Integrin activation - the importance of a positive feedback

Integrins mediate cell adhesion and are essential receptors for the development and functioning of multicellular organisms. Integrin activation is known to require both ligand and talin binding and to correlate with cluster formation but the activation mechanism and precise roles of these processes are not yet resolved. Here mathematical modeling, with known experimental parameters, is used to show that the binding of a stabilizing factor, such as talin, is alone insufficient to enable ligand-dependent integrin activation for all observed conditions; an additional positive feedback is required.Comment: in press in Bulletin of Mathematical Biolog

Turnip mosaic potyvirus probably first spread to Eurasian brassica crops from wild orchids about 1000 years ago

Turnip mosaic potyvirus (TuMV) is probably the most widespread and damaging virus that infects cultivated brassicas worldwide. Previous work has indicated that the virus originated in western Eurasia, with all of its closest relatives being viruses of monocotyledonous plants. Here we report that we have identified a sister lineage of TuMV-like potyviruses (TuMV-OM) from European orchids. The isolates of TuMV-OM form a monophyletic sister lineage to the brassica-infecting TuMVs (TuMV-BIs), and are nested within a clade of monocotyledon-infecting viruses. Extensive host-range tests showed that all of the TuMV-OMs are biologically similar to, but distinct from, TuMV-BIs and do not readily infect brassicas. We conclude that it is more likely that TuMV evolved from a TuMV-OM-like ancestor than the reverse. We did Bayesian coalescent analyses using a combination of novel and published sequence data from four TuMV genes [helper component-proteinase protein (HC-Pro), protein 3(P3), nuclear inclusion b protein (NIb), and coat protein (CP)]. Three genes (HC-Pro, P3, and NIb), but not the CP gene, gave results indicating that the TuMV-BI viruses diverged from TuMV-OMs around 1000 years ago. Only 150 years later, the four lineages of the present global population of TuMV-BIs diverged from one another. These dates are congruent with historical records of the spread of agriculture in Western Europe. From about 1200 years ago, there was a warming of the climate, and agriculture and the human population of the region greatly increased. Farming replaced woodlands, fostering viruses and aphid vectors that could invade the crops, which included several brassica cultivars and weeds. Later, starting 500 years ago, inter-continental maritime trade probably spread the TuMV-BIs to the remainder of the world

OP0291 TOFACITINIB FOR THE TREATMENT OF POLYARTICULAR COURSE JUVENILE IDIOPATHIC ARTHRITIS: RESULTS OF A PHASE 3, RANDOMISED, DOUBLE-BLIND, PLACEBO-CONTROLLED WITHDRAWAL STUDY

Background:Tofacitinib is an oral JAK inhibitor that is being investigated for JIA.Objectives:To assess tofacitinib efficacy and safety in JIA patients (pts).Methods:This was a Phase 3, randomised, double-blind (DB), placebo (PBO)-controlled withdrawal study in pts aged 2−<18 years with polyarticular course JIA (pcJIA), PsA or ERA (NCT02592434). In the 18-week open-label Part 1, pts received weight-based tofacitinib doses (5 mg BID or lower). Pts with ≥JIA ACR30 response at Week (W)18 were randomised 1:1 in the DB Part 2 (W18−44) to continue tofacitinib or switch to PBO. Primary endpoint: disease flare rate by W44. Key secondary endpoints: JIA ACR50/30/70 response rates; change from Part 2 baseline (Δ) in CHAQ-DI at W44. Other efficacy endpoints: time to disease flare in Part 2; JADAS27-CRP in Parts 1 and 2. PsA/ERA pts were excluded from these efficacy analyses. Safety was evaluated in all pts up to W44.Results:225 enrolled pts with pcJIA (n=184), PsA (n=20) or ERA (n=21) received tofacitinib in Part 1. At W18, 173/225 (76.9%) pts entered Part 2 (pcJIA n=142, PsA n=15, ERA n=16). In pcJIA pts, disease flare rate in Part 2 was significantly lower with tofacitinib vs PBO by W44 (p=0.0031; Fig 1a). JIA ACR50/30/70 response rates (Fig 1b) and ΔCHAQ-DI (Fig 1c) at W44, and time to disease flare in Part 2 (Fig 2a), were improved with tofacitinib vs PBO. Tofacitinib reduced JADAS27-CRP in Part 1; this effect was sustained in Part 2 (Fig 2b). Overall, safety was similar with tofacitinib or PBO (Table): 77.3% and 74.1% had adverse events (AEs); 1.1% and 2.4% had serious AEs. In Part 1, 2 pts had herpes zoster (non-serious) and 3 pts had serious infections (SIs). In Part 2, SIs occurred in 1 tofacitinib pt and 1 PBO pt. No pts died.Conclusion:In pcJIA pts, tofacitinib vs PBO resulted in significantly fewer disease flares, and improved time to flare, disease activity and physical functioning. Tofacitinib safety was consistent with that in RA pts.Table.Safety in all ptsPart 1Part 2TofacitinibaN=225TofacitinibaN=88PBO N=85Pts with events, n (%)AEs153 (68.0)68 (77.3)63 (74.1)SAEs7 (3.1)1 (1.1)2 (2.4)Permanent discontinuations due to AEs26 (11.6)16 (18.2)29 (34.1)AEs of special interest Death000 Gastrointestinal perforationb000 Hepatic eventb3 (1.3)00 Herpes zoster (non-serious and serious)2 (0.9)c00 Interstitial lung diseaseb000 Major adverse cardiovascular eventsb000 Malignancy (including non-melanoma skin cancer)b000 Macrophage activation syndromeb000 Opportunistic infectionb000 SI3 (1.3)1 (1.1)d1 (1.2) Thrombotic event (deep vein thrombosis, pulmonary embolismbor arterial thromboembolism)000 Tuberculosisb000a5 mg BID or equivalent weight-based lower dose in pts <40 kgbAdjudicated eventscBoth non-seriousdOne SAE of pilonidal cyst repair was coded to surgical procedures instead of infections, and was inadvertently not identified as an SI. Following adjudication, the SAE did not meet opportunistic infection criteria; it is also included in the table as an SIAE, adverse event; BID, twice daily; PBO, placebo; pts, patients; SAE, serious AE; SI, serious infectionAcknowledgments:Study sponsored by Pfizer Inc. Medical writing support was provided by Sarah Piggott of CMC Connect and funded by Pfizer Inc.Disclosure of Interests:Nicolino Ruperto Grant/research support from: Bristol-Myers Squibb, Eli Lily, F Hoffmann-La Roche, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sobi (paid to institution), Consultant of: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Speakers bureau: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Olga Synoverska Speakers bureau: Sanofi, Tracy Ting: None declared, Carlos Abud-Mendoza Speakers bureau: Eli Lilly, Pfizer Inc, Alberto Spindler Speakers bureau: Eli Lilly, Yulia Vyzhga Grant/research support from: Pfizer Inc, Katherine Marzan Grant/research support from: Novartis, Vladimir Keltsev: None declared, Irit Tirosh: None declared, Lisa Imundo: None declared, Rita Jerath: None declared, Daniel Kingsbury: None declared, Betül Sözeri: None declared, Sheetal Vora: None declared, Sampath Prahalad Grant/research support from: Novartis, Elena Zholobova Grant/research support from: Novartis and Pfizer Inc, Speakers bureau: AbbVie, Novartis, Pfizer Inc and Roche, Yonatan Butbul Aviel: None declared, Vyacheslav Chasnyk: None declared, Melissa Lerman Grant/research support from: Amgen, Kabita Nanda Grant/research support from: Abbott, AbbVie, Amgen and Roche, Heinrike Schmeling Grant/research support from: Janssen, Pfizer Inc, Roche and USB Bioscience, Heather Tory: None declared, Yosef Uziel Speakers bureau: Pfizer Inc, Diego O Viola Grant/research support from: Bristol-Myers Squibb, GSK, Janssen and Pfizer Inc, Speakers bureau: AbbVie and Bristol-Myers Squibb, Holly Posner Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Keith Kanik Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ann Wouters Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Cheng Chang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Richard Zhang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Irina Lazariciu Consultant of: Pfizer Inc, Employee of: IQVIA, Ming-Ann Hsu Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ricardo Suehiro Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Alberto Martini Consultant of: AbbVie, Eli Lily, EMD Serono, Janssen, Novartis, Pfizer, UCB, Daniel J Lovell Consultant of: Abbott (consulting and PI), AbbVie (PI), Amgen (consultant and DSMC Chairperson), AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb (PI), Celgene, Forest Research (DSMB Chairman), GlaxoSmithKline, Hoffman-La Roche, Janssen (co-PI), Novartis (consultant and PI), Pfizer (consultant and PI), Roche (PI), Takeda, UBC (consultant and PI), Wyeth, Employee of: Cincinnati Children's Hospital Medical Center, Speakers bureau: Wyeth, Hermine Brunner Consultant of: Hoffman-La Roche, Novartis, Pfizer, Sanofi Aventis, Merck Serono, AbbVie, Amgen, Alter, AstraZeneca, Baxalta Biosimilars, Biogen Idec, Boehringer, Bristol-Myers Squibb, Celgene, EMD Serono, Janssen, MedImmune, Novartis, Pfizer, and UCB Biosciences, Speakers bureau: GSK, Roche, and Novarti

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material

PKCε-CREB-Nrf2 signalling induces HO-1 in the vascular endothelium and enhances resistance to inflammation and apoptosis

Aims Vascular injury leading to endothelial dysfunction is a characteristic feature of chronic renal disease, diabetes mellitus, and systemic inflammatory conditions, and predisposes to apoptosis and atherogenesis. Thus, endothelial dysfunction represents a potential therapeutic target for atherosclerosis prevention. The observation that activity of either protein kinase C epsilon (PKCε) or haem oxygenase-1 (HO-1) enhances endothelial cell (EC) resistance to inflammation and apoptosis led us to test the hypothesis that HO-1 is a downstream target of PKCε. Methods and results Expression of constitutively active PKCε in human EC significantly increased HO-1 mRNA and protein, whereas conversely aortas or cardiac EC from PKCε-deficient mice exhibited reduced HO-1 when compared with wild-type littermates. Angiotensin II activated PKCε and induced HO-1 via a PKCε-dependent pathway. PKCε activation significantly attenuated TNFα-induced intercellular adhesion molecule-1, and increased resistance to serum starvation-induced apoptosis. These responses were reversed by the HO antagonist zinc protoporphyrin IX. Phosphokinase antibody array analysis identified CREB1(Ser133) phosphorylation as a PKCε signalling intermediary, and cAMP response element-binding protein 1 (CREB1) siRNA abrogated PKCε-induced HO-1 up-regulation. Likewise, nuclear factor (erythroid-derived 2)-like 2 (Nrf2) was identified as a PKCε target using nuclear translocation and DNA-binding assays, and Nrf2 siRNA prevented PKCε-mediated HO-1 induction. Moreover, depletion of CREB1 inhibited PKCε-induced Nrf2 DNA binding, suggestive of transcriptional co-operation between CREB1 and Nrf2. Conclusions PKCε activity in the vascular endothelium regulates HO-1 via a pathway requiring CREB1 and Nrf2. Given the potent protective actions of HO-1, we propose that this mechanism is an important contributor to the emerging role of PKCε in the maintenance of endothelial homeostasis and resistance to injury