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Ca 2+ entry into mitochondria is through the mitochondrial calcium uniporter complex (MCU cx ), a Ca 2+ -selective channel composed of five subunit types. Two MCU cx subunits (MCU and EMRE) span the inner mitochondrial membrane, while three Ca 2+ -regulatory subunits (MICU1, MICU2, and MICU3) reside in the intermembrane space. Here, we provide rigorous analysis of Ca 2+ and Na + fluxes via MCU cx in intact isolated mitochondria to understand the function of MICU subunits. We also perform direct patch clamp recordings of macroscopic and single MCU cx currents to gain further mechanistic insights. This comprehensive analysis shows that the MCU cx pore, composed of the EMRE and MCU subunits, is not occluded nor plugged by MICUs during the absence or presence of extramitochondrial Ca 2+ as has been widely reported. Instead, MICUs potentiate activity of MCU cx as extramitochondrial Ca 2+ is elevated. MICUs achieve this by modifying the gating properties of MCU cx allowing it to spend more time in the open state.

Systemic lupus erythematous (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. Here, we aim to identify SLE subgroups within a multiethnic cohort using an unsupervised clustering approach based on the American College of Rheumatology (ACR) classification criteria. We identify three patient clusters that vary according to disease severity. Methylation association analysis identifies a set of 256 differentially methylated CpGs across clusters, including 101 CpGs in genes in the Type I Interferon pathway, and we validate these associations in an external cohort. A cis-methylation quantitative trait loci analysis identifies 744 significant CpG-SNP pairs. The methylation signature is enriched for ethnic-associated CpGs suggesting that genetic and non-genetic factors may drive outcomes and ethnic-associated methylation differences. Our computational approach highlights molecular differences associated with clusters rather than single outcome measures. This work demonstrates the utility of applying integrative methods to address clinical heterogeneity in multifactorial multi-ethnic disease settings. Systemic lupus erythematosus (SLE) is an autoimmune disease of substantial phenotypic heterogeneity in different ethnic groups. Here, using data from a multi-ethnic cohort, the authors describe an approach based on clinical and molecular data to subtype SLE patients into three clusters of severity.

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD. Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

PurposeLimited mechanical ventilators (MV) during the Coronavirus disease (COVID-19) pandemic have led to the use of non-invasive ventilation (NIV) in hypoxemic patients, which has not been studied well. We aimed to assess the association of NIV versus MV with mortality and morbidity during respiratory intervention among hypoxemic patients admitted with COVID-19.MethodsWe performed a retrospective multi-center cohort study across 5 hospitals during March–April 2020. Outcomes included mortality, severe COVID-19-related symptoms, time to discharge, and final oxygen saturation (SpO2) at the conclusion of the respiratory intervention. Multivariable regression of outcomes was conducted in all hypoxemic participants, 4 subgroups, and propensity-matched analysis.ResultsOf 2381 participants with laboratory-confirmed SARS-CoV-2, 688 were included in the study who were hypoxemic upon initiation of respiratory intervention. During the study period, 299 participants died (43%), 163 were admitted to the ICU (24%), and 121 experienced severe COVID-19-related symptoms (18%). Participants on MV had increased mortality than those on NIV (128/154 [83%] versus 171/534 [32%], OR = 30, 95% CI 16–60) with a mean survival of 6 versus 15 days, respectively. The MV group experienced more severe COVID-19-related symptoms [55/154 (36%) versus 66/534 (12%), OR = 4.3, 95% CI 2.7–6.8], longer time to discharge (mean 17 versus 7.1 days), and lower final SpO2 (92 versus 94%). Across all subgroups and propensity-matched analysis, MV was associated with a greater OR of death than NIV.ConclusionsNIV was associated with lower respiratory intervention mortality and morbidity than MV. However, findings may be liable to unmeasured confounding and further study from randomized controlled trials is needed to definitively determine the role of NIV in hypoxemic patients with COVID-19.

Mitotically stable random monoallelic gene expression (RME) is documented for a small percentage of autosomal genes. We developed an in vivo genetic model to study the role of enhancers in RME using high-resolution single-cell analysis of natural killer (NK) cell receptor gene expression and enhancer deletions in the mouse germline. Enhancers of the RME NK receptor genes were accessible and enriched in H3K27ac on silent and active alleles alike in cells sorted according to allelic expression status, suggesting enhancer activation and gene expression status can be decoupled. In genes with multiple enhancers, enhancer deletion reduced gene expression frequency, in one instance converting the universally expressed gene encoding NKG2D into an RME gene, recapitulating all aspects of natural RME including mitotic stability of both the active and silent states. The results support the binary model of enhancer action, and suggest that RME is a consequence of general properties of gene regulation by enhancers rather than an RME-specific epigenetic program. Therefore, many and perhaps all genes may be subject to some degree of RME. Surprisingly, this was borne out by analysis of several genes that define different major hematopoietic lineages, that were previously thought to be universally expressed within those lineages: the genes encoding NKG2D, CD45, CD8α, and Thy-1. We propose that intrinsically probabilistic gene allele regulation is a general property of enhancer-controlled gene expression, with previously documented RME representing an extreme on a broad continuum.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Systemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups. We leverage cell-sorted RNA-seq data (CD14+ monocytes, B cells, CD4+ T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four-tier approach including unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning to identify SLE subgroups within this multiethnic cohort. K-means clustering on each cell-type resulted in three clusters for CD4 and CD14, and two for B and NK cells. To understand the identified clusters, correlation analysis revealed significant positive associations between the clusters and clinical parameters including disease activity as well as ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across cells were involved in SLE or other autoimmune-related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Finally, random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White individuals. The results from these analyses will help stratify patients based on their gene expression signatures to enable SLE precision medicine.Gaia Andreoletti et al. leverage cell-sorted RNA-seq data and machine learning to investigate gene expression patterns in immune cell subtypes purified from Asian and White patients with a medical diagnosis of systemic lupus erythematosus (SLE). They report distinct clinical subtypes of SLE and identify ethnicity- and disease-specific gene clusters, potentially contributing toward the future development of personalized therapeutic strategies for patients with SLE.

Preterm birth (PTB) is the leading cause of newborn deaths around the world. Spontaneous preterm birth (sPTB) accounts for two-thirds of all PTBs; however, there remains an unmet need of detecting and preventing sPTB. Although the dysregulation of the immune system has been implicated in various studies, small sizes and irreproducibility of results have limited identification of its role. Here, we present a cross-study meta-analysis to evaluate genome-wide differential gene expression signals in sPTB. A comprehensive search of the NIH genomic database for studies related to sPTB with maternal whole blood samples resulted in data from three separate studies consisting of 339 samples. After aggregating and normalizing these transcriptomic datasets and performing a meta-analysis, we identified 210 genes that were differentially expressed in sPTB relative to term birth. These genes were enriched in immune-related pathways, showing upregulation of innate immunity and downregulation of adaptive immunity in women who delivered preterm. An additional analysis found several of these differentially expressed at mid-gestation, suggesting their potential to be clinically relevant biomarkers. Furthermore, a complementary analysis identified 473 genes differentially expressed in preterm cord blood samples. However, these genes demonstrated downregulation of the innate immune system, a stark contrast to findings using maternal blood samples. These immune-related findings were further confirmed by cell deconvolution as well as upstream transcription and cytokine regulation analyses. Overall, this study identified a strong immune signature related to sPTB as well as several potential biomarkers that could be translated to clinical use.

PurposeTo investigate the relationship between metabolic syndrome (MS) and urinary abnormalities in stone-forming patients. Additionally, to delineate whether severity of urinary derangements is impacted by the number of co-occurring MS components.MethodsStone-forming patients who underwent initial metabolic workup prior to medical intervention at a comprehensive stone clinic were retrospectively reviewed and included in the study. Patients were given a six point (0–5) Metabolic Syndrome Severity Score (MSSS) based on the number of co-occurring MS components and split into six respective groups. Baseline clinical characteristics and metabolic profiles were compared between groups.ResultsFour-hundred-ninety-five patients were included in the study. Median age and median BMI was 58 years and 27.26 kg/m2, respectively. Several significant metabolic differences were noted, most notably a downward trend in median urinary pH (p < 0.001) and an upward trend in median urinary supersaturation uric acid (p < 0.001) across groups as MSSS increased. Multivariate analysis demonstrated an independent association between higher MSSS and increasing number of urinary abnormalities. A second multivariate analysis revealed that all MS components except hyperlipidemia were independently associated with low urinary pH. Additionally, obesity was independently associated with the greatest number of urinary abnormalities and had the strongest association with hyperuricosuria.ConclusionsPrior research has attributed the strong association of nephrolithiasis and MS to high prevalence of UA nephrolithiasis and low urinary pH. Our findings indicate that all MS components with the exception of hyperlipidemia were independently associated with low urinary pH suggesting a mechanism independent from insulin resistance.

ObjectiveThe COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive.DesignDemographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive.SettingAll patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system.ParticipantsParticipants over the age of 18 years were included.Primary outcomesWe investigated in-hospital mortality during the study period.ResultsA total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL.ConclusionsIn our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.