HLA-C*15:299 differs from HLA-C*15:02:01:01 by two nucleotide changes across exons 2 and 3.

HLA-DQB1*03:03:38 differs from HLA-DQB1*03:03:02:01 by one synonymous nucleotide substitution in codon 258 of exon 5.

The rapid advances in HLA genotyping technology and the massive amounts of associated data have created a demand for better and more efficient laboratory data management practices. However, while some standards have been developed, there is a need for comprehensive guidelines that include all laboratory data-related processes such as messaging, storage and retention, documentation, reporting, validation and quality control. An important consideration in developing these recommendations is the feasibility of application in a laboratory setting without posing a substantial staff and cost burden for implementation and long-term maintenance and the availability of publicly available tools. This article presents evidence-based recommendations for multiple laboratory general data practices, focusing on HLA genotyping data and associated meta-data. These recommendations are compiled by experts in the fields of histocompatibility and immunogenetics (H&I) and representation from multiple H&I worldwide professional society leadership with the long-term goal of adopting these recommendations in future laboratory accreditation requirements.

ABSTRACT Standardised data representation is fundamental to the integrity, interoperability and reproducibility of immunogenetics and histocompatibility testing. While well‐established standards such as the genotype list (GL) String, histoimmunogenetics markup language (HML) and minimum information for reporting next‐generation sequence genotyping exist for encoding HLA genotyping results, no equivalent syntax currently exists for describing phenotype‐ or antigen‐level information that underlies antibody testing and immunological risk assessment. We introduce the phenotype list (PL) String grammar and the phenotype list string code (PLSC) syntax, structured, machine‐readable grammars designed to represent antigen‐ and protein‐level data across three contexts: (1) the HLA phenotype composition of assay reagents, (2) test results derived from those reagents and (3) clinical interpretations of HLA antibody specificities. PL String extends the hierarchical logic of the GL String grammar, while adapting a subset of GL String delimiters and rules to describe ambiguity, heterodimeric relationships and phenotypic composition. PL String incorporates explicit namespace definitions (e.g., World Health Organization HLA Nomenclature Committee for Factors of the HLA System, Organ Procurement and Transplantation Network, Eurotransplant and NMDP) to ensure traceability and compatibility with existing regulatory and operational frameworks. By harmonising how HLA phenotypic and antibody data are encoded, PL Strings enable automated data exchange between laboratories, registries and information systems, reducing transcription errors and improving computational interpretation. This new grammar establishes a foundation for interoperable representation of serologic and functional HLA data, facilitating reproducible research and enhancing clinical decision support in transplantation and immunogenetics. Technical specifications for PL Strings and PLSCs are available at plstring.org .

Tacrolimus (TAC) is recommended for the patients with drug tolerance or respond poorly to other conventional immunosuppressants in myasthenia gravis (MG), but its effectiveness is influenced by dose and genetic polymorphism. Studies suggest that HLA polymorphisms are significantly associated with susceptibility to MG, but the relationship between HLA and TAC efficacy in MG has not been explored. In this paper, we investigated the correlation between TAC therapeutic efficacy and HLA in different MG subgroups. HLA-C*03 was associated with a better response to TAC in patients with early-onset MG, generalized MG, and those with a high concentration of TAC. While HLA-A*11:01 showed poor responsiveness to TAC in generalized MG. Furthermore, computational docking was used to demonstrate the binding situation of HLA-A with the TAC binding protein FKBP1A. It showed that HLA-A*11:01 had a higher binding strength with FKBP1A than HLA-C*03, indicating that the combination of different types of HLA and FKBP1A may be one of the reasons affecting its therapeutic efficacy. This study highlights the influence of HLA on the efficacy of TAC among different MG subgroups, providing clues for precise treatment of MG with TAC.

HLA-C*07:167 differs from HLA-C*07:02:01:89 by a single nucleotide substitution in codon 211 within exon 4.

The HLA-DQB1*03:506:01:01 allele is characterised by a single nucleotide substitution in codon 130 in exon 3.

HLA-A*02:1040:02 differs from HLA-A*02:01:01:01 by a single nucleotide substitution in codon 283 (CCC>TCC) in exon 5.

HLA-DRB3*01:01:25 differs from DRB3*01:01:02:08 by one nucleotide substitution in exon 2, and intronic changes.