Y-Chromosome haplotypes reveal relationships between populations of the Arabian Peninsula, North Africa and South Asia

<p><b>Background:</b> The United Arab Emirates (UAE) is positioned at the crossroads of human migration out of Africa and through to Asia and Europe.</p> <p><b>Aim:</b> To compare the degree of genetic diversity of the Arabian UAE population with populations in other countries from the Middle East, South Asia and North Africa.</p> <p><b>Subjects and methods:</b> Twenty-seven Y-STR were analysed in 217 individuals. Y-STR haplotypes from this study were compared to population data stored in YHRD, using MDS and AMOVA.</p> <p><b>Results:</b> Two hundred and twelve haplotypes were observed in the 217 individuals studied. Although the reduction in Y-STR loci from 27 to 17 resulted in a decrease in discriminatory power, comparisons of populations were possible. The UAE population clustered closer with other populations of the Middle East. The South Asian and North African populations were separated by Middle Eastern populations in between both clusters.</p> <p><b>Conclusion:</b> This is the first study to report the diversity of a population of the Arabian Peninsula using 27 Y-STR. MDS plots show that Middle Eastern populations are positioned in the centre, with African, Asian and European populations around the Arab population cluster. The findings of this study are consistent with this region being at the epicentre of human migration between continents.</p

Inhibition of Human Amylin Aggregation and Cellular Toxicity by Lipoic Acid and Ascorbic Acid

More than 30 human degenerative diseases result from protein aggregation such as Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM). Islet amyloid deposits, a hallmark in T2DM, are found in pancreatic islets of more than 90% of T2DM patients. An association between amylin aggregation and reduction in β-cell mass was also established by post-mortem studies. A strategy in preventing protein aggregation-related disorders is to inhibit the protein aggregation and associated toxicity. In this study, we demonstrated that two inhibitors, lipoic acid and ascorbic acid, significantly inhibited amylin aggregation. Compared to amylin (15 μM) as 100%, lipoic acid and ascorbic acid reduced amylin fibril formation to 42.1 ± 17.2% and 42.9 ± 12.8%, respectively, which is confirmed by fluorescence and TEM images. In cell viability tests, both inhibitors protected RIN-m5f β-cells from the toxicity of amylin aggregates. At 10:1 molar ratio of lipoic acid to amylin, lipoic acid with amylin increased the cell viability to 70.3%, whereas only 42.8% RIN-m5f β-cells survived in amylin aggregates. For ascorbic acid, an equimolar ratio achieved the highest cell viability of 63.3% as compared to 42.8% with amylin aggregates only. Docking results showed that lipoic acid and ascorbic acid physically interact with amylin amyloidogenic region (residues Ser20-Ser29) via hydrophobic interactions; hence reducing aggregation levels. Therefore, lipoic acid and ascorbic acid prevented amylin aggregation via hydrophobic interactions, which resulted in the prevention of cell toxicity <i>in vitro</i>

CARD/RGI alignment results showing reads coverage of gene <i>mexB</i>.

Note that more than 95% of the gene is covered with at least one read in all samples.</p

Genes identified by CARD/RGI but not by Resistomap.

Genes identified by CARD/RGI but not by Resistomap.</p

Pearson correlation breakdown by antibiotic class for single-match genes across all locations.

Pearson correlation breakdown by antibiotic class for single-match genes across all locations.</p

Heatmap displaying the relative abundance data of 45 genes with Resistomap primers matching multiple genes in CARD.

Heatmap displaying the relative abundance data of 45 genes with Resistomap primers matching multiple genes in CARD.</p

The region of the <i>mexB</i> gene targeted by Resistomap primers.

The regions are shown as blue and red lines below the CARD sequence for mexB. In grey are conserved amino acids and mutations are represented by colors.</p

Rarefaction landscape using different gene coverage thresholds for number of antibiotic resistance ontology (ARO) terms, drug classes and AMR gene families for sample S1.

Each line graph contains lines representing the different gene cover thresholds used in the rarefaction analysis (indicated in the legend).</p

The distribution of reads assigned to “Class” taxon using the Kraken tool for the four wastewater samples.

S1 was collected from an urban residential site, S2 from an industrial site, S3 from a healthcare facility, and S4 was collected from a rural sewage treatment plant influent.</p

Taxa of bacterial groups in all four samples.

Surveillance methods of circulating antibiotic resistance genes (ARGs) are of utmost importance in order to tackle what has been described as one of the greatest threats to humanity in the 21st century. In order to be effective, these methods have to be accurate, quickly deployable, and scalable. In this study, we compare metagenomic shotgun sequencing (TruSeq DNA sequencing) of wastewater samples with a state-of-the-art PCR-based method (Resistomap HT-qPCR) on four wastewater samples that were taken from hospital, industrial, urban and rural areas. ARGs that confer resistance to 11 antibiotic classes have been identified in these wastewater samples using both methods, with the most abundant observed classes of ARGs conferring resistance to aminoglycoside, multidrug-resistance (MDR), macrolide-lincosamide-streptogramin B (MLSB), tetracycline and beta-lactams. In comparing the methods, we observed a strong correlation of relative abundance of ARGs obtained by the two tested methods for the majority of antibiotic classes. Finally, we investigated the source of discrepancies in the results obtained by the two methods. This analysis revealed that false negatives were more likely to occur in qPCR due to mutated primer target sites, whereas ARGs with incomplete or low coverage were not detected by the sequencing method due to the parameters set in the bioinformatics pipeline. Indeed, despite the good correlation between the methods, each has its advantages and disadvantages which are also discussed here. By using both methods together, a more robust ARG surveillance program can be established. Overall, the work described here can aid wastewater treatment plants that plan on implementing an ARG surveillance program.</div