Infrared spectroscopy is a prominent molecular technique for bacterial analysis. Within its context, near infrared spectroscopy in particular brings benefits over other vibrational approaches; these advantages include, for example, lower sensitivity to water, high penetration depth and low cost. However, near infrared spectroscopy is not popular within microbiology, because the spectra of organic samples are difficult to interpret. We propose a comparison of spectral curve-fitting methods, namely, techniques that facilitate the interpretation of most peaks, simplify the spectra and improve the prediction of bacterial species from the relevant near infrared spectra. The performances of three common curve-fitting algorithms and the technique based on the differential evolution were compared via a synthesized experimental spectrum. Utilizing the obtained results, the spectra of three different bacterial species were curve fit by optimized algorithm. The proposed algorithm decomposed the spectra to specific absorption peaks, whose parameters were estimated via the differential evolution approach initialized through Levenberg-Marquardt optimization; subsequently, the spectra were classified with conventional procedures and using the parameters of the revealed peaks. On a limited data set, the correct classification rate computed by partial least squares discriminant analysis was 95%. When we employed the peak parameters for the classification, the rate corresponded to 91.7%. According to the Gaussian formula, the parameters comprise the spectral peak position, amplitude and width. The most important peaks for bacterial discrimination were identified by analysis of variance and interpreted as N-H stretching bonds in proteins, cis bonds and CH 2 absorption in fatty acids. We examined some aspects of the behaviour of standard curve-fitting algorithms and proposed differential evolution to optimize the fitting process. Based on the correct use of these algorithms, the near infrared spectra of bacteria can be interpreted and the full potential of near infrared spectroscopy in microbiology exploited.
miRNAs have an immense potential to serve as diagnostic, prognostic and prediction biomarkers in the whole field of oncology. Their utilization in liquid biopsy as a non-invasive diagnostic tool is very promising as well. However, to our knowledge, no miRNA-based diagnostics is currently used in clinical laboratories. We believe the reason is simple - the lack of easy, fast, sensitive and reproducible measurement technology, which would have passed the strict clinical validation criteria. On this count we have optimized a very promising miRNA detection technique, utilizing the enzyme Chlorella virus DNA ligase (SplintR® ligase, New England Biolabs), for clinical use. This two-step method involves ligation of two adjacent DNA oligonucleotides hybridized to a miRNA target, followed by TaqMan probe real-time quantitative PCR (qPCR). We managed to reduce the total procedure time from previous 5 hours to 2 hours and 15 minutes maintaining the high sensitivity (1 amol/µl in original clinical sample) and large dynamic range (7 logs). Moreover, we enabled the use of two PCR detection chemistries - more cost-effective SYBR green or more specific TaqMan probe. Based on this optimized method we developed an assay for detection of miR-142-5p, potential marker of colorectal cancer. We measured miR-142-5p profiles in the real samples of whole blood, plasma and serum of healthy donors. The results showed an excellent correlation with the TaqMan qPCR assay and miREIA® immuno-based technology for absolute quantification of miRNA. We conclude, that using SplintR® ligase for miRNA detection is potentially useful for clinical diagnostics. This work was funded by the Ministry of Industry and Trade of Czech Republic, project No. CZ.01.1.02/0.0/0.0/16_084/0008832. Citation Format: Iveta Hynstova, Tereza Mrackova, Michaela Adamcova, Barbora Dvorakova, Zlata Stastna, Ondrej Slaby, Milan Bartos, Viktor Ruzicka. New clinical diagnostic approach for miRNA quantification using the Chlorella virus DNA ligase [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-388.
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