Damaging mutations of the Angiotensin I-converting enzyme (ACE) that result in low ACE levels may increase the risk of developing late-onset Alzheimer’s disease (AD). We quantified blood ACE levels in EDTA-plasma from 147 subjects with 23 different heterozygous ACE mutations (and 70 controls) and estimated the effect of these mutations on ACE phenotype, using a set of monoclonal antibodies (mAbs) to ACE and two ACE substrates. We identified several mutations in both ACE domains (including the most frequent ACE mutation, Y215C), which led to decreased ACE levels in the blood, and thus could be considered as putative risk factors for late-onset AD. The precipitation of several ACE mutants (Q259R, A725P, C734Y) by specific mAbs changed significantly, and therefore, these mAbs could be markers of these mutations. Analysis of 50 of the most frequent ACE mutations demonstrates that more than 1.5% of the adult population may have mutations which lead to decreased ACE levels, and thus, the role of low ACE levels in the development of AD may be underappreciated. Intriguingly, statistical and cluster analyses of longevity patients revealed trends towards higher frequency of cognitive impairment among affected individuals with damaging ACE mutations. Systematic analysis of blood ACE levels in patients with various ACE mutations identifies individuals with low blood ACE levels who may be at increased risk for late-onset AD. Patients with transport-deficient ACE mutations theoretically could benefit from therapeutic treatment with a combination of chemical and pharmacological chaperones and proteasome inhibitors, as was demonstrated previously on a cell model of the transport-deficient ACE mutation Q1069R. Moreover, clinical association analysis suggests a trend linking damaging ACE mutations with increased risk of cognitive impairment.

Background: Analysis of existing mutations of Angiotensin-I-Converting Enzyme (ACE) led us to hypothesize that the carriers of damaging ACE mutations (accompanied by low ACE levels) could be at risk for the development of late-onset Alzheimer’s disease (AD). Methodology/Principal findings: We quantified blood ACE levels in EDTA-containing plasma from 15 patients with 11 different heterozygous ACE mutations and estimated the effects of these mutations on ACE phenotypes, using a set of mAbs to ACE and two ACE substrates. We confirmed prior observations that the relatively frequent Y215C mutation in the N domain of ACE (present in ~1% of the population) is associated with both Alzheimer’s disease (AD) and reduced plasma levels of ACE (~50% of controls), indicating that it likely results in a transport-deficient protein. In addition, we identified another 4 mutations in both ACE domains (M118T, C734Y, V992M and V997M) which are also associated with decreased ACE levels in the blood, and, thus, could be putative risk factors for late-onset AD. One of these mutations, C734Y, is likely transport-deficient, while the other mutations appear to influence ACE catalytic properties. The precipitation of mutant M118T by mAb 2D1 and ACE mutant C734Y by mAb 3F10 increased 2-3-fold compared to native ACE, and therefore, these mAbs could be markers of these mutations. Also, we identified a mutation I989T, which is associated with increased ACE levels in the blood. Conclusions/Significance: Conducting a systematic analysis of blood ACE levels in patients with ACE mutations holds promise for identifying individuals with low blood ACE levels. Such individuals may be at increased risk for late-onset AD. The patients with transport-deficient ACE mutations may benefit from therapeutic treatment with a combination of chemical and pharmacological chaperones and proteasome inhibitors, as was demonstrated previously using a cell model of the transport-deficient ACE mutation, Q1069R [Danilov et al, PLoS One, 2010].

Whole exome sequencing (WES) is essential for identifying genetic variants linked to diseases. This study compares available to date four exome enrichment kits: Agilent SureSelect Human All Exon v8, Roche KAPA HyperExome Probes, Vazyme VAHTS Target Capture Core Exome Panel, and Nanodigmbio NEXome Plus Panel v1. We evaluated target design, coverage statistics, and variant calling accuracy across these four different exome capture products. All kits showed high coverage completeness, with mean x10 coverage exceeding 97.5% and x20 coverage above 95%. Roche exhibited the most uniform coverage, indicated by the lowest fold-80 scores, while Nanodigmbio had more on-target reads due to fewer off-target reads. Variant calling performance, evaluated using in-lab standard E701 DNA sample, showed high recall rates for all kits, especially Agilent v8. All kits achieved an F-measure above 95.87%. Nanodigmbio had the highest precision with the fewest false positives but a slightly lower F-measure than other kits. This study also highlights the performance of new Chinese solutions from Vazyme and Nanodigmbio, which were comparable to Agilent v8 and Roche KAPA kits. These findings assist researchers and clinicians in selecting appropriate exome capture solutions.

Background: Personalized pharmacotherapy requires systematic consideration of genetic factors influencing drug efficacy and safety. The accumulation of large-scale whole-exome sequencing (WES) resources provides an opportunity to assess population frequencies of clinically significant pharmacogenetic variants; however, the applicability of exome-based pharmacogenomics across populations, particularly those underrepresented in existing reference datasets, requires further evaluation. Materials and methods: A retrospective analysis of 6102 anonymized sequencing datasets obtained between 2020 and 2025 was performed using the DNBSEQ-G400 (MGI) platform and Agilent SureSelect Human All Exon v6/v7/v8 enrichment kits. SNV and indel detection, CNV analysis, high-resolution HLA typing, and diplotype assignment for key pharmacogenes were conducted. Pharmacogenomic annotations were derived from ClinPGx (formerly PharmGKB) (levels of evidence 1 A–2B), CPIC, and PharmVar. Haplotype phasing (SHAPEIT5), genotype imputation (IMPUTE5), and phased linkage disequilibrium analysis were performed using a reference panel of 814 Russian whole-genome sequences generated with the same bioinformatic workflow to assess the feasibility of reconstructing clinically relevant non-coding pharmacogenetic variants not captured by WES. Results: WES reliably detected 33 of 34 Very Important Pharmacogenes (VIPs), allowing determination of allele frequencies, metabolizer statuses for 13 VIPs, and HLA diversity. The highest allelic and phenotypic variability was observed in CYP2D6 , CYP2C19 , and CYP2B6 . A total of 663 ClinPGx annotations were identified, predominantly related to drug metabolism (50.38%) and toxicity (29.56%), involving psychotropic drugs, anticoagulants, statins, opioid analgesics, antineoplastic agents, and immunosuppressants. However, at least 32 drugs require assessment of non-coding pharmacogenetic variants or accurate CYP2D6 copy number determination – both beyond standard WES capability. Phased haplotype analysis demonstrated that haplotype-based imputation was feasible for only a limited subset of clinically relevant non-coding pharmacogenetic variants, whereas the majority lacked informative exonic proxies required for accurate inference from WES data. Conclusion: These findings represent one of the largest reference resource to date of pharmacogenetically significant variant and HLA allele frequencies in the Russian population. The results confirm WES as a robust and scalable platform for population-level pharmacogenomic screening and broader clinical genomic applications, while demonstrating that haplotype-based imputation cannot fully overcome the intrinsic limitations of exome sequencing for comprehensive pharmacogenomic profiling. Selection of genomic testing strategies should balance diagnostic completeness, analytical complexity, cost, scalability, and long-term potential for reanalysis.

Background: The ACE Y215C mutation is a common, functionally damaging missense variant (~1.5% allele frequency) associated with reduced plasma ACE levels and increased Alzheimer’s disease (AD) risk. In CHO and HEK cell models, this mutation caused a ~3–6-fold decrease in ACE surface expression, soluble ACE levels, and ACE enzymatic activity compared to those of wild-type ACE. Methods: Circulating ACE levels and activity were measured in EDTA plasma obtained from 84 carriers of the ACE Y215C mutation using a set of mAbs to the ACE. The mAbs 5B3/1G12 binding ratio was revealed as a sensitive marker for the circulating Y215C ACE mutant. Whole-exome and whole-genome sequencing (WES/WGS) were performed to identify genetic variants potentially modifying circulating ACE levels. In parallel, published sequencing and proteomic data from 35,559 Icelanders participants were analyzed to identify genes influencing ACE shedding. Sequence comparison was performed between carriers with elevated and reduced ACE concentrations to identify the potential protective variants that may compensate for decreased ACE levels due to the Y215C mutation itself. Results: Most carriers of the Y215C ACE mutation demonstrated significantly decreased ACE levels (median is 62% of control ACE levels). However, substantial inter-individual variability was observed in plasma ACE activity among carriers. Comparative sequencing analysis revealed 9648 variants unique to individuals with elevated ACE, mapping to 5779 protein-coding genes and enriched for pathways related to intracellular and transmembrane transport. Conclusions: The presence of the damaging ACE mutation Y215C does not invariably result in low plasma ACE or, likely, elevated AD risk. Therefore, combined blood ACE phenotyping and whole-exome sequencing are recommended to more accurately assess ACE-related AD susceptibility in mutation carriers.

Introduction: The application of pharmacogenomics (PGx) in pediatrics is limited by the lack of age-oriented interpretation approaches, as algorithms developed for adults do not account for ontogenetic changes in the activity of drug-metabolizing enzymes and transport proteins. The aim of this study was to evaluate the clinical applicability of pharmacogenomic data in Russian children, assess the concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes, and develop recommendations for the generation of age-oriented PGx reports. Methods: We analyzed whole-exome sequencing (WES) data from 524 pediatric patients and 635 newborns, filtering pharmacogenomic annotations according to PharmGKB/ClinPGx evidence levels (1A–2B) and the presence of the “Pediatrics” tag. The concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes was assessed in newborns. In a pediatric subgroup of 100 patients, a retrospective analysis of medical records was performed to evaluate the structure of pharmacotherapy and the frequency of adverse drug reactions (ADRs). A “PGx–ADR–cost” database was created, and the relative population burden index was calculated for 27 gene–variant–drug–ADR associations. Results: Clinically relevant annotations (requiring drug avoidance or dose modification) accounted for only 5% of all initial pharmacogenomic annotations in both cohorts; 67.6% (pediatric cohort) and 67.2% (neonatal cohort) of these were related to alleles with altered function. Concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes in newborns was observed in only 5 of 14 (35.71%) gene–drug pairs. ADRs were identified in 21% of the 100 pediatric patients; however, only two cases could be explained by high-evidence PharmGKB/ClinPGx annotations. Ranking by relative population burden identified UGT1A1*28 –irinotecan-induced neutropenia and HLA-A*31:01 –carbamazepine-induced severe cutaneous reactions as priority associations. Conclusions: Age represents a critical factor in the interpretation of pharmacogenomic data in children, as current approaches to PGx reporting do not adequately incorporate the ontogenetic context. We propose a pediatric PGx interpretation model that includes mandatory reporting of patient age, ontogenetic adjustment, evidence-level stratification, and multidisciplinary clinical assessment. Prospective validation is required to confirm the clinical utility of the proposed approach.