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Identification of post-translationally or chemically modified peptides in mass spectrometry-based proteomics experiments is a crucial yet challenging task. We have recently introduced a fragment ion indexing method and the MSFragger search engine to empower an open search strategy for comprehensive analysis of modified peptides. However, this strategy does not consider fragment ions shifted by unknown modifications, preventing modification localization and limiting the sensitivity of the search. Here we present a localization-aware open search method, in which both modification-containing (shifted) and regular fragment ions are indexed and used in scoring. this website We also implement a fast mass calibration and optimization method, allowing optimization of the mass tolerances and other key search parameters. We demonstrate that MSFragger with mass calibration and localization-aware open search identifies modified peptides with significantly higher sensitivity and accuracy. Comparing MSFragger to other modification-focused tools (pFind3, MetaMorpheus, and TagGraph) shows that MSFragger remains an excellent option for fast, comprehensive, and sensitive searches for modified peptides in shotgun proteomics data.Costello syndrome is an autosomal dominant disorder that is caused by germline HRAS mutations. Patients with Costello syndrome present craniofacial abnormalities, cardiac defects, and cancer predisposition, as well as skin abnormalities, including papillomas, keratosis pilaris, and eczematous dermatitis. However, the mechanisms underlying the dermatological abnormalities remain unclear. Here, we demonstrated that knock-in mice expressing an Hras G12S mutation (HrasG12S/+ mice) are susceptible to develop atopic dermatitis (AD)-like skin lesions, including eczema, pruritus, elevated serum IgE levels, acanthosis, and the infiltration of mast cells, basophils, and type-2 innate lymphoid cells in the dermis, after stimulation with house dust mite allergens (Dermatophagoides farinae, Dfb). Reduced skin barrier function, increased proliferation of phosphorylated ERK (p-ERK)-positive epidermal cells, and increased Th2-type cytokines as well as epithelial cell-derived cytokines, including IL-33, were observed in the skin tissue of HrasG12S/+ mice compared with Hras+/+ mice. Cultured HrasG12S/+ keratinocytes exhibited increased IL-33 expression after Dfb stimulation. PD0325901, an MEK inhibitor, ameliorated AD-like symptoms in HrasG12S/+ mice, showing decreased proliferation of p-ERK-positive epidermal cells and decreased expression of IL-33. Our findings indicate that the epidermis of HrasG12S/+ mice stimulated by Dfb strongly induced IL-33 expression and type-2 innate lymphoid cells, resulting in AD-like skin lesions. These results suggest that the epidermis of HrasG12S/+ mice are prone to development of eczematous dermatitis stimulated with house dust mite allergens.Antibodies against programmed cell death protein 1 (PD-1) and its ligand (PD-L1) have dramatically changed the landscape of therapies for non-small cell lung carcinoma (NSCLC); however, the majority of patients do not respond to these agents. In addition, hyperprogressive disease (HPD) develops in a larger portion of NSCLC patients treated with PD-1/PD-L1 inhibitors than in patients treated with standard chemotherapy. The use of chimeric antigen receptor (CAR) T cells has been successful to treat blood cancers but not for solid tumors like NSCLC. In this work, we constructed CAR T cells that target PD-L1 and evaluated their efficacy in NSCLC with either high or low PD-L1 expression. PD-L1-CAR T cells exhibited antigen-specific activation, cytokine production, and cytotoxic activity against PD-L1high NSCLC cells and xenograft tumors. Furthermore, the addition of a subtherapeutic dose of local radiotherapy improved the efficacy of PD-L1-CAR T cells against PD-L1low NSCLC cells and tumors. Our findings indicate that PD-L1-CAR T cells represent a novel therapeutic strategy for patients with PD-L1-positive NSCLC, particularly for those who are susceptible to HPD.While food insecurity is a persistent public health challenge, its long-term association with depression at a national level is unknown. We investigated the spatial heterogeneity of food insecurity and its association with depression in South Africa (SA), using nationally-representative panel data from the South African National Income Dynamics Study (years 2008-2015). Geographical clusters ("hotpots") of food insecurity were identified using Kulldorff spatial scan statistic in SaTScan. Regression models were fitted to assess association between residing in food insecure hotspot communities and depression. Surprisingly, we found food insecurity hotspots (p  less then  0.001) in high-suitability agricultural crop and livestock production areas with reliable rainfall and fertile soils. At baseline (N = 15,630), we found greater likelihood of depression in individuals residing in food insecure hotspot communities [adjusted relative risk (aRR) = 1.13, 95% CI1.01-1.27] using a generalized linear regression model. When the panel analysis was limited to 8,801 participants who were depression free at baseline, residing in a food insecure hotspot community was significantly associated with higher subsequent incidence of depression (aRR = 1.11, 95% CI1.01-1.22) using a generalized estimating equation regression model. The association persisted even after controlling for multiple socioeconomic factors and household food insecurity. We identified spatial heterogeneity of food insecurity at a national scale in SA, with a demonstrated greater risk of incident depression in hotspots. More importantly, our finding points to the "Food Security Paradox", food insecurity in areas with high food-producing potential. There is a need for place-based policy interventions that target communities vulnerable to food insecurity, to reduce the burden of depression.Animal-mediated nutrient dynamics are critical processes in ecosystems. Previous research has found animal-mediated nutrient supply (excretion) to be highly predictable based on allometric scaling, but similar efforts to find universal predictive relationships for an organism's body nutrient content have been inconclusive. We use a large dataset from a diverse tropical marine community to test three frameworks for predicting body nutrient content. We show that body nutrient content does not follow allometric scaling laws and that it is not well explained by trophic status. Instead, we find strong support for taxonomic identity (particularly at the family level) as a predictor of body nutrient content, indicating that evolutionary history plays a crucial role in determining an organism's composition. We further find that nutrients are "stoichiometrically linked" (e.g., %C predicts %N), but that the direction of these relationships does not always conform to expectations, especially for invertebrates. Our findings demonstrate that taxonomic identity, not trophic status or body size, is the best baseline from which to predict organismal body nutrient content.
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