An Algorithm for the Selection of Probes for Specific Detection of Human Disease Pathogens Using the DNA Microarray Technology

Filatova E.N., Chaikina A.S., Brusnigina N.F., Makhova M.A., Utkin O.V.

Key words: probe selection algorithm; DNA microarray; DNA microarray design; community-acquired pneumonia; Chlamydophila pneumoniae.

2022, volume 14, issue 1, page 6.

DOI: https://doi.org/10.17691/stm2022.14.1.01

Full text

html pdf

883

1116

Abstract
References
How to cite

The aim of the study was to develop an algorithm for the selection of discriminating probes to identify a wide range of causative agents of human infectious diseases.

Materials and Methods. The algorithm for selecting the probes was implemented in the form of the disprose (DIScrimination PRObe SElection) computer program written in the R language. Additionally, third-party software was used: the BLAST+ and ViennaRNA Package programs. The developed algorithm was tested by selecting specific probes for detecting Chlamydophila (Chlamydia) pneumoniae — an atypical bacterial pathogen causing community-acquired pneumonia (CAP). Nucleotide sequences for analysis were downloaded from the NCBI databank.

Results. An algorithm for the selection of specific probes capable of detecting human infectious pathogens has been developed. The algorithm is implemented in the form of the disprose modular program, which allows for performing all stages of the probe selection process: loading the nucleotide sequences and their metadata from available databanks, creating local databases, forming a pool of probes, calculating their physicochemical parameters, aligning the probes and sequences contained in local databases, processing and evaluating the alignment results. The algorithm was successfully tested and its performance was confirmed by selecting a set of probes for the specific detection of Chlamydophila pneumoniae. The specificity of the selected probes calculated in silico indicated a low risk of their nonspecific binding and a high potential of using them as molecular genetic diagnostic tools (DNA microarrays, PCR).

Conclusion. An algorithm for the selection of specific probes detecting a wide range of human pathogens in clinical biomaterial has been developed and implemented in the form of the disprose modular program. The probes selected using this program can serve as the functional basis of DNA-oriented microarrays able to identify causative agents of polyetiological diseases, such as CAP. Due to the flexibility and openness of the program, the scope of its application can be expanded.

Kostić T., Sessitsch A. Microbial diagnostic microarrays for the detection and typing of food- and water-borne (bacterial) pathogens. Microarrays (Basel) 2011; 1(1): 3–24, https://doi.org/10.3390/microarrays1010003.
Rouillard J.M., Zuker M., Gulari E. OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res 2003; 31(12): 3057–3062, https://doi.org/10.1093/nar/gkg426.
Sung W.K., Lee W.H. Fast and accurate probe selection algorithm for large genomes. Proc IEEE Comput Soc Bioinform Conf 2003; 2: 65–74, https://doi.org/10.1109/csb.2003.1227305.
Urisman A., Fischer K.F., Chiu C.Y., Kistler A.L., Beck S., Wang D., DeRisi J.L. E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns. Genome Biol 2005; 6(9): R78, https://doi.org/10.1186/gb-2005-6-9-r78.
Watson M., Dukes J., Abu-Median A.B., King D.P., Britton P. DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data. Genome Biol 2007; 8(9): R190, https://doi.org/10.1186/gb-2007-8-9-r190.
National Center for Biotechnology Information. Nucleotide. Bethesda (MD): National Library of Medicine (US); 2021. URL: https://www.ncbi.nlm.nih.gov/nucleotide/.
Lorenz R., Bernhart S.H., Höner Zu Siederdissen C., Tafer H., Flamm C., Stadler P.F., Hofacker I.L. ViennaRNA Package 2.0. Algorithms Mol Biol 2011; 6(1): 26, https://doi.org/10.1186/1748-7188-6-26.
McCaskill J.S. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 1990; 29(6–7): 1105–1119, https://doi.org/10.1002/bip.360290621.
Junhui L. TmCalculator: melting temperature of nucleic acid sequences. R package version 1.0.1. 2020. URL: https://CRAN.R-project.org/package=TmCalculator.
SantaLucia J. Jr. A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proc Natl Acad Sci U S A 1998; 95(4): 1460–1465, https://doi.org/10.1073/pnas.95.4.1460.
Camacho C., Coulouris G., Avagyan V., Ma N., Papadopoulos J., Bealer K., Madden T.L. BLAST+: architecture and applications. BMC Bioinformatics 2009; 10: 421, https://doi.org/10.1186/1471-2105-10-421.
Bodrossy L., Sessitsch A. Oligonucleotide microarrays in microbial diagnostics. Curr Opin Microbiol 2004; 7(3): 245–254, https://doi.org/10.1016/j.mib.2004.04.005.
Sanguin H., Herrera A., Oger-Desfeux C., Dechesne A., Simonet P., Navarro E., Vogel T.M., Moënne-Loccoz Y., Nesme X., Grundmann G.L. Development and validation of a prototype 16S rRNA-based taxonomic microarray for Alphaproteobacteria. Environ Microbiol 2006; 8(2): 289–307, https://doi.org/10.1111/j.1462-2920.2005.00895.x.
Maskos U., Southern E.M. A study of oligonucleotide reassociation using large arrays of oligonucleotides synthesised on a glass support. Nucleic Acids Res 1993; 21(20): 4663–4669, https://doi.org/10.1093/nar/21.20.4663.
Raddatz G., Dehio M., Meyer T.F., Dehio C. PrimeArray: genome-scale primer design for DNA-microarray construction. Bioinformatics 2001; 17(1): 98–99, https://doi.org/10.1093/bioinformatics/17.1.98.
Wong C.W., Albert T.J., Vega V.B., Norton J.E., Cutler D.J., Richmond T.A., Stanton L.W., Liu E.T., Miller L.D. Tracking the evolution of the SARS coronavirus using high-throughput, high-density resequencing arrays. Genome Res 2004; 14(3): 398–405, https://doi.org/10.1101/gr.2141004.
Wong C.W., Heng C.L.W., Wan Yee L., Soh S.W.L., Kartasasmita C.B., Simoes E.A.F., Hibberd M.L., Sung W.K., Miller L.D. Optimization and clinical validation of a pathogen detection microarray. Genome Bio 2007; 8(5): R93, https://doi.org/10.1186/gb-2007-8-5-r93.
Yoo S.M., Keum K.C., Yoo S.Y., Choi J.Y., Chang K.H., Yoo N.C., Yoo W.M., Kim J.M., Lee D., Lee S.Y. Development of DNA microarray for pathogen detection. Biotechnol Bioprocess Engin 2004; 9(2): 93–99, https://doi.org/10.1007/bf02932990.
Zuker M., Mathews D.H., Turner D.H. Algorithms and thermodynamics for rna secondary structure prediction: a practical guide. In: Barciszewski J., Clark B.F.C. (editors). RNA biochemistry and biotechnology. Springer; 1999; p. 11–43, https://doi.org/10.1007/978-94-011-4485-8_2.
Pais F.S.M., Ruy P.C., Oliveria G., Coimbra R.S. Assessing the efficiency of multiple sequence alignment programs. Algorithms Mol Biol 2014; 9(1): 4, https://doi.org/10.1186/1748-7188-9-4.
Rachina S.A., Bobylev A.A. Atypical pathogens of community-acquired pneumonia: epidemiology, diagnosis, and treatment. Prakticeskaa pul’monologia 2016; 2: 20–27.
Nair G.B., Niederman M.S. Updates on community acquired pneumonia management in the ICU. Pharmacol Ther 2021; 217: 107663, https://doi.org/10.1016/j.pharmthera.2020.107663.
Zaitsev A.A. Community-acquired pneumonia: diagnostic, treatment and vaccine prevention opportunities in the context of the COVID-19 pandemic. Prakticeskaa pul’monologia 2020; 1: 14–20.

Filatova E.N., Chaikina A.S., Brusnigina N.F., Makhova M.A., Utkin O.V. An Algorithm for the Selection of Probes for Specific Detection of Human Disease Pathogens Using the DNA Microarray Technology. Sovremennye tehnologii v medicine 2022; 14(1): 6, https://doi.org/10.17691/stm2022.14.1.01