A Map of Toll-like Receptor Expression in the Intestinal Epithelium Reveals Distinct Spatial, Cell Type-Specific, and Temporal Patterns.

Signaling by Toll-like receptors (TLRs) on intestinal epithelial cells (IECs) is critical for intestinal homeostasis. To visualize epithelial expression of individual TLRs in vivo, we generated five strains of reporter mice. These mice revealed that TLR expression varied dramatically along the length of the intestine. Indeed, small intestine (SI) IECs expressed low levels of multiple TLRs that were highly expressed by colonic IECs. TLR5 expression was restricted to Paneth cells in the SI epithelium. Intestinal organoid experiments revealed that TLR signaling in Paneth cells or colonic IECs induced a core set of host defense genes, but this set did not include antimicrobial peptides, which instead were induced indirectly by inflammatory cytokines. This comprehensive blueprint of TLR expression and function in IECs reveals unexpected diversity in the responsiveness of IECs to microbial stimuli, and together with the associated reporter strains, provides a resource for further study of innate immunity.

Tissue-Resident Macrophages Are Locally Programmed for Silent Clearance of Apoptotic Cells.

Although apoptotic cells (ACs) contain nucleic acids that can be recognized by Toll-like receptors (TLRs), engulfment of ACs does not initiate inflammation in healthy organisms. Here we identified macrophage populations that continually engulf ACs in distinct tissues and found that these macrophages share characteristics compatible with immunologically silent clearance of ACs; such characteristics include high expression of AC recognition receptors, low expression of TLR9, and reduced TLR responsiveness to nucleic acids. Removal of the macrophages from tissues resulted in loss of many of these characteristics and the ability to generate inflammatory responses to AC-derived nucleic acids, suggesting that cues from the tissue microenvironment program macrophages for silent AC clearance. The transcription factors KLF2 and KLF4 control the expression of many genes within this AC clearance program. The coordinated expression of AC receptors with genes that limit responses to nucleic acids might ensure maintenance of homeostasis and thus represent a central feature of tissue macrophages.

Co-opting a Bioorthogonal Reaction for Oncometabolite Detection.

Dysregulated metabolism is a hallmark of many diseases, including cancer. Methods to fluorescently detect metabolites have the potential to enable new approaches to cancer detection and imaging. However, fluorescent sensing methods for naturally occurring cellular metabolites are relatively unexplored. Here we report the development of a chemical approach to detect the oncometabolite fumarate. Our strategy exploits a known bioorthogonal reaction, the 1,3-dipolar cycloaddition of nitrileimines and electron-poor olefins, to detect fumarate via fluorescent pyrazoline cycloadduct formation. We demonstrate hydrazonyl chlorides serve as readily accessible nitrileimine precursors, whose reactivity and spectral properties can be tuned to enable detection of fumarate and other dipolarophile metabolites. Finally, we show this reaction can be used to detect enzyme activity changes caused by mutations in fumarate hydratase, which underlie the familial cancer predisposition syndrome hereditary leiomyomatosis and renal cell cancer. Our studies define a novel intersection of bioorthogonal chemistry and metabolite reactivity that may be harnessed to enable biological profiling, imaging, and diagnostic applications.

Interactions of NK cell receptor KIR3DL1*004 with chaperones and conformation-specific antibody reveal a functional folded state as well as predominant intracellular retention.

Variable interaction between the Bw4 epitope of HLA-B and the polymorphic KIR3DL1/S1 system of inhibitory and activating NK cell receptors diversifies the development, repertoire formation, and response of human NK cells. KIR3DL1*004, a common KIR3DL1 allotype, in combination with Bw4(+) HLA-B, slows progression of HIV infection to AIDS. Analysis in this study of KIR3DL1*004 membrane traffic in NK cells shows this allotype is largely misfolded but stably retained in the endoplasmic reticulum, where it binds to the chaperone calreticulin and does not induce the unfolded protein response. A small fraction of KIR3DL1*004 folds correctly and leaves the endoplasmic reticulum to be expressed on the surface of primary NK and transfected NKL cells, in a form that can be triggered to inhibit NK cell activation and secretion of IFN-γ. Consistent with this small proportion of correctly folded molecules, trace amounts of MHC class I coimmunoprecipitated with KIR3DL1*004. There was no indication of any extensive intracellular interaction between unfolded KIR3DL1*004 and cognate Bw4(+) HLA-B. A similarly limited interaction of Bw4 with KIR3DL1*002, when both were expressed by the same cell, was observed despite the efficient folding of KIR3DL1*002 and its abundance on the NK cell surface. Several positions of polymorphism modulate KIR3DL1 abundance at the cell surface, differences that do not necessarily correlate with the potency of allotype function. In this context, our results suggest the possibility that the effect of Bw4(+) HLA-B and KIR3DL1*004 in slowing progression to AIDS is mediated by interaction of Bw4(+) HLA-B with the small fraction of cell surface KIR3DL1*004.

Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study.

BACKGROUND: Fatal drug overdose surveillance informs prevention but is often delayed because of autopsy report processing and death certificate coding. Autopsy reports contain narrative text describing scene evidence and medical history (similar to preliminary death scene investigation reports) and may serve as early data sources for identifying fatal drug overdoses. To facilitate timely fatal overdose reporting, natural language processing was applied to narrative texts from autopsies. OBJECTIVE: This study aimed to develop a natural language processing-based model that predicts the likelihood that an autopsy report narrative describes an accidental or undetermined fatal drug overdose. METHODS: Autopsy reports of all manners of death (2019-2021) were obtained from the Tennessee Office of the State Chief Medical Examiner. The text was extracted from autopsy reports (PDFs) using optical character recognition. Three common narrative text sections were identified, concatenated, and preprocessed (bag-of-words) using term frequency-inverse document frequency scoring. Logistic regression, support vector machine (SVM), random forest, and gradient boosted tree classifiers were developed and validated. Models were trained and calibrated using autopsies from 2019 to 2020 and tested using those from 2021. Model discrimination was evaluated using the area under the receiver operating characteristic, precision, recall, F1-score, and F2-score (prioritizes recall over precision). Calibration was performed using logistic regression (Platt scaling) and evaluated using the Spiegelhalter z test. Shapley additive explanations values were generated for models compatible with this method. In a post hoc subgroup analysis of the random forest classifier, model discrimination was evaluated by forensic center, race, age, sex, and education level. RESULTS: A total of 17,342 autopsies (n=5934, 34.22% cases) were used for model development and validation. The training set included 10,215 autopsies (n=3342, 32.72% cases), the calibration set included 538 autopsies (n=183, 34.01% cases), and the test set included 6589 autopsies (n=2409, 36.56% cases). The vocabulary set contained 4002 terms. All models showed excellent performance (area under the receiver operating characteristic ≥0.95, precision ≥0.94, recall ≥0.92, F1-score ≥0.94, and F2-score ≥0.92). The SVM and random forest classifiers achieved the highest F2-scores (0.948 and 0.947, respectively). The logistic regression and random forest were calibrated (P=.95 and P=.85, respectively), whereas the SVM and gradient boosted tree classifiers were miscalibrated (P=.03 and P<.001, respectively). "Fentanyl" and "accident" had the highest Shapley additive explanations values. Post hoc subgroup analyses revealed lower F2-scores for autopsies from forensic centers D and E. Lower F2-score were observed for the American Indian, Asian, ≤14 years, and ≥65 years subgroups, but larger sample sizes are needed to validate these findings. CONCLUSIONS: The random forest classifier may be suitable for identifying potential accidental and undetermined fatal overdose autopsies. Further validation studies should be conducted to ensure early detection of accidental and undetermined fatal drug overdoses across all subgroups.

Autophagy restricts Mycobacterium tuberculosis during acute infection in mice.

Whether or not autophagy has a role in defence against Mycobacterium tuberculosis infection remains unresolved. Previously, conditional knockdown of the core autophagy component ATG5 in myeloid cells was reported to confer extreme susceptibility to M. tuberculosis in mice, whereas depletion of other autophagy factors had no effect on infection. We show that doubling cre gene dosage to more robustly deplete ATG16L1 or ATG7 resulted in increased M. tuberculosis growth and host susceptibility in mice, although ATG5-depleted mice are more sensitive than ATG16L1- or ATG7-depleted mice. We imaged individual macrophages infected with M. tuberculosis and identified a shift from apoptosis to rapid necrosis in autophagy-depleted cells. This effect was dependent on phagosome permeabilization by M. tuberculosis. We monitored infected cells by electron microscopy, showing that autophagy protects the host macrophage by partially reducing mycobacterial access to the cytosol. We conclude that autophagy has an important role in defence against M. tuberculosis in mammals.

Mapping of UV-C dose and SARS-CoV-2 viral inactivation across N95 respirators during decontamination.

During public health crises like the COVID-19 pandemic, ultraviolet-C (UV-C) decontamination of N95 respirators for emergency reuse has been implemented to mitigate shortages. Pathogen photoinactivation efficacy depends critically on UV-C dose, which is distance- and angle-dependent and thus varies substantially across N95 surfaces within a decontamination system. Due to nonuniform and system-dependent UV-C dose distributions, characterizing UV-C dose and resulting pathogen inactivation with sufficient spatial resolution on-N95 is key to designing and validating UV-C decontamination protocols. However, robust quantification of UV-C dose across N95 facepieces presents challenges, as few UV-C measurement tools have sufficient (1) small, flexible form factor, and (2) angular response. To address this gap, we combine optical modeling and quantitative photochromic indicator (PCI) dosimetry with viral inactivation assays to generate high-resolution maps of "on-N95" UV-C dose and concomitant SARS-CoV-2 viral inactivation across N95 facepieces within a commercial decontamination chamber. Using modeling to rapidly identify on-N95 locations of interest, in-situ measurements report a 17.4 ± 5.0-fold dose difference across N95 facepieces in the chamber, yielding 2.9 ± 0.2-log variation in SARS-CoV-2 inactivation. UV-C dose at several on-N95 locations was lower than the lowest-dose locations on the chamber floor, highlighting the importance of on-N95 dose validation. Overall, we integrate optical simulation with in-situ PCI dosimetry to relate UV-C dose and viral inactivation at specific on-N95 locations, establishing a versatile approach to characterize UV-C photoinactivation of pathogens contaminating complex substrates such as N95s.

Practical considerations for Ultraviolet-C radiation mediated decontamination of N95 respirator against SARS-CoV-2 virus.

Decontaminating N95 respirators for reuse could mitigate shortages during the COVID-19 pandemic. Although the United States Center for Disease Control has identified Ultraviolet-C irradiation as one of the most promising methods for N95 decontamination, very few studies have evaluated the efficacy of Ultraviolet-C for SARS-CoV-2 inactivation. In addition, most decontamination studies are performed using mask coupons that do not recapitulate the complexity of whole masks. We sought to directly evaluate the efficacy of Ultraviolet-C mediated inactivation of SARS-CoV-2 on N95 respirators. To that end we created a portable UV-C light-emitting diode disinfection chamber and tested decontamination of SARS-CoV-2 at different sites on two models of N95 respirator. We found that decontamination efficacy depends on mask model, material and location of the contamination on the mask. Our results emphasize the need for caution when interpreting efficacy data of UV-C decontamination methods.

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee.

OBJECTIVE: To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication. MATERIALS AND METHODS: Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric "weak learner" models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic. RESULTS: Within 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79-0.83; Spiegelhalter P value: 0-.12). Risk concentration captured 47-52% of cases in the top quantiles of predicted probabilities. DISCUSSION: Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed. CONCLUSION: Predicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

Screening a Library of FDA-Approved and Bioactive Compounds for Antiviral Activity against SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has emerged as a major global health threat. The COVID-19 pandemic has resulted in over 168 million cases and 3.4 million deaths to date, while the number of cases continues to rise. With limited therapeutic options, the identification of safe and effective therapeutics is urgently needed. The repurposing of known clinical compounds holds the potential for rapid identification of drugs effective against SARS-CoV-2. Here, we utilized a library of FDA-approved and well-studied preclinical and clinical compounds to screen for antivirals against SARS-CoV-2 in human pulmonary epithelial cells. We identified 13 compounds that exhibit potent antiviral activity across multiple orthogonal assays. Hits include known antivirals, compounds with anti-inflammatory activity, and compounds targeting host pathways such as kinases and proteases critical for SARS-CoV-2 replication. We identified seven compounds not previously reported to have activity against SARS-CoV-2, including B02, a human RAD51 inhibitor. We further demonstrated that B02 exhibits synergy with remdesivir, the only antiviral approved by the FDA to treat COVID-19, highlighting the potential for combination therapy. Taken together, our comparative compound screening strategy highlights the potential of drug repurposing screens to identify novel starting points for development of effective antiviral mono- or combination therapies to treat COVID-19.

Dynamic post-translational modification profiling of Mycobacterium tuberculosis-infected primary macrophages.

Macrophages are highly plastic cells with critical roles in immunity, cancer, and tissue homeostasis, but how these distinct cellular fates are triggered by environmental cues is poorly understood. To uncover how primary murine macrophages respond to bacterial pathogens, we globally assessed changes in post-translational modifications of proteins during infection with Mycobacterium tuberculosis, a notorious intracellular pathogen. We identified hundreds of dynamically regulated phosphorylation and ubiquitylation sites, indicating that dramatic remodeling of multiple host pathways, both expected and unexpected, occurred during infection. Most of these cellular changes were not captured by mRNA profiling, and included activation of ubiquitin-mediated autophagy, an evolutionarily ancient cellular antimicrobial system. This analysis also revealed that a particular autophagy receptor, TAX1BP1, mediates clearance of ubiquitylated Mtb and targets bacteria to LC3-positive phagophores. These studies provide a new resource for understanding how macrophages shape their proteome to meet the challenge of infection.

Cas9+ conditionally-immortalized macrophages as a tool for bacterial pathogenesis and beyond.

Macrophages play critical roles in immunity, development, tissue repair, and cancer, but studies of their function have been hampered by poorly-differentiated tumor cell lines and genetically-intractable primary cells. Here we report a facile system for genome editing in non-transformed macrophages by differentiating ER-Hoxb8 myeloid progenitors from Cas9-expressing transgenic mice. These conditionally immortalized macrophages (CIMs) retain characteristics of primary macrophages derived from the bone marrow yet allow for easy genetic manipulation and a virtually unlimited supply of cells. We demonstrate the utility of this system for dissection of host genetics during intracellular bacterial infection using two important human pathogens: Listeria monocytogenes and Mycobacterium tuberculosis.

Activity-based protein profiling for mapping and pharmacologically interrogating proteome-wide ligandable hotspots.

Despite the completion of human genome sequencing efforts nearly 15 years ago that brought with it the promise of genome-based discoveries that would cure human diseases, most protein targets that control human diseases have remained largely untranslated, in-part because they represent difficult protein targets to drug. In addition, many of these protein targets lack screening assays or accessible binding pockets, making the development of small-molecule modulators very challenging. Here, we discuss modern methods for activity-based protein profiling-based chemoproteomic strategies to map 'ligandable' hotspots in proteomes using activity and reactivity-based chemical probes to allow for pharmacological interrogation of these previously difficult targets. We will showcase several recent examples of how these technologies have been used to develop highly selective small-molecule inhibitors against disease-related protein targets.

Magnetic nanoformulations for prostate cancer.

Magnetic nanoparticles (MNPs) play a vital role for improved imaging applications. Recently, a number of studies demonstrate MNPs can be applied for targeted delivery, sustained release of therapeutics, and hyperthermia. Based on stable particle size and shape, biocompatibility, and inherent contrast enhancement characteristics, MNPs have been encouraged for pre-clinical studies and human use. As a theranostic platform development, MNPs need to balance both delivery and imaging aspects. Thus, this review provides significant insight and advances in the theranostic role of MNPs through the documentation of unique magnetic nanoparticles used in prostate cancer, their interaction with prostate cancer cells, in vivo fate, targeting, and biodistribution. Specific and custom-made applications of various novel nanoformulations in prostate cancer are discussed.

Chemoproteomic Screening of Covalent Ligands Reveals UBA5 As a Novel Pancreatic Cancer Target.

Chemical genetic screening of small-molecule libraries has been a promising strategy for discovering unique and novel therapeutic compounds. However, identifying the targets of lead molecules that arise from these screens has remained a major bottleneck in understanding the mechanism of action of these compounds. Here, we have coupled the screening of a cysteine-reactive fragment-based covalent ligand library with an isotopic tandem orthogonal proteolysis-enabled activity-based protein profiling (isoTOP-ABPP) chemoproteomic platform to rapidly couple the discovery of lead small molecules that impair pancreatic cancer pathogenicity with the identification of druggable hotspots for potential cancer therapy. Through this coupled approach, we have discovered a covalent ligand DKM 2-93 that impairs pancreatic cancer cell survival and in vivo tumor growth through covalently modifying the catalytic cysteine of the ubiquitin-like modifier activating enzyme 5 (UBA5), thereby inhibiting its activity as a protein that activates the ubiquitin-like protein UFM1 to UFMylate proteins. We show that UBA5 is a novel pancreatic cancer therapeutic target and show DKM 2-93 as a relatively selective lead inhibitor of UBA5. Our results underscore the utility of coupling the screening of covalent ligand libraries with isoTOP-ABPP platforms for mining the proteome for druggable hotspots for cancer therapy.

Discovering Targets of Non-enzymatic Acylation by Thioester Reactivity Profiling.

Non-enzymatic protein modification driven by thioester reactivity is thought to play a major role in the establishment of cellular lysine acylation. However, the specific protein targets of this process are largely unknown. Here we report an experimental strategy to investigate non-enzymatic acylation in cells. Specifically, we develop a chemoproteomic method that separates thioester reactivity from enzymatic utilization, allowing selective enrichment of non-enzymatic acylation targets. Applying this method to cancer cell lines identifies numerous candidate targets of non-enzymatic acylation, including several enzymes in lower glycolysis. Functional studies highlight malonyl-CoA as a reactive thioester metabolite that can modify and inhibit glycolytic enzyme activity. Finally, we show that synthetic thioesters can be used as novel reagents to probe non-enzymatic acylation in living cells. Our studies provide new insights into the targets and drivers of non-enzymatic acylation, and demonstrate the utility of reactivity-based methods to experimentally investigate this phenomenon in biology and disease.

Microfluidic Mobility Shift Profiling of Lysine Acetyltransferases Enables Screening and Mechanistic Analysis of Cellular Acetylation Inhibitors.

Lysine acetyltransferases (KATs) are critical regulators of signaling in many diseases, including cancer. A major challenge in establishing the targetable functions of KATs in disease is a lack of well-characterized, cell-active KAT inhibitors. To confront this challenge, here we report a microfluidic mobility shift platform for the discovery and characterization of small molecule KAT inhibitors. Novel fluorescent peptide substrates were developed for four well-known KAT enzymes (p300, Crebbp, Morf, and Gcn5). Enzyme-catalyzed acetylation alters the electrophoretic mobility of these peptides in a microfluidic chip, allowing facile and direct monitoring of KAT activity. A pilot screen was used to demonstrate the utility of microfluidic mobility shift profiling to identify known and novel modulators of KAT activity. Real-time kinetic monitoring of KAT activity revealed that garcinol, a natural product KAT inhibitor used in cellular studies, exhibits time-dependent and detergent-sensitive inhibition, consistent with an aggregation-based mechanism. In contrast, the cell-permeable bisubstrate inhibitor Tat-CoA exhibited potent and time-independent KAT inhibition, highlighting its potential utility as a cellular inhibitor of KAT activity. These studies define microfluidic mobility shift profiling as a powerful platform for the discovery and characterization of small molecule inhibitors of KAT activity, and provide mechanistic insights potentially important for the application of KAT inhibitors in cellular contexts.

The labor market consequences of family illness.

BACKGROUND: This study examines the impact of mental illness on the labor market performance of family members of afflicted individuals. Numerous research projects have attempted to measure the impact of mental illness and related disorders on the ill individual, yet have traditionally neglected estimating potential costs accruing to family members of the ill. AIMS OF THE STUDY: Previous research estimating the impact of illness on the time allocation decisions of family caregivers has been limited in scope. I obtain estimates of the impact of mental illness on the probability of labor force participation and hours of work of all family members. The general analysis used in this study will pave the way for more accurate assessments of the costs of all types of illness and the estimates obtained will provide policy makers with a much more complete picture of the costs of mental illness. METHODS: The main empirical work in this study includes a probit estimation of labor force participation and a tobit regression of hours worked (including sample selection correction). The data sample, taken from the 1987 National Medical Expenditure Survey, is also partitioned by gender to clarify effects of family illness on labor supply for both females and males. RESULTS: Adult males are found to increase their probability of labor force participation in the presence of mental illness in the family (all else equal) when the mental illness is accompanied by a chronic physical illness. However, females are surprisingly found to have no significant impact on their probability of being a member of the labor market when a family member is afflicted with mental illness. On the other hand, hours of work are significantly reduced for both females and males when the mentally ill family member is afflicted with additional illnesses (physical and/or mental). DISCUSSION: Previous studies have traditionally not considered the effects of family illness on males because females are typically found to be the primary caregiver when a family member falls ill. The findings in this study indicate that men suffer reductions in their hours of work in an equivalent magnitude to females. Thus, males should not be ignored when estimating the opportunity costs of illness in families. IMPLICATIONS FOR HEALTH POLICIES: Current federal and state policies provide for some of the medical costs and replace some of the lost income of ill individuals, but generally do not support family members who are negatively affected by illness. This research provides evidence supporting the arguments of advocates for policy to ameliorate the financial burden borne by family members of the ill. IMPLICATIONS FOR FUTURE RESEARCH: The estimates obtained in this study show that women and men both need to be studied when determining the effects of family illness on labor supply, and should be studied separately to obtain clear results. Also, future research should include examining particular mental illnesses to see whether there is a higher cost of one over the other (e.g., schizophrenia versus major depression), as this may provide valuable information to policy makers. In addition, comparison of the costs of psychological disorders to chronic physical illnesses (such as cancer and heart disease) should be undertaken.