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Ribonuclease A (RNase A) plays significant roles in several physiological and pathological conditions and can be used as a valuable diagnostic biomarker for human diseases such as myocardial infarction and cancer. Hence, it is of great importance to develop a rapid and cost-effective method for the highly sensitive detection of RNase A. The significance of RNase A assay is further enhanced by the growing attention from the biotechnology and pharmaceutical industries to develop RNA-based vaccines and drugs in large part as a result of the successful development of mRNA vaccines in the COVID-19 pandemic. Herein, we report a label-free method for the detection of RNase A by monitoring its proteolytic cleavage of an RNA substrate in a nanopore. The method is ultra-sensitive with the limit of detection reaching as low as 30 femtogram per milliliter. Furthermore, sensor selectivity and the effects of temperature, incubation time, metal ion, salt concentration on sensor sensitivity were also investigated. 

Iron is an essential element that plays critical roles in many biological/metabolic processes, ranging from oxygen transport, mitochondrial respiration, to host defense and cell signaling. Maintaining an appropriate iron level in the body is vital to the human health. Iron deficiency or overload can cause life-threatening conditions. Thus, developing a new, rapid, cost-effective, and easy to use method for iron detection is significant not only for environmental monitoring but also for disease prevention. In this study, we report an innovative Fe3+ detection strategy by using both a ligand probe and an engineered nanopore with two binding sites. In our design, one binding site of the nanopore has a strong interaction with the ligand probe, while the other is more selective toward interfering species. Based on the difference in the number of ligand DTPMPA events in the absence and presence of ferric ions, micromolar concentrations of Fe3+ could be detected within minutes. Our method is selective: micromolar concentrations of Mg2+, Ca2+, Cd2+, Zn2+, Ni2+, Co2+, Mn2+, and Cu2+ would not interfere with the detection of ferric ions. Furthermore, Cu2+, Ni2+, Co2+, Zn2+, and Mn2+ produced current blockage events with quite different signatures from each other, enabling their simultaneous detection. In addition, simulated water and serum samples were successfully analyzed. The nanopore sensing strategy developed in this work should find useful application in the development of stochastic sensors for other substances, especially in situations where multi-analyte concurrent detection is desired. 

MicroRNAs (miRNAs) play important roles in posttranscriptional gene regulation. Aberrations in the miRNA levels have been the cause behind various diseases, including periodontitis. Therefore, sensitive, specific, and accurate detection of disease‐associated miRNAs is vital to early diagnosis and can facilitate inhibitor screening and drug design. In this study, we developed a label‐free, real‐time sensing method for the detection of miR31, which has been frequently linked to periodontitis, using an engineered protein nanopore and in the presence of a complementary ssDNA as a molecular probe. Our method is rapid and highly sensitive with nanomolar concentration of miR31 that could be determined in minutes. Furthermore, our sensor showed high selectivity toward the target miR31 sequence even in the presence of interfering nucleic acids. In addition, artificial saliva and human saliva samples were successfully analyzed. Our developed nanopore sensing platform could be used to detect other miRNAs and offers a potential application for the clinical diagnosis of disease biomarkers.

 

Abstract. Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-Series Study in the North Atlantic Ocean collected by in situ pumps and the AUV Clio were distributed with a paired metagenome, and one-dimensional liquid chromatographic data dependent acquisition mass spectrometry analyses was stipulated. Analysis of mass spectra from seven laboratories through a common informatic pipeline identified a shared set of 1056 proteins from 1395 shared peptides constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R2 values ranging from 0.43 to 0.83, and a Sørensen similarity analysis of the top 1,000 proteins revealed 70–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. An informatic intercomparison study, involving 10 laboratories using 8 software packages successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research including integration into global scale ocean surveys and ocean biogeochemical models.

 

Cellulose is an essential component of plant cell walls and an economically important source of food, paper, textiles, and biofuel. Despite its economic and biological significance, the regulation of cellulose biosynthesis is poorly understood. Phosphorylation and dephosphorylation of cellulose synthases (CESAs) were shown to impact the direction and velocity of cellulose synthase complexes (CSCs). However, the protein kinases that phosphorylate CESAs are largely unknown. We conducted research inArabidopsis thalianato reveal protein kinases that phosphorylate CESAs.

In this study, we used yeast two‐hybrid, protein biochemistry, genetics, and live‐cell imaging to reveal the role of calcium‐dependent protein kinase32 (CPK32) in the regulation of cellulose biosynthesis inA. thaliana.

We identified CPK32 using CESA3 as a bait in a yeast two‐hybrid assay. We showed that CPK32 phosphorylates CESA3 while it interacts with both CESA1 and CESA3. Overexpressing functionally defective CPK32 variant and phospho‐dead mutation of CESA3 led to decreased motility of CSCs and reduced crystalline cellulose content in etiolated seedlings. Deregulation of CPKs impacted the stability of CSCs.

We uncovered a new function of CPKs that regulates cellulose biosynthesis and a novel mechanism by which phosphorylation regulates the stability of CSCs.