Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Chitosomes Loaded with Docetaxel as a Promising Drug Delivery System to Laryngeal Cancer Cells: An In Vitro Cytotoxic Study

Current delivery of chemotherapy, either intra-venous or intra-arterial, remains suboptimal for patients with head and neck tumors. The free form of chemotherapy drugs, such as docetaxel, has non-specific tissue targeting and poor solubility in blood that deters treatment efficacy. Upon reaching the tumors, these drugs can also be easily washed away by the interstitial fluids. Liposomes have been used as nanocarriers to enhance docetaxel bioavailability. However, they are affected by potential interstitial dislodging due to insufficient intratumoral permeability and retention capabilities. Here, we developed and characterized docetaxel-loaded anionic nanoliposomes coated with a layer of mucoadhesive chitosan (chitosomes) for the application of chemotherapy drug delivery. The anionic liposomes were 99.4 ± 1.5 nm in diameter with a zeta potential of −26 ± 2.0 mV. The chitosan coating increased the liposome size to 120 ± 2.2 nm and the surface charge to 24.8 ± 2.6 mV. Chitosome formation was confirmed via FTIR spectroscopy and mucoadhesive analysis with anionic mucin dispersions. Blank liposomes and chitosomes showed no cytotoxic effect on human laryngeal stromal and cancer cells. Chitosomes were also internalized into the cytoplasm of human laryngeal cancer cells, indicating effective nanocarrier delivery. A higher cytotoxicity (p < 0.05) of docetaxel-loaded chitosomes towards human laryngeal cancer cells was observed compared to human stromal cells and control treatments. No hemolytic effect was observed on human red blood cells after a 3 h exposure, proving the proposed intra-arterial administration. Our in vitro results supported the potential of docetaxel-loaded chitosomes for locoregional chemotherapy delivery to laryngeal cancer cells

In Situ Visualization for 3D Agent-Based Vocal Fold Inflammation and Repair Simulation

A fast and insightful visualization is essential in modeling biological system behaviors and understanding underlying inter-cellular mechanisms. High fidelity models produce billions of data points per time step, making in situ visualization techniques extremely desirable as they mitigate I/O bottlenecks and provide computational steering capability. In this work, we present a novel high-performance scheme to couple in situ visualization with the simulation of the vocal fold inflammation and repair using little to no extra cost in execution time or computing resources. The visualization component is first optimized with an adaptive sampling scheme to accelerate the rendering process while maintaining the precision of the displayed visual results. Our software employs VirtualGL to perform visualization in situ. The scheme overlaps visualization and simulation, resulting in the optimal utilization of computing resources. This results in an in situ system biology simulation suite capable of remote simulation of 17 million biological cells and 1.2 billion chemical data points, remote visualization of the results, and delivery of visualized frames with aggregated statistics to remote clients in real-time

High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair

Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed

Unraveling the Relevance of Tissue‐Specific Decellularized Extracellular Matrix Hydrogels for Vocal Fold Regenerative Biomaterials: A Comprehensive Proteomic and In Vitro Study

Decellularized extracellular matrix (dECM) is a promising material for tissue engineering applications. Tissue‐specific dECM is seen as a favorable material that recapitulates a native‐like microenvironment for cellular remodeling. However, the minute quantity of dECM derivable from small organs like the vocal fold (VF) hampers manufacturing scalability. Small intestinal submucosa (SIS), a commercial product with proven regenerative capacity, may be a viable option for VF applications. This study aims to compare SIS and VF dECM hydrogels with respect to protein content and functionality using mass spectrometry‐based proteomics and in vitro studies. Proteomic analysis reveals that VF and SIS dECM share 75% of core matrisome proteins. Although VF dECM proteins have greater overlap with native VF, SIS dECM shows less cross‐sample variability. Following decellularization, significant reductions of soluble collagen (61%), elastin (81%), and hyaluronan (44%) are noted in VF dECM. SIS dECM contains comparable elastin and hyaluronan but 67% greater soluble collagen than VF dECM. Cells deposit more neo‐collagen on SIS than VF‐dECM hydrogels, but comparable neo‐elastin (≈50 μg scaffold−1) and neo‐hyaluronan (≈6 μg scaffold−1). Overall, SIS dECM possesses a reasonably similar proteomic profile and regenerative capacity to VF dECM. SIS dECM is a promising alternative for dECM‐derived biomaterials for VF regeneration

Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology

Mobile health wearables are often embedded with small processors for signal acquisition and analysis. These embedded wearable systems are, however, limited with low available memory and computational power. Advances in machine learning, especially deep neural networks (DNNs), have been adopted for efficient and intelligent applications to overcome constrained computational environments. Herein, evolutionary algorithms are used to find novel DNNs that are accurate in classifying airway symptoms while allowing wearable deployment. As opposed to typical microphone‐acoustic signals, mechano‐acoustic data signals, which did not contain identifiable speech information for better privacy protection, are acquired from laboratory‐generated and publicly available datasets. The optimized DNNs had a low model file size of less than 150 kB and predicted airway symptoms of interest with 81.49% accuracy on unseen data. By performing explainable AI techniques, namely occlusion experiments and class activation maps, mel‐frequency bands up to 8,000 Hz are found as the most important feature for the classification. It is further found that DNN decisions are consistently relying on these specific features, fostering trust and transparency of the proposed DNNs. The proposed efficient and explainable DNN is expected to support edge computing on mechano‐acoustic sensing wearables for remote, long‐term monitoring of airway symptoms