Izvestiya of Saratov University.


ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)

For citation:

Ulyanov S. S., Ulianova O. V., Zaitsev S. S., Saltykov I. V., Ulyanov A. S., Feodorova V. A. The interference of GB-speckles in molecular discrimination of bacterial pathogens: The use of the s-LASCA method on the Chlamydia psittaci model. Izvestiya of Saratov University. Physics , 2021, vol. 21, iss. 4, pp. 315-328. DOI: 10.18500/1817-3020-2021-21-4-315-328, EDN: QTABPN

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
Full text:
(downloads: 187)
Article type: 

The interference of GB-speckles in molecular discrimination of bacterial pathogens: The use of the s-LASCA method on the Chlamydia psittaci model

Ulyanov Sergei Sergeevich, Saratov State University
Ulianova Onega Vladimirovna, Saratov Branch of Federal Research Center for Virology and Microbiology
Zaitsev Sergei Sergeevich, Saratov Branch of Federal Research Center for Virology and Microbiology
Saltykov Iurii Vladimirovich, Saratov State Agrarian University named after N.I. Vavilov
Ulyanov Aleksandr Sergeevich, Saratov State University
Feodorova Valentina Anatol'evna, Saratov State University

Background and Objectives: In the article it has been demonstrated how virtual optical speckles may be generated from the nucleotide sequences of 7 housekeeping genes of Chlamydia psittaci. Such speckles are called as GB- speckles (gene-based speckles). In the article specific features of the formation of interference patterns have been studied with the superposition, as raw GB-speckle and GB-speckle, processed by the s-LASCA method (Laser Speckle Contrast Analysis). It has been shown, that the contrast of interfering GB-speckles may be used to identify the presence of polymorphism in nucleotide sequences of bacterial pathogens which are used for Multi Locus Sequence Typing (MLST). Materials and Methods: In this work, using the interference of generated GB-speckle implementations, a comparison of concatenated nucleotide sequences, 7 household genes (namely, gatA, gidA, enoA, fumC, hemN, hflX, oppA) was performed for three bacterial strains of the C. psittaci, belonging to different sequence types (ST): ST 28, ST24, ST43. The generated GB-speckles were processed using the s-LASCA method. The polymorphism of these genes allows for intraspecific typing of bacteria by MLST. This method is widely used in molecular evolutionary analysis to establish the phylogenetic relationship and population structure of microorganisms, as well as to investigate the origin of epidemiologically significant strains of pathogens in molecular epidemiology. Results: When multiple SNP appear in the compared bacterial nucleotide sequences, the system of interference fringes may be destroyed due to the appearance of random amplitude modulation. If there is interference of GB-speckles that have been processed by the s-LASCA method, then the interference pattern does not have any interference fringes at all, but contains only non-Gaussian speckles with a contrast of more than 4. However, such high contrast values of interfering speckles are an indicator of the presence of an evident polymorphism in the targeted DNA fragments of bacterial pathogens. It has been shown that the new class of speckles, namely, GB-speckles that have been processed by the s-LASCA method, have unique statistical properties and have no analogues in nature. Special attention has been paid to the study of GB-speckle interference issues. It has been established that when the initial speckle fields interfere, regular fringes can appear in the interference pattern (with minimal differences in the initial nucleotide sequences belonging to different sequence types). Conclusion: The structure of the resulting interference patterns is unique. Interference fringes are completely absent in such pictures. The speckle structure is described by non-Gaussian statistics, and the speckles themselves look like bright flashes on a dark background. The contrast of interfering GB-speckles processed by the s-LASCA method is significantly higher than unity and lies in the range [4.4; 10.6]. Such high contrast values can serve as a reliable and unmistakable sign of the presence of polymorphisms in bacterial target genes.

This study was funded by the Russian Science Foundation (project No. 17-16-01099) (continued).
  1. Briers J., Webster J. S. Laser speckle contrast analysis (LASCA): A nonscanning, full-field technique for monitoring capillary blood flow. Journal of Biomedical Optics, 1996, vol. 1, no. 2, pp. 174–179. https://doi.org/10.1117/12.231359
  2. Li P., Ni S., Zhang L., Zeng S., Luo Q. Imaging cerebral blood flow through the intact rat scull with temporal laser speckle imaging. Optics Letters, 2006, vol. 31, pp. 1824–1826. https://doi.org/10.1364/OL.31.001824
  3. Sini M., Linsely J., Sini M. Analysis of cerebral blood flow imaging by regis-tered laser speckle contrast analysis (rLASCA). Proceedings of 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN 2011), pp. 207–212. https://doi.org/10.1109/ICSCCN.2011.6024545
  4. Ickinger C., Lambrecht V., Tikly M., Vanhaecke A., Cutolo M., Smith V. Laser speckle contrast analysis is a reliable measure of digital blood perfusion in Black Africans with systemic sclerosis. Clinical and Experimental Rheumatology, 2021, vol. 131, no. 4, pp. 119–123.
  5. Aleksiev T., Ivanova Z., Dobrev H., Atanasov N. Application of a novel finger temperature device in the assessment of subjects with Raynaud’s phenomenon. Skin Research and Technology, 2021, pp. 1–6. https://doi.org/10.1111/srt.13070
  6. Unal-Cevik I., Orhan D., Acar-Ozen N. P., MamakEkinci E. B. Small Fiber Functionality in Patients with Diabetic Neuropathic Pain. Pain Medicine, 2021, vol. 22, no. 2. https://doi.org/10.1093/pm/pnab150
  7. Gigante A., Villa A., Rosato E. Laser speckle contrast analysis predicts major vascular complications and mortality of patients with systemic sclerosis. Rheumatology (Oxford), 2021. vol. 60, no. 4, pp. 1850–1857. https://doi.org/10.1093/rheumatology/keaa514
  8. Forstenpointner J., Sendel M., Moeller P., Reimer M., Canaan-Kühl S., Gaedeke J., Rehm S., Hüllemann P., Gierthmühlen J., Baron R. Bridging the Gap Between Vessels and Nerves in Fabry Disease. Frontiers in Neuroscience, 2020, vol. 14, pp. 448–458. https://doi.org/10.3389/fnins.2020.00448
  9. Cutolo M., Vanhaecke A., Ruaro B., Deschepper E., Ickinger C., Melsens K., Piette Y., Trombetta A. C., De Keyser F., Smith V. Is laser speckle contrast analysis (LASCA) the new kid on the block in systemic sclerosis? A systematic literature review and pilot study to evaluate reliability of LASCA to measure peripheral blood perfusion in scleroderma patients. Autoimmunity Reviews, 2018, vol. 17, no. 8, pp. 775–780. https://doi.org/10.1016/j.autrev.2018.01.023
  10. Wang G., Zhang Y. P., Gao Z., Shields L. B. E., Li F., Chu T., Lv H., Moriarty T., Xu X. M., Yang X., Shields C. B., Cai J. Pathophysiological and behavioral deficits in developing mice following rotational acceleration-deceleration traumatic brain injury. Disease Models & Mechanisms, 2018, vol. 11, no. 1, pp. 1210–1242. https://doi.org/10.1242/dmm.030387
  11. Tarantini S., Fulop GA., Kiss T., Farkas E., ZöleiSzénási D., Galvan V., Toth P., Csiszar A., Ungvari Z., Yabluchanskiy A. Demonstration of impaired neurovascular coupling responses in TG2576 mouse model of Alzheimer’s disease using functional laser speckle contrast imaging. Geroscience, 2017, vol. 39, no. 4, pp. 465–473. https://doi.org/10.1007/s11357-017-9980-z
  12. Orozco Merino M. Y., Lasca L. Iliopectineal bursitis. Rev Fac Cien Med Univ Nac Cordoba, 2016, vol. 73, no. 4, pp. 306 (in Spanish). PMID: 28152373
  13. Brauer J. I., Beech I. B., Sunner J. Mass Spectrometric Imaging Using Laser Ablation and Solvent Capture by Aspiration (LASCA). J Am Soc Mass Spectrom, 2015, vol. 26, no. 9, pp. 1538–1547. https://doi.org/10.1007/s13361-015-1176-0
  14. Koshoji N. H., Bussadori S. K., Bortoletto C. C., Prates R. A., Oliveira M. T., Deana A. M. Laser speckle imaging: A novel method for detecting dental erosion. PLoS One, 2015, vol. 10, no. 2, pp. 1–9. https://doi.org/10.1371/journal.pone.0118429
  15. Ulianova O. V., Ulyanov S. S., Li P. Qingming Luo Estimation of reactogenicity of preparations produced on the basis of photoinactivated live vaccines against brucellosis and tularaemia on the organismic level. Quantum Electronics., 2011, vol. 41, no. 4, pp. 340–343. https://doi.org/10.1070/QE2011v041n04ABEH014600.
  16. Ulyanov S. S., Ganilova Y., Zhu D., Qiu J., Li P., Ulianova O. V., Luo Q. LASCA with a small number of scatterers: Application for monitoring of microflow. Europhysics Letters, 2008, vol. 82, no. 1, pp. 18005. https://doi.org/10.1209/0295-5075/82/18005
  17. Ulianova O. V., Rebeza O., Ulyanov S. S. Investigations of processes of the growth of colonies of bacterial cells by the method of LASCA. Optics and Spectroscopy, 2016, vol. 120, no. 1, pp. 88–93. https://doi.org/10.1134/S0030400X16010227
  18. Ulianova O. V., Ulyanov S. S., Zaytsev S. S., Saltykov Y. V., Feodorova V. A. LASCA-imaging of GB-speckles: Application for detection of the gene polymorphism in bacterial model. Laser Physics Letters, 2020, vol. 17, no. 6, pp. 065603–065609. https://doi.org/10.1088/1612-202X/ab8b66
  19. Lesk A. M. Introduction to Bioinformatics. Oxford, Oxford University Press, 2002. 314 p. https://doi.org/10.1002/biot.200800277
  20. Sintchenko V. Roper M. Pathogen genome bioinformatics. Methods in Molecular Biology, 2014, vol. 41, no. 4, pp. 173–193. https://doi.org/10.1007/978-1-4939-0847-9_10
  21. Collier J. H., Allison L., Lesk A. M., Stuckey P. J., Garcia de la Banda M., Konagurthu A. S. Statistical inference of protein structural alignments using information and compression. Bioinformatics, 2017, vol. 33, no. 7, pp. 1005–1013. PMID: 28065899. https://doi.org/10.1093/bioinformatics/btw757
  22. Collier J. H., Allison L., Lesk A. M., Garcia de la Banda M., Konagurthu A. S. A new statistical framework to assess structural alignment quality using information compression. Bioinformatics, 2014, vol. 30, no. 17, pp. 512–518. PMID: 25161241. https://doi.org/10.1093/bioinformatics/btu460
  23. d’Humières C., Salmona M., Dellière S., Leo S., Rodriguez C., Angebault C., Alanio A., Fourati S., Lazarevic V., Woerther P. L., Schrenzel J., Ruppé E. The Potential Role of Clinical Metagenomics in Infectious Diseases: Therapeutic Perspectives. Drugs, 2021, vol. 81, pp. 1453–1456. https://doi.org/10.1007/s40265-021-01572-4
  24. Kosvyra A., Ntzioni E., Chouvarda I. Network analysis with biological data of cancer patients: A scoping review. Journal of Biomedical Informatics, 2021, vol. 120, pp. 103873–103884. https://doi.org/10.1016/j.jbi.2021.103873
  25. Rahmatbakhsh M., Gagarinova A., Babu M. Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections. Front Genet., 2021, vol. 12, pp. 667936–667942. https://doi.org/10.3389/fgene.2021.667936
  26. Soltaninejad H., Zare-Zardini H., Ordooei M., Ghelmani Y., Ghadiri-Anari A., Mojahedi S., Hamidieh A. A. Antimicrobial Peptides from Amphibian Innate Immune System as Potent Antidiabetic Agents: A Literature Review and Bioinformatics Analysis. Journal of Diabetes Research, 2021, vol. 3, pp. 1–10. https://doi.org/10.1155/2021/2894722
  27. Diwan A. D., Harke S. N., Gopalkrishna, Panche A. N. Aquaculture industry prospective from gut microbiome of fish and shellfish: An overview. Journal of Animal Physiologgy and Animal Nutrittion, 2021, pp. 1–29. https://doi.org/10.1111/jpn.13619
  28. Gawlik A., Salonen A., Jian C., Yanover C., Antosz A., Shmoish M., Wasniewska M., Bereket A., Wudy S. A., Hartmann M. F., Thivel D., Matusik P., Weghuber D., Hochberg Z. Personalized approach to childhood obesity: Lessons from gut microbiota and omics studies. Narrative review and insights from the 29th European childhood obesity congress. Pediatric Obesity, 2021, vol. 16, no. 10, pp. 1–9. Published online. https://doi.org/10.1111/ijpo.12835
  29. Mandeles S. Nucleic Acid Sequence Analysis. New York, London, Columbia University Press, 1972. 282 p.
  30. Ulyanov S. S., Zaytsev S. S., Ulianova O. V., Saltykov Y. V., Feodorova V. A. Using of methods of speckle optics for Chlamydia trachomatis typing. Proceedings of SPIE. Bellingham, Washington, SPIE Press, 2017, vol. 10336, pp. 03360D. https://doi.org/10.1117/12.2270760
  31. Ulyanov S. S., Ulianova O. V., Zaytsev S. S., Saltykov Y. V., Feodorova V. A. Statistics on gene-based laser speckles with a small number of scatterers: Implications for the detection of polymorphism in the Chlamydia trachomatis omp1 gene. Laser Physics Letters, 2018, vol. 15, no. 4, pp. 1–6. https://doi.org/10.1088/1612-202X/aaa11c
  32. Feodorova V. A., Ulyanov S. S., Zaytsev S. S., Saltykov Y. V., Ulianova O. V. Optimization of algorithm of coding of genetic information of Chlamydia. Proceedings of SPIE. Bellingham, Washington, SPIE Press, 2018, vol. 10716, pp. 107160Q. https://doi.org/10.1117/12.2314640
  33. Feodorova V. A., Saltykov Y. V., Zaytsev S. S., Ulyanov S. S., Ulianova O. V. Application of virtual phase shifting speckle-interferometry for detection of polymorphism in the Chlamydia trachomatis omp1 gene. Proceedings of SPIE. Bellingham, Washington, SPIE Press, 2018, vol. 10716, pp. 107160M. https://doi.org/10.1117/12.2314700
  34. Ulyanov S. S., Ulianova O. V., Zaitsev S. S., Khizhnyakova M. A., Saltykov Yu. V., Filonova N. N., Subbotina I. A., Lyapina A. M., Feodorova V. A. Study of Statistical Characteristics of GB-speckles, Forming at Scattering of Light on Virtual Structures of Nucleotide Gene Sequences of Enterobacteria. Izvestiya of Saratov University. Physics, 2018, vol. 18, iss. 2, pp. 123–137 (in Russian). https://doi.org/10.18500/1817-3020-2018-18-2-123-137
  35. Jelocnik M., Polkinghorne A., Pannekoek Y. Multilocus Sequence Typing (MLST) of Chlamydiales. Methods Molecular Biology, 2019, vol. 2042, pp. 69–86. https://doi.org/10.1038/emi.2016.135
  36. Pérez-Losada M., Arenas M., Castro-Nallar E. Microbial sequence typing in the genomic era. Infect Genet Evol, 2018, vol. 63, pp. 346–359. https://doi.org/10.1016/j.meegid.2017.09.022
  37. Glaeser S. P., Kämpfer P. Multilocus sequence analysis (MLSA) in prokaryotic taxonomy. Syst Appl Microbiol, 2015, vol. 38, no. 4, pp. 237–245. https://doi.org/10.1016/j.syapm.2015.03.007
  38. Liang W. T., Liu H., Deng Y. [Multilocus sequence typing and its application on population genetic structure analysis of parasites]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi, 2014, vol. 26, no. 4, pp. 449–452 (in Chinese). PMID: 25434151
  39. Matsumura Y. [Multilocus sequence typing (MLST) analysis]. Rinsho Byori, 2013, vol. 61, no. 12, pp. 1116–1122 (in Japanese). PMID: 24605545