Complete HLA genotyping of type 1 diabetes patients and controls from Mali reveals both expected and novel disease associations.

HLA genotyping was performed on 99 type 1 diabetes (T1D) patients and 200 controls from Mali. Next-generation sequencing of the classical HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, -DQA1, -DQB1, -DPA1, and -DPB1 loci revealed strong T1D association for all loci except HLA-C and -DPA1. Class II association is stronger than class I association, with most observed associations predisposing or protective as expected based on previous studies. For example, HLA-DRB1*03:01, HLA-DRB1*09:01, and HLA-DRB1*04:05 predispose for T1D, whereas HLA-DRB1*15:03 is protective. HLA-DPB1*04:02 (OR = 12.73, p = 2.92 × 10-05 ) and HLA-B*27:05 (OR = 21.36, p = 3.72 × 10-05 ) appear highly predisposing, although previous studies involving multiple populations have reported HLA-DPB1*04:02 as T1D-protective and HLA-B*27:05 as neutral. This result may reflect the linkage disequilibrium between alleles on the extended HLA-A*24:02~HLA-B*27:05~HLA-C*02:02~HLA-DRB1*04:05~HLA-DRB4*01:03~HLA-DQB1*02:02~HLA-DQA1*02:01~HLA-DPB1*04:02~HLA-DPA1*01:03 haplotype in this population rather than an effect of either allele itself. Individual amino acid (AA) analyses are consistent with most T1D association attributable to HLA class II rather than class I in this data set. AA-level analyses reveal previously undescribed differences of the HLA-C locus from the HLA-A and HLA-B loci, with more polymorphic positions, spanning a larger portion of the gene. This may reflect additional mechanisms for HLA-C to influence T1D risk, for example, through expression differences or through its role as the dominant ligand for killer cell immunoglobulin-like receptors (KIR). Comparison of these data to those from larger studies and on other populations may facilitate T1D prediction and help elucidate elusive mechanisms of how HLA contributes to T1D risk and autoimmunity.

PCR standard curve quantification in an extensive wastewater surveillance program: results from the Dutch SARS-CoV-2 wastewater surveillance.

Since the start of the COVID-19 pandemic in 2020, wastewater surveillance programs were established, or upscaled, in many countries around the world and have proven to be a cost-effective way of monitoring infectious disease pathogens. Many of these programs use RT-qPCR, and quantify the viral concentrations in samples based on standard curves, by including preparations of a reference material with known nucleic acid or virus concentrations in the RT-qPCR analyses. In high-throughput monitoring programs it is possible to combine data from multiple previous runs, circumventing the need for duplication and resulting in decreased costs and prolonged periods during which the reference material is obtained from the same batch. However, over time, systematic shifts in standard curves are likely to occur. This would affect the reliability and usefulness of wastewater surveillance as a whole. We aim to find an optimal combination of standard curve data to compensate for run-to-run measurement variance while ensuring enough flexibility to capture systematic longitudinal shifts. Based on more than 4000 observations obtained with the CDC N1 and N2 assays, taken as a part of the National Sewage Surveillance program at the Dutch National Institute for Public Health and the Environment, we show that seasonal and long-term shifts in RT-qPCR efficiency and sensitivity occur. We find that in our setting, using five days of standard-curve data to quantify, results in the least error prone curve or best approximation. This results in differences up to 100% in quantified viral loads when averaged out over a nationwide program of >300 treatment plants. Results show that combining standard curves from a limited set of runs can be a valid approach to quantification without obscuring the trends in the viral load of interest.