Early enforcement of cell identity by a functional component of the terminally differentiated state.

How progenitor cells can attain a distinct differentiated cell identity is a challenging problem given the fluctuating signaling environment in which cells exist and that critical transcription factors are often not unique to a differentiation process. Here, we test the hypothesis that a unique differentiated cell identity can result from a core component of the differentiated state doubling up as a signaling protein that also drives differentiation. Using live single-cell imaging in the adipocyte differentiation system, we show that progenitor fat cells (preadipocytes) can only commit to terminally differentiate after up-regulating FABP4, a lipid buffer that is highly enriched in mature adipocytes. Upon induction of adipogenesis in mouse preadipocyte cells, we show that after a long delay, cells first abruptly start to engage a positive feedback between CEBPA and PPARG before then engaging, after a second delay, a positive feedback between FABP4 and PPARG. These sequential positive feedbacks both need to engage in order to drive PPARG levels past the threshold for irreversible differentiation. In the last step before commitment, PPARG transcriptionally increases FABP4 expression while fatty acid-loaded FABP4 increases PPARG activity. Together, our study suggests a control principle for robust cell identity whereby a core component of the differentiated state also promotes differentiation from its own progenitor state.

Interdependence and the cost of uncoordinated responses to COVID-19.

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state's social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state's own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the "loss from anarchy" in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

The Relationship Between Broadband Speeds, Device Type, Demographic Characteristics, and Care-Seeking Via Telehealth.

Background: This study sought to examine the complex relationship between individual and environmental characteristics, broadband access, device type (computer or smartphone), and telehealth utilization as it relates to the digital divide. Methods: We analyzed a combination of electronic health record and publicly available zip code-level data for 2,770 men seeking treatment on a large, nationally available, direct-to-consumer telehealth platform. Using logistic regression, we determined the likelihood of accessing the platform through a smartphone (vs. a computer) based on key features of the environment, including broadband access and income, and demographic characteristics, including age and race. Results: We found that living in areas with higher rates of broadband adoption significantly decreased the likelihood of accessing virtual care using a smartphone (odds ratio [OR] = 0.17, p < 0.001). Compared with the 18-29 age category, the odds of accessing virtual care using a smartphone decreased for men between the age categories of 40-59 (OR = 0.63, p < 0.01) and over 60 (OR = 0.29, p < 0.001) years. Belonging to historically marginalized communities of color (Black, Hispanic, and Native American) almost doubled the odds of using a smartphone to access the platform (OR = 1.8, p < 0.001). Broadband availability and median area income were not significantly associated with mobile use. Conclusions: Telehealth platform design and policy solutions intended to expand access to virtual care should be flexible enough to accommodate the sometimes competing needs of patients who are at the greatest risk of being left behind.

The effect of indoor daylight spectrum and intensity on viability of indoor pathogens on different surface materials.

Built environments play a key role in the transmission of infectious diseases. Ventilation rates, air temperature, and humidity affect airborne transmission while cleaning protocols, material properties and light exposure can influence viability of pathogens on surfaces. We investigated how indoor daylight intensity and spectrum through electrochromic (EC) windows can impact the growth rate and viability of indoor pathogens on different surface materials (polyvinyl chloride [PVC] fabric, polystyrene, and glass) compared to traditional blinds. Results showed that tinted EC windows let in higher energy, shorter wavelength daylight than those with clear window and blind. The growth rates of pathogenic bacteria and fungi were significantly lower in spaces with EC windows compared to blinds: nearly 100% growth rate reduction was observed when EC windows were in their clear state followed by 41%-100% reduction in bacterial growth rate and 26%-42% reduction in fungal growth rate when EC windows were in their darkest tint. Moreover, bacterial viabilities were significantly lower on PVC fabric when they were exposed to indoor light at EC-tinted window. These findings are deemed fundamental to the design of healthy modern buildings, especially those that encompass sick and vulnerable individuals.

A memory of eS25 loss drives resistance phenotypes.

In order to maintain cellular protein homeostasis, ribosomes are safeguarded against dysregulation by myriad processes. Remarkably, many cell types can withstand genetic lesions of certain ribosomal protein genes, some of which are linked to diverse cellular phenotypes and human disease. Yet the direct and indirect consequences from these lesions are poorly understood. To address this knowledge gap, we studied in vitro and cellular consequences that follow genetic knockout of the ribosomal proteins RPS25 or RACK1 in a human cell line, as both proteins are implicated in direct translational control. Prompted by the unexpected detection of an off-target ribosome alteration in the RPS25 knockout, we closely interrogated cellular phenotypes. We found that multiple RPS25 knockout clones display viral- and toxin-resistance phenotypes that cannot be rescued by functional cDNA expression, suggesting that RPS25 loss elicits a cell state transition. We characterized this state and found that it underlies pleiotropic phenotypes and has a common rewiring of gene expression. Rescuing RPS25 expression by genomic locus repair failed to correct for the phenotypic and expression hysteresis. Our findings illustrate how the elasticity of cells to a ribosome perturbation can drive specific phenotypic outcomes that are indirectly linked to translation and suggests caution in the interpretation of ribosomal protein gene mutation data.

Expression variation and covariation impair analog and enable binary signaling control.

Due to noise in the synthesis and degradation of proteins, the concentrations of individual vertebrate signaling proteins were estimated to vary with a coefficient of variation (CV) of approximately 25% between cells. Such high variation is beneficial for population-level regulation of cell functions but abolishes accurate single-cell signal transmission. Here, we measure cell-to-cell variability of relative protein abundance using quantitative proteomics of individual Xenopus laevis eggs and cultured human cells and show that variation is typically much lower, in the range of 5-15%, compatible with accurate single-cell transmission. Focusing on bimodal ERK signaling, we show that variation and covariation in MEK and ERK expression improves controllability of the percentage of activated cells, demonstrating how variation and covariation in expression enables population-level control of binary cell-fate decisions. Together, our study argues for a control principle whereby low expression variation enables accurate control of analog single-cell signaling, while increased variation, covariation, and numbers of pathway components are required to widen the stimulus range over which external inputs regulate binary cell activation to enable precise control of the fraction of activated cells in a population.

Organ preservation: from the past to the future.

Organ transplantation is the most effective therapy for patients with end-stage disease. Preservation solutions and techniques are crucial for donor organ quality, which is directly related to morbidity and survival after transplantation. Currently, static cold storage (SCS) is the standard method for organ preservation. However, preservation time with SCS is limited as prolonged cold storage increases the risk of early graft dysfunction that contributes to chronic complications. Furthermore, the growing demand for the use of marginal donor organs requires methods for organ assessment and repair. Machine perfusion has resurfaced and dominates current research on organ preservation. It is credited to its dynamic nature and physiological-like environment. The development of more sophisticated machine perfusion techniques and better perfusates may lead to organ repair/reconditioning. This review describes the history of organ preservation, summarizes the progresses that has been made to date, and discusses future directions for organ preservation.

An Expanded Porphycene with High NIR Absorptivity That Stabilizes Two Different Kinds of Metal Complexes.

A new expanded porphycene with 26 π-electrons has been prepared by the McMurry coupling of 1,4-bis(3,4-diethyl-2-pyrryl)benzene dialdehyde. Expansion of the porphycene framework provides a ligand capable of stabilizing a bis(rhodium) and a monoruthenium complex. These new porphycene derivatives absorb strongly in the NIR spectral region, with appreciable absorptivity up to 1300 nm. On the basis of their ground- and excited-state spectroscopic features and structural parameters, both the free-base system and the bis(rhodium) complex are considered to be Hückel-type aromatic systems. This conclusion is supported by DFT calculations.

An Object-Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes.

Recent genome-wide association studies confirm that human leukocyte antigen (HLA) genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA-based disease predictive model. However, conventional model-building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as "objects" or "exemplars," one focuses on systemic associations of disease phenotype with "objects" via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease-associated genotypes or alleles. Effectively, it transforms large, discrete, and sparse HLA genotypes into a matrix of similarity-based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease-associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set, indicating that this methodology is useful to build predictive models with complex HLA genotypes.

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

AIM: It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. METHODS: Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. RESULTS: In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10-92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. CONCLUSION: Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations.

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor.

BACKGROUND: Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods' restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. RESULTS: The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions. CONCLUSION: Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors' training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

ROS-dependent degeneration of human neurons induced by environmentally relevant levels of micro- and nanoplastics of diverse shapes and forms.

Our study explores the pressing issue of micro- and nanoplastics (MNPs) inhalation and their subsequent penetration into the brain, highlighting a significant environmental health concern. We demonstrate that MNPs can indeed penetrate murine brain, warranting further investigation into their neurotoxic effects in humans. We then proceed to test the impact of MNPs at environmentally relevant concentrations, with focusing on variations in size and shape. Our findings reveal that these MNPs induce oxidative stress, cytotoxicity, and neurodegeneration in human neurons, with cortical neurons being more susceptible than nociceptors. Furthermore, we examine the role of biofilms on MNPs, demonstrating that MNPs can serve as a vehicle for pathogenic biofilms that significantly exacerbate these neurotoxic effects. This sequence of investigations reveals that minimal MNPs accumulation can cause oxidative stress and neurodegeneration in human neurons, significantly risking brain health and highlights the need to understand the neurological consequences of inhaling MNPs. Overall, our developed in vitro testing battery has significance in elucidating the effects of environmental factors and their associated pathological mechanisms in human neurons.

Using Haplotype-Based Artificial Intelligence to Evaluate SARS-CoV-2 Novel Variants and Mutations.

Importance: Earlier detection of emerging novel SARS-COV-2 variants is important for public health surveillance of potential viral threats and for earlier prevention research. Artificial intelligence may facilitate early detection of SARS-CoV2 emerging novel variants based on variant-specific mutation haplotypes and, in turn, be associated with enhanced implementation of risk-stratified public health prevention strategies. Objective: To develop a haplotype-based artificial intelligence (HAI) model for identifying novel variants, including mixture variants (MVs) of known variants and new variants with novel mutations. Design, Setting, and Participants: This cross-sectional study used serially observed viral genomic sequences globally (prior to March 14, 2022) to train and validate the HAI model and used it to identify variants arising from a prospective set of viruses from March 15 to May 18, 2022. Main Outcomes and Measures: Viral sequences, collection dates, and locations were subjected to statistical learning analysis to estimate variant-specific core mutations and haplotype frequencies, which were then used to construct an HAI model to identify novel variants. Results: Through training on more than 5 million viral sequences, an HAI model was built, and its identification performance was validated on an independent validation set of more than 5 million viruses. Its identification performance was assessed on a prospective set of 344 901 viruses. In addition to achieving an accuracy of 92.8% (95% CI within 0.1%), the HAI model identified 4 Omicron MVs (Omicron-Alpha, Omicron-Delta, Omicron-Epsilon, and Omicron-Zeta), 2 Delta MVs (Delta-Kappa and Delta-Zeta), and 1 Alpha-Epsilon MV, among which Omicron-Epsilon MVs were most frequent (609/657 MVs [92.7%]). Furthermore, the HAI model found that 1699 Omicron viruses had unidentifiable variants given that these variants acquired novel mutations. Lastly, 524 variant-unassigned and variant-unidentifiable viruses carried 16 novel mutations, 8 of which were increasing in prevalence percentages as of May 2022. Conclusions and Relevance: In this cross-sectional study, an HAI model found SARS-COV-2 viruses with MV or novel mutations in the global population, which may require closer examination and monitoring. These results suggest that HAI may complement phylogenic variant assignment, providing additional insights into emerging novel variants in the population.

Context-dependent regulation of lipid accumulation in adipocytes by a HIF1α-PPARγ feedback network.

Hypoxia-induced upregulation of HIF1α triggers adipose tissue dysfunction and insulin resistance in obese patients. HIF1α closely interacts with PPARγ, the master regulator of adipocyte differentiation and lipid accumulation, but there are conflicting results regarding how this interaction controls the excessive lipid accumulation that drives adipocyte dysfunction. To directly address these conflicts, we established a differentiation system that recapitulated prior seemingly opposing observations made across different experimental settings. Using single-cell imaging and coarse-grained mathematical modeling, we show how HIF1α can both promote and repress lipid accumulation during adipogenesis. Our model predicted and our experiments confirmed that the opposing roles of HIF1α are isolated from each other by the positive-feedback-mediated upregulation of PPARγ that drives adipocyte differentiation. Finally, we identify three factors: strength of the differentiation cue, timing of hypoxic perturbation, and strength of HIF1α expression changes that, when considered together, provide an explanation for many of the previous conflicting reports.

Magnetic Levitation System Isolates and Purifies Airborne Viruses.

Detection of viable viruses in the air is critical in order to determine the level of risk associated with the airborne diffusion of viruses. Different methods have been developed for the isolation, purification, and detection of viable airborne viruses, but they require an extensive processing time and often present limitations including low physical efficiency (i.e., the amount of collected viruses), low biological efficiency (i.e., the number of viable viruses), or a combination of all. To mitigate such limitations, we have employed an efficient technique based on the magnetic levitation (Maglev) technique with a paramagnetic solution and successfully identified distinct variations in levitation and density characteristics among bacteria (Escherichia coli), phages (MS2), and human viruses (SARS-CoV-2 and influenza H1N1). Notably, the Maglev approach enabled a significant enrichment of viable airborne viruses in air samples. Furthermore, the enriched viruses obtained through Maglev exhibited high purity, rendering them suitable for direct utilization in subsequent analyses such as reverse transcription-polymerase chain reaction (RT-PCR) or colorimetric assays. The system is portable, easy to use, and cost-efficient and can potentially provide proactive surveillance data for monitoring future outbreaks of airborne infectious diseases and allow for the induction of various preventative and mitigative measures.

Large-Vessel Vasculitis in Ophthalmology: Giant Cell Arteritis and Takayasu Arteritis.

ABSTRACT: Giant cell arteritis and Takayasu arteritis are large-vessel vasculitides that share multiple common features but also have significant differences in epidemiology, demographics, clinical presentation, evaluation, and treatment. Giant cell arteritis is more common in elderly patients of Caucasian descent versus Takayasu arteritis, which is more prevalent in younger patients of Asian descent. Although traditionally age has been the main criterion for differentiating the 2 etiologies, modifications in the diagnostic criteria have recognized the overlap between the 2 conditions. In this monograph, we review the diagnostic criteria for both conditions and describe the epidemiology, pathogenesis, histology, evaluation, and management for large-vessel vasculitis in ophthalmology. Additionally, we describe ocular imaging techniques that may be utilized by ophthalmologists to identify manifestations of large-vessel vasculiti- des in patients. Lastly, we compare and contrast the key clinical, laboratory, and pathologic features that might help ophthalmologists to differentiate the 2 entities.

TRPA1 contributes to cold hypersensitivity.

TRPA1 is a nonselective cation channel expressed by nociceptors. Although it is widely accepted that TRPA1 serves as a broad irritancy receptor for a variety of reactive chemicals, its role in cold sensation remains controversial. Here, we demonstrate that mild cooling markedly increases agonist-evoked rat TRPA1 currents. In the absence of an agonist, even noxious cold only increases current amplitude slightly. These results suggest that TRPA1 is a key mediator of cold hypersensitivity in pathological conditions in which reactive oxygen species and proinflammatory activators of the channel are present, but likely plays a comparatively minor role in acute cold sensation. Supporting this, cold hypersensitivity can be induced in wild-type but not Trpa1(-/-) mice by subcutaneous administration of a TRPA1 agonist. Furthermore, the selective TRPA1 antagonist HC-030031 [2-(1,3-dimethyl-2,6-dioxo-1,2,3,6-tetrahydro-7H-purin-7-yl)-N-(4-isopropylphenyl)acetamide] reduces cold hypersensitivity in rodent models of inflammatory and neuropathic pain.

Interdependent program evaluation: Geographic and social spillovers in COVID-19 closures and reopenings in the United States.

In an interconnected world, understanding policy spillovers is essential. We propose a program evaluation framework to measure policy spillover effects and apply that framework to study the governmental responses to COVID-19 in the United States. Our analysis suggests the presence of social spillovers. We estimate that while state closures directly reduced mobility by 3 to 4%, all other states locking down further decreased mobility in the focal state by 8 to 14%. Similarly, while reopening directly increased mobility by 2 to 3%, all other states' reopening increased mobility in the focal state by 12 to 21%. Our analysis also suggests geographic spillovers: Travel from locked down origins to open destinations increased by 12 to 29%. In contrast, travel from reopened origins to locked down destinations decreased by 6 to 7% for nearby counties and by 14 to 18% for distant counties. Despite its limitations, we believe that our approach takes the first steps toward creating a framework for interdependent program evaluation across policy domains.

Notch-induced endoplasmic reticulum-associated degradation governs mouse thymocyte ß-selection.

Signals from the pre-T cell receptor and Notch coordinately instruct ß-selection of CD4-CD8-double negative (DN) thymocytes to generate αß T cells in the thymus. However, how these signals ensure a high-fidelity proteome and safeguard the clonal diversification of the pre-selection TCR repertoire given the considerable translational activity imposed by ß-selection is largely unknown. Here, we identify the endoplasmic reticulum (ER)-associated degradation (ERAD) machinery as a critical proteostasis checkpoint during ß-selection. Expression of the SEL1L-HRD1 complex, the most conserved branch of ERAD, is directly regulated by the transcriptional activity of the Notch intracellular domain. Deletion of Sel1l impaired DN3 to DN4 thymocyte transition and severely impaired mouse αß T cell development. Mechanistically, Sel1l deficiency induced unresolved ER stress that triggered thymocyte apoptosis through the PERK pathway. Accordingly, genetically inactivating PERK rescued T cell development from Sel1l-deficient thymocytes. In contrast, IRE1α/XBP1 pathway was induced as a compensatory adaptation to alleviate Sel1l-deficiency-induced ER stress. Dual loss of Sel1l and Xbp1 markedly exacerbated the thymic defect. Our study reveals a critical developmental signal controlled proteostasis mechanism that enforces T cell development to ensure a healthy adaptive immunity.

In vitro studies of deferasirox derivatives as potential organelle-targeting traceable anti-cancer therapeutics.

We report here strategic functionalization of the FDA approved chelator deferasirox (1) in an effort to produce organelle-targeting iron chelators with enhanced activity against A549 lung cancer cells. Derivative 8 was found to have improved antiproliferative activity relative to 1. Fluorescent cell imaging revealed that compound 8 preferentially localises within the lysosome.