8 research outputs found
Substantiation of technology for pates of milt from salmon fishes
Salmon milt is a valued raw material with high content of biologically active nucleoproteins, as DNA and RNA, and lipids enriched by phospholipids, sterols, fat-soluble vitamins, and polyene fatty acids, therefore development of new products on the base of fish milt with high nutritional value, caloric content and good organoleptic properties is actual for fish industry. New technology for pates of the milt from salmon fishes is presented. Its key condition is an optimal ratio between protein, fat and water necessary for stability of the pate mass. There is determined that this ratio is provided by the milt : oil : water ratio as (45-55) : (15-20) : (15-20). So far as the milt has incomplete composition of proteins, some chopped muscle tissue of salmon (5-20 %) should be added to the raw material for the pates to heighten their nutritional value and to improve their organoleptic properties. Besides, 10-15 % of shrimps, scallops and squids should be added to the raw material to enhance biological value of the pates. Optimal time for the pates emulsification before thermal processing is determined as 7-9 minutes that ensures their homogeneous, tender structure. Several recipes for pates of salmon milt are proposed. Thermal treatment of the pates is recommended under the temperature 85oC until the temperature inside the pate mass reaches 72oC. The finished products can be stored up to 72 hours under the temperature 2-6oC
Comparative study of chemical and functional-technological properties for milts of commercial fishes
Milts are produced as food waste after processing of such raw fishes of high nutritional value as salmons, cod, and herring. Their using as a basement for food products is a perspective way of fish-processing technology development. Chemical and function-technology properties of chopped milt are investigated to determine their suitability for producing of food products, as sausages, pates, souses, and pastes and these properties are compared for the milts from salmon, cod, and herring. The milts are 3-26 % of the raw fish weight for these commercial species that is a significant amount of food waste. The milt tissue is slightly saturated by fats (coefficient of food saturation is 0.3 for all investigated fishes) and highly saturated by water, so the milt should be combined with other raw materials for increasing the food value. The milt lipids differ from the lipids of muscular tissue by high concentration of essential polyunsaturated fatty acids with five and six connections; the amount of these fatty acids is twice higher than the amount of saturated ones (49-52 % of total fatty acids). Moreover, the milt lipids have high coefficients of metabolism comparing with the lipids of muscular tissue, thatβs why they are easy digested by human. The milts of all investigated fish species are distinguished by ability for fat emulsification. These high functional-technological properties allow to recommend the milts of salmons, cod and herring for high spectrum of food products, including emulsion ones
Investigation of functional and technological properties of crushed milts
Fresh milts of salmon, cod, and herring possess high technological properties which are lowered in the processes of freezing and storage. The lowering is insignificant in the first month of storage but becomes more essential after two months of storage, in particular for the milt of cod. One of these properties is a high emulsifying ability. Stability of the emulsion systems with use of milt depends on the fish species, freshness of the raw materials, and preliminary thermal processing: the emulsions with milt of salmon have higher stability relative to the milts of other species and the unprocessed milt provides the highest stability of the emulsion systems. The milts could be used in emulsified products both as the emulsifiers and as functional ingredients that include proteins, nucleic acids, phospholipids, and polyunsaturated fatty acids
Prevalence and spatiotemporal dynamics of HIV-1 Circulating Recombinant Form 03_AB (CRF03_AB) in the Former Soviet Union countries.
BackgroundHIV-1 circulating recombinant forms (CRFs) infections has been increasing in Former Soviet Union (FSU) countries in the recent decade. One is the CRF03_AB, which circulated in the region since late 1990s and probably became widespread in northwestern FSU countries. However, there is not much information provided about the dissemination of this recombinant. Here, we examine the prevalence, evolutionary dynamics and dispersion pattern of HIV-1 CRF03_AB recombinant.MethodsWe analyzed 32 independent studies and 151 HIV-1 CRF03_AB pol sequences isolated from different FSU countries over a period of 22 years. Pooled prevalence was estimated using a random effects model. Bayesian coalescent-based method was used to estimate the evolutionary, phylogeographic and demographic parameters.ResultsOur meta-analysis showed that the pooled prevalence of CRF03_AB infection in northwestern FSU region was 5.9% [95%CI: 4.1-7.8]. Lithuania (11.6%), Russia (5.9%) and Belarus (2.9%) were the most affected by CRF03_AB. We found that early region wide spread of HIV-1 CRF03_AB originated from one viral clade that arose in the city of Kaliningrad in 1992 [95%HPD: 1990-1995]. Fourteen migration route of this variant were found. The city of Kaliningrad is involved in most of these, confirming its leading role in CRF03_AB spread within FSU. Demographic reconstruction point to this is that CRF03_AB clade seems to have experienced an exponential growth until the mid-2000s and a decrease in recent years.ConclusionThese data provide new insights into the molecular epidemiology of CRF03_AB as well as contributing to the fundamental understanding of HIV epidemic in FSU
Correction: Prevalence and spatiotemporal dynamics of HIV-1 Circulating Recombinant Form 03_AB (CRF03_AB) in the Former Soviet Union countries.
[This corrects the article DOI: 10.1371/journal.pone.0241269.]
1H-NMR analysis of feces: new possibilities in the helminthes infections research
Abstract Background Analysis of the stool samples is an essential part of routine diagnostics of the helminthes infections. However, the standard methods such Kato and Kato-Katz utilize only a fraction of the information available. Here we present a method based on the nuclear magnetic resonance spectroscopy (NMR) which could be auxiliary to the standard procedures by evaluating the complex metabolic profiles (or phenotypes) of the samples. Method The samples were collected over the period of June-July 2015, frozen at β20Β Β°C at the site of collection and transferred within four hours for the permanent storage at β80Β Β°C. Fecal metabolites were extracted by mixing aliquots of about 100Β mg thawed stool material with 0.5Β mL phosphate buffer saline, followed by the homogenization and centrifugations steps. All NMR data were recorded using a Bruker 600Β MHz AVANCE II spectrometer equipped with a 5Β mm triple resonance inverse cryoprobe and a z-gradient system. Results Here we report an optimized method for NMR based metabolic profiling/phenotyping of the stools samples. Overall, 62 metabolites were annotated in the pool sample using the 2D NMR spectra and the Bruker Biorefcode database. The compounds cover a wide range of the metabolome including amino acids and their derivatives, short chain fatty acids (SCFAs), carboxylic acids and their derivatives, amines, carbohydrates, purines, alcohols and others. An exploratory analysis of the metabolic profiles reveals no strong trends associated with the infection status of the patients. However, using the penalized regression as a variable selection method we succeeded in finding a subset of eleven variables which enables to discriminate the patients on basis of their infections status. Conclusions A simple method for metabolic profiling/phenotyping of the stools samples is reported and tested on a pilot opisthorchiasis cohort. To our knowledge this is the first report of a NMR-based feces analysis in the context of the helminthic infections
[Studying the structure of a gene pool population of the Russian White chicken breed by genome-wide SNP scan] ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΡΡΡΠΊΡΡΡΡ Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΡΡΡΠΊΠΎΠΉ Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ ΠΊΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ SNP-ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
A population of the Russian White chickens, bred at the gene pool farm of ARRIFAGB for 25 generations using individual selection, is characterized by resistance to a lowered temperature in the early postnatal period and white colour of the embryonic down. In 2002-2012, breeding was carried out by panmixia, and by now a new population of the Russian White chickens has been formed on the basis of the surviving stock. Comparison of the genetic variability of this population and the archival DNA of representatives of the 2001 population using microarray screening technology will help to assess the population structure and the preservation of the unique characteristics of its genome. The material for the study was DNA extracted from 162 chicken blood samples. Two groups of the Russian White breed were studied, the 2001 population and the current population. Genome-wide analysis using single nucleotide markers (SNP) included screening by means of the Illumina Chicken 60K SNP iSelect BeadChip microarray. Quality control of genotyping, determination of the population genetic structure by multidimensional scaling (MDS), calculation of linkage disequilibrium (LD) and allele frequency in the groups were carried out using PLINK 1.9 software program. The construction of a cluster delimitation model based on SNP genotypes was carried out using the ADMIXTURE program. According to the MDS analysis results, the current population can be divided into four MDS groups, which, when compared to the data of the pedigree, adequately reflect the origin of the studied individuals. The representatives of the ancestral population were genetically similar to the MDS3 group of the current population. Using the F-statistic of the two-way analysis of variance, a significant effect of the group, chromosome, chromosome in the group, and the distance between SNP markers on LD (r2) values was observed. In the 2001 group, the maximum r2 and the high incidence of LD equal to 1 were observed for all chromosomes, with a distance between SNP markers being 500-1000 Kb. There was also the greatest number of monomorphic alleles in this group. Based on the SNP analysis, we may conclude that the current Russian White chicken population is characterized by the disintegration of long LD regions of the ancestral population. Modelling clusters using the ADMIXTURE program revealed differences between the current population groups determined by MDS analysis. The groups composed of individuals included in MDS1 and MDS2 had a homogeneous structure and differed from each other at K = 4 and K = 5. The MDS4 group formed a genetically heterogeneous cluster different from the MDS1 and MDS2 groups at K of 2-5. The MDS3 group was phylogenetically close to the 2001 population (at K of 2-5). In general, the analysis of the current gene pool population of the Russian White chickens showed its heterogeneity while one of its groups (MDS3) was similar to the ancestral population of 2001, which in turn is characterized by a large number of monomorphic alleles and a high frequency of long LD regions. Thus, SNP scanning allowed evaluating the genetic similarity of individuals and the population structure of the Russian White chicken breed. Understanding the genetic structure is an important point in the panmictic breeding and tracking of historical changes in the molecular organization of the genome of a gene pool population with a limited number of animals.
ΠΠΎΠΏΡΠ»ΡΡΠΈΡ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ ΡΠ΅Π»Π΅ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π»Π°ΡΡ Π² Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΌ Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅ ΠΡΠ΅ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΠΠΠ Π³Π΅Π½Π΅ΡΠΈΠΊΠΈ ΠΈ ΡΠ°Π·Π²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
(ΠΠΠΠΠΠ Π) Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 25 ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Π±ΠΎΡΠ°. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΡΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ β ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΡ ΠΊ ΠΏΠΎΠ½ΠΈΠΆΠ΅Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ΅ Π²ΡΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ Π² ΡΠ°Π½Π½ΠΈΠΉ ΠΏΠΎΡΡΠ½Π°ΡΠ°Π»ΡΠ½ΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ ΠΈ Π±Π΅Π»ΡΠΉ ΡΠ²Π΅Ρ ΡΠΌΠ±ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡ
Π°. Π 2002-2012 Π³ΠΎΠ΄Π°Ρ
Π΅Π΅ ΡΠ°Π·Π²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΠΎΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΏΠ°Π½ΠΌΠΈΠΊΡΠΈΠΈ, ΠΈ ΠΊ Π½Π°ΡΡΠΎΡΡΠ΅ΠΌΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΎΡ
ΡΠ°Π½ΠΈΠ²ΡΠ΅Π³ΠΎΡΡ ΠΏΠΎΠ³ΠΎΠ»ΠΎΠ²ΡΡ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π° Π½ΠΎΠ²Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ. ΠΠ°ΡΠ΅ΠΉ ΡΠ΅Π»ΡΡ Π±ΡΠ»ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ SNP-ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ (single nucleotide polymorphisms) Π΄Π»Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΡΡΡΡΠΊΡΡΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΌΠ°Π»ΠΎΡΠΈΡΠ»Π΅Π½Π½ΡΡ
ΠΏΠΎΡΠΎΠ΄ ΠΊΡΡ ΠΎΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠΈΡΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎΠΉ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΡΡΡΠΊΠΎΠΉ Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ Ρ ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠ΅ΠΉ 2001 Π³ΠΎΠ΄Π°. ΠΡΠ»ΠΈ ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Π΄Π²Π΅ Π³ΡΡΠΏΠΏΡ ΠΊΡΡ: ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ 2001 Π³ΠΎΠ΄Π° (6 Π³ΠΎΠ»., Π½Π΅ΡΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΡΠΎΠ±ΠΈ ΠΈΠ· Π΄Π²ΡΡ
Π»ΠΈΠ½ΠΈΠΉ) ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ (156 Π³ΠΎΠ».). SNP-Π°Π½Π°Π»ΠΈΠ· Π²ΠΊΠ»ΡΡΠ°Π» ΡΠΊΡΠΈΠ½ΠΈΠ½Π³ 162 ΠΎΠ±ΡΠ°Π·ΡΠΎΠ² ΠΠΠ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠΈΠΊΡΠΎΡΠΈΠΏΠ° Illumina Chicken 60K SNP iSelect BeadChip (Β«IlluminaΒ», Π‘Π¨Π). ΠΠΎΠ½ΡΡΠΎΠ»Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π³Π΅Π½ΠΎΡΠΈΠΏΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΡΠΊΡΡΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΊΠ°Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ (multidimensional scaling, MDS), ΡΠ°ΡΡΠ΅Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π½Π΅ΡΠ°Π²Π½ΠΎΠ²Π΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ΅ΠΏΠ»Π΅Π½ΠΈΡ (linkage disequilibrium, LD) ΠΈ ΡΠ°ΡΡΠΎΡΡ Π²ΡΡΡΠ΅ΡΠ°Π΅ΠΌΠΎΡΡΠΈ Π°Π»Π»Π΅Π΅ΠΉ ΠΏΠΎ Π³ΡΡΠΏΠΏΠ°ΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ PLINK 1.9. ΠΠΎΡΡΡΠΎΠ΅Π½ΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ°Π·Π³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ SNP-Π³Π΅Π½ΠΎΡΠΈΠΏΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ADMIXTURE. ΠΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ MDS-Π°Π½Π°Π»ΠΈΠ·Π° ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ Π±ΡΠ»Π° ΡΡΠ»ΠΎΠ²Π½ΠΎ ΡΠ°Π·Π΄Π΅Π»Π΅Π½Π° Π½Π° ΡΠ΅ΡΡΡΠ΅ MDS-Π³ΡΡΠΏΠΏΡ, ΡΡΠΎ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π΄Π°Π½Π½ΡΠΌΠΈ ΡΠΎΠ΄ΠΎΡΠ»ΠΎΠ²Π½ΠΎΠΉ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎ ΠΎΡΡΠ°ΠΆΠ°Π΅Ρ ΠΏΡΠΎΠΈΡΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΈΠ·ΡΡΠ΅Π½Π½ΡΡ
ΠΎΡΠΎΠ±Π΅ΠΉ. ΠΡΠ΅Π΄ΡΡΠ°Π²ΠΈΡΠ΅Π»ΠΈ ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Π±ΡΠ»ΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΡ
ΠΎΠ΄Π½Ρ Ρ Π³ΡΡΠΏΠΏΠΎΠΉ MDS3. Π‘ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ F-ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΠΌΠ½ΠΎΠ³ΠΎΡΠ°ΠΊΡΠΎΡΠ½ΠΎΠ³ΠΎ Π΄ΠΈΡΠΏΠ΅ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π²ΡΡΠ²Π»Π΅Π½ΠΎ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π³ΡΡΠΏΠΏΡ, Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΡ, Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΡ Π² Π³ΡΡΠΏΠΏΠ΅ ΠΈ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ SNP-ΠΌΠ°ΡΠΊΠ΅ΡΠ°ΠΌΠΈ Π½Π° Π·Π½Π°ΡΠ΅Π½ΠΈΡ LD (r2). Π Π³ΡΡΠΏΠΏΠ΅ 2001 Π³ΠΎΠ΄Π° ΠΏΠΎ Π²ΡΠ΅ΠΌ Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΠ°ΠΌ Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΈΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ r2 ΠΈ Π²ΡΡΠΎΠΊΠ°Ρ ΡΠ°ΡΡΠΎΡΠ° Π²ΡΡΡΠ΅ΡΠ°Π΅ΠΌΠΎΡΡΠΈ LD, ΡΠ°Π²Π½ΠΎΠ³ΠΎ 1, ΠΏΡΠΈ ΡΠ°ΡΡΡΠΎΡΠ½ΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ SNP-ΠΌΠ°ΡΠΊΠ΅ΡΠ°ΠΌΠΈ 500-1000 ΠΠ±. ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΌΠΎΠ½ΠΎΠΌΠΎΡΡΠ½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ Π² ΡΡΠΎΠΉ Π³ΡΡΠΏΠΏΠ΅ ΡΠ°ΠΊΠΆΠ΅ Π±ΡΠ»ΠΎ ΡΠ°ΠΌΡΠΌ Π²ΡΡΠΎΠΊΠΈΠΌ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ SNP-Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΠ΅ΡΡΡ ΡΠ°ΡΠΏΠ°Π΄ΠΎΠΌ Π΄Π»ΠΈΠ½Π½ΡΡ
LD-ΡΠ°ΠΉΠΎΠ½ΠΎΠ² ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ. ΠΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ ADMIXTURE Π²ΡΡΠ²ΠΈΠ»ΠΎ ΡΠ°Π·Π»ΠΈΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠΌΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ MDS-Π°Π½Π°Π»ΠΈΠ·Π°. ΠΡΡΠΏΠΏΡ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΈΠ· ΠΎΡΠΎΠ±Π΅ΠΉ, Π²Ρ
ΠΎΠ΄ΡΡΠΈΡ
Π² MDS1 ΠΈ MDS2, ΠΈΠΌΠ΅Π»ΠΈ ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΡ ΡΡΡΡΠΊΡΡΡΡ ΠΈ ΡΠ°Π·Π»ΠΈΡΠ°Π»ΠΈΡΡ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΎΠ±ΠΎΠΉ ΠΏΡΠΈ K = 4 ΠΈ K = 5. ΠΡΡΠΏΠΏΠ° MDS4 ΠΎΠ±ΡΠ°Π·ΠΎΠ²ΡΠ²Π°Π»Π° Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΠΉ ΠΊΠ»Π°ΡΡΠ΅Ρ, ΠΎΡΠ»ΠΈΡΠ°ΡΡΠΈΠΉΡΡ ΠΎΡ Π³ΡΡΠΏΠΏ MDS1 ΠΈ MDS2 ΠΏΡΠΈ Π·Π½Π°ΡΠ΅Π½ΠΈΡΡ
K ΠΎΡ 2 Π΄ΠΎ 5. ΠΡΡΠΏΠΏΠ° MDS3 Π±ΡΠ»Π° ΡΠΈΠ»ΠΎΠ³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈ Π±Π»ΠΈΠ·ΠΊΠ° ΠΊ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ 2001 Π³ΠΎΠ΄Π° (ΠΏΡΠΈ K ΠΎΡ 2 Π΄ΠΎ 5). Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, Π°Π½Π°Π»ΠΈΠ· ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π» Π΅Π΅ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΠΎΡΡΡ ΠΈ ΡΡ
ΠΎΠ΄ΡΡΠ²ΠΎ Π³ΡΡΠΏΠΏΡ MDS3 Ρ ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠ΅ΠΉ 2001 Π³ΠΎΠ΄Π°, ΠΊΠΎΡΠΎΡΠ°Ρ, Π² ΡΠ²ΠΎΡ ΠΎΡΠ΅ΡΠ΅Π΄Ρ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π»Π°ΡΡ Π±ΠΎΠ»ΡΡΠΈΠΌ ΡΠΈΡΠ»ΠΎΠΌ ΠΌΠΎΠ½ΠΎΠΌΠΎΡΡΠ½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΈ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠ°ΡΡΠΎΡΠΎΠΉ Π²ΡΡΡΠ΅ΡΠ°Π΅ΠΌΠΎΡΡΠΈ Π΄Π»ΠΈΠ½Π½ΡΡ
LD-ΡΠ°ΠΉΠΎΠ½ΠΎΠ². SNP-ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΎΡΠ΅Π½ΠΈΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΡ
ΠΎΠ΄ΡΡΠ²ΠΎ ΠΎΡΠΎΠ±Π΅ΠΉ ΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΡ ΡΡΡΡΠΊΡΡΡΡ ΡΡΡΡΠΊΠΎΠΉ Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ ΠΊΡΡ. ΠΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΡΠΊΡΡΡΡ Π²Π°ΠΆΠ½ΠΎ ΠΏΡΠΈ ΠΏΠ°Π½ΠΌΠΈΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ°Π·Π²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΈ ΠΎΡΡΠ»Π΅ΠΆΠΈΠ²Π°Π½ΠΈΠΈ ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π³Π΅Π½ΠΎΠΌΠ° Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Ρ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΡΠΌ ΠΏΠΎΠ³ΠΎΠ»ΠΎΠ²ΡΠ΅ΠΌ
HIV-1 genotyping tropism profile in an HIV-positive population throughout the Russian Federation
<p>Most HIV-1 tropism studies have involved non-A subtypes. Our aim was to study the prevalence of R5- and non-R5-tropic HIV-1 variants and the tropism occurrence relative to the CD4 counts, treatment experiences, transmission routes and other features of infection in Russia, where subtype A is presumably predominant. In this multicenter, single-step, cross-sectional, epidemiologic study, 943 HIV-1-infected patients were enrolled at 12 AIDS centers throughout Russia. Viral tropism was determined using a genotype method-based kit. The V3 loop sequences were analyzed using the geno2pheno resource. The tropism was successfully predicted for 823 (87.3%) patients. Frequencies of R5-tropic and non-R5-tropic viruses in successfully analyzed samples were 70.2% (578) and 29.8% (245), respectively. Co-receptor usage correlated significantly only with the treatment experiences (<i>p</i>Β =Β 0.018) and CD4 counts (<i>p</i>Β =Β 0.004). But there was no dependence of R5/non-R5 co-receptor usage frequencies on presence/absence of a therapy change (<i>p</i>Β =Β 0.664) or HIV infection duration (<i>p</i>Β =Β 0.458). According to the env sequences, 457 (83.6%) of the samples in study were subtype A and 70 (12.8%) were subtype B. This indicates a stabilizing of immune system and thus little emergence of X4 viruses. We suggest that CCR5-antagonists could be used in both naΓ―ve and experienced patients in Russia after determination of HIV tropism.</p