Browsing by Author "Berka, Noureddine"

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    Characteristics of Donor-Specific anti-HLA Antibodies (DSA) Impacting different Renal Allograft Outcomes
    (2015-04-20) Ghandorah, Salim; Berka, Noureddine; Khan, Faisal
    AMR is the major complication of renal transplantation that significantly affects the allograft survival. Complement fixation of Donor specific anti-HLA antibodies is the hallmark of AMR. Different methods have been developed to detect harmful DSAs. However, not all DSAs have the same detrimental effect, as different antibody characteristics (timing, specificity, strength, and complement-fixing ability) might trigger distinct immune responses. Here, we assessed DSA characteristics in renal transplant patients. Thirty-nine patients with de novo DSA were analyzed for the DSA MFI values, C1q positivity and IgG subclasses. IgG1 was the predominant IgG subclass (49.4%), followed by IgG3 (24.7%), IgG2 (16%), and IgG4 (9.9%). Among DSA characteristics, DSA MFI and IgG1 subclass were strongly correlated with C1q positivity (p= 0.01 and p= 0.009, respectively). Further studies are needed to investigate the clinical relevance of high MFI DSA and IgG1 subclass in improving the utility of the C1q assay in predicting graft outcomes.
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    Relevant SARS-CoV-2 Genome Variation through Six Months of Worldwide Monitoring
    (2021-06-29) Hakmaoui, Abdelmalek; Khan, Faisal; Liacini, Abdelhamid; Kaur, Amanjot; Berka, Yacine; Machraoui, Safaa; Soualhine, Hafid; Berka, Noureddine; Rais, Hanane; Admou, Brahim
    Real-time genome monitoring of the SARS-CoV-2 pandemic outbreak is of utmost importance for designing diagnostic tools, guiding antiviral treatment and vaccination strategies. In this study, we present an accurate method for temporal and geographical comparison of mutational events based on GISAID database genome sequencing. Among 42523 SARS-CoV-2 genomes analyzed, we found 23202 variants compared to the reference genome. The Ti/Tv (transition/transversion) ratio was used to filter out possible false-positive errors. Transition mutations generally occurred more frequently than transversions. Our clustering analysis revealed remarkable hotspot mutation patterns for SARS-CoV-2. Mutations were clustered based on how their frequencies changed over time according to each geographical location. We observed some clusters showing a clear variation in mutation frequency and continuously evolving in the world. However, many mutations appeared in specific periods without a clear pattern over time. Various important nonsynonymous mutations were observed, mainly in Oceania and Asia. More than half of these mutations were observed only once. Four hotspot mutations were found in all geographical locations at least once: T265I (NSP2), P314L (NSP12), D614G (S), and Q57H (ORF3a). The current analysis of SARS-CoV-2 genomes provides valuable information on the geographical and temporal mutational evolution of SARS-CoV-2.