This is achievable by ultrafiltration or polyethyleneglycol precipitation but often at the expense of increasingstructural aggregation due to prolonged centrifugation and final redispersionin low buffer volumes. Here, we show that lyophilization and subsequentredispersion in low buffer volumes can achieve high concentrationsof DNA origami while drastically reducing aggregation due to initiallyvery low DNA origami concentrations in low salt buffers. We demonstratethis for four structurally different types of three-dimensional DNAorigami. All of these structures exhibit different aggregation behaviorsat high concentrations , which can be drastically reduced by dispersionin larger volumes of a low salt buffer and subsequent lyophilization.Finally, we show that this procedure can also be applied to silicifiedDNA origami to achieve high concentrations with low aggregation. Wethus find that lyophilization is not only a tool for long-term storageof biomolecules but also an excellent way for up-concentrating whilemaintaining well-dispersed solutions of DNA origami.Although for many purposes, low concentrations of DNAorigami aresufficient, certain applications such as cryo electron microscopy,measurements involving small-angle X-ray scattering, or  Such amounts and concentrations are sufficient for mostDNA origami applications. However, for certain applications such assingle-particle analysis by cryo electron microscopy (cryoEM),13 small-angle X-ray scattering (SAXS),16 for in vivo applications,19 or for DNA origami silicification,21 high concentrations (> 200 nM) are oftenrequired. Such concentrations can be achieved by various means includingultrafiltration or polyethylene glycol (PEG) precipitation.22 However, each of these methods has certain drawbacks:Ultrafiltration is often the method of choice for many DNA origamistructures for both purification from excess staples, buffer exchange,and up-concentration.22 However, it onlyefficiently works using small filter tubes, allowing only a maximumof 500 μL of solution to be filtered at a time. For large volumesof DNA origami, this requires many filter units, which can becomecostly very quickly. It can also result in high losses of rod-shapedstructures due to leakage or aggregation.23 PEG precipitation, on the other hand, is very cost-effective andcan, in principle, be applied to any DNA origami to achieve both purificationfrom excess staple strands as well as for up-concentration. Here,freshly folded structures at typical concentrations of 10–20nM are mixed with a buffer containing 15% PEG, a molecular crowdingagent.22 Centrifugation then pellets thestructures and allows for redispersion in any desired volume of abuffer of choice. However, it was shown that volumes <150 μLresulted in less efficient redispersion.22 A major issue with both methods with respect to sample quality isthe requirement for prolonged centrifugation. This can enhance aggregationdue to stacking interactions or structural interlocking of certaintypes of 3D DNA origami. Aggregation is further enhanced by redispersionof DNA origami in low buffer volumes after centrifugation if highconcentrations are required. Especially for structural analysis or in vivo applications, aggregates are highly undesirableand must be minimized. Aggregates may be removed by filtration butat the expense of a loss in yield and hence concentration. To overcomethese issues and achieve well-dispersed DNA origami solutions withhigh concentrations, displaying minimized aggregation, we here introducelyophilization as an effective tool. We show that if DNA origami structures,after purification, are kept at low concentrations in low salt buffers,subsequent lyophilization and final redispersion in ultrapure watercan be utilized to achieve highly concentrated samples of 3D DNA origamiwith significantly reduced aggregation compared to samples up-concentratedby ultrafiltration or PEG precipitation.The complete control over size and shapetogether with full site-specificaddressability makes DNA origami nanostructures perfect breadboardsfor the arrangement of functional molecules. This has led to proposedapplications ranging from nanophotonics24 In this technique, a liquid sample is shock-frozen in liquidnitrogen followed by sublimation of the liquid under vacuum, leavingbehind only solid components. Lyophilization is a gentle techniqueas molecules go straight from the solid frozen to the solid dry state,without an intermediate solution step. Lyophilization has also beenproposed as a long-term storage option for DNA nanostructures, including2D DNA origami and tetrahedral nanostructures.26 However, the effect of lyophilization on 3D DNA origami and especiallyits effect on DNA origami aggregation has thus far not been explored.Here, we exemplify the advantage of using lyophilization for up-concentrationof 3D DNA origami with reduced aggregation by using different designs(14- and 24-helix bundles (HBs), a four-layer block (4LB), a cube,and a tensegrity triangle (SC), see Figures S1–S5 for CaDNAno designs), which all display different aggregation behaviorsincluding tip-to-tip stacking, side-to-side binding, or structuralinterlocking at high concentrations. These types of aggregates canbe significantly reduced by lyophilization. It should be noted thatthe types of aggregates that already occur during folding, e.g., completeor partial misfolding or structures sharing one scaffold, remain unaffectedand cannot be reduced by this technique.Freeze-drying or lyophilizationis a technique commonly employedfor the long-term storage of biomolecules such as proteins, peptides,DNA, and even whole mammalian or bacterial cells.2) at room temperature, confirming recentresults by Keller and co-workers.27 However,we also found that tightly packed structures required slightly moresalt for prolonged structural integrity. Starting with an initiallylow concentration of DNA origami results in very well-dispersed structureswith minimal interaction. This is maintained during shock-freezingand subsequent lyophilization, resulting in very low amounts of aggregation,even after redispersion in low buffer volumes to achieve high concentrations.Finally, we show that not only bare DNA origami but also silicifiedstructures can be easily and gently up-concentrated by lyophilization.We thus establish the freeze-drying process as a very efficient wayto achieve high concentrations of bare and silicified DNA origamiwith excellent storage capabilities and significantly reduced aggregation.During lyophilization,not only the DNA origami but also buffercomponents, including salts, are left as solid residues, and structuresmust initially be dispersed in a low salt buffer so as to avoid exceedinglyhigh salt concentrations after lyophilization and redispersion insmall volumes of water. We show that in the absence of EDTA, structuresare stable for at least 4 weeks in low salt buffers  or via PEG precipitation.22 For Amicon ultrafiltration, 500 μL of the folding solutionwas applied to each pre-wetted filter unit and centrifuged at 8000rcf for 8 min. After centrifugation, the flow-through was discardedand the filter units were refilled with 400 μL of the respectivebuffer. This procedure was repeated four times. After the elution,ultrafiltration was repeated with new filter units and at least threemore washing steps. For PEG precipitation, 1 mL of EDTA-free PEG buffercontaining 15% (w/v) of PEG-8000, 1× TA, and 500 mM NaCl wasadded to 1 mL of folding solution and the MgCl2 contentwas adjusted to 10 mM MgCl2. After careful mixing, thesolution was centrifuged at 20,000 rcf for 30 min. The supernatantwas discarded immediately after centrifugation, and the pellet wasresuspended in at least 500 μL of buffer containing 500 μMor 2.5 mM MgCl2, depending on the respective DNA origamistructure. The sample was then placed on a shaker at 500 rpm at 30°C for at least 16 h. For lyophilization, samples were dilutedto 10 nM DNA origami concentration using the respective low salt bufferimmediately after purification. For strongly concentrated samples(no lyophilization), buffers containing 10 mM MgCl2 wereused for resuspension to reach final origami concentrations of 200–250nM. PEG precipitation was employed for all DNA origami nanostructuresin this study. Optionally, ultrafiltration was used for the 24HB andthe SC only.DNAorigami nanostructures were assembled from the p8064 (Tilibit Nanosystems)or the p8634 scaffold (produced in-house) and the respective staplestrands (IDT) in 1× Tris-acetate-EDTA (TAE) buffer containing18 or 20 mM MgCl16 DNA origami solutionswere used at a concentration of 200 nM in a total reaction volumeof 50 μL and were dispersed in 1× TAE buffer containing3 mM MgCl2. After placing the samples on a thermo shaker,the first silica precursor N-trimethoxysilylpropyl-N,N,N-trimethylammoniumchloride (TMAPS)  was added to thesample in 5-fold molar excess to the number of nucleobases. The sampleswere then left shaking on the thermo shaker for 1 min  before the second silica precursor tetraethyl orthosilicate(TEOS)  was added to the samplesin 12.5-fold molar excess to the number of nucleobases. After another15 min of shaking on the thermo shaker, the samples were transferredto a tube revolver rotator (Thermo Fisher Scientific) and rotatedat 40 rpm at 21 °C for 4 h. Subsequently, one round of ultrafiltration was performed on the samplesfor purification from excess silica precursors.Implementing our previouslyestablished protocol,For the lyophilization procedure, upto 750 μL of the DNA origami sample dispersed in low salt bufferat a concentration of around 10 nM was placed in a 2 mL Eppendorftube. The lid of the tube was opened, and a second lid with severalsmall holes was placed on top of the tube. The sample was shock-frozenin liquid nitrogen and placed into a pre-cooled Eppendorf rack. Thesamples were immediately transferred to the lyophilizer (Lyovac) andlyophilized overnight. After the lyophilization procedure, sampleswere either placed on the lab bench for storage at room temperatureor directly resuspended in ultrapure water to reach a concentrationof about 200–250 nM. N.B.: as all buffer components, includingsalt, are retained in the sample, redispersion in ultrapure wateris sufficient.2 and 1× TAE buffer. Thegels were run in 1× TAE buffer with 11 mM MgCl2 at75 V for 90–120 min at 4 °C.All DNA origami samplesand the scaffolds used as a reference were diluted to a concentrationof 10 nM prior to the addition of Ficoll loading buffer containingorange G. Then, all samples were applied onto 0.7/2% (w/v) agarosegels containing 11 mM MgCl28 and the integrated densities (intensities) of the selected areasand of the gel background without any sample were extracted. Afterbackground subtraction, the values for the monomer bands alone andthe corresponding gel lane were compared for all DNA origami nanostructurestested. Three different gels of three different lyophilization experimentswere used for the analysis of each structure.To determine the relative amountof monomers in a sample, the brightness of the monomer bands and ofthe entire gel lane including the (aggregates in the) wells was analyzed.The regions of interest were selected by drawing a rectangular boxaround them using the ImageJ softwareTEM imaging was carried out on a Jeol-JEM-1230TEM operating at an acceleration voltage of 80 kV. For sample preparation,10 μL of DNA origami samples diluted to 5 nM immediately beforeTEM grid preparation was applied to plasma-cleaned TEM grids  for 5 min. After careful removal of thesample liquid with a filter paper, the grid was washed with 5 μLof a 2% uranyl formate solution. Subsequently, the uranyl formatesolution was applied to the grid for 45 s for staining purposes (forbare samples). Grids with silicified samples were instead washed oncewith 5 μL of ultrapure water. After removal of the excess liquid,the grid was left for air-drying and subsequent imaging.For several applications, highconcentrations of DNA origami solutionsare required. However, commonly applied up-concentration proceduresoften also result in aggregation. In low concentrations, however,aggregation is often not a problem. We therefore hypothesized thataggregation could be reduced if structures were directly transferredfrom a well-dispersed state at a low concentration to a solid statewithout slow volume reduction or centrifugation. A method that allowsfor this is lyophilization. 29 However, these methods have different benefits and disadvantagesand are not equally well suited for different shapes and types ofDNA origami nanostructures. For instance, ultrafiltration is not wellsuited for the purification of thin, rod-like DNA origami such asthe 14HB and 24HB as high losses can occur due to DNA origami rodsgetting stuck in the filter membrane. AGE, SEC, and magnetic beadpurification are excellent for achieving samples of high purity butare unsuitable for obtaining high concentrations of DNA origami. Therefore,we here employed PEG precipitation and ultrafiltration as high concentrationsof DNA origami can be reached using these techniques. However, dueto ease of use, compatibility with all DNA origami structures, andcost-effectiveness, PEG precipitation was preferably used. Sampleswere initially folded at 10 nM scaffold concentration and subsequentlypurified by PEG precipitation. The resulting DNA origami pellets werethen resuspended in large volumes  of a lowsalt buffer (50 μM to 2.5 mM MgCl2) and subsequentlyplaced on a thermo shaker at elevated temperatures (∼30 °C)to ensure a proper dissolution of the DNA origami pellet and achievea well-dispersed sample with relatively low concentrations (10 toa maximum of 30 nM). The choice of buffer in which the DNA origamiis dispersed is of great significance for the subsequent lyophilizationprocedure. As all salts and buffer components remain in the sampleduring and after lyophilization, an increase in the concentrationof the DNA origami by resuspension of the lyophilized powder in asignificantly lower liquid volume than in the original sample beforelyophilization will also inevitably lead to a significantly increasedconcentration of the salts .This can lead to enhanced aggregation. An initial buffer or aqueoussolution with a low MgCl2 content is therefore highly recommendablesince it also aids to decrease the potential of DNA origami aggregationdue to reduced electrostatic screening. Importantly, the initial amountof MgCl2 in the sample solution must be chosen so thatstability and structural integrity of the DNA origami are not negativelyaffected. Kielar et al. recently showed that 2D DNA origami structureswere stable at very low concentrations of Mg2+ or evenin pure water in the absence of EDTA.27 However, for 3D structures, this had not been thoroughly tested.We therefore investigated the stability of all four different 3D DNAorigami nanostructures employed here as well as their tendency toaggregate before and after the lyophilization procedure in a rangeof different MgCl2 concentrations . All structures,apart from the cube, were found to be stable in low Mg2+ solutions (50–500 μM) as can be seen from TEM analysisshown in 2 solution . Therefore, a higher salt content of 2.5 mM MgCl2 waschosen for this nanostructure . Allstructures displayed excellent long-term stability in the low saltbuffer, suggesting that solutions of purified origami could also easilybe stored under ambient conditions before up-concentration by lyophilization.It should be noted that when using buffers with a low MgCl2 content (<1 mM), the addition of EDTA or acetic acid should beavoided as these can reduce the effective amount of MgCl2 available for DNA origami stabilization.30 As such, we found no detrimental effect of pure Tris buffer (10mM) on the structural integrity of the DNA origami structures, whereasthe use of 1× Tris acetate (TA) buffer led to the disintegrationof all structures at a low MgCl2 content .The most commonly used DNA origami purificationmethods involveultrafiltration in centrifugal filter units, PEG precipitation, sizeexclusion chromatography (SEC), AGE, or the use of different typesof solid support such as magnetic beads.Figure S10) followed by gentle shakingfor several hours on a thermo shaker. Importantly, the volume of ultrapurewater used for resuspension should be at least 25–30 μLto ensure proper dissolution of the whole sample. The most importantexperimental parameters for the lyophilization procedure are summarizedin Table S1.After establishing the stability of all structuresin the requiredlow salt buffer, samples were prepared for lyophilization. For this,DNA origami samples were prepared at 10 nM concentration in up to750 μL low salt buffer in 2 mL round-bottom Eppendorf tubes.These were fitted with an additional lid that was perforated witha syringe needle to contain several (8–10) small holes see 1b. Subse25 it was found thattetrahedral DNA nanostructures, depending on the Mg2+ contentof the buffer, exhibited a reduced quality after lyophilization andredispersion.26 Moreover, the effect oflyophilization on the structural integrity of 3D DNA origami is thusfar unknown. The procedure involves two stresses, freezing and drying,that could possibly be damaging to the DNA origami. Although it waspreviously established that DNA origami can withstand several freeze–thawcycles without loss of structural integrity,31 we analyzed each structure after lyophilization and dissolutionin water by TEM and AGE. Encouragingly, as can be seen from Figures S10 and S11). This suggests that lyophilizedDNA origamican be stored in powder form without any observable detrimental effects.Although lyophilizationis a well-established technique for thepreservation of many biomolecules and has successfully been testedfor 2D DNA origami,Figure S12). Types of aggregationcommonly observed in 3D DNA origami are tip-to-tip stacking or side-to-sidebinding . Tip-to-tip stackingcan be reduced by adding either single-stranded scaffold loops tothe tips of the nanostructures/leaving out the end staples duringDNA origami folding or adding poly-T or poly-C tails as well as dispersionin low Mg2+ buffers.22 However,although such strategies were employed for the DNA origami structuresused in this study, aggregate formation due to tip-to-tip stackingat high concentrations was still observable. Another potential causefor DNA origami aggregation/clustering is structural interlocking.This will most likely occur in nanostructures with complex 3D shapessuch as the SC in this study, where struts of one structure get tangledup in another structure. Nevertheless, most of these aggregation behaviorscan be minimized and almost completely avoided by keeping DNA origamiat low concentrations and in low salt buffers.22 Thus, after synthesis and purification, we redispersedthe origami in larger volumes of low salt buffer achieving DNA origamiconcentrations of only ∼10 to a maximum of 30 nM before lyophilization.Shock-freezing subsequently keeps structures in this “well-dispersed”(now frozen) state. As the lyophilization technique is based on sublimation,frozen structures are directly transferred from the frozen solid state(no interactions possible) to the dry solid state (no significantinteractions possible). Subsequent redispersion then results in themaintenance of this well-dispersed state, even at high concentrations.Losses during lyophilization are generally very small; therefore,the required amount of water for redispersion of the origami to adesired final concentration can be easily back-calculated from theinitial origami concentration and solution volume. Similarly, thefinal MgCl2 concentration in the redispersed sample canbe determined. Therefore, dissolution of the DNA origami powder inlow volumes of ultrapure water can result in very high concentrationsof DNA origami. In our experiments, DNA origami concentrations ina range of 200–250 nM could be easily achieved from 10 nM startingconcentrations.After ensuring that the structures were not negativelyaffectedby lyophilization, we next investigated if the procedure could beused for up-concentration while reducing aggregation. Conventionalmethods used for up-concentration ) rely on centrifugation and/or volume reductionoften leading to aggregation and subsequently bad sample quality , confirmingour hypothesis that lyophilization can indeed reduce aggregation inhighly concentrated samples. We analyzed and compared samples of allDNA origami structures up-concentrated by the two different methodsusing TEM  compared to samplesobtained at a similar concentration by PEG precipitation . The most pronounced improvement of sample qualitycould be observed for the SC and cube origami structures . For the 14HB origami, already the diluted sample exhibiteda stronger tendency to aggregate in the agarose gel compared to otherstructures, and the amount of monomers in the strongly concentratedsamples appears to have been only slightly increased by lyophilization. Additionally, it has to be noted that the aggregationfor 14HB samples worsened with increasing MgCl2 concentrationboth before and after lyophilization .Although the magnitude of the positive effects ofthe lyophilizationprocedure was observed to vary with DNA origami shape, we found thatthe overall amount and the average size of aggregates were significantlyreduced in the lyophilized samples . We have shown here that the lyophilization procedure can alleviatethese problems.Finally, we also tested our lyophilization procedureon silicifiedDNA origami samples. DNA origami-templated silica nanostructures haverecently gained more and more attention due to their excellent combinationof properties of both the DNA origami and the inorganic silica component.gate see 6. As sil2 content , lyophilization, and resuspension in ultrapure water.Additionally, TEM and AGE analyses of strongly concentrated DNA origamisamples showed an increased proportion of well-dispersed monomersand/or a decreased number of very large aggregates in the lyophilizedsamples. This positive effect was found to be dependent on the shapeof the 3D DNA origami. Especially for more complex 3D DNA origamishapes (SC and cube), the sample quality could be significantly improvedusing this up-concentration method compared to conventional up-concentrationmethods such as PEG precipitation.In conclusion, we present a procedure forthe up-concentrationof 3D DNA origami to concentrations >200 nM via lyophilization,applicableto both bare and silicified nanostructures, that can contribute toa significant improvement of the sample quality by reducing aggregationin strongly concentrated DNA origami solutions. For all tested typesand shapes of 3D DNA origami, their structural integrity was verywell preserved after dispersion in a solution with a very low MgCl2 content; (ii) theavoidance of intense centrifugation during the concentration procedure;and (iii) the sublimation procedure allowing structures to go froma well-dispersed solution to a frozen solid and finally to a dry solidstate, hindering excessive detrimental interactions between structures.Aggregation behaviors such as tip-to-tip stacking, side-to-side binding,and structural interlocking can be significantly reduced by lyophilization/redispersion.Nevertheless, it has to be noted that other types of aggregation causedby scaffold sharing of two monomers or other types of misfolding cannoteffectively be reduced by lyophilization but must be improved throughdesign and folding optimizations. However, aggregation caused by defectivestructures with unfolded scaffold sections that entangle correctlyfolded monomers can potentially be marginally improved by initialdispersion at a low concentration and subsequent lyophilization.We attribute the reductionin DNA origami aggregation to the followingthree main points: (i) the use of low initial DNA origami concentrationsdispersed in a buffer with a low MgClWe anticipate that our findings can significantly facilitate samplepreparation and improve sample quality for applications where veryhigh DNA origami concentrations above 200 nM are required. Furthermore,the lyophilization procedure can also be applied to silicified DNAorigami. Storage of bare or silicified structures as solid powderwithout the need for cooling or freezing furthermore enables moresustainable transport possibilities. All in all, we establish lyophilizationas an excellent tool for the preparation of well-dispersed (silicified)DNA origami solutions with high concentrations.


Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks’ capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models’ extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. Our findings highlight how each architecture’s inductive biases prime them to learn different aspects of the protein fitness landscape. Machine learning (ML) accelerates the protein engineering process by integrating experimental data into predictive models to direct the landscape search2. These models improve the quality of protein variants tested and reduce the total number of experiments needed to engineer a protein3. ML-assisted protein engineering approaches have expedited engineering of diverse proteins such as viral capsids, improved fluorescent proteins, and highly efficient biocatalysts7.Protein engineering can be envisioned as a search over the sequence-function landscape to discover new proteins with useful properties11. We focus on supervised learning, which learns from experimental sequence-function examples to predict new sequence configurations with some intended biochemical/biophysical properties. There are many different classes of models that each make different assumptions about the underlying landscape, influencing the way sequence-function relationships are learned2. For example, linear models assume additive contributions from individual mutations and are unable to capture epistatic effects. More sophisticated convolutional neural networks use convolving kernels to extract meaningful patterns from the input data that capture long range interactions and complex, non-linear functions12. Many studies have assessed the predictive performance of ML models on existing protein sequence-function datasets16, but there is little work that rigorously benchmarks performance in real-world protein design scenarios with experimental validation17. ML-guided protein design is inherently an extrapolation task that requires making predictions far beyond the training data, and evaluating models in this task is challenging due to the massive number of sequence configurations that must be searched and tested18.Broad classes of machine learning and deep learning techniques approach protein design from different perspectivesin silico search over the sequence-function landscape. We use this approach with different model architectures to design thousands of protein G (GB1) variants that sample a massive sequence space far outside the model’s training regime. The different models prioritize distinct regions of the landscape and display unique preferences for the sequence positions mutated and types of amino acid substitutions. We then experimentally test the designs using a high-throughput yeast display assay that evaluates variant foldability and IgG binding. We find all models show the ability to extrapolate to 2.5-5x more mutations than the training data, but the design performance decreases sharply with further extrapolation. The simple fully connected neural networks showed the best performance for designing variants with improved binding relative to wildtype GB1. Intriguingly, we also found that the parameter-sharing convolutional models could design folded, but non-functional, proteins with sequence identity as low as 10% from wildtype, suggesting these models are capturing more fundamental biophysical properties related to protein folding. Our high-throughput screen identified multiple designs with improved binding relative to wildtype GB1 and previously designed variants. Our work provides a rigorous assessment of model architectures’ capacities for protein design and will support ongoing advances in ML-driven protein engineering.In this paper, we evaluate different neural network architectures’ ability to extrapolate beyond training data for protein design. We develop a general protein design framework that uses an ML model to guide an 1. Machine learning (ML) models trained on sparse sequence-function data infer the full fitness landscape and can make predictions for previously unobserved protein sequences. These models can guide a search through sequence space to discover protein designs with high predicted fitness  that can capture nonlinear behavior and epistasis, sequence-based convolutional neural networks (CNN) that share parameters across the protein sequence, and structure-based graph convolutional neural networks (GCN) that consider residues’ context within the protein structure  is a small 8-kDa domain that binds to the mammalian Immunoglobin G (IgG) fragment crystallizable (Fc) region with nM affinity and to the fragment antigen-binding (Fab) region with tructure . We prev21. The values of these unconstrained parameters are greatly influenced by the random initialization during training, and we hypothesized these parameters may lead to model divergence when predicting away from the training data regime. We trained 100 CNNs with the same model architecture, hyperparameters, and training data but with different random initializations of model parameters. We then evaluated each model’s predictions along a mutational pathway across the fitness landscape  to optimize a model over sequence space to identify high fitness peaks. The approach executes hundreds of independent design runs to broadly search the landscape, clusters the final designs to remove redundant or similar solutions, and then selects the most fit sequence from each cluster to provide a diverse sampling of sequences predicted to have high fitness , GCN, and the ensembles EnsM and EnsC. For each model, we designed variants at six extrapolation distances: 5, 10, 20, 30, 40, and 50 mutations from wild-type GB1. For each model-distance combination, we ran at least 500 design runs and clustered the designs into 41 clusters, to obtain 41 diverse sequences for each criterion. We visualized the design space using multi-dimensional scaling, which organizes sequences in a 2D space that attempts to preserve sequence interrelationships. We observe that sequences occupy concentric rings expanding outward from wildtype GB1, with each successive ring representing an increased mutation distance and the outermost ring corresponding to the 50 mutants .We observed notable differences in the sequences designed by each model, suggesting each architecture prioritizes distinct regions of the landscape . The LR 20. This suggests the CNN and GCN models can exploit epistasis to design sequences composed of synergistic mutations.We further explored the unique mutational variation of each architecture’s designs by analyzing the amino acid diversity at each site . Generaldisplay and IgG binding enrichment scores, We used yeast surface display to characterize the expression and IgG binding of the designed GB1 variants. We sorted variants into “displaying” and “IgG binding” populations using fluorescence-activated cell sorting (FACS) , b and sng study . The dis12 with the extrapolative design ability of simpler LR/FCN models.We found all models were successful at designing functional GB1 variants that displayed and bound IgG . The desThe model design results for the display score were strikingly different than for IgG binding. While the LR and FCN design success decreased sharply with increasing extrapolation, the CNN and especially the GCN designed sequences that displayed out to 50 mutations. The picture becomes more complete when we jointly visualize the binding and display scores over extrapolation distances . The seq23. In this case, the LR/FCN designs are inactive because they are unfolded, while the CNN/GCN designs are folded but may have a defective IgG-binding interface. The model architectures and their intrinsic inductive biases may explain the differences in each model’s design behavior. The LR/FCN models put an emphasis on specific sites and how those sites contribute to function. In contrast the CNN/GCN models have parameter sharing architectures that learn general patterns across the protein sequence. The site-focused designs from the LR/FCN models will maintain an intact IgG-binding interface, at the potential cost of losing protein stability. While the CNN/GCN models learn more general rules related to protein folding, but in the process, put less emphasis on the IgG binding interface.We can contextualize these results if we consider that stable, folded proteins are more likely to display on the yeast surface, while unstable proteins are prone to proteolytic degradation24. We found designs with 20 or more mutations are less likely to display if they have a KEX2 site , WT-like binding , lower than WT binding (low-bind), and no IgG binding (display only) . Our res(Supplementary Table 2). We chose ten designs with 40-50 mutations that displayed but did not show IgG binding and we chose another ten designs with 5-20 mutations that showed IgG binding. We quantitatively evaluated display and IgG binding for each of these 20 designs in a clonal yeast display assay , the maximum binding signals are significantly larger for the designs, suggesting our designs have decreased 20 may have captured 5/5 of the “high binding” designs with 5-10 mutations showed IgG binding greater than wildtype; however, the 5 “binding” designs with 20 mutations showed diminished binding. We performed IgG titrations to assess the binding curves for several designs . While tProtein engineering has broad applications across biocatalysts, biomanufacturing, agriculture, and human health, but engineering proteins is slow and requires expensive rounds of experimental characterization to search the fitness landscape. Machine learning accelerates the protein engineering process by inferring the underlying landscape structure and reducing the experimental burden required to explore protein sequence space. In this work, we assessed different supervised machine learning architectures’ ability to extrapolate beyond the training data for protein design. Adapting previously developed models trained on a large GB1 sequence-function dataset, we performed an ML-guided search to design sequences predicted to have high fitness. We found each model had markedly different perceptions of the underlying landscape that gave rise to unique preferences for the designed sequences. All models showed strong potential to extrapolate to 2.5-5x more mutations than represented in the training data, but the simpler fully connected architecture performed the best for designing functional and highly fit proteins.25. These models become increasingly inaccurate further from the training data due to the challenge of generalizing local sequence rules to the global landscape, which is exacerbated by factors like landscape ruggedness, data sparsity, and magnitude of epistasis. Models that effectively capture epistasis and that can learn generalizable biophysical concepts will be more capable of extrapolating on the fitness landscape. Assessing a model’s extrapolation potential requires more than simply holding out some subset of variants from a localized and fixed data set. Indeed, our analysis found CNN/GCN models that perform well in predicting 4-mutants  ability to bypass local optima, we observed notable differences in the spread of each model’s designs , suggest27, and the LR and FCN’s inductive biases are set up to capture these simple relationships. FCNs have the additional advantage that they can learn epistatic relationships between residues, which resulted in increased performance relative to LR. The primary distinguishing feature of the convolutional models is their parameter sharing architecture that learns filters that are applied across the protein sequence/structure. Convolutional models have the capacity to learn more general relationships, but their inductive biases are not primed to capture the simpler additive and epistatic relationships that dominate local landscapes.We found the LR and FCN models strongly outperformed the convolutional models in designing functional 5- and 10-mutants that bound IgG tighter than wildtype GB1. These models have a simple architecture where each position in the amino acid sequence contributes to function and highlights the relative simplicity of the local landscape for a given property. Additive mutational effects have been known and leveraged in protein engineering for decades28 and this observation was noted in the original GB1 deep mutational scanning manuscript20. The combination of data that’s dominated by folding effects and the convolutional models’ inductive bias to recognize patterns led to models that learned more general rules of protein folding.We interpret our results primarily through the lens of protein engineering where our goal is to design GB1 variants with improved binding to IgG. But we found the CNN, and especially the structure-based GCN, were highly effective at designing GB1s with up to 50 mutations that display on the yeast surface and presumably fold, but do not bind IgG. It’s worth noting that this phenomenon doesn’t necessarily indicate subpar model performance, and instead we postulate the parameter sharing architectures may have focused more on learning general rules of protein folding and, in the process, ignored IgG binding activity. While the original sequence-function training data was based on IgG binding, this experimental mapping is dominated by folding effects because a majority of a protein’s residues are involved with maintaining structural stability, while relatively few are directly involved with binding. In other words, a mutation can inactivate a protein due to disrupted folding or disrupted binding, but the disrupted folding mechanism is statistically much more likely. It’s well known that most mutations destabilize proteins29. If the models tend to agree, we can be more confident in the prediction, while if the ensembles’ predictions are highly variable, then the random parameter initialization may be influencing the models’ prediction. Evaluating this consensus between models is important for neural network models that may have millions or more internal parameters that are not fully determined by the data. Other work has used model uncertainty to guide designs toward regions of sequence space that are generally more confident32. For our EnsC predictor, we do not restrict design space based on uncertainty, but rather use the ensemble to make a conservative fitness prediction where 95% of the models agree the fitness is above a certain value. Designing sequences that maximize EnsC result in designs that most models agree have high fitness. We found the EnsC design strategy was the best overall with similar performance to LR and FCN for designing highly fit 5- and 10-mutants and some ability to design functional sequences with 20 mutations.We found ensembles of CNNs outperform individual CNNs in designing high fitness variants. Ensembles highlight model variation arising from random parameter initialization and can identify regions of sequence space that are poorly understood by the model. Being able to identify where a model is not confident is critical for robust design. Model confidence can be estimated by inputting a single sequence into an ensemble of models and evaluating the agreement in prediction across the models33. Another design strategy could aim to jointly optimize multiple models simultaneously using multi-objective criteria34.Protein function is multifaceted; a single output property such as binding may be influenced by mutations that affect structure, the binding interface, pH sensitivity, or protein expression. Similar to the adage ‘you get what you screen for,’ in ML-guided protein engineering, you get what you train on. It is difficult to know a priori the mechanisms though which individual mutations affect fitness. We found different model architectures are more primed to learn distinct underlying mechanisms. This naturally leads to the idea that these differing architectures could be used together to design functional proteins more robustly. A collection of model architectures, each with their own inductive biases, could be used together in an ensemble, similar to EnsC. Ensembles of different model classes have been successfully used for protein and metabolic engineeringDeep learning and artificial intelligence are revolutionizing the fields of protein science and engineering. These tools can process, learn from, and make sense of large quantities of data to decode the complex inner workings of proteins with a scale and resolution beyond human comprehension. Continued advances will help realize the potential of protein design to address society's most pressing problems and future global challenges.20 and the Gelman et al. model architectures12 with the same parameter initializations, train-validation-test splits, and hyperparameters. We additionally trained 100 models of each architecture with different random initializations to assess the effects of parameter initialization and for ensemble learning. We constructed two CNN ensemble predictors: a median predictor (EnsM) that inputs a sequence into 100 CNN models and outputs the median fitness prediction, and a conservative predictor (EnsC) that inputs a sequence into 100 CNN models and outputs the 5th percentile fitness prediction.We used the Olson et al. GB1-IgG binding dataset22 This dataset consists of a complete 4-site combinatorial saturation mutagenesis GB1 library screened for binding to IgG. The models were trained on single and double mutant data from Olson et al.20 and were used to predict the fitness values of the 3- and 4-mutants. We also tested the ability of the models to identify the 4-mutants with the highest fitness values. The model recall was calculated by giving a model a design budget We evaluated the models' ability to extrapolate to 3- and 4-mutants using the four-site combinatorial GB1 data set from Wu et al.Supplementary Table 1). For each model-distance combination, we designed 41 diverse GB1 variants with high predicted fitness. For a given model-distance combination, we used simulated annealing (SA) to maximize the model’s predicted fitness constraining the number of mutations allowed in the designed protein. To ensure the number of mutations in a design were fixed, we exchanged random mutations. For example, when designing a 5-mutant, we might randomly choose two current mutations, mutate those back to the wildtype amino acids, and then choose two new random mutations to keep the total mutations fixed at five. At each step of simulated annealing, one or more mutations  and (FCN 5 mutants) = 10,000 steps and (LR-10 mutants), (FCN-10 mutants), (EnsM-5 mutants), and (EnsC-5 mutants) = 25,000 steps. All sequence designs were run in parallel using high-throughput computing35. To select a diverse and representative set of designs, we performed K-means clustering on the 500 independent designs using 41 clusters and selected the variant from each cluster with the highest predicted fitness for experimental characterization. This resulted in 41 unique GB1 designs for 8 models at 6 distances, for a total of 1968 designs.We designed GB1 variants with eight different models at 5, 10, 20, 30, 40, and 50 mutations from wildtype GB1 (S. Cerevisiae using GenSmart Codon Optimization (GenScript) and excluded the BsaI, NheI, BamHI restriction enzyme sites. We also identified 25 sequences from the training data with a broad range of fitness values to correlate our fitness measurements with the original data from Olson et al.20. Each of these variants were designed with two different synonymous codon sequences to provide internal controls to ensure reproducibility of our fitness measurements. The designed genes and control sequences were synthesized as an oligonucleotide pool by Twist Biosciences with flanking sequences to allow PCR amplification and downstream cloning.We codon optimized our GB1 designs for expression in 36, transformed into 10G supreme electrocompetent E. coli (Lucigen), and harvested the plasmid DNA using the Qiaprep Spin miniprep kit (Qiagen). We then transformed the GB1 library into yeast display Saccharomyces cerevisiae strain EBY100 made competent using the Zymo Research Frozen EZ Yeast Transformation II kit. We grew the transformed library in pH 4.5 Sabouraud Dextrose Casamino Acid media , 5 g Casamino Acids (VWR), 10.4 g sodium citrate, 7.4 g citric acid monohydrate) at 30°C and 250 rpm for two days. We plated an aliquot of the transformant pool on SD -Trp agar to quantify the number of library transformants.We amplified the oligonucleotide pool using either Phusion Hot Start Flex 2X Master Mix (New England Biolabs) or KAPA HiFi HotStart ReadyMix (Roche), cloned the gene library into the yeast surface display vector pCTCON2 myc IgY (Aves Labs) conjugated with Alexa488 using NHS chemistry. Following the overnight incubation, we washed the yeast in PBS/0.2% BSA and resuspended in ice cold PBS for FACS. We performed FACS using a FACS Aria II (Becton Dickinson) and analyzed yeast cells for display at 488 nm and IgG binding at 647 nm. Our “qualitative” experiments sorted cells into “display only” and “bind” populations, while our “quantitative” experiments sorted cells into “display only,” “low bind,” “WT-like bind,” and “high bind” populations.We analyzed and sorted the GB1 library using florescence-activated cell sorting (FACS). We induced the library expression in SGCAA media overnight, harvested approximately 3e6 yeast cells by centrifugation, washed once in pH 7.4 Phosphate Buffered Saline (PBS) containing 0.2% (w/v) bovine serum albumin (BSA), and incubated overnight at 4 °C on a tube rotator at 18 rpm in 800 μL of PBS/0.2% BSA containing 15 nM human IgG1 (BioLegend) that had been conjugated with Alexa647 using NHS chemistry (Molecular Probes) and 3 μg/mL anti-E. coli, cultured overnight in LB + carbenecillin media shaking at 250 rpm at 30°C, and harvested the plasmid DNA using the Qiaprep Spin miniprep kit (Qiagen). We cut out the GB1 gene insert using XhoI and PstI restriction enzymes, excised the corresponding band using agarose gel extraction, and purified using the Zymo Research gel extraction kit. The UW-Madison Biotechnology Center DNA sequencing core prepared a sequencing library using the NEBNext Ultra II kit and sequences the samples using an Illumina NovaSeq6000 with 2x150 bp reads.We recovered the sorted yeast populations, as well as the initial unsorted library, and grew them in SDCAA media overnight. The following morning, we expanded the cultures into SDCAA media at an optical density of 0.1, grew them until reaching density of ~1.0, harvested and centrifuged the cultures, and extracted the plasmids using the Zymo Research Yeast Plasmid Miniprep II kit. We transformed the extracted plasmid DNA into 10G supreme electrocompetent We aligned forward and reverse Illumina reads to wildtype GB1, using a predetermined offset, and merged the two reads by selecting the base with the higher quality score in overlapping regions. A design’s count was equal to the number of sequencing reads that exactly matched the designed nucleotide sequence and we filtered out any designs if they had fewer than 10 counts in the unsorted population.The initial experiments consisted of two replicates each of unsorted ectively . We alsoectively . Here, tFor the quantitative screening of designs, we obtained unsorted  l, w, h . Each se l, w, h . All datSupplementary Table 2). We chose five 5- and 10- mutants with high binding across all replicates, five 20-mutants with some binding activity across all replicates, and ten 40- and 50-mutants with high levels of display across all replicates. We resynthesized their DNA sequences as individual gene fragments (Twist Biosciences) and cloned them into the same yeast display vector used for library screening. We transformed the plasmid DNA into EBY100 made competent using the Zymo Research Frozen EZ Yeast Transformation II kit and grew the transformants on synthetic dropout (SD) -Trp  agar plates for two days at 30°C. After two days, we picked individual colonies into 4 mL of pH 4.5 Sabouraud Dextrose Casamino Acid media , 5 g Casamino Acids (VWR), 10.4 g sodium citrate, 7.4 g citric acid monohydrate) and grew them overnight at 30 °C and 250 rpm. We induced GB1 display in a 5 mL pH 7.4 SGCAA culture started at an optical density as measured at 600 nm of 0.5 and shaken overnight at 250 rpm and 20 °C.We identified twenty designs to test more thoroughly for binding and display in clonal yeast display assays  containing 0.2% (w/v) bovine serum albumin (BSA), and incubated for three hours at 4°C on a tube rotator at 18 rpm in between 100 μL and 1 mL of PBS/0.2% BSA containing various concentrations of Alexa647 human IgG1 and 3 μg/mL Alexa 488 anti-myc IgY. We varied the volumes of Alexa647 IgG-containing incubation solution to prevent ligand depletion from occurring at the lowest IgG concentrations. Following incubation, we washed the yeast once in PBS/0.2% BSA and resuspended in ice cold PBS for flow cytometric analysis. We analyzed the samples using a Fortessa analyzer (Becton Dickinson).For IgG binding titrations, we harvested approximately 2*10Supplement 1


Advances in research and technology to boost human space exploration proceed at a brisk pace, although a major obstacle remains: the cold and empty outer space. Can we capitalize on the unique properties of specific materials and structures to address this problem? Considered among the best insulating materials available, aerogels have become prime candidates in this field owing to their ultralight weight, large surface area, deformability, excellent fire and corrosion resistance, as well as low thermal conductivity . HoweverNational Science Review, Yu et al. overcome this critical issue by using an innovative approach featuring hybrid 3D aerogels made of chemically bonded 2D inorganic materials, namely graphene and amorphous boron nitride (a-BNGA) [3NH3) is thermally decomposed and polymerized (at 1000°C) on graphene aerogels, leading to the formation of a-BNGA , being the lowest value recorded in vacuum among solid materials to date. Such an exceptional performance is due to the ‘encasing effect’ of amorphous boron nitride over the graphene layer, whose conduction paths are reduced and phonon transport is hindered because of an enhanced scattering at the boron nitride/graphene interface. Finally, the ultralight, superelastic and thermally superinsulating a-BNGAs were tested for possible deep-space applications. The interior environment of a lunar base model wrapped with such a system experiences a longer (10-h) heating–cooling cycle compared with pristine boron nitride, graphene and silica aerogels, demonstrating the superior performance of a-BNGA in suppressing thermal radiation and heat conduction.Writing in (a-BNGA) . They derized at 000°C on 3C2Tx MXene shows extremely low emissivity in the infrared range [The promising results achieved by combining different 2D materials suggest an opportunity for further material engineering to improve the aerogel performance, since hundreds of 2D materials are available and some of them show superior thermal properties . For exaed range  and can This novel strategy represents a great advance in the field, paving the way toward the production of hybrid 3D aerogels made of 2D materials showing superelasticity and ultralow thermal conductivity for a variety of applications, such as extraterrestrial exploration. However, the use of mechanically robust aerogels in industrial applications at elevated temperatures, when elastic polymer foams cannot work, or at extremely low temperatures in the Arctic and Antarctic, is even more promising. Those terrestrial applications may also provide testing opportunities under practical use conditions that will ensure the reliability of aerogels in future space applications.


C. difficile infection (CSI), and surgical-site infection (SSI) rates were negatively correlated with COVID-19 intensity in CSIP facilities.We analyzed efficacy of a centralized surveillance infection prevention (CSIP) program in a healthcare system on healthcare-associated infection (HAI) rates amid the coronavirus disease 2019 (COVID-19) pandemic. HAI rates were variable in CSIP and non-CSIP facilities. Central-line–associated bloodstream infection (CLABSI),  The coronavirus disease 2019 (COVID-19) pandemic has introduced various challenges in hospital settings, including staffing and capacity management issues. Previous studies have shown that HAI rates have increased during the COVID-19 pandemic.Healthcare-associated infections (HAIs) are a serious complication in patient care that negatively affects patient outcomes and staff safety: ∼4% of hospitalized patients will develop an HAI and ∼11% will die during hospitalization. In our health system, we have developed and implemented a centralized surveillance infection prevention (CSIP) program that includes a team of system-level infection preventionists responsible for HAI surveillance, allowing local infection preventionists to refocus their time on harm reduction efforts.Typically, HAI transmission in healthcare settings is monitored by a team of local infection preventionists that work onsite at a single facility, in collaboration with other healthcare workers.The aim of this quality improvement work was to analyze HAI rates between sites with CSIP program surveillance and sites without CSIP program surveillance. We also sought to determine the effect of the CSIP program on the relationship between COVID-19 and HAI rates.Observations and data collection for this quality improvement initiative were conducted beginning January 2018 and concluding December 2021 at a 40-hospital academic healthcare system. In this analysis, all facilities implementing CSIP programs during the analysis period were assigned identifiers beginning with C (denoting a CSIP facility) and ending with a number assigned in order of the date of CSIP implementation. In total, 6 facilities implemented CSIP in 2019 (C1–C6), 2 facilities implemented CSIP in 2020 , and 4 facilities implemented CSIP in 2021 (C9–C12).Any facilities that produced data used for this report that did not implement CSIP during the analysis period were designated “local” facilities and were alphabetically assigned an identifier beginning with “L” and ending with a sequential number.The CSIP program comprises senior infection preventionists performing HAI surveillance and data analysis for facilities adopting centralized surveillance.We performed 2 analyses estimating the impact of CSIP deployment as an effect modifier of the relationship between the COVID-19 pandemic and HAI rates. The first was a comparison of HAI rates in a pre-CSIP pre–COVID-19 period (January–December 2018) to a period when the CSIP program had been deployed at 6 facilities  during the COVID-19 pandemic (January–December 2020). Second, we compared COVID-19 surge intensity to the outcome of monthly facility HAI rates during March 2020 through December 2021. COVID-19 surge intensity was defined as the proportion of inpatient beds assigned to COVID-19–contagious patients. Surgeries were not limited in these facilities due to COVID-19. Scatter plots were created comparing COVID-19 surge intensity to each HAI rate for each facility month. In our organization, severe acute respiratory coronavirus virus 2 (SARS-CoV-2)–positive patient admissions were not restricted to specific facilities; care was provided at the appropriate facility for the patient’s needs, independent of COVID-19 status. Because HAI incidence is highest in intensive care units and HAI risk during the COVID-19 pandemic is hypothesized to be related to this level of care, we performed a post-hoc correlation analysis restricted to ICU-level care.In total, 8 CSIP hospitals and 17 hospitals that have not yet implemented CSIP but were eligible based on their electronic health record system (facilities L1–L13) or implemented CSIP after the 2020 analysis period (facilities C9–C12) were included in this analysis. Periods were chosen to accommodate for CSIP implementation. HAIs were determined using NHSN definitions.C. difficile infection) or catheter days (for CAUTI and CLABSI) in 2020 from the corresponding HAI rate in 2018. To analyze individual facility monthly HAI rates compared to COVID-19 inpatient care intensity, we calculated a Spearman correlation coefficient.The comparison of HAI rates in 2018 and 2020 were descriptive in nature and were calculated by subtracting the HAI per 1,000 patient days  as a quality improvement project by the Quality Improvement Review Committee.C. difficile infection rates in 25 facilities in our system: 6 fully participating in the CSIP program in 2018 and 2020, 2 that implemented CSIP during the 2020 analysis period, and 7 that are eligible for future CSIP program implementation . Non-CSIP hospitals were more likely than CSIP hospitals to report an increase in CLABSI and CAUTI rates in 2020 compared to 2018. Nearly all hospitals had a similar or decreased C. difficile rate in 2020.We compared CLABSI, CAUTI, and S1). In the analysis of HAI rates and COVID-19 surge intensity, the correlations in all facilities for each of the 4 HAIs, and the correlations stratified by CSIP and non-CSIP programs all had a Spearman correlation coefficient ρ <0.20 .COVID-19 intensity was variable over time for all facilities (Supplementary Fig. In this quality improvement study characterizing the impacts of CSIP implementation and the COVID-19 pandemic on HAI rates, we identified several major themes. Across our acute-care facilities, there was not a clear pattern of worsening HAI rates because of the COVID-19 pandemic; however, we did observe some evidence that a CSIP-based HAI surveillance program may improve the ability of acute-care facilities to maintain a safe patient environment during major disruptions to providing healthcare. These observations are a substantial contribution to the published experience of centralized surveillance. These results will be important to multiple-facility health systems considering centralized models for HAI surveillance, and our findings provide a suggestion that such a model may improve patient safety in acute-care settings. Our subpopulation analysis restricted to intensive care unit–level care did not demonstrate that the impact of COVID-19 on HAI rates are attributable to severely ill patients. Other health systems likely also find that the COVID-19 pandemic did not universally negatively affect HAI rates. We have also demonstrated a preliminary signal that CSIP programs allow facilities to maintain HAI prevention progress during times of high patient volume. In the correlation coefficient models we controlled for autoregression and therefore facility trends, but we did not control for HAI reduction initiatives or adherence to infection prevention process measures, which may also have influenced HAI rates.CSIP programs are ultimately intended to reduce HAI. Our data provide an indeterminate answer regarding whether CSIP program adoption reduces HAI, and these data are confounded by the COVID-19 pandemic. Unlike other studies, however, our analyses showed a mix of both modestly rising and falling rates; consistent with other literature, CAUTI and CLABSI were most adversely impacted.In this quality improvement project report, we demonstrated that a CSIP program allowed hospitals to maintain HAI prevention progress during COVID-19 surges. Future investigations should continue to characterize ways that CSIP programs can improve infection-related patient safety.


Bicuspid aortic valve (BAV) is associated with ascending aorta aneurysms and dissections. Presently, genetic factors and pathological flow patterns are considered responsible for aneurysm formation in BAV while the exact role of inflammatory processes remains unknown.In order to objectify inflammation, we employ a highly sensitive, quantitative immunohistochemistry approach. Whole slides of dissected, dilated and non-dilated ascending aortas from BAV patients were quantitatively analyzed.Dilated aortas show a 4-fold increase of lymphocytes and a 25-fold increase in B lymphocytes in the adventitia compared to non-dilated aortas. Tertiary lymphoid structures with B cell follicles and helper T cell expansion were identified in dilated and dissected aortas. Dilated aortas were associated with an increase in M1-like macrophages in the aorta media, in contrast the number of M2-like macrophages did not change significantly.This study finds unexpected large numbers of immune cells in dilating aortas of BAV patients. These findings raise the question whether immune cells in BAV aortopathy are innocent bystanders or contribute to the deterioration of the aortic wall. A BAV iInflammation is seen as an important player in vascular diseases, ranging from hypertension to atherosclerosis and has proven to be an effective therapeutic target –20. StriInflammation is a protective response involving blood vessels, molecular mediators, and many cell types of the immune system. Immune cells can be studied by using markers to specifically identify cell subsets exerting particular functions. The presence of high numbers of B cells in combination with neovascularization can be indicative for chronic inflammation, whereas neutrophils indicate acute inflammation , 27. ChaIn this study we aimed to provide an overview of inflammatory processes in ascending aorta dilatation in patients with BAV. Inflammatory processes were studied with a wide variety of markers, focusing on both the innate and the adaptive immune system. An eight-color immunohistochemistry (IHC) method with automated quantitative analysis was applied to detect differences in immune cell involvement between non-dilated, dilated and dissected aortas. These finding have led to new hypotheses which could explain an additional factor in the pathogenesis of an aortic dissection in BAV patients.2.2.1.Patient material was collected from 3 academic centers in the Netherlands; (1) patients with a BAV undergoing elective surgical repair of ascending aorta aneurysms or acute surgical repair of an acute Stanford type A aortic dissection at the Radboudumc in Nijmegen, (2) patients with a BAV undergoing elective surgical replacement of the aortic valve with or without concomitant proximal aortic replacement at the Leiden University Medical Centre, Leiden, (3) post-mortem non-dilated BAV aortic wall samples from the Heart Valve Bank of the Erasmus Medical Centre in Rotterdam. The Leiden and Rotterdam samples have been used in a prior study . Approvan = 13) defined as a maximum ascending aorta diameter below 40 mm; (2) dilated (n = 10) defined as an ascending aorta diameter of 45 mm and larger and (3) hyperacute dissection (n = 4) defined as patients with a Stanford type A dissection proven on CT or trans-thoracic ultrasound and symptoms for less than 24 h, in accordance with the IRAD definition (The study population was divided in three groups: (1) non-dilated  part of the aorta as assessed by the surgeon. The dissection samples were taken from the area of the entry tear and the immediate distal portion down-stream. In the case of a dissection the intimal flap from the propagating tear and the standing adventitia portions of the aorta were embedded. The thrombotic tissue adhering to the entry and propagating tear were excluded from analysis. All samples were formalin fixed and paraffin embedded: samples were fixed directly after excision in buffered 4% formaldehyde for at least 24 h. Samples were carefully embedded in paraffin to include all aorta layers. Full thickness transverse sections of 4 μm were mounted on silane coated glass slides .2.3.Tissue sections were stained with two multiplex IHC panels ; one forTissue sections were deparaffinized, rehydrated and washed with demi water before heat induced antigen retrieval in EnVision™ FLEX target retrieval solution  for 10 min. Protein blocking was performed by covering the tissue in Akoya Antibody Diluent/Block . Primary antibodies were incubated for 1 h, followed by Polymer HRP Ms. + Rb  as a secondary antibody for 30 min. Visualization was done with tyramide signal amplification (TSA) using an Opal fluorophore  dissolved 1:50 in 1 X Plus Amplification Diluent . All incubations steps were performed at room temperature. Please see the 2.4.Slides were scanned at 20× magnification using the PerkinElmer Vectra . Multispectral images were unmixed using spectral libraries and inForm Advanced Image Analysis software . Spectral libraries were built from images of single stained tissues for each reagent and a non-stained slide for the elastin autofluorescence.2.5.Whole slides consist of 2–4 tissue sections spanning multiple centimeters of aorta wall. The entire slide was scanned, resulting in up to 400 20×-views per subject. 20× magnification was chosen as it allowed accurate cell segmentation while simultaneously sampling a large aorta section which minimized sampling bias. Single cells were segmented using inForm Advance Image Analysis software which uses DAPI to identify cells and improves upon this segmentation using the membrane makers CD20 and CD3 when present. Artifact staining is very sparce and only present in necrotic areas void of cells. Excluding artifact staining in the analysis was possible by the DAPI based cell segmentation since only fluorescence associated with a cell is analyzed. Consequently, cell membranes without a nucleus -because it is outside of the section- will not be analyzed. This is most notable with large and irregularly shaped cells such as macrophages. Cell data  and 20× view images were merged to form single flow cytometry standard files for each slide. These data were subsequently analyzed using FlowJo . The tissue slides were divided into intima, media and adventitia based on the trained inForm tissue segmentation data . Cells wSamples were graded on presence and magnitude of atherosclerosis and media degeneration using the AHA classification and the most recent consensus statement by the AECVP/SCVP , 40.2.6.Nearest neighbor analysis was performed on M1-like macrophages (CD68+ CD206-) and endothelial cells (CD31+ CD68-) located in the media and adventitia of all aortas as phenotyped by inForm cell phenotyper. All data belonging to one slide were merged in order to find nearest neighbors on adjacent tiles. Slides that contained less than 150 cells of one phenotype were excluded because of a high chance for a sample error. An adapted version of the Akoya Biosciences ‘phenoptr’  nearest 2.7.p < 0.05 was considered statistically significant.Statistical analysis was performed with SPSS for Windows . Visualization of results was done with PRISM 8 . Continuous data were expressed as mean ± standard deviation (SD), or in case of non-Gaussian distribution median (interquartile range) (IQR). The non-Gaussian distributed variables were compared between two groups with the Kolmogorov–Smirnov test. Testing between three groups was performed with the independent Kruskal-Wallis test adjusted with Bonferroni correction for multiple testing. Binary variables were tested for differences using the Fisher exact test. Interobserver variability was calculated with the intraclass correlation coefficient (ICC). Correlations between continuous non-Gaussian distributed variables were studied with Kendall’s tau because of low numbers per group. 3.3.1.Bicuspid aortic valve ascending aorta samples were collected and divided into 3 groups: non-dilated, dilated and dissection. The diameter of dilated and dissected aortas did not differ significantly . The med3.2.Two antibody panels to identify cells of either the innate or the adaptive immune system were developed . These pin situ single cell phenotype data. First, the inForm tissue segmenter, based on supervised machine learning, was employed to distinguish the intima, media and adventitia layers as shown in After unmixing, images were refined in a three-step process to extract whole slide in situ, localized, single cell analysis of entire tissue slides with little inter-observer variability and IHC artefacts.In the second step, cells were virtually dissociated with the inForm cell segmentation tool . Finally3.3.The adaptive immune system is studied by markers discriminating B cells, T cells and various subsets thereof . DistingThe adventitia of dilated BAV ascending aortas was compared to non-dilated BAV samples ,B. The ar = 0.411 (CI 0.087–0.671), p = 0.007). The number of CD45RO+ memory T cells was significantly higher in the dilated compared to the non-dilated group (p < 0.01). Finally, regulatory T cells were rarely observed in the adventitia of dilated BAV ascending aortas  MMP9 between the three groups, this could indicate that this specific metalloproteinase did not contribute to BAV aortopathy .Although the adventitia of dissections was disrupted, visually a strong increase in neutrophils was observed compared to non-dilated samples -(NWO) open technology grant [STW-14716] and a SCAN consortium: European Research Area - CardioVascualar Diseases (ERA-CVD) grant [JTC2017-044].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pectobacterium atrosepticum, a bacterial pathogen that causes black-leg disease and extensive crop damage.This inquiry aimed to investigate the potential of bacteriophages vB_PatP_CB3, vB_PatP_CB4, and vB_PatP_CB5 as methods for controlling the spread of Structure of PodovirusMorphology of typical N4-like podovirus )Soft Rot in PotatoPectobacterium atrosepticum infection in potato )Pectobacterium atrosepticum infection among potato crops. Phages were narrowed down by the specificity of their receptor-binding proteins for the protein receptors on the bacterium cell wall, as determined by FASTA sequence comparison between phage tail fiber RBPs and bacterial integral glycoproteins. A standard serial dilution and phage titration protocol in conjunction with a plaque assay were subsequently used to evaluate the efficacy of each phage, both individually and in a cocktail.Phages were initially selected based on the location of isolation of the phage and its proximity to a field with a known current, or recent previous, Plaque Assay DiagramStandard plaque assay procedure used inoculate plates to obtain plaquesPlaque assays showed that vB_PatP_CB5 produced a large number of plaques, indicating successful cell lysis and bacteriophage propagation within the bacterial colony. In contrast, vB_PatP_CB3 and vB_PatP_CB4 produced no measurable plaques.Plaques on Bacterial LawnObserved plaque formation after initial 24 hour periodPlaque Charts from Trial 1Plaque formation from trial 1Plaque Charts from Trial 3Plaque Charts from Trial 3P. atrosepticum. These results have implications for the development of new phage-based treatments for black-leg disease and for limiting the associated crop damage.Based on these findings, it was concluded that vB_PatP_CB5 is the most effective at lysing and controlling growth of All Authors: No reported disclosures


Autologous hematopoietic cell transplantation (AHCT) is a well-established treatment for lymphoma. Unintended effects of this therapy include oral mucositis (OM) and gastrointestinal toxicities, resulting in poor clinical outcomes. The gut microbiome has been previously linked to transplant toxicities among allogeneic recipients, but little is known about the effects of AHCT on the oral microbiome.Seven patients with non-Hodgkin or Hodgkin lymphoma undergoing AHCT with palifermin (keratinocyte growth factor) were included. Buccal swab samples were collected at baseline and 14- and 28-days post-treatment. Oral microbial communities were characterized with 16 S rRNA amplicon sequencing. Temporal trends in community composition, alpha diversity, and beta diversity were investigated.Gemella and Actinomyces were observed from baseline. No significant temporal differences in alpha diversity were observed. Significant changes in beta diversity were recorded.A significant reduction in the relative abundance of the genera Results of this pilot study suggest treatment with AHCT and palifermin affects the oral microbiome, resulting in temporal shifts in oral microbial community composition. Future studies are warranted to confirm these trends and further investigate the effects of AHCT on the oral microbiome and how these shifts may affect health outcomes. High-dose chemotherapy followed by autologous hematopoietic cell transplantation (AHCT) is a well-established curative treatment for chemotherapy-sensitive relapsed lymphoma. Patients undergoing AHCT experience regimen-related toxicities, including oral mucositis (OM) and gastrointestinal toxicities, leading to considerable morbidity and high readmission rates with significant impact on clinical and economic outcomes –6. CondiHere we present a pilot study investigating the temporal changes in the oral microbiome of patients undergoing AHCT for lymphoma with the addition of palifermin, a keratinocyte growth factor that is approved to ameliorate OM and other gastrointestinal toxicities in the setting of AHCT –20. PaliEligible patients were > 18 years of age, had a diagnosis of non-Hodgkin lymphoma (NHL) or Hodgkin lymphoma with a plan to undergo AHCT after conditioning with BEAM . Patients received granulocyte colony-stimulating factor (G-CSF) mobilized peripheral blood stem cell graft. There were no early deaths, and all patients were followed up for at least 30 days after AHCT. All patients were on palifermin (60 mcg/kg/day), administered as an intravenous bolus injection for 3 consecutive days before and 3 consecutive days after myelotoxic therapy (two hours after stem cell graft infusion and the two subsequent days 1 and 2), for a total of 6 doses per FDA indication. Patients also received antimicrobial prophylaxis with levofloxacin (250 mg) orally daily and fluconazole (200 mg) orally daily. All patients started fluconazole on the day of start of conditioning (day − 6) and continued through day 60 post-AHCT. Levofloxacin was started on day − 1 through day 14 or later depending on neutrophil recovery (> 1000 cells/microL ) but discontinued before day 21 for all patients. Longitudinal buccal swab samples were collected with a 10-second rotating swab of the left and right buccal mucosa 5 times starting from the upper to the lower jaw with Puritan™ PurFlock™ Ultra Flocked swabs, placed directly in RNAlater vial. Samples were collected at least one hour after a meal. A total of three samples were collected from each patient: prior to AHCT (baseline), 14 days (D14) and 28 days (D28) from the day of hematopoietic cell infusion. The severity of oral mucositis and gastrointestinal toxicities were not prospectively collected for the study period. All participants provided informed consent to participating in this study. The study was approved by the Institutional Review Board at the University of Minnesota.Swab tips were cut using flame-sterilized scissors and placed into PowerBead tubes . Bacterial DNA was extracted with QIAGEN DNeasy® PowerSoil® Pro Kit on the automated QIAcube system , following the manufacturer’s instructions. The V4 hypervariable region of the 16 S rRNA gene was amplified using the 515 F/806R primer set . PolymerSequencing data were processed and analyzed with mothur (v. 1.31.1) . Sequencα = 0.05, with Bonferroni correction for multiple comparisons.Differences in alpha diversity between the three time-points were investigated with analysis of variance (ANOVA) and pairwise differences with simple linear regression models. Beta diversity was explored with principal coordinate analysis (PCoA) and evaluated statistically using analysis of similarity (ANOSIM) . DiffereWe enrolled 7 patients with median age of 63 years (range 26–75), 5 males and 2 females, one with a diagnosis of Hodgkin lymphoma and 6 with NHL Table . Mean esStreptococcus and Veillonella. For most participants (4/7), the relative abundance of Streptococcus decreased from baseline (mean = 55.0%) to D14 (mean = 28.3%), rebounding by D28 (mean = 36.0%) post-AHCT; these trends were not statistically significant . Genera Gemella and Actinomyces were most altered by AHCT procedures. The relative abundance of Gemella was significantly greater at baseline (mean = 9.8%) compared to D14  and remained low at D28 . The relative abundance of Actinomyces also decreased significantly from baseline (mean = 2.8%) to D14 , but recovered by D28 . Less abundant taxa were not interrogated due to data sparsity and a small number of samples that limited statistical analyses. An overall difference in community composition was observed across the three time points  diversity similar to previous reports, notable changes in beta  diversity were recorded [This pilot prospective study investigated the temporal changes in the oral microbiome of patients with lymphoma undergoing AHCT and treated with palifermin, added for mucosal cytoprotection. We observed that significant shifts in microbial community composition from baseline to D14 and D28 post-treatment, marked by reduction in Streptococcus and Gemella have been previously reported to dominate the oral microbial community in patients with breast cancer after chemotherapy exposure [Streptococcus increased one week post-transplant followed by a subsequent decrease [Streptococcus, Actinomyces and Gemella [Our observations have potentially unique clinical implications. exposure , while idecrease . Our findecrease , with itdecrease . Additiodecrease  may be ddecrease –37. Our  Gemella ; althoug Gemella . As note Gemella , composi Gemella –41. The This study has several noteworthy limitations. Firstly, due to the compositional nature of microbiome data, any observed changes in relative abundance of a given taxon are relative to the total abundance captured in the sample and thus may not be reflective of absolute changes in the microbiome or its biomass. Low microbial biomass in samples led to insufficient sequence results obtained, and the removal of 5 samples from some analyses. Due to this small sample size, the generalizability of our study and our ability to adjust for confounders was limited, further exacerbating the effects of confounding biases, as is the case with any observational study. However, due to the temporal nature of sampling, confounding due to non-time varying characteristics of the patients is less likely to be at play. Additionally, the direct effect of palifermin addition on the temporal changes in microbiome composition and diversity are not clear without a comparative cohort. Future investigations are warranted to further investigate our findings in a larger patient population, ideally comparing those changes to patients with similar disease and transplant characteristics not receiving palifermin. This pilot study serves as an important first step towards understanding the effects of AHCT with palifermin on the oral microbiome, uniquely reporting on longitudinal dynamic changes in oral microbiome composition in this patient population.


The prostone analog, lubiprostone, is approved to manage constipation-predominant irritable bowel syndrome. Lubiprostone also protects intestinal mucosal barrier function in animal models of colitis. The aim of this study was to determine if lubiprostone improves barrier properties in isolated colonic biopsies from Crohn’s disease (CD) and ulcerative colitis (UC) patients. Sigmoid colon biopsies from healthy subjects, CD and UC patients in remission, and CD patients with active disease were mounted in Ussing chambers. Tissues were treated with lubiprostone or vehicle to determine the effects on transepithelial electrical resistance (TER), FITC-dextran 4kD (FD4) permeability, and electrogenic ion transport responses to forskolin and carbachol. Localization of the tight junction protein, occludin, was determined by immunofluorescence. Lubiprostone significantly increased ion transport across control, CD and UC remission biopsies but not active CD. Lubiprostone selectively improved TER in both CD remission and active disease biopsies but not in control or UC biopsies. The improved TER was associated with increased membrane localization of occludin. Lubiprostone selectively improved barrier properties of biopsies from CD patients vs. UC and independent of an ion transport response. These data indicate that lubiprostone has potential efficacy in improving mucosal integrity in Crohn’s disease. TM; Takeda Pharmaceuticals Inc.), a bicyclic fatty acid prostone derivative, was developed to enhance intestinal fluid secretion in treating constipation. Lubiprostone activates luminally directed chloride secretion in intestinal epithelial cells (IEC), thereby increasing luminal fluid content and stool hydration. This alleviates symptoms associated with irritable bowel syndrome associated with constipation (IBS-C) and chronic idiopathic constipation (CIC) [- secretion has proven controversial as a number of conflicting studies have indicated a role for the CFTR chloride channel in the overall ion transport and Cl- secretory response to lubiprostone using human IEC lines, isolated human intestinal tissue and enteroids generated from CFTR knockout mice and cystic fibrosis patients harboring the DF508 mutation that prevents appropriate CFTR trafficking to the epithelial apical membrane [The current mainstay of IBD treatment is the use of broad-spectrum anti-inflammatory drugs, but there are several complications and limitations with this approach including the lack of effect in rectifying an underlying permeability defect in the intestinal epithelium that permits excessive access of luminal antigens to the lamina propria. Lubiprostone (Amitizaon (CIC) ,2,3,4. Ion (CIC) ,6,7,8,9.membrane ,11,12,13membrane .Clc2−/− mice [While much of the attention on lubiprostone has focused on its ability to promote fluid secretion to alleviate constipation–regardless of the precise mechanism of chloride secretion—this agent has also demonstrated efficacy in modulating other aspects of intestinal epithelial function ,16. Lubi−/− mice ,27,28,29In this study, we set out to identify if lubiprostone can improve barrier function defects associated with IBD by using explanted colonic tissue isolated from control subjects as well as Crohn’s disease (CD) and ulcerative colitis (UC) patients in remission or CD patients with active disease. We functionally characterized a beneficial effect of lubiprostone in partially improving ex vivo intestinal permeability in Crohn’s disease through modifying membrane localization of the tight junction protein occludin. These studies extrapolate the findings of a number of experimental models to human IBD and indicate a possible therapeutic potential for lubiprostone in restoring intestinal barrier function in the setting of inflammation.Given that the focus of our study was on analyzing epithelial transport and barrier properties, we first determined that colonic biopsies from CD and UC patients had an intact epithelial layer. Representative histological H&E-staining of intestinal biopsies derived from control subjects, patients with UC (in remission) and patients with CD (active vs. in remission) showed that colonic biopsies from healthy control patients and patients with Crohn’s disease or UC in remission had no signs of significant inflammation while an intact epithelium was present in all cases . Biopsiesc)) in colonic biopsies from control subjects vs. CD or UC in remission , compared with vehicle control (DMSO only), across colonic biopsies from control subjects with responses reaching a peak plateau at 0.1 and 1.0 μM  but this was significantly lower than the response to the same concentration in control subjects  subjects and CD patients in remission previously exposed to lubiprostone (30 min) in Ussing chambers, the magnitude of forskolin -induced ion transport responses was decreased in a largely lubiprostone concentration-dependent manner. Specifically, the highest concentration of lubiprostone pre-treatment (1.0 μM) significantly inhibited subsequent responses to forskolin  and permeability to FITC-dextran (4kD) over time (120 min). Tissues were kept in open-circuit conditions for the duration of the experiment apart from recording of individual TER measurements which were taken under closed circuit conditions. The change in TER over the course of the experiment was equivalent in tissues from control subjects, CD and UC patients in remission A. HoweveModulation of epithelial permeability is regulated by transmembrane proteins in the apical junctional complex, specifically, proteins that make up the apical tight junction. Since lubiprostone has been shown to promote occludin localization at the tight junction in ischemic porcine intestine, we focused on this tight junction protein as a molecular target that may contribute to the improved barrier performance induced by lubiprostone.Lubiprostone  did not appear to modulate localization of the TJ protein occludin (red) in colonic biopsies from normal control subjects A. Howevep < 0.05; p < 0.05; We next determined if lubiprostone could also improve TER in biopsies from patients with active CD. Biopsies from Crohn’s patients with active disease had a significantly lower resting baseline mucosal TER than control subjects (sc for tissues subsequently treated with either vehicle control (DMSO) or lubiprostone  that effectively increased TER , cytokine profiles, unique vs. overlapping genetic risk variants, and most pertinently the differential responses to various treatment approaches , it should not be surprising that lubiprostone also displayed a selective effect between IBD subtypes . In animStudies were performed according to the guidelines of the Declaration of Helsinki. Approval was granted by the Human Research Protections Program, University of California San Diego (IRB #071794) and written informed consent was obtained from all study subjects.n = 17), Crohn’s disease  and ulcerative colitis  patients in remission and CD patients with active disease (n = 4), by a gastroenterologist with expertise in IBD  at University of California, San Diego Medical Center. Patient characteristics are included in Control subjects were individuals referred for colonoscopy for general evaluation . IBD subjects in remission had an established diagnosis of IBD for at least 3 months prior to enrollment and were referred for colonoscopy for disease assessment or surveillance. Patients were classified as being in remission based on an absence of visible inflammation by colonoscopy and with a Mayo endoscopy score of zero (UC), and a simple endoscopic score for Crohn’s Disease (SES-CD) endoscopy score of zero. Active Crohn’s disease (CD) patients had a diagnosis of CD for at least 3 months prior to the start of the study and were undergoing colonoscopy to assess disease activity. Six biopsies from macroscopically normal sigmoid colon were obtained from normal subjects undergoing routine colorectal cancer screening  and placed on gel foam inserts   by a gastroenterologist . Biopsied tissues were immediately placed into cold, pre-oxygenated Ringer’s solution (pH 7.4) with the following composition (in mM): 140 Na2O. Tissues were stained with hematoxylin and eosin to identify histological features and primarily to confirm that tissues had an intact epithelium. Images were taken with an Olympus IX71 microscope.Immunostaining was performed on 4 mm-thick, formalin-fixed, paraffin-embedded tissue sections mounted on positively charged X-tra slides . Paraffin sections were deparaffinized in xylene, rehydrated, and washed in HThe 4 mm-thick colonic biopsies were placed in 4% paraformaldehyde, embedded in paraffin, and sectioned (5 μM) onto positively charged X-tra slides . Sections were de-waxed using xylene, hydrated with graded ethanol and heated for antigen retrieval (10mM sodium citrate buffer). After rehydration, an outline was drawn around the sections using a PAP pen. The sections were then treated with proteinase K (0.2 μg/mL) Tris-EDTA buffer and blocked ).The primary rabbit anti-occludin antibody (Invitrogen) was diluted at 1:50 in 5% BSA/PBS and pipetted onto the designated sections as a bubble held by the PAP pen outline. The slides were then incubated overnight at 4 °C in a humidifying chamber. The slides were then washed with PBS (×3), and secondary Alexa Fluor 568-conjugated goat anti-rabbit (Molecular Probes) was pipetted as a 1:100 dilution in 5% BSA/PBS onto the slides as described above in the primary step and incubated for 30 min at 37 °C to evaluate localization of occludin. The slides were washed three times with PBS. The sections were then incubated with Hoechst 33258 (Molecular Probes) in PBS (1:100) for 5 min at 37 °C and washed three times in PBS. Finally, a coverslip was mounted on the sections using ProLong Gold antifade reagent (Molecular Probes) and allowed to dry at room temperature. Imaging was performed using the Zeiss LSM 510 confocal imaging system. Occludin immunofluorescence staining of intestinal tissues was performed using ImageJ as previously [2 . Tissues were bathed bilaterally in 5 mL oxygenated Ringer’s solution (composition as above) at 37 °C. The tissues were short-circuited by an automated multichannel voltage/current clamp (VCC MC8) and the short-circuit current (Isc), expressed in μA, across the tissues was monitored at intervals as an indication of net active ion transport. Tissues were allowed to equilibrate for a 30 min period, at which point baseline potential difference (PD) expressed in mV, short circuit current (Isc) and tissue conductance (G) were measured to confirm both tissue viability and calculate baseline mucosal transepithelial electrical resistance (TER), prior to administration of any reagents [Mucosal biopsies were mounted on specially designed Ussing chamber inserts with a window area of 0.031 cmreagents ,46. For FITC-dextran 4kD (FD4)  was added to the mucosal bathing solution of colonic biopsies mounted in Ussing chambers to reach a final concentration of 2.2 mg/mL. Serosal bathing media samples were collected at 30 min intervals over a period of 120 min and an equal volume of physiological solution was added to the serosal side to negate the hydrostatic pressure imbalance. Collected samples were stored in the dark at 4 °C until fluorescence intensity (492-nm excitation and 515-nm emission wavelengths) was measured using a microplate spectrophotometer .2+-dependent agonist Carbachol  to the serosal side of chamber. The concentrations of individual agonists used were based on maximally induced responses observed in intestinal tissues mounted in Ussing chambers, including ex vivo human intestine studies [Mucosal biopsies mounted in Ussing chambers were treated with either DMSO (0.045%) as a negative control, or increasing concentrations  of lubiprostone  on both the mucosal and serosal surfaces added to individual tissue preparations. After 120 min, tissues were treated with the cAMP-dependent agonist forskolin  applied to the mucosal and serosal side. After approximately 5 min (at the peak of the forskolin response plateau phase), tissues were treated with the Ca studies . This apsc responses (sc) to lubiprostone were calculated by subtracting the baseline Isc from peak Isc. ΔIsc responses to forskolin and carbachol were calculated in the same manner. Transepithelial electrical resistance (TER) was calculated from the conductance and Isc based on Ohm’s law (R = V/I). Percent change in TER was also calculated from basal and post-treatment TER.It-test where appropriate, using GraphPad prism software . A p-value of Data are presented as the mean ± standard error of mean (SEM). Comparisons between groups were performed by using analysis of variance (ANOVA) followed by the Newman–Student–Keuls post-test or unpaired Student’s