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Record W4388667350 · doi:10.1371/journal.pone.0291378

Genetic diversity, haplotype analysis, and prevalence of Hepatitis B virus MHR mutations among isolates from Kenyan blood donors

2023· article· en· W4388667350 on OpenAlexfundno aff
Benard Langat, Kevin O. Ochwedo, Jamie Borlang, Carla Osiowy, Alex Mutai, Fredrick Okoth, Edward Muge, Anton Andonov, Elijah Songok Maritim

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsnot available
FundersNational Commission for Science, Technology and InnovationPublic Health AgencyPublic Health Agency of Canada
KeywordsHBsAgKenyaGenotypeHaplotypeHepatitis B virusGenetic diversityPopulationBiologyVirologyHepatitis BImmunologyMedicineGeneticsGeneVirusEnvironmental health

Abstract

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BACKGROUND: The rapid spread of HBV has resulted in the emergence of new variants. These viral genotypes and variants, in addition to carcinogenic risk, can be key predictors of therapy response and outcomes. As a result, a better knowledge of these emerging HBV traits will aid in the development of a treatment for HBV infection. However, many Sub-Saharan African nations, including Kenya, have insufficient molecular data on HBV strains circulating locally. This study conducted a population-genetics analysis to evaluate the genetic diversity of HBV among Kenyan blood donors. In addition, within the same cohort, the incidence and features of immune-associated escape mutations and stop-codons in Hepatitis B surface antigen (HBsAg) were determined. METHODS: In September 2015 to October 2016, 194 serum samples were obtained from HBsAg-positive blood donors residing in eleven different Kenyan counties: Kisumu, Machakos, Uasin Gishu, Nairobi, Nakuru, Embu, Garissa, Kisii, Mombasa, Nyeri, and Turkana. For the HBV surface (S) gene, HBV DNA was isolated, amplified, and sequenced. The sequences obtained were utilized to investigate the genetic and haplotype diversity within the S genes. RESULTS: Among the blood donors, 74.74% were male, and the overall mean age was 25.36 years. HBV genotype A1 (88.14%) was the most common, followed by genotype D (10.82%), genotype C (0.52%), and HBV genotype E (0.52%). The phylogenetic analysis revealed twelve major clades, with cluster III comprising solely of 68 blood donor isolates (68/194-35.05%). A high haplotype diversity (Hd = 0.94) and low nucleotide diversity (π = 0.02) were observed. Kisumu county had high number of haplotypes (22), but low haplotype (gene) diversity (Hd = 0.90). Generally, a total of 90 haplotypes with some consisting of more than one sequence were observed. The gene exhibited negative values for Tajima's D (-2.04, p<0.05) and Fu's Fs (-88.84). Several mutations were found in 139 isolates, either within or outside the Major Hydrophilic Area (MHR). There were 29 mutations found, with 37.9% of them situated inside the "a" determinant. The most common mutations in this research were T143M and K122R. Escape mutations linked to diagnostic failure, vaccination and immunoglobulin treatment evasion were also discovered. Also, one stop-codon, W163STP, inside the MHR, was found in one sample from genotype A. CONCLUSION: In Kenya, HBV/A1 is still the most common genotype. Despite limited genetic and nucleotide diversity, haplotype network analysis revealed haplotype variance among HBV genotypes from Kenyan blood donors. The virological properties of immune escape, which may be the source of viral replication endurance, were discovered in the viral strains studied and included immune-escape mutations and stop-codon. The discovery of HBsAg mutations in MHR in all isolates highlighted the need of monitoring MHR mutations in Kenya.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.237
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2023
Admission routes1
Has abstractyes

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