89 research outputs found
An ARID family protein binds to the African swine fever virus encoded ubiquitin conjugating enzyme, UBCv1
No full textAbstractThe NH2-terminal end of a protein, named SMCp, which contains an ARID (A/T rich interaction domain) DNA binding domain and is similar to the mammalian SMCY/SMCX proteins and retinoblastoma binding protein 2, was shown to bind the African swine fever virus encoded ubiquitin conjugating enzyme (UBCv1) using the yeast two hybrid system and in in vitro binding assays. Antisera raised against the SMCp protein were used to show that the protein is present in the cell nucleus. Immunofluorescence showed that although UBCv1 is present in the nucleus in most cells, in some cells it is in the cytoplasm, suggesting that it shuttles between the nucleus and cytoplasm. The interaction and co-localisation of UBCv1 with SMCp suggest that SMCp may be a substrate in vivo for the enzyme
Assessing antigenic drift and phylogeny of influenza A (H1N1) pdm09 virus in Kenya using HA1 sub-unit of the hemagglutinin gene
No full textInfluenza A (H1N1) pdm09 virus emerged in North America in 2009 and has been established as a seasonal strain in humans. After an antigenic stasis of about six years, new antigenically distinct variants of the virus emerged globally in 2016 necessitating a change in the vaccine formulation for the first time in 2017. Herein, we analyzed thirty-eight HA sequences of influenza A (H1N1) pdm09 strains isolated in Kenya during 2015–2018 seasons, to evaluate their antigenic and molecular properties based on the HA1 sub-unit. Our analyses revealed that the A (H1N1) pdm09 strains that circulated in Kenya during this period belonged to genetic clade 6B, subclade 6B.1 and 6B.2. The Kenyan 2015 and 2016 isolates differed from the vaccine strain A/California/07/2009 at nine and fourteen antigenic sites in the HA1 respectively. Further, those isolated in 2017 and 2018 correspondingly varied from A/Michigan/45/2015 vaccine strain at three and fifteen antigenic sites. The predicted vaccine efficacy of A/California/07/2009 against Kenyan 2015/2016 was estimated to be 32.4% while A/Michigan/45/2015 showed estimated vaccine efficacies of 39.6% - 41.8% and 32.4% - 42.1% against Kenyan 2017 and 2018 strains, respectively. Hemagglutination-inhibition (HAI) assay using ferret post-infection reference antiserum showed that the titers for the Kenyan 2015/2016 isolates were 2–8-fold lower compared to the vaccine strain. Overall, our results suggest the A (H1N1) pdm09 viruses that circulated in Kenya during 2015/2016 influenza seasons were antigenic variants of the recommended vaccine strains, denoting sub-optimal vaccine efficacy. Additionally, data generated point to a swiftly evolving influenza A (H1N1) pdm09 virus in recent post pandemic era, underscoring the need for sustained surveillance coupled with molecular and antigenic analyses, to inform appropriate and timely influenza vaccine update.</div
Amplification of 1-amino-cyclopropane-1-carboxylic (ACC) deaminase from plant growth promoting rhizobacteria in Striga-infested soil
Get PDFExperiments were conducted in pots to determine the growth effect of different rhizobacteria on maize under Striga hermonthica infestation. Three bacteria were selected based on their plant growth promoting effects. Whole bacterial cells of the rhizobacteria were used to amplify 1-amino-cyclopropane-1-carboxylic acid (ACC) deaminase gene by polymerase chain reaction (PCR). Each bacterial inoculation increased agronomic characteristics of maize although not always to a statistically significant extent. The extent of growth enhancement differs between the isolates. Enterobacter sakazakii 8MR5 had the ability to stimulate plant growth, however in the PCR study, ACC deaminase was not amplified from this isolate, indicating that not all plant growth-promoting rhizobacteria contain the enzyme ACC deaminase. In contrast, an ACC deaminase specific product was amplified from Pseudomonas sp. 4MKS8 and Klebsiella oxytoca 10MKR7. This is the first report of ACC deaminase in K. oxytoca
Assessing antigenic drift and phylogeny of influenza A (H1N1) pdm09 virus in Kenya using HA1 sub-unit of the hemagglutinin gene - Fig 2
No full textWebLogo depicting frequency of amino acid changes at the epitope sites (A-E) within the HA1 protein of Kenyan influenza A (H1N1) pdm09 strains isolated between 2015 and 2018. Amino acid alignment positions along the x-axis in (A) indicate variable sites among Kenyan 2015–2016 strains relative to the vaccine strain A/California/2009 while those in (B) depict variable sites among the 2017–2018 isolates relative to the vaccine strain A/Michigan/45/2015. The height of the residue indicates the relative frequency of each amino acid at that particular position. These graphics were created using WebLogo (https://weblogo.berkeley.edu/).</p
Amplification of 1-amino-cyclopropane-1-carboxylic (ACC) deaminase from plant growth promoting rhizobacteria in Striga-infested soil
Get PDFExperiments were conducted in pots to determine the growth effect of different rhizobacteria on maize under Striga hermonthica infestation. Three bacteria were selected based on their plant growth promoting effects. Whole bacterial cells of the rhizobacteria were used to amplify 1-amino-cyclopropane-1-carboxylic acid (ACC) deaminase gene by polymerase chain reaction (PCR). Each bacterial inoculation increased agronomic characteristics of maize although not always to a statistically significant extent. The extent of growth enhancement differs between the isolates. Enterobacter sakazakii 8MR5 had the ability to stimulate plant growth, however in the PCR study, ACC deaminase was not amplified from this isolate, indicating that not all plant growth-promoting rhizobacteria contain the enzyme ACC deaminase. In contrast, an ACC deaminase specific product was amplified from Pseudomonas sp. 4MKS8 and Klebsiella oxytoca 10MKR7. This is the first report of ACC deaminase in K. oxytoca.
Key words: 1-amino-cyclopropane-1-carboxylic acid, ACC deaminase, PCR, rhizobacteria, Striga hermonthica.
(African Journal of Biotechnology: 2003 2(6): 157-160
Effect of ABCB1 C3435T Polymorphism on Clinical Outcomes in Kenyan HIV Patients on Lopinavir-Based Regimens
No full textChemometrics-Enabled Raman Spectrometric Qualitative Determination and Assessment of Biochemical Alterations during Early Prostate Cancer Proliferation in Model Tissue
No full textThe use of Raman spectroscopy combined with multivariate chemometrics for disease diagnosis has attracted great attention from researchers in recent years. This is because it is a noninvasive and nondestructive detection approach with enhanced sensitivity. However, a major challenge when analyzing spectra from biological samples has been the detection of subtle biochemical alterations buried in background and fluorescence noise. This work reports a qualitative chemometrics-assisted investigation of subtle biochemical alterations associated with prostate malignancy in model biological tissue (metastatic androgen insensitive (PC3) and immortalized normal (PNT1a) prostate cell lines). Raman spectra were acquired from PC3 and PNT1a cells at various stages of growth, and their biochemical alterations were determined from difference spectra between the two cell lines (for prominent alterations) and principal component analysis (PCA) (for subtle alterations). The Raman difference spectra were computed by subtracting the normalized mean spectral intensities of PNT1a cells from the normalized mean spectral intensities of PC3 cells. These difference spectra revealed prominent biochemical alterations associated with the malignant PC3 cells at 566 ± 0.70 cm−1, 630 cm−1, 1370 ± 0.86 cm−1, and 1618 ± 1.73 cm−1 bands. The band intensity ratios at 566 ± 0.70 cm−1 and 630 cm−1 suggested that prostate malignancy can be associated with an increase in relative amounts of nucleic acids and lipids, respectively, whereas those at 1370 ± 0.86 cm−1 and 1618 ± 1.73 cm−1 suggested that prostate malignancy can be associated with a decrease in relative amounts of saccharides and tryptophan, respectively. In the analysis using PCA, intermediate-order and high-order principal components (PCs) were used to extract the subtle biochemical fingerprints associated with the cell lines. This revealed subtle biochemical differences at 1076 cm−1, (1232, 1234 cm−1), (1276, 1278 cm−1), (1330, 1333 cm−1), (1434, 1442 cm−1), and (1471, 1479 cm−1). The band intensity ratios at 1076 cm−1 and 1232 cm−1 suggested that prostate malignancy can be associated with an increase in subtle amounts of nucleic acids and amide III components, respectively. The method reported here has demonstrated that subtle biochemical alterations can be extracted from Raman spectra of normal and malignant cell lines. The identified subtle bands could play an important role in quantitative monitoring of early biomarker alterations associated with prostate cancer proliferation
Molecular characterization of human coronaviruses and their circulation dynamics in Kenya, 2009–2012
No full textData_Sheet_1_Mathematical modelling of non-pharmaceutical interventions to control infectious diseases: application to COVID-19 in Kenya.docx
No full textIntroductionThe first case of COVID-19 in Kenya was reported on March 13, 2020, prompting the collection of baseline data during the initial spread of the disease. Subsequently, the Kenyan government implemented non-pharmaceutical interventions (NPIs) on April 9, 2020, to mitigate disease transmission over a two-month period. These measures were later gradually relaxed starting from June 9, 2020.MethodsWe applied a deterministic mathematical model to simulate the dynamics of COVID-19 transmission in Kenya. Using baseline data, we estimated transmission and recovery rates and proposed a mathematical model of how NPIs affect disease transmission rates. The model extends to interventions that yield an increase in disease transmission, unlike previous models that were limited to a decrease in transmission. We computed the mitigation and relaxation fractions and hence deduced the impact of the interventions.ResultsThe mitigation measures imposed from April 9, 2020, reduced the disease transmission by 43.7% from the baseline level, while the relaxation from June 9, 2020, increased the transmission by 32% over the mitigation level. Without intervention, the model predicts that infections would have peaked at 30% by late May 2020. However, due to the combined effect of mitigation and relaxation, the epidemic peaked at 13% infection in mid-July 2020.DiscussionThe model’s projections closely align with observed data, providing valuable insights for planning. Ongoing research aims to refine the model to capture sub-waves and spikes, as well as simulate multiple waves of infection. These efforts will enhance our understanding of COVID-19 dynamics and inform effective public health strategies. The estimated basic reproduction number R0=2.76, consistent with previous findings, underscores the validity of our model and its relevance in predicting disease transmission dynamics.</p
Evidence in Kenya of Reassortment Between of Reassortment BetweenSeasonal Influenza A(H3N2) and Influenza A(H1N1)pdm09 to yield A(H3N2) Variants With the Matrix Gene Segment of A(H1N1)pdm09
No full textInfluenza vaccines and antiviral drugs are the mainstay for preventing influenza and reducing the impact of influenza epidemics. Currently, there are two classes of antiviral drugs available for preventing and treating the M gene. The neuraminidase inhibitors (NAI’s) interrupt the replication cycle by preventing virus release and allowing progeny virus to clump (Monto et al, 2002). The rapid emergence of adamantine drug resistant influenza A virus strains has limited these drugs’ clinical effectiveness.The origin and evolution of antiviral drug resistance amongst influenza viruses can occur through different molecular mechanisms that also drive the evolution of the virus. The most crucial of these mechanisms result from the segmented nature of its genome. This permits the formation of new progeny viruses with novel combinations of segments through reassortment when two or more different virus subtypes infect a single cell, a phenomenon referred to as antigenic shift. This process is capable of introducing new genes in circulating viral populations that can drastically change the biological properties of the virus. Studies have shown that reassortment led to an increase in the frequency of amantadine-resistant seasonal influenza A(H1N1) viruses since the 2005-2006 season (Yang et al, 2011). This underscores the necessity to monitor genome dynamics in circulating influenza viruses because it is through such molecular surveillance that we are able to understand the evolution and mechanisms of the emergence and spread of antiviral resistance among influenza A viruses (Boni et al, 2010).Thus, we set out to qualitatively analyze human influenza A(H3N2) viruses that circulated in Kenya in 2010, the period when influenza A(H3N2) and A(H1N1)pdm09 [previously referred to as swine flu] (WHO, 2011a) begun to co-circulate in the human population in the country, determine evidence of reassortment amongst the co-circulating subtypes and relate any such events to influenza antiviral resistance in the country. We applied the current laboratory testing protocol which involves routinely sequencing the HA, M and NA gene segments of the influenza viruses. These three gene segments were selected because they are the main antigens (NA & HA) and drug targets (M & NA) of the influenza A virus. Herein we provide evidence that indeed there was reassortment involving at least the M gene segment amongst cocirculating influenza A viruses in Kenya during this period
- …
