EAP1 regulation of GnRH promoter activity is important for human pubertal timing.

The initiation of puberty is orchestrated by an augmentation of gonadotropin-releasing hormone (GnRH) secretion from a few thousand hypothalamic neurons. Recent findings have indicated that the neuroendocrine control of puberty may be regulated by a hierarchically organized network of transcriptional factors acting upstream of GnRH. These include enhanced at puberty 1 (EAP1), which contributes to the initiation of female puberty through transactivation of the GnRH promoter. However, no EAP1 mutations have been found in humans with disorders of pubertal timing. We performed whole-exome sequencing in 67 probands and 93 relatives from a large cohort of familial self-limited delayed puberty (DP). Variants were analyzed for rare, potentially pathogenic variants enriched in case versus controls and relevant to the biological control of puberty. We identified one in-frame deletion (Ala221del) and one rare missense variant (Asn770His) in EAP1 in two unrelated families; these variants were highly conserved and potentially pathogenic. Expression studies revealed Eap1 mRNA abundance in peri-pubertal mouse hypothalamus. EAP1 binding to the GnRH1 promoter increased in monkey hypothalamus at the onset of puberty as determined by chromatin immunoprecipitation. Using a luciferase reporter assay, EAP1 mutants showed a reduced ability to trans-activate the GnRH promoter compared to wild-type EAP1, due to reduced protein levels caused by the Ala221del mutation and subcellular mislocation caused by the Asn770His mutation, as revealed by western blot and immunofluorescence, respectively. In conclusion, we have identified the first EAP1 mutations leading to reduced GnRH transcriptional activity resulting in a phenotype of self-limited DP.

Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants.

The interpretation of human genetic variation is one of the greatest challenges of modern genetics. New approaches are urgently needed to prioritize variants, especially those that are rare or lack a definitive clinical interpretation. We examined 10,136,597 human missense genetic variants from GnomAD, ClinVar and UniProt. We were able to perform large-scale atom-based mapping and phenotype interpretation of 3,960,015 of these variants onto 18,874 experimental and 84,818 in house predicted three-dimensional coordinates of the human proteome. We demonstrate that 14% of amino acid substitutions from the GnomAD database that could be structurally analysed are predicted to affect protein structure (n = 568,548, of which 566,439 rare or extremely rare) and may, therefore, have a yet unknown disease-causing effect. The same is true for 19.0% (n = 6266) of variants of unknown clinical significance or conflicting interpretation reported in the ClinVar database. The results of the structural analysis are available in the dedicated web catalogue Missense3D-DB ( http://missense3d.bc.ic.ac.uk/ ). For each of the 4 M variants, the results of the structural analysis are presented in a friendly concise format that can be included in clinical genetic reports. A detailed report of the structural analysis is also available for the non-experts in structural biology. Population frequency and predictions from SIFT and PolyPhen are included for a more comprehensive variant interpretation. This is the first large-scale atom-based structural interpretation of human genetic variation and offers geneticists and the biomedical community a new approach to genetic variant interpretation.

Identification of disease-associated loci using machine learning for genotype and network data integration.

MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritization of weak loci. RESULTS: We developed cNMTF (corrected non-negative matrix tri-factorization), an integrative algorithm based on clustering techniques of biological data. This method assesses the inter-relatedness between genotypes, phenotypes, the damaging effect of the variants and gene networks in order to identify loci-trait associations. cNMTF was used to prioritize genes associated with lipid traits in two population cohorts. We replicated 129 genes reported in GWAS world-wide and provided evidence that supports 85% of our findings (226 out of 265 genes), including recent associations in literature (NLGN1), regulators of lipid metabolism (DAB1) and pleiotropic genes for lipid traits (CARM1). Moreover, cNMTF performed efficiently against strong population structures by accounting for the individuals' ancestry. As the method is flexible in the incorporation of diverse omics data sources, it can be easily adapted to the user's research needs. AVAILABILITY AND IMPLEMENTATION: An R package (cnmtf) is available at https://lgl15.github.io/cnmtf_web/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ligand-Specific Factors Influencing GLP-1 Receptor Post-Endocytic Trafficking and Degradation in Pancreatic Beta Cells.

The glucagon-like peptide-1 receptor (GLP-1R) is an important regulator of blood glucose homeostasis. Ligand-specific differences in membrane trafficking of the GLP-1R influence its signalling properties and therapeutic potential in type 2 diabetes. Here, we have evaluated how different factors combine to control the post-endocytic trafficking of GLP-1R to recycling versus degradative pathways. Experiments were performed in primary islet cells, INS-1 832/3 clonal beta cells and HEK293 cells, using biorthogonal labelling of GLP-1R to determine its localisation and degradation after treatment with GLP-1, exendin-4 and several further GLP-1R agonist peptides. We also characterised the effect of a rare GLP1R coding variant, T149M, and the role of endosomal peptidase endothelin-converting enzyme-1 (ECE-1), in GLP1R trafficking. Our data reveal how treatment with GLP-1 versus exendin-4 is associated with preferential GLP-1R targeting towards a recycling pathway. GLP-1, but not exendin-4, is a substrate for ECE-1, and the resultant propensity to intra-endosomal degradation, in conjunction with differences in binding affinity, contributes to alterations in GLP-1R trafficking behaviours and degradation. The T149M GLP-1R variant shows reduced signalling and internalisation responses, which is likely to be due to disruption of the cytoplasmic region that couples to intracellular effectors. These observations provide insights into how ligand- and genotype-specific factors can influence GLP-1R trafficking.

PhenoRank: reducing study bias in gene prioritization through simulation.

Motivation: Genome-wide association studies have identified thousands of loci associated with human disease, but identifying the causal genes at these loci is often difficult. Several methods prioritize genes most likely to be disease causing through the integration of biological data, including protein-protein interaction and phenotypic data. Data availability is not the same for all genes however, potentially influencing the performance of these methods. Results: We demonstrate that whilst disease genes tend to be associated with greater numbers of data, this may be at least partially a result of them being better studied. With this observation we develop PhenoRank, which prioritizes disease genes whilst avoiding being biased towards genes with more available data. Bias is avoided by comparing gene scores generated for the query disease against gene scores generated using simulated sets of phenotype terms, which ensures that differences in data availability do not affect the ranking of genes. We demonstrate that whilst existing prioritization methods are biased by data availability, PhenoRank is not similarly biased. Avoiding this bias allows PhenoRank to effectively prioritize genes with fewer available data and improves its overall performance. PhenoRank outperforms three available prioritization methods in cross-validation (PhenoRank area under receiver operating characteristic curve [AUC]=0.89, DADA AUC = 0.87, EXOMISER AUC = 0.71, PRINCE AUC = 0.83, P < 2.2 × 10-16). Availability and implementation: PhenoRank is freely available for download at https://github.com/alexjcornish/PhenoRank. Supplementary information: Supplementary data are available at Bioinformatics online.

Properties of human genes guided by their enrichment in rare and common variants.

We analyzed 563,099 common (minor allele frequency, MAF≥0.01) and rare (MAF < 0.01) genetic variants annotated in ExAC and UniProt and 26,884 disease-causing variants from ClinVar and UniProt occurring in the coding region of 17,975 human protein-coding genes. Three novel sets of genes were identified: those enriched in rare variants (n = 32 genes), in common variants (n = 282 genes), and in disease-causing variants (n = 800 genes). Genes enriched in rare variants have far greater similarities in terms of biological and network properties to genes enriched in disease-causing variants, than to genes enriched in common variants. However, in half of the genes enriched in rare variants (AOC2, MAMDC4, ANKHD1, CDC42BPB, SPAG5, TRRAP, TANC2, IQCH, USP54, SRRM2, DOPEY2, and PITPNM1), no disease-causing variants have been identified in major, publicly available databases. Thus, genetic variants in these genes are strong candidates for disease and their identification, as part of sequencing studies, should prompt further in vitro analyses.

Landscape of Pleiotropic Proteins Causing Human Disease: Structural and System Biology Insights.

Pleiotropy is the phenomenon by which the same gene can result in multiple phenotypes. Pleiotropic proteins are emerging as important contributors to rare and common disorders. Nevertheless, little is known on the mechanisms underlying pleiotropy and the characteristic of pleiotropic proteins. We analyzed disease-causing proteins reported in UniProt and observed that 12% are pleiotropic (variants in the same protein cause more than one disease). Pleiotropic proteins were enriched in deleterious and rare variants, but not in common variants. Pleiotropic proteins were more likely to be involved in the pathogenesis of neoplasms, neurological, and circulatory diseases and congenital malformations, whereas non-pleiotropic proteins in endocrine and metabolic disorders. Pleiotropic proteins were more essential and had a higher number of interacting partners compared with non-pleiotropic proteins. Significantly more pleiotropic than non-pleiotropic proteins contained at least one intrinsically long disordered region (P < 0.001). Deleterious variants occurring in structurally disordered regions were more commonly found in pleiotropic, rather than non-pleiotropic proteins. In conclusion, pleiotropic proteins are an important contributor to human disease. They represent a biologically different class of proteins compared with non-pleiotropic proteins and a better understanding of their characteristics and genetic variants can greatly aid in the interpretation of genetic studies and drug design.

Three-Dimensional Model of Human Nicotinamide Nucleotide Transhydrogenase (NNT) and Sequence-Structure Analysis of its Disease-Causing Variations.

Defective mitochondrial proteins are emerging as major contributors to human disease. Nicotinamide nucleotide transhydrogenase (NNT), a widely expressed mitochondrial protein, has a crucial role in the defence against oxidative stress. NNT variations have recently been reported in patients with familial glucocorticoid deficiency (FGD) and in patients with heart failure. Moreover, knockout animal models suggest that NNT has a major role in diabetes mellitus and obesity. In this study, we used experimental structures of bacterial transhydrogenases to generate a structural model of human NNT (H-NNT). Structure-based analysis allowed the identification of H-NNT residues forming the NAD binding site, the proton canal and the large interaction site on the H-NNT dimer. In addition, we were able to identify key motifs that allow conformational changes adopted by domain III in relation to its functional status, such as the flexible linker between domains II and III and the salt bridge formed by H-NNT Arg882 and Asp830. Moreover, integration of sequence and structure data allowed us to study the structural and functional effect of deleterious amino acid substitutions causing FGD and left ventricular non-compaction cardiomyopathy. In conclusion, interpretation of the function-structure relationship of H-NNT contributes to our understanding of mitochondrial disorders.

Mutations in MRAP, encoding a new interacting partner of the ACTH receptor, cause familial glucocorticoid deficiency type 2.

Familial glucocorticoid deficiency (FGD), or hereditary unresponsiveness to adrenocorticotropin (ACTH; OMIM 202200), is an autosomal recessive disorder resulting from resistance to the action of ACTH on the adrenal cortex, which stimulates glucocorticoid production. Affected individuals are deficient in cortisol and, if untreated, are likely to succumb to hypoglycemia or overwhelming infection in infancy or childhood. Mutations of the ACTH receptor (melanocortin 2 receptor, MC2R) account for approximately 25% of cases of FGD. FGD without mutations of MC2R is called FGD type 2. Using SNP array genotyping, we mapped a locus involved in FGD type 2 to chromosome 21q22.1. We identified mutations in a gene encoding a 19-kDa single-transmembrane domain protein, now known as melanocortin 2 receptor accessory protein (MRAP). We show that MRAP interacts with MC2R and may have a role in the trafficking of MC2R from the endoplasmic reticulum to the cell surface.

Missense3D-TM: Predicting the Effect of Missense Variants in Helical Transmembrane Protein Regions Using 3D Protein Structures.

Variant effect predictors assess if a substitution is pathogenic or benign. Most predictors, including those that are structure-based, are designed for globular proteins in aqueous environments and do not consider that the variant residue is located within the membrane. We report Missense3D-TM that provides a structure-based assessment of the impact of a missense variant located within a membrane. On a dataset of 2,078 pathogenic and 1,060 benign variants, spanning 711 proteins from 706 structures, Missense3D-TM achieved an accuracy of 66%, Mathews correlation coefficient of 0.37, sensitivity of 58% and specificity of 81%. Missense3D-TM performed similarly to mCSM-membrane: accuracy 66% vs 61% (p = 0.02) on an unbalanced test set and 70% vs 67% (p = 0.20) on a balanced test set. The Missense3D-TM website provides an analysis of the structural effects of the variant along with its predicted position within the membrane. The web server is available at http://missense3d.bc.ic.ac.uk/.

Protein structure-based evaluation of missense variants: Resources, challenges and future directions.

We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.

The effect of adjusting LDL-cholesterol for Lp(a)-cholesterol on the diagnosis of familial hypercholesterolaemia.

BACKGROUND: Familial hypercholesterolaemia (FH) diagnostic tools help prioritise patients for genetic testing and include LDL-C estimates commonly calculated using the Friedewald equation. However, cholesterol contributions from lipoprotein(a) (Lp(a)) can overestimate 'true' LDL-C, leading to potentially inappropriate clinical FH diagnosis. OBJECTIVE: To assess how adjusting LDL-C for Lp(a)-cholesterol affects FH diagnoses using Simon Broome (SB) and Dutch Lipid Clinic Network (DLCN) criteria. METHODS: Adults referred to a tertiary lipid clinic in London, UK were included if they had undergone FH genetic testing based on SB or DLCN criteria. LDL-C was adjusted for Lp(a)-cholesterol using estimated cholesterol contents of 17.3%, 30% and 45%, and the effects of these adjustments on reclassification to 'unlikely' FH and diagnostic accuracy were determined. RESULTS: Depending on the estimated cholesterol content applied, LDL-C adjustment reclassified 8-23% and 6-17% of patients to 'unlikely' FH using SB and DLCN criteria, respectively. The highest reclassification rates were observed following 45% adjustment in mutation-negative patients with higher Lp(a) levels. This led to an improvement in diagnostic accuracy (46% to 57% with SB, and 32% to 44% with DLCN following 45% adjustment) through increased specificity. However all adjustment factors led to erroneous reclassification of mutation-positive patients to 'unlikely' FH. CONCLUSION: LDL-C adjustment for Lp(a)-cholesterol improves the accuracy of clinical FH diagnostic tools. Adopting this approach would reduce unnecessary genetic testing but also incorrectly reclassify mutation-positive patients. Health economic analysis is needed to balance the risks of over- and under-diagnosis before LDL-C adjustments for Lp(a) can be recommended.

Missense3D-PPI: A Web Resource to Predict the Impact of Missense Variants at Protein Interfaces Using 3D Structural Data.

In 2019, we released Missense3D which identifies stereochemical features that are disrupted by a missense variant, such as introducing a buried charge. Missense3D analyses the effect of a missense variant on a single structure and thus may fail to identify as damaging surface variants disrupting a protein interface i.e., a protein-protein interaction (PPI) site. Here we present Missense3D-PPI designed to predict missense variants at PPI interfaces. Our development dataset comprised of 1,279 missense variants (pathogenic n = 733, benign n = 546) in 434 proteins and 545 experimental structures of PPI complexes. Benchmarking of Missense3D-PPI was performed after dividing the dataset in training (320 benign and 320 pathogenic variants) and testing (226 benign and 413 pathogenic). Structural features affecting PPI, such as disruption of interchain bonds and introduction of unbalanced charged interface residues, were analysed to assess the impact of the variant at PPI. The performance of Missense3D-PPI was superior to that of Missense3D: sensitivity 44 % versus 8% and accuracy 58% versus 40%, p = 4.23 × 10-16. However, the specificity of Missense3D-PPI was lower compared to Missense3D (84% versus 98%). On our dataset, Missense3D-PPI's accuracy was superior to BeAtMuSiC (p = 3.4 × 10-5), mCSM-PPI2 (p = 1.5 × 10-12) and MutaBind2 (p = 0.0025). Missense3D-PPI represents a valuable tool for predicting the structural effect of missense variants on biological protein networks and is available at the Missense3D web portal (http://missense3d.bc.ic.ac.uk).

Rare variants in the MECP2 gene in girls with central precocious puberty: a translational cohort study.

BACKGROUND: Identification of genetic causes of central precocious puberty have revealed epigenetic mechanisms as regulators of human pubertal timing. MECP2, an X-linked gene, encodes a chromatin-associated protein with a role in gene transcription. MECP2 loss-of-function mutations usually cause Rett syndrome, a severe neurodevelopmental disorder. Early pubertal development has been shown in several patients with Rett syndrome. The aim of this study was to explore whether MECP2 variants are associated with an idiopathic central precocious puberty phenotype. METHODS: In this translational cohort study, participants were recruited from seven tertiary centres from five countries (Brazil, Spain, France, the USA, and the UK). Patients with idiopathic central precocious puberty were investigated for rare potentially damaging variants in the MECP2 gene, to assess whether MECP2 might contribute to the cause of central precocious puberty. Inclusion criteria were the development of progressive pubertal signs (Tanner stage 2) before the age of 8 years in girls and 9 years in boys and basal or GnRH-stimulated LH pubertal concentrations. Exclusion criteria were the diagnosis of peripheral precocious puberty and the presence of any recognised cause of central precocious puberty (CNS lesions, known monogenic causes, genetic syndromes, or early exposure to sex steroids). All patients included were followed up at the outpatient clinics of participating academic centres. We used high-throughput sequencing in 133 patients and Sanger sequencing of MECP2 in an additional 271 patients. Hypothalamic expression of Mecp2 and colocalisation with GnRH neurons were determined in mice to show expression of Mecp2 in key nuclei related to pubertal timing regulation. FINDINGS: Between Jun 15, 2020, and Jun 15, 2022, 404 patients with idiopathic central precocious puberty (383 [95%] girls and 21 [5%] boys; 261 [65%] sporadic cases and 143 [35%] familial cases from 134 unrelated families) were enrolled and assessed. We identified three rare heterozygous likely damaging coding variants in MECP2 in five girls: a de novo missense variant (Arg97Cys) in two monozygotic twin sisters with central precocious puberty and microcephaly; a de novo missense variant (Ser176Arg) in one girl with sporadic central precocious puberty, obesity, and autism; and an insertion (Ala6_Ala8dup) in two unrelated girls with sporadic central precocious puberty. Additionally, we identified one rare heterozygous 3'UTR MECP2 insertion (36_37insT) in two unrelated girls with sporadic central precocious puberty. None of them manifested Rett syndrome. Mecp2 protein colocalised with GnRH expression in hypothalamic nuclei responsible for GnRH regulation in mice. INTERPRETATION: We identified rare MECP2 variants in girls with central precocious puberty, with or without mild neurodevelopmental abnormalities. MECP2 might have a role in the hypothalamic control of human pubertal timing, adding to the evidence of involvement of epigenetic and genetic mechanisms in this crucial biological process. FUNDING: Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, and the Wellcome Trust.

GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification.

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.

Somatic mutations of CADM1 in aldosterone-producing adenomas and gap junction-dependent regulation of aldosterone production.

Aldosterone-producing adenomas (APAs) are the commonest curable cause of hypertension. Most have gain-of-function somatic mutations of ion channels or transporters. Herein we report the discovery, replication and phenotype of mutations in the neuronal cell adhesion gene CADM1. Independent whole exome sequencing of 40 and 81 APAs found intramembranous p.Val380Asp or p.Gly379Asp variants in two patients whose hypertension and periodic primary aldosteronism were cured by adrenalectomy. Replication identified two more APAs with each variant (total, n = 6). The most upregulated gene (10- to 25-fold) in human adrenocortical H295R cells transduced with the mutations (compared to wildtype) was CYP11B2 (aldosterone synthase), and biological rhythms were the most differentially expressed process. CADM1 knockdown or mutation inhibited gap junction (GJ)-permeable dye transfer. GJ blockade by Gap27 increased CYP11B2 similarly to CADM1 mutation. Human adrenal zona glomerulosa (ZG) expression of GJA1 (the main GJ protein) was patchy, and annular GJs (sequelae of GJ communication) were less prominent in CYP11B2-positive micronodules than adjacent ZG. Somatic mutations of CADM1 cause reversible hypertension and reveal a role for GJ communication in suppressing physiological aldosterone production.

Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs.

Many nonsynonymous single nucleotide polymorphisms (nsSNPs) are disease causing due to effects at protein-protein interfaces. We have integrated a database of the three-dimensional (3D) structures of human protein/protein complexes and the humsavar database of nsSNPs. We analyzed the location of nsSNPS in terms of their location in the protein core, at protein-protein interfaces, and on the surface when not at an interface. Disease-causing nsSNPs that do not occur in the protein core are preferentially located at protein-protein interfaces rather than surface noninterface regions when compared to random segregation. The disruption of the protein-protein interaction can be explained by a range of structural effects including the loss of an electrostatic salt bridge, the destabilization due to reduction of the hydrophobic effect, the formation of a steric clash, and the introduction of a proline altering the main-chain conformation.

The AlphaFold Database of Protein Structures: A Biologist's Guide.

AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the AlphaFold models from the perspective of a biologist, who may not be an expert in structural biology.

GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes.

Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein-protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein-protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.

Lipid lowering with inclisiran: a real-world single-centre experience.

OBJECTIVE: The reduction in circulating low-density lipoprotein cholesterol (LDL-c) is the primary aim of lipid-lowering therapies as a method of atherosclerotic cardiovascular disease risk reduction. Inclisiran is a new and potent lipid-lowering drug that is shown to be effective in reducing LDL-c in randomised controlled trials, however, real-world data of its use are not yet known. We sought to analyse the early effects of this drug in a tertiary centre lipid and cardiovascular risk clinic. METHODS: We performed a retrospective analysis of the first 80 patients who received a single dose of inclisiran at our lipid clinic between 1 December 2021 and 1 September 2022. Data were collected using electronic healthcare records. Baseline blood tests were taken prior to start of treatment and were repeated at 2 months follow-up. Data on adverse events were also recorded. RESULTS: At 2 months after treatment initiation, mean baseline LDL-c fell from 3.5±1.1 mmol/L by 48.6% to 1.8±1.0 mmol/L and total cholesterol from 5.7±1.3 mmol/L by 33.3% to 3.8±1.1 mmol/L (both p<0.0001). Mean high-density lipoprotein-c rose by 7.7% to 1.4±0.4 mmol/L (p=0.02) and median triglycerides fell by 31.3% to 1.1 mmol/L (IQR 0.9-2) (p=0.001). Adverse events (injection site reaction, fatigue and headache) were recorded in three patients and all had self-resolved by time of follow-up. CONCLUSION: Inclisiran use in line with National Institute for Health and Care Excellence guidelines led to significant lowering of LDL-c at 2 months, with efficacy similar to that reported in trials with good tolerability.