The bacterial antiporter GadC plays a central role in the glutamate (Glu)-dependent acid resistance system, which protects enteric bacteria against the extreme acidity of the human stomach. Upon acid shock, GadC imports Glu into the cytoplasm, where Glu decarboxylases consume a cytoplasmic proton, which ends up as a “virtual” proton in the decarboxylated product γ-aminobutyric acid (GABA) and is then exported via GadC. It was therefore proposed that GadC counters intracellular acidification by continually pumping out virtual protons. This scenario, however, is oversimplified. In gastric environments, GadC encounters substrates in multiple carboxyl protonation forms (outside: Glu − , Glu 0 , Glu + ; inside: GABA 0 , GABA + ). Of the six possible combinations of antiport partners, Glu + /GABA 0 results in proton influx, Glu 0 /GABA 0 and Glu + /GABA + are proton neutral, and Glu − /GABA 0 , Glu − /GABA + , or Glu 0 /GABA + lead to proton extrusion. Which of these exchanges does GadC catalyze? To attack this problem, we developed an oriented GadC liposome system holding a three-unit inward pH gradient to mimic the conditions facing bacteria in the stomach. By assessing the electrogenicity of substrate transport, we demonstrate that GadC selectively exchanges Glu − or Glu 0 with GABA + , resulting in effective proton extrusion of >0.9 H + per turnover to counter proton invasion into acid-challenged bacteria. We further show that GadC selects among protonated substrates using a charge-based mechanism, rather than directly recognizing the protonation status of the carboxyl groups. This result paves the way for future work to identify the molecular basis of GadC’s substrate selectivity.

The COVID-19 pandemic has revealed a crucial need for rapid, straightforward collection and testing of biological samples. Serological antibody assays can analyze patient blood samples to confirm immune response following mRNA vaccine administration or to verify past exposure to the SARS-CoV-2 virus. While blood tests provide vital information for clinical analysis and epidemiology, sample collection is not trivial; this process requires a visit to the doctor’s office, a professionally trained phlebotomist to draw several milliliters of blood, processing to yield plasma or serum, and necessitates appropriate cold chain storage to preserve the specimen. A novel whole blood collection kit (truCOLLECT) allows for a lancet-based, decentralized capillary blood collection of metered low volumes and eliminates the need for refrigerated transport and storage through the process of active desiccation. Anti-SARS-CoV-2 spike (total and neutralizing) and nucleocapsid protein antibody titers in plasma samples obtained via venipuncture were compared to antibodies extracted from desiccated whole blood using Adaptive Focused Acoustics (AFA). Paired plasma versus desiccated blood extracts yields Pearson correlation coefficients of 0.98; 95% CI [0.96, 0.99] for anti-SARS-CoV-2 spike protein antibodies, 0.97; 95% CI [0.95, 0.99] for neutralizing antibodies, and 0.97; 95% CI [0.94, 0.99] for anti-SARS-CoV-2 nucleocapsid protein antibodies. These data suggest that serology testing using desiccated and stabilized whole blood samples can be a convenient and cost-effective alternative to phlebotomy.

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Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffinembedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows: standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC− MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.