Simple and rapid detection of common fetal aneuploidies using peptide nucleic acid probe-based real-time polymerase chain reaction

To examine the detection performance of a peptide nucleic acid (PNA) probe-based real-time time polymerase chain reaction (PCR) assay to detect common aneuploidies. Using amniotic fluid samples, PNA probe based real-time PCR (Patio DEP Detection Kit; SeaSun Biomaterials, Korea) assay was performed. PNA probe was designed to hybridize to similar sequences located on different segments of target chromosomes (21, 18, and 13) and a reference chromosome. Amplification of target sequences and melting curve analysis was performed. When analyzing the melting curve, the ratio of the peak height of the target and reference chromosome was calculated and determined as aneuploidy if the ratio of peak height was abnormal. All the results from the PNA probe-based real-time PCR and melting curve analyses were compared to those from conventional karyotyping. Forty-two cases with common aneuploidies (24 of trisomy 21, 12 of trisomy 18, and 6 of trisomy 13) and 131 cases with normal karyotype were analyzed. When comparing the karyotyping results, the sensitivity and specificity of the PNA probe-based real-time PCR assay were both 100%. The level of agreement was almost perfect (k = 1.00). PNA real-time PCR assay is a rapid and easy method for detecting common aneuploidies.

A New PNA-FISH Probe Targeting Fannyhessea vaginae

Bacterial vaginosis (BV) is the most common vaginal infection in women of reproductive age and has been associated with serious health complications, mainly in pregnant women. It is characterized by a decrease in the number of Lactobacillus species in the healthy vaginal microbiota and an overgrowth of strict and facultative anaerobic bacteria that develop a polymicrobial biofilm. Despite over 60 years of research investigating BV, its etiology is not fully understood. Gardnerella spp. is a crucial microorganism that contributes to the formation of the biofilm and the development of BV, but the role of other BV-associated bacteria is not clear. Nevertheless, Fannyhessea vaginae (previously known as Atopobium vaginae) is a highly specific species for BV, and co-colonization with Gardnerella is thought to be a very specific diagnostic marker. The diagnosis of BV still presents some limitations, since currently used methods often fail to accurately detect BV. This work aims to develop a novel peptide nucleic acid (PNA) probe targeting F. vaginae. This probe was further validated in a multiplex assay, which included a Gardnerella-specific PNA probe, as a possible method for diagnosis of BV, and was compared with quantification by qPCR. The new PNA probe showed excellent sensitivity and specificity and could discriminate F. vaginae-Gardnerella biofilms, confirming the potential to be used for the detection of BV-associated pathogens.

OaAEP1-mediated PNA-protein conjugation enables erasable imaging of membrane protein

Methods to efficiently and site-specifically conjugate proteins to nucleic acids could enable exciting application in bioanalytics and biotechnology. Here, we report the use of the strict protein ligase to covalently ligate a protein to a peptide nucleic acid (PNA). The rapid ligation requires only a short N-terminal GL dipeptide in target protein and a C-terminal NGL tripeptide in PNA. We demonstrate the versatility of this approach by conjugating three different types of proteins with a PNA strand. The biostable PNA strand then serves as a generic landing platform for nucleic acid hybridization. Lastly, we show the erasable imaging of EGFR on HEK293 cell membrane through toehold-mediated strand displacement. This work provides a controlled tool for precise conjugation of proteins with nucleic acids through an extremely small peptide linker and facilitates further study of membrane proteins.

Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design

Peptide nucleic acids (PNAs) are potential antisense therapies for genetic, acquired, and viral diseases. Efficiently selecting candidate PNA sequences for synthesis and evaluation from a genome containing hundreds to thousands of options can be challenging. To facilitate this process, we leverage here machine learning (ML) algorithms and automated synthesis technology to predict PNA synthesis efficiency and guide rational PNA sequence design. The training data was collected from individual fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions performed on a fully automated PNA synthesizer. Our optimized ML model allows for 93% prediction accuracy and 0.97 Pearson’s r. The predicted synthesis scores were validated to be correlated with the experimental HPLC crude purities (correlation coefficient R2 = 0.95). Furthermore, we demonstrated a general applicability of ML through designing synthetically accessible antisense PNA sequences from 102,315 predicted candidates targeting exon 44 of the human dystrophin gene, SARS-CoV-2, HIV, as well as selected genes associated with cardiovascular diseases, type II diabetes, and various cancers. Collectively, ML provides an accurate prediction of PNA synthesis quality and serves as a useful computational tool for rational PNA sequence design.

Pre-concentration of microRNAs by LNA-modified magnetic beads for enhancement of electrochemical detection

MicroRNAs are extremely promising candidates for early cancer diagnosis and prognosis. The levels of circulating microRNAs provide valuable information about cancer disease at its early stages. However, the levels of microRNAs that need to be detected are extremely low and difficult to discriminate from a large pool of oligonucleotides. There is the need for accurate, rapid and sensitive detection methodologies for detection of microRNAs. We developed electrochemical impedance spectroscopy peptide nucleic acid (PNA)-based sensors that can detect miRNAs in diluted serum with a limit of detection of 0.38 fM. In order to further improve the accuracy and reliability of the sensors, we developed an assay using magnetic beads for simple and rapid fishing of target microRNAs from solution and its pre-concentration prior to electrochemical detection. Our methodology utilizes magnetic beads for the capture of the target microRNA from solution and brings the concentrated sample to the sensor surface. We modify the magnetic beads with locked nucleic acids (LNA), which have high affinity and specificity to their complementary microRNA sequence. The separated and concentrated microRNA is then detected using the PNA-based sensors. By exposing the sensing electrodes only to the captured microRNAs, interferences from other nucleotides or biomolecules from the sample are eliminated.