A keyword-based search of comprehensive databases such as PubMed may returnirrelevant papers, especially if the keywords are used in multiple fields ofstudy. In such cases, domain experts (curators) need to verify the results andremove the irrelevant articles. Automating this filtering process will savetime, but it has to be done well enough to ensure few relevant papers arerejected and few irrelevant papers are accepted. A good solution would be fast,work with the limited amount of data freely available (full paper body may bemissing), handle ambiguous keywords and be as domain-neutral as possible. Inthis paper, we evaluate a number of classification algorithms for identifying adomain-specific set of papers about echinoderm species and show that theresulting tool satisfies most of the abovementioned requirements. Echinodermsconsist of a number of very different organisms, including brittle stars, seastars (starfish), sea urchins and sea cucumbers. While their taxonomicidentifiers are specific, the common names are used in many other contexts,creating ambiguity and making a keyword search prone to error. We tryclassifiers using Linear, Naïve Bayes, Nearest Neighbor, Tree, SVM,Bagging, AdaBoost and Neural Network learning models and compare theirperformance. We show how effective the resulting classifiers are in filteringirrelevant articles returned from PubMed. The methodology used is more dependenton the good selection of training data and is a practical solution that can beapplied to other fields of study facing similar challenges.http://xenbaseturbofrog.org/pub/Text-Topic-Classifier/Database URL The code and date reported in this paper are freely available at Text mining in biological data has been of much interest , includiwww.echinobase.org) (Echinobase (ase.org) , the onlase.org) . Our aimThe initial approach was for the Echinobase curation team to inspect the downloadedpapers and reject the irrelevant ones. Our keyword search returned over18 000 papers from PubMed, and the curation team, after verifying a subset ofthem, estimated that between 30% and 50% were not relevant or not ofinterest to Echinobase users. Other possible solutions to reducing the number ofreturned papers include limiting the search to titles and abstracts only andsearching for multi-word keywords when possible, which reduce the number ofirrelevant returns but do not filter all of them. As a policy, Echinobase hasdecided to err on the side of not missing relevant papers, and given thetime-consuming nature of manually identifying false positives, we developed theautomated classification system described in this paper, to help the curationprocess.As shown in ref. , text miRelying on only titles and abstracts both limits the amount of information(undesirable) and focuses on the parts with the main message of the papers(desirable). As with any supervised learning effort, having an appropriate mix ofrelevant and irrelevant papers as training data is the key to achieving goodclassification accuracy. We tried to solve the ambiguity problem through carefulselection of training papers, making sure common species names appear often enoughto be learned by the classifiers. We note that all domain-dependent parts of thiswork are encapsulated in the training data, and the following methodology isdomain-neutral.We used Python to develop the code and used classifiers offered by the Scikit-learn(sklearn) machine learning library . ForeacWe used accuracy, precision and recall to measure the performance of the classifiers.Scikit-learn provides methods to calculate and report these metrics, which weemployed. We developed command line tools for training and testing the classifiersand for running the classifiers on one or more text files.http://xenbaseturbofrog.org/pub/Text-Topic-Classifier/. The code wasdeveloped and tested with Python 3.7.4. The package includes code for training andtesting the classifiers, a tool for running the classifiers on individual articles,as well as a tool for running the classifiers on multiple articles in batch mode.These tools allow the readers to incorporate this filtering mechanism into theirexisting literature loader pipelines: An existing software system to download papersfrom a resource such as PubMed can pass the contents of downloaded papers to thetools and decide to add or reject the papers based on the classifier’soutput.All code and data needed to run the tool and reproduce the results are freelyavailable at All computations reported in the next section were performed on a PC with an Inteli7-8700 CPU with 12 logical (hyper-threaded) cores and 32 GB of RAM. The time ittakes to train a classifier varies with the type of classifier, with ensemble onesbeing the slowest, as expected. When possible, we instructed Scikit-learn to use allavailable cores. During the training phase, CPU usage was mostly at 100% forensemble classifiers, with the PC being very sluggish, and around60–80% for other classifiers. The maximum RAM usage during thetraining phase was around 4 GB, with 1 GB being the usual usage. Training, excludingthe optional cross-validation phase, took less than 5 seconds for all theclassifiers except Bagging and MLPClassifier, which took around a minute each.Please note that even small differences in training times are amplified when doing alarge number of cross validations (CVs). Classification times mirror the trainingtimes. As an example, the Bagging classifier, using all available cores, took about30 seconds to classify the 248 papers mentioned below, while the Ridgeclassifier took less than 2 seconds to process the same files. We used the results of our PubMed E-Utilities keyword search for echinoderms, whichcontained numerous relevant and irrelevant papers, to create training and testingsets. Professional curators manually selected a set of 496 relevant (positive)papers and a set of 405 irrelevant (negative) papers to train the classifiers. Wetried to keep the selected papers, both relevant and irrelevant, as varied aspossible, so care was taken for the relevant set to contain most of the echinodermspecies of interest in Echinobase, though due to their high number, not all werecovered.To test the classifiers, 229 relevant and 220 irrelevant papers were selected by thecurators. We performed 20-fold CV on the training dataset to get a measure of theirsuitability, then fitted the data to the training set and finally tried theresulting classifiers on the test dataset. The results appear in While most classifiers achieve similar results for training and test accuracy,Decision Tree, AdaBoost and Bagging perform much better on the test data. Ingeneral, we expect the results to be data- and domain-dependent, so we encourage thereaders to try as many of the classifiers as possible on their own data. We alsonote that results may change with fine-tuning classifier parameters. The exactparameters used by us are in the source code.Since accuracy does not provide a complete picture, we also measured precision,recall and F-score values for the test data, as shown in In another test, we measured the ability of the Bagging classifier to rejectirrelevant papers returned from PubMed. To do so, we collected 248 papers fromPubMed which were returned when searching for keywords in the titles, abstracts andbodies but not when excluding the bodies. We expect these papers to containambiguous uses of our keywords and hence be mostly irrelevant. A domain expertreviewed the papers and determined 242 of them to be indeed irrelevant and 6 to berelevant. The classifier declared 240 papers as being irrelevant and 8 as relevant.The classifier and the domain expert agreed on 5 out of the 6 relevant papers and239 out of 242 irrelevant papers, resulting in a 98.3% accuracy (4 mistakesout of 248). We consider the results to confirm that leaving out the full body inechinoderm paper search from PubMed will greatly reduce the number of irrelevantreturns, although it is possible to lose relevant papers.We compared our test accuracy results with NCBI’s LitSuggest , an onliOur problem involves sorting relevant and irrelevant publications in the presence ofambiguous keywords. One possible line of action is to remove some of the irrelevantsearch returns by excluding the paper body and look for multi-word keywords as muchas possible but that increases the probability of missing relevant papers. Analternative approach is to cast a wide search net and retrieve more papers fromresources such as PubMed and then use classifiers to reject the irrelevant ones. Todo so, we evaluated a variety of classification methods with very differentunderlying algorithms. In our case, we chose the Bagging method based on highaccuracy, as well as balanced precision and recall values.We consider the training data to be the key factor in obtaining our results. A numberof different curators, over a course of few months, were involved in creating thetraining and test data. These were professional biologists with varying degrees offamiliarity with echinoderms. During the development phase, we noticed that somepapers were classified as both relevant and irrelevant. We also noted that in a fewcases the same paper switched from relevant to irrelevant or vice versa over time.We attribute these to the fact that different curators may have different opinionsabout what papers should be included in Echinobase. We also expect thedatabase’s priorities and focus to change over time. This means thatimproving the classifiers with different or better training data is a continuingprocess. Over time, clearly miss-classified papers can be added to the relevant andirrelevant training sets, to hopefully achieve better results with retrainedclassifiers.It is important to note that while we used specific domain knowledge (echinoderms) tocreate the classifiers, the underlying software design is domain-neutral, and thesame methodology can be applied in other fields. Given this data dependency, weencourage the readers to try all or most of these classification algorithms whensolving similar problems in other domains and choose the better ones. If a number ofclassifiers show clear advantages over others, they can be combined in an ensembleclassifier, such as sklearn’s VotingClassifier, to hopefully improve theresults further.http://geneontology.org/). Considering the encouraging results ofthis paper, we intend to extend the work and use it to perform multi-class tagging,where papers downloaded from PubMed are classified as containing different topics ofinterest. Such a classification system will speed up the paper curation process.Established model organism databases such as Xenbase  focus on


Plasmodium berghei are the important proteins in erythrocyte invasion and the replication of parasites. In this study, we generated three VLPs expressing MSP-8, MSP-9 or RAP1 together with influenza virus matrix protein M1 as a core protein, and the protection and alleviated inflammation induced by VLP immunization were investigated. Mice were immunized with a mixture of three VLPs, MSP-8, MSP-9 and RAP1, and challenge-infected with P. berghei. As a result, VLPs immunization elicited higher levels of P. berghei or VLPs-specific IgG antibody responses in the sera upon boost compared to that upon prime and naive. Upon challenge infection with P. berghei, higher levels of CD4+ T cell and memory B cell responses in the spleen were also found in VLPs-immunized mice compared to non-immunized control. Importantly, VLP immunization significantly alleviated inflammatory cytokine responses  both in the sera and spleen. VLP vaccine immunization also assisted in diminishing the parasitic burden in the peripheral blood and prolonged the survival of immunized mice. These results indicated that a VLPs vaccine containing MSP-8, MSP-9 and RAP1 could be a vaccine candidate for P. berghei infection.Virus-like particles (VLP) are a highly efficient vaccine platform used to present multiple antigenic proteins. Merozoite surface protein 8 (MSP-8), 9 (MSP-9) and rhoptry-associated protein 1 (RAP1) of  Plasmodium parasite remains a serious disease threatening public health. In 2016, significant funds were invested to implement a malaria control and elimination program, which helped to reduce the burden of malaria disease [Malaria caused by the  disease . Current disease .P. berghei, a rodent Plasmodium parasite, has been used extensively in vaccine development studies due to its structure, physiology and life cycle similar to that of human and other primate Plasmodium parasites [Plasmodium parasite can lead to death due to the severity of symptoms caused by inflammation [arasites ,6. Infecammation ,8,9. Becammation ,11.Plasmodium, which is considered an excellent target for vaccine development as an antigen that sufficiently stimulates the host’s immunity and is located in the parasitophorous vacuole (PV) during an erythrocytes infection of the host [In our previous studies, we investigated the efficacy of virus-like particles (VLPs) vaccines by targeting the merozoite surface proteins (MSP). The MSP family forms the most abundant protein in the merozoite form of the host ,13,14. Ithe host ,16. Howethe host ,17,18. VPlasmodium berghei rhoptry-associated protein 1 (RAP1) is located in the rhoptry bulb organ of merozoites. Furthermore, RAP1 is formed into a complex with other rhoptry-associated proteins (RAP2 and RAP3) and is secreted from it, and the RAP complex is located in the parasitophorous vacuole (PV) formed after Plasmodium invades the host cell [Plasmodium parasites have been reported to have longer survival times in mice than normal Plasmodium parasites [ost cell ,20,21. Aarasites , indicat+ T and memory B cell responses in the spleen were observed in VLPs-immunized mice than non-immunized control upon challenge infection. Importantly, VLPs vaccination significantly reduced the parasitemia level and inflammatory cytokine  responses both in blood and spleen, and significantly prolonged survival time.In the study, we investigated the vaccine efficacy induced by multiple proteins MSP-8, MSP-9 and RAP1 containing VLPs. We found that a multiple-protein containing VLPs vaccine induces a parasite or VLPs-specific IgG antibody response. A higher population of CD4All animal experiments were performed in strict accordance with the Kyung Hee University IACUC guidelines (permit number: KHSASP(SE)-19-188). Animals were housed in approved facilities with a 12 h day and night cycle, with easy access to food and water. To minimize animal suffering, mice were humanely euthanized upon reaching the humane intervention point which was determined to be at a weight loss exceeding 20% of initial bodyweight.Plasmodium berghei ANKA strain 2.34 was serially maintained in mice through intraperitoneal passaging. Recombinant baculovirus (rBV) and VLPs were generated using Spodoptera frugiperda Sf9 cells. The Sf9 cells were cultured in spinner flasks with SF900II media purchased from Invitrogen . P. berghei whole antigens were prepared following the method previously described [P. brerghei-infected mice at 8 weeks post-infection via retro-orbital plexus puncture for serum acquisition. Monoclonal anti-M1 antibody and horseradish peroxidase (HRP)-conjugated secondary mouse IgG antibodies were purchased from Abcam  and Southern Biotech , respectively. Fluorophore-conjugated antibodies were purchased from BD Bioscience  and used to perform flow cytometry.BALB/c mice  were purchased from NARA Biotech . escribed ,16,23,24P. berghei merozoite surface protein 8 , merozoite surface protein 9 (MSP-9) and influenza matrix protein 1 (M1) genes were cloned, as described previously [P. berghei rhoptry-associated protein 1 (RAP1) gene was acquired from GenScript . For plasmid constructions, eviously ,16,25. Cg. VLPs present within the supernatants were purified and stored at 4 °C as described elsewhere [The recombinant plasmid was transformed into DH10Bac competent cell. Colonies were screened and bacmid DNA from successful clonal construct was extracted using FavorPrep Gel Purification Kit . MSP-8, MSP-9, RAP1, or influenza M1-expressing recombinant baculoviruses (rBVs) were prepared as previously described ,27. Threlsewhere ,27. P. berghei antigens were characterized using a transmission electron microscope (TEM) as described previously [P. berghei-infected mice (1:500 dilution) or monoclonal mouse M1 antibody (1:2000 dilution) overnight at 4 °C. After washing, membranes were probed with HRP-conjugated mouse IgG secondary antibodies (1:3000) dilution for 1 h, RT.VLPs expressing the three eviously ,16. Purieviously . Twenty-eviously ,16 by usn = 12 per group). The VLPs containing MSP-8, MSP-9, or RAP1 were mixed at a ratio of 1:1:1 . At weeks 0 and 4, a total of 150 µg VLPs comprising equal concentrations of MSP-8, MSP-9, and RAP1 VLPs were intramuscularly (IM) administered into mice. On week 8, which corresponds to 4 weeks after boost immunization, mice were intraperitoneally (IP) challenge-infected with 0.5% of P. berghei in 100 µL of PBS, which includes all forms of the parasite. At 7 dpi, six mice were randomly selected from each group and sacrificed for blood and spleen sample acquisition. The remaining mice in each group were monitored daily to record bodyweight changes and survival rates. Six-week-old female BALB/c mice were evenly distributed with appropriate control groups , and supernatants from the individual mouse were carefully collected for cytokine detection as described previously ,16. BothP. berghei-infected mice. Blood samples were collected at 3, 7, 15, 22, 28, 34, 44, 50, and 56 days post-challenge infection. Briefly, 2 µL of blood samples were mixed with 100 µL of PBS pre-mixed with 500 U/mL of heparin. Prior to flow cytometry, samples were stained with 1 µL of SYBR Green I  and subsequently incubated for 30 min, 37 °C. Samples (104 RBCs) were acquired using the BD Accuri C6 flow cytometer  for analysis.Retro-orbital plexus puncture was performed to acquire blood samples from P < 0.05.Statistical significance between the groups was determined by performing a one-way analysis of variance (ANOVA) with Tukey’s post hoc test or Student’s t-test using PC-SAS 9.4 . Data are expressed as mean ± SEM and asterisks were used to denote statistical significance. Differences between the groups were considered to be statistically significant for EcoRI/HindIII. Sf9 cells were co-infected with rBVs expressing P. berghei RAP1 and influenza M1 to generate RAP1 VLPs. The morphology of VLPs was determined by electron microscopy , splenic T cell (CD4+ and CD8+) and memory B cell (MB) responses were evaluated (+ T cell and MB cell populations were detected from spleens of immunized mice compared to non-immunized mice control (* P < 0.05). These results indicate that VLPs immunization contributed to significantly increased CD4+ T cell and memory B cell populations.Mice were sacrificed after valuated . T cells* P < 0.05, ** P < 0.01). These results indicated that VLP immunization induced significant reductions of inflammatory cytokines in blood and spleen.Serum and spleens were harvested at 7 dpi to evaluate the production of inflammatory cytokines TNF-α and IFN-γ. As shown in P. berghei infection to evaluate the parasitic burden in mice (** P < 0.01). Mice immunized with VLPs maintained their normal weight for 48 days, while the non-immunized mice control began to experience reduced body weight from day 4 post-challenge infection  containing virus-like particles (VLPs) vaccine for ytokines ,16,29. HP. berghei infection. As expected, vaccination with VLPs containing multiple proteins MSP-8, MSP-9 and RAP1 showed a significant reduction (nine-fold) of parasitemia in the blood compared to single MSP-8 or MSP-9 VLPs vaccination. RAP1 is known to form a parasitophorous vacuole (PV), which is necessary for parasite growth in host cells and contributes to parasite toxicity ,20,21,22In our previous studies, MSP-8 or MSP-9 expressing VLPs immunization limited the production of pro-inflammatory cytokines, which contributed to the survival and reduced parasite burden in immunized mice ,16. In aPlasmodium berghei is known to overproduce pro-inflammatory cytokines IFN-γ and TNF-α [P. berghei is highly pathogenic and uniformly lethal, resulting in severe pathology in mice upon IP route infection [Severe pathology in mice infected with nd TNF-α ,8. Additnd TNF-α . It has nd TNF-α . The incnd TNF-α ,31,32,33nfection ,16. The nfection . The connfection . In the Mouse survival and body weight changes are important indicators for the evaluation of vaccine efficacy. Compared to single VLPs vaccines, multiple proteins containing VLPs vaccine showed significantly improved body weight loss and survival. Individual AMA1, MSP-8 or MSP-9 expressing VLPs immunization showed significant increases in parasite-specific IgG antibody responses, T cell responses, and B cell responses, correlating with significant reductions of parasitemia and prolonged survival time ,16,29.P. berghei MSP-8, MSP-9 and RAP1 in mice. We found that VLPs vaccination led to higher levels of P. berghei-specific IgG antibody response, CD4+ T cell and memory B cell responses. Vaccination with VLPs significantly reduced the pro-inflammatory cytokine responses, parasitemia and body weight loss. These results indicated that multiple protein-containing VLPs could be an effective vaccine candidate for malaria parasite infections.In conclusion, we investigated the vaccine efficacy induced by VLPs vaccine containing


Heart failure with preserved ejection fraction (HFpEF) is an unsolved and growing concern in cardiovascular medicine. While no treatment options that improve prognosis in HFpEF patients has been established so far, SGLT2 inhibitors (SGLT2i) are currently being investigated for the treatment of HFpEF patients. SGLT2i have already been shown to mitigate comorbidities associated with HFpEF such as type 2 diabetes and chronic renal disease, however, more recently there has been evidence that they may also directly improve diastolic function. In this article, we discuss some potential beneficial mechanisms of SGLT2i in the pathophysiology of HFpEF with focus on contractile function. Heart failure (HF) with preserved ejection fraction (HFpEF) is diagnosed in a growing proportion of patients presenting with symptoms of HF . PatientSGLT2i were initially used as oral anti-diabetes agents via blood glucose reduction from the inhibition of SGLT2 transporters in the kidney. Remarkably, SGLT2i showed distinct beneficial effects on cardiovascular outcomes in patients with type 2 diabetes mellitus (T2DM) but also in patients with HF independent of T2DM . The DAPGiven the favorable cardiovascular outcomes of SGLT2i in patients with T2DM and established cardiovascular disease or at high risk and in patients with HFrEF, SGLT2i are currently being investigated in HFpEF. The recently published EMPEROR-Preserved trial investigated the effect of empagliflozin on the composite endpoint of cardiovascular death or HF hospitalization in 5988 patients with HF and an EF > 40% . Empagliflozin significantly reduced the primary endpoint, which was mainly driven by a ∼29% reduced risk of hospitalization for HF . AlthougA recent meta-analysis of randomized controlled studies regarding effects of SGLT2i in ∼16,000 patients with HF with or without T2DM, indicated that the subgroup of patients with HFpEF may also achieve a risk reduction of the composite endpoint of cardiovascular death or HF hospitalization . The SOLInhibition of SGLT2 transporters in the kidney causes glucosuria, natriuresis, and osmotic diuresis. This results in lower blood glucose levels, obesity, blood pressure and improved lipid metabolism . All of However, some arguments against a major contribution of classical cardiovascular risk factors for the improvement of clinical outcomes should be discussed. It has been suggested that at least in diabetic patients the reduction of cardiovascular risk factors like blood pressure , cholestAnother important consideration is cardiorenal syndrome: The hallmark feature of HF is salt and water retention, both of which are regulated by the kidneys, therefore the intimate interaction between the heart and kidneys cannot be discounted. The Study to Evaluate the Effect of Dapagliflozin on Renal Outcomes and Cardiovascular Mortality in Patients With Chronic Kidney Disease (Dapa-CKD) and Evaluation of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Participants With Diabetic Nephropathy (CREDENCE) trials both showed striking benefits in hard renal outcomes including progression of CKD, end stage renal disease and death . InteresIt is known that patients with either clinically stable HFpEF, or at hospital admission due to HFpEF, worsening of renal function is independently associated with all-cause mortality . Taken tHFpEF on the myocardial level is characterized by diastolic dysfunction with impaired relaxation leading to compromised filling of the ventricles. Thus, improving diastolic function should theoretically be the ultimate treatment strategy for patients with HFpEF. While there is currently no approved therapy for the specific treatment of diastolic dysfunction, there is growing evidence suggesting that SGLT2i may directly target diastolic function. In small but prospective clinical studies in patients with T2DM and normal EF, SGLT2i improved diastolic function as determined by echocardiography after 3  and 6 moin vitro, in the absence of systemic confounders  as may occur in any in vivo model. Thus, these experiments indicate a direct cardiac effect of SGLT2i-induced improvement of diastolic function  the myocardial stiffness based on the viscoelastic properties mediated largely by myofilament stiffness as well as structural remodeling of the extracellular matrix and (2) myocardial relaxation mediated by Caeticulum . The folMyofilament function critically determines diastolic cardiomyocyte stiffness, and myofilament stiffness is abnormally increased in HFpEF patients . The giaIncreased cardiac fibrosis adversely affects diastolic function and is a common feature in HFpEF patients . The etiPreventing the development of cardiac fibrosis using SGLT2i likely impedes the progression of myocardial stiffness and may therefore be advantageous for the causal treatment HFpEF patients. Nevertheless, evidence of these specific and direct mechanisms of SGLT2i on fibrosis is limited. Moreover, the effect of these agents on myocardial fibrosis may be confounded by their other cardiovascular effects which may deter disease progression via different mechanisms.per se  indicate that systolic Ca2+ release and cell shortening could be unaltered or increased, while Ca2+ reuptake and relaxation may be impaired along with elevated diastolic Ca2+ depending on the model studied (+ homeostasis and thereby Ca2+ handling (firstly reported by in vitro with empagliflozin (24 h) Ca2+/calmodulin-dependent protein kinase IIδ (CaMKII) activity has been found to be diminished  models varied. In human cardiomyocytes from patients with HFrEF acute treatment with empagliflozin did not change systolic Ca2+ transient or diastolic cytosolic Ca2+ , which were first reported for rabbit myocardium  and inflammation  in human HFpEF myocardium after in vitro treatment (In HFpEF, also inflammation/oxidative stress-mediated endothelial dysfunction may impair cardiomyocyte function . A recenreatment .2+ leak as well as reduced late Na+ current (As empagliflozin reduced oxidative stress and inflammation in human HFpEF myocardium, NO bioavailability and PKG signaling were improved upon exposure to empagliflozin leading to lower myofilament stiffness and thereby improved diastolic function in human myocardium . Thus, t current . Finally current . HoweverAs we are on the cusp of welcoming the first prognostically beneficial drug class in HFpEF, understanding the mechanistic effects of SGLT2i on the myocardium will be key in maximizing its potential in this important patient population. While some putative targets and pathways are still rather speculative, evidence from human myocardium including human HFpEF hearts indicate direct favorable effects on diastolic function via reduced myofilament stiffness due to improved PKG signaling. While this review discusses some potentially relevant mechanisms of SGLT2i in HFpEF, also other pleiotropic effects of SGLT2i have been described as discussed elsewhere . Both thSP, NH, JS, and SS drafted and revised the manuscript. All authors contributed to the article and approved the submitted version.SP received speaker’s honoraria from AstraZeneca. JS has received speaker’s honoraria from Boehringer Ingelheim Pharma GmbH and AstraZeneca. SS received speaker’s/consultancy honoraria from Boehringer Ingelheim Pharma GmbH and AstraZeneca. The remaining author declares 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.


A20 (Tnfaip3) and Abin-1 (Tnip1), in intestinal epithelial cells (IECs) sensitized mice to both TNF-dependent and TNF-independent death. Here we show that TNF-independent IEC death after A20 and Abin-1 deletion was rescued by germ-free derivation or deletion of MyD88, while deletion of Trif provided only partial protection. Combined deletion of Ripk3 and Casp8, which inhibits both apoptotic and necroptotic death, completely protected against death after acute deletion of A20 and Abin-1 in IECs. A20- and Abin-1–deficient IECs were sensitized to TNF-independent, TNFR1-mediated death in response to lymphotoxin α (LTα) homotrimers. Blockade of LTα in vivo reduced weight loss and improved survival when combined with partial deletion of MyD88. Biopsies of inflamed colon mucosa from patients with IBD exhibited increased LTA and IL1B expression, including a subset of patients with active colitis on anti-TNF therapy. These data show that microbial signals, MyD88, and LTα all contribute to TNF-independent intestinal injury.Anti-TNF antibodies are effective for treating patients with inflammatory bowel disease (IBD), but many patients fail to respond to anti-TNF therapy, highlighting the importance of TNF-independent disease. We previously demonstrated that acute deletion of 2 IBD susceptibility genes,   ITLN1, NOS2,ATG16L1, XBP1, A20 (TNFAIP3), ABIN-1 (TNIP1), among others  is the result of an abnormal immune response to microbial stimuli in genetically susceptible individuals, culminating in intestinal epithelial cell (IEC) injury –6. IECs g others –12. UndeA20 (TNFAIP3) and ABIN-1 (TNIP1) are linked to a variety of inflammatory disorders affecting multiple tissues, including IBD , Zbp1, IFN signaling, ripoptosome activation, or other inflammatory death pathways  and floxed Abin-1 (fl/flAbin-1) on a T2+Vil-cre-ER background (A20/Abin-1T-ΔIEC) undergo acute deletion of A20 and Abin-1 in IECs upon treatment with tamoxifen, culminating in spontaneous apoptotic IEC death, severe enterocolitis, and rapid mouse lethality  oil injection has been reported to cause peritoneal inflammation, foam cell formation, and depletion of resident macrophages , and with this approach A20/Abin-1T-ΔIEC–/–Tnf mice died with similar kinetics to those of A20/Abin-1T-ΔIEC mice (A20/Abin-1T-ΔIEC–/–Tnf mice undergo deletion of A20 and Abin-1 when treated with 200 nM 4-hydroxytamoxifen (4-OHT) in vitro, but they are protected from spontaneous cell death  mice in our facility. Germ-free mice conventionalized with cecal contents from SPF A20/Abin-1T-ΔIEC–/–Tnf mice (GF-CONV) exhibited rapid mortality upon deletion of A20 and Abin-1 -induced death, and we observed that deletion of A20 and Abin-1 in IECs dramatically increased susceptibility to poly(I:C)-induced death (Trif in A20/Abin-1T-ΔIEC–/–Tnf mice using CRISPR/Cas9 editing and generated a strain of mice with a deletion and premature stop codon at the Trif locus (A20/Abin-1T-ΔIEC–/–Tnf–/–Trif mice exhibited significantly reduced IFN-β production in response to poly(I:C), confirming functional deletion at the protein level - and LPS-induced cell death (TRIF contains a RIP homotypic interaction motif (RHIM) domain and interacts with receptor-interacting serine/threonine kinase 3 (RIPK3) to induce death signaling downstream of TLR3 in response to the dsRNA analog polyinosinic-polycytidylic acid [poly(I:C)] , 57–60. ed death . LPS, ined death . Both poed death . We deleif locus . Splenocin level . To detell death . TherefoTrif deletion rescued TNF-independent death in response to poly(I:C) and LPS in vitro, we treated A20/Abin-1T-ΔIEC–/–Tnf–/–Trif mice with tamoxifen to see if Trif deletion also protected A20- and Abin-1–deficient IECs in vivo. Deletion of Trif in A20/Abin-1T-ΔIEC–/–Tnf mice provided a modest survival benefit, with slightly delayed median survival relative to +/+Trif controls , Fas ligand, and TRAIL (TNF-related apoptosis-inducing ligand) failed to induce significant death of A20/Abin-1T-ΔIEC–/–Tnf enteroids in vitro  to TNF by 1000-fold in IECs  are reported to bind to TNFR1 and activate death signaling  induced significant death in A20/Abin-1T-ΔIEC–/–Tnf enteroids  but not TNFR1 , unlike most prior studies investigating TNF- and LTα3-induced death , and cleaved PARP in response to LTα3 as compared with control fl/flA20fl/flAbin-1–/–Tnf enteroids  for Lta in the intestine after IEC deletion of A20 and Abin-1. The A20/Abin-1T-ΔIEC–/–Tnf mice exhibited increased Lta-positive cells in both the small intestine and colon as compared with fl/flA20fl/flAbin-1–/–Tnf mice, correlating increased local Lta-expressing cells with histologic severity upon deletion of A20 and Abin-1 in IECs  is a TNF- and NF-κB–inducible gene, and mRNA levels were predictably increased in inflamed areas of IBD patients as compared with noninflamed IBD biopsies and non-IBD controls (ABIN-1 (TNIP1) mRNA levels were not significantly different among these groups .To further determine whether lymphotoxin or IL-1β contributes to intestinal injury in patients with IBD, we measured ammation . Anotherd tissue . These td tissue . Interesammation , suggestid stain  and quanid stain . NotablyGenome-wide association studies have implicated hundreds of genes in CD and UC pathogenesis, but it is unclear how these genes increase disease susceptibility or influence treatment response . Since ISetdb1–/– IECs die primarily from necroptotic cell death. The TNF-independent IEC death observed in A20/Abin-1T-ΔIEC–/–Tnf mice is also unusual when compared with other models. The colitis in mice with conditional IEC knockout of NEMO, FADD, CASP8, and RIPK1 mice is largely reversed by TNF or TNFR1 deletion , suggesting that ZBP1 contributes to TNFR1-independent death in the ileum  pyroptotic death is another potential TNF-independent death pathway to consider in A20- and ABIN-1–deficient IECs. A20 restricts NLRP3 inflammasome activation , 85, so ic death , 89, 90,ic death , 92. Thificiency , 93, 94.3 and MyD88 contribute to TNF-independent intestinal damage in this model of severe enterocolitis. Deletion of A20 and Abin-1 unveils a role for LTα3-induced TNF-independent IEC death in primary cells both in vivo and in vitro. To our knowledge, this is the first description of TNF-independent LTα3-induced cytotoxicity in IECs in the absence of death-sensitizing agents. There are many potential mechanisms by which patients with IBD may fail to respond to anti-TNF therapy, including activation of alternative inflammatory pathways, neutralizing antibodies, subtherapeutic levels, or inability to block autocrine IEC-derived TNF, but it is tempting to consider whether LTα3-mediated TNFR1-induced IEC death could play a role in a subset of patients. Polymorphisms in LTA were associated with anti-TNF nonresponse in a small cohort of patients  provided –/–Ripk3 mice, and transgenic mice with a tamoxifen-inducible Cre recombinase under the control of the villin promoter (T2+Vil-cre-ER) mice were a gift from Sylvie Robine  for 3 consecutive days as indicated for survival analysis. +/–MyD88 and +/–Trif breeders were maintained on antibiotic water ad libitum with 1 mg/mL ampicillin, 0.5 mg/mL vancomycin, and 1 mg/mL neomycin , and offspring were transitioned to regular water at weaning. For in vivo monoclonal antibody treatment, mice were injected with 370 μg of antibody on days –2, 0, 1, 2, 5, 7, and 9. For all experimental mice, genotypes were confirmed twice. When possible, littermates were used as controls. Both males and females were included in all experiments, with no observable sex differences. Mice were analyzed between 7 and 12 weeks of age for all experiments. All mice were on the C57BL/6 background.eviously , 61, 109N-acetylcysteine , supplemented with Y-27632 . Samples were immediately placed on ice and transported to the laboratory for processing.Patients undergoing colonoscopy or sigmoidoscopy for standard-of-care indications were screened for study eligibility. All patients gave written informed consent and approval. Cold-forceps biopsy samples were obtained from patients with CD or UC (IBD), and individuals without IBD (non-IBD). Baseline demographic and clinical data for the study participants are provided in A20/Abin-1T-ΔIEC–/–Tnf mice were derived by cesarean delivery into the UCSF Gnotobiotic Core Facility (gnotobiotics.ucsf.edu) and maintained in germ-free isolators. Stool pellets from isolators in the Gnotobiotic Core Facility were screened every 2 to 3 weeks by culture and qPCR, whenever mice were transferred from breeding to experimental isolators, and at the beginning and end of each experiment. 515F and 806R universal primers were used to amplify the V4 region: 515F-GTGCCAGCMGCCGCGGTAA and 806R-GGACTACHVGGGTWTCTAAT. For cecal content conventionalization experiments, cecal contents from SPF mice were resuspended in 10% (w/v) Bacto brain heart infusion (BHI) (VWR) supplemented with 0.05% (w/v) L-cysteine (MilliporeSigma) in aerobic conditions, passed through a 100-μm filter, and 200 μL was administered to germ-free mice by oral gavage 5–7 days prior to tamoxifen treatment.Germ-free A20/Abin-1T-ΔIEC–/–Tnf mice: MyD88 guide 1 CCCACGTTAAGCGCGACCAA and MyD88 guide 2 AAGGAGCTGAAGTCGCGCAT targeting exon 1 of MyD88; and Trif guide 1 TCTGGAACGCTAATTTCGTG and Trif guide 2 CAAGCTATGTAACACACCGC targeting exon 2 of Trif. The following gRNAs were selected for editing A20/Abin-1T-ΔIEC–/–Ripk3 mice: Casp8 guide 1 TAGCTTCTGGGCATCCTCGA and Casp8 guide 2 CTTCCTAGACTGCAACCGAG targeting exon 2 of Casp8. The corresponding crRNAs targeting each gene and tracrRNA were annealed and ribonucleoprotein (RNP) complexes were prepared with HiFi Cas9 nuclease ribonucleoprotein (IDT) according to the manufacturer’s instructions. RNPs were electroporated into mouse zygotes as previously described . For human colonoid culture, biopsies were cryopreserved, thawed, and digested as previously described (15] gastrin-1 human (MilliporeSigma), human R-spondin-3 instead of R-spondin-1 (Peprotech), human stem cell factor (STEMCELL Technologies), human FGF-basic (Peprotech), and human IGF-1 (BioLegend). Confocal imaging of organoids was performed on a Leica SP5 laser scanning confocal system using a 10× dry objective. Images were acquired in a 512 × 512 format, with a line average of at least 3, scan speed of 400 Hz, and pinhole airy unit 1. Excitation for both PI or SYTOX was done with the 488 nm laser line at 30% power with a detection band of 550 to 732 nm (PI) or 520–550 nm (SYTOX). Image analysis was performed using the Leica Application Suite.For murine enteroid culture, intestinal crypts were isolated from the small intestine as previously described , with thescribed . Human cescribed , with WNA20 or Abin-1 in vitro was performed via treatment with 200 nM 4-OHT for 24 hours, at which point the enteroids were stimulated as indicated. Viability was measured using the CellTiter Glo 3D assay (Promega) according to the manufacturer’s specifications, with the exception that 100 μL of reagent was added to 200 μL of culture for a final volume of 300 μL prior to reading. For colonoids, stimulations were performed as indicated for 48 hours and viability was measured using the CellTiter Glo 3D assay according to the manufacturer’s specifications. Luminescence was read on a SpectraMax M5 (Molecular Devices), analyzed using SoftMax Pro (Molecular Devices), and expressed as percentage viability relative to vehicle control.Enteroid death assays were performed by resuspending in Matrigel (Corning) and plating 25 μL per well in 96-well flat-bottom opaque plates (Nunc). Deletion of g for 5 minutes. Cell pellets were lysed in ice-cold NP40 lysis buffer  supplemented with complete EDTA-free Protease Inhibitor Cocktail (Roche), phosphatase inhibitors (1 mM Na3VO4 and 10 mM NaF), and 10 mM N-ethylmaleimide (MilliporeSigma). Colonic mucosal biopsy samples were lysed in Lysing Matrix D tubes  containing RIPA lysis buffer  supplemented with complete EDTA-free Protease Inhibitor Cocktail, phosphatase inhibitors (1 mM Na3VO4 and 10 mM NaF), 10 mM N-ethylmaleimide, and antifoaming Reagent DX (Qiagen). Samples were homogenized using a FastPrep-24 homogenizer . RKO  lysate was used as a reference. Biopsy samples with low lysate protein concentration (below 1000 μg/mL) were excluded. After lysis, samples were centrifuged for 20 minutes at 21,130g to remove debris, and the supernatants were quantified using the BCA Protein Assay Kit (Pierce). Lysates were normalized and denatured in LDS Sample Buffer (Invitrogen), followed by resolution in NuPage precast 4% to 12% Bis-Tris gels (Invitrogen) and transferred to PVDF for immunoblotting. Quantitation was performed using Image Lab (Bio-Rad).For enteroid lysates, cultures were resuspended in Cell Recovery Solution (Corning) supplemented with 10 μM Y-27632  and incubated for 15 minutes on ice, followed by centrifugation at 500Lta (Mm00440228_gH), mouse Il1b (Mm00434228_m1), and mouse Actb (Mm02619580_g1), and the TaqMan Universal Master Mix II with UNG on the QuantStudio 6 (Thermo Fisher Scientific). qPCR on human samples was performed using TaqMan probes for human LTA (Hs99999086_m1), IL1B (Hs01555410_m1), IL18 (Hs01038788_m1), TNFAIP3 (Hs00234713_m1), TNIP1 (Hs00374581_m1), and GAPDH  with the Taqman Multiplex Master Mix (Thermo Fisher Scientific 4461884) on the QuantStudio 6 (Thermo Fisher Scientific). Relative gene abundance was normalized to the mean expression of the housekeeping gene Actb or GAPDH, and 2–ΔΔCt was calculated relative to a reference sample. All samples were run in duplicate. For mucosal biopsies, if the colon was entirely inflamed or noninflamed, values from the 2 sets of biopsies were averaged. If biopsies were obtained separately from inflamed and noninflamed colon, then they were processed and displayed separately. Samples with low RNA concentration and amplification of GAPDH at Ct greater than 24 were excluded. Data were analyzed using QuantStudio Real-Time PCR Software (Thermo Fisher Scientific).Mouse small intestine or colon (1 cm) was flushed with saline solution and placed in RNAlater (Thermo Fisher Scientific) and stored for 16 to 72 hours at 4°C. For colonic mucosal biopsies, samples were placed in RNAlater and stored for 16 to 72 hours at 4°C. The solution was then aspirated, and samples were stored at –70°C. Samples were then thawed and homogenized in Buffer RLT (Qiagen) supplemented with β-mercaptoethanol using Lysing Matrix D tubes  on the FastPrep-24 , followed by QIAshredder homogenization (Qiagen). RNA was prepared using the RNeasy Mini Kit (Qiagen) with on-column DNase digestion according to the manufacturer’s instructions. Quality was confirmed using the NanoDrop ND-1000 (Thermo Fisher Scientific) and Agilent RNA 6000 Nano Kit on the Agilent 2100 Bioanalyzer, according to manufacturers’ instructions. cDNA was synthesized from 2000 ng of total RNA using the High-Capacity RNA-to-cDNA kit (Thermo Fisher Scientific). qPCR on murine samples was performed using TaqMan probes for mouse t test. Multigroup comparisons were performed by 1-way analysis of variance (ANOVA) if comparing 1 variable per group or 2-way ANOVA if there were multiple variables per group. When comparing every mean to every other mean by ANOVA, Tukey’s multiple comparison test was used. When comparing each mean to a control, Dunnett’s multiple comparison test was used. When comparing only a subset of means, Bonferroni’s multiple-comparison test was used. Categorical variables were analyzed by Fisher’s exact or χ2 tests where appropriate. A P value of less than 0.05 was used as the threshold for statistical significance.Statistical analysis was performed with GraphPad Prism 9. Kaplan-Meier survival curve comparisons were performed using the log-rank Mantel-Cox test. Comparisons between 2 groups were performed by 2-tailed, unpaired Student’s 3-Fc CM are described in Histology and immunohistochemistry, antibodies, reagents, and LTαAll animal studies were conducted in accordance with the UCSF Institutional Animal Care and Use Committee (AN183350). The study was conducted according to Declaration of Helsinki principles and was approved by the Institutional Review Board of UCSF (15-17757 and 19-27302). Written informed consent was received from participants prior to inclusion in the study.3-Fc CM. EM performed enteroid immunoblot analysis and in vitro assays. EM, IR, and MN performed RNA-ISH analysis with advice and guidance from ODK. JLB consented patients and collected and processed patient biospecimens. ZL performed CRISPR/Cas9 zygote editing with guidance from A Marson. JLB, XS, YYR, ZW, RA, and PA assisted with mouse breeding, genotyping, and histology quantitation. KML, PJT, and JAT derived germ-free mice and performed germ-free experiments in the UCSF Gnotobiotics Core Facility. BR provided a key observation that lymphotoxin could contribute to TNF-independent death in this model. BAM, BR, and LS critically reviewed the manuscript and provided helpful discussion. MGK performed the statistical analyses and wrote the manuscript with editing from IR, EM, BAM, A Ma, and input from all co-authors.MGK and A Ma conceived of the project. MGK oversaw the project and designed and performed the experiments with assistance from co–first authors IR and EM. Co–first author order was determined by duration of time spent on the project. IR performed in vivo experiments, in vitro death assays, and helped generate LTα


Heart failure (HF) has no cure and, for HF with preserved ejection fraction (HFpEF), no life-extending treatments. Defining the clinical epidemiology of HF could facilitate earlier identification of high-risk individuals. We define the clinical epidemiology of HF subtypes (HFpEF and HF with reduced ejection fraction [HFrEF]), identified among 2.7 million individuals receiving routine clinical care. Differences in patterns and rates of accumulation of comorbidities, frequency of hospitalization, use of specialty care, were defined for each HF subtype. Among 28,156 HF cases, 8322 (30%) were HFpEF and 11,677 (42%) were HFrEF. HFpEF was the more prevalent subtype among older women. 177 Phenotypes differentially associated with HFpEF versus HFrEF. HFrEF was more frequently associated with diagnoses related to ischemic cardiac injury while HFpEF was associated more with non-cardiac comorbidities and HF symptoms. These comorbidity patterns were frequently present 3 years prior to a HFpEF diagnosis. HF subtypes demonstrated distinct patterns of clinical co-morbidities and disease progression. For HFpEF, these comorbidities were often non-cardiac and manifested prior to the onset of a HF diagnosis. Recognizing these comorbidity patterns, along the care continuum, may present a window of opportunity to identify individuals at risk for developing incident HFpEF. In 2018, there were about 6.2 million adults with heart failure in the United States, and 379,800 death certificates listed heart failure (13.4%)2. Heart failure is broadly subtyped by left ventricular ejection fraction (LVEF) into HF with reduced ejection fraction  and HF with preserved ejection fraction 3. Currently, HFpEF patients account for about 50% of all heart failure patients4. There is no cure for HF and, for HFpEF, there are currently no life-extending treatments7. Hence, prevention is paramount for reducing the burden of HF, especially for HFpEF, which is becoming the more prevalent subtype7.Heart failure (HF), a clinical syndrome driven by the heart’s inability to meet the oxygen needs of the body, is a significant cause of annually increasing morbidity and mortality. HF accounted for 1 in 8 deaths in the U.S. in 201711. In particular, a clearer understanding of the spectrum and temporality of the clinical manifestations of early HF could better enable primary care providers to identify high risk patients with sufficient lead time to enable early intervention to delay or prevent the onset of HF and sequelae13. Defining the relevant clinical epidemiology requires large, contemporary clinical cohorts, such as those who seek care at large medical centers20.Identifying high risk individuals is essential for effective prevention, and this requires a clear understanding of the evolving epidemiology of HFWe leveraged electronic health records (EHR) from ~ 2.7 million individuals seeking routine clinical care at Vanderbilt University Medical Center. We identified a large collection of HF cases and characterized the comorbidity profiles of HF subtypes prior to and at the time of their clinical diagnosis. We asked if there were distinct longitudinal patterns of comorbidity accumulation between the HF subtypes, as patients moved through the healthcare system, and if this could have implications for HF management.24. This resource comprises inpatient and outpatient clinical data from multiple sources including diagnostic and procedure codes , demographics, text from clinical notes, laboratory values, procedural reports , and medications extracted individual clinical records25. The study was reviewed and approved by Vanderbilt’s Institutional Review Board and was determined to be non-human subjects research. These data contain no HIPAA or other personal identifiers.All individuals were derived from Vanderbilt University Medical Center’s Synthetic Derivative database, a research tool for conducting epidemiological studies using de-identified EHR data, especially suited to work with tools of machine learning and big data26. Keyword features were extracted from source documents  and occurrences were excluded if a negation term (such as “not” or “ruled out”) was within 100 characters of the keyword , blood pressure, heart rate, body mass index (BMI), BNP values, and clinician visit dates and types  were extracted from structured tables in the SD. Diagnoses of myocardial infarction (MI), coronary artery disease (CAD), hypertension, dyslipidemia, atrial fibrillation (AF), type 2 diabetes (T2D), and chronic kidney disease (CKD) were based on previously validated EHR algorithms , diastolic function, and cardiac chamber dimensions. For each HF case with one or more LVEF measurements, the following HF subtypes were defined:HFpEF: All LVEF measurements ≥ 50%.HFmEF (Heart Failure with Mid-Range Ejection Fraction): Any LVEF < 50% and no LVEF < 40%HFrEF: Any LVEF ≤ 40%.Measures, extracted from transthoracic echocardiography (TTE) reports, using previously described approaches32. A complete list of PheWAS codes and their mappings to ICD-9 codes can be found at https://www.vumc.org/cpm/cpm-blog/phewas-phenome-wide-association-studies. For a given PheWAS code, cases are individuals with 1 or more codes and controls are individuals with no related codes. After excluding phenotypes affecting a single sex or with ≤ 100 cases, 1224 clinical phenotypes were included in analyses.Phenome-wide association study (PheWAS) codes are validated groupings of related ICD-9 billing codes that capture the extended range of clinical diagnoses within an EHR data set20 universal heart failure algorithm criteria based on the features available to the machine learning algorithm. In line with the guidelines, we tested for the presence of any of the following symptoms: pulmonary edema, pleural effusion, orthopnea, paroxysmal nocturnal dyspnea, nocturnal cough, lower extremity edema, dyspnea on exertion, jugular venous distension/elevated jugular venous pressure/elevated JVP, cardiomegaly, third heart sound, hepatomegaly, rales, ejection fraction < 50%, and an outpatient NT-Pro-BNP, pg/ml ≥ 125. The proportion of individuals who met all of these criteria was determined.We examined how many of the cases used in the Validation set met the Bozkurt et al.ScikitLearn (v0.18.1)33, was used to identify heart failure cases34. The approach and implementation are detailed in the Supplementary Methods. In brief, the machine learning model was developed on a training set (n = 1091) and validated on a testing set (n = 468). Based on the final set of features selected, an independent testing set demonstrated that the model had a positive predictive value (PPV) of 0.92 and a case sensitivity and specificity of 0.67 and 0.99, respectively, as compared to manual clinical record review by a cardiologist  separately. Between group differences were assessed using the Mann–Whitney U test or Chi-square test for continuous and dichotomous variables, respectively.We also conducted stratified analyses comparing HF cases identified before and after 2005, the approximate midpoint of the time range for which EHR data were available, to characterize secular trends in HF epidemiology. Subjects were classified by LVEF in 10-year age ranges, centered around decades for which LVEF data were present, and stratified by sex and date of HF diagnosis.35 q-value < 0.1 were then jointly analyzed using a multivariable logistic model in conjunction with a stepwise selection feature (using Proc Logistic in SAS) that retained all phenotypes with an independent association p < 0.05. PheWAS analyses were stratified by 10-year age ranges.PheWAS analyses were used to comprehensively scan the medical phenome to identify clinical diagnoses differentially associated with HFrEF versus HFpEF, since these subtypes had the largest numbers of individuals. Because PheWAS phenotypes are highly correlated, we conducted a 2-step analysis to identify phenotypes independently associated with the HF subtypes. First, multivariable logistic regression analysis adjusting for age at HF diagnosis, sex, and self-reported race was used to test the association between each PheWAS code and the HF subtype . Phenotypes with a Benjamini–Hochberg (B–H) false discovery rate (FDR)The ML algorithm also assigned a diagnoses date for each case. To better define the clinical events leading up to a HF diagnosis, we extracted ICD-9 based diagnoses during each of the three years prior to diagnosis of HF and mapped them to corresponding PheWAS phenotypes. Because ascertainment of diagnoses during this time may be incomplete in individuals referred for specialty care, analyses were limited to those individuals receiving medical care at VUMC prior to diagnosis of HF, defined as two or more primary care or cardiology outpatient visits over the three-year period. Descriptive statistics were generated for PheWAS diagnoses and medical encounters prior to HF diagnosis for both HFpEF and HFrEF subjects and were also stratified by 10-year age groups.2), and had a higher prevalence of hypertension (91% vs 87%) . Subjects with HFmEF variably differed with respect to HFpEF and HFrEF for these features.The ML classifier identified 28,156 HF cases  (Supplementary Table –8) and a slightly higher LVEF nadir .HF cases entered the medical system between 1989 and 2017, with the majority entering after the 2005 (the middle timepoint) . The relative proportions of HF subtypes, by age and sex, were similar before and after 2005  more likely to have diagnosis of HFpEF   and the assigned ML subtype Fig. . For insAcross all ages, most diagnoses prevalent among HFrEF cases were related to the circulatory system . It was also associated with a diagnosis of hypertrophic obstructive cardiomyopathy among individuals ages 65–75 .In contrast, diseases associated with HFpEF cases were more heterogeneous and reflected a higher burden of comorbidities across the clinical disease spectrum. Among these were diagnoses related to symptomatic HF, such as respiratory failure, volume overload and edema, as well as obesity and related complications. For example, HFpEF was associated with sleep apnea at all ages, a diagnosis of obesity after age 45, and non-alcoholic liver disease after age 55 , as well as metabolic risk factors Fig. .Hospitalizations and cardiovascular evaluations became increasingly common in the years immediately prior to HF diagnosis Fig. . In the Overall patterns were similar between HFpEF and HFrEF, though IHD was more common in HFrEF and hypertension more common in HFpEF. HFpEF subjects also accumulated comorbid diagnoses for a slightly longer duration before a HF diagnosis, as compared to subjects with HFrEF (~ 2 years vs ~ 1 year). We examined the timing of the first mention of diuretic use, as a marker of early symptomatic heart failure, in the 3 years prior to a HF diagnosis. For HFpEF and HFrEF, respectively, 98% and 99% of loop diuretics were first mentioned only within the 6 months prior to a diagnosis  cardiomyopathies among individuals < 55 years old, structural heart disease  at older ages, and ischemic heart disease across all age ranges. HFmEF patients in our study, comprising 10% of the HF cases, were a hybrid of HFpEF , and HFrEF (CAD burden) features, in agreement with prior observations40. In that study, HFpEF had a higher average burden of comorbidities, as compared to HFrEF. Importantly, a higher comorbidity burden was associated with higher mortality rates. Our study extends that by demonstrating that the higher comorbidity burden extends to a far broader spectrum of comorbidities. We also add to their findings by demonstrating that these comorbidities are antecedent in nature, are along the care continuum, and may carry a differential risk for developing incident HFpEF.Our overall findings are corroborated by a recent study that examined the burden of 15 comorbidities among participants in the Atherosclerosis Risk In Communities (ARIC) study diagnosed with acute, decompensated heart failureThe majority of encounters occurring prior to a HF diagnosis were with non-cardiologist providers, especially for patients who went on to develop HFpEF. Approximately 50% of HFpEF patients received loop diuretics before their HF diagnosis, and most (98%) received them in the 6 months prior to HF diagnosis. Sensitizing primary providers that a loop diuretic requirement may suggest the onset of symptomatic heart failure could advance the lead time for a HF diagnosis and potentially enhance outcomes.4. Less research focuses on the multi-organ system processes that may play a role in the development of HFpEF. Our findings support the notion that HFpEF is a layered continuum characterized by premorbid states, such as obesity, that ultimately can lead to HFpEF (Fig. 7. Better characterization of the premorbid state may identify timepoints where therapies may demonstrate greater benefit.A large portion of research related to HFpEF has focused on adverse cardiac remodeling including diastolic dysfunction, cardiac hypertrophy and myocardial fibrosisThere are several novel aspects of the current study. The combination of a large data set, a broad representation of cardiac and non-cardiac phenotypes and large numbers of individuals across the full adult age spectrum provided an opportunity to identify important drivers of HF across the life course. In the younger age group, ischemic heart disease was a less common HF comorbidity, as compared to older ages. Cardiometabolic risk factors were common among this age group, and structural heart disease related to congenital heart defects were also more prevalent. Across all ages, HFpEF is a condition associated with a broad range of comorbidities, many of which are related to obesity. Prior to a HF diagnosis, most clinical encounters were with non-cardiologists for both HF subtypes, especially HFpEF. Importantly, the methods used here are easily portable to other EHR environments, and implementation of this approach across a broad range of EHR data sets will enable efficient, contemporaneous characterization of the evolving epidemiology of HF and will highlight important differences across diverse populations which will directed enhance treatment and prevention.There are limitations to this study. The VUMC EHR is an observational, single site data set, which can be associated with differentially missing data elements and ascertainment biases. Thus, for instance, TTE measurements performed at outside institutions were not available for these analyses. VUMC is a tertiary care center and may represent a sicker cohort than the general population. The data elements available for analysis are influenced by clinical decision making and practice patterns, which can lead to systematic biases. There was no active follow-up of study subjects, which can lead to differential censoring of data.20, only 35% had sufficient data available in an electronic format to confirm that they met the case definition. Thus, the validity of the case definition with respect to a contemporary epidemiological case definition of heart failure is not well-defined. To fully address the robustness of the approach, it will need to be systematically evaluated in other native clinical environments.Finally, these analyses used a random forest algorithm to identify cases. While the threshold to define HF cases had PPV of 92%, up to 8% of cases may be misclassified. Importantly, the machine-learning algorithm heavily loaded on the frequency of instances that an individual was assigned billing codes for a heart failure diagnosis. Thus, the case definition is heavily based on the summative assessment of the clinical provider using clinical criteria relevant to the time period when the diagnosis was made. When cases used in the Validation set were evaluated using a Universal heart failure definitionIn summary, we characterized the clinical epidemiology of HF subtypes derived from a large clinical data set. HF subtypes demonstrated distinct patterns of clinical co-morbidities and disease progression. Of direct clinical relevance, we demonstrate that, for HFpEF, these comorbidities are often not related to cardiac disease and manifest prior to the onset of a HF diagnosis. Awareness of these stereotypical patterns of clinical presentation will enhance early detection and prevention strategies, which is essential for HFpEF, which has no proven life-extending therapies.Supplementary Information.


TNF is a proinflammatory cytokine that is critical for the coordination of tissue homeostasis. RIPK1 and TRADD are the main participants in the transduction of TNF signaling. However, data on the cell fate-controlling functions of both molecules are quite controversial. Here, we address the functions of RIPK1 and TRADD in TNF signaling by generating RIPK1- or TRADD-deficient human cell lines. We demonstrate that RIPK1 is relevant for TNF-induced apoptosis and necroptosis in conditions with depleted IAPs. In addition, TRADD is dispensable for necroptosis but required for apoptosis. We reveal a new possible function of TRADD as a negative regulator of NIK stabilization and subsequent ripoptosome formation. Furthermore, we show that RIPK1 and TRADD do not appear to be essential for the activation of MAPK signaling. Moreover, partially repressing NF-κB activation in both RIPK1 and TRADD KO cells does not result in sensitization to TNF alone due to the absence of NIK stabilization. Importantly, we demonstrate that RIPK1 is essential for preventing TRADD from undergoing TNF-induced ubiquitination and degradation. Taken together, our findings provide further insights into the specific functions of RIPK1 and TRADD in the regulation of TNF-dependent signaling, which controls the balance between cell death and survival. Tumor necrosis factor (TNF) is a proinflammatory cytokine of critical importance for maintaining tissue homeostasis. . ThroughReceptor interacting protein 1 (RIPK1) and TNFR1-associated death domain (TRADD) are essential molecules for TNF signal transduction. RIPK1 has both protein kinase and scaffolding functions. The best studied RIPK1 function is its role in the induction of necroptosis ,7. HowevTRADD is an adaptor molecule that binds to activated TNFR1 and subsequently recruits further signaling molecules to the complex ,14. SimiIn this study, we addressed the function of RIPK1 and TRADD in TNF signaling by generating RIPK1- or TRADD-deficient human cell lines, respectively. We show that RIPK1 is relevant for TNF-induced apoptosis and necroptosis in conditions of depleted IAPs but is not absolutely necessary for the activation of NF-κB or MAPK signaling. In addition, we show that TRADD is dispensable for necroptotic cell death signaling but not for apoptotic cell death signaling. We show that similar to RIPK1, TRADD appears to not be critically required for the activation of NF-κB and MAPK signaling. Of note, partial repression of canonical NF-κB activation in both RIPK1 and TRADD KO cells does not result in sensitization to TNF alone due to the absence of NIK stabilization. Moreover, we confirm that NIK stabilization is the major prerequisite for ripoptosome formation. We reveal that RIPK1 is essential for protecting TRADD from TNF-induced ubiquitination and degradation.We generated HeLa cells deficient in RIPK1 using CRISPR/Cas9 technology and examined the induction of apoptosis in RIPK1 KO cells stimulated with TNF alone or in combination with the protein synthesis inhibitor cycloheximide (CHX) or IAP antagonist. In order to quantify the number of dead cells, PI staining and FACS analysis were performed A. As expSince the IAP antagonist can induce necroptosis through RIPK3 activation, we next analyzed the effect of RIPK1 deficiency on TNF-induced necroptotic signaling. We thus expressed either a functional RIPK3 or kinase-dead (KD) RIPK3 mutant in HeLa cells, which endogenously lack RIPK3 expression B. We theRIPK1 has a controversial role in apoptosis and necroptosis execution, which depends on the initial conditions in the cell. RIPK1 has a pro-cell death role (both apoptotic and necroptotic) in IAP-depleted conditions, while in the case of translation blockade with CHX, RIPK1 protects against apoptosis.CXCL8 upon TNF stimulation  and the transcription and translation inhibitor CHX B. BlockiTo investigate the function of TRADD in TNFR1 signaling in more detail, we generated TRADD-deficient HeLa cell lines by CRISPR/Cas9 technology. We then studied quantitative and qualitative apoptotic cell death responses to TNF upon cIAP inhibition and CXH, respectively. Cell death sensitivity was analyzed by FACS analysis A. Loss oCXCL8 upon TNF stimulation was largely reduced in TRADD KO cells , and finally RIPK1, which is relevant to the stability of TRADD protein . DetaileThe following antibodies (Abs) were used for WB: RIPK1 (R41220), TRADD T50320), and FADD (F36620) Abs were purchased from BD Transduction Laboratories, San Diego, California. Caspase-8 ; caspase-10 (M059-3) from MBL, Woburn, MA, USA. RIPK3 (IMG-5846A) from Imgenex, San Diego, CA, USA. Rat Abs were used against cIAP1 0, and FA. β-actinCRISPR cell line generation:http://crispr.mit.edu/ accessed date 1 March 2017. The gRNA sequences used were as follows:TRADD KO cells were generated using the pSpCas9(BB)-2A-GFP (PX458) plasmid , and single clones were isolated and analyzed to confirm successful deletion of TRADD.The RIPK1 KO cell line was generated exactly as described in .HeLa cells were provided by Michael Boutros  and were cultured in RPMI medium containing 10% fetal calf serum (FCS).The generation of A20- and IKK2-KD OE cell lines is explained in detail elsewhere . The ectThe following stimulation conditions were used: pre-stimulation with zVAD-fmk (10 mM), necrostatin-1 (50 mM), and IAP antagonist (100 nM) alone or in the respective combinations for 1 h. Pre-stimulation with CHX (5 µg/mL) and BTZ (1 µM) was performed for 5 h. HF-TNF stimulation concentration for the crystal violet assay, propidium iodide (PI) staining, and WB was 250 ng/mL. Stimulation for complex immunoprecipitation was performed as follows: caspase-8 co-immunoprecipitation-1 mg/mL HF-TNF for 2 h and ligand affinity precipitation-TNF-Fc supernatant for 5 min.g rpm.For analysis of proteins, the following lysis buffer was used: 30 mM TRIS-HCL (pH 7.5), 120 mM NaCl, 10% glycerol, 1% Triton X, 2 tablets Complete (Protease Inhibitor) per 100 mL. The lysis was performed for 20 min on ice, followed by 10 min centrifugation at 14,000× v/v) glycerol, 1% (v/v) Triton X-100, 2 mm EDTA, 50 mm sodium β-glycerophosphate, 20 mm sodium pyrophosphate, 1 mm Pefabloc, 5 mg/mL of aprotinine, 5 mg/mL of leupeptin, 5 mm benzamidine, and 1 mm sodium orthovanadate. All lysates were homogenized by a syringe and 0.4 mm needle, followed by 10 min centrifugation at 14,000× g rpm.For analysis of phospho-proteins, the respective cells were starved in serum free medium for 6 h. The scraped cells were resuspended in the following triton lysis buffer: 20 mm Tris (pH 7.4), 137 mm NaCl, 10%  and transferred to nitrocellulose or PVDF membranes. After the blocking step, the membranes were incubated with primary and appropriate secondary Abs as described previously . Bands wIP of caspase-8-bound complexes and TNF-Fc ligand affinity precipitation were performed exactly as explained in .Crystal violet staining of attached live cells was performed 18–24 h after stimulation with HF-TNF as described in the “cell stimulation conditions”. All experiments were completed in 96-well plates in triplicate as previously described . The optA total of 2 × 104 cells were stimulated as described in the “cell stimulation conditions” for 18 h. After trypsinization, the cells were washed with PBS and stained with 10 µg/mL PI for 15 min (dark). BD Accuri C6 flow cytometer was used for analysis.CXCL8: For-CACCCCAAATTTATCAAAGA and Rev-ACTGGCATCTTCACTGATTC: and actin: For-CGCCTTTGCCGATCC and Rev-ACGATGGAGGGGAAGAC.RNA isolation from HeLa cells was performed with RNeasy Kit . For cDNA synthesis: SuperScript II Reverse Transcriptase  and a mixture of random nanomers and oligo dT primers in a ratio 10:1 was used. RT qPCR analysis was performed using PowerUp™ SYBR™ Green Mastermix  in the QuantStudio 1 Real-Time-PCR System (Thermo Fischer Scientific). Equal cycling conditions were used to amplify genes of interest and reference gene products. Mean values were calculated using data obtained from three independent experiments. Normalization of each experiment was performed to β-actin expression. Primer sequences for t-test for two groups was used to assess the significance of differences.All data are expressed as the mean ± SEM. A two-tailed Student’s