Coenzyme A synthase controls pathogenic features of myelin-specific T cells by linking metabolic reprogramming to alteration of intracellular signaling pathways

In recent years, it has become clear that metabolism of immune cells critically influences the outcome of immune responses and contribute to the pathogenesis of autoimmunity. Thus, the emerging field of immunometabolism may hold the potential to discover new therapeutic targets for the treatment of autoimmune diseases. In the development of such pathologies, the involvement of Coenzyme A (CoA) metabolism was never directly investigated. The aim of this study was to investigate the involvement of CoA synthase (COASY), the enzyme that catalyzes the last two steps of CoA synthesis pathway, in the control of autoreactive T cell pathogenicity by using murine experimental autoimmune encephalomyelitis (EAE) as a model of autoimmune disease. By using metabolomics, proteomics and functional in vitro assays we investigated the pathogenic features of encephalitogenic PLP139-151(myelin)-specific effector T cells (PLP-T cells), which represent the major players in the pathogenesis of EAE in SJL mice. By performing metabolomics analysis, we have shown that PLP-T cells displayed reduced intracellular CoA synthesis, increased free fatty acids levels, and glycolysis-, Krebs cycle- and pentose phosphate pathway-related metabolites, compared to naïve T cells. In light of this, we investigated the immuno-modulatory effect of the CoA precursor pantethine (PTTH), on the pathogenic features of PLP-T cells and the impact of such immunomodulation on the development of EAE. CoA fueling, induced by PTTH treatment, reprogrammed autoreactive T cells, to a “naïve/resting-like state” leading them to reduce glycolysis, block pentose phosphate pathway, inhibit nucleic acid synthesis, and significantly alter lipid and protein content. Our phosphoproteomics analysis revealed that PTTH is able to affect crucial immune processes associated with the functionality and the pathogenicity of PLP-T cells, such as cell activation and proliferation, cytokine production and cell migration. These observations were confirmed by in vitro functional assays showing how CoA fueling strongly influenced PLP-T cells by reducing their antigen-specific proliferative capacity, pro-inflammatory cytokine production and their integrin-dependent adhesion. The role of PTTH, as an enhancer of the CoA synthesis pathway, was confirmed by the siRNA mediated silencing of COASY, which resulted in a significant loss of the inhibitory effect of PTTH on the proliferation rate of PLP-T cells. Interestingly, the knockdown of COASY in PLP-T cells increased their proliferation in absence of antigen stimulation, suggesting a key role of COASY in the control of autoreactive T cell activation. The potential role of COASY in the regulation of the immune response was corroborated by a bioinformatics analysis that showed a link between COASY and pathways like MAPK, RAC1 and mTOR. In light of the immuno-modulatory effect of CoA fueling on PLP-T cells in vitro, we sought to test the clinical potential of metabolic perturbation by PTTH in vivo. Pantethine treatment prevented the development of EAE by delaying the disease onset and acuteness. Furthermore, PTTH treatment started after disease onset significantly ameliorated the disease course. In conclusion, our data demonstrated a new role of CoA synthesis pathway in the metabolic reprogramming of autoreactive T cell necessary for their pathogenic features, suggesting the CoA fueling as a novel potential therapeutic target for the treatment of autoimmune diseases

Addressing Load Sensitivity of Rational Macromodels

Behavioral models are effective tools used to relieve the computational burden of large-scale system-level simulations. In electrical and electronic applications, the Vector Fitting (VF) iteration often represents the algorithm of choice for generating low order equivalent circuits for complex multiport components in a data-driven setting. Although accurate and reliable in general, macromodels generated via VF are inherently represented in terms of a rational approximation of one specific input-output transfer function of the structure under modeling, e.g., its scattering matrix. However, accuracy in the scattering representation does not necessarily imply a good accuracy when solving the macromodel in a system-level setting, under different termination conditions. In fact, the sensitivity of the macromodel with respect to its loading conditions may be large and needs to be addressed and controlled. In this work, we present a modified VF scheme that overcomes this issue, by introducing in the rational approximation algorithm the requirement that the macromodel remains accurate when interconnected with a known class of admissible networks. The proposed formulation is based on an augmentation of the cost function minimized at each VF iteration; further, it does not require additional expensive data gathering steps when compared to standard approaches. The effectiveness of the scheme is tested over a set of relevant examples, in particular for Power Integrity applications

A Compressed Multivariate Macromodeling Framework for Fast Transient Verification of System-Level Power Delivery Networks

This paper discusses a reduced-order modeling and simulation approach for fast transient power integrity verifi- cation at full system level. The reference structure is a com- plete power distribution network (PDN) from platform voltage regulator module (VRM) to multiple cores, including board, package, decoupling capacitors, and per-core fully integrated voltage regulators (FIVR). All blocks are characterized and known through high-fidelity models derived from first-principle solvers (full-wave electromagnetic and circuit-level extractions). The complexity of such detailed characterization grows very large and becomes intractable, especially for power integrity verification of massive multicore platforms subjected to real workload scenarios. We approach this problem by exploiting a multi-stage macromodeling and compression process, leading to a compact representation of the system dynamics in terms of a linearized state-space structure with multiple feedback loops from the FIVR controllers. The PDN macromodel is obtained through a data-driven approach starting from reference small- signal frequency responses, obtaining a sparse and structured representation specifically designed to match the behavior of the reference system. The resulting compact model is then solved in time-domain very efficiently. Results on mobile and enterprise server benchmarks demonstrate a speedup in runtime up to 50× with respect to HSPICE, with negligible loss of accuracy

Automatic Parameterization of the Purine Metabolism Pathway through Discrete Event-based Simulation

Model development and analysis of metabolic networks is recognized as a key requirement for integrating in-vitro and in-vivo experimental data. In-silico simulation of a biochemical model allows one to test different experimental conditions, helping in the discovery of the dynamics that regulate the system. Although qualitative characterizations of such complex mechanisms are, at least partially, available, a fully-parametrized quantitative description is often miss- ing. On the other hand, several characteristics and issues to model biological systems are common to the electronics system modelling, such as concurrency, reactivity, abstraction levels, automatic reverse engineering, as well as design space explosion during validation. This work presents a methodology that applies languages, techniques, and tools well established in the context of electronic design automation (EDA) for modelling and simulation of metabolic networks through Petri nets. The paper presents the results obtained by applying the proposed methodology to model the purine metabolism starting from the metabolomics data obtained from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders

Efficient Simulation and Parametrization of Stochastic Petri Nets in SystemC: A Case study from Systems Biology

Stochastic Petri nets (SPN) are a form of Petri net where the transitions fire after a probabilistic and randomly determined delay. They are adopted in a wide range of appli- cations thanks to their capability of incorporating randomness in the models and taking into account possible fluctuations and environmental noise. In Systems Biology, they are becoming a reference formalism to model metabolic networks, in which the noise due to molecule interactions in the environment plays a crucial role. Some frameworks have been proposed to implement and dynamically simulate SPN. Nevertheless, they do not allow for automatic model parametrization, which is a crucial task to identify the network configurations that lead the model to satisfy temporal properties of the model. This paper presents a framework that synthesizes the SPN models into SystemC code. The framework allows the user to formally define the network properties to be observed and to automatically extrapolate, thorough Assertion-based Verification (ABV), the parameter configurations that lead the network to satisfy such properties. We applied the framework to implement and simulate a complex biological network, i.e., the purine metabolism, with the aim of reproducing the metabolomics data obtained in-vitro from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders

Glycopatterns of the foregut in the striped dolphin Stenella coeruleoalba, Meyen 1833 from the Mediterranean Sea

AbstractThe glycopatterns of the glycans secreted by the mucosa of stomach and duodenal ampulla of the striped dolphin, Stenella coeruleoalba were studied by histochemical (Periodic acid‐Schiff, Alcian Blue pH 2.5, High Iron Diamine) and lectin‐binding (SBA, DBA, PNA, WGA, MAA‐II, SNA, ConA, UEA‐I, AAA, LTA) techniques. The stomach can be divided into four compartments: main stomach, two connecting chambers and pylorus. The pylorus is followed by the duodenal ampulla. Mucins are secreted by surface cells and intramucosal glands specific for each compartment. In the main stomach glands, neck cells were weakly sulphated, with prevailing glycosaminylated, glycosylated/mannosylated, and fucosylated residuals. Parietal and chief cells in general were scarcely reactive. In the connecting chambers glands, there were high levels of sulphation, glycosaminylation, glycosylation/mannosylation, and fucosylation, the latter with more complex patterns than those observed in the main stomach glands. In the pyloric glands sulphated, glycosaminylated and fucosylated residuals decreased, whereas the opposite was observed for galactosyl/galactosaminylated residuals. Glycosylation patterns in the glands of the duodenal ampulla differed from those of the pyloric ones, with similar levels of sulphation, lower levels of galactosyl/galactosaminylation and glycosaminylation, and higher level of fucosylation. The results are compared with those available in literature

LFA-1 Controls Th1 and Th17 Motility Behavior in the Inflamed Central Nervous System

Leukocyte trafficking is a key event during autoimmune and inflammatory responses. The subarachnoid space (SAS) and cerebrospinal fluid are major routes for the migration of encephalitogenic T cells into the central nervous system (CNS) during experimental autoimmune encephalomyelitis (EAE), the animal model of multiple sclerosis, and are sites of T cell activation before the invasion of CNS parenchyma. In particular, autoreactive Th1 and Th17 cell trafficking and reactivation in the CNS are required for the pathogenesis of EAE. However, the molecular mechanisms controlling T cell dynamics during EAE are unclear. We used two-photon laser microscopy to show that autoreactive Th1 and Th17 cells display distinct motility behavior within the SAS in the spinal cords of mice immunized with the myelin oligodendrocyte glycoprotein peptide MOG(35-55). Th1 cells showed a strong directional bias at the disease peak, moving in a straight line and covering long distances, whereas Th17 cells exhibited more constrained motility. The dynamics of both Th1 and Th17 cells were strongly affected by blocking the integrin LFA-1, which interfered with the deformability and biomechanics of Th1 but not Th17 cells. The intrathecal injection of a blocking anti-LFA-1 antibody at the onset of disease significantly inhibited EAE progression and also strongly reduced neuro-inflammation in the immunized mice. Our results show that LFA-1 plays a pivotal role in T cell motility during EAE and suggest that interfering with the molecular mechanisms controlling T cell motility can help to reduce the pathogenic potential of autoreactive lymphocytes

Identifying priority areas for spatial management of mixed fisheries using ensemble of multi‐species distribution models

Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures

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

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research