Twin study reveals non-heritable immune perturbations in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system underpinned by partially understood genetic risk factors and environmental triggers and their undefined interactions1,2. Here we investigated the peripheral immune signatures of 61 monozygotic twin pairs discordant for MS to dissect the influence of genetic predisposition and environmental factors. Using complementary multimodal high-throughput and high-dimensional single-cell technologies in conjunction with data-driven computational tools, we identified an inflammatory shift in a monocyte cluster of twins with MS, coupled with the emergence of a population of IL-2 hyper-responsive transitional naive helper T cells as MS-related immune alterations. By integrating data on the immune profiles of healthy monozygotic and dizygotic twin pairs, we estimated the variance in CD25 expression by helper T cells displaying a naive phenotype to be largely driven by genetic and shared early environmental influences. Nonetheless, the expanding helper T cells of twins with MS, which were also elevated in non-twin patients with MS, emerged independent of the individual genetic makeup. These cells expressed central nervous system-homing receptors, exhibited a dysregulated CD25-IL-2 axis, and their proliferative capacity positively correlated with MS severity. Together, our matched-pair analysis of the extended twin approach allowed us to discern genetically and environmentally determined features of an MS-associated immune signature.

High-dimensional immune profiling identifies a biomarker to monitor dimethyl fumarate response in multiple sclerosis.

Dimethyl fumarate (DMF) is an immunomodulatory treatment for multiple sclerosis (MS). Despite its wide clinical use, the mechanisms underlying clinical response are not understood. This study aimed to reveal immune markers of therapeutic response to DMF treatment in MS. For this purpose, we prospectively collected peripheral blood mononuclear cells (PBMCs) from a highly characterized cohort of 44 individuals with MS before and at 12 and 48 wk of DMF treatment. Single cells were profiled using high-dimensional mass cytometry. To capture the heterogeneity of different immune subsets, we adopted a bioinformatic multipanel approach that allowed cell population-cluster assignment of more than 50 different parameters, including lineage and activation markers as well as chemokine receptors and cytokines. Data were further analyzed in a semiunbiased fashion implementing a supervised representation learning approach to capture subtle longitudinal immune changes characteristic for therapy response. With this approach, we identified a population of memory T helper cells expressing high levels of neuroinflammatory cytokines (granulocyte-macrophage colony-stimulating factor [GM-CSF], interferon γ [IFNγ]) as well as CXCR3, whose abundance correlated with treatment response. Using spectral flow cytometry, we confirmed these findings in a second cohort of patients. Serum neurofilament light-chain levels confirmed the correlation of this immune cell signature with axonal damage. The identified cell population is expanded in peripheral blood under natalizumab treatment, substantiating a specific role in treatment response. We propose that depletion of GM-CSF-, IFNγ-, and CXCR3-expressing T helper cells is the main mechanism of action of DMF and allows monitoring of treatment response.

Potent neutralization by monoclonal human IgM against SARS-CoV-2 is impaired by class switch.

To investigate the class-dependent properties of anti-viral IgM antibodies, we use membrane antigen capture activated cell sorting to isolate spike-protein-specific B cells from donors recently infected with SARS-CoV-2, allowing production of recombinant antibodies. We isolate 20, spike-protein-specific antibodies of classes IgM, IgG, and IgA, none of which shows any antigen-independent binding to human cells. Two antibodies of class IgM mediate virus neutralization at picomolar concentrations, but this potency is lost following artificial switch to IgG. Although, as expected, the IgG versions of the antibodies appear to have lower avidity than their IgM parents, this is not sufficient to explain the loss of potency.

Immunological Predictors of Dimethyl Fumarate-Induced Lymphopenia.

Treatment with dimethyl fumarate (DMF) leads to lymphopenia and infectious complications in a subset of patients with multiple sclerosis (MS). Here, we aimed to reveal immune markers of DMF-associated lymphopenia. This prospective observational study longitudinally assessed 31 individuals with MS by single-cell mass cytometry before and after 12 and 48 weeks of DMF therapy. Employing a neural network-based representation learning approach, we identified a CCR4-expressing T helper cell population negatively associated with relevant lymphopenia. CCR4-expressing T helper cells represent a candidate prognostic biomarker for the development of relevant lymphopenia in patients undergoing DMF treatment. ANN NEUROL 2022;91:676-681.

Single-cell profiling of myasthenia gravis identifies a pathogenic T cell signature.

Myasthenia gravis (MG) is an autoimmune disease characterized by impaired neuromuscular signaling due to autoantibodies targeting the acetylcholine receptor. Although its auto-antigens and effector mechanisms are well defined, the cellular and molecular drivers underpinning MG remain elusive. Here, we employed high-dimensional single-cell mass and spectral cytometry of blood and thymus samples from MG patients in combination with supervised and unsupervised machine-learning tools to gain insight into the immune dysregulation underlying MG. By creating a comprehensive immune map, we identified two dysregulated subsets of inflammatory circulating memory T helper (Th) cells. These signature ThCD103 and ThGM cells populated the diseased thymus, were reduced in the blood of MG patients, and were inversely correlated with disease severity. Both signature Th subsets rebounded in the blood of MG patients after surgical thymus removal, indicative of their role as cellular markers of disease activity. Together, this in-depth analysis of the immune landscape of MG provides valuable insight into disease pathogenesis, suggests novel biomarkers and identifies new potential therapeutic targets for treatment.

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks.

Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network. The atomic-scale dynamics emulate leaky integrate and fire (LIF) mechanisms in biological neurons, leading to the generation of critical avalanches of signals. These avalanches are quantitatively the same as those observed in cortical tissue and are signatures of the correlations that are required for computation. We show that the avalanches are associated with dynamical restructuring of the networks which self-tune to balanced states consistent with self-organized criticality. Our simulations allow visualization of the network states and detailed mechanisms of signal propagation.

The end of omics? High dimensional single cell analysis in precision medicine.

High-dimensional single-cell (HDcyto) technologies, such as mass cytometry (CyTOF) and flow cytometry, are the key techniques that hold a great promise for deciphering complex biological processes. During the last decade, we witnessed an exponential increase of novel HDcyto technologies that are able to deliver an in-depth profiling in different settings, such as various autoimmune diseases and cancer. The concurrent advance of custom data-mining algorithms has provided a rich substrate for the development of novel tools in translational medicine research. HDcyto technologies have been successfully used to investigate cellular cues driving pathophysiological conditions, and to identify disease-specific signatures that may serve as diagnostic biomarkers or therapeutic targets. These technologies now also offer the possibility to describe a complete cellular environment, providing unanticipated insights into human biology. In this review, we present an update on the current cutting-edge HDcyto technologies and their applications, which are going to be fundamental in providing further insights into human immunology and pathophysiology of various diseases. Importantly, we further provide an overview of the main algorithms currently available for data mining, together with the conceptual workflow for high-dimensional cytometric data handling and analysis. Overall, this review aims to be a handy overview for immunologists on how to design, develop and read HDcyto data.

Escalating to medium- versus high-efficacy disease modifying therapy after low-efficacy treatment in relapsing remitting multiple sclerosis.

BACKGROUND: In patients with relapsing remitting multiple sclerosis (RRMS) on low-efficacy disease modifying therapies (DMT), the optimal strategy on how to escalate treatment once needed, remains unknown. METHODS: We studied RRMS patients on low-efficacy DMTs listed in the Swiss National Treatment Registry, who underwent escalation to either medium- or high-efficacy DMTs. Propensity score-based matching was applied using 12 clinically relevant variables. Both groups were also separately matched with control subjects who did not escalate therapy. Time to relapse and to disability worsening were evaluated using Cox proportional hazard models. RESULTS: Of 1037 eligible patients, we 1:1 matched 450 MS patients who switched from low-efficacy to medium-efficacy (n = 225; 76.0% females, aged 42.4 ± 9.9 years [mean ± SD], median EDSS 3.0 [IQR 2-4]) or high-efficacy DMTs (n = 225; 72.4% females, aged 42.2 ± 10.6 years, median EDSS 3.0 [IQR 2-4]). Escalation to high-efficacy DMTs was associated with lower hazards of relapses than medium-efficacy DMTs (HR = 0.67, 95% CI 0.47-0.95, p = .027) or control subjects (HR = 0.61, 95% CI 0.44-0.84, p = .003). By contrast, escalation from low to medium-efficacy DMTs did not alter the hazard for relapses when compared to controls (i.e. patients on low-efficacy DMT who did not escalate DMT during follow-up) CONCLUSION: Our nationwide registry analysis suggests that, once escalation from a low-efficacy DMT is indicated, switching directly to a high-efficacy treatment is superior to a stepwise escalation starting with a moderate-efficacy treatment.

B cell depletion attenuates CD27 signaling of T helper cells in multiple sclerosis.

Multiple sclerosis is a chronic inflammatory disease of the central nervous system. Whereas T cells are likely the main drivers of disease development, the striking efficacy of B cell-depleting therapies (BCDTs) underscore B cells' involvement in disease progression. How B cells contribute to multiple sclerosis (MS) pathogenesis-and consequently the precise mechanism of action of BCDTs-remains elusive. Here, we analyze the impact of BCDTs on the immune landscape in patients with MS using high-dimensional single-cell immunophenotyping. Algorithm-guided analysis reveals a decrease in circulating T follicular helper-like (Tfh-like) cells alongside increases in CD27 expression in memory T helper cells and Tfh-like cells. Elevated CD27 indicates disrupted CD27/CD70 signaling, as sustained CD27 activation in T cells leads to its cleavage. Immunohistological analysis shows CD70-expressing B cells at MS lesion sites. These results suggest that the efficacy of BCDTs may partly hinge upon the disruption of Th cell and B cell interactions.

Biocontrol Using Torulaspora delbrueckii in Sequential Fermentation: New Insights into Low-Sulfite Verdicchio Wines.

Torulaspora delbrueckii has attracted renewed interest in recent years, for its biotechnological potential linked to its ability to enhance the flavor and aroma complexity of wine. Sequential fermentations with a selected native strain of T. delbrueckii (DiSVA 130) and low-sulfite native strain of Saccharomyces cerevisiae (DiSVA 709) were carried out to establish their contribution in biocontrol and the aroma profile. A first set of trials were conducted to evaluate the effect of the sulfur dioxide addition on pure and T. debrueckii/S. cerevisiae sequential fermentations. A second set of sequential fermentations without SO2 addition were conducted to evaluate the biocontrol and aromatic effectiveness of T. delbrueckii. Native T. delbrueckii showed a biocontrol action in the first two days of fermentation (wild yeasts reduced by c.a. 1 log at the second day). Finally, trials with the combination of both native and commercial T. delbrueckii/S. cerevisiae led to distinctive aromatic profiles of wines, with a significant enhancement in isoamyl acetate, phenyl ethyl acetate, supported by positive appreciations from the tasters, for ripe and tropical fruits, citrus, and balance. The whole results indicate that native T. delbrueckii could be a potential biocontrol tool against wild yeasts in the first phase of fermentation, contributing to improving the final wine aroma.

Neuromorphic behaviour in discontinuous metal films.

Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple evaporation processes, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex. We further demonstrate that these signals meet established criteria for criticality. We perform a detailed experimental investigation of the atomic-scale switching processes that are responsible for these signals, and show that they mimic the integrate-and-fire mechanism of biological neurons. Using numerical simulations and a simple circuit model, we show that the characteristic features of the switching events are dependent on the network state and the local position of the switch within the complex network. We conclude that discontinuous films provide an interesting potential platform for brain-inspired computing.

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy.

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.

Introduction: Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. Methods: We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. Results: Of 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. Conclusions: In this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.

Update on treatment in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. In recent years, many disease-modifying therapies (DMT) have been approved for MS treatment. For this reason, a profound knowledge of the characteristics and indications of the available compounds is required to tailor the therapeutic strategy to the individual patient characteristics. This should include the mechanism of action and pharmacokinetic of the drug, the safety and efficacy profile provided by clinical trials, as well as the understanding of possible side effects. Moreover, the evolving knowledge of the disease is paving the way to new and innovative therapeutic approaches, as well as the development of new biomarkers to monitor the therapeutic response and to guide the clinician's therapeutic choices. In this review we provide a comprehensive overview on currently approved therapies in MS and the emerging evidence-based strategies to adopt for initiating, monitoring, and eventually adapting a therapeutic regimen with DMT.

Starmerella bombicola and Saccharomyces cerevisiae in Wine Sequential Fermentation in Aeration Condition: Evaluation of Ethanol Reduction and Analytical Profile.

In the last few decades, the increase of ethanol in wine, due to global climate change and consumers' choice is one of the main concerns in winemaking. One of the most promising approaches in reducing the ethanol content in wine is the use of non-Saccharomyces yeasts in co-fermentation or sequential fermentation with Saccharomyces cerevisiae. In this work, we evaluate the use of Starmerella bombicola and S. cerevisiae in sequential fermentation under aeration condition with the aim of reducing the ethanol content with valuable analytical profile. After a preliminary screening in synthetic grape juice, bench-top fermentation trials were conducted in natural grape juice by evaluating the aeration condition (20 mL/L/min during the first 72 h) on ethanol reduction and on the analytical profile of wines. The results showed that S. bombicola/S. cerevisiae sequential fermentation under aeration condition determined an ethanol reduction of 1.46% (v/v) compared with S. cerevisiae pure fermentation. Aeration condition did not negatively affect the analytical profile of sequential fermentation S. bombicola/S. cerevisiae particularly an overproduction of volatile acidity and ethyl acetate. On the other hand, these conditions strongly improved the production of glycerol and succinic acid that positively affect the structure and body of wine.

Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation.

There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic spiking behavior that is strikingly similar to that observed in biological neurons. The spiking rate can be controlled by the input stimulus, similar to "rate coding" in biology, and the distributions of times between events are log-normal, providing insights into the atomic-scale spiking mechanism. The stochasticity of the spiking behavior is then used for true random number generation, and the high quality of the generated random bit-streams is demonstrated, opening up promising routes toward integration of neuromorphic computing with secure information processing.

Mass Cytometry of CSF Identifies an MS-Associated B-cell Population.

OBJECTIVE: To identify an MS-specific immune cell population by deep immune phenotyping and relate it to soluble signaling molecules in CSF. METHODS: We analyzed surface expression of 22 markers in paired blood/CSF samples from 39 patients using mass cytometry (cytometry by time of flight). We also measured the concentrations of 296 signaling molecules in CSF using proximity extension assay. Results were analyzed using highly automated unsupervised algorithmic informatics. RESULTS: Mass cytometry objectively identified a B-cell population characterized by the expression of CD49d, CD69, CD27, CXCR3, and human leukocyte antigen (HLA)-DR as clearly associated with MS. Concentrations of the B cell-related factors, notably FCRL2, were increased in MS CSF, especially in early stages of the disease. The B-cell trophic factor B cell activating factor (BAFF) was decreased in MS. Proteins involved in neural plasticity were also reduced in MS. CONCLUSION: When analyzed without a priori assumptions, both the soluble and the cellular compartments of the CSF in MS were characterized by markers related to B cells, and the strongest candidate for an MS-specific cell type has a B-cell phenotype.

Purification and Characterization of WA18, a New Mycocin Produced by Wickerhamomyces anomalus Active in Wine Against Brettanomyces bruxellensis Spoilage Yeasts.

Wickerhamomyces anomalus strain 18, isolated from a natural underground cheese ripening pit, secretes a mycocin named WA18 that inhibits wine spoilage yeasts belonging to Brettanomyces bruxellensis species, with a broad-spectrum of activity. WA18 was purified, and the purified protein was digested with specific restriction enzymes (lysine K and arginine R cut sites). The LC-MS and LC-MS/MS analysis after enzymatic digestions revealed a molecular weight of 31 kDa. Bioinformatics processing and database research of digested pure killer protein showed 99% identity with a UDP-glycosyltransferase protein. Competitive inhibition assay of killer activity by cell-wall polysaccharides suggests that branched glucans represent the first receptor site of the toxin on the envelope of the sensitive target. The WA18 partially purified crude extract (PPCE) showed high stability of antimicrobial activity at the physicochemical conditions suitable for the winemaking process. Indeed, in wine WA18 was able to counteract B. bruxellensis and control the production of ethyl phenols. In addition, the strain WA18 was compatible with Saccharomyces cerevisiae in co-culture conditions with a potential application together with commercial starter cultures. These data suggest that WA18 mycocin is a promising biocontrol agent against spoilage yeasts in winemaking, particularly during wine storage.

Long-range temporal correlations in scale-free neuromorphic networks.

Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing.