Citations

Abstract Regulatory T-cell (Treg) deficiency causes lethal, CD4+T cell-driven autoimmune diseases. Stem cell transplantation is used to treat these diseases, but this procedure is limited by the availability of a suitable donor. The intestinal microbiota drives host immune homeostasis by regulating the development of Treg, Th1 and Th2 cells. It is currently unclear if Treg-deficiency autoimmune disorders can be treated by targeting the enteric microbiota. Our aims are to determine the autoimmunity, gut microbiota, and plasma metabolomics affected by probiotic Lactobacillus reuteri DSM 17938 (LR), and to further identify the mechanism of modulated metabolite(s) in suppressing autoimmunity in Treg-deficient scurfy (SF) mice. We demonstrated that Foxp3+Treg deficiency results in gut microbial dysbiosis and autoimmunity over the lifespan of SF mouse. Remodeling microbiota with LR prolonged survival and reduced multi-organ inflammation in SF mice. LR changed the metabolomics profile disrupted by Treg-deficiency with a major effect of restoring levels of the purine metabolite inosine. Feeding inosine itself prolonged life and inhibited multi-organ inflammation by reducing Th1/Th2 cells and their associated cytokines. Mechanistically, the inhibition of inosine on the differentiation of Th1 and Th2 cells in vitro depended on adenosine A2A receptors. Both A2A receptor specific antagonist or genetically knockout A2A to SF mice reversed the anti-inflammatory effects of both inosine and LR in vivo. In conclusions, A2A receptors mediate a substantial protective effect of inosine and LR. The LR-modulated-microbiota-inosine-A2A axis might represent a potential avenue for combatting autoimmune diseases mediated by Treg dysfunction.

Abstract Among all the genetic factors linked with multiple sclerosis (MS), MHC class-II molecules show the strongest association. Generation of HLA class-II transgenic mice has helped to elucidate the role of HLA class-II gene in MS. We have shown that the human HLA-DR3 (DRβ1*0301) gene predisposes to proteolipid protein (PLP)-induced EAE, whereas HLA-DQβ1*0601 (DQ6) was resistant. We also showed that the DQ6 molecule protects from EAE in DR3DQ6 double transgenic mice by producing anti-inflammatory interferon gamma (IFNγ). To further confirm the role of IFNγ in protection, we generated DR3DQ8 mice lacking IFNγ (DR3DQ8.IFNγ-/-). Immunization with PLP91-110 peptide caused atypical EAE in DR3DQ8.IFNγ-/- mice characterized by ataxia and spasticity, hallmarks of brain-specific disease. Severe brain specific inflammation and demyelination in DR3DQ8.IFNγ-/- mice with minimal spinal cord pathology further confirmed brain-specific pathology. Severe disease in DR3DQ8.IFNγ-/- mice was due to increased encephalitogenicity of CD4 T cells and its ability to produce higher levels of IL-17 and GM-CSF compared to DR3DQ8 mice. Further, areas with demyelination showed increased presence of CD68+ inflammatory cells suggesting an important role for monocytes/microglia in causing brain pathology. Thus, our study supports a protective role for IFNγ in inflammatory and demyelinating disease through down regulation of IL-17 and induction of neuro-protective factors in the brain by monocytes/microglial cells.

Microglia play a critical role in the clearance of myelin debris, thereby ensuring functional recovery from neural injury. Here, using mouse model of demyelination following two-point LPC injection, we show that the microglial autophagic–lysosomal pathway becomes overactivated in response to severe demyelination, leading to lipid droplet accumulation and a dysfunctional and pro-inflammatory microglial state, and finally failed myelin debris clearance and spatial learning deficits. Data from genetic approaches and pharmacological modulations, via microglial Atg5 deficient mice and intraventricular BAF A1 administration, respectively, demonstrate that staged suppression of excessive autophagic–lysosomal activation in microglia, but not sustained inhibition, results in better myelin debris degradation and exerts protective effects against demyelination. Combined multi-omics results in vitro further showed that enhanced lipid metabolism, especially the activation of the linoleic acid pathway, underlies this protective effect. Supplementation with conjugated linoleic acid (CLA), both in vivo and in vitro, could mimic these effects, including attenuating inflammation and restoring microglial pro-regenerative properties, finally resulting in better recovery from demyelination injuries and improved spatial learning function, by activating the peroxisome proliferator-activated receptor (PPAR-γ) pathway. Therefore, we propose that pharmacological inhibition targeting microglial autophagic–lysosomal overactivation or supplementation with CLA could represent a potential therapeutic strategy in demyelinated disorders.

AbstractBackgroundChronic traumatic encephalopathy (CTE) is a neurodegenerative disease that is characterized by perivascular foci of phosphorylated tau (p‐tau) notably in the depths of sulci, with varying patterns in the cerebral cortex. Prior studies show elevated flortaucipir (FTP) PET binding in former American professional football players(PRO). However, the association of p‐tau with clinical measures remains unclear using regional FTP SUVR as early signs of CTE related tau pathology. Graph theory (GT) takes advantages of the spatial variability in p‐tau load and allows the integration of regional measures to define GT based measures. Here we examine the GT based measures in two CTE cohorts to evaluate their utility in assessing p‐tau related pathology in CTE.MethodThe graph procedure was optimized in FTP PET images in a group of 26 PRO with reported cognitive and/or behavorial symptoms and 30 asymptomatic controls without a history of traumatic brain injury (the DETECT Study) and identified the GT measure, entorhinal tau strength, as the target metric with the best group separation between PRO and controls. The optimized graph procedures were then applied to the DIAGNOSE CTE Research Project dataset (99 former PRO, 51 former college football players [COL], and 52 controls without exposure to repetitive head impacts [CTL]) and we examined entorhinal tau strength’s ability to differentiate the exposure groups and relationship to consensus based diagnoses of Traumatic Encephalopathy Syndrome (TES), the clinical syndrome associated with CTE.ResultEntorhinal tau strength was significantly different among the three exposure groups (p=0.0001) similar to regional SUVR measures (Figure 1). Regional SUVR did not differentiate players (PRO+COL) with and without TES, while entorhinal tau strength approached although did not reach significance (Figure 2). Entorhinal tau strength is the only FTP measure that showed significant differences (p=0.006) between those players with cognitive impairment and players without (Figure 3).ConclusionWhile there is still considerable overlap in between‐group measurements, GT based entorhinal tau strength, is potentially more sensitive in detecting tau changes in players with and without TES. Additional studies are necessary to clarify the value of this new measure in detecting underlying CTE pathology.

AbstractBackgroundAPOE4 gene dose, the number of apolipoprotein E ‐ɛ4 (APOE4) ɛ4 alleles in a person’s genotype, is associated with higher Alzheimer’s disease (AD) risk and younger median age at dementia onset. We previously found relationships between PET, plasma, and measurements of core AD CSF biomarkers in APOE4 gene dose. Here, we characterize longitudinal neurofilament light chain (NfL) changes in the Arizona APOE cohort, including cognitively unimpaired participants, with longitudinal plasma NfL measurements in APOE4 homozygotes (HMs), heterozygotes (HTs) and non‐carriers (NCs).MethodPlasma NfL measurements were performed at the University of Gothenburg using a Single molecule array (Simoa) immunoassay. Annual log‐transformed plasma NfL changes were compared with linear‐tend and univariate ANOVA adjusted for age, sex, and education in 45 HMs, 107 HTs, and 201 NCs who were cognitively unimpaired, 47‐85 years old and did not differ significantly in their age, sex, or educational level. Pearson r correlations with age and annual log‐transformed NfL changes were assessed in 353 participants with at least 2 visits (range 2‐6 visits with an average of ∼3) and an average follow‐up of ∼7 years (range 1‐12 years).ResultAnnual log‐transformed NfL changes were linearly associated with APOE4 gene dose (linear trend HM>HT>NC, P=0.01) and positively associated with age (r = 0.31, P<0.0001). Pairwise comparisons from the univariate ANOVA adjusted for age, sex, and education (P=0.001) showed significantly higher annual NfL changes in HM and HT groups versus NC controls (P=0.01).ConclusionAge and APOE4 gene dose are associated with greater annual plasma NfL changes in CU subjects close to their estimated ages at clinical onset. Studies are needed to better elucidate relationships between different Aβ, tau, inflammatory, and other CSF, or blood‐based biomarkers, and APOE4 gene dose at other ages and in larger subject groups.

PurposeTo improve calibrationless parallel imaging using pre‐learned subspaces of coil sensitivity functions.Theory and MethodsA subspace‐based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method.ResultsWith no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state‐of‐the‐art methods including JSENSE, DeepSENSE, P‐LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system.ConclusionA subspace‐based method named JSENSE‐Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

Background: Clostridioides difficile infection (CDI) is associated with poor outcomes in patients with inflammatory bowel diseases (IBD). Objectives: We conducted a nationally representative cohort study to evaluate the impact of recurrent CDI (rCDI)-related hospitalization on longitudinal unplanned healthcare utilization in patients with IBD. Design: This was a retrospective cohort study that utilized the 2017 Nationwide Readmissions Database (NRD). Methods: We identified 13,446 patients with IBD, hospitalized at least twice from January to June 2017 and followed through December 2017; of these, 1,148 had CDI-related hospitalizations. We compared the annual burden of hospitalization and IBD-related surgery in IBD patients with rCDI-related admission versus single CDI-related admission (primary reference), and those with one or more CDI-related admission versus no CDI-related admission (secondary reference). Results: There were no significant differences in risk and burden of unplanned healthcare utilization (time spent in-hospital, 27 days versus 27 days, p = 0.62), 6-month readmission (63% versus 64.3%, p = 0.8) or IBD-related surgery in patients with recurrent (two or more) CDI-related hospitalizations versus single CDI-related admission. However, patients with ⩾1 CDI-related admission versus no CDI admissions experienced higher rate of 6-month readmission (61.1% versus 55.7%, p<.001), total days spent in the hospital per year (median: 26 days versus 21 days, p<.001), total cost across all hospitalizations per year ($212,524 versus $184,384, p < 0.01), and inpatient mortality (3.28% versus 1.81%, p = 0.01), without an increase in risk of IBD-related surgery (6.7% versus 6.4%, p = 0.79). Conclusion: While patients with IBD hospitalized for CDI have poor longitudinal inpatient outcomes, recurrent admissions for CDI may not increase risk of adverse outcomes compared to one-time admission.

AbstractObjectiveThe aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used.Materials and MethodsA sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries.ResultsMost of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27.DiscussionDespite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints.ConclusionsThe phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

Abstract The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.