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HLA-DQA1 and -DQB1 genes have significant and potentially causal associations with autoimmune type 1 diabetes (T1D). To follow up on the earlier analysis on high-risk HLA-DQ2.5 and DQ8.1, the current analysis uncovers seven residues (αa1, α157, α196, β9, β30, β57, and β70) that are resistant to T1D among subjects with DQ4-, 5-, 6-, and 7-resistant DQ haplotypes. These 7 residues form 13 common motifs: 6 motifs are significantly resistant, 6 motifs have modest or no associations (P values >0.05), and 1 motif has 7 copies observed among control subjects only. The motifs “DAAFYDG,” “DAAYHDG,” and “DAAYYDR” have significant resistance to T1D (odds ratios [ORs] 0.03, 0.25, and 0.18; P = 6.11 × 10−24, 3.54 × 10−15, and 1.03 × 10−21, respectively). Remarkably, a change of a single residue from the motif “DAAYHDG” to “DAAYHSG” (D to S at β57) alters the resistance potential, from resistant motif (OR 0.15; P = 3.54 × 10−15) to a neutral motif (P = 0.183), the change of which was significant (Fisher P value = 0.0065). The extended set of linked residues associated with T1D resistance and unique to each cluster of HLA-DQ haplotypes represents facets of all known features and functions of these molecules: antigenic peptide binding, peptide–MHC class II complex stability, β167-169 RGD loop, T-cell receptor binding, formation of homodimer of α-β heterodimers, and cholesterol binding in the cell membrane rafts. Identification of these residues is a novel understanding of resistant DQ associations with T1D. Our analyses endow potential molecular approaches to identify immunological mechanisms that control disease susceptibility or resistance to provide novel targets for immunotherapeutic strategies.

HLA-DQA1 and -DQB1 are strongly associated with type 1 diabetes (T1D), and DQ8.1 and DQ2.5 are major risk haplotypes. Next-generation targeted sequencing of HLA-DQA1 and -DQB1 in Swedish newly diagnosed 1- to 18 year-old patients (n = 962) and control subjects (n = 636) was used to construct abbreviated DQ haplotypes, converted into amino acid (AA) residues, and assessed for their associations with T1D. A hierarchically organized haplotype (HOH) association analysis allowed 45 unique DQ haplotypes to be categorized into seven clusters. The DQ8/9 cluster included two DQ8.1 risk and the DQ9 resistant haplotypes, and the DQ2 cluster included the DQ2.5 risk and DQ2.2 resistant haplotypes. Within each cluster, HOH found residues α44Q (odds ratio [OR] 3.29, P = 2.38 * 10−85) and β57A (OR 3.44, P = 3.80 * 10−84) to be associated with T1D in the DQ8/9 cluster representing all ten residues (α22, α23, α44, α49, α51, α53, α54, α73, α184, β57) due to complete linkage disequilibrium (LD) of α44 with eight such residues. Within the DQ2 cluster and due to LD, HOH analysis found α44C and β135D to share the risk for T1D (OR 2.10, P = 1.96 * 10−20). The motif “QAD” of α44, β57, and β135 captured the T1D risk association of DQ8.1 (OR 3.44, P = 3.80 * 10−84), and the corresponding motif “CAD” captured the risk association of DQ2.5 (OR 2.10, P = 1.96 * 10−20). Two risk associations were related to GAD65 autoantibody (GADA) and IA-2 autoantibody (IA-2A) but in opposite directions. CAD was positively associated with GADA (OR 1.56, P = 6.35 * 10−8) but negatively with IA-2A (OR 0.59, P = 6.55 * 10−11). QAD was negatively associated with GADA (OR 0.88; P = 3.70 * 10−3) but positively with IA-2A (OR 1.64; P = 2.40 * 10−14), despite a single difference at α44. The residues are found in and around anchor pockets 1 and 9, as potential T-cell receptor contacts, in the areas for CD4 binding and putative homodimer formation. The identification of three HLA-DQ AAs (α44, β57, β135) conferring T1D risk should sharpen functional and translational studies.

Background: SARS-CoV-2 is continuously evolving with the emergence of variants of interest (VOI) or with variants of concern (VOC). While Variants of High Consequence (VOHC) are well defined, no such variants have been formally documented. Here we propose an integrated strategy and application towards discovering VOHC. Methods: We utilized 7,137 viral sequences collected from COVID-19 cases in Washington State from January 19, 2020 to January 31, 2021, to identify genome-wide viral single nucleotide variants (SNVs). Utilizing a non-parametric regression model, we selected a subset of SNVs that had significant and substantial expansions over the collection period. Further, using unsupervised learning, we identified multiple SNVs forming haplotypes. To evaluate their clinical relevance, we assembled a discovery cohort of COVID-19 cases (388 inpatients and 295 outpatients) to identify SNVs and haplotypes associated with hospitalization status, a proxy for disease severity. A logistic regression model was used to assess associations of SNVs with hospitalization status in the discovery cohort. These results were validated on an independent cohort of 964 genome sequences derived from COVID-19 cases in Washington State from June 1, 2020 to March 31, 2021. Finding: The analysis of the 7,137 sequences led to identification of 107 SNVs that were statistically significant (false positive error rate q-value 0.10). Forty-one SNVs were considered urgent, because their SNV proportions persisted or expanded above 10% in January 2021, the last month of the current investigation period. Correlating with clinical data, eight SNVs were found to significantly associate with inpatient status (p-values

Background: HLA-DR4, a common antigen of HLA-DRB1, has multiple subtypes that are strongly associated with risk of type 1 diabetes (T1D); however, some are risk neutral or resistant. The pathobiological mechanism of HLA-DR4 subtypes remains to be elucidated. Methods: We used a population-based case-control study of T1D (962 patients and 636 controls) to decipher genetic associations of HLA-DR4 subtypes and specific residues with susceptibility to T1D. Using a birth cohort of 7865 children with periodically measured islet autoantibodies (GADA, IAA or IA-2A), we proposed to validate discovered genetic associations with a totally different study design and time-to-seroconversions prior to clinical onset of T1D. A novel analytic strategy hierarchically organized the HLA-DRB1 alleles by sequence similarity and identified critical amino acid residues by minimizing local genomic architecture and higher-order interactions. Findings: Three amino acid residues of HLA-DRB1 (β71, β74, β86) were found to be predictive of T1D risk in the population-based study. The “KAG” motif, corresponding to HLA-DRB1×04:01, was most strongly associated with T1D risk ([O]dds [R]atio=3.64, p = 3.19 × 10−64). Three less frequent motifs (“EAV”, OR = 2.55, p = 0.025; “RAG”, OR = 1.93, p = 0.043; and “RAV”, OR = 1.56, p = 0.003) were associated with T1D risk, while two motifs (“REG” and “REV”) were equally protective (OR = 0.11, p = 4.23 × 10−4). In an independent birth cohort of HLA-DR3 and HLA-DR4 subjects, those having the “KAG” motif had increased risk for time-to-seroconversion (Hazard Ratio = 1.74, p = 6.51 × 10−14) after adjusting potential confounders. Interpretations: DNA sequence variation in HLA-DRB1 at positions β71, β74, and β86 are non-conservative (β74 A→E, β71 E vs K vs R and β86 G vs V). They result in substantial differences in peptide antigen anchor pocket preferences at p1, p4 and potentially neighboring regions such as pocket p7. Differential peptide antigen binding is likely to be affected. These sequence substitutions may account for most of the HLA-DR4 contribution to T1D risk as illustrated in two HLA-peptide model complexes of the T1D autoantigens preproinsulin and GAD65. Funding: National Institute of Diabetes and Digestive and Kidney Diseases and the Swedish Child Diabetes Foundation and the Swedish Research Council.

A line of investigations indicate that genes in the human leukocyte antigen (HLA) complex are involved in a successful acceptance of the semiallogeneic fetus during pregnancy. In this study, associations between specific HLA class Ia (HLA-A and -B) and class II (HLA-DRB1, -DQA1, -DQB1, -DPA1 and -DPB1) alleles and the risk of developing severe preeclampsia/eclampsia were investigated in a detailed and large-scale study. In total, 259 women diagnosed with severe preeclampsia or eclampsia and 260 matched control women with no preeclampsia, together with their neonates, were included in the study. HLA genotyping for mothers and neonates was performed using next-generation sequencing. The HLA-DPB1*04:01:01G allele was significantly more frequent (Pc=0.044) among women diagnosed with severe preeclampsia/eclampsia compared with controls, and the DQA1*01:02:01G allele frequency was significantly lower (Pc=0.042) among newborns born by women with severe preeclampsia/eclampsia compared with controls. In mothers with severe preeclampsia/eclampsia, homozygosity was significantly more common compared with controls at the HLA-DPB1 locus (Pc=0.0028). Although the current large study shows some positive results, more studies, also with a functional focus, are needed to further clarify a possible role of the classical HLA genes in preeclampsia.

The possible contribution of HLA-DRB3, -DRB4, and -DRB5 alleles to type 1 diabetes risk and to insulin autoantibody (IAA), GAD65 (GAD autoantibody [GADA]), IA-2 antigen (IA-2A), or ZnT8 against either of the three amino acid variants R, W, or Q at position 325 (ZnT8RA, ZnT8WA, and ZnT8QA, respectively) at clinical diagnosis is unclear. Next-generation sequencing (NGS) was used to determine all DRB alleles in consecutively diagnosed patients ages 1–18 years with islet autoantibody–positive type 1 diabetes (n = 970) and control subjects (n = 448). DRB3, DRB4, or DRB5 alleles were tested for an association with the risk of DRB1 for autoantibodies, type 1 diabetes, or both. The association between type 1 diabetes and DRB1*03:01:01 was affected by DRB3*01:01:02 and DRB3*02:02:01. These DRB3 alleles were associated positively with GADA but negatively with ZnT8WA, IA-2A, and IAA. The negative association between type 1 diabetes and DRB1*13:01:01 was affected by DRB3*01:01:02 to increase the risk and by DRB3*02:02:01 to maintain a negative association. DRB4*01:03:01 was strongly associated with type 1 diabetes (P = 10−36), yet its association was extensively affected by DRB1 alleles from protective (DRB1*04:03:01) to high (DRB1*04:01:01) risk, but its association with DRB1*04:05:01 decreased the risk. HLA-DRB3, -DRB4, and -DRB5 affect type 1 diabetes risk and islet autoantibodies. HLA typing with NGS should prove useful to select participants for prevention or intervention trials.

Introduction Accuracy and precision are critically important in the assessment of minimal residual disease (MRD) and chimerism following hematopoietic cell transplantation. Reliable and sensitive detection of changes in engraftment status can impact patient care by early detection of potential failure of engraftment or relapse. We evaluated a commercially available NGS technology for engraftment monitoring for sensitivity, accuracy and reproducibility. Objective Introduction of next generation sequencing technology for engraftment monitoring. Methods Samples analyzed by NGS at Baylor Transplant Immunology Laboratory included archived clinical specimens with historic results by STR, n=19, or qPCR, n=15, test results; 72 artificial mixtures of volunteer donors, and 5 ASHI external proficiency specimens. NGS analysis assessed 182 SNPs validated in the 1000 Genomes HapMap project and distributed across all 23 chromosomes, including the x chromosome. The rapid workflow allows a TAT of 2 days for up to 24 samples, with a less than 6 hour NGS library preparation protocol, followed by a 6 hour sequencing run time and 30 minute analysis. The low sample requirement, 1 ng DNA per PCR, facilitates chimerism assessment in multiple cell subsets including low frequency populations such as CD34-positive cells. Results NGS showed close concordance with historic results from STR and qPCR (figure 1), providing validation of NGS technology. Figure 2 compares NGS results versus the reference values of artificial mixtures and demonstrates NGS accuracy, while the low tech to tech variation data reveals assay precision. Limit of detection experiments showed reliable 0.1% sensitivity, as illustrated in Figure 3, with NGS analysis of 4 mixtures at 0.1%.. In addition, NGS reported results for the external proficiency samples correlated closely with the mean % donor reported throughout the full range of donor/recipient proportions including concordance with a sample of 100% donor. Conclusions Application of NGS technology to chimerism analysis in HCT provides accurate and precise test results. With the reliable 0.1% sensitivity and low input DNA that allows testing of multiple critical cell subsets, NGS is particularly relevant to early detection of MRD, graft failure, and relapse.

AIM: It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies.METHODS: Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D.RESULTS: In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10(-92) ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime.CONCLUSION: Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations. (Less)