Genetic Patterns of Oral Cavity Microbiome in Patients with Sickle Cell Disease.
Faisal Al-SarrajRaed S AlbiheyriMohammed QariMohammed AlotaibiMajid Al-ZahraniYasir AnwarMashail A AlghamdiNada M NassThamer BoubackIbrahim A AlotibiOsman Omer RadhwiBayan H SajerAlya RedhwanMohammed A Al-MataryEnas A AlmanzalawiHazem S ElshafiePublished in: International journal of molecular sciences (2024)
The Middle Eastern prevalence of sickle cell anemia, a genetic disorder that affects red blood cells, necessitates additional research. On a molecular level, we sought to identify and sort the oral microbiota of healthy individuals and those with sickle cell anemia. Furthermore, it is crucial to comprehend how changes in the genetic makeup of the oral microbiota impact the state of sickle cell anemia. Using next-generation sequencing, the 16S rRNA amplicon was examined using saliva samples from 36 individuals with sickle cell anemia and healthy individuals. These samples were obtained from sickle cell anemia patients (18 samples) and healthy control participants (controls, 18 samples). Various analyses are conducted using bioinformatic techniques to identify distinct species and their relative abundance. Streptococcus , followed by Fusobacterium nucleatum , Prevotella , and Veillonella were the most prevalent genera of bacteria in the saliva of the SCA and non-SCA individuals according to our findings. Rothia mucilaginosa , Prevotella scoposa , and Veillonella dispar species were the dominant species in both sickle cell anemia and non-sickle cell anemia subjects. Streptococcus salivarius , Actinomyces graevenitzii , Actinomyces odontolyticus , and Actinomyces georgiae spp. were the most prevalent bacterial spp. in the studied SCA cases. The sequencing of the 16S rRNA gene yielded relative abundance values that were visualized through a heatmap analysis. Alterations in the oral microflora's constitution can significantly affect the susceptibility of sickle cell anemia patients to develop more severe health complications. Salivary diagnosis is a potential tool for predicting and preventing oral microbiome-related diseases in the future.