A Tool for the Assessment of HLA-DQ Heterodimer Variation in Hematopoietic Cell Transplantation.

When optimizing transplants, clinical decision-makers consider HLA-A, -B, -C, -DRB1 (8 matched alleles out of 8), and sometimes HLA-DQB1 (10 out of 10) matching between the patient and donor. HLA-DQ is a heterodimer formed by the ß chain product of HLA-DQB1 and an α chain product of HLA-DQA1. In addition to molecules defined by the parentally inherited cis haplotypes, α-ß trans-dimerization is possible between certain alleles, leading to unique molecules and a potential source of mismatched molecules. Recently, researchers uncovered that clinical outcome after HLA-DQB1-mismatched unrelated donor HCT depends on the total number of HLA-DQ molecule mismatches and the specific α-ß heterodimer mismatch. Our objective in this study is to develop an automated tool for analyzing HLA-DQ heterodimer data and validating it through numerous datasets and analyses. By doing so, we provide an HLA-DQ heterodimer tool for DQα-DQß trans-heterodimer evaluation, HLA-DQ imputation, and HLA-DQ-featured source selection to the transplant field. In our study, we leverage 352,148 high-confidence, statistically phased (via a modified expectation-maximization algorithm) HLA-DRB1∼DQA1∼DQB1 haplotypes, 1,052 pedigree-phased HLA-DQA1∼DQB1 haplotypes, and 13,663 historical transplants to characterize HLA-DQ heterodimers data. Using our developed QLASSy (HLA-DQA1 and HLA-DQB1 Heterodimers Assessment) tool, we first assessed the data quality of HLA-DQ heterodimers in our data for trans-dimers, missing HLA-DQA1 typing, and unexpected HLA-DQA1 and HLA-DQB1 combinations. Since trans-dimers enable up to four unique HLA-DQ molecules in individuals, we provide in-silico validations for 99.7% of 275 unique trans-dimers generated by 176,074 U.S. donors with HLA-DQA1 and HLA-DQB1 data. Many individuals lack HLA-DQA1 typing, so we developed and validated high-confidence HLA-DQ annotation imputation via HLA-DRB1 with >99% correct predictions in 23,698 individuals. A select few individuals displayed unexpected HLA-DQ combinations. We revisited the typing of 61 donors with unexpected HLA-DQ combinations based on their HLA-DQA1 and HLA-DQB1 typing and corrected 22 out of 61 (36%) cases of donors through data review or retyping and used imputation to resolve unexpected combinations. After verifying the data quality of our datasets, we analyzed our datasets further: we explored the frequencies of observed HLA-DQ combinations to compare HLA-DQ across populations (for instance, we found more high-risk molecules in Asian/Pacific Islander and Black/African American populations), demonstrated the effect of HLA-DQA1 and HLA-DQB1 mismatching on HLA-DQ molecular mismatches, and highlighted where donor selections could be improved at the time of search for historical transplants with this new HLA-DQ information (where 51.9% of G2-mismatched transplants had lower-risk, G2-matched alternatives). We encapsulated our findings into a tool that imputes missing HLA-DQA1 as needed, annotates HLA-DQ (mis)matches, and highlights other important HLA-DQ data to consider for the present and future. Altogether, these valuable datasets, analyses, and a culminating tool serve as actionable resources to enhance donor selection and improve patient outcomes.

Simulation of elution profiles in liquid chromatography - II: Investigation of injection volume overload under gradient elution conditions applied to second dimension separations in two-dimensional liquid chromatography.

An important research direction in the continued development of two-dimensional liquid chromatography (2D-LC) is to improve the detection sensitivity of the method. This is especially important in applications where injection of large volumes of effluent from the first dimension (1D) column into the second dimension (2D) column leads to severe 2D peak broadening and peak shape distortion. For example, this is common when coupling two reversed-phase columns and the organic solvent content of the 1D mobile phase overwhelms the 2D column with each injection of 1D effluent, leading to low resolution in the second dimension. In a previous study we validated a simulation approach based on the Craig distribution model and adapted from the work of Czok and Guiochon [1] that enabled accurate simulation of simple isocratic and gradient separations with very small injection volumes, and isocratic separations with mismatched injection and mobile phase solvents [2]. In the present study we have extended this simulation approach to simulate separations relevant to 2D-LC. Specifically, we have focused on simulating 2D separations where gradient elution conditions are used, there is mismatch between the sample solvent and the starting point in the gradient elution program, injection volumes approach or even exceed the dead volume of the 2D column, and the extent of sample loop filling is varied. To validate this simulation we have compared results from simulations and experiments for 101 different conditions, including variation in injection volume (0.4-80µL), loop filling level (25-100%), and degree of mismatch between sample organic solvent and the starting point in the gradient elution program (-20 to +20% ACN). We find that that the simulation is accurate enough (median errors in retention time and peak width of -1.0 and -4.9%, without corrections for extra-column dispersion) to be useful in guiding optimization of 2D-LC separations. However, this requires that real injection profiles obtained from 2D-LC interface valves are used to simulate the introduction of samples into the 2D column. These profiles are highly asymmetric - simulation using simple rectangular pulses leads to peak widths that are far too narrow under many conditions. We believe the simulation approach developed here will be useful for addressing practical questions in the development of 2D-LC methods.