The perpetuation of inflammation is an important pathophysiological contributor to the global medical burden. Chronic inflammation is promoted by non-programmed cell death1,2; however, how inflammation is instigated, its cellular and molecular mediators, and its therapeutic value are poorly defined. Here we use mouse models of atherosclerosis—a major underlying cause of mortality worldwide—to demonstrate that extracellular histone H4-mediated membrane lysis of smooth muscle cells (SMCs) triggers arterial tissue damage and inflammation. We show that activated lesional SMCs attract neutrophils, triggering the ejection of neutrophil extracellular traps that contain nuclear proteins. Among them, histone H4 binds to and lyses SMCs, leading to the destabilization of plaques; conversely, the neutralization of histone H4 prevents cell death of SMCs and stabilizes atherosclerotic lesions. Our data identify a form of cell death found at the core of chronic vascular disease that is instigated by leukocytes and can be targeted therapeutically.

Background: Alpha-synuclein (αS) is a nerve cell protein associated with Parkinson disease (PD). Accumulation of αS within the enteric nervous system (ENS) and its traffic from the gut to the brain are implicated in the pathogenesis and progression of PD. αS has no known function in humans and the reason for its accumulation within the ENS is unknown. Several recent studies conducted in rodents have linked αS to immune cell activation in the central nervous system. We hypothesized that αS in the ENS might play a role in the innate immune defenses of the human gastrointestinal (GI) tract. Methods: We immunostained endoscopic biopsies for αS from children with documented gastric and duodenal inflammation and intestinal allograft recipients who contracted norovirus. To determine whether αS exhibited immune-modulatory activity, we examined whether human αS induced leukocyte migration and dendritic cell maturation. Findings: We showed that the expression of αS in the enteric neurites of the upper GI tract of pediatric patients positively correlated with the degree of acute and chronic inflammation in the intestinal wall. In intestinal allograft subjects who were closely monitored for infection, expression of αS was induced during norovirus infection. We also demonstrated that both monomeric and oligomeric αS have potent chemoattractant activity, causing the migration of neutrophils and monocytes dependent on the presence of the integrin subunit, CD11b, and that both forms of αS stimulate dendritic cell maturation. Interpretation: These findings strongly suggest that αS is expressed within the human ENS to direct intestinal inflammation and implicates common GI infections in the pathogenesis of PD.

There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature of their functional commonality and sequence homology. SVM is used to search the undiscovered peptide sequence space and identify Pareto-optimal candidates that simultaneously maximize the distance σ from the SVM hyperplane (thus maximize its "antimicrobialness") and its ⍺-helicity, but minimize mutational distance to known AMPs. By calibrating SVM machine learning results with killing assays and small-angle X-ray scattering (SAXS), we find that the SVM metric σ correlates not with a peptide's minimum inhibitory concentration (MIC), but rather its ability to generate negative Gaussian membrane curvature. This surprising result provides a topological basis for membrane activity common to AMPs. Moreover, we highlight an important distinction between the maximal recognizability of a sequence to a trained AMP classifier (its ability to generate membrane curvature) and its maximal antimicrobial efficacy. As mutational distances are increased from known AMPs, we find AMP-like sequences that are increasingly difficult for nature to discover via simple mutation. Using the sequence map as a discovery tool, we find a unexpectedly diverse taxonomy of sequences that are just as membrane-active as known AMPs, but with a broad range of primary functions distinct from AMP functions, including endogenous neuropeptides, viral fusion proteins, topogenic peptides, and amyloids. The SVM classifier is useful as a general detector of membrane activity in peptide sequences.machine learning | membrane curvature | membrane permeation | antimicrobial peptides | cell-penetrating peptides T he ∼1,100 known antimicrobial peptides (AMPs) (1-6) are known collectively to have broad spectrum antimicrobial activity (1, 3, 5) via nonspecific interactions to target generic features in the many pathogen membranes (1, 7). Machine learning can in principle be used to help discover the "blueprint" for natural AMP sequences; however, such an enterprise presents significant structural difficulties. AMPs do not share a common core structure, but tend to be short (<50 amino acids), cationic (+2 to +9), and amphiphilic (1-6). One of the principal components of AMP activity involves the selective permeabilization of microbial membranes (1-3, 5, 8-11). However, there is increasing evidence that membrane activity is but one of several modes of antimicrobial activity: Translocated AMPs can interact with intracellular targets to inhibit cell wall synthesis, nucleic acid synthesis, protein synthesis, and enzymatic activity (12-16). Recent studies have shown that AMPs can be immunomodulatory (17, 18): In fact, LL-37 plays a role in autoimmune disorders such as lupus and psoriasis (18). These confounding factors make it difficult to implement adaptive learning for AMPs.Prior AMP machine-learning studies have focused pri...

Using multigenerational, single-cell tracking we explore the earliest events of biofilm formation by During initial stages of surface engagement (≤20 h), the surface cell population of this microbe comprises overwhelmingly cells that attach poorly (∼95% stay<30 s, well below the ∼1-h division time) with little increase in surface population. If we harvest cells previously exposed to a surface and direct them to a virgin surface, we find that these surface-exposed cells and their descendants attach strongly and then rapidly increase the surface cell population. This "adaptive," time-delayed adhesion requires determinants we showed previously are critical for surface sensing: type IV pili (TFP) and cAMP signaling via the Pil-Chp-TFP system. We show that these surface-adapted cells exhibit damped, coupled out-of-phase oscillations of intracellular cAMP levels and associated TFP activity that persist for multiple generations, whereas surface-naïve cells show uncorrelated cAMP and TFP activity. These correlated cAMP-TFP oscillations, which effectively impart intergenerational memory to cells in a lineage, can be understood in terms of a Turing stochastic model based on the Pil-Chp-TFP framework. Importantly, these cAMP-TFP oscillations create a state characterized by a suppression of TFP motility coordinated across entire lineages and lead to a drastic increase in the number of surface-associated cells with near-zero translational motion. The appearance of this surface-adapted state, which can serve to define the historical classification of "irreversibly attached" cells, correlates with family tree architectures that facilitate exponential increases in surface cell populations necessary for biofilm formation.