alternative strategy established in the frame of this study could be beneficially applied. In any case, appropriate negative controls should always be included in the analysis in order to assess any potential bias related to the chosen strategy.Supplementary Data are available at NAR Online.


CLN6. It is a recessive genetic lysosomal storage disease characterised by progressive neurodegeneration. It starts insidiously and leads to blindness, epilepsy and dementia in affected children. Sheep that are homozygous for a natural mutation in CLN6 have an ovine form of Batten disease Here, we used in vivo magnetic resonance imaging to track brain changes in 4 unaffected carriers and 6 affected Batten disease sheep. We scanned each sheep 4 times, between 17 and 22 months of age. Cortical atrophy in all sheep was pronounced at the baseline scan in all affected Batten disease sheep. Significant atrophy was also present in other brain regions . Atrophy continued measurably in all of these regions during the study. Longitudinal MRI in sheep was sensitive enough to measure significant volume changes over the relatively short study period, even in the cortex, where nearly 40% of volume was already lost at the start of the study. Thus longitudinal MRI could be used to study the dynamics of progression of neurodegenerative changes in sheep models of Batten disease, as well as to assess therapeutic efficacy.Variant late-infantile Batten disease is a neuronal ceroid lipofuscinosis caused by mutations in  CLN5 and CLN6 causing late-infantile variants [CLN7 and CLN8. The original infantile, late- infantile, juvenile adult descriptions were made on the basis of presenting symptoms but subsequent studies have shown that individual genetic forms can present many ways; for instance there are both congenital and adult-onset CLN10 cases.Batten disease is the term by which the neuronal ceroid lipoficinoses (NCL), a group of rare genetic lysosomal diseases, are commonly known . They arvariants ,4. ThereOnset of late-infantile Batten disease and variants typically occurs between 18 months and eight years of age, with symptoms of delayed and then regressive motor function, blindness, dysarthria, ataxia and dementia followed by premature death, usually during the second decade of life . In tCLN6 [in vivo MRI scans on a sheep model of Huntington disease [A number of natural animal models of Batten disease exist (for reviews see –9). One CLN6 –12 that CLN6 ,13–15; fCLN6 . They thCLN6 . With su disease . To our in vivo the progressive brain atrophy in Batten disease affected sheep. We conducted in vivo MRI scans of the brains of 4 unaffected heterozygous carriers of CLN6 mutation (here after described as control sheep) and 6 homozygous Batten disease affected sheep (hereafter described as Batten disease affected sheep) over a period of 5 months. We found that measurable brain atrophy in Batten disease affected sheep continued throughout the study period, not only in cerebral cortex , but also in brain regions that were normal at the beginning of the study. Thus longitudinal MRI in sheep is both feasible and sensitive, and gives insight into the progression of the disease.The aim of this study was to determine whether or not longitudinal MRI could be used to track The objective of the research was to determine if (1) longitudinal scans could be conducted on animals with overt signs of neurodegenerative disease; (2) progressive atrophy was measurable in animals in which frank atrophy had already occurred; (3) progression of regional atrophy could be detected with longitudinal scanning. The affected animals showed signs of disease at the first time point, so the scans could not be conducted blind to genotype. Nevertheless, all MRI data was coded for analysis blind to genotype by another investigator (SJS) and the full details of each sheep were concealed until the data had been fully processed. Despite the evident cortical atrophy making groups obvious, analysis proceeded by automated scripts that handled each dataset identically with the same parameters so there was no possibility of introducing bias.Sample size was 6 Batten disease affected sheep and 4 normal sheep . Such a study had not been conducted previously, so a power analysis was not possible. All data from all animals were included.Rules for terminating the study of any individual sheep were defined by humane end points. One animal had seizures in the scanner during its first scan and was euthanized. After this, all procedures were reviewed, and the anaesthesia changed for all subsequent scans to an anaesthetic that would prevent further occurrences of scanner-induced seizures (probably due to noise). The endpoint was prospectively planned as the completion of 4 sequential scans.ad libitum access to water and feed. Batten disease affected sheep were housed in covered pens for the last few months of the study. A routine clinical assessment was carried out daily on all Batten disease affected sheep to monitor health, as well as seizures.Animals were derived from 32 frozen embryos that were shipped from Lincoln University to South Australian Research and Development Institute where they were implanted into primed ewes. Of the resulting 12 pregnancies, 10 lambs survived. Animals of both sexes were used , N = 4 female; all Batten disease affected). Males were castrated at 6 weeks. After weaning, all sheep were maintained in a single flock outdoors with All of the sheep were given MRI scans, with their first baseline scan conducted at ~16 months. Nine sheep survived for the duration of the experiment and were each scanned four times; one Batten disease affected sheep was euthanized shortly after its first scan (see below). Repeated scans were performed at baseline +7±2 weeks, baseline +12±1 weeks and baseline +18±2 weeks  analysis at baseline and to produce the study templates. For the longitudinal statistics, the euthanized sheep was excluded.All studies were carried out in South Australia according to the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes (2004) and under the approvals granted by the Primary Industries and Regions South Australia (PIRSA) (#18/10) and South Australia Pathology (#144/12) Animal Ethics Committees.2, a ventilation tidal volume of 450–500l, and a respiratory rate of 12–15 breaths per minute. End tidal CO2 concentration is used to monitor ventilator adequacy. Because the third sheep scanned (No. 945) had a convulsive seizure in the scanner and then again in the recovery pen, and did not respond to intravenous doses of diazepam to control the seizures adequately, it was humanely killed on welfare grounds. For all subsequent scans sheep anaesthesia was induced with intravenous diazepam (0.3mg/kg), then 4% isoflurane in oxygen by face mask, and maintained with isoflurane at 2–2.5%, 5 l/min O2 via a semi-close circle absorber system driven by a pressure cycled ventilator . Ketamine was eliminated from the protocol in order to remove its possible extrapyramidal effects.For the conduct of MRI scans of sheep, anaesthesia is induced with diazepam 0.3mg/kg i.v. and ketamine 5mg/kg i.v., intubated and maintained with isoflurane at 2–2.5%, 5 l/min OSheep were scanned supine in a 1.5T Siemens Sonata system using a variable flip angle 3D T2-weighted sequence . Signal transmission and excitation were performed with the manufacturer CP knee/extremity coil .The scan sequences were designed to give sufficiently high-resolution data, in a reasonable time, with optimal contrast between grey and white matter and good signal-to-noise ratios.http://www.spmmouse.org). The unified segmentation algorithm of SPM8 was used [Templates for VBM were created following the same procedure we used for the mouse brain . Brieflywas used  with DARwas used  to produ6 and regularisation parameters of zero absolute displacement and membrane energy, 100 for bending energy and 25 for linear elasticity mu and 100 for the penalty on velocity divergence, lambda. The resulting template structural images at the average time point were segmented and registered together with DARTEL, following the same process as the baseline study. These transforms were applied to the divergence of velocity maps to assess the rate of change of volume on a voxel-wise basis in a common stereotactic space for comparison.Each MRI was warped to create an average image at the median time point of scans for that animal, separately for each subject, using symmetric diffeomorphic modelling . This frMaps of longitudinal rate changes were compared with voxel-wise Student's t-tests using SPM8. The false-discovery rate was used to control type I errors expected due to multiple comparisons with a threshold of pFDR<0.05 .To clarify the longitudinal changes in plotting volumes for each sheep, volumes were normalised by adding a constant offset to the volume of each structure at each time point, for each sheep, so that baseline volumes were the same for all of the sheep within each group. The solid lines on each graph show a linear fit to these points and the variability of the data is shown by shading a region equal to two standard deviations of the post-baseline data above and below the line.Statistical significance for longitudinal changes in the regions of interest shown was determined by comparing the product-moment coefficient of the adjusted data after a Fisher's z-transformation with p < 0.05 used as a threshold for rejecting the null hypothesis, without controlling for multiple comparisons.Body weights of the Batten disease affected sheep were the same as those of control sheep until they were 10 months old, after which their rate of growth slowed relative to that of unaffected sheep . All of post mortem compared to the control sheep . We wondered if, despite the fact that there was already significant structural change in the Batten disease affected sheep at baseline, we would be able to detect further degeneration after subsequent scans. Longitudinal changes were assessed quantitatively by comparing the divergence of velocity fields for the registration parameters derived in the image registration process. Mean maps of local volume changes per year are shown for each group of sheep in r = -0.6), but this was not statistically significant (p = 0.3).We checked to see if there was a correlation between brain atrophy and clinical stage, as assessed by age of onset, body weight/ seizures. There was a tendency for a negative correlation between seizures and cortical volume , althouCLN6 mutations [CLN6 Borderdale Batten disease sheep from the same flock reared under different conditions [CLN6 (Merino) Batten disease sheep [nclf mouse that has a natural disease-causing mutation in CLN6 [Our study highlights both the usefulness and sensitivity of longitudinal MRI studies, and the feasibility of using MRI to study progressive pathology in sheep with neurological diseases. Longitudinal MRI gave insight into pathology that might have been missed in a cross-sectional study. For example, it was interesting to note that after the cortex, the most affected areas were basal ganglia nuclei , where no neuronal loss has been seen in a 2 year old CLN6 sheep . Subcortutations , althougutations . As wellutations  and basautations  althoughutations . The thanditions , althougse sheep , the ncl in CLN6  and in sCLN2 gene in humans [Cln3 [Detailed statistical analysis of longitudinal MRI gave additional insights, such as the selective atrophy of deep layers of the cortex  as well n humans  and in ans [Cln3  as well ns [Cln3 ,35. The ns [Cln3 . We do nIn the present study that the sex ratios were not the same between control and Batten affected groups. Sexual dimorphism can be seen by MRI, but is a much smaller effect than the large scale differences we report here. Total brain volumes were ~30% lower in the male affected sheep compared to the male control sheep. Within the group of affected sheep, the four females had brains that were only 10% smaller than the male. Thus the effect of disease has a much stronger effect on brain size than the natural differences of sexual dimorphism. For the longitudinal study, each animal served as its own control, so sex was not a factor. An alternative to the analysis we have performed would be to normalise by overall brain volume before comparing the groups. Since so much of the brain is affected, however, this normalisation will tend to remove the appearance of disease effects and introduce artefactual findings in comparatively spared structures, especially the cerebellum.The naturally occurring Batten diseases that exist in large animals make sheep ideal models in which to develop therapies such as gene therapy –40. The S1 FigMRI sections showing longitudinal progression in a control sheep (A) and the least- (B) and worst-affected (C) Batten disease affected sheep. To aid comparison in the figure, gridlines were drawn to frame the grey matter surface of each of brain at the last time point, and these lines are reproduced on the earlier scans for comparison. Gridlines on each set for scans are a box enclosing the grey matter surface on the final scan reproduced at each of the earlier time points. Scale bar is 1cm.(TIF)Click here for additional data file.S2 FigColour bar indicates Student’s t-score with seven degrees of freedom. All changes shown are significant at p < 0.05 adjusted for multiple comparisons by controlling the false-discovery rate.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Transporting over two billion passengers per year, global airline travel has the potential to spread emerging infectious diseases, both via transportation of infectious cases and through in-flight transmission. Current World Health Organization (WHO) guidance recommends contact tracing of passengers seated within two rows of a case of influenza during air travel.The objectives of this study were to describe flight-related transmission of influenza A(H1N1)pdm09 during a commercial flight carrying the first cases reported in the United Kingdom and to test the specific hypothesis that passengers seated within two rows of an infectious case are at greater risk of infection.An historical cohort study, supplemented by contact tracing, enhanced surveillance data and laboratory testing, was used to establish a case status for passengers on board the flight.P = 1·00). Laboratory testing using PCR and/or serology, available for 118 of 239 (49·4%) of the passengers, was largely consistent with clinically defined case status.Data were available for 239 of 278 (86·0%) of passengers on the flight, of whom six were considered infectious in-flight and one immune. The attack rate (AR) was 10 of 232 . There was no evidence that the AR for those seated within two rows of an infectious case was different from those who were not pdm09 does not support current WHO guidance regarding the contact tracing of passengers seated within two rows of an infectious case of influenza during air travel. An emergent novel swine-origin influenza AH1N1)pdm09 virus was first identified in humans on 15th April 2009; on 25th April 2009, the World Health Organization declared the outbreak a ‘public health emergency of international concern’.N1pdm09 v3With over 2 billion passengers per year, global airline travel represents an important vehicle for the spread of emerging infectious diseases such as pandemic influenza, both through the transportation of infected passengers and through the potential for in-flight transmission of disease. Previous studies have described the spread of influenza during air travel,11The aims of this study are to describe the in-flight transmission of influenza A(H1N1)pdm09 during a commercial flight carrying the first cases reported in the United Kingdom and to test the specific hypothesis that passengers seated within two rows of an infected case are at greater risk of infection. Knowledge of in-flight transmission dynamics of influenza A(H1N1)pdm09 will inform public health policy regarding advice on dealing with potentially infectious passengers and the role, if any, of contact tracing.On 27th April 2009, a couple returning by air travel from vacation in Mexico became the first case of influenza A(H1N1)pdm09 to be reported to the public health agencies in the United Kingdom (UK). The couple travelled on a Boeing 767–300 that departed Cancun, Mexico, at 0000 GMT on 21st April 2009, and arrived in Birmingham, UK, 9·5 hours later. This cohort study, supplemented by multiple overlapping data sources, examines possible transmission dynamics between passengers travelling on board the same commercial flight.The data sources used were an historical cohort study using a telephone questionnaire administered following the flight; initial contact tracing of passengers seated within two rows of the first identified case and enhanced surveillance data collected by the Health Protection Agency (HPA) at the time of the pandemic.A flight manifest was obtained to identify a study cohort, the sampling frame of which comprised all passengers travelling on the flight. The HPA attempted to contact all passengers to request completion of a telephone-administered questionnaire to investigate demographics, location of stay and activities in Mexico, travelling group links and symptoms. Passengers were asked which seat they sat in, as some did not take up their allocated seat. Eight public health officials conducted the interviews between 9th June 2009 and 5th August 2009. Passengers unable to be contacted by telephone after three attempts were sent an invitation to participate by post, but there were no further responses to this. All passengers contacted were invited to attend for serological testing (see section Laboratory testing).The West Midlands HPA regional epidemiology unit attempted to contact those seated within two rows of the first two reported cases on the flight, to assess case status and the individual passenger's need for antiviral medication. All contact tracing was performed between 29th April 2009 and 1st May 2009, by two officials.Data linkage was used to cross-match the flight manifest with HPA information collected at the time of the pandemic using the first few hundred (FF100) and Data Mart surveillance systems.The Data Mart project was developed during the pandemic as an automated surveillance tool to collect H1N1 testing information from UK laboratories, in an effort to improve timeliness and completion of reporting. Data Mart data fields include clinical information and the results of PCR testing.The USA Centre for Disease Control and Prevention (CDC) clinical definition of influenza-like illness (ILI) was used, defined as fever (self-reported in this study), with at least one symptom of sore throat or cough.To establish the case status of passengers, an incubation time for influenza A(H1N1)pdm09 of 0–6 days was used, with a period of communicability from 1 day pre- to 1 day post-symptom resolution.9Any passenger with symptoms compatible with ILI and onset prior to the day of the flight, with recovery of symptoms no earlier than the day before the flight.Any passenger with a history of symptoms compatible with ILI, but who had recovered at least 2 days before the flight.Any passenger with a history of symptoms compatible with ILI that began 0–6 days after the day of the flight.A passenger with clinical data who did not have symptoms consistent with ILI or a date of onset more than 6 days post-flight.All data analysis was performed using Stata 12.0 . As the data sources were overlapping, they were considered to be a hierarchy, with the cohort study considered the most complete and reliable for clinical data, followed by contact tracing and then laboratory surveillance data. Initial descriptive analysis was performed, mapping the flight seating plan, followed by calculation of the attack rate and testing the primary hypothesis using Fisher's exact test.Combined nose and throat swab specimens were analysed by RT-PCR for detection of influenza A (H1N1)pdm09 virus as described previously.infected in-flight and 94 non-cases).Table infected in-flight, leading to an overall attack rate of 10 of 232 at risk . Of the ten passengers defined as infected in-flight, four were tested using PCR, and of these, 4 of 4 (100%) were positive, one PCR positive case was also serology positive, and a further passenger who did not undergo PCR testing was serology negative. Six passengers were defined as infectious, of whom four underwent PCR testing, with one-fourth (25%) testing positive; no infectious cases underwent serological testing. One passenger was defined as immune during the flight and underwent no laboratory testing. Figure infected in-flight cases were seated in the rear section of the plane, with one seated at the front. The seat location of one infected in-flight case was unknown. Infectious cases were seated in all sections of the flight. There was no evidence that passengers seated within two rows of an infectious case were at an increased risk of infection compared with those who were not , with the attack rates similar for both groups ; seating data were unavailable for 12 passengers at risk.Clinical data sufficient to establish a case definition were available for 239 of the 278 (86·0%) passengers who travelled on the flight of which 224 were part of the cohort study; an additional 12 were added through contact tracing. Enhanced surveillance data were available for 17 passengers, but only contributed information regarding an additional three cases. Ten passengers were defined clinically as infected in-flight occurred between 0 and 6 days post-flight with 9 of 10 occurring within 3 days.Figure The median age of all passengers with ILI was 16 years , compared with 37 years  for those without. A similar proportion of passengers with or without ILI were male (48·2% versus 47·1%).infectious case 5 and infected in-flight case 13  of the infected in-flight passengers staying at resorts where those considered infectious had been on vacation; infected in-flight cases 9 and 10 stayed at the same resort as infectious case 2; infected in-flight case 16 stayed at the same resort as infectious case 4. All passengers travelled to the airport on tour buses, an estimated journey time of 30–120 minutes depending on starting location. Where information was available, passengers with ILI stayed at either Cancun , Riviera Maya  or Akumal (one infected in-flight). Cancun is north of Cancun airport, whereas Riviera Maya and Akumal are south; therefore, it seems unlikely that these passengers travelled together to the airport. No apparent links between those with ILI could be attributed to day trips and visits while in Mexico.Apart from the first two UK-reported cases, 13 Table , no otheinfected in-flight . Four of eight  of the infected in-flight cases spent more than two hours in the departure lounge compared with 114 of 208  of non-cases.There was no association between time spent in the departure lounge and being infectious case used the front right toilet, with information not available for the remaining five. The relative risk of infection by exposure to the toilet used by infectious cases could therefore not be calculated. Six of ten cases infected in-flight provided information on toilet use, using three different toilets, with two using the front right toilet. Data regarding movement in-flight were available for five of the six infectious passengers, with one stating they spent 1–2 hours away from their seat and a further two away from their seat for over two hours, whereas all infected in-flight cases spent <1 hour away from their allocated seat.Participants in the cohort study were asked about toilet use on board the flight, one This study estimates the overall attack rate (AR) for influenza A(H1N1)pdm09 on a nine-hour commercial flight with six potentially infectious passengers on board to be 4·3%, but did not demonstrate an increased risk to those seated within two rows of an infectious case.et al.The AR in our present study is largely consistent with the published data from four previous reports that have suggested transmission of influenza A(H1N1)pdm09 during long-haul modern commercial air travel; 0·5% for all sections of a 13-hour flight with a single infectious passengeret al.et al.16Our study does not demonstrate an increased risk of becoming infected with influenza A(H1N1)pdm09 in those seated within two rows of an infectious case compared with those who were not. Two separate reports of single cases infected in-flight demonstrated that they sat two and six rows away from the known infectious case.infected in-flight cases tested using PCR were positive, suggesting specificity of the CDC clinical case definition; only one of four infectious cases tested was positive, but this is unsurprising given the length of time (5–15 days) between symptom onset in Mexico and return to the UK. Of the four infected in-flight cases confirmed using PCR testing, two of four (50%) cases were seated within two rows of an infectious case . Serological testing was only available for two cases (both infected in-flight) with ILI and was negative in one case with a previously positive PCR, perhaps as a result of early timing of the test or a false-negative result following administration of oseltamivir. Serological data were considered in the light of potential cross-reactivity of antibody from seasonal influenza A(H1N1)The results of laboratory testing in this present study are largely consistent with the clinical case definitions. All four f time 5– days betet al.Influenza transmission in-flight can occur via several pathways: direct physical contact, fomites, direct droplet spread and suspended small particles.Limitations of this study include selection and measurement biases, limited sample size and the potential for influenza A(H1N1)pdm09 transmission beyond the flight.infected in-flight cases tested with PCR provides some validation to the CDC case definition chosen. The validity of defining true infectious cases was potentially reduced by the low PCR positivity rate  and lack of serological testing among this group. The potential for observer bias was minimised as interviewers would not be aware of the location of infectious cases in the cohort study, but recall bias among respondents aware of the reported cases remains a possibility.Information sufficient to determine case status was available for 239 of 278 (86·0%) passengers on board the flight, with more detailed clinical data obtained from 224 of 278 (80·6%) passengers, the high response reducing the potential effect of selection bias. No data are available regarding the demographics or case status of non-responders to the cohort study and contact tracing exercise, unless recorded during enhanced surveillance. However, there is no suggestion that this group were aware of, and therefore could be selected by, the exposure studied. The majority of passengers with unknown case status belonged to groups of four to six travellers, where only contact details of the lead passenger were available, further suggesting that non-response was unrelated to individual case status or exposure, and therefore unlikely to bias the results of this study. The use of a clinical case definition has the potential to result in incorrect classification of case status, thereby under- or overestimating the attack rate. The specificity of 100% for the four Despite the large number of passengers on the flight, the low attack rate reduces the power in this study to test the primary hypothesis, the AR in those seated or not seated within two rows of an infectious case was almost identical, but a much larger study, in practical terms a meta-analysis of several flights, would be required to test the hypothesis with statistical integrity.In calculating the AR, the presumption is made that all newly infected cases contracted influenza A(H1N1)pdm09 during the course of the flight, but this requires the validity of several assumptions. The incubation period used in this study was 0–6 days, estimated from modelling studies among the UK population following the pandemic.29Despite the first reported cases of influenza A(H1N1)pdm09 arriving on the flight studied, there is evidence to suggest circulating disease in the UK prior to this. A phylogenetic study estimated from divergence that two of 13 strains of influenza A(H1N1)pdm09 may have been circulating up to 8 days before the arrival of the flight, although the risk of transmission to the passengers studied remains unknown.30infectious case 5 and infected in-flight case 13, but the potential for becoming infected prior to the flight in Mexico remains for all infected passengers, and therefore the AR may be an overestimate.Exhaustive attempts were made in the cohort study to identify common links, such as accommodation and excursions in Mexico, family groups and toilet use on board. No clear links were identified apart from that between Other potential opportunities for infection include via aerosol spread while travelling in close proximity to an infectious case on a low-humidity air-conditioned charter bus to the airport and via direct contact in a potentially crowded departure lounge.infectious case was unlikely to be identified by this study. The onset of symptoms 5–15 days prior to the flight for infectious cases raises the possibility of resort exposure leading to the infection of those infected in-flight and demonstrates the additional possible exposure to circulating disease in Mexico at the time of the flight. The shape of the epidemic curve, albeit for a small number of cases, does suggest increased transmission around the time of the flight.At the time of the cohort study, the aircraft toilet locations were not known to health protection staff, and there is likely to be considerable non-differential misclassification in self-reported use. Therefore, any increased risk of illness, if present, associated with using the same toilet as an The findings of this study do not support current WHO guidance regarding the contact tracing of passengers seated within two rows of an infectious case of influenza A(H1N1)pdm09. The complex transmission dynamics of influenza, including airborne, direct and surface transmission, make clear identification of those at risk and decisions about subsequent contact tracing challenging.