Details

Autor(en) / Beteiligte

• Kabwe, E.
• Davidyuk, Y.
• Shamsutdinov, A.
• Ismagilova, R.
• Shakirova, V.
• Khaiboullina, S.
• Rizvanov, A.A.
• Morzunov, S.

Titel

Molecular Analysis of Puumala orthohantavirus Strains in Hemorrhagic Fever with Renal Syndrome Patients in Tatarstan

Ist Teil von

• International journal of infectious diseases, 2022-03, Vol.116, p.S70-S71

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2022

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

• Puumala hantavirus (PUUV) is the main causative agent of hemorrhagic fever with renal syndrome (HRFS) in the Republic of Tatarstan (RT). The natural host of PUUV is the bank vole (Myodes glareolus), where virus establishes life-long infection. The goal of this work was to identify the PUUV strains in HFRS patients from Kazan and search for related strains circulating in the bank vole populations in RT. Total RNA was extracted from HFRS blood samples. The overlapping PCR-products were obtained by RT-PCR using S segment specific primers and automated sequencing. The PUUV RNA was detected in three HFRS samples. Analysis of a complete PUUV S segment coding region (1302 bp) revealed high genetic identity (99.7%) between GL436 and GL437 HFRS strains. Sequence identity between these two strains and GL427 strain was within 92.2-92.3% range. Strains GL436 and GL437 were 99.5-99.8% similar to PUUV strains previously identified in RT. Also, these strains clustered with the RUS genetic lineage, which was shown to circulate in the bank vole population near Kazan. Therefore, it could be suggested that these two HFRS patients were infected in the same region near Kazan. Surprisingly, the sequence identity between GL427 strain and other PUUV strains of the RUS lineage was lower, ranging between 90.6-92.4%. Therefore, the source of this PUUV strain remains unknown. Sequence analysis revealed a potential link between PUUV strains detected in HFRS patients and circulating in the bank vole population near Kazan. These data suggest that molecular methods could be used to identify the regions where human infection is highly likely.