Multiple Reaction Monitoring Profiling (MRM-Profiling) of Lipids To Distinguish Strain-Level Differences in Microbial Resistance in Escherichia coli

The worldwide increase in antimicrobial resistance is due to antibiotic overuse in agriculture and overprescription in medicine. For appropriate and timely patient support, faster diagnosis of antimicrobial resistance is required. Current methods for bacterial identification rely on genomics and proteomics and use comparisons with databases of known strains, but the diagnostic value of metabolites and lipids has not been explored significantly. Standard mass spectrometry/chromatography methods involve multiple dilutions during sample preparation and separation. To increase the amount of chemical information acquired and the speed of analysis of lipids, multiple reaction monitoring profiling (MRM-Profiling) has been applied. The MRM-Profiling workflow includes a discovery stage and a screening stage. The discovery stage employs precursor (PREC) ion and neutral loss (NL) scans to screen representative pooled samples for functional groups associated with particular lipid classes. The information from the first stage is organized in precursor/product ion pairs, or MRMs, and the screening stage rapidly interrogates individual samples for these MRMs. In this study, we performed MRM-Profiling of lipid extracts from four different strains of Escherichia coli cultured with amoxicillin or amoxicillin/clavulanate, a β-lactam and β-lactamase inhibitor, respectively. t tests, analysis of variance and receiver operating characteristic (ROC) curves were used to determine the significance of each MRM. Principal component analysis was applied to distinguish different strains cultured under conditions that allowed or disallowed development of bacterial resistance. The results demonstrate that MRM-Profiling distinguishes the lipid profiles of resistant and nonresistant E. coli strains

Utilization of electronic health records for the assessment of adiponectin receptor autoantibodies during the progression of cardio-metabolic comorbidities

Background: Diabetes is a complex, multi-symptomatic disease whose complications drives increases in healthcare costs as the diabetes prevalence grows rapidly world-wide. Real-world electronic health records (EHRs) coupled with patient biospecimens, biological understanding, and technologies can characterize emerging diagnostic autoimmune markers resulting from proteomic discoveries. Methods: Circulating autoantibodies for C‑terminal fragments of adiponectin receptor 1 (IgG-CTF) were measured by immunoassay to establish the reference range using midpoint samples from 1862 participants in a 20-year observational study of type 2 diabetes and cardiovascular arterial disease (CVAD) conducted by the Fairbanks Institute. The White Blood Cell elastase activity in these patients was assessed using immunoassays for Bikunin and Uristatin. Participants were assigned to four cohorts (healthy, T2D, CV, CV+T2D) based on analysis of their EHRs and the diagnostic biomarkers values and patient status were assessed ten-years post-sample. Results: The IgG-CTF reference range was determined to be 75–821 ng/mL and IgG-CTF out-ofrange values did not predict cohort or comorbidity as determined from the EHRs at 10 years after sample collection nor did IgG-CTF demonstrate a significant risk for comorbidity or death. Many patients at sample collection time had other conditions (hypertension, hyperlipidemia, or other risk factors) of which only hypertension, Uristatin and Bikunin values correlated with increased risk of developing additional comorbidities (odds ratio 2.58–13.11, P<0.05). Conclusions: This study confirms that retrospective analysis of biorepositories coupled with EHRs can establish reference ranges for novel autoimmune diagnostic markers and provide insights into prediction of specific health outcomes and correlations to other markers

Enumeration of Rare Cells in Whole Blood by Signal Ion Emission Reactive Release Amplification with Same-Sample RNA Analysis

Herein is presented a platform capable of detecting less than 30 cells from a whole blood sample by size-exclusion filtration, microfluidic sample handling, and mass spectrometric detection through signal ion emission reactive release amplification (SIERRA). This represents an approximate 10-fold improvement in detection limits from previous work. Detection by SIERRA is accomplished through the use of novel nanoparticle reagents coupled with custom fluidic fixtures for precise sample transfer. Sample processing is performed in standardized 96-well microtiter plates with commonly available laboratory instrumentation to facilitate assay automation. The detection system is easily amenable to multiplex detection, and compatibility with PCR-based gene assays is demonstrated