Effects of ibrutinib on effector B cells in patients with systemic sclerosis

Systemic sclerosis (SSc) is a connective tissue disease with significant morbidity and reduced survival of patients. Currently available treatment strategies only alleviate symptoms and slow disease progression. Previous attempts of immunomodulating therapies addressing B cell pathology like rituximab and tocilizumab in SSc showed insufficient efficacy. Here, we investigated the therapeutic potential of ibrutinib, a Bruton’s tyrosine kinase (BTK) inhibitor used in B cell malignancies, to alter B cell pathology in SSc in an in vitro model of autoimmunity. Our data show that ibrutinib was able to reduce the production of the profibrotic hallmark cytokines IL-6 and TNF-α, which are mainly released by the effector B cell population, in response to TLR9-stimulation, while preserving the release of immunoregulatory IL-10 and IFN-γ from B cells. This investigation supports efforts for a potential future clinical application of ibrutinib in patients with SSc as a novel treatment for the underlying pathogenetic immune imbalance contributing to disease onset and progression

Towards multiomic analysis of oral mucosal pathologies

Oral mucosal pathologies comprise an array of diseases with worldwide prevalence and medical relevance. Affecting a confined space with crucial physiological and social functions, oral pathologies can be mutilating and drastically reduce quality of life. Despite their relevance, treatment for these diseases is often far from curative and remains vastly understudied. While multiple factors are involved in the pathogenesis of oral mucosal pathologies, the host's immune system plays a major role in the development, maintenance, and resolution of these diseases. Consequently, a precise understanding of immunological mechanisms implicated in oral mucosal pathologies is critical (1) to identify accurate, mechanistic biomarkers of clinical outcomes; (2) to develop targeted immunotherapeutic strategies; and (3) to individualize prevention and treatment approaches. Here, we review key elements of the immune system's role in oral mucosal pathologies that hold promise to overcome limitations in current diagnostic and therapeutic approaches. We emphasize recent and ongoing multiomic and single-cell approaches that enable an integrative view of these pathophysiological processes and thereby provide unifying and clinically relevant biological signatures

Systemic Immunological Consequences of Chronic Periodontitis

Chronic Periodontitis (ChP) is a prevalent inflammatory disease affecting 46% of the US population. ChP produces a profound local inflammatory response to dysbiotic oral microbiota that leads to destruction of alveolar bone and tooth loss. ChP is also associated with systemic illnesses including cardiovascular diseases, malignancies, and adverse pregnancy outcomes. However, the mechanisms underlying these adverse health outcomes are poorly understood. We used a highly multiplex mass cytometry immunoassay to perform an in-depth analysis of the systemic consequences of ChP in patients, before and after periodontal treatment in this prospective cohort study. A high-dimensional analysis of intracellular signaling networks revealed immune system-wide dysfunctions differentiating patients with ChP from healthy controls. Notably, we observed exaggerated pro-inflammatory responses to P. gingivalis-derived lipopolysaccharide in circulating neutrophils and monocytes from patients with ChP. Simultaneously, natural killer cell responses to inflammatory cytokines were attenuated. Importantly, the immune alterations associated with ChP were no longer detectable three weeks after periodontal treatment. Our findings demarcate systemic and cell-specific immune dysfunctions in patients with ChP which can be temporarily reversed by the local treatment of ChP

Discovery of sparse, reliable omic biomarkers with Stabl

<p><span>Adoption of high-content omic technologies in clinical studies, coupled with computational </span><span>methods, have yielded an abundance of candidate biomarkers. However, translating such find</span><span>ings into bona fide clinical biomarkers remains challenging.</span> <span>To facilitate this process, we </span><span>introduce Stabl, a general machine learning framework that identifies a sparse, reliable set </span><span>of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into </span><span>multivariable predictive modeling.</span> <span>Evaluation of Stabl on synthetic datasets and five inde</span><span>pendent clinical studies demonstrates improved biomarker sparsity and reliability compared to </span><span>commonly used sparsity-promoting regularization methods while maintaining predictive per</span><span>formance; it distills datasets containing 1,400 to 35,000 features down to 4 to 34 candidate </span><span>biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of </span><span>complex predictive models, as it hones in on a shortlist of proteomic, metabolomic, and cyto</span><span>metric events predicting labor onset, microbial biomarkers of preterm birth, and a pre-operative </span><span>immune signature of post-surgical infections.</span></p><p>Funding provided by: Stanford University<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100005492<br>Award Number: </p&gt