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IntroductionThe COVID-19 pandemic significantly disrupted civic life, particularly for older adults at increased risk for severe morbidity and mortality. Yet, little is known about the longer-term impacts on their daily routines and how this may affect health and wellbeing. MethodsThis qualitative study utilized data from older US adults who participated in the COVID-19 Coping Study’s three-year follow-up online survey, conducted in April–May 2023. The primary aim was to understandhowandwhydaily routines have changed among older Americans (N = 1,309). ResultsParticipants had an average age of 71 years, with approximately 74% female and 93% identifying as Non-Hispanic White. We conducted content and thematic analysis of open-ended survey responses to identify five key reasons for still-altered routines 3 years after the pandemic onset: (1) COVID-19 risk and exposure, (2) altered access, (3) broader life circumstances, (4) emotional health, and (5) physical health. DiscussionThese findings highlight the enduring impacts of the pandemic on older adults’ routines and underscore the importance of integrating public health strategies that prioritize routine stability to enhance mental, physical, and social health. To support older adults’ wellbeing during and beyond public health emergencies, we recommend strengthening community-based programs, improving access to health and social services, and designing adaptable interventions that help individuals rebuild and maintain meaningful daily routines. 

Abstract BACKGROUNDThe strongest genetic drivers of late‐onset Alzheimer's disease (AD) are apolipoprotein E4 (ApoE4) and TREM2^R47H. Despite their critical roles, the mechanisms underlying their interactions remain poorly understood. METHODSWe conducted microsecond‐long molecular dynamics simulations of TREM2‐ApoE complexes, including TREM2^R47H, validating our findings through comparison with published experimental data on TREM2‐ApoE binding interactions. RESULTSOur simulations reveal TREM2^WTcan sample an “open” CDR2 conformation, challenging the prevailing notion that this conformation is pathogenic. TREM2^WTexhibits greater flexibility, accessing diverse CDR2 conformations, while rigidity in TREM2^R47H’s CDR2 may explain its reduced ligand‐binding properties. ApoE2 facilitates dynamic reconfiguration of TREM2‐ApoE2 complexes, which is absent with ApoE4. TREM2^R47Hand ApoE4 mutually rigidify each other, suppressing interfacial flexibility. DISCUSSIONOur findings suggest mechanisms underlying ApoE2's neuroprotective functions, ApoE4's pathogenicity, and the synergistic effects of ApoE4 and TREM2^R47Hin AD. TREM2^WT’s flexibility and reconfiguration with ApoE2 may support microglial activation, while rigidity in TREM2^R47H‐ApoE4 interactions may drive pathogenic signaling. HighlightsTREM2^WTsamples diverse CDR2 conformations, challenging prior assumptions that an “open” CDR2 state is solely pathogenic.ApoE2 promotes dynamic reconfiguration of TREM2‐ApoE2 complexes, preserving TREM2^WT's flexibility.ApoE4's hinge forms a unique binding pocket that enhances TREM2 binding.The TREM2^R47H‐ApoE4 complex exhibits mutual rigidity, suppressing CDR2 and hinge flexibility. 

Abstract Little is known about biological soil crust (BSC) formation during the early stages of primary succession following glacial retreat. Here, we report on focused sampling of twelve discrete BSC colonies near the snout of a retreating glacier in the High Arctic and show that BSC colonies had significantly higher 16S and 18S rRNA gene diversity than the simpler communities of bare sediments sampled next to each colony. Surprisingly, the colonies also had a higher degree of community dispersion than the more clustered bare sediment controls. There were only eight 16S amplicons that showed 100% prevalence in all 12 of the colonies, and the three most abundant of these keystone amplicons were cyanobacteria, including a nitrogen fixing Nostoc. The only 18S amplicon common to all colonies was a diatom related to Sellaphora. This prominence of phototrophs indicates that early-successional BSC colonies are being supported by photosynthesis rather than ancient- or aeolian-derived organic matter. Co-occurrence network analysis among the phototrophs and fungi identified several potential early-successional soil lichens. Overall, our fine-scaled sampling revealed new insights into community assembly and function in actual communities of interacting microbes (as opposed to mixed communities in bulk soil samples) during the early stages of primary succession. 

BackgroundUnderstanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible. MethodsWe present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools’ resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls. ResultsWe first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5’ scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn’s disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases. 

Abstract Transcription by RNA polymerases is an exquisitely regulated step of the central dogma. Transcription is the primary determinant of cell-state, and most cellular perturbations impact transcription by altering polymerase activity. Thus, detecting changes in polymerase activity yields insight into most cellular processes. Nascent run-on sequencing provides a direct readout of polymerase activity, but no tools exist to model all aspects of this activity at genes. We focus on RNA polymerase II—responsible for transcribing protein-coding genes. We present the first model to capture the complete process of gene transcription. For individual genes, this model parameterizes each distinct stage of transcription—loading, initiation, elongation, and termination, hence LIET—in a biologically interpretable Bayesian mixture, which is applied to nascent run-on data. Our improved modeling of loading/initiation demonstrates these stages are characteristically different between sense and antisense strands. Applying LIET to 24 human cell-types, our analysis indicates the position of dissociation (the last step of termination) appears to be highly consistent, indicative of a tightly regulated process. Furthermore, by applying LIET to perturbation experiments, we demonstrate its ability to detect specific changes in pausing (5′ end), strand-bias, and dissociation location (3′ end)—opening the door to differential assessment of transcription at individual stages of individual genes. 

ABSTRACT Studies have suggested that phytochemicals in green tea have systemic anti-inflammatory and neuroprotective effects. However, the mechanisms behind these effects are poorly understood, possibly due to the differential metabolism of phytochemicals resulting from variations in gut microbiome composition. To unravel this complex relationship, our team utilized a novel combined microbiome analysis and metabolomics approach applied to low complexity microbiome (LCM) and human colonized (HU) gnotobiotic mice treated with an acute dose of powdered matcha green tea. A total of 20 LCM mice received 10 distinct human fecal slurries for ann= 2 mice per human gut microbiome; 9 LCM mice remained un-colonized with human slurries throughout the experiment. We performed untargeted metabolomics on green tea and plasma to identify green tea compounds that were found in the plasma of LCM and HU mice that had consumed green tea. 16S ribosomal RNA gene sequencing was performed on feces of all mice at study end to assess microbiome composition. We found multiple green tea compounds in plasma associated with microbiome presence and diversity (including acetylagmatine, lactiflorin, and aspartic acid negatively associated with diversity). Additionally, we detected strong associations between bioactive green tea compounds in plasma and specific gut bacteria, including associations between spiramycin andGemmigerand between wildforlide andAnaerorhabdus. Notably, some of the physiologically relevant green tea compounds are likely derived from plant-associated microbes, highlighting the importance of considering foods and food products as meta-organisms. Overall, we describe a novel workflow for discovering relationships between individual food compounds and the composition of the gut microbiome. IMPORTANCEFoods contain thousands of unique and biologically important compounds beyond the macro- and micro-nutrients listed on nutrition facts labels. In mammals, many of these compounds are metabolized or co-metabolized by the community of microbes in the colon. These microbes may impact the thousands of biologically important compounds we consume; therefore, understanding microbial metabolism of food compounds will be important for understanding how foods impact health. We used metabolomics to track green tea compounds in plasma of mice with and without complex microbiomes. From this, we can start to recognize certain groups of green tea-derived compounds that are impacted by mammalian microbiomes. This research presents a novel technique for understanding microbial metabolism of food-derived compounds in the gut, which can be applied to other foods. 

ImportanceNeurodevelopmental outcomes for children with congenital heart defects (CHD) have improved minimally over the past 20 years. ObjectivesTo assess the feasibility and tolerability of maternal progesterone therapy as well as the magnitude of the effect on neurodevelopment for fetuses with CHD. Design, Setting, and ParticipantsThis double-blinded individually randomized parallel-group clinical trial of vaginal natural progesterone therapy vs placebo in participants carrying fetuses with CHD was conducted between July 2014 and November 2021 at a quaternary care children’s hospital. Participants included maternal-fetal dyads where the fetus had CHD identified before 28 weeks’ gestational age and was likely to need surgery with cardiopulmonary bypass in the neonatal period. Exclusion criteria included a major genetic or extracardiac anomaly other than 22q11 deletion syndrome and known contraindication to progesterone. Statistical analysis was performed June 2022 to April 2024. InterventionParticipants were 1:1 block-randomized to vaginal progesterone or placebo by diagnosis: hypoplastic left heart syndrome (HLHS), transposition of the great arteries (TGA), and other CHD diagnoses. Treatment was administered twice daily between 28 and up to 39 weeks’ gestational age. Main Outcomes and MeasuresThe primary outcome was the motor score of the Bayley Scales of Infant and Toddler Development-III; secondary outcomes included language and cognitive scales. Exploratory prespecified subgroups included cardiac diagnosis, fetal sex, genetic profile, and maternal fetal environment. ResultsThe 102 enrolled fetuses primarily had HLHS (n = 52 [50.9%]) and TGA (n = 38 [37.3%]), were more frequently male (n = 67 [65.7%]), and without genetic anomalies (n = 61 [59.8%]). The mean motor score differed by 2.5 units (90% CI, −1.9 to 6.9 units;P = .34) for progesterone compared with placebo, a value not statistically different from 0. Exploratory subgroup analyses suggested treatment heterogeneity for the motor score for cardiac diagnosis (Pfor interaction = .03) and fetal sex (Pfor interaction = .04), but not genetic profile (Pfor interaction = .16) or maternal-fetal environment (Pfor interaction = .70). Conclusions and RelevanceIn this randomized clinical trial of maternal progesterone therapy, the overall effect was not statistically different from 0. Subgroup analyses suggest heterogeneity of the response to progesterone among CHD diagnosis and fetal sex. Trial RegistrationClinicalTrials.gov Identifier:NCT02133573 

OBJECTIVE:To determine biomarkers other than CA 125 that could be used in identifying early-stage ovarian cancer. DATA SOURCES:Ovid MEDLINE ALL, EMBASE, Web of Science Core Collection, ScienceDirect, Clinicaltrials.gov, and CAB Direct were searched for English-language studies between January 2008 and April 2023 for the concepts of high-grade serous ovarian cancer, testing, and prevention or early diagnosis. METHODS OF STUDY SELECTION:The 5,523 related articles were uploaded to Covidence. Screening by two independent reviewers of the article abstracts led to the identification of 245 peer-reviewed primary research articles for full-text review. Full-text review by those reviewers led to the identification of 131 peer-reviewed primary research articles used for this review. TABULATION, INTEGRATION, AND RESULTSOf 131 studies, only 55 reported sensitivity, specificity, or area under the curve (AUC), with 36 of the studies reporting at least one biomarker with a specificity of 80% or greater specificity or 0.9 or greater AUC. CONCLUSION:These findings suggest that although many types of biomarkers are being tested in ovarian cancer, most have similar or worse detection rates compared with CA 125 and have the same limitations of poor detection rates in early-stage disease. However, 27.5% of articles (36/131) reported biomarkers with better sensitivity and an AUC greater than 0.9 compared with CA 125 alone and deserve further exploration. 

Abstract Polymeric micro‐ and nanoparticles are useful vehicles for delivering cytokines to diseased tissues such as solid tumors. Double emulsion solvent evaporation is one of the most common techniques to formulate cytokines into vehicles made from hydrophobic polymers; however, the liquid–liquid interfaces formed during emulsification can greatly affect the stability and therapeutic performance of encapsulated cytokines. To develop more effective cytokine‐delivery systems, a clear molecular understanding of the interactions between relevant proteins and solvents used in the preparation of such particles is needed. We utilized an integrated computational and experimental approach for studying the governing mechanisms by which interleukin‐12 (IL‐12), a clinically relevant cytokine, is protected from denaturation by albumin, a common stabilizing protein, at an organic‐aqueous solvent interface formed during double emulsification. We investigated protein–protein interactions between human (h)IL‐12 and albumin and simulated these components in pure water, dichloromethane (DCM), and along a water/DCM interface to replicate the solvent regimes formed during double emulsification. We observed that (i) hIL‐12 experiences increased structural deviations near the water/DCM interface, and (ii) hIL‐12 structural deviations are reduced in the presence of albumin. Experimentally, we found that hIL‐12 bioactivity is retained when released from particles in which albumin is added to the aqueous phase in molar excess to hIL‐12 and sufficient time is allowed for albumin‐hIL‐12 binding. Findings from this work have implications in establishing design principles to enhance the stability of cytokines and other unstable proteins in particles formed by double emulsification for improved stability and therapeutic efficacy. 

Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.