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Background: Glucocorticoids (GCs) can cause osteoporosis (OP). Prior observational research on bone density and the effects of GCs in polymyalgia rheumatica (PMR) and vasculitides is scarce and inconclusive. Methods: Rh-GIOP is a prospective cohort study of bone health in patients with inflammatory rheumatic diseases. In this cross-sectional baseline analysis, we focused on patients with PMR and different forms of vasculitides. Multivariable linear regression was used to model the effect of current and cumulative GC intake on the minimum T-score at any site (mTs; at either lumbar spine or hip), with comprehensive adjustment for confounders. In separate models, GCs were modelled both as continuous and categorical predictors. Sensitivity analyses, stratifying by measurement site and disease, were conducted. Results: A total of 198 patients, with a mean age of 67.7 ± 11.4 years and a mean disease duration of 5.3 ± 6.3 years, were included. Most patients suffered from PMR (36%), giant cell arteritis (26%) or granulomatosis with polyangiitis (17%). Women comprised 66.7% of the patients, and 87.4% were currently taking GCs. The mean CRP was 13.2 ± 26.1 mg/L. OP diagnosed by dual energy X-ray absorptiometry (DXA) (T-score ≤ −2.5) was present in 19.7% of the patients. While 88% were taking vitamin D supplements, calcium supplementation (4%) and treatment with anti-resorptive agents (17%) were relatively infrequent. Only 7% had a vitamin D deficit. Neither current (β(continuous model) = −0.01, 97.5% CI –0.02 to 0.01; p(all models) ≥ 0.49) nor cumulative (β(continuous model) = 0.01, 97.5% CI −0.04 to 0.07; p(all models) ≥ 0.35) GC doses were associated with mTs in any model. CRP was not associated with mTs in any model (p(all models) ≥ 0.56), and no interaction between CRP and GC intake was observed (p for interaction(all models) ≥ 0.32). Across all analyses, lower body mass index (p(all models) ≤ 0.01), history of vertebral fractures (p(all models) ≤ 0.02) and proton-pump inhibitor intake (p(all models) ≤ 0.04) were associated with bone loss. Sensitivity analyses with femoral neck and lumbar spine T-scores as dependent variables led to similar results as the analysis that excluded patients with PMR. Conclusions: In this cohort of PMR and vasculitides, we found a similar prevalence of OP by DXA to the overall elderly German population. Vitamin D supplementation was very common, and vitamin D insufficiency was less frequent than expected in Germans. There was no association between current or cumulative GC intake, CRP and impaired bone density. Proton-pump inhibitors seem to be a major, but somewhat neglected, risk factor for OP and should be given more attention. Our findings require confirmation from longitudinal analyses of the Rh-GIOP and other cohorts.

BackgroundTreatments for nonalcoholic steatohepatitis (NASH) are urgently needed. Hepatic fat fraction and shear stiffness quantified by magnetic resonance imaging (MRI‐HFF) and magnetic resonance elastography (MRE‐SS), respectively, are biomarkers for hepatic steatosis and fibrosis.PurposeThis study assessed the longitudinal effects of fibroblast growth factor 21 variant (polyethylene glycol [PEG]‐FGF21v) on MRI‐HFF and MRE‐SS in a NASH mouse model.Study TypePreclinical.Animal ModelThis study included a choline‐deficient, amino acid‐defined, high‐fat diet (CDAHFD) model and 6‐week‐old, male C57BL/6J mice (N = 78).Field Strength/SequenceThis study was performed using: 3T: gradient‐echo two‐point Dixon and spin‐echo (SE) echo‐planar imaging elastography (200 Hz) and 7T: SE two‐point Dixon and SE elastography (200 Hz).AssessmentMRI and MRE were performed before control diet (CD) or CDAHFD (BD), before PEG‐FGF21v dosing (baseline), and after PEG‐FGF21v treatment (WK4/8). Regions of interest for MRI‐HFF and MRE‐SS were delineated by J.L. and H.T. (>5 years of experience each). Fibrosis and steatosis were measured histologically after picrosirius red and H&E staining. Alkaline phosphatase, alanine transaminase, bile acids, and triglycerides (TGs) were measured.Statistical TestsTwo‐tailed Dunnett's tests were used for statistical analysis; untreated CDAHFD or baseline was used for comparisons. Imaging and histology/biochemistry data were determined using Spearman correlations. Bayesian posterior distributions for MRE‐SS at WK8, posterior means, and 95% credible intervals were presented.ResultsCDAHFD significantly increased baseline MRI‐HFF (3T: 21.97% ± 0.29%; 7T: 40.12% ± 0.35%) and MRE‐SS (3T: 1.25 ± 0.02; 7T: 1.78 ± 0.06 kPa) vs. CD (3T: 3.45% ± 0.7%; 7T: 12.06% ± 1.4% and 3T: 1.01 ± 0.02; 7T: 0.89 ± 0.06 kPa). At 7T, PEG‐FGF21v significantly decreased MRI‐HFF (WK4: 28.97% ± 1.22%; WK8: 20.93% ± 1.15%) and MRE‐SS (WK4: 1.57 ± 0.04; WK8: 1.36 ± 0.05 kPa) vs. untreated (WK4: 36.36% ± 0.62%; WK8: 30.58% ± 0.81% and WK4: 2.03 ± 0.06; WK8: 2.01 ± 0.04 kPa); 3T trends were similar. WK8 SS posterior mean percent attenuation ratios (RDI) were −68% (−90%, −44%; 3T) and −64% (−78%, −52%; 7T). MRI‐HFF was significantly correlated with H&E (3T, r = 0.93; 7T, r = 0.94) and TGs (both, r = 0.92).Data ConclusionsMRI‐HFF and MRE‐SS showed PEG‐FGF21v effects on hepatic steatosis and fibrosis across 3 and 7T, consistent with histological and biochemical data.Level of Evidence1Technical Efficacy Stage2

High-resolution 3D images of organelles are of paramount importance in cellular biology. Although light microscopy and transmission electron microscopy (TEM) have provided the standard for imaging cellular structures, they cannot provide 3D images. However, recent technological advances such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion beam scanning electron microscopy (FIB-SEM) provide the tools to create 3D images for the ultrastructural analysis of organelles. Here, we describe a standardized protocol using the visualization software, Amira, to quantify organelle morphologies in 3D, thereby providing accurate and reproducible measurements of these cellular substructures. We demonstrate applications of SBF-SEM and Amira to quantify mitochondria and endoplasmic reticulum (ER) structures.

Abstract Study Objectives Post-traumatic stress disorder (PTSD) and rapid eye movement (REM) sleep behavior disorder (RBD) share some common features including prominent nightmares and sleep disturbances. We aimed to comparatively analyze REM sleep without atonia (RSWA) between patients with chronic PTSD with and without dream enactment behavior (DEB), isolated RBD (iRBD), and controls. Methods In this retrospective study, we comparatively analyzed 18 PTSD with DEB (PTSD+DEB), 18 PTSD without DEB, 15 iRBD, and 51 controls matched for age and sex. We reviewed medical records to determine PTSD clinical features and quantitatively analyzed RSWA. We used nonparametric analyses to compare clinical and polysomnographic features. Results PTSD patients, both with and without DEB, had significantly higher RSWA than controls (all p < .025, excepting submentalis phasic duration in PTSD+DEB). Most RSWA measures were also higher in PTSD+DEB than in PTSD without DEB patients (all p < .025). Conclusions PTSD patients have higher RSWA than controls, whether DEB is present or not, indicating that REM sleep atonia control is abnormal in chronic PTSD. Further prospective studies are needed to determine whether neurodegenerative risk and disease markers similar to RBD might occur in PTSD patients.

Abstract Introduction: Multiple myeloma (MM) is a complex hematological malignancy with the heterogenous immune bone marrow (BM) environment contributing to tumor growth, drug resistance, and immune escape. T-Cells play a critical role in the clearance of malignant plasma cells from the tumor environment. However, T-Cells in multiple myeloma demonstrate impaired cytotoxicity, proliferation, and cytokine production due to the activation of immune inhibitory receptors from ligands produced by the myeloma cells. In this study, we investigate the behavior of T-Cells in MM patients by using single-cell RNA-Seq (scRNA-Seq) to compare the transcriptomic profiles of BM T-Cells of patients with rapid progressing (FP; PFS < 18mo) and non-progressing (NP; PFS > 4yrs) disease. Methods: Newly diagnosed MM patients (n=18) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) were identified as either rapid progressors or non-progressors based on their progression free survival since diagnosis. To capture transcriptomic data, scRNA-Seq was performed on 48 aliquots of frozen CD138-negative BM cells at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis, and Mount Sinai School of Medicine, NYC). Samples were collected at diagnosis prior to treatment. Surface marker expression for 29 proteins was captured for at least one sample per patient using CITE-Seq. After integration and batch correction, clustering was performed to identify cells of T or NK lineage. Uniform Manifold Approximation and Projection (UMAP) and differential expression were used to identify T-Lymphoid subtypes, and differences in NP and FP samples. Results: In this study, single cell transcriptomic profiles were identified for ~102,207 cells from 48 samples of 18 MM patients. 40,328 T (CD3+) and NK (CD3-, NKG7+) cells were isolated, and subclustered for further analysis (Fig 1A). Using differentially expressed markers for each cluster, the T-Lymphoid subset was refined into seven subtypes, consisting of various CD4+ T-Cells, CD8+ T-Cells, and NK cells (Fig 1B). The CD8+ cells were divided into three distinct phenotypes, namely a GZMK-, GZMB- CD8+ T-Cell cluster, a GZMK+ CD8+ Exhausted T-Cell cluster enriched in TIGIT and multiple chemokines (CCL3, CCL4, XCL2), and a GZMB+ NkT cluster enriched in cytolytic markers (PRF1, GNLY, NKG7) (Fig 1C). Differential expression between NP and FP samples in this CD8+ subset showed enrichment of the NkT cytotoxic markers in NP samples, while FP samples were enriched in the CD8+ Exhausted chemokine markers (Fig 1D). Furthermore, the proportion of CD8+ Exhausted T-Cells was enriched in FP samples (p.val < 0.05) (Fig 1E). Exhaustion markers were measured through both RNA and surface marker levels. In RNA, TIGIT was uniquely associated with the FP-enriched CD8+ Exhausted T-Cell cluster, and CD160 was uniquely expressed in FP samples (Fig 1F). CITE-Seq surface marker expression confirms enrichment of both TIGIT and PD1 in the CD8+ Exhausted T-Cell cluster, and along with more exhaustion in FP samples (p.val < 0.01). Conclusion: In this study, we have identified significant differences in T-Cell activity in patients with non-progressing and rapid-progressing multiple myeloma. T-Cells in rapid progressing patients appear to be in a suppressed state, with low cytolytic activity and enriched exhaustion markers. This GZMK+ T-Cell population shows strong similarities with an aging-associated subtype of effector memory T-Cells found to be enriched in older populations (Mogilenko et al, Immunity 54, 2021). These findings will be further validated in an expanded study, consisting both of a larger number of samples, and multiple samples at different timepoints from the same patient. Figure 1 Figure 1. Disclosures Jayasinghe: MMRF: Consultancy; WUGEN: Consultancy. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Kumar: Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Bluebird Bio: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Abstract Multiple Myeloma (MM) is the second most common hematologic malignancy, marked by uncontrolled clonal expansion of plasma cells. MM genome is complex and heterogeneous, with a high frequency of structural variants (SVs) and copy-number abnormalities (CNAs). Single-cell sequencing technologies offer advantages over traditional bulk methods in cancer genomics research for evaluating cellular heterogeneity and investigating evolution of cellular subpopulations within the tumor. Thus, there is an impetus to translate the growing understanding of the genomic landscape of MM into dissecting the tumor heterogeneity using scRNA-seq. So far, there are no reported studies systematically comparing tumor and immune populations differences between MM NON-progressors (NPs) and RAPID-progressor (RPs) and investigating how clonal plasma cells contribute to progression in a large cohort. In addition, the bone marrow microenvironment plays an important role in the evolution of premalignant MM and MM progression. A previous study of single-cell transcriptomics analysis of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) revealed that natural killer (NK) cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. This study shed the light on the role of immune cells on disease progression from asymptomatic MGUS to symptomatic MM. However to date, how immune cells influence disease progression within symptomatic MM is still unclear. Here, we subjected 344 CD138-negative Bone Marrow Mononuclear cells (BMMC) samples to scRNA-seq. To control for the first line therapy, 272 patients were treated with RVD and Autologous stem cell transplant (ASCT) and were stratified into 3 groups based on progression: 200 NON-progressors (NPs) (PFS> 5 years), 7 Intermediate Progressors (IPs, PFS 2-4 years) and 38 RAPID-progressors (RPs) (PFS<=18 months). In addition, there are 72 subjects treated with RVD but without ASCT, including 34 NPs, 2 IPs and 36 RPs. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Our preliminary analysis of scRNA-seq of 6 samples treated by the same first-line therapies (RVD but without ASCT) revealed that plasma cells (PCs) from two patients (MMRF2550 and MMRF2562) with chromosome 13q deletion clustered together, whereas PCs from a patient (MMRF2187) with t(8;14) Myc translocation formed a distinct cluster. Interestingly, PCs from a patient (MMRF2271) with both chromosome 13q deletion and t(8;14) formed another independent cluster. These observations highlight the important role of genetic drivers in transcriptome profiles of plasma cells. In addition, we further investigated how progression features correlate with the immune microenvironment and identified differentially expressed genes between NK cells from RPs versus those from NPs, which could potentially serve as MM progression markers. Interestingly, several cytotoxicity genes are significantly upregulated in FPs, such as GZMB and KLRB1 (log Fold Change=1.44, and 8.41 respectively). Overall, our preliminary results provide a small glimpse of the interconnected nature of driver genetic alterations and progression features in MM tumor and TME. This study will provide a sufficiently broad, deep, and diverse vast dataset for accurately characterizing MM at single-cell resolution to help interrogate how genetic alterations and disease progression interplay MM tumor and TME. We hope this study could identify novel candidate targets for therapeutic approaches, and ultimately stratify patients by risk of progression for early intervention in the clinic. Disclosures Kumar: Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Carsgen: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; BMS: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

With novel mutant SARS-CoV-2 variants of concern on the rise, knowledge of immune specificities against SARS-CoV-2 proteins is increasingly important for understanding the impact of structural changes in antibody-reactive protein epitopes on naturally acquired and vaccine-induced immunity, as well as broader topics of cross-reactivity and viral evolution. A multi-coronavirus protein microarray used to map the binding of COVID-19 patient antibodies to SARS-CoV-2 proteins and protein fragments as well as to the proteins of four other coronaviruses that infect humans has shown specific regions of SARS-CoV-2 proteins that are highly reactive with patient antibodies and revealed cross-reactivity of these antibodies with other human coronaviruses.