Spontaneous CD4+ T Cell Activation and Differentiation in Lupus-Prone B6.Nba2 Mice Is IFNAR-Independent.

Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by dysregulated T and B lymphocytes. Type I interferons (IFN-I) have been shown to play important pathogenic roles in both SLE patients and mouse models of lupus. Recent studies have shown that B cell intrinsic responses to IFN-I are enough to drive B cell differentiation into autoantibody-secreting memory B cells and plasma cells, although lower levels of residual auto-reactive cells remain present. We speculated that IFN-I stimulation of T cells would similarly drive specific T-cell associated lupus phenotypes including the upregulation of T follicular helper cells and Th17, thereby affecting autoantibody production and the development of glomerulonephritis. Using the B6.Nba2 mouse model of lupus, we evaluated disease parameters in T cell specific IFN-I receptor (IFNAR)-deficient mice (cKO). Surprisingly, all measured CD4+ T cell abnormalities and associated intra-splenic cytokine levels (IFNγ, IL-6, IL-10, IL-17, IL-21) were unchanged and thus independent of IFN-I. In contrast B6.Nba2 cKO mice displayed reduced levels of effector CD8+ T cells and increased levels of Foxp3+ CD8+ regulatory T cells, suggesting that IFN-I induced signaling specifically affecting CD8+ T cells. These data suggest a role for both pathogenic and immunosuppressive CD8+ T cells in Nba2-driven autoimmunity, providing a model to further evaluate the role of these cell subsets during lupus-like disease development in vivo.

Deep learning classification method for boar sperm morphology analysis.

BACKGROUND: Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics. OBJECTIVE: To overcome these challenges, we propose a novel approach integrating deep-learning technology with high-throughput image-based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label-free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide. MATERIALS AND METHODS: Images of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy. RESULTS: Using the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification. DISCUSSION AND CONCLUSIONS: We have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label-free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.

Reporting population size in wastewater-based epidemiology: A scoping review.

Knowledge of the number of people present in a catchment is fundamental for the assessment of spatio-temporal trends in wastewater-based epidemiology (WBE). Accurately estimating the number of people connected to wastewater catchments is challenging however, because populations are dynamic. Methods used to estimate population size can significantly influence the calculation and interpretation of population-normalised wastewater data (PNWD). This paper systematically reviews the reporting of population data in 339 WBE studies. Studies were evaluated based on their reporting of population size, the source of population data, the population calculation methods, and the uncertainties in population estimates. Most papers reported population size (96 %) and the source of population data (60 %). Fewer studies reported the uncertainties in their population data (50 %) and the methods used to calculate these estimates (28 %). This is relevant because different methods have unique strengths and limitations which can affect the accuracy of PNWD. Only 64 studies (19 %) reported all four components of population data. The reporting of population data has remained consistent in the past decade. Based on the findings, we recommend generalised reporting criteria for population data in WBE. As WBE is further mainstreamed and applied, the clear and comprehensive reporting of population data will only become increasingly important.

S100a9 Protects Male Lupus-Prone NZBWF1 Mice From Disease Development.

Systemic lupus erythematosus (SLE) is an autoimmune disorder disproportionally affecting women. A similar sex difference exists in the murine New Zealand Black/White hybrid model (NZBWF1) of SLE with all females, but only 30-40% of males, developing disease within the first year of life. Myeloid-derived suppressor cells (MDSCs) are prominent in NZBWF1 males and while depletion of these cells in males, but not females, promotes disease development, the mechanism of suppression remains unknown. S100a9, expressed by neutrophils and MDSCs, has previously been shown to exert immunosuppressive functions in cancer and inflammation. Here we investigated if S100a9 exerts immunosuppressive functions in NZBWF1 male and female mice. S100a9+/+, S100a9+/- and S100a9-/- NZBWF1 mice were followed for disease development for up to 8 months of age. Serum autoantibody levels, splenomegaly, lymphocyte activation, glomerulonephritis and proteinuria were measured longitudinally or at the time of harvest. In accordance with an immunosuppressive function of MDSCs in male mice, S100a9-deficient male NZBWF1 mice developed accelerated autoimmunity as indicated by increased numbers of differentiated effector B and T cells, elevated serum autoantibody levels, increased immune-complex deposition and renal inflammation, and accelerated development of proteinuria. In contrast, female mice showed either no response to S100a9-deficiency or even a slight reduction in disease symptoms. Furthermore, male, but not female, S100a9-/- NZBWF1 mice displayed an elevated type I interferon-induced gene signature, suggesting that S100a9 may dampen a pathogenic type I interferon signal in male mice. Taken together, S100a9 exerts an immunosuppressive function in male NZBWF1 mice effectively moderating lupus-like disease development via inhibition of type I interferon production, lymphocyte activation, autoantibody production and the development of renal disease.

Partial Protection From Lupus-Like Disease by B-Cell Specific Type I Interferon Receptor Deficiency.

Systemic lupus erythematosus (SLE) is an autoimmune disease that can present with many different permutations of symptom presentation. A large subset of SLE patients have been shown to present with elevated interferon stimulated gene (ISG) expression, and Type I IFNs (IFNαß) have been shown to drive disease in murine models through global IFNα Receptor (IFNAR) knockouts. However, the disease contribution of distinct immune cell subsets in response to constitutively increased levels of IFNαß is not fully understood. We utilized a B-cell specific IFNAR knockout (BΔIFNAR) on the B6.Nba2 spontaneous-lupus background to determine the contribution of IFNαß stimulated B cells in disease. We found that IFNαß signaling in B cells is driving increased splenomegaly, increased populations of activated B cells, and increased populations of germinal center (GC) B cells, memory B cells, and plasma blasts/cells, but did not affect the development of glomerulonephritis and immune-complex deposition. IFNAR expression by B cells also drove production of anti-chromatin IgG, and anti-dsDNA and -nRNP IgG and IgG2C auto-antibody levels, as well as increased Bcl2 expression, affecting GC B cell survival in B6.Nba2 mice.

Low Levels of Vitamin D Promote Memory B Cells in Lupus.

Background: Vitamin D deficiency is a known risk factor for Systemic Lupus Erythematosus (SLE), yet clinical trials have not demonstrated efficacy and few studies have utilized lupus models to understand the mechanism underlying this relationship. The Act1-/- mouse is a spontaneous model of lupus and Sjögren's syndrome, characterized by increased Th17 cells and peripheral B cell expansion. Vitamin D3 has anti-inflammatory properties, reduces Th17 cells and impairs B cell differentiation/activation. Therefore, we assessed how varying amounts of vitamin D3 affected lupus-like disease in the Act1-/- mouse. Methods: Act1-/- mice were fed either low/restricted (0 IU/kg), normal (2 IU/kg), or high/supplemented (10 IU/kg) vitamin D3 chow for 9 weeks, after which lupus-like features were analyzed. Results: While we found no differences in Th17 cells between vitamin D3 groups, vitamin D3 restriction specifically promoted memory B cell development, accompanied by elevated levels of serum IgM, IgG1, IgG3, and anti-dsDNA IgG. A similar significant negative association between serum vitamin D and memory B cells was confirmed in a cohort of SLE patients. Conclusion: Low levels of vitamin D3 are associated with elevated levels of memory B cells in an animal model of lupus and well-controlled SLE patients.

Limits of agricultural greenhouse gas calculators to predict soil N2O and CH4 fluxes in tropical agriculture.

Demand for tools to rapidly assess greenhouse gas impacts from policy and technological change in the agricultural sector has catalyzed the development of 'GHG calculators'- simple accounting approaches that use a mix of emission factors and empirical models to calculate GHG emissions with minimal input data. GHG calculators, however, rely on models calibrated from measurements conducted overwhelmingly under temperate, developed country conditions. Here we show that GHG calculators may poorly estimate emissions in tropical developing countries by comparing calculator predictions against measurements from Africa, Asia, and Latin America. Estimates based on GHG calculators were greater than measurements in 70% of the cases, exceeding twice the measured flux nearly half the time. For 41% of the comparisons, calculators incorrectly predicted whether emissions would increase or decrease with a change in management. These results raise concerns about applying GHG calculators to tropical farming systems and emphasize the need to broaden the scope of the underlying data.