BackgroundSmall non-coding RNAs (sRNAs) facilitate host-microbe interactions. They have a central function in the post-transcriptional regulation during pathogenic lifestyles. Hfq, an RNA-binding protein that many sRNAs act in conjunction with, is required for Y. pestis pathogenesis. However, information on how Yersinia pestis modulates the expression of sRNAs during infection is largely unknown.Methodology and Principal FindingsWe used RNA-seq technology to identify the sRNA candidates expressed from Y. pestis grown in vitro and in the infected lungs of mice. A total of 104 sRNAs were found, including 26 previously annotated sRNAs, by searching against the Rfam database with 78 novel sRNA candidates. Approximately 89% (93/104) of these sRNAs from Y. pestis are shared with its ancestor Y. pseudotuberculosis. Ninety-seven percent of these sRNAs (101/104) are shared among more than 80 sequenced genomes of 135 Y. pestis strains. These 78 novel sRNAs include 62 intergenic and 16 antisense sRNAs. Fourteen sRNAs were selected for verification by independent Northern blot analysis. Results showed that nine selected sRNA transcripts were Hfq-dependent. Interestingly, three novel sRNAs were identified as new members of the transcription factor CRP regulon. Semi-quantitative analysis revealed that Y. pestis from the infected lungs induced the expressions of six sRNAs including RyhB1, RyhB2, CyaR/RyeE, 6S RNA, RybB and sR039 and repressed the expressions of four sRNAs, including CsrB, CsrC, 4.5S RNA and sR027.Conclusions and SignificanceThis study is the first attempt to subject RNA from Y. pestis-infected samples to direct high-throughput sequencing. Many novel sRNAs were identified and the expression patterns of relevant sRNAs in Y. pestis during in vitro growth and in vivo infection were revealed. The annotated sRNAs accounted for the most abundant sRNAs either expressed in bacteria grown in vitro or differentially expressed in the infected lungs. These findings suggested these sRNAs may have important functions in Y. pestis physiology or pathogenesis.

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Multiplex biomolecular analysis with inductively coupled plasma mass spectrometry (ICP-MS) becomes increasingly important in clinical diagnosis and single cell analysis. However, the sensitivity of ICP-MS-based immunoassay is only comparable or lower than those of fluorescence methods at the present stage. Therefore, designing elemental tags with a large number of metal atoms is necessary to achieve high-sensitive detection. In this work, we proposed a new strategy to build up elemental tag loading with hundreds of rare earth ions by coupling alkyne-DNA chains with rare earth element (REE)-DOTA complexes a click chemistry reaction. There are about 2 orders of magnitude improvement in sensitivity compared with single metal-ion tags. DNA chains with multialkynyl groups were facilely prepared by PCR synthesis. Moreover, the DNA-based elemental tags own excellent water-solubility and biocompatibility. The tags would be potentially applied to mass cytometry and clinical diagnosis.

The global effort against the COVID-19 pandemic dictates that routine quantitative detection of SARS-CoV-2 neutralizing antibodies is vital for assessing immunity following periodic revaccination against new viral variants. Here, we report a dual-detection fluorescent immunochromatographic assay (DFIA), with a built-in self-calibration process, that enables rapid quantitative detection of neutralizing antibodies that block binding between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and the angiotensin-converting enzyme 2 (ACE2). Thus, this assay is based on the inhibition of binding between ACE2 and the RBD of the SARS-CoV-2 spike protein by neutralizing antibodies, and the affinity of anti-human immunoglobulins for these neutralizing antibodies. Our self-calibrating DFIA shows improved precision and sensitivity with a wider dynamic linear range, due to the incorporation of a ratiometric algorithm of two-reverse linkage signals responding to an analyte. This was evident by the fact that no positive results (0/14) were observed in verified negative samples, while 22 positives were detected in 23 samples from verified convalescent plasma. A comparative analysis of the ability to detect neutralizing antibodies in 266 clinical serum samples including those from vaccine recipients, indicated that the overall percent agreement between DFIA and the commercial ELISA kit was 90.98%. Thus, the proposed DFIA provides a more reliable and accurate rapid test for detecting SARS-CoV-2 infections and vaccinations in the community. Therefore, the DFIA based strategy for detecting biomarkers, which uses a ratiometric algorithm based on affinity and inhibition reactions, may be applied to improve the performance of immunochromatographic assays.

Objectives In this study, a new immunoassay for the simultaneous determination of pepsinogen I (PGI) and pepsinogen II (PGII) in serum based on element labeling strategy coupled with inductively coupled plasma mass spectrometry (ICP‐MS) detection was proposed. Methods The sandwich‐type immunoassay was used to simultaneously detect PGI and PGII in serum. PGI and PGII were captured by anti‐PGI and anti‐PGII antibody immobilized on the magnetic beads and then banded with Eu 3+ labeled anti‐PGI detection antibody and Sm 3+ labeled anti‐PGII detection antibody, followed by ICP‐MS detection. Results The linear correlation coefficient ( R 2 ) of PGI and PGII standard curves was .9938 and .9911, with the dynamic range of 0‐200 ng/mL and 0‐60 ng/mL, respectively. The limit of detection for PGI and PGII was 1.8 ng/mL and 0.3 ng/mL, respectively. The average recovery was 101.41% ± 6.74% for PGI and 101.47% ± 4.20% for PGII. Good correlations were obtained between the proposed method and CLIA ( r = .9588 for PGI, r = .9853 for PGII). Conclusions We established a mass spectrometry‐based immunoassay for the simultaneous detection of PGI and PGII in a single analysis. The element tagged immunoassay coupled with ICP‐MS detection has high sensitivity, accuracy, and specificity in clinical serum sample analysis.