Effectiveness of naturally occurring Aphis gossypii on tomato plants as a bio-indicator for heavy metals in Riyadh and Hafar Al-Batin,Saudi Arabia

Although certain pollutants can be biologically degraded by microorganisms, rendering their impact short-term, others can not be impaired, such that their effect persists. The present study evaluates the effectiveness of using a field-collected aphid, Aphis gossypii, as a bio-indicator for heavy metals in tomato farms in Riyadh and Hafar Al-Batin, Saudi Arabia. Heavy metals were selected (Cd, Cu, Zn, and Pb) and measured for comparative screening in field-collected plants, soil, and aphids using inductively coupled plasma-mass spectrometry (ICP-MS). Field-collected aphids from both studied regions were identified as Aphis gossypii. In Riyadh, there was no significant difference observed for Cd, Cu, and Zn for all experimental samples, while, Pb was showed differences among samples especially tomato leaves None of the studied samples in Hafar Al-Batin were showed statistically significant differences in Cd, in reverse to significant differences in the other heavy metals. Comparing concentrations of selected heavy metals between the two studied regions was showed that neither region showed a significant difference in heavy metals except for Cu. This study demonstrates that tomato leaf samples showed the highest concentrations of most studied heavy metals, followed by soil, then aphids. Aphids were utilized as a bio-indicator of heavy metals in the studied regions.     

Identification of a novel IVD mutation in a consanguineous family with isovaleric acidemia

Isovaleric acidemia (IVA) is a rare autosomal recessive disorder caused by a deficiency of isovaleryl-CoA dehydrogenase encoded by IVD gene. In this case study we report the first Saudi IVA patients from a consanguineous family with a novel transversion (p.G362V) and briefly discuss likely phenotype–genotype correlation of the disease in the Saudi population. We explored the functional consequences of the mutation by using various bioinformatics prediction algorithms and discussed the likely mechanism of the disease caused by the mutation.