Oxidative Stress is a shared characteristic of ME/CFS and Long COVID.

More than 65 million individuals worldwide are estimated to have Long COVID (LC), a complex multisystemic condition, wherein patients of all ages report fatigue, post-exertional malaise, and other symptoms resembling myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS). With no current treatments or reliable diagnostic markers, there is an urgent need to define the molecular underpinnings of these conditions. By studying bioenergetic characteristics of peripheral blood lymphocytes in over 16 healthy controls, 15 ME/CFS, and 15 LC, we find both ME/CFS and LC donors exhibit signs of elevated oxidative stress, relative to healthy controls, especially in the memory subset. Using a combination of flow cytometry, bulk RNA-seq analysis, mass spectrometry, and systems chemistry analysis, we also observed aberrations in ROS clearance pathways including elevated glutathione levels, decreases in mitochondrial superoxide dismutase levels, and glutathione peroxidase 4 mediated lipid oxidative damage. Critically, these changes in redox pathways show striking sex-specific trends. While females diagnosed with ME/CFS exhibit higher total ROS and mitochondrial calcium levels, males with an ME/CFS diagnosis have normal ROS levels, but larger changes in lipid oxidative damage. Further analyses show that higher ROS levels correlates with hyperproliferation of T cells in females, consistent with the known role of elevated ROS levels in the initiation of proliferation. This hyperproliferation of T cells can be attenuated by metformin, suggesting this FDA-approved drug as a possible treatment, as also suggested by a recent clinical study of LC patients. Thus, we report that both ME/CFS and LC are mechanistically related and could be diagnosed with quantitative blood cell measurements. We also suggest that effective, patient tailored drugs might be discovered using standard lymphocyte stimulation assays.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning.

PURPOSE: Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS: Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS: From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION: Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Rationale for LDH-targeted cancer immunotherapy.

Immunotherapies have significantly improved the survival of patients in many cancers over the last decade. However, primary and secondary resistances are encountered in most patients. Unravelling resistance mechanisms to cancer immunotherapies is an area of active investigation. Elevated levels of circulating enzyme lactate dehydrogenase (LDH) have been historically considered in oncology as a marker of bad prognosis, usually attributed to elevated tumour burden and cancer metabolism. Recent evidence suggests that elevated LDH levels could be independent from tumour burden and contain a negative predictive value, which could help in guiding treatment strategies in immuno-oncology. In this review, we decipher the rationale supporting the potential of LDH-targeted therapeutic strategies to tackle the direct immunosuppressive effects of LDH on a wide range of immune cells, and enhance the survival of patients treated with cancer immunotherapies.

Human Coronary Plaque T Cells Are Clonal and Cross-React to Virus and Self.

BACKGROUND: Coronary artery disease is an incurable, life-threatening disease that was once considered primarily a disorder of lipid deposition. Coronary artery disease is now also characterized by chronic inflammation' notable for the buildup of atherosclerotic plaques containing immune cells in various states of activation and differentiation. Understanding how these immune cells contribute to disease progression may lead to the development of novel therapeutic strategies. METHODS: We used single-cell technology and in vitro assays to interrogate the immune microenvironment of human coronary atherosclerotic plaque at different stages of maturity. RESULTS: In addition to macrophages, we found a high proportion of αß T cells in the coronary plaques. Most of these T cells lack high expression of CCR7 and L-selectin, indicating that they are primarily antigen-experienced memory cells. Notably, nearly one-third of these cells express the HLA-DRA surface marker, signifying activation through their TCRs (T-cell receptors). Consistent with this, TCR repertoire analysis confirmed the presence of activated αß T cells (CD4

SU086, an inhibitor of HSP90, impairs glycolysis and represents a treatment strategy for advanced prostate cancer.

Among men, prostate cancer is the second leading cause of cancer-associated mortality, with advanced disease remaining a major clinical challenge. We describe a small molecule, SU086, as a therapeutic strategy for advanced prostate cancer. We demonstrate that SU086 inhibits the growth of prostate cancer cells in vitro, cell-line and patient-derived xenografts in vivo, and ex vivo prostate cancer patient specimens. Furthermore, SU086 in combination with standard of care second-generation anti-androgen therapies displays increased impairment of prostate cancer cell and tumor growth in vitro and in vivo. Cellular thermal shift assay reveals that SU086 binds to heat shock protein 90 (HSP90) and leads to a decrease in HSP90 levels. Proteomic profiling demonstrates that SU086 binds to and decreases HSP90. Metabolomic profiling reveals that SU086 leads to perturbation of glycolysis. Our study identifies SU086 as a treatment for advanced prostate cancer as a single agent or when combined with second-generation anti-androgens.

CD20-Targeted Therapy Ablates De Novo Antibody Response to Vaccination but Spares Preestablished Immunity.

To obtain a deeper understanding of poor responses to COVID-19 vaccination in patients with lymphoma, we assessed blocking antibodies, total anti-spike IgG, and spike-specific memory B cells in the peripheral blood of 126 patients with lymphoma and 20 age-matched healthy controls 1 and 4 months after COVID-19 vaccination. Fifty-five percent of patients developed blocking antibodies postvaccination, compared with 100% of controls. When evaluating patients last treated from days to nearly 18 years prior to vaccination, time since last anti-CD20 was a significant independent predictor of vaccine response. None of 31 patients who had received anti-CD20 treatment within 6 months prior to vaccination developed blocking antibodies. In contrast, patients who initiated anti-CD20 treatment shortly after achieving a vaccine-induced antibody response tended to retain that response during treatment, suggesting a policy of immunizing prior to treatment whenever possible. SIGNIFICANCE: In a large cohort of patients with B-cell lymphoma, time since anti-CD20 treatment was an independent predictor of neutralizing antibody response to COVID-19 vaccination. Comparing patients who received anti-CD20 treatment before or after vaccination, we demonstrate that vaccinating first can generate an antibody response that endures through anti-CD20-containing treatment. This article is highlighted in the In This Issue feature, p. 85.

Development and validation of prognostic scoring system for COVID-19 severity in South India.

BACKGROUND: Development of a prediction model using baseline characteristics of COVID-19 patients at the time of diagnosis will aid us in early identification of the high-risk groups and devise pertinent strategies accordingly. Hence, we did this study to develop a prognostic-scoring system for predicting the COVID-19 severity in South India. METHODS: We undertook this retrospective cohort study among COVID-19 patients reporting to Hindu Mission Hospital, India. Multivariable logistic regression using the LASSO procedure was used to select variables for the model building, and the nomogram scoring system was developed with the final selected model. Model discrimination, calibration, and decision curve analysis (DCA) was performed. RESULTS: In total, 35.1% of the patients in the training set developed severe COVID-19 during their follow-up period. In the basic model, nine variables (age group, sex, education, chronic kidney disease, tobacco, cough, dyspnea, olfactory-gustatory dysfunction [OGD], and gastrointestinal symptoms) were selected and a nomogram was built using these variables. In the advanced model, in addition to these variables (except OGD), C-reactive protein, lactate dehydrogenase, ferritin, D-dimer, and CT severity score were selected. The discriminatory power (c-index) for basic model was 0.78 (95%CI: 0.74-0.82) and advanced model was 0.83 (95%CI: 0.79-0.87). DCA showed that both the models are beneficial at a threshold probability around 10-95% than treat-none or treat-all strategies. CONCLUSION: The present study has developed two separate prognostic-scoring systems to predict the COVID-19 severity. This scoring system could help the clinicians and policymakers to devise targeted interventions and in turn reduce the COVID-19 mortality in India.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

In situ DESI-MSI lipidomic profiles of mucosal margin of oral squamous cell carcinoma.

BACKGROUND: Although there is consensus that the optimal safe margin is ≥ 5mm, obtaining clear margins (≥5 mm) intraoperatively seems to be the major challenge. We applied a molecular diagnostic method at the lipidomic level to determine the safe surgical resection margin of OSCC by desorption electrospray ionisation mass spectrometry imaging (DESI-MSI). METHODS: By overlaying mass spectrometry images with hematoxylin-eosin staining (H&E) from 18 recruited OSCC participants, the mass spectra of all pixels across the diagnosed tumour and continuous mucosal margin regions were extracted to serve as the training and validation datasets. A Lasso regression model was used to evaluate the test performance. FINDINGS: By leave-one-out validation, the Lasso model achieved 88.6% accuracy in distinguishing between tumour and normal regions. To determine the safe surgical resection distance and margin status of OSCC, a set of 14 lipid ions that gradually decreased from tumour to normal tissue was assigned higher weight coefficients in the Lasso model. The safe surgical resection distance of OSCC was measured using the developed 14 lipid ion molecular diagnostic model for clinical reference. The overall accuracy of predicting tumours, positive margins, and negative margins was 92.6%. INTERPRETATION: The spatial segmentation results based on our diagnostic model not only clearly delineated the tumour and normal tissue, but also distinguished the different status of surgical margins. Meanwhile, the safe surgical resection margin of OSCC on frozen sections can also be accurately measured using the developed diagnostic model. FUNDING: This study was supported by Nanjing Municipal Key Medical Laboratory Constructional Project Funding (since 2016) and the Centre of Nanjing Clinical Medicine Tumour (since 2014).

MassExplorer: a computational tool for analyzing desorption electrospray ionization mass spectrometry data

Summary: In the last few years, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) has been increasingly used for simultaneous detection of thousands of metabolites and lipids from human tissues and biofluids. To successfully find the most significant differences between two sets of DESI-MSI data (e.g., healthy vs disease) requires the application of accurate computational and statistical methods that can pre-process the data under various normalization settings and help identify these changes among thousands of detected metabolites. Here, we report MassExplorer, a novel computational tool, to help pre-process DESI-MSI data, visualize raw data, build predictive models using the statistical lasso approach to select for a sparse set of significant molecular changes, and interpret selected metabolites. This tool, which is available for both online and offline use, is flexible for both chemists and biologists and statisticians as it helps in visualizing structure of DESI-MSI data and in analyzing the statistically significant metabolites that are differentially expressed across both sample types. Based on the modules in MassExplorer, we expect it to be immediately useful for various biological and chemical applications in mass spectrometry. Availability and implementation: MassExplorer is available as an online R-Shiny application or Mac OS X compatible standalone application. The application, sample performance, source code and corresponding guide can be found at: https://zarelab.com/research/massexplorer-a-tool-to-help-guide-analysis-of-mass-spectrometry-samples/. Supplementary informationMATION: Supplementary data are available at Bioinformatics online.

Oral squamous cell carcinoma diagnosed from saliva metabolic profiling.

Saliva is a noninvasive biofluid that can contain metabolite signatures of oral squamous cell carcinoma (OSCC). Conductive polymer spray ionization mass spectrometry (CPSI-MS) is employed to record a wide range of metabolite species within a few seconds, making this technique appealing as a point-of-care method for the early detection of OSCC. Saliva samples from 373 volunteers, 124 who are healthy, 124 who have premalignant lesions, and 125 who are OSCC patients, were collected for discovering and validating dysregulated metabolites and determining altered metabolic pathways. Metabolite markers were reconfirmed at the primary tissue level by desorption electrospray ionization MS imaging (DESI-MSI), demonstrating the reliability of diagnoses based on saliva metabolomics. With the aid of machine learning (ML), OSCC and premalignant lesions can be distinguished from the normal physical condition in real time with an accuracy of 86.7%, on a person by person basis. These results suggest that the combination of CPSI-MS and ML is a feasible tool for accurate, automated diagnosis of OSCC in clinical practice.

Metabolite therapy guided by liquid biopsy proteomics delays retinal neurodegeneration.

BACKGROUND: Neurodegenerative diseases are incurable disorders caused by progressive neuronal cell death. Retinitis pigmentosa (RP) is a blinding neurodegenerative disease that results in photoreceptor death and progresses to the loss of the entire retinal network. We previously found that proteomic analysis of the adjacent vitreous served as way to indirectly biopsy the retina and identify changes in the retinal proteome. METHODS: We analyzed protein expression in liquid vitreous biopsies from autosomal recessive (ar)RP patients with PDE6A mutations and arRP mice with Pde6ɑ mutations. Proteomic analysis of retina and vitreous samples identified molecular pathways affected at the onset of photoreceptor death. Based on affected molecular pathways, arRP mice were treated with a ketogenic diet or metabolites involved in fatty-acid synthesis, oxidative phosphorylation, and the tricarboxylic acid (TCA) cycle. FINDINGS: Dietary supplementation of a single metabolite, ɑ-ketoglutarate, increased docosahexaeonic acid levels, provided neuroprotection, and enhanced visual function in arRP mice. A ketogenic diet delayed photoreceptor cell loss, while vitamin B supplementation had a limited effect. Finally, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) on ɑ-ketoglutarate-treated mice revealed restoration of metabolites that correlated with our proteomic findings: uridine, dihydrouridine, and thymidine (pyrimidine and purine metabolism), glutamine and glutamate (glutamine/glutamate conversion), and succinic and aconitic acid (TCA cycle). INTERPRETATION: This study demonstrates that replenishing TCA cycle metabolites via oral supplementation prolongs retinal function and provides a neuroprotective effect on the photoreceptor cells and inner retinal network. FUNDING: NIH grants [R01EY026682, R01EY024665, R01EY025225, R01EY024698, R21AG050437, P30EY026877, 5P30EY019007, R01EY018213, F30EYE027986, T32GM007337, 5P30CA013696], NSF grant CHE-1734082.

Identification of diagnostic metabolic signatures in clear cell renal cell carcinoma using mass spectrometry imaging.

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. Intraoperative frozen section (IFS) analysis is used to confirm the diagnosis during partial nephrectomy. However, surgical margin evaluation using IFS analysis is time consuming and unreliable, leading to relatively low utilization. In our study, we demonstrated the use of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) as a molecular diagnostic and prognostic tool for ccRCC. DESI-MSI was conducted on fresh-frozen 23 normal tumor paired nephrectomy specimens of ccRCC. An independent validation cohort of 17 normal tumor pairs was analyzed. DESI-MSI provides two-dimensional molecular images of tissues with mass spectra representing small metabolites, fatty acids and lipids. These tissues were subjected to histopathologic evaluation. A set of metabolites that distinguish ccRCC from normal kidney were identified by performing least absolute shrinkage and selection operator (Lasso) and log-ratio Lasso analysis. Lasso analysis with leave-one-patient-out cross-validation selected 57 peaks from over 27,000 metabolic features across 37,608 pixels obtained using DESI-MSI of ccRCC and normal tissues. Baseline Lasso of metabolites predicted the class of each tissue to be normal or cancerous tissue with an accuracy of 94 and 76%, respectively. Combining the baseline Lasso with the ratio of glucose to arachidonic acid could potentially reduce scan time and improve accuracy to identify normal (82%) and ccRCC (88%) tissue. DESI-MSI allows rapid detection of metabolites associated with normal and ccRCC with high accuracy. As this technology advances, it could be used for rapid intraoperative assessment of surgical margin status.

The 3D Structure of Human DP Prostaglandin G-Protein-Coupled Receptor Bound to Cyclopentanoindole Antagonist, Predicted Using the DuplexBiHelix Modification of the GEnSeMBLE Method.

Prostaglandins play a critical physiological role in both cardiovascular and immune systems, acting through their interactions with 9 prostanoid G protein-coupled receptors (GPCRs). These receptors are important therapeutic targets for a variety of diseases including arthritis, allergies, type 2 diabetes, and cancer. The DP prostaglandin receptor is of interest because it has unique structural and physiological properties. Most notably, DP does not have the 3-6 ionic lock common to Class A GPCRs. However, the lack of X-ray structures for any of the 9 prostaglandin GPCRs hampers the application of structure-based drug design methods to develop more selective and active medications to specific receptors. We predict here 3D structures for the DP prostaglandin GPCR, based on the GEnSeMBLE complete sampling with hierarchical scoring (CS-HS) methodology. This involves evaluating the energy of 13 trillion packings to finally select the best 20 that are stable enough to be relevant for binding to antagonists, agonists, and modulators. To validate the predicted structures, we predict the binding site for the Merck cyclopentanoindole (CPI) selective antagonist docked to DP. We find that the CPI binds vertically in the 1-2-7 binding pocket, interacting favorably with residues R3107.40 and K762.54 with additional interactions with S3137.43, S3167.46, S191.35, etc. This binding site differs significantly from that of antagonists to known Class A GPCRs where the ligand binds in the 3-4-5-6 region. We find that the predicted binding site leads to reasonable agreement with experimental Structure-Activity Relationship (SAR). We suggest additional mutation experiments including K762.54, E1293.49, L1233.43, M2706.40, F2746.44 to further validate the structure, function, and activation mechanism of receptors in the prostaglandin family. Our structures and binding sites are largely consistent and improve upon the predictions by Li et al. ( J. Am. Chem. Soc. 2007 , 129 ( 35 ), 10720 ) that used our earlier MembStruk prediction methodology.

Comparative effectiveness and cost-effectiveness of treat-to-target versus benefit-based tailored treatment of type 2 diabetes in low-income and middle-income countries: a modelling analysis.

BACKGROUND: Optimal prescription of blood pressure, lipid, and glycaemic control treatments for adults with type 2 diabetes remains unclear. We aimed to compare the effectiveness and cost-effectiveness of two treatment approaches for diabetes management in five low-income and middle-income countries. METHODS: We developed a microsimulation model to compare a treat-to-target (TTT) strategy, aiming to achieve target levels of biomarkers (blood pressure <130/80 mm Hg, LDL <2·59 mmol/L, and HbA1c <7% [ie, 53·0 mmol/mol]), with a benefit-based tailored treatment (BTT) strategy, aiming to lower estimated risk for complications (to a 10 year cardiovascular risk <10% and lifetime microvascular risk <5%) on the basis of age, sex, and biomarker values. Data were obtained from cohorts in China, Ghana, India, Mexico, and South Africa to span a spectrum of risk profiles. FINDINGS: The TTT strategy recommended treatment to a larger number of people-who were generally at lower risk of diabetes complications-than the BTT. The BTT strategy recommended treatment to fewer people at higher risk. Compared with the TTT strategy, the BTT strategy would be expected to avert 24·4-30·5% more complications and be more cost-effective from a societal perspective (saving US$4·0-300·0 per disability-adjusted life-year averted in the countries simulated). Alternative treatment thresholds, matched by total cost or population size treated, did not change the comparative superiority of the BTT strategy, nor did titrating treatment using fasting plasma glucose (for areas without HbA1c testing). However, if insulin were unavailable, the BTT strategy would no longer be superior for preventing microvascular events and was superior only for preventing cardiovascular events. INTERPRETATION: A BTT strategy is more effective and cost-effective than a TTT strategy in low-income and middle-income countries for prevention of both cardiovascular and microvascular complications of type 2 diabetes. However, the superiority of the BTT strategy for averting microvascular complications is contingent on insulin availability. FUNDING: Rosenkranz Prize for Healthcare Research in Developing Countries and US National Institutes of Health (U54 MD010724, DP2 MD010478).