Spatial and Temporal Characteristics of Yersinia pestis Strain Properties in the Natural Plague Foci of Kazakhstan

Spatial and Temporal Characteristics of Yersinia pestis Strain Properties in the Natural Plague Foci of Kazakhstan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial and Temporal Characteristics of Yersinia pestis Strain Properties in the Natural Plague Foci of Kazakhstan Abdel Ziyat Zh., Zhumadilova Zauresh. B., Mussagalieva Raikhan S., and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6864612/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents a comprehensive analysis of Yersinia pestis (Y. pestis) strains circulating in the natural plague foci of Kazakhstan, based on integrated epizootological monitoring, microbiological, and advanced molecular genetic methods. The research aims to assess the genetic biodiversity of Y. pestis , evaluate the effectiveness of analytical approaches, and develop a structured algorithm for the application of genotyping tools in building a national biorepository of natural isolates. Uniquely, the study combines classical microbiological techniques with high-throughput technologies including PCR, MLVA (VNTR), VITEK 2 Compact, MiniION (Oxford Nanopore), MiSeq (Illumina), and GIS-based spatial mapping. A total of 1,220 Y. pestis strains (2010–2023) were phenotypically and genotypically characterized, revealing that 94.8% were typical for their ecological settings, while 5.2% exhibited deviations. Additionally, whole-genome and multilocus analyses of 82 DNA samples allowed for the construction of three phylogenetic trees and GIS-integrated visualizations, offering new insights into the spatial and temporal dynamics of plague in Central Asia. A genetic repository was established, forming the foundation for future research on the evolution, distribution, and risk of plague in endemic regions. These findings represent the first large-scale genomic profiling of Y. pestis in Kazakhstan and provide essential tools for public health surveillance and biosecurity. Infectious Diseases Yersinia pestis phenotype genotype biorepository molecular epidemiology natural plague foci Kazakhstan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Plague, caused by Yersinia pestis , remains a global health threat under the International Health Regulations due to its historical pandemics and high mortality rates [ 1 ]. Its persistence in natural foci is influenced by complex ecological systems involving the microbe, its rodent hosts, and parasitic vectors. Landscape transformation and climate change contribute to the expansion and reactivation of these foci. Research has advanced understanding of Y. pestis genetics and epizootology. T.V. Meka-Mechenko et al. [ 2 ] and M. Keller et al. [ 3 ] explored phenotypic and molecular characteristics affecting microbe stability and virulence. Studies by G. Venugopal, R.D. Pechous [ 4 ], and C. Demeure et al. [ 5 ] emphasized the role of genomic sequencing in mapping phylogenetic relationships. Regional work by A.K. Dzhaparova et al. [ 6 ] and G. Sariyeva et al. [ 7 ] highlights Kazakhstan's use of genetic data to improve plague surveillance systems. Central Asia, particularly Kazakhstan, remains endemic, with over 1.1 million km² of natural foci. From 1990 to 2020, these areas expanded by 79,710 km² due to environmental and human factors [ 8 , 9 ]. Historically, Kazakhstan has seen over 1,000 plague cases with 80% mortality [ 10 ]. Kazakhstan’s health services conduct routine monitoring, disinsection, and deratisation in active zones, supported by laboratory analyses and public education. This has reduced the risk of human and animal infection. This study aims to assess the genetic diversity and spatial-temporal dynamics of Y. pestis strains, supporting the development of a national biorepository and enhancing preventive strategies against future outbreaks. 2. Materials and methods 2.1. Epizootological monitoring The established methods were used in epizootological monitoring, the data of which were analysed in the system of geographic information system (GIS) technology using ArcGIS 10.2.2 [ 11 ]. The study was conducted at the Central Reference Laboratory of Masgut Aikimbayev National Scientific Centre for Particularly Dangerous Infections of the Ministry of Healthcare of the Republic of Kazakhstan (MH RK). Full genomic DNA sequencing of plague microbe strains was carried out at the laboratory of the National Centre for Biotechnology in Astana, Republic of Kazakhstan. The basis for epizootological monitoring is the Resolution of the Ministry of Health of the Republic of Kazakhstan No. 8 “On Making Amendments and Additions to the Resolution of the Chief State Sanitary Doctor of Kostanay Region No. 5” [ 12 ]. The materials were based on the accumulated long-term observations in the course of epizootic monitoring within the plague-affected areas of the Republic of Kazakhstan. There are 6 natural and 15 autonomous foci in the republic, within which more than 90 landscape-epizootological districts are allocated (Fig. 1 ). In Kazakhstan, plague was detected in more than 40 species of rodents, predatory and insectivorous mammals, hares, ungulates, and 2 species of birds. The main carriers of the plague are gerbils, gophers, marmots, and voles. Fleas are the carriers of natural plague foci. The modern fauna of specific flea species in Kazakhstan and Central Asia consists of 48 forms belonging to 9 genera and 6 families: Pulicidae , Hystrichopsyllidae , Coptopsyllidae , Leptopsyllidae , Ceratophyllidae , Ctenophthalmidae . In Kazakhstan, 51 species of fleas spontaneously infected with the plague microbe were recorded. 2.2. Analysis of Strain Properties This study analyzed laboratory data from 1,212 Yersinia pestis strains and DNA from 82 isolates collected from natural plague foci in Kazakhstan. All manipulations were conducted under biosafety regulations and plague pathogen handling protocols [ 11 ]. Identification was performed using standard microbiological, biochemical, and serological methods, including indirect haemagglutination tests with diagnostic erythrocyte immunoglobulins. Antibiotic susceptibility and species confirmation were assessed using the automated VITEK 2 Compact 30 analyzer (BioMerieux, USA), based on turbidimetry and colorimetry, with proprietary test cards and VITEK®2 Systems 7.01 software. DNA from 225 strains (2007–2022) from the Masgut Aikimbayev National Scientific Centre was used to construct phylogenetic profiles. Full-genome sequencing was carried out on 48 DNA samples and genotyping was performed on 34 strains, with isolates stored at − 70°C and dated 1950–2022. DNA was extracted using QIAamp® DNA Mini Kit (Qiagen, USA) [ 15 , 16 ], and quantified via NanoDrop1000 and Qubit fluorimeters. Genetic analysis used primers for plague-specific genes ( caf1 , pla , YopE ) with commercial PCR kits: “AmpliSens®Y. pestis-FL”, “GenPest”, “GenPak”, Dream Taq (Thermo Scientific), and the experimental "Pest-Quest" kit developed in Kazakhstan. Molecular typing by MLVA was performed on 34 strains at 25 VNTR loci using Veriti PCR (Applied Biosystems) and gel electrophoresis. Whole-genome sequencing of 32 strains was conducted with Illumina MiSeq (MiSeq® Reagent Kit v3, 600 cycles), and library preparation was done using Nextera®XT DNA Library Kit. Additional sequencing of 16 strains was performed using the Oxford Nanopore MiniION Mk1C platform. Data were integrated using MultiQC software (v1.12), and phylogenetic analysis was conducted using PAUP 4.0 with UPGMA clustering. Sequence data were compared against EMBL and GenBank databases within the International Nucleotide Sequence Database Collaboration. 2.3. Research quality control Reference strains obtained from the depository and working collections of the Museum of Living Cultures of Masgut Aikimbayev National Scientific Centre for Particularly Dangerous Infections, including 17 reference strains of Y. pestis from different plague foci of Kazakhstan, 1 strain of Y. pestis EV, 4 strains of Y. pseudotuberculosis were used for quality control. Strains representing the main biovars of the plague pathogen, Y. pestis , were also used as reference samples: Pestoides F (Microtus/Antiqua biovar), Nepal516 (Antiqua biovar), KIM10+ (Mediaevalis biovar) and CO92 (Orientalis biovar). 3. Results 3.1. Epidemiological monitoring Each natural focal point of plague in Kazakhstan is autonomous, differing in landscape, spatial and biocenotic structure, types of carriers and vectors, frequency and intensity of epizootics, epidemic and epizootic potentials, as well as the risk of epidemiological complications [8, 9, 17]. The results of the data on spatial and temporal characteristics of epizootics among wild animals and Y. pestis strains circulating in natural foci of plague in Kazakhstan using GIS tools for the period 2010–2023 are illustrated in Fig. 2. The results of epizootological monitoring demonstrated that all natural plague foci are epizootically active and pose a real threat of human infection, except for the steppe plague foci of Kazakhstan. Of all the foci, the Central Asian desert plague centre by its size (83.1%), epidemic manifestations in the past and epizootic activity to date occupies the main position, where there are four nuclei of plague enzootics. In general, during the analysed period (2010–2023) the epizootic process was registered only within the group of desert plague foci virtually continuously (except for two steppe plague foci), but in some autonomous foci seasonality and cyclicity of epizootic processes are sharply expressed. Thanks to a complex of preventive measures, since 2003, no plague morbidity among humans has been observed in Kazakhstan. 3.2. Phenotypic Properties Between 2011 and 2023, phenotypic and molecular-genetic properties of over 1,220 Yersinia pestis strains were analyzed. These strains were isolated from 12 autonomous Central Asian desert plague foci and two high-mountain foci in the Tien Shan region. An additional 28 strains from highland and Caspian Depression foci were used for genotyping. Y. pestis strains did not hydrolyze urea, nor produce oxidase, indole, or hydrogen sulfide, but were catalase-positive. They fermented glucose, maltose, mannitol, and other carbohydrates, but not lactose or sucrose. Reactions to substrates like glycerol and rhamnose were variable and diagnostically significant. Gelatin was not liquefied. These biochemical profiles supported epizootological differentiation and classification of strains into two groups based on epidemic activity: group 1 included foci with frequent human plague cases; group 2 — without or with rare human infections (see Table 1). Table 1 Results of a study of plague microbe strains isolated from desert plague foci in Kazakhstan in 2010–2023, in % Name of the desert plague foci Total studied strains Of these was revealed by phenotypic properties Of these was revealed by genotypic properties Typicals Atypicals Typicals Atypicals Group 1 Ili intermountain 26.5 96.4 3.6 100 0 Mangystau 0.4 100 0 100 0 Pre-Ustyurt 2.7 100 0 100 0 Pre-Aral-Karakum 12.2 100 0 100 0 Pre-Balkhash 44.2 85.7 14.3 93.8 6.2 North Pre-Aral 3 95.5 4.5 100 0 Ustyurt 11 100 0 100 0 Total by group 59.8 92.6 7.4 97.3 2.7 Group 2 Aryskum-Daryalyktakyr 5.7 100 0 100 0 Betpak-Dala 12.2 100 0 100 0 Kyzylkum 12.2 100 0 100 0 Muyun-Kum 47.6 97.4 2.6 100 0 Tau-Kum 22.2 97.3 2.7 100 0 Total of by group 40.2 98.2 1.8 100 0 Total 100 94.8 5.2 98.4 1.6 Source: compiled by the authors. The majority of strains exhibited typical cultural, morphological, and enzymatic features of Y. pestis . All fermented glycerol, glucose, and related sugars; were pesticinogenic; and lacked denitrification ability. Genotypically, 98.4% were standard, while 1.6% lacked the caf1 gene (pFra plasmid). Atypical strains were more frequent in group 1 foci. Virulence factors are encoded by multiple genes located on the chromosome and plasmids. A total of 1,220 FI antigen tests showed positive results in 1,198 strains; 23 strains were FI-negative. Pigment sorption (Pgm+) was observed in 94.7% of strains; 5.3% were Pgm–. Pesticin production was detected in 91.1% of strains; 0.9% (from Pre-Balkhash) did not produce pesticin. Most Y. pestis strains are natural auxotrophs requiring methionine, phenylalanine, and threonine. In this study, 97% of strains needed these amino acids. Threonine and arginine dependence was also recorded in small proportions (up to 2.45% in group 1, and up to 1.02% in group 2), with no correlation to epizootic activity. In summary, 94.8% of strains showed typical phenotypic traits and 98.4% had standard genotypic profiles. All strains conformed to the biological characteristics of Y. pestis from the Central Asian desert plague region. 3.3. Whole-genome sequencing and VNTR analysis In 2022, DNA fragments of 32 Y. pestis strains were selected and isolated for whole-genome sequencing on the MiSeq platform (Illumina, USA) using MiSeq Reagent Kit v3 reagents. DNA concentrations were measured by spectrophotometric and fluorometric methods using a NanoDrop1000 spectrophotometer and a Qubit fluorometer, respectively (Table 2). Table 2 Results of DNA concentration measurements of 32 Y. pestis strains Sample ID NanoDrop, ng/uL Qubit, ng/uL A260 A280 260/280 260/230 Constant Cursor pos. Cursor abs. 340 raw IM-1 77.57 60 1.551 0.853 1.82 2.66 50 230 0.583 0.038 IM-2 70.18 60 1.404 0.758 1.85 1.45 50 230 0.967 1.76 IM-3 58.44 60 1.169 0.637 1.84 1.61 50 230 0.726 2.004 IM-5 58.02 56 1.16 0.611 1.9 2.29 50 230 0.507 0.305 IM-6 52.35 49.4 1.047 0.706 1.48 1.24 50 230 0.844 7.617 KK-1 47.32 28.2 0.946 0.587 1.61 1.42 50 230 0.666 2.636 MK-1 38.2 26.5 0.764 0.429 1.78 1.5 50 230 0.511 -0.076 MK-2 36.02 25.5 0.72 0.378 1.91 4.53 50 230 0.159 0.47 PAK-1 28.37 24 0.567 0.348 1.63 1.73 50 230 0.328 0.871 PAK-2 21.51 17.5 0.43 0.254 1.69 1.38 50 230 0.312 0.718 PAK-4 20.17 12.1 0.403 0.212 1.9 2.83 50 230 0.142 0.379 PAK-5 19.07 9.27 0.381 0.283 1.35 1.99 50 230 0.192 3.923 PB-1 18.01 7.88 0.36 0.193 1.87 1.82 50 230 0.198 0.674 PB-2 17.86 6.69 0.357 0.189 1.89 1.53 50 230 0.233 0.231 PB-3 17.1 6.52 0.342 0.192 1.78 3.38 50 230 0.101 0.369 PB-4 16.24 5.67 0.325 0.191 1.7 8.34 50 230 0.039 0.019 SZ-12 15.09 5.13 0.302 0.177 1.7 4.75 50 230 0.063 1.183 SZ-13 14.82 4.28 0.296 0.135 2.19 11.72 50 230 0.025 0.139 SZ-14 14.7 3.85 0.294 0.189 1.55 3.31 50 230 0.089 1.098 SZ-15 13.75 3.67 0.275 0.202 1.36 7.92 50 230 0.035 2.394 SZ-16 12.21 2.68 0.244 0.154 1.58 0.65 50 230 0.373 -0.092 SZ-4 11.01 2.33 0.22 0.145 1.52 734.24 50 230 0 0.639 SZ-9 10.31 2.32 0.206 0.1 2.07 1.5 50 230 0.138 -0.114 TK-1 9.88 2.29 0.198 0.148 1.33 3.47 50 230 0.057 1.063 TK-2 9.53 2 0.191 0.103 1.84 3.7 50 230 0.051 1.983 TL-2 9.09 1.97 0.182 0.106 1.72 0.94 50 230 0.194 -0.08 TL-3 9.04 1.8 0.181 0.083 2.19 1.09 50 230 0.166 -0.078 TL-6 8.52 1.58 0.17 0.073 2.34 7.66 50 230 0.022 0.504 TL-7 8.40 1.57 0.168 0.111 1.51 -4.42 50 230 -0.038 0.019 UE-1 7.35 1.4 0.147 0.091 1.61 2.95 50 230 0.05 1.118 UE-3 7.23 1.06 0.145 0.08 1.82 1.64 50 230 0.088 -0.098 UE-4 7.03 0.754 0.141 0.091 1.54 3.29 50 230 0.043 0.002 Source: compiled by the authors. Sequencing was performed with universal primers 8F (5'-AGAGAGTTTGATCCTGGCTCAG-3') and 806R (5'-GGACTACCAGGGTATCTAAT-3'). The sequences obtained were identical to the National Centre for Biotechnology Information Basic Local Alignment Search Tool database regarding the database “16S ribosomal RNA sequences (Bacteria and Archaea)”. The results of species identification of all samples were specific and identified as Y. pestis . The results of whole-genome sequencing were entered into a modular tool for combining the results of bioinformatic analysis of multiple samples into a single report of the MultiQC software (Version V 1.12; report created on 11.04.2022, 11:02 a.m.): /home/lpg/Desktop/Chuma-2/qc). The MultiQC reporting tool analysed and displayed the results and statistics of the main console output data (Table 3). The table of overall statistics shows the scores from the various instruments collected for each sample (sample example samples: L001_R2_001, 22.8%, 47%, 258 bp, 0.8; L001_R1_001, 24.4%, 47%, 262 bp, 1, etc.). Collecting data in a single report provides a quick and easy way to view key statistics. For instance, the genome of Y. pestis -IM-1 consisted of a single ring chromosome with a length of 4,567,859 base pairs with an average G + C content of 47%. Of this total, 209 were included in the final contig map. Across all samples, the total read length was 262 bp. Table 3 Results of whole-genome sequencing of DNA samples of 32 Y. pestis strains on the MiSeq platform (Illumina, USA) Key, strain CanSNPer2_e dited nb of SKESA contigs Total size N50 Min contig size Max contig size N75 N90 auN Mapping coverage on reference genome CO92 Y-pestis-IM-1 2MED1 209 4567859 48673 213 119804 27409 12568 51295.3 327368 Y-pestis-IM-2 2MED1 399 4538639 29690 200 115027 18438 8563 34876.15 352529 Y-pestis-IM-3 2MED1 193 4378134 48672 214 119804 27583 12749 51549.65 508518 Y-pestis-IM-5 2MED1 61 4296314 191930 203 554502 91610 45450 248394.11 889618 Y-pestis-IM-6 2MED1 206 4574107 44960 214 119793 27320 12749 47773.13 318017 Y-pestis-KK-1 2MED1 189 4466992 48673 213 119811 27845 13869 51719.99 425541 Y-pestis-MK-1 2MED1 272 4555863 37790 213 115027 23439 10708 39638.15 330899 Y-pestis-MK-2 2MED1 253 4555164 37524 205 115024 21738 10231 39996.76 335309 Y-pestis-PAK-1 2MED1 189 4465035 49170 211 119803 27872 14709 52757.66 428803 Y-pestis-PAK-2 2MED1 197 4466961 48443 205 123069 27760 13132 51262.54 426077 Y-pestis-PAK-4 2MED1 197 4475427 48443 213 119797 27409 13132 51486.14 416847 Y-pestis-PAK-5 2MED1 227 4565135 39584 214 119796 25206 12568 44685.54 325286 Y-pestis-PB-1 0.ANT3a 237 4389007 39619 205 115036 25295 19646 44854.62 513832 Y-pestis-PB-2 2MED1 201 4457064 44425 211 95188 26641 12719 44411.64 439234 Y-pestis-PB-3 2MED1 232 4565323 43969 200 100582 25926 12568 46213.83 328589 Y-pestis-PB-4 2MED1 189 4443797 48673 214 115026 27737 13132 50708.21 443640 Y-pestis-SZ-12 0.ANT2b 203 4532038 49661 210 115033 29015 14945 53513.96 384218 Y-pestis-SZ-13 0.ANT2b 221 4624555 48675 203 115033 27891 12592 50730.29 289371 Y-pestis-SZ-14 0.ANT3a 262 4377810 37275 213 97558 22647 10553 38969.53 522926 Y-pestis-SZ-15 0.ANT3a 202 4330771 44992 210 111345 27850 14093 48298.89 576702 Y-pestis-SZ-16 0.ANT2b 252 4510376 39114 207 115048 25222 11045 44594.2 398766 Y-pestis-SZ-4 0.ANT2b 115 4285578 78180 217 171839 37185 23204 81536.35 894676 Y-pestis-SZ-9 0.ANT2b 213 4530861 47932 212 111392 27348 13297 48455.44 384950 Y-pestis-TK-1 2MED1 274 4550304 37091 207 115027 21093 10634 42425.62 334501 Y-pestis-TK-2 2MED1 223 4569668 48549 207 119787 27348 12337 50197.47 323829 Y-pestis-TL-2 0.PE4a.c 230 4580278 41204 209 150584 27318 13926 49624.35 335410 Y-pestis-TL-3 0.PE4a.c 228 4584816 39501 205 150547 25870 13816 47212.86 334420 Y-pestis-TL-6 0.PE4a.c 365 4544857 31877 204 91076 19417 10039 34610.96 362709 Y-pestis-TL-7 0.PE4a.c 198 4587741 50895 213 150584 35123 18112 57030.37 330570 Y-pestis-UE-1 2MED1 219 4566759 41243 207 116976 25798 12418 45647.97 324404 Y-pestis-UE-3 2MED1 213 4385393 39393 233 115045 25754 12319 43900.83 505420 Y-pestis-UE-4 2MED1 243 4503644 39115 205 115045 22619 10767 42388.76 397292 Source: compiled by the authors. MultiQC scans the specified analysis directories for log files and quality control reports and generates a single summary report that visualises the results for all DNA samples of 32 Y. pestis strains, which are given in the first section “General Statistics”, where rows and columns with parameters are specified: sample name, % duplicates, % GC, read length and M/sec (Table 4). Table 4 General statistics of a modular tool for combining the results of bioinformatic analysis of multiple samples into a single report of whole-genome sequencing of 32 Y. pestis strains Sample name % Dups % GC Read length M Seqs PB_2_Pre-Balkhash 24.4% 47% 262 bp 1 PB_2_Pre-Balkhash 22.4% 47% 263 bp 1 SZ_4_Tien Shan high-mountain 11.2% 47% 225 bp 0.5 SZ_4_Tien Shan high-mountain 9.7% 47% 226 bp 0.5 SZ_9_Tien Shan high-mountain 27.1% 47% 270 bp 0.8 SZ_9_Tien Shan high-mountain 25% 47% 270 bp 0.8 MK_2_Muyun-Kum desert 21.6% 48% 261 bp 0.7 MK_2_Muyun-Kum desert 19% 48% 261 bp 0.7 UE_3_Ural-Embi desert 16.8% 47% 256 bp 0.7 UE_3_Ural-Embi desert 15.2% 47% 257 bp 0.7 SZ_13_Tien Shan high-mountain 23.9% 47% 271 bp 0.8 SZ_13_Tien Shan high-mountain 22.1% 47% 271 bp 0.8 Gis_2_Hissar mountain 19.9% 47% 262 bp 0.7 Gis_2_Hissar mountain 18.3% 47% 263 bp 0.7 TL_2_Talas mountain 11.3% 47% 263 bp 0.3 TL_2_Talas mountain 9.4% 47% 264 bp 0.3 TL_3_Talas mountain 16.9% 47% 270 bp 0.6 TL_3_Talas mountain 14.9% 47% 271 bp 0.6 SZ_12_Tien Shan high-mountain 29.1% 47% 266 bp 1 SZ_12_Tien Shan high-mountain 27.3% 47% 267 bp 1 TL_1891_ 22.5% 47% 271 bp 0.7 Source: compiled by the authors. In the MultQC programme, the main sections are distributed in tabs – FastQC. For the bioinformatics analysis, FastQC is presented according to the following indicators: number of sequences, sequence quality histograms, quality indicators by sequence, base sequence content, GC content for each sequence, by base N content, sequence length distribution, sequence duplication levels, over-represented sequences, adapter content and status checks. Examples of FastQC metrics are shown in Fig. 3. In 2023, whole-genome sequencing was performed on DNA fragments from 16 Y. pestis strains isolated from desert plague foci in Kazakhstan. Sequencing was conducted using the MiniION Mk1C (MIN-101C) platform (Oxford Nanopore, UK). Data quality was assessed using FastQC and MultiQC, with read lengths averaging 225–280 bp. Trimmomatic was used for adapter trimming and filtering low-quality reads. Alignment quality was evaluated with SAMtools, examining metrics such as coverage depth and base quality. Read counts per sample ranged from 100k to 1.4M, with read uniqueness between 74.5% and 90.6%. Base quality, GC content, and base composition were analyzed, with most samples showing typical distributions. Duplication levels and overrepresented sequences were reported, and FastQC summary statuses ranged from normal (green) to slightly abnormal (orange). Adapter content and sequence length distributions were also assessed. Comparative analysis of reads against the CO92 reference genome was conducted to determine gene order. As next-generation sequencing technologies advance, new bioinformatics tools enhance data resolution and accuracy. A phylogenetic tree based on whole-genome data was constructed using the Maximum Likelihood method in MEGA11. The resulting dendrogram (Fig. 4) grouped 32 Y. pestis strains into three major clusters: Cluster I: Medievalis biovar (2.MED1), comprising strains from desert foci (e.g., IM-1, TK-1, MK-1, PAK-1, UE-1). Cluster II: Pestoides biovar (0.PE4a.c), represented by strains from Talas highland foci (e.g., TL-2, TL-3). Cluster III: Antiqua biovar (0.ANT2b, 0.ANT3a), consisting of all remaining strains. These results confirm the genetic differentiation of Y. pestis populations by geographic origin and ecological focus. Isolates SZ-12, SZ-13, SZ-16, and SZ-9 (subcluster 0.ANT2b) originated from the Sary-Djaz highland focus, while subcluster 0.ANT3a included one desert isolate (PB-1, Pre-Balkhash) and three highland isolates (SZ-4, SZ-14, SZ-15). The presence of a highland-type strain in a desert focus may be due to transmission by animals or rivers originating from the Tian Shan Mountains. A phylogenetic tree of 16 desert Y. pestis strains revealed four main clusters: Cluster I – Y. pseudotuberculosis ; Cluster II – Pestoides F; Cluster III – reference strains Nepal516 (2.ANT1) and CO92 (1.ORI1); Cluster IV – strain KIM10 and all 16 tested strains, all belonging to the Medievalis biovar (2.MED1). All isolates from Central Asian desert foci (e.g., Ural-Embi, Pre-Aral-Karakum, Pre-Ustyurt, Pre-Balkhash) fell within Cluster IV, further divided into subclusters IVa, IVb, and IVc. These subgroups reflected geographic origins and genetic proximity. The studied strains were genetically similar to Y. pestis KIM10+, confirming their classification within the Medievalis biovar. This underscores the relevance of phylogenetic analysis in monitoring strain evolution and focus-specific variation. Additionally, multilocus typing (MLVA at 25 VNTR loci) was conducted on 34 isolates (1950–2022), including strains from Volga-Ural, Aryskum, Pre-Aral, and Ili regions. Data were encoded into a binary matrix and analyzed via UPGMA in PAUP, then visualized using FigTree. The resulting dendrogram divided Y. pestis and Y. pseudotuberculosis into eight clusters. Cluster VI (2.MED1) included all 34 Y. pestis isolates, further grouped into subclusters VII and VIII, comprising multiple regional genotypes. This study clarified species and subspecies status, virulence gene presence, and genetic relationships of Y. pestis strains in Kazakhstan. Such data improve the resolution and reliability of plague surveillance and genetic characterization. 3.4. Biodiversity analysis with a plague microbe strain Masgut Aikimbayev National Scientific Centre for Particularly Dangerous Infections researchers genotyped a total of 225 Y. pestis strains [14, 15] during the period 2007–2023, including DNA from 82 Y. pestis strains isolated from desert areas. As a result of a comprehensive analysis obtained in the course of laboratory experiments, the study identified 203 sequenced strains of Y. pestis of the phylogenetic branch of the biovar Medievalis (2.MED1 and 2.MED0), 12 sequenced strains of Y. pestis biovar Antiqua (0.ANT2b and 0.ANT3a), 6 sequenced strains of Y. pestis biovar Microtus/Antiqua 0.PE4 and 0.PE4a.c (Fig. 5). Thus, the results of laboratory studies showed that all Y. pestis strains were typical for the Central Asian desert plague focus by phenotypic and molecular-genetic characteristics and belonged to the medieval biovar – Medievalis of the causative agent Y. pestis , except for single atypical variants by some biochemical properties. 4. Discussion Research on plague natural foci in Kazakhstan highlights the interplay of natural and anthropogenic factors shaping the biodiversity and genetic variability of Yersinia pestis over the past 30 years. Key to this are microbiological features of the biocenosis and regional epizootology. The Y. pestis genome consists of a 4.65 Mbp chromosome and three plasmids – pCad (70.3 kbp), pFra (96.2 kbp), and pPst (9.6 kbp) – which define its virulence and diagnostic value. The pCad plasmid, also found in Y. pseudotuberculosis and Y. enterocolitica, is genus-specific, while pFra and pPst are species-specific and critical for capsule and toxin production [ 18 ]. Studies show variation in plasmid sizes and genetic structure even among strains from the same focus. The chromosome contains pseudogenes affected by IS-elements, deletions, and mutations. Variable number tandem repeats (VNTRs) influence strain differentiation, essential for genotyping and tracing infection origins [ 19 , 20 ]. Comparative studies confirm that Y. pseudotuberculosis is the ancestor of Y. pestis, sharing 98.9% homology in O-antigen genes [ 21 ]. Evolutionary analyses indicate divergence of Y. pestis from Y. pseudotuberculosis predates known pandemics by over 1500 years [ 22 ]. Experiments demonstrate phenotypic changes during adaptation to new hosts, reflecting microevolutionary dynamics [ 23 ]. The detection of atypical and low-virulence strains in natural foci underscores the need for continuous microbiological surveillance and genome comparison using next-generation sequencing (NGS) and bioinformatics tools [ 20 ]. While classical phenotypic methods remain relevant, advances in genomics have elevated genodiagnostics to a new level. Climatic shifts and human activity (e.g., land use, urbanization) affect rodent populations and flea vectors, influencing plague dynamics [ 24 ]. Molecular epidemiology tools such as whole-genome sequencing and VNTR typing support geographic and ecological tracking of Y. pestis strains. Kazakhstan’s role in regional and global surveillance is strategic due to transboundary animal migration and active foci. Monitoring antimicrobial resistance, though rare, is crucial for biosecurity and preparedness against bioterrorism threats. In summary, integrating classical microbiology with modern genomics enhances understanding of Y. pestis biology and informs effective surveillance, prevention, and global health security strategies [ 25 ]. 5. Conclusions The results of the analysis and long-term monitoring confirm that the natural plague foci of Kazakhstan remain active, cover vast areas, and pose a potential threat to human health. In the context of climate change and anthropogenic pressure, it is essential to implement modern surveillance strategies using molecular genetic methods and geoinformation systems. A key achievement was the creation of a DNA biorepository of Yersinia pestis strains and the passportisation of natural foci at the landscape-epizootological level, enabling a more systematic and efficient approach to plague control. Continuous monitoring and preventive measures have ensured the absence of human cases in Kazakhstan since 2003, confirming the effectiveness of current strategies. The established electronic database serves as a critical tool for analyzing genetic diversity, studying phylogenetic relationships, and identifying strains of unknown origin, including in emergency scenarios. Regular updates will support research, diagnostics, and the development of new preventive and therapeutic tools. Ongoing research into the genetic variability of Y. pestis and monitoring of natural foci will strengthen epidemiological surveillance, enable early detection of changes in pathogen circulation, and improve response capacity. The study contributes significantly to Kazakhstan’s biological security and supports the advancement of public health and sustainable development. Declarations Funding The research was carried out as part of the project of the Ministry of Science and Higher Education of the Republic of Kazakhstan on the topic “Study of antibiotic resistance genes of plague and cholera pathogens, design of PCR test system”, the project IRN – AP19679355, funding source – the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan. References World Health Organization. 2022. Plague. https://www.who.int/news-room/fact-sheets/detail/plague Meka-Mechenko, T.V., Erubaev, T.K., Begimbaeva, E.Zh., Kovaleva, G.G., Abdel, Z.Zh., Sutyagin, V.V., Izbanova, U.A. 2022. Multilevel system of studying plague microbe strains proprties in the Republic of Kazakhstan. Problems of Particularly Dangerous Infections , 4, 23-28. https://doi.org/10.21055/0370-1069-2022-4-23-28 Keller, M., Spyrou, M.A., Scheib, C.L., Neumann, G.U., Kröpelin, A., Haas-Gebhard, B., Päffgen, B., Haberstroh, J., Ribera I. Lacomba, A., Raynaud, C., Cessford, C., Durand, R., Stadler, P., Nägele, K., Bates, J.S., Trautmann, B., Inskip, S.A., Peters, J., Robb, J.E., Kivisild, T., Castex, D., McCormick, M., Bos, K.I., Harbeck, M., Herbig, A., Krause, J. 2019. Ancient Yersinia pestis genomes from across Western Europe reveal early diversification during the First Pandemic (541-750). Proceedings of the National Academy of Sciences of the United States of America , 116(25), 12363-12372. https://doi.org/10.1073/pnas.1820447116 Venugopal, G., Pechous, R.D. 2024. Yersinia pestis and pneumonic plague: Insight into how a lethal pathogen interfaces with innate immune populations in the lung to cause severe disease. Cellular Immunology , 403-404, 104856. https://doi.org/10.1016/j.cellimm.2024.104856 Demeure, C., Dussurget, O., Fiol, G., Le Guern, A., Savin, C., Pizarro-Cerdá, J. 2019. Yersinia pestis and plague: An updated view on evolution, virulence determinants, immune subversion, vaccination, and diagnostics. Genes and Immunity , 20(5), 357-370. https://doi.org/10.1038/s41435-019-0065-0 Dzhaparova, A.K., Eroshenko, G.A., Nikiforov, K.A., Kukleva, L.M., Alkhova, Zh.V., Berdiev, S.K., Kutyrev, V.V. 2021. Characteristics and philogenetic analysis of Yersinia pseudotuberculosis strains from the Sarydzhaz high-mountain focus in the Tien-Shan. Problems of Particularly Dangerous Infections , 2, 87-93. https://doi.org/10.21055/0370-1069-2021-2-87-93 Sariyeva, G., Bazarkanova, G., Maimulov, R., Abdikarimov, S., Kurmanov, B., Abdirassilova, A., Shabunin, A., Sagiyev, Z., Dzhaparova, A., Abdel, Z., Mussagaliyeva, R., Morand, S., Motin, V., Kosoy, M. 2019. Marmots and Yersinia pestis strains in two plague endemic areas of Tien Shan mountains. Frontiers in Veterinary Science , 6, 207. https://doi.org/10.3389/fvets.2019.00207 Abdel, Z.Zh., Erubaev, T.K., Tokmurzieva, G.Zh., Aimakhanov, B.K., Dalibaev, Zh.S., Musagalieva, R.S., Zhumadilova, Z.B., Meka-Mechenko, V.G., Meka-Mechenko, T.V., Matzhanova, A.M., Abdrasilova, A.A., Umarova, S.K., Rysbekova, A.K., Esimseit, D.T., Abdeliev, B.Z., Konyratbaev, K.K., Iskakov, B.G., Bely, D.G., Eskermesov, M.K., Kulemin, M.V., Askar, Zh.S., Kaldybaev, T.E., Mukhtarov, R.K., Davletov, S.B., Sutyagin, V.V., Lezdinsh, I.A. 2021. Demarcation of the boundaries of the Central Asian desert natural focus of plague of Kazakhstan and monitoring the areal of the main carrier Rhombomys opimus . Problems of Particularly Dangerous Infections , 2, 71-78. https://doi.org/10.21055/0370-1069-2021-2-71-78 Balykova, A.N., Katyshev, A.D., Eroshenko, G.A., Kukleva, L.M., Dzhaparova, A.K., Naryshkina, E.A., Oglodin, E.G., Berdiev, S.K., Kutyrev, V.V. 2024. Whole genome sequences of Yersinia pestis strains of ancient phylogenetic branch 0.ANT5 isolated in the 21st century in the Tien-Shan in Kyrgyzstan. Microbiology Resource Announcements , 13(10), e00469-24. https://doi.org/10.1128/mra.00469-24 Rametov, N.M., Steiner, M., Bizhanova, N.A., Abdel, Z.Zh., Yessimseit, D.T., Abdeliyev, B.Z., Mussagalieva, R.S. 2023. Mapping plague risk using Super Species Distribution Models and forecasts for rodents in the Zhambyl region, Kazakhstan. GeoHealth , 7(11), e2023GH000853. https://doi.org/10.1029/2023GH000853 World Health Organization. 2023. Laboratory biosafety manual . Geneva: World Health Organization. https://iris.who.int/handle/10665/365602 Resolution of the Ministry of Health of the Republic of Kazakhstan No. 8 “On Making Amendments and Additions to the Resolution of the Chief State Sanitary Doctor of Kostanay Region No. 5”. 2021. https://www.gov.kz/memleket/entities/departament-kkbtu-kostanay/documents/details/138918?lang=ru Atshabar, B., Nurtazhin, S.T., Shevtsov, A., Ramankulov, E.M., Sayakova, Z., Rysbekova, A., Stenseth, N.C., Utepova, I.B., Sadovskaya, V.P., Abdirasilova, A.A., Begimbaeva, E.Z., Abdel, Z.Z. 2021. Populations of the major carrier Rhombomys opimus , vectors of Xenopsylla fleas and the causative agent of Yersinia pestisin the Central Asian desert natural focus of plague. Bulletin the National academy of sciences of the Republic of Kazakhstan , 1(389), 26-34. https://doi.org/10.32014/2021.2518-1467.4 Meka-Mechenko, T.V., Izbanova, U.A., Abdel, Z.Zh., Nakisbekov, N.O., Lukhnova, L.Yu., Baitursyn, B., Dalibayev, Zh.S., Umarova, S.K. 2022. Genotypic properties of collection plague microbes strains from the natural plague foci of Kazakhstan. Acts of Biomedical Science , 7(6), 111-118. https://doi.org/10.29413/ABS.2022-7.6.11 Abdel, Z., Abdeliyev, B., Yessimseit, D., Begimbayeva, E., Mussagalieva, R. 2023. Natural foci of plague in Kazakhstan in the space-time continuum. Comparative Immunology, Microbiology and Infectious Diseases , 100, 102025. https://doi.org/10.1016/j.cimid.2023.102025 Sagiyev, Z., Berdibekov, A., Bolger, T., Merekenova, A., Ashirova, S., Nurgozhin, Z., Dalibayev, Zh. 2019. Human response to live plague vaccine EV, Almaty region, Kazakhstan, 2014-2015. PLoS ONE , 14(6), e0218366. https://doi.org/10.1371/journal.pone.0218366 Pauling, C.D., Beerntsen, B.T., Song, Q., Anderson, D.M. 2024. Transovarial transmission of Yersinia pestis in its flea vector Xenopsylla cheopis . Nature Communications , 15, 7266. https://doi.org/10.1038/s41467-024-51668-0 Byvalov, A.A., Dudina, L.G., Kravchenko, T.B., Ivanov, S.A., Konyshev, I.V., Morozova, N.A., Chernyadiev, A.V., Dentovskaya, S.V. 2024. The role of Yersinia pestis antigens in the reception of plague diagnostic bacteriophage L-413C. Applied Biochemistry and Microbiology , 60(4), 740-748. https://doi.org/10.1134/S0003683824604438 Sarfraz, A., Qurrat-Ul-Ain Fatima, S., Shehroz, M., Ahmad, I., Zaman, A., Nishan, U., Tayyab, M., Sheheryar, Moura, A.A., Ullah, R., Ali, E.A., Shah, M. 2024. Decrypting the multi-genome data for chimeric vaccine designing against the antibiotic resistant Yersinia pestis . International Immunopharmacology , 132, 111952. https://doi.org/10.1016/j.intimp.2024.111952 Nistane, N.T., Kale, M.B., Das, R.J., Umare, M.D., Umekar, M.J., Hemke, A.T., Gajbhiye, V.R. 2024. Herbal remedies: An emerging alternative for the treatment of pandemic diseases. Current Traditional Medicine , 10(6), e030823219384. http://doi.org/10.2174/2215083810666230803101424 Andrades Valtueña, A., Neumann, G.U., Spyrou, M.A., Musralina, L., Aron, F., Beisenov, A., Belinskiy, A.B., Bos, K.I., Buzhilova, A., Conrad, M., Djansugurova, L.B., Dobeš, M., Ernée, M., Fernández-Eraso, J., Frohlich, B., Furmanek, M., Hałuszko, A., Hansen, S., Harney, É., Hiss, A.N., Hübner, A., Key, F.M., Khussainova, E., Kitov, Y., Kitova, A.O., Knipper, C., Kühnert, D., Lalueza-Fox, C., Littleton, J., Massy, K., Mittnik, A., Mujika-Alustiza, J.A., Olalde, I., Papac, L., Penske, S., Peška, J., Pinhasi, R., Reich, D., Reinhold, S., Stahl, R., Stäuble, H., Tukhbatova, R.I., Vasilyev, S., Veselovskaya, E., Warinner, C., Stockhammer, P.W., Haak, W., Krause, J., Herbig, A. 2022. Stone Age Yersinia pestis genomes shed light on the early evolution, diversity, and ecology of plague. Proceedings of the National Academy of Sciences , 119(17), e2116722119. https://doi.org/10.1073/pnas.2116722119 Hussain, T., Singh, S., Danish, M., Pervez, R., Hussain, K., Husain, R. 2020. Natural metabolites: An eco-friendly approach to manage plant diseases and for better agriculture farming. In: J. Singh, A. Yadav (Eds.), Natural Bioactive Products in Sustainable Agriculture (pp. 1-13). Singapore: Springer. https://doi.org/10.1007/978-981-15-3024-1_1 Trzilova, D., Tamayo, R. 2021. Site-specific recombination-how simple DNA inversions produce complex phenotypic heterogeneity in bacterial populations. Trend in Genetics , 37(1), 59-72. https://doi.org/10.1016/j.tig.2020.09.004 Zhang, L., Wang, Z., Chang, N., Shang, M., Wei, X., Li, K., Li, J., Lun, X., Ji, H., Liu, Q. 2024. Relationship between climatic factors and the flea index of two plague hosts in Xilingol League, Inner Mongolia Autonomous Region. Biosafety and Health , 6(4), 244-250. https://doi.org/10.1016/j.bsheal.2024.07.004 Begon, M., Davis, S., Laudisoit, A., Leirs, H., Reijniers, J. 2019. Sylvatic plague in Central Asia: A case study of abundance thresholds. In: K. Wilson, A. Fenton, D. Tompkins (Eds.), Wildlife Disease Ecology: Linking Theory to Data and Application (pp. 623-643). Cambridge: Cambridge University Press. https://doi.org/10.1017/9781316479964.022 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6864612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469331921,"identity":"eb942787-9262-40b8-b0c4-3c6184011f33","order_by":0,"name":"Abdel Ziyat Zh.","email":"","orcid":"","institution":"Abdel Ziyat Zh.","correspondingAuthor":false,"prefix":"","firstName":"Abdel","middleName":"Ziyat","lastName":"Zh.","suffix":""},{"id":469331922,"identity":"71b00548-138b-4ad2-babb-30ae535e5f75","order_by":1,"name":"Zhumadilova Zauresh. B.","email":"","orcid":"","institution":"Masgut Aikimbayev’s National Scientific Center Especially Dangerous Infections","correspondingAuthor":false,"prefix":"","firstName":"Zhumadilova","middleName":"Zauresh.","lastName":"B.","suffix":""},{"id":469331923,"identity":"53874959-5db2-4dd7-8a0b-6757778511f8","order_by":2,"name":"Mussagalieva Raikhan S.","email":"","orcid":"","institution":"Masgut Aikimbayev’s National Scientific Center Especially Dangerous Infections","correspondingAuthor":false,"prefix":"","firstName":"Mussagalieva","middleName":"Raikhan","lastName":"S.","suffix":""},{"id":469331924,"identity":"3ec320a5-8c2c-4b57-b9e1-b7d6aa3d15a9","order_by":3,"name":"Shakiyev Nurbol N.","email":"","orcid":"","institution":"Masgut Aikimbayev’s National Scientific Center Especially Dangerous Infections","correspondingAuthor":false,"prefix":"","firstName":"Shakiyev","middleName":"Nurbol","lastName":"N.","suffix":""},{"id":469331925,"identity":"e295a0de-1bc1-40d6-8460-a736a74b305e","order_by":4,"name":"Otebay Dinmukhammed M.","email":"","orcid":"","institution":"Masgut Aikimbayev’s National Scientific Center Especially Dangerous Infections","correspondingAuthor":false,"prefix":"","firstName":"Otebay","middleName":"Dinmukhammed","lastName":"M.","suffix":""},{"id":469331926,"identity":"dced3b40-86d9-4e1d-842b-7808aac2f481","order_by":5,"name":"Abdeliyev Beck Z.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCQYDhocNQJodSDAYWBCpJRGkhecASIsEKVokEiBcgoB/dvPGB4k7bOQlZz6/uuFHgQQDf3t3An5L7hwrNkg8k2Y4Wzqn7GYP0GESZ85uwG/NjRwzicS2wwly0jlpN3iAWgwkcvFrkb+RY/4jse1/gpzkmbSbf4jRYgC0hSGx7UCCtAT7sdtE2WII9ItE4plkw5k9OWy3ZQwkeAj6Re5288YPH3fYyUscP/7s5ps/NnL87b0EvI8APAZgkljlIMD+gBTVo2AUjIJRMIIAAI5OSl2NdXO9AAAAAElFTkSuQmCC","orcid":"","institution":"Masgut Aikimbayev’s National Scientific Center Especially Dangerous Infections","correspondingAuthor":true,"prefix":"","firstName":"Abdeliyev","middleName":"Beck","lastName":"Z.","suffix":""}],"badges":[],"createdAt":"2025-06-10 15:27:22","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6864612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6864612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84391146,"identity":"697394c0-5d48-4c7e-a0af-15cb3cca0fd8","added_by":"auto","created_at":"2025-06-11 11:34:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":863152,"visible":true,"origin":"","legend":"\u003cp\u003eNatural foci of plague in Kazakhstan. Codes and names of plague foci\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: North Pre-Caspian region: 15 – Volga-Ural steppe; 16 – Volga-Ural sandy; 17 – Ural-Uilsky steppe. Central-Asian desert region: 18 – Ural-Embi; 19 – Pre-Ustyurt; 20 – Ustyurt; 21 – North Pre-Aral; 22 – Aryskum-Daryalyktakyr; 23 – Mangystau; 24 – Pre-Aral-Karakum; 27 – Kyzylkum; 28 – Muyun-Kum; 29 – Tau-Kum; 30 – Pre-Balkhash; 42 – Betpak-Dala; 45 – Pre-Аlakol low-mountain; 46 – Ili intermountain. Tien Shan high-mountain: 31 – Sarydzhaz; 40 – Talas; 44 – Dzhungar.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6864612/v1/33b5e12f4cfa05ae150bf129.png"},{"id":84390915,"identity":"33353363-1cf6-4412-8abb-82aaecebec59","added_by":"auto","created_at":"2025-06-11 11:26:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1152993,"visible":true,"origin":"","legend":"\u003cp\u003eEpizootological indicators of places of isolation of \u003cem\u003eY. pestis\u003c/em\u003e strains from rodents and their ectoparasites and detection of seropositive animals with antibodies to F1 plague microbe in plague foci of Kazakhstan in 2010-2023\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6864612/v1/9378236dbd38731f76f78279.png"},{"id":84390916,"identity":"7f207c85-df8e-4470-95c9-d3ff0ba46878","added_by":"auto","created_at":"2025-06-11 11:26:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":942334,"visible":true,"origin":"","legend":"\u003cp\u003eFastQC tabs of the results of whole-genome sequencing of 32 \u003cem\u003eY. pestis\u003c/em\u003e strains: a) sequence count for each sample; b) GC content for each sample sequence; c) adapter and sequence status check content; d) average quality value for each base position in the sample sequence reads\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6864612/v1/3470fdc4e3a550c9c79e0aa9.png"},{"id":84390917,"identity":"07be4735-7934-4206-a11c-9f29eaac7a7d","added_by":"auto","created_at":"2025-06-11 11:26:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1475956,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic parameters (tree and dendrogram) of whole-genome sequencing and DNA genotyping of 82 \u003cem\u003eY. pestis\u003c/em\u003e strains circulating in natural foci of Kazakhstan: a) tree of 32 isolates; b) tree of 16 isolates; c) dendrogram of 34 isolates; d) tree of 34 isolates\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6864612/v1/f50eda46f7eb8f7f994a96de.png"},{"id":84390914,"identity":"8d1f9253-3417-4b3f-b29c-1b7747d73232","added_by":"auto","created_at":"2025-06-11 11:26:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103881,"visible":true,"origin":"","legend":"\u003cp\u003eResults of laboratory studies to determine the molecular-genetic properties of the plague microbe circulating in the Central Asian desert and Tien Shan high-mountain natural plague foci of Kazakhstan\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: compiled by the author based on T.V. Meka-Mechenko [14] and Z. Abdel [15].\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6864612/v1/e451987c9b3d07daf099a119.png"},{"id":84392092,"identity":"b40a8c40-8c41-4aa9-8867-7c7e7a80811a","added_by":"auto","created_at":"2025-06-11 11:42:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5799033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6864612/v1/0dbbfd20-0d28-42b9-8f12-079516a77b02.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSpatial and Temporal Characteristics of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eYersinia pestis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Strain Properties in the Natural Plague Foci of Kazakhstan\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePlague, caused by \u003cem\u003eYersinia pestis\u003c/em\u003e, remains a global health threat under the International Health Regulations due to its historical pandemics and high mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its persistence in natural foci is influenced by complex ecological systems involving the microbe, its rodent hosts, and parasitic vectors. Landscape transformation and climate change contribute to the expansion and reactivation of these foci.\u003c/p\u003e \u003cp\u003eResearch has advanced understanding of \u003cem\u003eY. pestis\u003c/em\u003e genetics and epizootology. T.V. Meka-Mechenko et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and M. Keller et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] explored phenotypic and molecular characteristics affecting microbe stability and virulence. Studies by G. Venugopal, R.D. Pechous [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and C. Demeure et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] emphasized the role of genomic sequencing in mapping phylogenetic relationships. Regional work by A.K. Dzhaparova et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and G. Sariyeva et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] highlights Kazakhstan's use of genetic data to improve plague surveillance systems.\u003c/p\u003e \u003cp\u003eCentral Asia, particularly Kazakhstan, remains endemic, with over 1.1\u0026nbsp;million km\u0026sup2; of natural foci. From 1990 to 2020, these areas expanded by 79,710 km\u0026sup2; due to environmental and human factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Historically, Kazakhstan has seen over 1,000 plague cases with 80% mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKazakhstan\u0026rsquo;s health services conduct routine monitoring, disinsection, and deratisation in active zones, supported by laboratory analyses and public education. This has reduced the risk of human and animal infection.\u003c/p\u003e \u003cp\u003eThis study aims to assess the genetic diversity and spatial-temporal dynamics of \u003cem\u003eY. pestis\u003c/em\u003e strains, supporting the development of a national biorepository and enhancing preventive strategies against future outbreaks.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Epizootological monitoring\u003c/h2\u003e\n \u003cp\u003eThe established methods were used in epizootological monitoring, the data of which were analysed in the system of geographic information system (GIS) technology using ArcGIS 10.2.2 [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. The study was conducted at the Central Reference Laboratory of Masgut Aikimbayev National Scientific Centre for Particularly Dangerous Infections of the Ministry of Healthcare of the Republic of Kazakhstan (MH RK). Full genomic DNA sequencing of plague microbe strains was carried out at the laboratory of the National Centre for Biotechnology in Astana, Republic of Kazakhstan. The basis for epizootological monitoring is the Resolution of the Ministry of Health of the Republic of Kazakhstan No. 8 \u0026ldquo;On Making Amendments and Additions to the Resolution of the Chief State Sanitary Doctor of Kostanay Region No. 5\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe materials were based on the accumulated long-term observations in the course of epizootic monitoring within the plague-affected areas of the Republic of Kazakhstan. There are 6 natural and 15 autonomous foci in the republic, within which more than 90 landscape-epizootological districts are allocated (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn Kazakhstan, plague was detected in more than 40 species of rodents, predatory and insectivorous mammals, hares, ungulates, and 2 species of birds. The main carriers of the plague are gerbils, gophers, marmots, and voles. Fleas are the carriers of natural plague foci. The modern fauna of specific flea species in Kazakhstan and Central Asia consists of 48 forms belonging to 9 genera and 6 families: \u003cem\u003ePulicidae\u003c/em\u003e, \u003cem\u003eHystrichopsyllidae\u003c/em\u003e, \u003cem\u003eCoptopsyllidae\u003c/em\u003e, \u003cem\u003eLeptopsyllidae\u003c/em\u003e, \u003cem\u003eCeratophyllidae\u003c/em\u003e, \u003cem\u003eCtenophthalmidae\u003c/em\u003e. In Kazakhstan, 51 species of fleas spontaneously infected with the plague microbe were recorded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Analysis of Strain Properties\u003c/h2\u003e\n \u003cp\u003eThis study analyzed laboratory data from 1,212 \u003cem\u003eYersinia pestis\u003c/em\u003e strains and DNA from 82 isolates collected from natural plague foci in Kazakhstan. All manipulations were conducted under biosafety regulations and plague pathogen handling protocols [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Identification was performed using standard microbiological, biochemical, and serological methods, including indirect haemagglutination tests with diagnostic erythrocyte immunoglobulins.\u003c/p\u003e\n \u003cp\u003eAntibiotic susceptibility and species confirmation were assessed using the automated VITEK 2 Compact 30 analyzer (BioMerieux, USA), based on turbidimetry and colorimetry, with proprietary test cards and VITEK\u0026reg;2 Systems 7.01 software.\u003c/p\u003e\n \u003cp\u003eDNA from 225 strains (2007\u0026ndash;2022) from the Masgut Aikimbayev National Scientific Centre was used to construct phylogenetic profiles. Full-genome sequencing was carried out on 48 DNA samples and genotyping was performed on 34 strains, with isolates stored at \u0026minus;\u0026thinsp;70\u0026deg;C and dated 1950\u0026ndash;2022. DNA was extracted using QIAamp\u0026reg; DNA Mini Kit (Qiagen, USA) [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], and quantified via NanoDrop1000 and Qubit fluorimeters. Genetic analysis used primers for plague-specific genes (\u003cem\u003ecaf1\u003c/em\u003e, \u003cem\u003epla\u003c/em\u003e, \u003cem\u003eYopE\u003c/em\u003e) with commercial PCR kits: \u0026ldquo;AmpliSens\u0026reg;Y. pestis-FL\u0026rdquo;, \u0026ldquo;GenPest\u0026rdquo;, \u0026ldquo;GenPak\u0026rdquo;, Dream Taq (Thermo Scientific), and the experimental \u0026quot;Pest-Quest\u0026quot; kit developed in Kazakhstan.\u003c/p\u003e\n \u003cp\u003eMolecular typing by MLVA was performed on 34 strains at 25 VNTR loci using Veriti PCR (Applied Biosystems) and gel electrophoresis. Whole-genome sequencing of 32 strains was conducted with Illumina MiSeq (MiSeq\u0026reg; Reagent Kit v3, 600 cycles), and library preparation was done using Nextera\u0026reg;XT DNA Library Kit. Additional sequencing of 16 strains was performed using the Oxford Nanopore MiniION Mk1C platform.\u003c/p\u003e\n \u003cp\u003eData were integrated using MultiQC software (v1.12), and phylogenetic analysis was conducted using PAUP 4.0 with UPGMA clustering. Sequence data were compared against EMBL and GenBank databases within the International Nucleotide Sequence Database Collaboration.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Research quality control\u003c/h2\u003e\n \u003cp\u003eReference strains obtained from the depository and working collections of the Museum of Living Cultures of Masgut Aikimbayev National Scientific Centre for Particularly Dangerous Infections, including 17 reference strains of \u003cem\u003eY. pestis\u003c/em\u003e from different plague foci of Kazakhstan, 1 strain of \u003cem\u003eY. pestis\u003c/em\u003e EV, 4 strains of \u003cem\u003eY. pseudotuberculosis\u003c/em\u003e were used for quality control. Strains representing the main biovars of the plague pathogen, \u003cem\u003eY. pestis\u003c/em\u003e, were also used as reference samples: Pestoides F (Microtus/Antiqua biovar), Nepal516 (Antiqua biovar), KIM10+ (Mediaevalis biovar) and CO92 (Orientalis biovar).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1. Epidemiological monitoring\u003c/h2\u003e\n \u003cp\u003eEach natural focal point of plague in Kazakhstan is autonomous, differing in landscape, spatial and biocenotic structure, types of carriers and vectors, frequency and intensity of epizootics, epidemic and epizootic potentials, as well as the risk of epidemiological complications [8, 9, 17]. The results of the data on spatial and temporal characteristics of epizootics among wild animals and \u003cem\u003eY. pestis\u003c/em\u003e strains circulating in natural foci of plague in Kazakhstan using GIS tools for the period 2010\u0026ndash;2023 are illustrated in Fig. 2.\u003c/p\u003e\n \u003cp\u003eThe results of epizootological monitoring demonstrated that all natural plague foci are epizootically active and pose a real threat of human infection, except for the steppe plague foci of Kazakhstan. Of all the foci, the Central Asian desert plague centre by its size (83.1%), epidemic manifestations in the past and epizootic activity to date occupies the main position, where there are four nuclei of plague enzootics. In general, during the analysed period (2010\u0026ndash;2023) the epizootic process was registered only within the group of desert plague foci virtually continuously (except for two steppe plague foci), but in some autonomous foci seasonality and cyclicity of epizootic processes are sharply expressed. Thanks to a complex of preventive measures, since 2003, no plague morbidity among humans has been observed in Kazakhstan.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.2. Phenotypic Properties\u003c/h2\u003e\n \u003cp\u003eBetween 2011 and 2023, phenotypic and molecular-genetic properties of over 1,220 \u003cem\u003eYersinia pestis\u003c/em\u003e strains were analyzed. These strains were isolated from 12 autonomous Central Asian desert plague foci and two high-mountain foci in the Tien Shan region. An additional 28 strains from highland and Caspian Depression foci were used for genotyping.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eY. pestis\u003c/em\u003e strains did not hydrolyze urea, nor produce oxidase, indole, or hydrogen sulfide, but were catalase-positive. They fermented glucose, maltose, mannitol, and other carbohydrates, but not lactose or sucrose. Reactions to substrates like glycerol and rhamnose were variable and diagnostically significant. Gelatin was not liquefied.\u003c/p\u003e\n \u003cp\u003eThese biochemical profiles supported epizootological differentiation and classification of strains into two groups based on epidemic activity: group 1 included foci with frequent human plague cases; group 2 \u0026mdash; without or with rare human infections (see Table 1).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of a study of plague microbe strains isolated from desert plague foci in Kazakhstan in 2010\u0026ndash;2023, in %\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eName of the desert plague foci\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal studied strains\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOf these was revealed by phenotypic properties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOf these was revealed by genotypic properties\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTypicals\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAtypicals\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTypicals\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAtypicals\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIli intermountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMangystau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-Ustyurt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-Aral-Karakum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-Balkhash\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth Pre-Aral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUstyurt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal by group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAryskum-Daryalyktakyr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBetpak-Dala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKyzylkum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMuyun-Kum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTau-Kum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal of by group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe majority of strains exhibited typical cultural, morphological, and enzymatic features of \u003cem\u003eY. pestis\u003c/em\u003e. All fermented glycerol, glucose, and related sugars; were pesticinogenic; and lacked denitrification ability. Genotypically, 98.4% were standard, while 1.6% lacked the \u003cem\u003ecaf1\u003c/em\u003e gene (pFra plasmid). Atypical strains were more frequent in group 1 foci.\u003c/p\u003e\n \u003cp\u003eVirulence factors are encoded by multiple genes located on the chromosome and plasmids. A total of 1,220 FI antigen tests showed positive results in 1,198 strains; 23 strains were FI-negative. Pigment sorption (Pgm+) was observed in 94.7% of strains; 5.3% were Pgm\u0026ndash;. Pesticin production was detected in 91.1% of strains; 0.9% (from Pre-Balkhash) did not produce pesticin.\u003c/p\u003e\n \u003cp\u003eMost \u003cem\u003eY. pestis\u003c/em\u003e strains are natural auxotrophs requiring methionine, phenylalanine, and threonine. In this study, 97% of strains needed these amino acids. Threonine and arginine dependence was also recorded in small proportions (up to 2.45% in group 1, and up to 1.02% in group 2), with no correlation to epizootic activity.\u003c/p\u003e\n \u003cp\u003eIn summary, 94.8% of strains showed typical phenotypic traits and 98.4% had standard genotypic profiles. All strains conformed to the biological characteristics of \u003cem\u003eY. pestis\u003c/em\u003e from the Central Asian desert plague region.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.3. Whole-genome sequencing and VNTR analysis\u003c/h2\u003e\n \u003cp\u003eIn 2022, DNA fragments of 32 \u003cem\u003eY. pestis\u003c/em\u003e strains were selected and isolated for whole-genome sequencing on the MiSeq platform (Illumina, USA) using MiSeq Reagent Kit v3 reagents. DNA concentrations were measured by spectrophotometric and fluorometric methods using a NanoDrop1000 spectrophotometer and a Qubit fluorometer, respectively (Table 2).\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of DNA concentration measurements of 32 \u003cem\u003eY. pestis\u003c/em\u003e strains\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNanoDrop, ng/uL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQubit, ng/uL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA260\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA280\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e260/280\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e260/230\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCursor pos.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCursor abs.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e340 raw\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMK-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAK-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAK-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAK-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e734.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTK-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUE-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUE-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUE-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eSequencing was performed with universal primers 8F (5\u0026apos;-AGAGAGTTTGATCCTGGCTCAG-3\u0026apos;) and 806R (5\u0026apos;-GGACTACCAGGGTATCTAAT-3\u0026apos;). The sequences obtained were identical to the National Centre for Biotechnology Information Basic Local Alignment Search Tool database regarding the database \u0026ldquo;16S ribosomal RNA sequences (Bacteria and Archaea)\u0026rdquo;. The results of species identification of all samples were specific and identified as \u003cem\u003eY. pestis\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eThe results of whole-genome sequencing were entered into a modular tool for combining the results of bioinformatic analysis of multiple samples into a single report of the MultiQC software (Version V 1.12; report created on 11.04.2022, 11:02 a.m.): /home/lpg/Desktop/Chuma-2/qc). The MultiQC reporting tool analysed and displayed the results and statistics of the main console output data (Table 3).\u003c/p\u003e\n \u003cp\u003eThe table of overall statistics shows the scores from the various instruments collected for each sample (sample example samples: L001_R2_001, 22.8%, 47%, 258 bp, 0.8; L001_R1_001, 24.4%, 47%, 262 bp, 1, etc.). Collecting data in a single report provides a quick and easy way to view key statistics. For instance, the genome of \u003cem\u003eY. pestis\u003c/em\u003e-IM-1 consisted of a single ring chromosome with a length of 4,567,859 base pairs with an average G\u0026thinsp;+\u0026thinsp;C content of 47%. Of this total, 209 were included in the final contig map. Across all samples, the total read length was 262 bp.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of whole-genome sequencing of DNA samples of 32 \u003cem\u003eY. pestis\u003c/em\u003e strains on the MiSeq platform (Illumina, USA)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKey, strain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCanSNPer2_e dited\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enb of SKESA contigs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN50\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin contig size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax contig size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN75\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN90\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eauN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMapping coverage on reference genome CO92\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-IM-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4567859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51295.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e327368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-IM-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4538639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34876.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-IM-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4378134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51549.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e508518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-IM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4296314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e554502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e248394.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e889618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-IM-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4574107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47773.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-KK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4466992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51719.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e425541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-MK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4555863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39638.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-MK-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4555164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39996.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PAK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4465035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52757.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e428803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PAK-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4466961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51262.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e426077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PAK-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4475427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51486.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e416847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PAK-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4565135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44685.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e325286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PB-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4389007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44854.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e513832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PB-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4457064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44411.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e439234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PB-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4565323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46213.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e328589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-PB-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4443797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50708.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e443640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-SZ-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4532038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53513.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e384218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-SZ-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4624555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50730.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e289371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-SZ-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4377810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38969.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-SZ-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4330771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48298.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e576702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-SZ-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4510376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44594.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e398766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-SZ-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4285578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81536.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e894676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-SZ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.ANT2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4530861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48455.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e384950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-TK-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4550304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42425.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e334501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-TK-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4569668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50197.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e323829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-TL-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.PE4a.c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4580278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49624.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e335410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-TL-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.PE4a.c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4584816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47212.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e334420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-TL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.PE4a.c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4544857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34610.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e362709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-TL-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.PE4a.c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4587741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57030.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-UE-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4566759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45647.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e324404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-UE-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4385393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43900.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e505420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY-pestis-UE-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2MED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4503644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42388.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e397292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMultiQC scans the specified analysis directories for log files and quality control reports and generates a single summary report that visualises the results for all DNA samples of 32 \u003cem\u003eY. pestis\u003c/em\u003e strains, which are given in the first section \u0026ldquo;General Statistics\u0026rdquo;, where rows and columns with parameters are specified: sample name, % duplicates, % GC, read length and M/sec (Table 4).\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGeneral statistics of a modular tool for combining the results of bioinformatic analysis of multiple samples into a single report of whole-genome sequencing of 32 \u003cem\u003eY. pestis\u003c/em\u003e strains\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% Dups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% GC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRead length\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM Seqs\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB_2_Pre-Balkhash\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB_2_Pre-Balkhash\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_4_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_4_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_9_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_9_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMK_2_Muyun-Kum desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMK_2_Muyun-Kum desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUE_3_Ural-Embi desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUE_3_Ural-Embi desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_13_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_13_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGis_2_Hissar mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGis_2_Hissar mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL_2_Talas mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL_2_Talas mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL_3_Talas mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL_3_Talas mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_12_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSZ_12_Tien Shan high-mountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTL_1891_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e271 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eSource: compiled by the authors.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIn the MultQC programme, the main sections are distributed in tabs \u0026ndash; FastQC. For the bioinformatics analysis, FastQC is presented according to the following indicators: number of sequences, sequence quality histograms, quality indicators by sequence, base sequence content, GC content for each sequence, by base N content, sequence length distribution, sequence duplication levels, over-represented sequences, adapter content and status checks. Examples of FastQC metrics are shown in Fig. 3.\u003c/p\u003e\n \u003cp\u003eIn 2023, whole-genome sequencing was performed on DNA fragments from 16 \u003cem\u003eY. pestis\u003c/em\u003e strains isolated from desert plague foci in Kazakhstan. Sequencing was conducted using the MiniION Mk1C (MIN-101C) platform (Oxford Nanopore, UK). Data quality was assessed using FastQC and MultiQC, with read lengths averaging 225\u0026ndash;280 bp. Trimmomatic was used for adapter trimming and filtering low-quality reads. Alignment quality was evaluated with SAMtools, examining metrics such as coverage depth and base quality.\u003c/p\u003e\n \u003cp\u003eRead counts per sample ranged from 100k to 1.4M, with read uniqueness between 74.5% and 90.6%. Base quality, GC content, and base composition were analyzed, with most samples showing typical distributions. Duplication levels and overrepresented sequences were reported, and FastQC summary statuses ranged from normal (green) to slightly abnormal (orange). Adapter content and sequence length distributions were also assessed.\u003c/p\u003e\n \u003cp\u003eComparative analysis of reads against the CO92 reference genome was conducted to determine gene order. As next-generation sequencing technologies advance, new bioinformatics tools enhance data resolution and accuracy.\u003c/p\u003e\n \u003cp\u003eA phylogenetic tree based on whole-genome data was constructed using the Maximum Likelihood method in MEGA11. The resulting dendrogram (Fig. 4) grouped 32 \u003cem\u003eY. pestis\u003c/em\u003e strains into three major clusters:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eCluster I: Medievalis biovar (2.MED1), comprising strains from desert foci (e.g., IM-1, TK-1, MK-1, PAK-1, UE-1).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCluster II: Pestoides biovar (0.PE4a.c), represented by strains from Talas highland foci (e.g., TL-2, TL-3).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCluster III: Antiqua biovar (0.ANT2b, 0.ANT3a), consisting of all remaining strains.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThese results confirm the genetic differentiation of \u003cem\u003eY. pestis\u003c/em\u003e populations by geographic origin and ecological focus.\u003c/p\u003e\n \u003cp\u003eIsolates SZ-12, SZ-13, SZ-16, and SZ-9 (subcluster 0.ANT2b) originated from the Sary-Djaz highland focus, while subcluster 0.ANT3a included one desert isolate (PB-1, Pre-Balkhash) and three highland isolates (SZ-4, SZ-14, SZ-15). The presence of a highland-type strain in a desert focus may be due to transmission by animals or rivers originating from the Tian Shan Mountains.\u003c/p\u003e\n \u003cp\u003eA phylogenetic tree of 16 desert \u003cem\u003eY. pestis\u003c/em\u003e strains revealed four main clusters: Cluster I \u0026ndash; \u003cem\u003eY. pseudotuberculosis\u003c/em\u003e; Cluster II \u0026ndash; Pestoides F; Cluster III \u0026ndash; reference strains Nepal516 (2.ANT1) and CO92 (1.ORI1); Cluster IV \u0026ndash; strain KIM10 and all 16 tested strains, all belonging to the Medievalis biovar (2.MED1).\u003c/p\u003e\n \u003cp\u003eAll isolates from Central Asian desert foci (e.g., Ural-Embi, Pre-Aral-Karakum, Pre-Ustyurt, Pre-Balkhash) fell within Cluster IV, further divided into subclusters IVa, IVb, and IVc. These subgroups reflected geographic origins and genetic proximity.\u003c/p\u003e\n \u003cp\u003eThe studied strains were genetically similar to \u003cem\u003eY. pestis\u003c/em\u003e KIM10+, confirming their classification within the Medievalis biovar. This underscores the relevance of phylogenetic analysis in monitoring strain evolution and focus-specific variation.\u003c/p\u003e\n \u003cp\u003eAdditionally, multilocus typing (MLVA at 25 VNTR loci) was conducted on 34 isolates (1950\u0026ndash;2022), including strains from Volga-Ural, Aryskum, Pre-Aral, and Ili regions. Data were encoded into a binary matrix and analyzed via UPGMA in PAUP, then visualized using FigTree.\u003c/p\u003e\n \u003cp\u003eThe resulting dendrogram divided \u003cem\u003eY. pestis\u003c/em\u003e and \u003cem\u003eY. pseudotuberculosis\u003c/em\u003e into eight clusters. Cluster VI (2.MED1) included all 34 \u003cem\u003eY. pestis\u003c/em\u003e isolates, further grouped into subclusters VII and VIII, comprising multiple regional genotypes.\u003c/p\u003e\n \u003cp\u003eThis study clarified species and subspecies status, virulence gene presence, and genetic relationships of \u003cem\u003eY. pestis\u003c/em\u003e strains in Kazakhstan. Such data improve the resolution and reliability of plague surveillance and genetic characterization.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.4. Biodiversity analysis with a plague microbe strain\u003c/h2\u003e\n \u003cp\u003eMasgut Aikimbayev National Scientific Centre for Particularly Dangerous Infections researchers genotyped a total of 225 \u003cem\u003eY. pestis\u003c/em\u003e strains [14, 15] during the period 2007\u0026ndash;2023, including DNA from 82 \u003cem\u003eY. pestis\u003c/em\u003e strains isolated from desert areas.\u003c/p\u003e\n \u003cp\u003eAs a result of a comprehensive analysis obtained in the course of laboratory experiments, the study identified 203 sequenced strains of \u003cem\u003eY. pestis\u003c/em\u003e of the phylogenetic branch of the biovar Medievalis (2.MED1 and 2.MED0), 12 sequenced strains of \u003cem\u003eY. pestis\u003c/em\u003e biovar Antiqua (0.ANT2b and 0.ANT3a), 6 sequenced strains of \u003cem\u003eY. pestis\u003c/em\u003e biovar Microtus/Antiqua 0.PE4 and 0.PE4a.c (Fig. 5).\u003c/p\u003e\n \u003cp\u003eThus, the results of laboratory studies showed that all \u003cem\u003eY. pestis\u003c/em\u003e strains were typical for the Central Asian desert plague focus by phenotypic and molecular-genetic characteristics and belonged to the medieval biovar \u0026ndash; \u003cem\u003eMedievalis\u003c/em\u003e of the causative agent \u003cem\u003eY. pestis\u003c/em\u003e, except for single atypical variants by some biochemical properties.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eResearch on plague natural foci in Kazakhstan highlights the interplay of natural and anthropogenic factors shaping the biodiversity and genetic variability of Yersinia pestis over the past 30 years. Key to this are microbiological features of the biocenosis and regional epizootology. The Y. pestis genome consists of a 4.65 Mbp chromosome and three plasmids \u0026ndash; pCad (70.3 kbp), pFra (96.2 kbp), and pPst (9.6 kbp) \u0026ndash; which define its virulence and diagnostic value. The pCad plasmid, also found in Y. pseudotuberculosis and Y. enterocolitica, is genus-specific, while pFra and pPst are species-specific and critical for capsule and toxin production [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Studies show variation in plasmid sizes and genetic structure even among strains from the same focus.\u003c/p\u003e \u003cp\u003eThe chromosome contains pseudogenes affected by IS-elements, deletions, and mutations. Variable number tandem repeats (VNTRs) influence strain differentiation, essential for genotyping and tracing infection origins [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Comparative studies confirm that Y. pseudotuberculosis is the ancestor of Y. pestis, sharing 98.9% homology in O-antigen genes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvolutionary analyses indicate divergence of Y. pestis from Y. pseudotuberculosis predates known pandemics by over 1500 years [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Experiments demonstrate phenotypic changes during adaptation to new hosts, reflecting microevolutionary dynamics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe detection of atypical and low-virulence strains in natural foci underscores the need for continuous microbiological surveillance and genome comparison using next-generation sequencing (NGS) and bioinformatics tools [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While classical phenotypic methods remain relevant, advances in genomics have elevated genodiagnostics to a new level. Climatic shifts and human activity (e.g., land use, urbanization) affect rodent populations and flea vectors, influencing plague dynamics [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Molecular epidemiology tools such as whole-genome sequencing and VNTR typing support geographic and ecological tracking of Y. pestis strains.\u003c/p\u003e \u003cp\u003eKazakhstan\u0026rsquo;s role in regional and global surveillance is strategic due to transboundary animal migration and active foci. Monitoring antimicrobial resistance, though rare, is crucial for biosecurity and preparedness against bioterrorism threats. In summary, integrating classical microbiology with modern genomics enhances understanding of Y. pestis biology and informs effective surveillance, prevention, and global health security strategies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe results of the analysis and long-term monitoring confirm that the natural plague foci of Kazakhstan remain active, cover vast areas, and pose a potential threat to human health. In the context of climate change and anthropogenic pressure, it is essential to implement modern surveillance strategies using molecular genetic methods and geoinformation systems.\u003c/p\u003e \u003cp\u003eA key achievement was the creation of a DNA biorepository of \u003cem\u003eYersinia pestis\u003c/em\u003e strains and the passportisation of natural foci at the landscape-epizootological level, enabling a more systematic and efficient approach to plague control. Continuous monitoring and preventive measures have ensured the absence of human cases in Kazakhstan since 2003, confirming the effectiveness of current strategies.\u003c/p\u003e \u003cp\u003eThe established electronic database serves as a critical tool for analyzing genetic diversity, studying phylogenetic relationships, and identifying strains of unknown origin, including in emergency scenarios. Regular updates will support research, diagnostics, and the development of new preventive and therapeutic tools.\u003c/p\u003e \u003cp\u003eOngoing research into the genetic variability of \u003cem\u003eY. pestis\u003c/em\u003e and monitoring of natural foci will strengthen epidemiological surveillance, enable early detection of changes in pathogen circulation, and improve response capacity. The study contributes significantly to Kazakhstan\u0026rsquo;s biological security and supports the advancement of public health and sustainable development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe research was carried out as part of the project of the Ministry of Science and Higher Education of the Republic of Kazakhstan on the topic \u0026ldquo;Study of antibiotic resistance genes of plague and cholera pathogens, design of PCR test system\u0026rdquo;, the project IRN \u0026ndash; AP19679355, funding source \u0026ndash; the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. 2022. Plague. https://www.who.int/news-room/fact-sheets/detail/plague \u003c/li\u003e\n\u003cli\u003eMeka-Mechenko, T.V., Erubaev, T.K., Begimbaeva, E.Zh., Kovaleva, G.G., Abdel, Z.Zh., Sutyagin, V.V., Izbanova, U.A. 2022. Multilevel system of studying plague microbe strains proprties in the Republic of Kazakhstan. \u003cem\u003eProblems of Particularly Dangerous Infections\u003c/em\u003e, 4, 23-28. https://doi.org/10.21055/0370-1069-2022-4-23-28 \u003c/li\u003e\n\u003cli\u003eKeller, M., Spyrou, M.A., Scheib, C.L., Neumann, G.U., Kr\u0026ouml;pelin, A., Haas-Gebhard, B., P\u0026auml;ffgen, B., Haberstroh, J., Ribera I. Lacomba, A., Raynaud, C., Cessford, C., Durand, R., Stadler, P., N\u0026auml;gele, K., Bates, J.S., Trautmann, B., Inskip, S.A., Peters, J., Robb, J.E., Kivisild, T., Castex, D., McCormick, M., Bos, K.I., Harbeck, M., Herbig, A., Krause, J. 2019. Ancient \u003cem\u003eYersinia\u003c/em\u003e\u003cem\u003epestis\u003c/em\u003e genomes from across Western Europe reveal early diversification during the First Pandemic (541-750). \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, 116(25), 12363-12372. https://doi.org/10.1073/pnas.1820447116 \u003c/li\u003e\n\u003cli\u003eVenugopal, G., Pechous, R.D. 2024.\u003cem\u003e \u003c/em\u003e\u003cem\u003eYersinia pestis\u003c/em\u003e and pneumonic plague: Insight into how a lethal pathogen interfaces with innate immune populations in the lung to cause severe disease. \u003cem\u003eCellular Immunology\u003c/em\u003e, 403-404, 104856. https://doi.org/10.1016/j.cellimm.2024.104856 \u003c/li\u003e\n\u003cli\u003eDemeure, C., Dussurget, O., Fiol, G., Le Guern, A., Savin, C., Pizarro-Cerd\u0026aacute;, J. 2019. \u003cem\u003eYersinia pestis\u003c/em\u003e and plague: An updated view on evolution, virulence determinants, immune subversion, vaccination, and diagnostics. \u003cem\u003eGenes and Immunity\u003c/em\u003e, 20(5), 357-370. https://doi.org/10.1038/s41435-019-0065-0 \u003c/li\u003e\n\u003cli\u003eDzhaparova, A.K., Eroshenko, G.A., Nikiforov, K.A., Kukleva, L.M., Alkhova, Zh.V., Berdiev, S.K., Kutyrev, V.V. 2021. Characteristics and philogenetic analysis of \u003cem\u003eYersinia pseudotuberculosis\u003c/em\u003e strains from the Sarydzhaz high-mountain focus in the Tien-Shan. \u003cem\u003eProblems of Particularly Dangerous Infections\u003c/em\u003e, 2, 87-93. https://doi.org/10.21055/0370-1069-2021-2-87-93 \u003c/li\u003e\n\u003cli\u003eSariyeva, G., Bazarkanova, G., Maimulov, R., Abdikarimov, S., Kurmanov, B., Abdirassilova, A., Shabunin, A., Sagiyev, Z., Dzhaparova, A., Abdel, Z., Mussagaliyeva, R., Morand, S., Motin, V., Kosoy, M. 2019. Marmots and \u003cem\u003eYersinia pestis\u003c/em\u003e strains in two plague endemic areas of Tien Shan mountains. \u003cem\u003eFrontiers in Veterinary Science\u003c/em\u003e, 6, 207. https://doi.org/10.3389/fvets.2019.00207 \u003c/li\u003e\n\u003cli\u003eAbdel, Z.Zh., Erubaev, T.K., Tokmurzieva, G.Zh., Aimakhanov, B.K., Dalibaev, Zh.S., Musagalieva, R.S., Zhumadilova, Z.B., Meka-Mechenko, V.G., Meka-Mechenko, T.V., Matzhanova, A.M., Abdrasilova, A.A., Umarova, S.K., Rysbekova, A.K., Esimseit, D.T., Abdeliev, B.Z., Konyratbaev, K.K., Iskakov, B.G., Bely, D.G., Eskermesov, M.K., Kulemin, M.V., Askar, Zh.S., Kaldybaev, T.E., Mukhtarov, R.K., Davletov, S.B., Sutyagin, V.V., Lezdinsh, I.A. 2021. Demarcation of the boundaries of the Central Asian desert natural focus of plague of Kazakhstan and monitoring the areal of the main carrier \u003cem\u003eRhombomys opimus\u003c/em\u003e. \u003cem\u003eProblems of Particularly Dangerous Infections\u003c/em\u003e, 2, 71-78. https://doi.org/10.21055/0370-1069-2021-2-71-78 \u003c/li\u003e\n\u003cli\u003eBalykova, A.N., Katyshev, A.D., Eroshenko, G.A., Kukleva, L.M., Dzhaparova, A.K., Naryshkina, E.A., Oglodin, E.G., Berdiev, S.K., Kutyrev, V.V. 2024. Whole genome sequences of \u003cem\u003eYersinia pestis\u003c/em\u003e strains of ancient phylogenetic branch 0.ANT5 isolated in the 21st century in the Tien-Shan in Kyrgyzstan. \u003cem\u003eMicrobiology Resource Announcements\u003c/em\u003e, 13(10), e00469-24. https://doi.org/10.1128/mra.00469-24 \u003c/li\u003e\n\u003cli\u003eRametov, N.M., Steiner, M., Bizhanova, N.A., Abdel, Z.Zh., Yessimseit, D.T., Abdeliyev, B.Z., Mussagalieva, R.S. 2023. Mapping plague risk using Super Species Distribution Models and forecasts for rodents in the Zhambyl region, Kazakhstan. \u003cem\u003eGeoHealth\u003c/em\u003e, 7(11), e2023GH000853. https://doi.org/10.1029/2023GH000853 \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. 2023. \u003cem\u003eLaboratory biosafety manual\u003c/em\u003e. Geneva: World Health Organization. https://iris.who.int/handle/10665/365602 \u003c/li\u003e\n\u003cli\u003eResolution of the Ministry of Health of the Republic of Kazakhstan No. 8 \u0026ldquo;On Making Amendments and Additions to the Resolution of the Chief State Sanitary Doctor of Kostanay Region No. 5\u0026rdquo;. 2021. https://www.gov.kz/memleket/entities/departament-kkbtu-kostanay/documents/details/138918?lang=ru \u003c/li\u003e\n\u003cli\u003eAtshabar, B., Nurtazhin, S.T., Shevtsov, A., Ramankulov, E.M., Sayakova, Z., Rysbekova, A., Stenseth, N.C., Utepova, I.B., Sadovskaya, V.P., Abdirasilova, A.A., Begimbaeva, E.Z., Abdel, Z.Z. 2021. Populations of the major carrier \u003cem\u003eRhombomys opimus\u003c/em\u003e, vectors of \u003cem\u003eXenopsylla\u003c/em\u003e fleas and the causative agent of \u003cem\u003eYersinia pestisin\u003c/em\u003e the Central Asian desert natural focus of plague. \u003cem\u003eBulletin the National academy of sciences of the Republic of Kazakhstan\u003c/em\u003e, 1(389), 26-34. https://doi.org/10.32014/2021.2518-1467.4 \u003c/li\u003e\n\u003cli\u003eMeka-Mechenko, T.V., Izbanova, U.A., Abdel, Z.Zh., Nakisbekov, N.O., Lukhnova, L.Yu., Baitursyn, B., Dalibayev, Zh.S., Umarova, S.K. 2022. Genotypic properties of collection plague microbes strains from the natural plague foci of Kazakhstan. \u003cem\u003eActs of Biomedical Science\u003c/em\u003e, 7(6), 111-118. https://doi.org/10.29413/ABS.2022-7.6.11 \u003c/li\u003e\n\u003cli\u003eAbdel, Z., Abdeliyev, B., Yessimseit, D., Begimbayeva, E., Mussagalieva, R. 2023. Natural foci of plague in Kazakhstan in the space-time continuum. \u003cem\u003eComparative Immunology, Microbiology and Infectious Diseases\u003c/em\u003e, 100, 102025. https://doi.org/10.1016/j.cimid.2023.102025 \u003c/li\u003e\n\u003cli\u003eSagiyev, Z., Berdibekov, A., Bolger, T., Merekenova, A., Ashirova, S., Nurgozhin, Z., Dalibayev, Zh. 2019. Human response to live plague vaccine EV, Almaty region, Kazakhstan, 2014-2015. \u003cem\u003ePLoS ONE\u003c/em\u003e, 14(6), e0218366. https://doi.org/10.1371/journal.pone.0218366 \u003c/li\u003e\n\u003cli\u003ePauling, C.D., Beerntsen, B.T., Song, Q., Anderson, D.M. 2024. Transovarial transmission of \u003cem\u003eYersinia pestis\u003c/em\u003e in its flea vector \u003cem\u003eXenopsylla cheopis\u003c/em\u003e. \u003cem\u003eNature Communications\u003c/em\u003e, 15, 7266. https://doi.org/10.1038/s41467-024-51668-0 \u003c/li\u003e\n\u003cli\u003eByvalov, A.A., Dudina, L.G., Kravchenko, T.B., Ivanov, S.A., Konyshev, I.V., Morozova, N.A., Chernyadiev, A.V., Dentovskaya, S.V. 2024. The role of \u003cem\u003eYersinia pestis\u003c/em\u003e antigens in the reception of plague diagnostic bacteriophage L-413C. \u003cem\u003eApplied Biochemistry and Microbiology\u003c/em\u003e, 60(4), 740-748. https://doi.org/10.1134/S0003683824604438 \u003c/li\u003e\n\u003cli\u003eSarfraz, A., Qurrat-Ul-Ain Fatima, S., Shehroz, M., Ahmad, I., Zaman, A., Nishan, U., Tayyab, M., Sheheryar, Moura, A.A., Ullah, R., Ali, E.A., Shah, M. 2024. Decrypting the multi-genome data for chimeric vaccine designing against the antibiotic resistant \u003cem\u003eYersinia pestis\u003c/em\u003e. \u003cem\u003eInternational Immunopharmacology\u003c/em\u003e, 132, 111952. https://doi.org/10.1016/j.intimp.2024.111952 \u003c/li\u003e\n\u003cli\u003eNistane, N.T., Kale, M.B., Das, R.J., Umare, M.D., Umekar, M.J., Hemke, A.T., Gajbhiye, V.R. 2024. Herbal remedies: An emerging alternative for the treatment of pandemic diseases. \u003cem\u003eCurrent Traditional Medicine\u003c/em\u003e, 10(6), e030823219384. http://doi.org/10.2174/2215083810666230803101424 \u003c/li\u003e\n\u003cli\u003eAndrades Valtue\u0026ntilde;a, A., Neumann, G.U., Spyrou, M.A., Musralina, L., Aron, F., Beisenov, A., Belinskiy, A.B., Bos, K.I., Buzhilova, A., Conrad, M., Djansugurova, L.B., Dobe\u0026scaron;, M., Ern\u0026eacute;e, M., Fern\u0026aacute;ndez-Eraso, J., Frohlich, B., Furmanek, M., Hałuszko, A., Hansen, S., Harney, \u0026Eacute;., Hiss, A.N., H\u0026uuml;bner, A., Key, F.M., Khussainova, E., Kitov, Y., Kitova, A.O., Knipper, C., K\u0026uuml;hnert, D., Lalueza-Fox, C., Littleton, J., Massy, K., Mittnik, A., Mujika-Alustiza, J.A., Olalde, I., Papac, L., Penske, S., Pe\u0026scaron;ka, J., Pinhasi, R., Reich, D., Reinhold, S., Stahl, R., St\u0026auml;uble, H., Tukhbatova, R.I., Vasilyev, S., Veselovskaya, E., Warinner, C., Stockhammer, P.W., Haak, W., Krause, J., Herbig, A. 2022. Stone Age \u003cem\u003eYersinia pestis\u003c/em\u003e genomes shed light on the early evolution, diversity, and ecology of plague. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, 119(17), e2116722119. https://doi.org/10.1073/pnas.2116722119 \u003c/li\u003e\n\u003cli\u003eHussain, T., Singh, S., Danish, M., Pervez, R., Hussain, K., Husain, R. 2020. Natural metabolites: An eco-friendly approach to manage plant diseases and for better agriculture farming. In: J. Singh, A. Yadav (Eds.), \u003cem\u003eNatural Bioactive Products in Sustainable Agriculture\u003c/em\u003e (pp. 1-13). Singapore: Springer. https://doi.org/10.1007/978-981-15-3024-1_1 \u003c/li\u003e\n\u003cli\u003eTrzilova, D., Tamayo, R. 2021. Site-specific recombination-how simple DNA inversions produce complex phenotypic heterogeneity in bacterial populations. \u003cem\u003eTrend in Genetics\u003c/em\u003e, 37(1), 59-72. https://doi.org/10.1016/j.tig.2020.09.004 \u003c/li\u003e\n\u003cli\u003eZhang, L., Wang, Z., Chang, N., Shang, M., Wei, X., Li, K., Li, J., Lun, X., Ji, H., Liu, Q. 2024. Relationship between climatic factors and the flea index of two plague hosts in Xilingol League, Inner Mongolia Autonomous Region. \u003cem\u003eBiosafety and Health\u003c/em\u003e, 6(4), 244-250. https://doi.org/10.1016/j.bsheal.2024.07.004 \u003c/li\u003e\n\u003cli\u003eBegon, M., Davis, S., Laudisoit, A., Leirs, H., Reijniers, J. 2019. Sylvatic plague in Central Asia: A case study of abundance thresholds. In: K. Wilson, A. Fenton, D. Tompkins (Eds.), \u003cem\u003eWildlife Disease Ecology: Linking Theory to Data and Application\u003c/em\u003e (pp. 623-643). Cambridge: Cambridge University Press. https://doi.org/10.1017/9781316479964.022 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Yersinia pestis, phenotype, genotype, biorepository, molecular epidemiology, natural plague foci, Kazakhstan","lastPublishedDoi":"10.21203/rs.3.rs-6864612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6864612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comprehensive analysis of \u003cem\u003eYersinia pestis\u003c/em\u003e (Y. pestis) strains circulating in the natural plague foci of Kazakhstan, based on integrated epizootological monitoring, microbiological, and advanced molecular genetic methods. The research aims to assess the genetic biodiversity of \u003cem\u003eY. pestis\u003c/em\u003e, evaluate the effectiveness of analytical approaches, and develop a structured algorithm for the application of genotyping tools in building a national biorepository of natural isolates. Uniquely, the study combines classical microbiological techniques with high-throughput technologies including PCR, MLVA (VNTR), VITEK 2 Compact, MiniION (Oxford Nanopore), MiSeq (Illumina), and GIS-based spatial mapping.\u003c/p\u003e \u003cp\u003eA total of 1,220 \u003cem\u003eY. pestis\u003c/em\u003e strains (2010\u0026ndash;2023) were phenotypically and genotypically characterized, revealing that 94.8% were typical for their ecological settings, while 5.2% exhibited deviations. Additionally, whole-genome and multilocus analyses of 82 DNA samples allowed for the construction of three phylogenetic trees and GIS-integrated visualizations, offering new insights into the spatial and temporal dynamics of plague in Central Asia. A genetic repository was established, forming the foundation for future research on the evolution, distribution, and risk of plague in endemic regions. These findings represent the first large-scale genomic profiling of \u003cem\u003eY. pestis\u003c/em\u003e in Kazakhstan and provide essential tools for public health surveillance and biosecurity.\u003c/p\u003e","manuscriptTitle":"Spatial and Temporal Characteristics of Yersinia pestis Strain Properties in the Natural Plague Foci of Kazakhstan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 11:26:17","doi":"10.21203/rs.3.rs-6864612/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d45645aa-f72b-494c-9593-b0771bae91b2","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49830531,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2025-06-11T11:26:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-11 11:26:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6864612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6864612","identity":"rs-6864612","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}