Evasion of NKG2D-mediated cytotoxic immunity by sarbecoviruses.

SARS-CoV-2 and other sarbecoviruses continue to threaten humanity, highlighting the need to characterize common mechanisms of viral immune evasion for pandemic preparedness. Cytotoxic lymphocytes are vital for antiviral immunity and express NKG2D, an activating receptor conserved among mammals that recognizes infection-induced stress ligands (e.g., MIC-A/B). We found that SARS-CoV-2 evades NKG2D recognition by surface downregulation of MIC-A/B via shedding, observed in human lung tissue and COVID-19 patient serum. Systematic testing of SARS-CoV-2 proteins revealed that ORF6, an accessory protein uniquely conserved among sarbecoviruses, was responsible for MIC-A/B downregulation via shedding. Further investigation demonstrated that natural killer (NK) cells efficiently killed SARS-CoV-2-infected cells and limited viral spread. However, inhibition of MIC-A/B shedding with a monoclonal antibody, 7C6, further enhanced NK-cell activity toward SARS-CoV-2-infected cells. Our findings unveil a strategy employed by SARS-CoV-2 to evade cytotoxic immunity, identify the culprit immunevasin shared among sarbecoviruses, and suggest a potential novel antiviral immunotherapy.

Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.

Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools.

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer.

BACKGROUND: Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data. METHODS: Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively. RESULTS: The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance. CONCLUSION: We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Cross-reactive SARS-CoV-2 epitope targeted across donors informs immunogen design.

The emergence of the antigenically distinct and highly transmissible Omicron variant highlights the possibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immune escape due to viral evolution. This continued evolution, along with the possible introduction of new sarbecoviruses from zoonotic reservoirs, may evade host immunity elicited by current SARS-CoV-2 vaccines. Identifying cross-reactive antibodies and defining their epitope(s) can provide templates for rational immunogen design strategies for next-generation vaccines. Here, we characterize the receptor-binding-domain-directed, cross-reactive humoral repertoire across 10 human vaccinated donors. We identify cross-reactive antibodies from diverse gene rearrangements targeting two conserved receptor-binding domain epitopes. An engineered immunogen enriches antibody responses to one of these conserved epitopes in mice with pre-existing SARS-CoV-2 immunity; elicited responses neutralize SARS-CoV-2, variants, and related sarbecoviruses. These data show how immune focusing to a conserved epitope targeted by human cross-reactive antibodies may guide pan-sarbecovirus vaccine development, providing a template for identifying such epitopes and translating to immunogen design.

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer.

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

Evaluation of Automatic Blood Analyzer as Screening Method in Fetomaternal Hemorrhage.

Screening of fetomaternal hemorrhage (FMH) is essential in management of fetomaternal antigen incompatibilities of blood. The objective in this study was to evaluate the ability of automatic blood analyzer (ABA) to screen FMH, also comparing this method with flow cytometry (FCM). The contents of fetal red blood cells and fetal hemoglobin were evaluated by FCM and ABA, respectively, using both blood samples of male adults laced with umbilical cord blood diluted at 1/10, 1/100, 1/1,000, and 1/10,000, or blood from puerperal women collected within 48 hours following delivery. FCM had better performance (area under curve, AUC = 0.8723) than ABA (AUC = 0.6569) in detecting fetal blood laced with blood from male adults. At a critical level of 0.5%, ABA indicated that 27.5% of puerperal women would have FMH while FCM did not detect FMH. Our results showed that ABA overestimates FMH and disagrees with FCM on indicating puerperal women with FMH. ABA is inadequate for being used to screen for or to measure FMH.