Systemic Lupus Erythematosus (SLE) is an autoimmune disease characterized by loss of tolerance to self nucleic acids. The source of autoantigen that drives disease onset and progression is unclear. A candidate source of autoantigen is the Neutrophil Extracellular Trap (NET), which results in the release of nucleic acids into the extracellular environment, generating a structure composed of DNA coated with antimicrobial proteins. Based on in vitro and patient correlative studies, several groups have suggested NETs may provide lupus autoantigens. The observation that NET release (NETosis) relies on activity of the phagocyte NAPDH oxidase (Nox2) in neutrophils of both humans and mice provided a genetic strategy to test this hypothesis in vivo. To do so, we have crossed an X-linked nox2 null allele onto the lupus-prone MRL.Fas lpr genetic background and assessed immune activation, autoantibody generation, and SLE pathology. Strikingly, and counter to the prevailing hypothesis, Nox2-deficient lupus-prone mice have markedly exacerbated lupus, including increased spleen weight, increased renal disease, and elevated and altered autoantibody profiles. Intriguingly, heterozygous female mice, which have Nox2-deficiency in 50% of neutrophils, also had exacerbated lupus and altered autoantibody patterns, suggesting that failure to undergo normal Nox2-dependent cell death may result in release of immunogenic self-constituents that stimulate lupus. Our results indicate that NETosis does not contribute to SLE in vivo, and rather that Nox2 acts to inhibit disease pathogenesis.

Biomedical researchers are generating high-throughput, high-dimensional single-cell 5 data at a staggering rate. As costs of data generation decrease, experimental design is mov-6 ing towards measurement of many different single-cell samples in the same dataset. These 7 samples can correspond to different patients, conditions, or treatments. While scalability of 8 methods to datasets of these sizes is a challenge on its own, dealing with large-scale exper-9 imental design presents a whole new set of problems, including batch effects and sample 10 1 .

Cytosolic DNA-sensing pathways that signal via Stimulator of interferon genes (STING) mediate immunity to pathogens and also promote autoimmune pathology in DNaseII- and DNaseIII-deficient mice. In contrast, we report here that STING potently suppresses inflammation in a model of systemic lupus erythematosus (SLE). Lymphoid hypertrophy, autoantibody production, serum cytokine levels, and other indicators of immune activation were markedly increased in STING-deficient autoimmune-prone mice compared with STING-sufficient littermates. As a result, STING-deficient autoimmune-prone mice had significantly shorter lifespans than controls. Importantly, Toll-like receptor (TLR)-dependent systemic inflammation during 2,6,10,14-tetramethylpentadecane (TMPD)-mediated peritonitis was similarly aggravated in STING-deficient mice. Mechanistically, STING-deficient macrophages failed to express negative regulators of immune activation and thus were hyperresponsive to TLR ligands, producing abnormally high levels of proinflammatory cytokines. This hyperreactivity corresponds to dramatically elevated numbers of inflammatory macrophages and granulocytes in vivo. Collectively these findings reveal an unexpected negative regulatory role for STING, having important implications for STING-directed therapies.

Handling the vast amounts of single-cell RNA-sequencing and CyTOF data, which are now being generated in patient cohorts, presents a computational challenge due to the noise, complexity, sparsity and batch effects present. Here, we propose a unified deep neural network-based approach to automatically process and extract structure from these massive datasets. Our unsupervised architecture, called SAUCIE (Sparse Autoencoder for Unsupervised Clustering, Imputation, and Embedding), simultaneously performs several key tasks for single-cell data analysis including 1) clustering, 2) batch correction, 3) visualization, and 4) denoising/imputation. SAUCIE is trained to recreate its own input after reducing its dimensionality in a 2-D embedding layer which can be used to visualize the data. Additionally, it uses two novel regularizations: (1) an information dimension regularization to penalize entropy as computed on normalized activation values of the layer, and thereby encourage binary-like encodings that are amenable to clustering and (2) a Maximal Mean Discrepancy penalty to correct batch effects. Thus SAUCIE has a single architecture that denoises, batch-corrects, visualizes and clusters data using a unified 1 . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a

Though recent reports suggest that neutrophil extracellular traps (NETs) are a source of antigenic nucleic acids in systemic lupus erythematosus (SLE), we recently showed that inhibition of NETs by targeting the NADPH oxidase complex via cytochrome b-245, β polypeptide (cybb) deletion exacerbated disease in the MRL.Faslpr lupus mouse model. While these data challenge the paradigm that NETs promote lupus, it is conceivable that global regulatory properties of cybb and cybb-independent NETs confound these findings. Furthermore, recent reports indicate that inhibitors of peptidyl arginine deiminase, type IV (Padi4), a distal mediator of NET formation, improve lupus in murine models. Here, to clarify the contribution of NETs to SLE, we employed a genetic approach to delete Padi4 in the MRL.Faslpr model and used a pharmacological approach to inhibit PADs in both the anti-glomerular basement membrane model of proliferative nephritis and a human-serum-transfer model of SLE. In contrast to prior inhibitor studies, we found that deletion of Padi4 did not ameliorate any aspect of nephritis, loss of tolerance, or immune activation. Pharmacological inhibition of PAD activity had no effect on end-organ damage in inducible models of glomerulonephritis. These data provide a direct challenge to the concept that NETs promote autoimmunity and target organ injury in SLE.