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Substance use disparities among sexual and gender minority (SGM) people are attributed to minority stress, but few studies have examined minority stress and cannabis use over time or investigated differences in cannabis use trajectories by less-studied gender subgroups. We examined if longitudinal cannabis use trajectories are related to baseline minority stressors and if gender differences persisted after accounting for minority stress. Cannabis use risk was measured annually over four years (2017-2021) within a longitudinal cohort study of SGM adults in the United States (N = 11,813). Discrimination and victimization, internalized stigma, disclosure and concealment, and safety and acceptance comprised minority stress (n = 5,673). Latent class growth curve mixture models identified five cannabis use trajectories: 'low or no risk', 'low moderate risk', 'high moderate risk', 'steep risk increase', and 'highest risk'. Participants who reported past-year discrimination and/or victimization at baseline had greater odds of membership in any cannabis risk category compared to the 'low risk' category (odds ratios [OR] 1.17-1.33). Internalized stigma was related to 'high moderate' and 'highest risk' cannabis use (ORs 1.27-1.38). After accounting for minority stress, compared to cisgender men, gender expansive people and transgender men had higher odds of 'low moderate risk' (ORs 1.61, 1.67) or 'high moderate risk' (ORs 2.09, 1.99), and transgender men had higher odds of 'highest risk' (OR 2.36) cannabis use. This study indicates minority stress is related to prospective cannabis use risk trajectories among SGM people, and transgender men and gender expansive people have greater odds of trajectories reflecting cannabis use risk.

Objective: Circulating microRNAs show cross-sectional associations with overweight and obesity. Few studies provided data to differentiate between a snapshot perspective on these associations versus how microRNAs characterize prodromal risk from disease pathology and complications. This study assessed longitudinal relationships between circulating microRNAs and weight at multiple time-points in the Diabetes Prevention Program trial. Research design and methods: A subset of participants (n=150) from the Diabetes Prevention Program were included. MicroRNAs were measured from banked plasma using a Fireplex Assay. We used generalized linear mixed models to evaluate relationships between microRNAs and changes in weight at baseline, year-1, and year-2. Logistic regression was used to evaluate whether microRNAs at baseline were associated with weight change after 2 years. Results: In fully adjusted models that included relevant covariates, seven miRs (i.e., miR-126, miR-15a, miR-192, miR-23a, and miR-27a) were statistically associated with weight over 2 years. MiR-197 and miR-320a remained significant after adjustment for multiple comparisons. Baseline levels of let-7f, miR-17, and miR-320c were significantly associated with 3% weight loss after 2 years in fully adjusted models. Discussion: This study provided evidence for longitudinal relationships between circulating microRNAs and weight. Because microRNAs characterize the combined effects of genetic determinants and responses to behavioral determinants, they may provide insights about the etiology of overweight and obesity in the context or risk for common, complex diseases. Additional studies are needed to validate the potential genes and biological pathways that might be targeted by these microRNA biomarkers and have mechanistic implications for weight loss and disease prevention.

Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis.

Purpose: Sexual and gender minority (SGM) people are at greater risk for substance use than heterosexual and cisgender people, but most prior work is limited by cross-sectional analyses or the examination of single substance use. This study examined substance use over time among SGM people to identify patterns of polysubstance use at the intersection of sex and gender. Methods: Data were collected annually over 4 years from SGM respondents (n = 11,822) in The Population Research in Identity and Disparities for Equality (PRIDE) Study. Differences in substance use patterns (any prior 30-day use of 15 substances) by gender subgroup were examined with latent class analysis, and multinomial regression models tested relationships between gender subgroup and substance use. Results: Eight classes of substance use were observed. The three most common patterns were low substance use (49%), heavy episodic alcohol use (≥5 alcoholic drinks on one occasion) with some cannabis and tobacco use (14%), and cannabis use with some tobacco and declining heavy episodic alcohol use (13%). Differences observed included lower odds of patterns defined by heavy episodic alcohol use with some cannabis and tobacco use in all gender subgroups relative to cisgender men and persons with low substance use (odds ratios [ORs] 0.26-0.60). Gender expansive people assigned female at birth, gender expansive people assigned male at birth, and transgender men had greater odds of reporting cannabis use with small percentages of heavy episodic alcohol and tobacco use (ORs: 1.41-1.60). Conclusion: This study suggests that there are unique patterns of polysubstance use over time among gender subgroups of SGM people.

Introduction: Metabolic syndrome (MetS) is a prominent risk factor for both cardiovascular disease (CVD) and type 2 diabetes (T2D). MicroRNAs (miRs) are small noncoding RNA molecules that target messenger RNAs to alter gene expression. Circulating miRs have been studied as potential clinically meaningful biomarkers of risk for MetS as they are readily measured from blood. Though associations among MetS components and social constructs of race, ethnicity, and gender have previously been established, grouping according to these constructs alone may conflate genetic ancestry with the environmental/behavioral effects of being characterized as a particular gender, race, or ethnicity. The purpose of this study was to identify differences in circulating miRs associated with demographic factors and MetS components to better illuminate the nuanced health impacts of race, ethnicity and gender. Methods: This was a secondary analysis of a subset of participants (N=1000) from the Diabetes Prevention Program (DPP). A custom Fireplex assay was used to quantify miRs from banked plasma collected at baseline. Correlations between miRs and metabolic syndrome components were assessed by Pearson’s correlation coefficient. Multivariable linear models adjusted for age and weight were used to analyze associations between gender, race, ethnicity, and miR expression. The Benjamini-Hochberg false discovery rate (FDR) method was applied. Results: The sample was 68% female, 19% Black, 15% Hispanic, and mean age was 52 ± 10 years. After adjusting for multiple comparisons (FDR

Abstract Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.

BackgroundCervical cancer oncogenesis starts with human papillomavirus (HPV) cell entry after binding to host cell surface receptors; however, the mechanism is not fully known. We examined polymorphisms in receptor genes hypothesized to be necessary for HPV cell entry and assessed their associations with clinical progression to precancer.MethodsAfrican American women (N = 1,728) from the MACS/WIHS Combined Cohort Study were included. Two case-control study designs were used-cases with histology-based precancer (CIN3+) and controls without; and cases with cytology-based precancer [high-grade squamous intraepithelial lesions (HSIL)] and controls without. SNPs in candidate genes (SDC1, SDC2, SDC3, SDC4, GPC1, GPC2, GPC3, GPC4, GPC5, GPC6, and ITGA6) were genotyped using an Illumina Omni2.5-quad beadchip. Logistic regression was used to assess the associations in all participants and by HPV genotypes, after adjusting for age, human immunodeficiency virus serostatus, CD4 T cells, and three principal components for ancestry.ResultsMinor alleles in SNPs rs77122854 (SDC3), rs73971695, rs79336862 (ITGA6), rs57528020, rs201337456, rs11987725 (SDC2), rs115880588, rs115738853, and rs9301825 (GPC5) were associated with increased odds of both CIN3+ and HSIL, whereas, rs35927186 (GPC5) was found to decrease the odds for both outcomes (P value ≤ 0.01). Among those infected with Alpha-9 HPV types, rs722377 (SDC3), rs16860468, rs2356798 (ITGA6), rs11987725 (SDC2), and rs3848051 (GPC5) were associated with increased odds of both precancer outcomes.ConclusionsPolymorphisms in genes that encode binding receptors for HPV cell entry may play a role in cervical precancer progression.ImpactOur findings are hypothesis generating and support further exploration of mechanisms of HPV entry genes that may help prevent progression to cervical precancer.

Metals play vital roles in biological systems, with iron/heme being essential for cellular and metabolic functions necessary for survival and/or virulence in many bacterial pathogens. Given the rise of bacterial resistance to current antibiotics, there is an urgent need for the development of non-toxic and novel antibiotics that do not contribute to resistance to other antibiotics. Gallium, which mimics iron, has emerged as a promising antimicrobial agent, offering a novel approach to combat bacterial infections. Gallium does not have any known functions in biological systems. Gallium exerts its effects primarily by replacing iron in redox enzymes, effectively inhibiting bacterial growth by targeting multiple iron/heme-dependent biological processes and suppressing the development of drug resistance. The aim of this review is to highlight recent findings on the mechanisms of action of gallium and provide further insights into the development of gallium-based compounds. Understanding the mechanisms underlying gallium’s biological activities is crucial for designing drugs that enhance their therapeutic therapies while minimizing side effects, offering promising avenues for the treatment of infectious diseases.

Abstract Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.

Ineffective recovery from pneumonia can lead to interstitial lung disease characterized by aberrant epithelial cells in fibrotic regions. In this issue of theJCI, Lin et al. define molecular pathways leading to the development and persistence of keratin [5.sup.+] ([Krt5.sup.+]) epithelial cells in the alveolar parenchyma when mice struggle to recover from influenza infection. The receptor for IFN-[gamma] on lung epithelium was essential for the formation of aberrant Krt5* cells and fibrotic lung disease. The transcription factor Yes-associated protein 1 (YAP) was necessary for persistence of these Krt5* cells, and IFN- [gamma] activated YAP in lung epithelial cells via JAK, focal adhesion kinase (FAK), and Src kinases. These findings establish a targetable pathway underlying some of the pulmonary postacute sequelae of pneumonia., Postacute sequelae of pneumonia The lungs have remarkable healing abilities. Most of the time, the lungs recover from even severe infection to become histologically normal but immunologically improved, with resident [...]

Microglia, the macrophages of the brain parenchyma, are key players in neurodegenerative diseases such as Alzheimer's disease. These cells adopt distinct transcriptional subtypes known as states. Understanding state function, especially in human microglia, has been elusive owing to a lack of tools to model and manipulate these cells. Here, we developed a platform for modeling human microglia transcriptional states in vitro. We found that exposure of human stem-cell-differentiated microglia to synaptosomes, myelin debris, apoptotic neurons or synthetic amyloid-beta fibrils generated transcriptional diversity that mapped to gene signatures identified in human brain microglia, including disease-associated microglia, a state enriched in neurodegenerative diseases. Using a new lentiviral approach, we demonstrated that the transcription factor MITF drives a disease-associated transcriptional signature and a highly phagocytic state. Together, these tools enable the manipulation and functional interrogation of human microglial states in both homeostatic and disease-relevant contexts.

ObjectiveCirculating microRNAs show cross-sectional associations with overweight and obesity. Few studies provided data to differentiate between a snapshot perspective on these associations versus how microRNAs characterize prodromal risk from disease pathology and complications. This study assessed longitudinal relationships between circulating microRNAs and weight at multiple time-points in the Diabetes Prevention Program trial.Research design and methodsA subset of participants (n=150) from the Diabetes Prevention Program were included. MicroRNAs were measured from banked plasma using a Fireplex Assay. We used generalized linear mixed models to evaluate relationships between microRNAs and changes in weight at baseline, year-1, and year-2. Logistic regression was used to evaluate whether microRNAs at baseline were associated with weight change after 2 years.ResultsIn fully adjusted models that included relevant covariates, seven miRs (i.e., miR-126, miR-15a, miR-192, miR-23a, and miR-27a) were statistically associated with weight over 2 years. MiR-197 and miR-320a remained significant after adjustment for multiple comparisons. Baseline levels of let-7f, miR-17, and miR-320c were significantly associated with 3% weight loss after 2 years in fully adjusted models.DiscussionThis study provided evidence for longitudinal relationships between circulating microRNAs and weight. Because microRNAs characterize the combined effects of genetic determinants and responses to behavioral determinants, they may provide insights about the etiology of overweight and obesity in the context or risk for common, complex diseases. Additional studies are needed to validate the potential genes and biological pathways that might be targeted by these microRNA biomarkers and have mechanistic implications for weight loss and disease prevention.

MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/.

BackgroundType 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy.ObjectiveThe purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D.MethodsThe data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss.ResultsAverage waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]).ConclusionsOur results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity.Trial registrationClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939.

Abstract In all terrestrial vertebrates, the parathyroid glands are critical regulators of calcium homeostasis and the sole source of parathyroid hormone (PTH). Hyperparathyroidism and hypoparathyroidism are clinically important disorders affecting multiple organs. However, our knowledge regarding regulatory mechanisms governing the parathyroids has remained limited. Here, we present the comprehensive maps of the chromatin landscape of the human parathyroid glands, identifying active regulatory elements and chromatin interactions. These data allow us to define regulatory circuits and previously unidentified genes that play crucial roles in parathyroid biology. We experimentally validate candidate parathyroid-specific enhancers and demonstrate their integration with GWAS SNPs for parathyroid-related diseases and traits. For instance, we observe reduced activity of a parathyroid-specific enhancer of the Calcium Sensing Receptor gene, which contains a risk allele associated with higher PTH levels compared to the wildtype allele. Our datasets provide a valuable resource for unraveling the mechanisms governing parathyroid gland regulation in health and disease.

IntroductionIntegrated transcriptome and microRNA differential gene expression (DEG) analyses may help to explain type 2 diabetes (T2D) pathogenesis in at-risk populations. The purpose of this study was to characterize DEG in banked biospecimens from underactive adult participants who responded to a randomized clinical trial measuring the effects of lifestyle interventions on T2D risk factors. DEGs were further examined within the context of annotated biological pathways.MethodsParticipants (n = 52) in a previously completed clinical trial that assessed a 12-week behavioural intervention for T2D risk reduction were included. Participants who showed >6mg/dL decrease in fasting blood glucose were identified as responders. Gene expression was measured by RNASeq, and overrepresentation analysis within KEGG pathways and weighted gene correlation network analysis (WGCNA) were performed.ResultsNo genes remained significantly differentially expressed after correction for multiple comparisons. One module derived by WGCNA related to body mass index was identified, which contained genes located in KEGG pathways related to known mechanisms underlying risk for T2D as well as pathways related to neurodegeneration and protein misfolding. A network analysis showed indirect connections between genes in this module and islet amyloid polypeptide (IAPP), which has previously been hypothesized as a mechanism for T2D.DiscussionWe validated prior studies that showed pathways related to metabolism, inflammation/immunity, and endocrine/hormone function are related to risk for T2D. We identified evidence for new potential mechanisms that include protein misfolding. Additional studies are needed to determine whether these are potential therapeutic targets to decrease risk for T2D.

PurposeGestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM.DesignThe systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.MethodsPubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM.ResultsSixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person.ConclusionsThe mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.

Background: The relationships between spirometric assessment of lung function and symptoms (including exacerbations) in patients with asthma and/or chronic obstructive pulmonary disease (COPD) in a real-life setting are uncertain. Objectives: To assess the relationships between baseline post-bronchodilator (post-BD) spirometry measures of lung function and symptoms and exacerbations in patients with a physician-assigned diagnosis of asthma and/or COPD. Design: The NOVEL observational longiTudinal studY (NOVELTY) is a global, prospective, 3-year observational study. Methods: Logistic regression analysis was used to evaluate relationships. Spirometry measures were assessed as percent predicted (%pred). Symptoms were assessed at baseline, and exacerbations were assessed at baseline and Year 1. Results: A total of 11,181 patients in NOVELTY had spirometry data (asthma, n = 5903; COPD, n = 3881; asthma + COPD, n = 1397). A 10% lower post-BD %pred forced expiratory volume in 1 s (FEV 1 ) and forced vital capacity (FVC) – adjusted for age and sex – were significantly associated with dyspnea (modified Medical Research Council ⩾ grade 2), frequent breathlessness [St George’s Respiratory Questionnaire (SGRQ)], frequent wheeze attacks (SGRQ), nocturnal awakening (Respiratory Symptoms Questionnaire; ⩾1 night/week), and frequent productive cough (SGRQ). Lower post-BD %pred FEV 1 and, to a lesser extent, lower post-BD %pred FVC were significantly associated with ⩾1 physician-reported exacerbation at baseline or Year 1. This association was stronger in patients with COPD than in those with asthma. Conclusion: In a real-life setting, reduced lung function is consistently associated with symptoms in patients with asthma, COPD, or asthma + COPD. The relationship with exacerbations is stronger in COPD only than in asthma. Trail registration: clinicaltrials.gov identifier: NCT02760329 ( www.clinicaltrials.gov ).

We examined behaviour and physiology of lake trout (Salvelinus namaycush) following ice-angling. Fish were ice- angled and placed in a water-filled tub for 0.5, 4, and 6 h to recover (n = 19). Reflex impairment and physiological status were assessed repeatedly for every individual. Longer fight times lead to higher lactate and glucose, and lower extracellular pH 0.5 h post-angling. Loss of orientation was the most common reflex impairment (84% of fish) 4 h post-angling. Mortality (36.8%) was observed during the study; however, variation in handling, barotrauma, and issues with sampling may have confounded angling effects. To determine if barotrauma impacted impairment and mortality, lake trout at a later sampling date (n = 29) were exposed to air for either 60, 120, 180, 240, 300, or 420 s before assessment (3.4% mortality). For fish air- exposed for 300 s or more, 14% lost orientation during immediate assessment. Bloating occurred in 20% of fish air-exposed for 60 s. An air exposure duration of 420 s significantly impaired reflexes. Recreationally caught lake trout show behavioural and physiological impairment with such impairments magnified by extended air exposure. Key words: catch-and-release, winter, stress, mortality, barotrauma, Introduction Catch-and-release angling (C&R) relies on the assumption that fish survive and exhibit negligible long-term impairment (Wydoski 1977). If those assumptions are satisfied, C&R can be a valuable conservation tool [...]

This multiple case study examines the extent and ways in which leaders and administrators in Florida College System (FCS) institutions engage in distributed leadership through data sharing with frontline staff. Based on focus groups and individual interviews with administrators, faculty, and staff (659 participants) from 21 state colleges, we found a continuum of three data cultures ranging from democratic data cultures to blended data cultures to "need to know" data cultures. We triangulate these results with survey data from FCS institutional leaders and find considerable variation in the extent of data sharing and perceptions of effectiveness of institutional data use. Institutions with democratic data cultures tended to have distributed leadership that encouraged information sharing and collaboration among staff to use data to inform change. Need-to-know institutions faced challenges, including weak data quality, concerns about adequate time and resources among staff for reviewing data, and perceptions that staff lack data literacy skills.