Disrupted Endoplasmic Reticulum Ca2+ Handling: A Harßinger of ß-Cell Failure.

The ß-cell workload increases in the setting of insulin resistance and reduced ß-cell mass, which occurs in type 2 and type 1 diabetes, respectively. The prolonged elevation of insulin production and secretion during the pathogenesis of diabetes results in ß-cell ER stress. The depletion of ß-cell Ca2+ER during ER stress activates the unfolded protein response, leading to ß-cell dysfunction. Ca2+ER is involved in many pathways that are critical to ß-cell function, such as protein processing, tuning organelle and cytosolic Ca2+ handling, and modulating lipid homeostasis. Mutations that promote ß-cell ER stress and deplete Ca2+ER stores are associated with or cause diabetes (e.g., mutations in ryanodine receptors and insulin). Thus, improving ß-cell Ca2+ER handling and reducing ER stress under diabetogenic conditions could preserve ß-cell function and delay or prevent the onset of diabetes. This review focuses on how mechanisms that control ß-cell Ca2+ER are perturbed during the pathogenesis of diabetes and contribute to ß-cell failure.

Sources and Prevalence of Cyclospora cayetanensis in Southeastern U.S. Growing Environments.

Recent cyclosporiasis outbreaks associated with fresh produce grown in the United States highlight the need to better understand Cyclospora cayetanensis prevalence in U.S. agricultural environments. In this study, C. cayetanensis occurrence was assessed in municipal wastewater sludge, on-farm portable toilets, irrigation pond water, and spent packing house dump tank water in a Southeastern Georgia growing region over two years. Detection of the C. cayetanensis 18S rRNA qPCR gene target in pond samples was 0%, 28%, and 42% (N = 217) depending on the detection definition used, and ≤1% in dump tank samples (N = 46). However, no qPCR detections were confirmed by sequencing, suggesting false detection occurred due to cross-reactions. C. cayetanensis qPCR detections were confirmed in 9% of wastewater sludge samples (N = 76). The human-specific fecal markers HF183 and crAssphage were detected in 33% and 6% of pond samples, respectively, and 4% and 0% of dump tank samples, respectively. Despite community Cyclospora shedding and evidence of human fecal contamination in irrigation water, there was no correlation between C. cayetanensis and HF183 qPCR detections, further supporting that 18S gene target qPCR amplifications were due to cross-reactions. When evaluating C. cayetanensis qPCR environmental detection data, the impact of assay specificity and detection criteria should be considered. Moreover, additional sequence-based testing may be needed to appropriately interpret Cyclospora qPCR environmental data.

The MODY-associated KCNK16 L114P mutation increases islet glucagon secretion and limits insulin secretion resulting in transient neonatal diabetes and glucose dyshomeostasis in adults.

The gain-of-function mutation in the TALK-1 K+ channel (p.L114P) is associated with maturity-onset diabetes of the young (MODY). TALK-1 is a key regulator of ß-cell electrical activity and glucose-stimulated insulin secretion. The KCNK16 gene encoding TALK-1 is the most abundant and ß-cell-restricted K+ channel transcript. To investigate the impact of KCNK16 L114P on glucose homeostasis and confirm its association with MODY, a mouse model containing the Kcnk16 L114P mutation was generated. Heterozygous and homozygous Kcnk16 L114P mice exhibit increased neonatal lethality in the C57BL/6J and the CD-1 (ICR) genetic background, respectively. Lethality is likely a result of severe hyperglycemia observed in the homozygous Kcnk16 L114P neonates due to lack of glucose-stimulated insulin secretion and can be reduced with insulin treatment. Kcnk16 L114P increased whole-cell ß-cell K+ currents resulting in blunted glucose-stimulated Ca2+ entry and loss of glucose-induced Ca2+ oscillations. Thus, adult Kcnk16 L114P mice have reduced glucose-stimulated insulin secretion and plasma insulin levels, which significantly impairs glucose homeostasis. Taken together, this study shows that the MODY-associated Kcnk16 L114P mutation disrupts glucose homeostasis in adult mice resembling a MODY phenotype and causes neonatal lethality by inhibiting islet insulin secretion during development. These data suggest that TALK-1 is an islet-restricted target for the treatment for diabetes.

Evaluation of the Increased Genetic Resolution and Utility for Source Tracking of a Recently Developed Method for Genotyping Cyclospora cayetanensis.

Cyclospora cayetanensis is a foodborne parasite that causes cyclosporiasis, an enteric illness in humans. Genotyping methods are used to genetically discriminate between specimens from cyclosporiasis cases and can complement source attribution investigations if the method is sufficiently sensitive for application to food items. A very sensitive targeted amplicon sequencing (TAS) assay for genotyping C. cayetanensis encompassing 52 loci was recently designed. In this study, we analyzed 66 genetically diverse clinical specimens to assess the change in phylogenetic resolution between the TAS assay and a currently employed eight-marker scheme. Of the 52 markers, ≥50 were successfully haplotyped for all specimens, and these results were used to generate a hierarchical cluster dendrogram. Using a previously described statistical approach to dissect hierarchical trees, the 66 specimens resolved into 24 and 27 distinct genetic clusters for the TAS and an 8-loci scheme, respectively. Although the specimen composition of 15 clusters was identical, there were substantial differences between the two dendrograms, highlighting the importance of both inclusion of additional genome coverage and choice of loci to target for genotyping. To evaluate the ability to genetically link contaminated food samples with clinical specimens, C. cayetanensis was genotyped from DNA extracted from raspberries inoculated with fecal specimens. The contaminated raspberry samples were assigned to clusters with the corresponding clinical specimen, demonstrating the utility of the TAS assay for traceback efforts.

Data-driven simulations for training AI-based segmentation of neutron images.

Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs → Validate PDFs → Design Image Masks → Generate Intensities → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks.

Endocrine islet ß-cell subtypes with differential function are derived from biochemically distinct embryonic endocrine islet progenitors that are regulated by maternal nutrients.

Endocrine islet b cells comprise heterogenous cell subsets. Yet when/how these subsets are produced and how stable they are remain unknown. Addressing these questions is important for preventing/curing diabetes, because lower numbers of b cells with better secretory function is a high risk of this disease. Using combinatorial cell lineage tracing, scRNA-seq, and DNA methylation analysis, we show here that embryonic islet progenitors with distinct gene expression and DNA methylation produce b-cell subtypes of different function and viability in adult mice. The subtype with better function is enriched for genes involved in vesicular production/trafficking, stress response, and Ca2+-secretion coupling, which further correspond to differential DNA methylation in putative enhancers of these genes. Maternal overnutrition, a major diabetes risk factor, reduces the proportion of endocrine progenitors of the b-cell subtype with better-function via deregulating DNA methyl transferase 3a. Intriguingly, the gene signature that defines mouse b-cell subtypes can reliably divide human cells into two sub-populations while the proportion of b cells with better-function is reduced in diabetic donors. The implication of these results is that modulating DNA methylation in islet progenitors using maternal food supplements can be explored to improve b-cell function in the prevention and therapy of diabetes.

Quantifying a light-induced energetic change in bacteriorhodopsin by force spectroscopy.

Ligand-induced conformational changes are critical to the function of many membrane proteins and arise from numerous intramolecular interactions. In the photocycle of the model membrane protein bacteriorhodopsin (bR), absorption of a photon by retinal triggers a conformational cascade that results in pumping a proton across the cell membrane. While decades of spectroscopy and structural studies have probed this photocycle in intricate detail, changes in intramolecular energetics that underlie protein motions have remained elusive to experimental quantification. Here, we measured these energetics on the millisecond time scale using atomic-force-microscopy-based single-molecule force spectroscopy. Precisely, timed light pulses triggered the bR photocycle while we measured the equilibrium unfolding and refolding of the terminal 8-amino-acid region of bR's G-helix. These dynamics changed when the EF-helix pair moved ~9 Å away from this end of the G helix during the "open" portion of bR's photocycle. In ~60% of the data, we observed abrupt light-induced destabilization of 3.4 ± 0.3 kcal/mol, lasting 38 ± 3 ms. The kinetics and pH-dependence of this destabilization were consistent with prior measurements of bR's open phase. The frequency of light-induced destabilization increased with the duration of illumination and was dramatically reduced in the triple mutant (D96G/F171C/F219L) thought to trap bR in its open phase. In the other ~40% of the data, photoexcitation unexpectedly stabilized a longer-lived putative misfolded state. Through this work, we establish a general single-molecule force spectroscopy approach for measuring ligand-induced energetics and lifetimes in membrane proteins.

TALK-1-mediated alterations of ß-cell mitochondrial function and insulin secretion impair glucose homeostasis on a diabetogenic diet.

Mitochondrial Ca2+ ([Ca2+]m) homeostasis is critical for ß-cell function and becomes disrupted during the pathogenesis of diabetes. [Ca2+]m uptake is dependent on elevations in cytoplasmic Ca2+ ([Ca2+]c) and endoplasmic reticulum Ca2+ ([Ca2+]ER) release, both of which are regulated by the two-pore domain K+ channel TALK-1. Here, utilizing a novel ß-cell TALK-1-knockout (ß-TALK-1-KO) mouse model, we found that TALK-1 limited ß-cell [Ca2+]m accumulation and ATP production. However, following exposure to a high-fat diet (HFD), ATP-linked respiration, glucose-stimulated oxygen consumption rate, and glucose-stimulated insulin secretion (GSIS) were increased in control but not TALK1-KO mice. Although ß-TALK-1-KO animals showed similar GSIS before and after HFD treatment, these mice were protected from HFD-induced glucose intolerance. Collectively, these data identify that TALK-1 channel control of ß-cell function reduces [Ca2+]m and suggest that metabolic remodeling in diabetes drives dysglycemia.

Purinergic Ca2+ signaling as a novel mechanism of drug tolerance in BRAF mutant melanoma.

Drug tolerance is a major cause of relapse after cancer treatment. In spite of intensive efforts1-9, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels), and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma, and points to actionable strategies to overcome it.

The MODY-associated KCNK16 L114P mutation increases islet glucagon secretion and limits insulin secretion resulting in transient neonatal diabetes and glucose dyshomeostasis in adults.

The gain-of-function mutation in the TALK-1 K + channel (p.L114P) is associated with maturity-onset diabetes of the young (MODY). TALK-1 is a key regulator of ß-cell electrical activity and glucose-stimulated insulin secretion (GSIS). The KCNK16 gene encoding TALK-1, is the most abundant and ß-cell-restricted K + channel transcript. To investigate the impact of KCNK16 L114P on glucose homeostasis and confirm its association with MODY, a mouse model containing the Kcnk16 L114P mutation was generated. Heterozygous and homozygous Kcnk16 L114P mice exhibit increased neonatal lethality in the C57BL/6J and the mixed C57BL/6J:CD-1(ICR) genetic background, respectively. Lethality is likely a result of severe hyperglycemia observed in the homozygous Kcnk16 L114P neonates due to lack of glucose-stimulated insulin secretion and can be reduced with insulin treatment. Kcnk16 L114P increased whole-cell ß-cell K + currents resulting in blunted glucose-stimulated Ca 2+ entry and loss of glucose-induced Ca 2+ oscillations. Thus, adult Kcnk16 L114P mice have reduced glucose-stimulated insulin secretion and plasma insulin levels, which significantly impaired glucose homeostasis. Taken together, this study shows that the MODY-associated Kcnk16 L114P mutation disrupts glucose homeostasis in adult mice resembling a MODY phenotype and causes neonatal lethality by inhibiting islet hormone secretion during development. These data strongly suggest that TALK-1 is an islet-restricted target for the treatment of diabetes.

Simultaneous multimaterial operando tomography of electrochemical devices.

The performance of electrochemical energy devices, such as fuel cells and batteries, is dictated by intricate physiochemical processes within. To better understand and rationally engineer these processes, we need robust operando characterization tools that detect and distinguish multiple interacting components/interfaces in high contrast. Here, we uniquely combine dual-modality tomography (simultaneous neutron and x-ray tomography) and advanced image processing (iterative reconstruction and metal artifact reduction) for high-contrast multimaterial imaging, with signal and contrast enhancements of up to 10 and 48 times, respectively, compared to conventional single-modality imaging. Targeted development and application of these methods to electrochemical devices allow us to resolve operando distributions of six interacting fuel cell components (including void space) with the highest reported pairwise contrast for simultaneous yet decoupled spatiotemporal characterization of component morphology and hydration. Such high-contrast tomography ushers in key gold standards for operando electrochemical characterization, with broader applicability to numerous multimaterial systems.

Novel insights on the genetic population structure of human-infecting Cyclospora spp. and evidence for rapid subtype selection among isolates from the USA.

Human-infecting Cyclospora was recently characterized as three species, two of which (C. cayetanensis and C. ashfordi) are currently responsible for all known human infections in the USA, yet much remains unknown about the genetic structure within these two species. Here, we investigate Cyclospora genotyping data from 2018 through 2022 to ascertain if there are temporal patterns in the genetic structure of Cyclospora parasites that cause infections in US residents from year to year. First, we investigate three levels of genetic characterization: species, subpopulation, and strain, to elucidate annual trends in Cyclospora infections. Next, we determine if shifts in genetic diversity can be linked to any of the eight loci used in our Cyclospora genotyping approach. We observed fluctuations in the abundance of Cyclospora types at the species and subpopulation levels, but no significant temporal trends were identified; however, we found recurrent and sporadic strains within both C. ashfordi and C. cayetanensis. We also uncovered major shifts in the mitochondrial genotypes in both species, where there was a universal increase in abundance of a specific mitochondrial genotype that was relatively abundant in 2018 but reached near fixation (was observed in over 96% of isolates) in C. ashfordi by 2022. Similarly, this allele jumped from 29% to 82% relative abundance of isolates belonging to C. cayetanensis. Overall, our analysis uncovers previously unknown temporal-genetic patterns in US Cyclospora types from 2018 through 2022 and is an important step to presenting a clearer picture of the factors influencing cyclosporiasis outbreaks in the USA.

An improved framework for detecting discrete epidemiologically meaningful partitions in hierarchically clustered genetic data.

Motivation: Hierarchical clustering of microbial genotypes has the limitation that hierarchical clusters are nested, where smaller groups of related isolates exist within larger groups that get progressively larger as relationships become increasingly distant. In an epidemiologic context, investigators must dissect hierarchical trees into discrete groupings that are epidemiologically meaningful. We recently described a statistical framework (Method A) for dissecting hierarchical trees that attempts to minimize investigator bias. Here, we apply a modified version of that framework (Method B) to a hierarchical tree constructed from 2111 genotypes of the foodborne parasite Cyclospora, including 639 genotypes linked to epidemiologically defined outbreaks. To evaluate Method B's performance, we examined the concordance between these epidemiologically defined groupings and the genetic partitions identified. We also used the same epidemiologic clusters to evaluate the performance of Method A, plus two tree-dissection methods (cutreeHybrid and cutreeDynamic) available within the Dynamic Tree Cut R package, in addition to the TreeCluster method and PARNAS. Results: Compared to the other methods, Method B, TreeCluster, and PARNAS were the most accurate (99.4%) in identifying genetic groups that reflected the epidemiologic groupings, noting that TreeCluster and PARNAS performed identically on our dataset. CutreeHybrid identified groups reflecting patterns in the wider Cyclospora population structure but lacked finer, strain-level discrimination (Simpson's D: cutreeHybrid=0.785). CutreeDynamic displayed good strain discrimination (Simpson's D = 0.933), though lacked sensitivity (77%). At two different threshold/radius settings TreeCluster/PARNAS displayed similar utility to Method B. However, Method B computes a tree-dissection threshold automatically, and the threshold/radius settings used when executing TreeCluster/PARNAS here were computed using Method B. Using a TreeCluster threshold of 0.045 as recommended in the TreeCluster documentation, epidemiologic utility dropped markedly below that of Method B. Availability and implementation: Relevant code and data are publicly available. Source code (Method B) and instructions for its use are available here: https://github.com/Joel-Barratt/Hierarchical-tree-dissection-framework.

The limit of detection of the BioFire® FilmArray® gastrointestinal panel for the foodborne parasite Cyclospora cayetanensis.

Cyclosporiasis is a foodborne diarrheal illness caused by the parasite Cyclospora cayetanensis. The BioFire® FilmArray® gastrointestinal (FilmArray GI) panel is a common method for diagnosing cyclosporiasis from clinical stool samples. The currently published limit of detection (LOD) of this panel is in genome equivalents; however, it is unclear how this relates to the number of C. cayetanensis oocysts in a clinical sample. In this study, we developed a technique to determine the LOD in terms of oocysts, using a cell sorter to sort 1 to 50 C. cayetanensis oocyst(s) previously purified from three human stool sources. We found the FilmArray GI panel detected samples with ≥20 C. cayetanensis oocysts in 100% of replicates, with varying detection among samples with 1, 5, or 10 C. cayetanensis oocysts. This method provides a parasitologically relevant LOD that should enable comparison among C. cayetanensis detection techniques, including the FilmArray GI panel.

Retrospective evaluation of an integrated molecular-epidemiological approach to cyclosporiasis outbreak investigations - United States, 2021.

Cyclosporiasis results from an infection of the small intestine by Cyclospora parasites after ingestion of contaminated food or water, often leading to gastrointestinal distress. Recent developments in temporally linking genetically related Cyclospora isolates demonstrated effectiveness in supporting epidemiological investigations. We used 'temporal-genetic clusters' (TGCs) to investigate reported cyclosporiasis cases in the United States during the 2021 peak-period (1 May - 31 August 2021). Our approach split 655 genotyped isolates into 55 genetic clusters and 31 TGCs. We linked two large multi-state epidemiological clusters (Epidemiologic Cluster 1 [n = 136 cases, 54 genotyped] and Epidemiologic Cluster 2 [n = 42 cases, 15 genotyped]) to consumption of lettuce varieties; however, product traceback did not identify a specific product for either cluster due to the lack of detailed product information. To evaluate the utility of TGCs, we performed a retrospective case study comparing investigation outcomes of outbreaks first detected using epidemiological methods with those of the same outbreaks had TGCs been used to first detect them. Our study results indicate that adjustments to routine epidemiological approaches could link additional cases to epidemiological clusters of cyclosporiasis. Overall, we show that CDC's integrated genotyping and epidemiological investigations provide valuable insights into cyclosporiasis outbreaks in the United States.

Oral metagenomes from Native American Ancestors reveal distinct microbial lineages in the pre-contact era.

OBJECTIVES: Limited studies have focused on how European contact and colonialism impacted Native American oral microbiomes, specifically, the diversity of commensal or opportunistically pathogenic oral microbes, which may be associated with oral diseases. Here, we studied the oral microbiomes of pre-contact Wichita Ancestors, in partnership with the Descendant community, The Wichita and Affiliated Tribes, Oklahoma, USA. MATERIALS AND METHODS: Skeletal remains of 28 Wichita Ancestors from 20 archeological sites (dating approximately to 1250-1450 CE) were paleopathologically assessed for presence of dental calculus and oral disease. DNA was extracted from calculus, and partial uracil deglycosylase-treated double-stranded DNA libraries were shotgun-sequenced using Illumina technology. DNA preservation was assessed, the microbial community was taxonomically profiled, and phylogenomic analyzes were conducted. RESULTS: Paleopathological analysis revealed signs of oral diseases such as caries and periodontitis. Calculus samples from 26 Ancestors yielded oral microbiomes with minimal extraneous contamination. Anaerolineaceae bacterium oral taxon 439 was found to be the most abundant bacterial species. Several Ancestors showed high abundance of bacteria typically associated with periodontitis such as Tannerella forsythia and Treponema denticola. Phylogenomic analyzes of Anaerolineaceae bacterium oral taxon 439 and T. forsythia revealed biogeographic structuring; strains present in the Wichita Ancestors clustered with strains from other pre-contact Native Americans and were distinct from European and/or post-contact American strains. DISCUSSION: We present the largest oral metagenome dataset from a pre-contact Native American population and demonstrate the presence of distinct lineages of oral microbes specific to the pre-contact Americas.

Optimizing hierarchical tree dissection parameters using historic epidemiologic data as 'ground truth'.

Hierarchical clustering of pathogen genotypes is widely used to complement epidemiologic investigations of outbreaks. Investigators must dissect trees to obtain genetic partitions that provide epidemiologists with meaningful information. Statistical approaches to tree dissection often require a user-defined parameter to predict the optimal partition number and augmenting this parameter can drastically impact resultant partition memberships. Here, we demonstrate how to optimize a given tree dissection parameter to maximize accuracy irrespective of the tree dissection method used. We hierarchically clustered 1,873 genotypes of the foodborne pathogen Cyclospora spp., including 587 possessing links to historic outbreaks. We dissected the resulting tree using a statistical method requiring users to select the value of a 'stringency parameter' (s), with a recommended value of 95% to 99.5%. We dissected this hierarchical tree across s-values from 94% to 99.5% (at increments of 0.25%), to identify a value that maximized partitioning accuracy, defined as the degree to which genetic partitions conform to known epidemiologic groupings. We show that s-values of 96.5% and 96.75% yield the highest accuracy (> 99.9%) when clustering Cyclospora sp. isolates with known epidemiologic linkages. In practice, the optimized s-value will generate robust genetic partitions comprising isolates likely derived from a common food source, even when the epidemiologic grouping is not known prior to genetic clustering. While the s-value is specific to the tree dissection method used here, the optimization approach described could be applied to any parameter/method used to dissect hierarchical trees.

IP3Rs puff along: A SNAPpy dance with IP3 and Ca2.

Concerted openings of clustered inositol 1,4,5-trisphosphate receptors (IP3Rs) result in short, localized Ca2+ bursts, also called puffs, which are crucial regulators of Ca2+-dependent signaling processes. However, the processes regulating Ca2+ puff amplitude (average ∼0.5 ΔF/F0) and duration (at half-maximal; average ∼25-30 ms) have yet to be elucidated. A recent study in JBC by Smith and Taylor determined that Ca2+ puff amplitude is independent of IP3R cluster density and that the termination of IP3R Ca2+ puff is regulated by IP3 dissociation, illuminating the steps of this regulatory dance.

Socioeconomic and Comorbid Factors Affecting Mortality and Length of Stay in COVID-19 Patients.

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic exposed and exacerbated health disparities between socioeconomic groups. Our purpose was to determine if age, sex, race, insurance, and comorbidities predicted patients' length of stay (LOS) in the hospital and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19) during the early pandemic. Methods Utilizing retrospective, secondarily sourced electronic health record (EHR) data for patients who tested positive for COVID-19 from HCA Healthcare facilities, predictors of LOS and in-hospital mortality were assessed using regression. LOS and in-hospital mortality were assessed using logistic regression and negative binomial regression, respectively. All models included age, insurance status, and sex, while additional covariates were selected using the least absolute shrinkage and selection operator (LASSO) regression. LOS data were presented as incidence rate ratios (IRR), and in-hospital mortality was presented as odds ratios (OR), followed by their 95% confidence intervals (CI). Results There were 111,849 qualifying patient records from March 1, 2020, to August 23, 2020. After excluding those with missing data (n = 7), without clinically confirmed COVID-19 (n = 27,225), and those from a carceral environment (n = 1,861), there were 84,624 eligible patients. Compared to the population of the United States of America, our COVID-19 cohort had a larger proportion of African American patients (23.17% versus 13.4%). The African American patients were more likely to have private insurance providers (28.52% versus 23.68%) and shorter LOS (IRR = 0.88, 95% CI = 0.86-0.90) than the White patient cohort. In addition, the African American versus White patients did not have increased odds (OR = 0.98, 95% CI = 0.96-1.00) of in-hospital mortality. Patients on Medicaid (OR = 1.04, 95% CI = 1.01-1.07) and self-pay (OR = 1.07, 95% CI = 1.00-1.14, noninclusive endpoints) had higher in-hospital mortality than private insurance. Several comorbidities were predictive of an increased LOS, including anxiety (IRR = 1.94, 95% CI = 1.87-2.01) and sedative abuse (IRR = 2.07, 95% CI = 1.63-2.64). Conclusions Race was not associated with increased LOS or in-hospital mortality in patients with COVID-19 infections during the early pandemic. Insurance type, psychiatric comorbidities, and medical comorbidities significantly impacted outcomes in patients with COVID-19. This research and future research in the field should help to determine rational public policies to help mitigate the risk of diseases and their impact on future pandemics.

Untargeted Fecal Metabolomic Analyses across an Industrialization Gradient Reveal Shared Metabolites and Impact of Industrialization on Fecal Microbiome-Metabolome Interactions.

The metabolome is a central determinant of human phenotypes and includes the plethora of small molecules produced by host and microbiome or taken up from exogenous sources. However, studies of the metabolome have so far focused predominantly on urban, industrialized populations. Through an untargeted metabolomic analysis of 90 fecal samples from human individuals from Africa and the Americas-the birthplace and the last continental expansion of our species, respectively-we characterized a shared human fecal metabolome. The majority of detected metabolite features were ubiquitous across populations, despite any geographic, dietary, or behavioral differences. Such shared metabolite features included hyocholic acid and cholesterol. However, any characterization of the shared human fecal metabolome is insufficient without exploring the influence of industrialization. Here, we show chemical differences along an industrialization gradient, where the degree of industrialization correlates with metabolomic changes. We identified differential metabolite features such as amino acid-conjugated bile acids and urobilin as major metabolic correlates of these behavioral shifts. Additionally, coanalyses with over 5,000 publicly available human fecal samples and cooccurrence probability analyses with the gut microbiome highlight connections between the human fecal metabolome and gut microbiome. Our results indicate that industrialization significantly influences the human fecal metabolome, but diverse human lifestyles and behavior still maintain a shared human fecal metabolome. This study represents the first characterization of the shared human fecal metabolome through untargeted analyses of populations along an industrialization gradient. IMPORTANCE As the world becomes increasingly industrialized, understanding the biological consequences of these lifestyle shifts and what it means for past, present, and future human health is critical. Indeed, industrialization is associated with rises in allergic and autoimmune health conditions and reduced microbial diversity. Exploring these health effects on a chemical level requires consideration of human lifestyle diversity, but understanding the significance of any differences also requires knowledge of what molecular components are shared between human groups. Our study reveals the key chemistry of the human gut as defined by varied industrialization-based differences and ubiquitous shared features. Ultimately, these novel findings extend our knowledge of human molecular biology, especially as it is influenced by lifestyle and behavior, and provide steps toward understanding how human biology has changed over our species' history.