Jasbi, Paniz

Abstract Background While evaluating COVID-19 vaccine responses using a rapid neutralizing antibody (NAb) test, we observed that 25% of RNA vaccine recipients did not neutralize >50%. We termed this group “vaccine poor responders” (VPRs). The objective of this study was to determine if individuals who neutralized <50% would remain VPRs, or if a third dose would elicit high levels of NAbs. Methods 269 healthy individuals ranging in age from 19 to 80 (Average age = 51; 165 females and 104 males) who received either BNT162b2 (Pfizer) or mRNA1273 (Moderna) vaccines were evaluated. NAb levels were measured: i) 2-4 weeks after a second vaccine dose, ii) 2-4 months after the second dose, iii) within 1-2 weeks prior to a third dose and iv) 2-4 weeks after a third RNA vaccine dose. Results Analysis of vaccine recipients revealed that 25% did not neutralize above 50% (Median neutralization = 21%, titers <1:80) within a month after their second dose. Twenty-three of these VPRs obtained a third dose of either BNT162b2 or mRNA-1273 vaccine 1-8 months (average = 5 months) after their second dose. Within a month after their third dose, VPRs showed an average 20-fold increase in NAb levels (range: 46%-99%). Conclusions The results suggest that VPRs are not permanently poor responders; they can generate high NAb levels with an additional vaccine dose. Although it is not known what levels of NAbs protect from infection or disease, those in high-risk professions may wish to keep peripheral NAb levels high, limiting infection, and potential transmission.

Coccidioidomycosis, or Valley Fever (VF), is a potentially lethal fungal infection that results in more than 200 deaths/year in the United States. Differential diagnosis and timely treatment of VF is made difficult due to presentation of vague symptoms that often mimic other pulmonary conditions. Accurate diagnosis therefore relies on a combination of clinical presentation, serology, radiography, histology and culture. These diagnostic methods are either costly, time‐consuming, invasive, or indeterminate. Previous studies have used MS‐based methods for the accurate diagnosis of similar fungal diseases as well as characterization of host‐fungal interactions. Herein, we present the first targeted plasma and urine liquid chromatography tandem mass spectrometry (LC‐MS/MS) profiling approach for the relatively non‐invasive, rapid and accurate detection of VF. In this targeted approach, 207 plasma metabolites and 231 urine metabolites were reliably detected using LC‐MS/MS. These metabolites, which are representative of more than 35 metabolic pathways of potential biological relevance, were monitored in 59 plasma samples and 88 urine samples acquired from excess clinical specimens taken from two groups of patients (48 VF patients and 99 non‐VF controls) at Mayo Clinic Arizona. Univariate and multivariate statistical analyses were performed to develop predictive plasma and urine biomarker panels. Pathway and enrichment analyses were performed to elucidate potential pathogenic mechanisms and therapeutic targets. Results of our univariate significance testing and multivariate model estimation informed the construction of a 3‐metabolite panel of plasma biomarkers ( q < 0.05 and VIP > 2; Fig 1) and a 9‐metabolite panel of urinary biomarkers ( q < 0.05 and VIP > 1; Fig 2). Receiver operating characteristic (ROC) curves (Fig 3) generated based on enhanced orthogonal partial least squares‐discriminant analysis (OPLS‐DA) models (Fig 4) showed excellent overall classification performance (99.5%), with 94.4% sensitivity and 97.6% specificity for plasma metabolites. Although urine metabolites were less accurate (92.5%), demonstrating 89.7% sensitivity and 88.1% specificity, they represent a substantial increase in diagnostic accuracy over currently available urinary antigen tests, which only report sensitivity of around 70%. Pathway analysis (Fig 5) and enrichment analysis (Fig 6) ubiquitously showed alterations in metabolic pathways related to fungal virulence, DNA/RNA synthesis and energy utilization, and host immune escape mechanisms. These results potentially reveal pathways or markers that could be targeted therapeutically. This study is the first‐ever MS‐based metabolomics approach for rapid and highly accurate diagnosis of VF. Support or Funding Information Support from the College of Health Solutions at Arizona State University, Arizona Biomedical Research Council (ABRC) grant# 16–162513 and American Achievement Rewards for College Scientists (ARCS) Foundation Award is gratefully acknowledged. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Abstract Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbiet al.J. Chromatogr. B.2019, 1105, 26−37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p &lt; 0.05 and AUROC &gt; 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895–0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).