Inositol-requiring enzyme 1 (IRE1) is an ER membrane-resident kinase/RNase that mediates signal transmission in the most evolutionarily conserved branch of the UPR. Dimerization and/or higher-order oligomerization of IRE1 are thought to be important for its activation mechanism, yet the actual oligomeric states of inactive, active, and attenuated mammalian IRE1 complexes remain unknown. We developed an automated two-color single-molecule tracking approach to dissect the oligomerization of tagged endogenous human IRE1 in live cells. In contrast to previous models, our data indicate that IRE1 exists as a constitutive homodimer at baseline and assembles into small oligomers upon ER stress. We demonstrate that the formation of inactive dimers and stress-dependent oligomers is fully governed by IRE1’s lumenal domain. Phosphorylation of IRE1’s kinase domain occurs more slowly than oligomerization and is retained after oligomers disassemble back into dimers. Our findings suggest that assembly of IRE1 dimers into larger oligomers specifically enables  Our cells contain many different compartments that each perform specific tasks. A cellular compartment known as the endoplasmic reticulum is responsible for making many of the proteins the cell requires and transporting them around the cell.It is important that the endoplasmic reticulum remains healthy and, therefore, cells use a protein called IRE1 that senses when this compartment is under stress. IRE1 then sends a signal to the control center of the cell (known as the nucleus) to ask for help. Previous studies suggest that IRE1 assembles into either pairs or larger groups of molecules known as oligomers when it senses that the endoplasmic reticulum is under stress. However, it remains unclear whether such assembly is the main switch that turns IRE1 on and, if so, how many molecules need to come together to flip the switch.Here, Belyy et al. genetically engineered human bone cancer cells to attach a mark known as a HaloTag to IRE1.The team developed a microscopy approach to count, in living cells, how many tagged IRE1 molecules join. The experiments indicated that IRE1 proteins were generally found as pairs in unstressed cells. When the endoplasmic reticulum experienced stress, IRE1 proteins briefly assembled into oligomers before disassembling back into pairs. Mutated versions of IRE1 revealed the exact parts of IRE1 that connect the pairs and the larger oligomers.These findings suggest that the assembly of IRE1 pairs into oligomers plays a major part in the activation of IRE1 to send a stress signal to the nucleus. IRE1 signaling is closely implicated in both cancer biology and aging, and therefore, understanding how it works may aid the development of new therapies for cancer, dementia, and other health conditions affecting older people. Furthermore, the microscopy approach developed in this work could be adapted to study other proteins that relay signals in living cells. Protein oligomerization is central to cell biology. The regulated assembly of membrane proteins into dimers or larger oligomers constitutes a fundamental cellular mechanism for relaying information across membranes. The majority of receptor superfamilies rely on some form of oligomerization, including G-protein coupled receptors , integritrans-autophosphorylation and catalyzes RNase-mediated non-conventional splicing of the XBP1 mRNA (HAC1 mRNA in yeast)  responsible for initiating the most evolutionarily conserved branch of the unfolded protein response (UPR) . The UPRBP1 mRNA  (in mammn yeast)  as well n yeast)  and RIDDn yeast) . While IL , generally accepted to be the primary dimerization interface, and IF2L . By repeating this analysis on data collected from cells expressing the 1 x and 2 x HaloTag controls in ten independent replicates ( > 10 cells and >1500 trajectories per condition in each replicate), we verified that the algorithm robustly and reproducibly distinguishes between monomeric and dimeric molecules in the ER membrane  from that of the tandem 2 x HaloTag protein, indicating that both HaloTag binding sites of a tandem construct remain fully accessible to dye molecules. The 4 x HaloTag construct, created by GST-induced dimerization of tandem HaloTag dimers, exhibited a substantial further increase in the percentage of correlated trajectories , we demonstrated that our assay provides excellent sensitivity for oligomers in the monomer to tetramer range, with sensitivity likely decreasing for oligomers comprised of more than ~10 protomers  to minimize the ambiguity in assignment of crossing tracks. Pearson’s correlation coefficients were then calculated between both the x- and y-coordinates of adjacent tracks within each sliding window. Every track in the JF-549 channel that contained at least one window with a correlation coefficient above a predetermined threshold was classified as a co-localizer . Since our approach does not reveal the individual oligomeric state of any given tracked protein, this observed change is most readily explained by a Tm-dependent shift in equilibrium towards a mixture of dimers and tetramers. Extending the treatment to 24 hr reversed the shift in correlated trajectories, suggesting that IRE1 oligomers dissociate back into dimers under prolonged stress. This finding parallels the previously observed attenuation of IRE1 activity upon prolonged, unmitigated ER stress , which disrupts ER calcium homeostasis by blocking sarco/endoplasmic reticulum Ca2+ pumps  and without (p = 0.18) ER stress (two-tailed t-test). Thus, both lumenal domain mutants fail to change their oligomeric states in response to ER stress, with K121Y remaining mostly monomeric and WLLI359-362-GSGS remaining mostly dimeric.To determine which regions of IRE1 are responsible for assembling the protein into dimers and oligomers, we applied our trajectory analysis to a number of key IRE1 mutants . Since cl domain , and WLLace IF2L . These tNext, we probed the potential contribution of the kinase/RNase domain. K599A, a mutation that abrogates IRE1’s kinase activity , closelyL interface) as well as oligomer formation in response to ER stress (via the IF2L interface). Indeed, the lumenal domain alone is sufficient for the formation of both resting-state dimers and stress-induced oligomers when tethered to the ER membrane, while the kinase/RNase domain alone remains strictly monomeric.We measured the stress-dependent oligomeric changes of fully active human IRE1 in live cells at physiological expression levels. Despite IRE1 not forming the massive clusters that were previously observed by us and others in the context of overexpression, we show that the formation of high-order oligomers remains a conserved feature of IRE1’s activation. Surprisingly, IRE1 forms constitutive inactive dimers in the absence of externally induced ER stress in U-2 OS cells, thus challenging the widely held notion  that theWhy might the formation of oligomers rather than dimers be the key step in IRE1’s activation? The kinase/RNase domains can adopt two distinct dimeric conformations, known as the back-to-back and face-to-face orientations . The bacOur results raise the question of whether the larger oligomers represent the maximally active form of IRE1 or are instead a phosphorylation-competent transient state en route to the generation of fully active phosphorylated dimers with and perhaps outside of oligomers. Our data showing the fast assembly and disassembly of high-order oligomers in response to saturating Tg, coupled with rapid hyper-phosphorylation, suggest that phosphorylated IRE1 may not need to remain oligomeric for its RNase to remain active. In fact, extensive phosphorylation either on the activation loop or elsewhere on IRE1’s kinase domain may serve as a negative feedback mechanism, as previously proposed for yeast IRE1 . A simplL interface, for example through the action of ER-lumenal chaperones  supplemented with 10% tetracycline-free fetal bovine serum , 6 mM L-glutaminfe, and 100 U/ml penicillin/streptomycin. All cell lines used in the study tested negative for mycoplasma contamination when assayed with either the Universal Mycoplasma Detection Kit (ATCC 30–1012 K) or the MycoAlert Detection Kit (Lonza LT07-418). Tunicamycin and thapsigargin were purchased from Sigma-Aldrich or from Tocris and dissolved in DMSO. JF549 and JF646 dyes conjugated with the HaloTag ligand were a kind gift of Luke Lavis (Janelia Farms). The antibodies used for immunoblotting are listed in the Immunoblotting section.U-2 OS Flp-In T-REx cells were a kind gift of the Ivan Dikic lab and were independently authenticated through the human STR profiling service offered by the American Type Culture Collection (ATCC). Cells were cultured at 37 °C with 5% COERN1, the gene encoding IRE1α (cell line ID: PWM359). This was done using the same CRISRP/Cas9-based approach that we used to generate a complete IRE1α knockout in our previous paper  template plasmid targeted at C-terminus of ERN1. Design of both plasmids followed the protocol published elsewhere , which were pre-coated with rat tail collagen type I (Corning 354236) at 10 µg/cm2 in accordance with the manufacturer’s instructions . Twenty-four hours prior to imaging, the growth medium was replaced with ‘Imaging medium’: FluoroBrite DMEM (ThermoFisher) supplemented with 10% tetracycline-free fetal bovine serum  and 6 mM L-glutamine, without antibiotics. For experiments requiring transfection, cells were transfected with a mixture of 50 ng of plasmid DNA and 50 ng of carrier salmon sperm DNA per well immediately following medium change (i.e. 24 hr prior to the start of imaging). Transfections were carried out in “Imaging medium” using the Fugene HD transfection reagent (Promega).Cells were seeded at a density of 1.6 × 10On the day of imaging, cells were treated with ER stressors at the indicated time points. Labeling with JF549 and JF646 dyes conjugated with the HaloTag ligand was initiated 1.5 hr prior to the start of imaging. First, the dyes were added to pre-warmed ‘Imaging medium’, and this medium was used to replace the cells’ growth medium. We experimentally found the optimal molar dye ratio to achieve ~50% labeling with each ligand to be 1:20 . The large difference in required concentrations is likely due to the difference in membrane permeability between the two dyes. We experimentally found the 5 nM JF549 /100 nM JF646 concentrations to be saturating under our labeling conditions since further increases in dye amounts did not lead to a further increase in the density of diffusing spots in IRE1-HaloTag cells. Following addition of the medium containing the two dyes (and any required ER stressors), cells were returned to the incubator for 1 hr. Then, cells were washed twice with warm PBS, washed once with pre-warmed ‘Imaging medium’, and returned to the incubator for an additional 5 min to give any unbound dye time to diffuse out of the cells. The medium was replaced one more time with pre-warmed ‘Imaging medium’ containing any required ER stressors to finish sample preparation.2 control to maintain the samples at 37 °C and 5% CO2, one using a custom-built enclosure (#1) and the other using an OkoLab Live stage insert (#2). All components of microscope #1 were controlled by the µManager open-source platform (All imaging was carried out on one of two Nikon Ti-E inverted microscopes (#1 and #2 hereafter), each equipped with a Nikon motorized TIRF module, an Agilent/Keysight MLC400 fiber-coupled laser light source, a Perfect Focus System , a 100 × 1.49 NA oil immersion objective , and a Hamamatsu Flash 4.0 CMOS camera. Microscope #1 held a ZET405/488/561/640m-TRFv2 quadruple bandpass filter cube (Chroma), while microscope #2 held a ZET488/561/640 m triple bandpass filter cube (Chroma). Additionally, microscope #1 included a Yokogawa CSU-X high-speed confocal scanner unit and an Andor iXon 512 × 512 EMCCD camera, which were used for spinning-disk confocal microscopy experiments. Both microscopes featured full temperature and COplatform , while mOblique-angle illumination conditions were achieved by focusing on a cell, engaging the PFS, and gradually increasing illumination angle with the motorized TIRF lens until single-molecule spots along the bottom surface of the cell became clearly visible. Videos were acquired with a 60ms combined frame time, split into a 25ms exposure in the JF549 channel  and a 25ms exposure in the JF646 channel , with the remaining 10ms accounting for channel switching times. The two channels were imaged sequentially by the same camera using camera-triggered switching of the acousto-optic tunable filter (AOTF) built into the light source. Frames were cropped to approximately 500 × 500 pixels prior to acquisition since the full camera sensor could not be read out fast enough to support the required frame rate. Typically, 100 combined frames were acquired per cell , which in our hands provided a good number of trajectories per cell while avoiding extensive photobleaching of the dyes. To locate and choose cells for imaging, we used the full size of the camera sensor and acquired a series of tiled snapshots of an area containing ~100 cells. We then selected cells that were morphologically normal, well-adhered, and spread out. When imaging transiently transfected cells, we chose cells in which the HaloTag-labeled proteins were expressed at sufficiently low levels to allow us to clearly see individual spots corresponding to single molecules.Single-molecule data were analyzed to identify co-localizing two-color trajectories using a pipeline developed in house, described in detail below. First, each movie was split into the two individual channels, JF549 and JF646. Next, spots were located using the Laplacian of Gaussian (LoG) detector implemented in the TrackMate plugin  for ImagAll subsequent analysis was performed in Python. The broad goal of this analysis was to identify tracks that correlated well in space and time between the JF549 and JF646 channels. To avoid problems imposed by uneven track durations and trajectories crossing each other, we decided to perform the analysis using a short sliding window. In other words, instead of considering the entire movie at once, we binned each movie into overlapping shorter movies containing a fixed number of frames each, and looked for co-localizing trajectories in each of the shorter movies. To achieve this, the TrackMate output files were parsed and filtered to only include tracks that span at least as many frames as the length of the sliding window. The sliding window was then moved across the duration of the movie in 1 frame increments. In each of the resulting windows, only tracks that were fully defined within that window (i.e. had position information for each frame) were selected for correlation analysis.N frames long in which the position of each spot is well-defined in every frame, 2) At least one such window yields a PCC value greater than T for both the X- and Y- coordinates of the tracked spot, and 3) The two tracks are at least partially overlapping in space. The value plotted in the figures, ‘% correlated trajectories’, is defined as follows:Pearson’s correlation coefficients (PCC) were then individually calculated for the X- and Y-coordinates of every pair of spatially adjacent tracks (adjacent meaning that at least a subset of data points of track B are contained within the rectangle that bounds track A). The requirement for tracks being spatially adjacent both increased the computational efficiency of the algorithm and helped eliminate false positives from short tracks of similar shapes that occurred by chance in different parts of the cell. A pair of tracks was determined to be correlated if all of the following conditions were met: 1) the two tracks share at least one window JF549total is the total number of trajectories in the JF549 channel that are at least as long as the length of the sliding window (N), while nJF549corr is the number of trajectories in the JF549 channel that are found to have correlated trajectories in the JF646 channel as described above.where nN  and T . Just as with the tracking algorithm, we first empirically found values of N and T that yielded robust identification of visually correlated tracks without giving too many false positives, and then used these values in all subsequent data analysis. We did find that due to differences in filters, laser intensity, and alignment between the two microscopes, a different combination of N and T yielded the highest dynamic range in our assay. Data from each microscope were fully internally consistent but we avoided showing data collected on two different microscopes on the same plot. Thus, each panel in the paper contains either data collected exclusively on microscope #1 or on microscope #2.In our analysis, the only user-selected parameters that tune the sensitivity of the approach are N and T parameters, along with a full list of folders containing the TrackMate XML files for each condition. The code reports the fraction of correlated tracks for each condition with 95% confidence intervals determined by bootstrapping. The README.md file included with the source code  to determine the respective particle’s diffusion coefficient. SA analysis was performed as described in the original manuscript (Apparent diffusion coefficients were obtained from single-particle trajectories from tracking data using one of two methods: mean squared displacement (MSD) or state array (SA). In MSD analysis, each single-particle trajectory was broken up into 5-frame segments that were then aligned and averaged to obtain the final MSD plot for that particle. Particles that were tracked for fewer than 15 frames were excluded from MSD analysis. Each MSD plot was then fitted to a straight line(<rnuscript  through nuscript .1P (JF549-bound), 2P (JF646-bound), and uP (undetectable). Because the combined probabilities must add up to unity, u = 1 – P1 – P2P. Then, for a cluster comprised of n individual molecules, we can express the total probability that the cluster contains at least one dye of each color as follows:A simple yet useful model for estimating cluster stoichiometry based on the fraction of correlated tracks works as follows. Assume that each HaloTag-conjugated protein can occupy one of three states: bound to an unbleached JF549 dye molecule, bound to an unbleached JF646 dye molecule, or undetectable. The latter category is a catch-all for every possible reason a protein may escape detection such as dye bleaching, incomplete labeling, new protein synthesis after labeling reaction, and false negatives in the spot detection algorithm. Let the probabilities of these three states be denoted as obsF:However, in our experiment, the clusters containing no detectable dyes are invisible, and what we measure experimentally is instead the observed fraction of all visible clusters that contain at least one dye of each color. Let’s call this quantity the fraction of observed co-localizers, obs, we first need a measurement of P1 and P2, which can be done using data from the constitutive 2 x HaloTag homodimer construct, where we know that n = 2. Let’s assume that 1 = P2 = PLP (labeling probability), since all our experiments are done in a regime where the labeling densities with the two different dyes are nearly identical. Plugging these assumptions into the expression for obsF, we obtain:To estimate cluster stoichiometry based on the experimentally measurable FL can be expressed in terms of obsF:Rearranging this expression, we find that PLP from the 2 x homodimer control , it can be simply plugged into the earlier expression for obsF and plotted as a function of n to yield an estimate of average cluster stoichiometry for any value of obsF. Of course, this model is a significant oversimplification of the true underlying processes , but it does provide a useful ballpark estimate.Once we have an estimate of While we did not observe the formation of stress-induced IRE1 clusters in cells expressing IRE1-HaloTag from the endogenous genomic locus, some small punctate structures were occasionally visible in confocal images of these cells both with and without stress. Such small puncta were also present in some cells expressing HaloTag control constructs. We attribute these puncta to autofluorescence, local enrichment of ER membrane components in organelles such as lysosomes or autophagosomes, and/or dense regions of dense ER folds that cannot be adequately resolved by confocal microscopy. We sought to distinguish between these spots and the visually distinct “classic” IRE1 clusters (as described by us and others) by developing a quantitative definition of IRE1 clustering. We define a cell as exhibiting IRE1 clustering if greater than 1% of the integrated IRE1 fluorescence intensity is contained within small punctate structures. To identify such structures automatically, the image is first smoothened with a Gaussian filter with a sigma of 1 pixel. Then, the image is background-corrected and its intensity normalized such that the mean pixel intensity becomes 1. The normalized image is then processed with a Laplacian filter with a 3x3 pixel kernel to identify regions of maximal contrast and thresholded with an empirically found value of 5. Any areas contained by pixels that pass this threshold cutoff are considered to be potential puncta. These areas are then dilated with a 3x3 pixel mask and the combined fluorescence intensity contained in the potential puncta is compared against the total fluorescence intensity of the cell. If the tentative puncta contain more than 1% of the cell’s total fluorescence, we conclude that IRE1 clusters are present in the cell. We find that these criteria robustly pass the visual test for a range of images across different illumination intensities and magnification levels.CGGAAGCCAAGGGGAATGAA) and VB_pr167 (ACTGGGTCCAAGTTGTCCAG). PCR was carried out with Taq polymerase (Thermo Fisher # 10342020) in the manufacturer-supplied Taq buffer supplemented with 1.5 µM Mg2+. The following PCR program was used:  in accordance with the manufacturer’s instructions. RNA was then extracted from the aqueous phase using a spin column-based purification kit  and reverse-transcribed into cDNA using SuperScript VILO Master Mix (Thermo Fisher # 11755050). The cDNA was diluted 1:10 and used as a template for PCR with the following primer pair: VB_pr259  FBS (Sigma), 2 mM glutamine (Gibco), and 100 U/mL penicillin plus 100 μg/mL streptomycin (Gibco), and treated as indicated. Thapsigargin (Tocris) was used at a concentration of 100 nM and tunicamycin (Tocris) at 5 μg/mL, dissolved in DMSO. DMSO was used as the untreated control. When 70–80% confluent, cells were washed in PBS and trypsinized using Trypsin-EDTA 0.05%. Cells were pelleted and stored at –20 for protein or RNA extraction.For immunoblotting, protein lysates were extracted in RIPA buffer (EMD Millipore) with Halt protease and phosphatase inhibitor cocktail (Thermo Scientific). The crude lysates were cleared by centrifugation at 13,000 rpm for 15 min and protein content was analyzed by Pierce BCA protein assay (Thermo Scientific).Equal amounts of protein (40 µg/condition) were run with SDS-PAGE and electrotransferred onto membranes that were blocked with 5% dried nonfat milk powder in TBST (blocking solution). Blots were incubated with 1/1000 dilution in blocking solution of primary antibodies overnight at 4°C. Antibodies (Abs) for IRE1α (3294), PERK (3192), ATF4 (11815), CHOP (2895) were from Cell Signaling Technology (CST). ATF6 antibody (66563–1) was from Proteintech. β-actin (5125) from CST was used as a housekeeping control. Abs for XBP1s and pIRE1 were generated at Genentech and have been described elsewhere . Blots wFor RT-qPCR, RNA extraction was performed with the RNeasy Plus kit (Qiagen #74134). 50 ng of RNA per sample, in technical triplicates, were reverse transcribed and amplified on the ABI QuantStudio 7 Flex Real-Time PCR System, using TaqMan RNA-to-CT 1-Step Kit (ThermoFisher Scientific #4392938). The RQ  was calculated by relating each individual CT value to the expression of the housekeeping HPRT1 gene and the control of the experiment. Taqman primers for IRE1 (Hs00176385_m1), XBP1u (Hs02856596_m1), XBP1s (Hs03929085_g1), DGAT2 (Hs01045913_m1), BCAM (Hs00170663_m1), TGOLN2 (Hs00197728_m1), and HPRT1 (Hs02800695_m1) were from ThermoFisher Scientific.Data Analysis section) with 95% confidence intervals. For pairwise comparisons of individual conditions, p-values for each pair of conditions were calculated both by two-tailed t-test and by an approximate permutation test with 10,000 iterations. Tables containing all pairwise p-values are included with every figure as source data. Individual data points are overlaid on the graphs, with each point representing the percent of correlated trajectories in one cell. Data for individual conditions were pooled from multiple experiments conducted on separate days . The exact data points and raw files going into each condition can be obtained from the human-readable JSON files that are published alongside the manuscript. The only files that were excluded from analysis were movies in which the cell was clearly out of focus, TIRF illumination was clearly out of alignment, or the expression level of the transfected construct was so high that individual spots were not resolvable. Otherwise, no data were excluded and no outliers were removed. No explicit power analysis to determine sample size was conducted prior to the start of the experiment; instead, sample sizes were chosen to comprise no fewer than ten individual cells , corresponding to ~100,000 or more individual particle trajectories.All single-particle tracking data are reported as mean values of cell-by cell percent correlated trajectories  stress. While prior studies indicated the existence of large Ire1 oligomers that arose in the ER membrane after stress, this study used single molecule tracking and native levels of Ire1 to demonstrate that Ire1 naturally exists in the inactive state as a dimer and that much smaller oligomers form after stress, a phenomenon governed by the Ire1 lumenal domain. Moreover, Ire1 trans-phosphorylation, which is required for activation, begins after oligomer formation. Overall, these studies yield unprecedented insight into the mechanism underlying the unfolded protein response and have revised our understanding of Ire1 dynamics. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Vivek Malhotra as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Endoplasmic reticulum stress activates human IRE1α through reversible assembly of inactive dimers into small oligomers" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Overall comments and conclusions.As you will note in the associated files, your manuscript was reviewed by three experts in the field, and their comments are now available to you. We are delighted to report that the reviewers universally agreed that the strengths of this study include the development of an exciting and clever approach to probe membrane protein oligomerization, the use of a technological advance that will be applicable to studies of other protein substrates, the fact that the experiments were expertly performed and controlled, and the potential that this study provides a new advance in our understanding of the early and later events during UPR induction.However, after much discussion, the reviewers universally and consistently hovered around the question of how to reconcile the conclusions from this study with prior work from your lab and others using fluorescently tagged IRE1 fusions. Thus, there was a general feeling that the current story needs to conclusively account for why select conclusions from earlier publications suffered from an inherent limitation. Additional, related comments included: (1) Better verification that the IRE-HALO protein remains active during the course of the experiment, specifically in the presence of ligands/fluorophores, since some downstream events after IRE1 activation were not investigated; (2) It appears that the cell type and expression levels seem to be unchanged between this study and prior studies, but this raises the question of whether there is another confounding factor. Two other questions also arose from the reviewers/discussion, including how this model explains IRE1 activation/oligomerization induced by lipid saturation, and whether there is a way to verify what a bona fide tetramer might look like using this new system. In short, is there a control substrate that can be examined?In sum, there is a strong feeling that additional work is needed to better resolve what will be noted as a discrepancy in the field, and thus the results-if published as is-might muddy the waters.We sincerely hope that the revisions will not prove overly burdensome, especially as the conclusions are novel and the method will be welcomed by many in the cell biology field.Reviewer #1 (Recommendations for the authors):1. An important control that appears missing is demonstrating that the IRE1-HALO tag remains active and functional under the same conditions used in the imaging. In other words, the authors should perform experiments showing that IRE1-HALO autophosphorylation and RNase activation are not impacted by the presence of the fluorophores used to map trajectories in the microscopy experiments. The presence of large fluorescent ligands could impact IRE1 activity, so it should be clearly demonstrated that this is not the case, especially in light of the comparisons between oligomerization and activity discussed.2. Similar to the above, it would be good to have quantitation of markers of IRE1 activity  to confirm the model whereby oligomerization precedes autophosphorylation/activation. Specifically, as indicated above, under the same conditions. This does appear accurate based on the presented data, but demonstrating this more explicitly would help support the manuscript and this important point more strongly.3. I have some questions about the relationship between the work described herein and previous work monitoring IRE1 oligomerization using IRE1-GFP, specifically with respect to the mutant constructs that were overexpressed in this work. I understand the limited sensitivity of confocal made it difficult to detect small oligomers with IRE1-GFP, but I'm curious as to why the larger oligomeric clusters were not observed with the HALO-tagged IRE1 when ectopically expressed. I assume this is coming from lower levels of expression with the CMVd3 promoter, but it would be good to show that similar experiments with IRE1-GFP do not show the larger clusters observed in previous studies. Especially with respect to the fact that the model described herein contradicts these previous studies.4. Lastly, the authors show that the luminal domain is primarily responsible for ER stress-dependent oligomerization of IRE1. However, lipid saturation can activate IRE1 through a mechanism that appears independent of its luminal domain. It would be good to include some potential discussion of how lipid saturation can bypass this oligomerization step to promote IRE1 oligomerization and/or autophosphorylation, especially because the presented results suggest IRE1 would be monomeric in the absence of the luminal domain.Reviewer #2 (Recommendations for the authors):Determining which form(s) of IRE1a are active and inactive in cells is fundamental to understanding the Unfolded Protein Response. A single particle tracking imaging approach is highly appropriate and probably the best approach. Unfortunately, some fundamental issues significantly dampen my enthusiasm for this manuscript.The main issue with the manuscript is the lack of apparent clustering of the IRE1a-Halo reporter. Given all of the other studies that observe clustering with different IRE1a reporters with different fusions in different cell types, including multiple studies from the Walter lab, the lack of clustering raises the reasonable question of: What is being studied here? The IRE1a clusters and clustering behavior described in the recent Belyy et al., study would likely produce very different % correlated trajectory results. If the results in the current study are unique to the reporter, it's unclear what the relevance is to the rest of the ER stress field. To provide confidence in this system, I think the authors need to determine 1. why there is a discrepancy and 2. which system(s) accurately report on IRE1a behavior. I appreciate that these tasks represent a significant amount of work and I am not making these suggestions lightly.Regarding the nature of the discrepancy, it likely revolves around the Halo Tag, the JF dyes, and/or the insertion site. Either the new reporter sets a new standard for an inert tag or the new reporter will raise a cautionary note for the Halo Tag system. One useful approach would be the addition of a minimal epitope tag to IRE1, such as an HA tag, and analysis of the reporter's behavior with immunofluorescence of stressed and unstressed cells. It is probably the most physiologic system, short of a great anti-IRE1a IF antibody. With such a reporter, please assess whether IRE1a-epitope tag clusters form in stressed and not in unstressed cells. If affirmed, then the authors will need to dissect and optimize the Halo Tag system. Note that an HA tag has been used in yeast and stress induced-Ire1 clustering was readily observed by Kimata et al. If no clusters are observed with stressed epitope-tagged mammalian IRE1a, it would be helpful for the ER stress field for the authors to determine how fluorescent proteins, even robust monomers such as mNG, impact clustering and whether a significant re-evaluation of IRE1a clustering models will be necessary.Reviewer #3 (Recommendations for the authors):Given that, previously, IRE1 structural rearrangements have been tied to the XBP1-splicing-versus-RIDD functionalities of IRE1, does the IRE1a-HALO activate RIDD to the same extent and with the same kinetics as endogenous?The tracking of multimerization through movement raises two related questions. One is whether the automated visualization is then biased for movement, such that, if higher ordered structures of IRE1 were relatively immobile, they would be missed. The flip side of this coin is that, presumably, embedded within the already-existing data is information on whether ER stress changes the mobility of IRE1, comparing correlated tracks to non-correlated tracks, and comparing correlated tracks in the presence and absence of stress. Overall comments and conclusions.As you will note in the associated files, your manuscript was reviewed by three experts in the field, and their comments are now available to you. We are delighted to report that the reviewers universally agreed that the strengths of this study include the development of an exciting and clever approach to probe membrane protein oligomerization, the use of a technological advance that will be applicable to studies of other protein substrates, the fact that the experiments were expertly performed and controlled, and the potential that this study provides a new advance in our understanding of the early and later events during UPR induction.However, after much discussion, the reviewers universally and consistently hovered around the question of how to reconcile the conclusions from this study with prior work from your lab and others using fluorescently tagged IRE1 fusions. Thus, there was a general feeling that the current story needs to conclusively account for why select conclusions from earlier publications suffered from an inherent limitation. Additional, related comments included: (1) Better verification that the IRE-HALO protein remains active during the course of the experiment, specifically in the presence of ligands/fluorophores, since some downstream events after IRE1 activation were not investigated; (2) It appears that the cell type and expression levels seem to be unchanged between this study and prior studies, but this raises the question of whether there is another confounding factor. Two other questions also arose from the reviewers/discussion, including how this model explains IRE1 activation/oligomerization induced by lipid saturation, and whether there is a way to verify what a bona fide tetramer might look like using this new system. In short, is there a control substrate that can be examined?In sum, there is a strong feeling that additional work is needed to better resolve what will be noted as a discrepancy in the field, and thus the results-if published as is-might muddy the waters.We sincerely hope that the revisions will not prove overly burdensome, especially as the conclusions are novel and the method will be welcomed by many in the cell biology field.eLife in its new form.We thank the editors and all three reviewers for their helpful comments and feedback. In the revised version of the manuscript, we have addressed all comments, performed several additional experiments, and directly addressed the question of reconciling the conclusions of this manuscript with prior work from our lab and others. We hope that the reviewers agree that the manuscript has been substantially improved by the revision process and warrants publication in Reviewer #1 (Recommendations for the authors):1. An important control that appears missing is demonstrating that the IRE1-HALO tag remains active and functional under the same conditions used in the imaging. In other words, the authors should perform experiments showing that IRE1-HALO autophosphorylation and RNase activation are not impacted by the presence of the fluorophores used to map trajectories in the microscopy experiments. The presence of large fluorescent ligands could impact IRE1 activity, so it should be clearly demonstrated that this is not the case, especially in light of the comparisons between oligomerization and activity discussed.This is a very fair point. We have repeated the key biochemical experiments under conditions identical to those used for imaging . The results, shown in the new Figure 4 —figure supplement 1, indicate that IRE1-HaloTag remains fully active under these conditions.2. Similar to the above, it would be good to have quantitation of markers of IRE1 activity  to confirm the model whereby oligomerization precedes autophosphorylation/activation. Specifically, as indicated above, under the same conditions. This does appear accurate based on the presented data, but demonstrating this more explicitly would help support the manuscript and this important point more strongly.We agree with this point. While quantification of Western blots is still inherently imprecise, we performed qPCR assays to quantitatively monitor XBP1 splicing and RIDD activity of IRE1. These data are shown in the new Figure 1 —figure supplement 2.3. I have some questions about the relationship between the work described herein and previous work monitoring IRE1 oligomerization using IRE1-GFP, specifically with respect to the mutant constructs that were overexpressed in this work. I understand the limited sensitivity of confocal made it difficult to detect small oligomers with IRE1-GFP, but I'm curious as to why the larger oligomeric clusters were not observed with the HALO-tagged IRE1 when ectopically expressed. I assume this is coming from lower levels of expression with the CMVd3 promoter, but it would be good to show that similar experiments with IRE1-GFP do not show the larger clusters observed in previous studies. Especially with respect to the fact that the model described herein contradicts these previous studies.We thank the reviewer for raising this important point. The reviewer is correct in assuming that the CMVd3 promoter generally results in low expression levels of IRE1-HaloTag upon transient transfection (which is exactly why we chose to use this promoter), but the IRE1-HaloTag construct is fully capable of forming clusters when overexpressed. In fact, even with the CMVd3 promoter, we see a small fraction of cells that express elevated levels of IRE1-HaloTag; these cells readily form IRE1 puncta upon induction of ER stress. We are showing example confocal images of these cells in the new Figure 1 —figure supplement 3.4. Lastly, the authors show that the luminal domain is primarily responsible for ER stress-dependent oligomerization of IRE1. However, lipid saturation can activate IRE1 through a mechanism that appears independent of its luminal domain. It would be good to include some potential discussion of how lipid saturation can bypass this oligomerization step to promote IRE1 oligomerization and/or autophosphorylation, especially because the presented results suggest IRE1 would be monomeric in the absence of the luminal domain.This is an interesting comment, and we actually pursued this direction briefly before submitting the initial version of the manuscript. What we found when we induced lipid stress by loading cells with palmitate was that diffusion in the ER membrane was severely slowed down, both for IRE1 molecules and control HaloTag constructs. A significant fraction of single molecules even appeared to be completely “stuck” in one place. While this is an intriguing observation, our method for detecting oligomerization is inextricably linked to measuring diffusive trajectories, so if diffusion in the ER is so massively perturbed by lipid stress, it is difficult for us to make meaningful mechanistic conclusions. Rather than presenting these confusing results to the readers, we added the following sentences to the discussion:“… Additionally, it remains to be determined how other signals ), impact IRE1’s oligomeric state and phosphorylation. Based on prior work in the field, it is likely that changes in the ER membrane’s lipid composition directly drive IRE1 molecules into phosphorylation-competent small oligomers via key features of its transmembrane helix .”Reviewer #2 (Recommendations for the authors):Determining which form(s) of IRE1a are active and inactive in cells is fundamental to understanding the Unfolded Protein Response. A single particle tracking imaging approach is highly appropriate and probably the best approach. Unfortunately, some fundamental issues significantly dampen my enthusiasm for this manuscript.We fully understand the reviewer’s concerns and believe that we have addressed them in the revised version of the manuscript, as detailed below.The main issue with the manuscript is the lack of apparent clustering of the IRE1a-Halo reporter. Given all of the other studies that observe clustering with different IRE1a reporters with different fusions in different cell types, including multiple studies from the Walter lab, the lack of clustering raises the reasonable question of: What is being studied here? The IRE1a clusters and clustering behavior described in the recent Belyy et al., study would likely produce very different % correlated trajectory results. If the results in the current study are unique to the reporter, it's unclear what the relevance is to the rest of the ER stress field. To provide confidence in this system, I think the authors need to determine 1. why there is a discrepancy and 2. which system(s) accurately report on IRE1a behavior. I appreciate that these tasks represent a significant amount of work and I am not making these suggestions lightly.As described in our response to the public review, we strongly believe that this apparent discrepancy in IRE1’s clustering behavior is rooted in its expression levels. First, the IRE1-HaloTag construct used in the present study is fully capable of assembling into stress-induced puncta when it is overexpressed . Second, conventional fluorescent protein fusion reporters do not always form clusters, as demonstrated in a parallel manuscript by Gómez-Puerta et al., (https://doi.org/10.1101/2021.11.15.468613). Third, our earlier work with an exogenously expressed IRE1-mNeonGreen construct  showed that nearly half of all cells overexpressing IRE1-mNeonGreen never form detectable clusters, despite IRE1 in these cells being fully active. We have clarified each of these points in the Discussion section, which now includes the following text:“The substantially lower expression levels achieved in this study are likely the reason that we did not observe endogenously tagged IRE1 assembling into large clusters. It is clear that the lack of large cluster formation is not a defect of the HaloTag construct, since the same construct readily assembles into stress-induced puncta when overexpressed . Conversely, we can rule out the possibility that IRE1 clustering is an artifact of fluorescent protein fusions. In a parallel study by Gómez-Puerta et al., the authors expressed IRE1-GFP in HeLa cells at near-endogenous levels and demonstrated that it remains fully active despite not forming microscopically detectable clusters (56). Furthermore, in an earlier study from our own lab, we found that nearly one half of cells overexpressing an IRE1-mNeonGreen reporter did not form clusters over the time course of stress, despite fully splicing XBP1 mRNA (22). We also do not contend that large IRE1 clusters artificially result from overexpression: IRE1 levels were found to be highly variable across a panel of cancer cell lines  and it is reasonable to suspect that large clusters of endogenous IRE1 do form in cell lines with elevated amounts of IRE1 protein.”Regarding the nature of the discrepancy, it likely revolves around the Halo Tag, the JF dyes, and/or the insertion site. Either the new reporter sets a new standard for an inert tag or the new reporter will raise a cautionary note for the Halo Tag system. One useful approach would be the addition of a minimal epitope tag to IRE1, such as an HA tag, and analysis of the reporter's behavior with immunofluorescence of stressed and unstressed cells. It is probably the most physiologic system, short of a great anti-IRE1a IF antibody. With such a reporter, please assess whether IRE1a-epitope tag clusters form in stressed and not in unstressed cells. If affirmed, then the authors will need to dissect and optimize the Halo Tag system. Note that an HA tag has been used in yeast and stress induced-Ire1 clustering was readily observed by Kimata et al. If no clusters are observed with stressed epitope-tagged mammalian IRE1a, it would be helpful for the ER stress field for the authors to determine how fluorescent proteins, even robust monomers such as mNG, impact clustering and whether a significant re-evaluation of IRE1a clustering models will be necessary.This is closely related to our response to the previous point. Since IRE1-HaloTag labeled with JF dyes is fully capable of clustering when overexpressed while IRE1-GFP can be active without clustering, we do not see this discrepancy as an issue with the tag but rather as a question of expression levels. We believe that this point is also addressed in our revised Discussion section, as cited above. Additionally, we included the following text in the Results section:“The lack of clustering was not a defect of the IRE1-HaloTag fusion construct, since overexpressing the same IRE1-HaloTag protein by transient transfection resulted in readily observed stress-induced clusters .”Reviewer #3 (Recommendations for the authors):Given that, previously, IRE1 structural rearrangements have been tied to the XBP1-splicing-versus-RIDD functionalities of IRE1, does the IRE1a-HALO activate RIDD to the same extent and with the same kinetics as endogenous?We thank the reviewer for raising this point. In response, we carried out qPCR experiments on both IRE1-HaloTag clones and showed that they are capable of RIDD activity in addition to XBP1 splicing. Please see the new Figure 1 —figure supplement 2 for details.The tracking of multimerization through movement raises two related questions. One is whether the automated visualization is then biased for movement, such that, if higher ordered structures of IRE1 were relatively immobile, they would be missed. The flip side of this coin is that, presumably, embedded within the already-existing data is information on whether ER stress changes the mobility of IRE1, comparing correlated tracks to non-correlated tracks, and comparing correlated tracks in the presence and absence of stress.This is yet another great question. Inspired by the reviewer’s suggestions, we looked more carefully at our existing single-particle data and showed that ER stress does not induce significant changes in IRE1 diffusion . The relevant data are shown in the new Figure 3 —figure supplement 4. The reviewer is correct that very large immobile structures would be missed by our approach. However, we see no evidence for the existence of such structures in our data either by visual inspection of microscopy images or by quantitative analysis of single-particle diffusive trajectories.


Human papillomavirus (HPV) is a common infection principally spread through sexual activity. Most HPV infections are asymptomatic and resolve spontaneously. However, persistent infection may progress to cervical cancer. Highly efficacious HPV vaccines have been available since 2006, yet uptake into national programs has been slow in part due to cost. WHO guidelines call for a two-dose  schedule for girls 9–14 years of age. Post-hoc analyses of randomized trials have found high vaccine effectiveness following a single dose of vaccine. In order to provide additional data on the potential impact of single dose HPV vaccination in a real-world setting, we are conducting an effectiveness study among Thai schoolgirls. This is an observational study of a single dose (SD) or two doses (2D) of the bivalent HPV vaccine CERVARIX® (GlaxoSmithKline plc.) administered in a school-based program to 8–9,000 Grade 8 female students in two provinces of Thailand beginning in 2018; one province is assigned the SD, and the other the standard 2D regimen. The reduction in HPV vaccine-type prevalence will be assessed in each province two and four years after vaccination by comparing HPV prevalence in urine samples obtained through cross-sectional surveys of the immunized grade cohort as they age and compared to a historical “baseline” HPV prevalence of same age students. Human Papillomavirus (HPV) infection of the reproductive tract is common, principally spread through sexual activity. The majority of HPV infections are asymptomatic and resolve spontaneously. However, persistent infection with specific HPV types (most frequently HPV 16 and 18) may lead to precancerous lesions which if untreated may progress to cervical and some other cancers . As a reAll available HPV vaccines are virus-like particle (VLP) vaccines and have been shown to have high efficacy in randomized controlled trials . CERVARIDespite reductions in the cost of these vaccines through the Global Alliance for Vaccine and Immunization (GAVI) or tiered-pricing, the uptake of HPV vaccines into national programs has been slow particularly in low- and middle-income countries (LMIC) . BarrierPost-hoc analyses of randomized trials have found high vaccine effectiveness following a single dose (SD) of bivalent or quadrivalent vaccine –9. HowevSD HPV vaccination would enable twice as many female students to be covered with the same amount of vaccine supply, facilitate catch-up vaccination campaigns, and alleviate the overall economic cost of vaccine supply and delivery, while averting treatment costs of disease. WHO acknowledges that more data are needed to issue such a recommendation .In order to provide additional data on the potential impact of SD HPV vaccination in a real-world setting, we are conducting an effectiveness study of the SD and the standard two-dose (2D) regimens in two provinces in Thailand. Vaccination is administered in schools, mirroring the already implemented national Grade 5 HPV immunization program, but targeting Grade 8 females outside the grade eligibility.Primary objectivesDemonstrate effectiveness of SD HPV vaccination by a reduction in vaccine-type HPV prevalence (HPV16 and/or 18) at two and four years post vaccination compared to the prevalence among unvaccinated same grade female students collected in a baseline survey.Demonstrate effectiveness of SD and 2D HPV vaccine regimens are similar by comparing reductions in vaccine HPV-type prevalence at four years post vaccination compared with the baseline prevalence in the two provinces.Secondary objectivesEstimate prevalence of HPV infection in female students in high school Grades 10 and 12  and vocational school years 1 and 3 Estimate the distribution of HPV types detectedAssess HPV type-specific antibody response pre- and post-vaccination in a subset of participantsExploratory objectivesAssess comparability of sexual behavior between the two provinces among female students in Grades 8, 10 and 12  and vocational school years 1 and 3 Assess comparability of risk for HPV infection between G10/V1 and G12/V3 female students at baseline and at years 2 and 4 post vaccinationAssess possible herd protection conferred by the HPV vaccination in unvaccinated female students“Effectiveness of Single Dose or Two Doses of Bivalent HPV Vaccine in Thailand (IVIHPV1)” and identifier NCT03747770. Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the The protocol was first approved by the IVI IRB (IVI HPV1 2018–005 V1.3) on 20 Nov 2018, the Thailand Ministry of Health EC  on 29 Nov 2018, and the Chulalongkorn University IRB (IRB No. 495/61 V1.0) on 4 Dec 2018. The US CDC IRB deferred to the IVI IRB 7 May 2019. This paper is based on the protocol version 6.0 dated 15 Dec 2021 This is an observational community effectiveness study of SD or 2D of CERVARIX® (containing HPV16 and HPV18) administered in a voluntary school-based program to Grade 8 female students less than age 15 years in two provinces of Thailand, Udon Thani and Buri Ram. The overall study is divided into four independent parts:Part A–Grade 8 vaccination and sexual behavior questionnaire (SBQ)Part B–Baseline cross-sectional survey (CSS) of HPV prevalence based on self-collected urine samples and SBQ (G10/V1 and G12/V3)Part C–CSS of HPV prevalence based on self-collected urine samples and SBQ two years after vaccination (G10/V1)Part D–CSS of HPV prevalence based on self-collected urine samples and SBQ four years after vaccination (G12/V3)Vaccine effectiveness will be assessed as the reduction in vaccine HPV-type infection measured in a CSS two and four years after vaccination, compared to the Baseline CSS. A subset of students in each province (N = 200) will be invited to participate in a sub-study to evaluate immune response to vaccination. This study will be conducted in two provinces in North-East Thailand, Udon Thani and Buri Ram, selected by the Ministry of Public Health based on the similarity of criteria such as size of the female student population, socio-economic parameters, comparability of self-reported sexual activity in a national survey, logistics feasibility, and high degree of acceptance of the Grade 5 HPV vaccine program by local authorities and communities. Each province is further sub-divided into Districts, each with a District Hospital and subordinate Health Centers . This puAll aspects of recruitment and enrollment will be conducted according to ICH E6(R2) GCP guidelines. For each of the activities described below, eligibility is assessed according to the inclusion and exclusion criteria . ParentsOpen label vaccination with either SD (Udon Thani) or 2D regimen (Buri Ram) are offered to all Grade 8 students younger than age 15 years between December 2018-February 2019 on a voluntary basis. A subset of 1500 students in each province are selected by a proportional and systematic sampling method and invited to complete the SBQ. For Grade 8 students less than 15 years of age, both a parental consent and student assent form is completed prior to enrollment for vaccination, and a separate assent form is obtained for the SBQ.Cross-sectional surveys (CSS) are intended to sample a representative subset of students in the province, and all schools willing to participate are included in the surveys. As sexual activity is expected to be higher among vocational students , and the overall prevalence of vaccine-type HPV infections is expected to be low, vocational students will be over sampled to achieve ~50% of all enrolled subjects. Based on the overall province and school-type sample size, school-specific target enrollments will be calculated to be proportionate to their grade enrollments. At each school, students will be selected to participate in the CSS based on a systematic sampling from an eligibility list. When selected students are either not eligible because of exclusion criteria or decide not to participate in the study, additional students are selected by the field staff using the same method until the school target enrollment is reached. If the school target enrollment cannot be met, study staff can over enroll at another school of the same type in order to meet the province target enrollment. In order to ensure student confidentiality, only student assent is required for enrollment in the CSS and SBQ. In the Year 2 and Year 4 CSS, the student vaccination status is verified and documented at the time of enrollment.For practical purposes, a non-random subset of 200 Grade 8 students in each province consenting to vaccination and living in close proximity of a District Hospital will be invited to participate in this sub-study. A blood sample will be taken prior to vaccination and at the time of the Year 2 and Year 4 CSS. Eligibility criteria are re-assessed by District Health staff at the time of blood sample collection. Both parental consent and student assent forms are completed for each blood draw prior to vaccination, and at G10/V1 and G12/V3.Parents or guardians, and participants are informed that participation in each activity is voluntary and that they are free to withdraw their consent or assent at any time upon request, without justification and without prejudice. Subjects who withdraw may request destruction of samples and deletion of their data. The Principal Investigator may also discontinue participation in case of any occurrence that in her/his judgment might be detrimental to the participants well-being, and the reason for their withdrawal will be recorded.CERVARIX® is a bivalent HPV vaccine manufactured by GlaxoSmithKline plc. and presented as a suspension containing purified viral L1 protein for HPV types 16 and 18 and administered by intramuscular injection. It is produced using a baculovirus expression system. Each 0.5 mL dose of the bivalent vaccine contains 20 μg of HPV16 L1 protein and 20 μg of HPV18 L1 protein formulated with AS04  recorded through the national surveillance system and reported as per national guidelines. The investigators will exercise due diligence in ascertaining, and accurately recording all reportable AEFI that vaccinated study participants may experience. Reported AEFI are linked to the study ID of the students. AEFI may also be reported to the MOPH EC as per local regulations.Urine samples are self-collected by the students at school using a commercially available urine collection device, Colli-Pee®  pre-labeled with coded subject ID. The device contains 7 ml of UCM preservative and collects up to 20 ml of the initial void of urine. This collection method has been successfully used in Bhutan and Rwanda for HPV prevalence assessments, and demonstrated a high degree of comparability to physician-collected cervical swabs in a pilot study in Thailand –18. UrinCobas® (Roche), a high throughput, qualitative assay system will be used for detection of HPV16 and 18 and 12 other HPV types  –21. ResiThe purpose of the immunogenicity study is to verify that the vaccine administered is immunogenic for HPV16 and 18 and to assess response to non-vaccine types  with potential cross-immunogenicity. Blood (5 ml) will be collected via antecubital venipuncture by trained phlebotomist into serum tubes and allowed to clot at room temperature. Sera will be aliquoted into 1 ml cryovials and stored at -70°C and shipped on dry ice to the US CDC laboratory for testing using multiplex direct IgG ELISA against L1/L2 HPV virus-like particles on the Meso Scale Discovery platform with chemiluminescent detection as described with minor modifications .The primary outcome is the vaccine effectiveness, or reduction in vaccine-type HPV prevalence (HPV16 and/or 18) as measured by Cobas® in a single time point urine sample collected through a CSS two and four years after vaccination compared to unvaccinated same grade female students from the Baseline CSS in each of the provinces. Four years after vaccination a non-inferiority comparison of SD and 2D vaccine effectiveness will be made in the SD and 2D provinces. As the analysis will use a non-contemporaneous control group (Baseline survey), and the composition of the groups and/or their sexual behavior may differ, an adjusted analysis will be conducted to account for imbalances in the populations between the two time points within the province, and between the provinces. The main indicator for imbalance in sexual behavior will be the prevalence of 7 non-vaccine, non-cross-reactive high-risk HPV types .The assumed prevalence of HPV16 and/or 18 in G10/V1 and G12/V3 female students in the two provinces for Year 2 and Year 4 CSS are 2% and 3%, respectively. Due to lack of data on HPV prevalence in young female students in Thailand and the uncertainty of our assumption, the sample size for Year 2 and Year 4 CSS will be re-assessed based on the actual prevalence of HPV infection in G10/V1 and G12/V3 from the Baseline CSS prior to the Year 2 CSS. Assuming a 90% vaccine coverage of Grade 8 students, the sample size of N = 2600 per province at Year 2, and N = 2,000 per province at Year 4 are calculated to provide >80% power to show the vaccine effectiveness of SD or 2D is greater than 50% using one-sided test at a 0.025 significance. At four years post vaccination, this sample size is calculated to provide 80% power to demonstrate the VE of SD is non-inferior to VE of 2D with non-inferiority margin of 10% using one-sided test at 0.025 significance level.For the assessment of antibody response to the vaccine, the sample size N = 200 per province is based on keeping the coefficient of variation (CV) of mean titer of HPV16 or HPV18 less than 2.5, so that the geometric mean titer (GMT) estimate can be compared between provinces.Vaccination. A limited amount of data is collected on a field source document including name, national ID, date of birth and date of vaccination. Each student agreeing to vaccination (or any other study activity) is assigned a pre-printed subject number in numerical and bar-code format which codes for province, school, a unique number, grade, and activity type . The pre-printed barcodes are affixed to the consent and assent documents and the source document, which are maintained only by the MOPH staff. Only the complete subject number, date of birth, date of consent or assent and date of vaccination are entered into the EDC system by study staff.Cross-sectional surveys. Each student’s assigned subject number in numerical and bar-code format on pre-printed barcodes is affixed to documents and urine sample containers. The SBQ is a web-based, self-administered questionnaire with a limited set of questions derived from the MOPH national survey regarding sexual behavior. Using their own cell phone or via a tablet or phone provided by study staff, students scan their assigned subject number barcode at a weblink and privately enter their responses independent of study or school staff.Serology. The same enrollment procedures are followed as described above. The blood sample collection is performed at the local health center.Vaccination. Vaccine coverage is the estimated percentage of female students who have received an HPV vaccination. Two types of vaccine coverage will be calculated in this study:Total vaccine coverage of Grade 8 schoolgirls: the total number of Grade 8 students that received at least one dose of the study HPV vaccine, or self-report previously receiving any HPV vaccine/ the total number of students in Grade 8 in the provincesPer protocol vaccine coverage of Grade 8 schoolgirls: the number of students in Grade 8 enrolled in the vaccination activity that completed the HPV vaccine regimen per protocol/ the number of eligible Grade 8 schoolgirlsOnly students vaccinated per protocol will be eligible to enroll as vaccinated students in the Year 2 and Year 4 post-vaccination CSS. The AEFI as defined in the Thailand national guidelines will be summarized as reported after each vaccination and analyzed descriptively.HPV prevalence. Cobas® results for vaccine-type HPV16 and 18 prevalence and Anyplex™ results for prevalence of HPV types 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68 (among Cobas-positive samples) will be reported by grade and by school type. The prevalence of HPV types 6, 11, 26, 40, 42, 43, 44, 53, 54, 61, 69, 70, 73, 82 will be calculated based on the Anyplex™ results of Cobas-positive samples, and of a subset of Cobas-negative samples, in order to estimate the frequency of these types in the Cobas-negative population. Because the sampling by school type is not proportional to enrollment, the HPV prevalence by province will be reported as a weighted prevalence of the two school types in the total population using Horvitz-Thompson adjustment [justment .Vaccine Effectiveness (VE). The crude VE is defined as the percent reduction of HPV16 and/or 18 prevalence in a vaccinated group compared to an unvaccinated group and given by:p1 is the prevalence of HPV infection (HPV16 and/or 18) at Year 2 or Year 4 post vaccination and p2 is the prevalence of HPV infection (HPV16 and/or 18) of unvaccinated students in the Baseline CSS in the corresponding grade and province.p1 and p2 denote the weighted HPV 16 and/or 18 prevalence rates in the Baseline and Year 2 and Year 4 surveys, respectively. Similarly, q1 and q2 denote the weighted overall prevalence rates for the non-target genotypes in the Baseline and Year 2 and Year 4 surveys, respectively. The VE will also be adjusted using differences in the proportion of self-reported sexual activity from the SBQ.In addition, an adjusted VE will be computed by three methods as part of a sensitivity analysis and to account for the potential confounding effects of imbalance in age distribution and sexual activity between the non-contemporaneous groups. First, a simple adjusted VE will be calculated which adjusts only for the relative proportion of the 7 non-target HPV types  with no evidence of vaccine cross protection using the formula:Finally, a propensity score will be computed from a logistic regression that contained age and binary test result of each of 7 non-target HPV types as covariates. VE stratified by quintiles of propensity scores will be computed.Non-inferiority test. The VE difference between the SD and 2D regimens (SD minus 2D) with the corresponding 95% confidence intervals (CI) will be calculated to test the VE of SD is non-inferior to VE of 2D with non-inferiority margin of 10% at Year 4.Herd protection. This exploratory analysis will only be conducted if there is a sufficient number of unvaccinated students who enroll in the Year 2 and Year 4 surveys. The crude herd protection (HP) will be the percent reduction of HPV16 and/or 18 prevalence among unvaccinated students at Year 2 or Year 4 post vaccination as compared to the prevalence in the corresponding grade and province in the Baseline CSS. An adjusted HP will be calculated using the same methods as for adjusted VE.Serology. The HPV type-specific antibody titers at Baseline, Year 2 and Year 4 post vaccination will be logarithmically transformed to better approximate normality and reported as a weighted GMT average with two-sided 95% CI by province.CI for a single proportion will be defined using the Wilson method and a CI for a difference between independent proportions will be computed using the extension of the Wilson method and CI for a ratio of proportions will be derived using the delta method , 27. AllThe VE will be calculated using only subjects who met all protocol requirements, received the designated vaccine regimen in G8 and enrolled in a later CSS two and/or four years after vaccination. The unvaccinated Baseline CSS data set will be restricted to include only subjects who based on their date of birth would have been eligible to receive vaccine (<15 years of age) in G8 so that the vaccinated set and the unvaccinated comparator set have the same age distribution.Since the vaccine is already in use in the national immunization program and administered within the approved age range, the establishment of a Data and Safety Monitoring Board (DSMB) is not required. Because the schoolgirls in one province are offered a reduced dosing schedule, a futility assessment will be performed after the Year 2 survey with pre-determined criteria that, if met, would indicate no public health value to the SD regimen. If the futility criteria are met, a second dose of HPV vaccine will be offered to the vaccinated students in Udon Thani.The futility criteria for considering the administration of a second dose of vaccine are:The estimated effectiveness of two doses after cross-sectional survey in Buri Ram must have a lower bound >50% ANDThe estimated effectiveness of single dose after Year 2 cross-sectional survey in Udon Thani must have an upper bound of <50%, ANDBe inferior to 2D with a margin of >25% difference in vaccine effectiveness.The sponsor has contracted independent local clinical monitors to ensure that the study is conducted according to protocol, standard operating procedures, the principles of GCP, and to verify that investigators are collecting and reporting quality data. During periods of field activities in the year of vaccination and Baseline CSS, and during the Year 2 and Year 4 CSS, the monitors are present at site to observe the informed consent process, including 100% review of ICFs and assent forms, and to verify adherence to study protocol. Data quality is further verified by monitors through direct source document verification against data in the electronic data capture system of at least 10% of randomly selected study participants.All study-related information and data is being securely managed including use of physical security (locked rooms and cabinets) for source documents in Thailand, and secure servers for storage of digital data. The primary data to be used for analysis is located on a server at IVI, however a duplicate set of data is stored in Thailand. Only the PI and limited MOPH staff have access to personally identifiable information, and all data stored at IVI and Chulalongkorn University use only study ID number or a laboratory code number as an identifier. The serum samples, labeled with a laboratory code, will be sent outside the country for analysis at the US CDC. Both urine and serum samples will be stored for five years after study completion.Upon completion of each phase of the study , an analysis will be conducted, and a study report produced. The report will be submitted to the competent authority and ethics committees according to national regulations and publication sought in peer-reviewed journals. In order to shorten the time between availability of data and public health decision-making, study reports will be made available to national (Thailand) and international (WHO) public health policy makers. We will report the study in accordance with the Consolidated Standards of Reporting Trials guidelines, with authorship based on the ICMJE recommendations.S1 Checklist(DOC)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file. 6 Apr 2022A community intervention effectiveness study of single dose or two doses of bivalent HPV vaccine (CERVARIX®) in female school students in ThailandPONE-D-22-06291Dear Dr. Lynch,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.As a Peer-Reviewed Funded Protocol, your submission is eligible for expedited review by in-house editors. Based on our evaluation, we are satisfied that your manuscript reports a protocol for a clinical trial that complies with our Study Protocol and Clinical Trial policies. Your Study Protocol is therefore considered to be suitable for publication subject to final journal requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. 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The ataxia-linked protein sacsin has three regions of partial homology to Hsp90’s N-terminal ATP binding domain. Although a crystal structure for this Hsp90-like domain has been reported the precise molecular interactions required for ATP-binding and hydrolysis are unclear and it is debatable whether ATP biding is compatible with these domains. Furthermore, the Identification of a sacsin domain(s) equivalent to the middle domain of Hsp90 has been elusive. Here we present the superimposition of an AlphaFold structure of sacsin with yeast Hsp90, which provides novel insights into sacsin’s structure. We identify residues within the sacsin Hsp90-like domains that are required for ATP binding and hydrolysis, including the putative catalytic arginine residues equivalent to that of the Hsp90 middle domain. Importantly, our analysis allows comparison of the Hsp90 middle domain with corresponding sacsin regions and identifies a shorter lid segment, in the sacsin ATP-binding domains, than the one found in the N-terminal domain of Hsp90. Our results show how a realignment of residues in the lid segment of sacsin that are involved in ATP binding can better match equivalent residues seen in Hsp90, which we then corroborated using molecular dynamic simulations. We speculate, from a structural viewpoint, why some ATP competitive inhibitors of Hsp90 may not bind sacsin, while others would. Together our analysis supports the hypothesis that sacsin’s function is ATP-driven and would be consistent with it having a role as a super molecular chaperone. We propose that the SR1 regions of sacsin be renamed as HSP-NRD  and the fragment immediately after as HSP-MRD . Mutations which lead to loss of function of the protein sacsin cause the neurodegenerative disorder Autosomal Recessive Spastic Ataxia of Charlevoix Saguenay or ARSACS . AlthougSacsin is an extremely large  multidomain protein that is conserved through vertebrate evolution . There iThe domain structure of sacsin provides a link to both the ubiquitin proteasome system and molecular chaperones, suggesting a potential function in protein quality control systems. Although there is some evidence supporting this , the preOne possibility is that sacsin functions directly as a super molecular chaperone and if this is the case its three regions of homology to Hsp90  could indicate its action, like Hsp90, is driven by ATP binding and hydrolysis. Currently, sequence alignment and crystallographic analysis have shown that the sacsin SR1 has structural similarity to the Bergerat protein fold of Hsp90, which forms a nucleotide binding pocket from a sandwich of four β-sheets and two α-helices . HoweverTo resolve this controversy and better understand the molecular function of sacsin we hypothesised that recent advances in protein structural modelling could be exploited to identify key residues within sacsin that could mediate ATP binding and catalysis.We hypothesis that sacsin is an ATP-dependent super molecular chaperone. This theory is based on an analysis showing that the structure of sacsin is compatible with the known mechanism for ATPase hydrolysis by Hsp90, although this may not extend to dimerization of the ATP binding domains of sacsin, as observed with Hsp90. We propose that a unique piece of structure, as part of the HSP-N-terminal Repeat Domain (HSP-NRD), not found in the middle domain of Hsp90, may provide stability for closure of the lid over bound ATP.Recently the structure of a region of human Sacsin (amino acids 1–177) has been determined using AlphaFold . Using tvia a water molecule to Asp 79 and to a nitrogen atom of the adenine ring of ATP. Thus, the side chain of valine, would point away from the bound ATP and would not therefore interfere with its binding.Using the structurally aligned superimposition of Hsp90 and sacsin, we next compared the amino acid residues of Hsp90 involved in binding and hydrolysis of ATP with the peptide sequence of sacsin . Using tOther main chain contacts between Hsp90 and ATP include Val 122 that would allow substitutions in sacsin and Gly 100 which is replaced by Lys 180 in sacsin. Finally, the main chain of Hsp90 Gln 119 contacts ATP and this interaction could be maintained with the substitutions seen in sacsin . On closer inspection, the greatest variability in ATP binding residues between Hsp90 and sacsin are those that occur on the lid region of the SR domains. We noted that the AlphaFold model confidence for the lid structure of sacsin was low. Nonetheless the sacsin lid structure is significantly shorter than that of Hsp90 . Sacsin Using molecular dynamics (MD) simulation see . These rOn closer inspection we find that ATP particularly interacts with sacsin residues such as Asn 120, Asn 160, Phe 164, Asp 168, Ser 178, Lys 180, Arg 189, Phe 190, Gly 191, Ile 192, Gly 193 and Phe 194 through both hydrogen bonds, ionic interactions, and hydrophobic interactions. Details of single established interactions are reported in . Asn 120In the AlphaFold analysis we previously predicted that sacsin Phe 164 aligned with Hsp90 Met 84, allowing Phe 164 to perhaps pi-stack with the adenine ring of ATP. The MD simulation shows indeed that this is the case . AnotherFinally, in Hsp90 Asn 92 forms a direct interaction with the 3′ hydroxyl group of the pentose ring of ATP . HoweverWith the N-terminal domain of Hsp90 correctly superimposed onto sacsin SIRPT-SR1, we were able to locate the putative catalytic arginine required for ATPase activity in the downstream (SIRPT1) region of sacsin, as observed in the Hsp90’s middle domain . We wereIn order to obtain the best superimposition of the middle domain of Hsp90 with the equivalent region of sacsin, an orientation of the Hsp90 middle domain (residues 262 to 444 used) was required. The middle domain of Hsp90 contains a 7-stranded β-sheet, whereas sacsin contains a continuous 13-stranded β-sheet that runs from the SR1 domain and into the downstream SIRPT1 region, of which the last 5 β-strands appear to form the central core of the domain . To get Further analysis of the C-terminal domain of Hsp90 with topologically corresponding regions of sacsin, did not appear to show any structural homology . We therin vitro ATPase assay which saw no effect of geldanamycin or radicicol on activity of a region of mouse sacsin from the N-terminus of the protein to the beginning of the second SIRPT domain  . Given tT domain . To provT domain . In Hsp9T domain . This suMD simulations for the sacsin-geldanamycin complex were carried out as described for the ATP bound structure of the Hsp-NRD of sacsin see .In order to investigate the motions of the loop in our simulations, we carried out clustering analysis on the entire trajectory. We observed that the loop explores virtually a single conformation, closed over the ligand, during the simulation time . OtherwiResults from the protein-ligand interactions analysis highlight that the most recurring interaction is a hydrogen bond between the ligand’s hydroxyl and the lid residue Lys 186, which is conserved during the majority of the simulation (90% of simulation time) . AnotherMD simulations for the sacsin-AUY922 complex were also carried out as described for the ATP bound structure of the HSP-NRD of sacsin . In ordeProtein-Ligand interactions analysis identify four key interactions established by AUY922 in the binding site. These interactions are categorized by type and summarized in In conclusion, it appears that sacsin is able to bind and hydrolyse ATP because a full complement of the machinery required for binding and catalysis is present. We see a few non-conserved positions, but the models explain how these residue differences with Hsp90 can still interact with ATP. In contrast, we are less certain if the Hsp90 ATPase inhibitors, geldanamycin and AUY922, are able to freely bind the Hsp-NRD’s of sacin.In order to investigate whether sacsins HSP-NRD could dimerise, as seen with the N-terminal domains of Hsp90, we used AlphaFold to generate models using Colab’s accelerated prediction from within ChimeraX . We founThis analysis suggests that sacsin contains three Hsp90-like N-terminal- and middle-domains, which we designate the HSP-NRD and HSP-MRD segments. We hypothesise that together they are responsible for an ATPase activity. Our alignments suggest that the lid segment of sacsin is about 12 residues shorter than for Hsp90 and this consequently results in some alteration of residues at specific positions that are required to maintain ATP binding. This is very apparent for the invariant Gly 100 of Hsp90, which is replaced by Lys 180 in SIRPT1-SR1 of sacsin. ARSACS causing mutations are found in the predicted ATP binding regions of sacsin, including mutations that would be predicted to specifically inhibit ATPase activity . This supports the notion that sacsin requires ATP activity for its function.The ATPase activity of Hsp90, and thus its function, is partly determined by dimerization mediated through the C-terminal domain . Crystalin vivo, with the SIRPT1 and the J-domain were having opposing effects, by respectively promoting and preventing filament assembly. The intermediate filament phenotype of motor neurons from the sacsin knockout mice was also altered by expression of the sacsin SIRPT1 or J-domain, with partial resolution of existing neurofilament bundles. That these isolated domains of sacsin influence neurofilament organisation is perhaps surprising but would again be consistent with the full-length protein functioning as a chaperone for intermediate filament assembly or disassembly.It is a common feature of chaperone machines that they are driven by ATP binding and hydrolysis to assist protein folding and unfolding . TherefoHsp90 works with other chaperones and cochaperones as part of a larger protein folding and remodelling machinery. Of particular importance is Hsp90’s collaboration with Hsp70 in protein folding and other chaperone functions . The preFinally, our analysis indicates that for the Hsp90 inhibitors we investigated, which target its ATP-binding site, AUY922 may bind sacsin, whereas geldanamycin may not. This is important as these drugs are in clinical trials as therapeutics for cancers and other diseases, such that off target effects on sacsin would be undesirable. However, further work is required to establish whether sacsin is a target of specific Hsp90 ATPase inhibitors.


Morbidity and mortality risk prediction tools are increasingly being used as part of preoperative assessment of patients presenting for major abdominal surgery. Cardiopulmonary exercise testing (CPET) can predict which patients undergoing major abdominal surgery are at risk of complications. The primary objective of this study was to identify preoperative variables including those derived from CPET, which were associated with inpatient morbidity in high-risk patients following major abdominal cancer surgery. The secondary objective was to use these variables to derive and validate a morbidity risk prediction tool.We conducted a retrospective cohort analysis of consecutive adult patients who had CPET as part of their preoperative work-up for major abdominal cancer surgery. Morbidity was a composite outcome, defined by the Clavien-Dindo score and/or the postoperative morbidity survey (POMS) score which was assessed on postoperative day 7. A risk prediction tool was devised using variables from the first analysis which was then applied prospectively to a matched cohort of patients.p < 0.01) were anaerobic threshold (AT), maximal oxygen consumption at maximal exercise capacity (VO2 max), and ventilatory equivalent for carbon dioxide at anaerobic threshold (AT VE/VCO2). In addition to the CPET findings and the type of surgery the patient underwent, eight preoperative variables that were associated with postoperative morbidity were identified. These include age, WHO category, body mass index (BMI), prior transient ischaemic attack (TIA) or stroke, chronic renal impairment, diabetes mellitus, chronic obstructive pulmonary disease (COPD), and cancer stage. Both sets of variables were then combined to produce a validated morbidity risk prediction scoring tool called the Marsden Morbidity Index. In the second phase of the analysis, this tool was applied prospectively to 424 patients between June 2017 and December 2018. With an area under the curve (AUC) of 0.79, this new model had a sensitivity of 74.2%, specificity of 78.1%, a positive predictive value (PPV) of 79.7%, and a negative predictive value of (NPV) of 79%.A total of 1398 patients were included in the first phase of the analysis between June 2010 and May 2017. Of these, 540 patients (38.6%) experienced postoperative morbidity. CPET variables deemed significant (Our study showed that of the CPET variables, AT, VO2 max, and AT VE/VCO2 were shown to be associated with postoperative surgical morbidity following major abdominal oncological surgery. When combined with a number of preoperative comorbidities commonly associated with increased risk of postoperative morbidity, we created a useful institutional scoring system for predicting which patients will experience adverse events. However, this system needs further validation in other centres performing oncological surgery. As the population becomes more elderly, the incidence of cancer increases concomitantly with other comorbidities . The criteria for CPET in patients scheduled for major oncological abdominal surgery at the Royal Marsden NHS Foundation Trust are as follows:≤ 4 metabolic equivalents*Significant cardiorespiratory comorbiditiesDo not meet any of the contraindications to CPET as stated by our institutional policy which is in line with the national guidance   prior to abdominal cancer surgery. They all had a CPET to assess their functional capacity, which was used to assess fitness for surgery and decision-making. Postoperatively, all patients were admitted to an intensive care or high dependency unit. Preoperative data was extracted from the electronic patient record (EPR) by the institution’s information support team who were not involved in the study. Preoperative comorbidities were documented in the patient’s EPR by a nurse and/or doctor at the pre-assessment and were collected based on the ICD-10  complication grading system  7Morbidity was a composite outcome defined as follows:A CD score ≥ grade 3 and/or POMS > 1 on POD 7 during the patient’s inpatient hospital stay were both classed as clinically significant complication  and American College of Physicians (ACCP)  for a variable to be included in the fitted multivariate model. Predicted probabilities were obtained for the primary and temporal validation datasets using the post estimation commands of the analysis software, which were then categorised at a cut-off point of 0.5 and summarized into binary classification table (observed and predicted morbidity) for a sensitivity analysis. Sensitivity, specificity, negative and positive predictive values, and classification accuracy of the model were calculated in the primary and validation datasets. Similarly, ROC (receiver operator characteristic) were fitted and AUC (area under the curve) values obtained. A nomograph was utilized to assign scores to the significant variables in the fitted multivariate logistic regression model to produce a graph that can be used clinically to identify the patient with high probability of morbidity risk. The preoperative status of the patients is applied against the nomograph variables to obtain the total scores, which can then be converted to probability to determine their morbidity risk level. The STATA version 13 was used for analysis  were excluded from the analysis. One-hundred and four (n = 104) patients were deemed too high risk for surgery following pre-assessment and CPET, and their CPET data is included in the Table 8 in n = 53) patients were excluded because of insufficient datasets. The remainder of patients (n = 458) was excluded for other reasons with the most common being decision for alternative oncological option (not related to fitness for surgery) and disease progression whereby surgery was not appropriate and surgery was not performed .Six-hundred and fifteen patients (Patient demographics and perioperative characteristics are summarised in Table Five-hundred and forty patients (36.8%) experienced postoperative morbidity, a composite outcome defined as Clavien-Dindo score of ≥ 3 and/or the score for POMS in POMS-defined morbidity on POD 7. The CPET variables on univariate analysis associated with postoperative morbidity were AT, VO2 max, AT VE/VCO2, and oxygen pulse  was used to fit the multivariate model. Table Binary logistic regression analysis method was used in the univariate and multivariate settings to identify the morbidity risk factors. Backward stepwise method using  scored POMS ≥ 1 on POD 7, 46 (8.5%) experienced a ≥ 3 CD complication, and 81 (15%) had both a POMS ≥ 1 and experienced a ≥ 3 CD complication (Table 2) of 0.59. This shows that when the significant CPET variables on multivariable analysis are removed from the model, its predictive and discriminatory ability for serious morbidity is markedly reduced. This supports the value of CPET variables as part of a risk predictor for morbidity for our patient cohort.Table IQR: 68–69)  validation. The median age of these patients was 68 years , CPET-generated data , pre-existing comorbidities , and whether a minimally invasive or an open approach was undertaken. These variables were shown to have good strength in discriminating postoperative morbidity in a prospective group of major abdominal cancer surgical patients. Using a scoring system based on the significance of each of these variables on postoperative morbidity, a simple risk scoring system called the “Marsden Morbidity Index” was devised. This index can be used in our institution to predict morbidity in patients scheduled for major abdominal surgery as a means of aiding decision-making, consent, and resource allocation. These variables were broadly reflective of the functional measures which deemed the need for CPET prior to surgery in this patient cohort.p < 0.001) variables at the multivariate analysis level and predictive of poor surgical outcomes. In the perioperative context, both have been shown to be strong predictors of postoperative complications and mortality in a number of cohorts analysing outcomes post major abdominal and thoracic surgery  post elective colorectal resection.The CPET variables we found to be associated with morbidity were in keeping with findings from previous studies where CPET was evaluated as a risk prediction tool in major abdominal surgery . Similar to our study, CPET was applied in patients deemed high risk, and POMS scores were used to assess postoperative morbidity. An AT VE/VCO2 of ≥ 34.5 ml/kg/min was found to have a specificity of 84% and a sensitivity of 47%, with a PPV of 76% and an NPV of 60%, for POMS-defined morbidity.In our analysis, VE/VCO2 at anaerobic threshold had the strongest weighting in the model for postoperative morbidity in major abdominal surgery. This is a measure of ventilatory efficiency and is elevated in conditions such as heart failure, pulmonary embolism, and chronic lung disease  was a significant CPET variable associated with postoperative morbidity in this analysis of high-risk patients undergoing major abdominal cancer surgery. This is consistent with one of our previous studies that demonstrated VO2 at AT < 10.2 ml/kg/min as a significant predictor of POMS-defined morbidity on POD 3 in patients undergoing major hepatic resection  and chronic conditions . The majority of these variables have been strongly validated in multiple risk prediction scores, like CHA2 max, are highly correlated with muscle mass  when compared to minimally invasive surgery, i.e. robotic assisted or laparoscopy. These findings are in keeping with the literature where the unique benefits and superiority of minimally invasive procedures over open procedures in selected patients have been shown . This makes it one of the largest published datasets looking at the association of CPET on postoperative surgical morbidity. This was a strongly validated study, and the result reflects the high-risk cohort of patients that present to the Royal Marsden Hospital as a tertiary oncological centre.The strength of this study is the large number of “high-risk” cancer patients that were studied (AUC 0.79) in the prospectively studied population. This suggests that despite a number of important factors not being accounted for in the preoperative variables, the model is a strong tool for our population. We would be interested in implementing its use in our institution which may provide further validation of the data.One of the major limitations of our study is that only high-risk patients based on our institutional criteria had CPET. The ideal study design would be that all patients had CPET to limit bias in the population studied. The risk calculator is thus only valid on our high-risk patient cohort. Nonetheless, when looking at real-world use of CPET, most published data is from a high-risk cohort of patients extracted from a general surgical population. The authors chose a POMS score of > 1 on POD 7, based on similar studies using POMS scores on this day as their preferred measure to discriminate morbidity in similar major surgical cohorts (Wong et al.