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Heterozygous mutations in SLC40A1, encoding a multi-pass membrane protein of the major facilitator superfamily known as ferroportin 1 (FPN1), are responsible for two distinct hereditary iron-overload diseases: ferroportin disease, which is associated with reduced FPN1 activity (i.e., decrease in cellular iron export), and SLC40A1-related hemochromatosis, which is associated with abnormally high FPN1 activity (i.e., resistance to hepcidin). Here, we report three SLC40A1 missense variants with opposite functional consequences. In cultured cells, the p.Arg40Gln and p.Ser47Phe substitutions partially reduced the ability of FPN1 to export iron and also partially reduced its sensitivity to hepcidin. The p.Ala350Val substitution had more profound effects, resulting in low FPN1 iron egress and weak FPN1/hepcidin interaction. Structural analyses helped to differentiate the first two substitutions, which are predicted to cause local instabilities, and the third, which is thought to prevent critical rigid-body movements that are essential to the iron transport cycle. The phenotypic traits observed in a total of 12 affected individuals are highly suggestive of ferroportin disease. Our findings dismantle the classical dualism of FPN1 loss versus gain of function, highlight some specific and unexpected functions of FPN1 transmembrane helices in the molecular mechanism of iron export and its regulation by hepcidin, and extend the spectrum of rare genetic variants that may cause ferroportin disease., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

SLC40A1 is the sole iron export protein reported in mammals. In humans, its dysfunction is responsible for ferroportin disease, an inborn error of iron metabolism transmitted as an autosomal dominant trait and observed in different ethnic groups. As a member of the major facilitator superfamily, SLC40A1 requires a series of conformational changes to enable iron translocation across the plasma membrane. The influence of lipids on protein stability and its conformational changes has been little investigated to date. Here, we combine molecular dynamics simulations of SLC40A1 embedded in membrane bilayers with experimental alanine scanning mutagenesis to analyze the specific role of glycerophospholipids. We identify four basic residues (Lys90, Arg365, Lys366, and Arg371) that are located at the membrane-cytosol interface and consistently interact with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) molecules. These residues surround a network of salt bridges and hydrogens bonds that play a critical role in stabilizing SLC40A1 in its basal outward-facing conformation. More deeply embedded in the plasma membrane, we identify Arg179 as a charged amino acid residue also tightly interacting with lipid polar heads. This results in a local deformation of the lipid bilayer. Interestingly, Arg179 is adjacent to Arg178, which forms a functionally important salt-bridge with Asp473 and is a recurrently associated with ferroportin disease when mutated to glutamine. We demonstrate that the two p.Arg178Gln and p.Arg179Thr missense variants have similar functional behaviors. These observations provide insights into the role of phospholipids in the formation/disruption of the SLC40A1 inner gate, and give a better understanding of the diversity of molecular mechanisms of ferroportin disease., (© 2024 The Author(s). The FASEB Journal published by Wiley Periodicals LLC on behalf of Federation of American Societies for Experimental Biology.)

Background: Single-nucleotide variants (SNVs) within gene coding sequences can significantly impact pre-mRNA splicing, bearing profound implications for pathogenic mechanisms and precision medicine. In this study, we aim to harness the well-established full-length gene splicing assay (FLGSA) in conjunction with SpliceAI to prospectively interpret the splicing effects of all potential coding SNVs within the four-exon SPINK1 gene, a gene associated with chronic pancreatitis., Results: Our study began with a retrospective analysis of 27 SPINK1 coding SNVs previously assessed using FLGSA, proceeded with a prospective analysis of 35 new FLGSA-tested SPINK1 coding SNVs, followed by data extrapolation, and ended with further validation. In total, we analyzed 67 SPINK1 coding SNVs, which account for 9.3% of the 720 possible coding SNVs. Among these 67 FLGSA-analyzed SNVs, 12 were found to impact splicing. Through detailed comparison of FLGSA results and SpliceAI predictions, we inferred that the remaining 653 untested coding SNVs in the SPINK1 gene are unlikely to significantly affect splicing. Of the 12 splice-altering events, nine produced both normally spliced and aberrantly spliced transcripts, while the remaining three only generated aberrantly spliced transcripts. These splice-impacting SNVs were found solely in exons 1 and 2, notably at the first and/or last coding nucleotides of these exons. Among the 12 splice-altering events, 11 were missense variants (2.17% of 506 potential missense variants), and one was synonymous (0.61% of 164 potential synonymous variants). Notably, adjusting the SpliceAI cut-off to 0.30 instead of the conventional 0.20 would improve specificity without reducing sensitivity., Conclusions: By integrating FLGSA with SpliceAI, we have determined that less than 2% (1.67%) of all possible coding SNVs in SPINK1 significantly influence splicing outcomes. Our findings emphasize the critical importance of conducting splicing analysis within the broader genomic sequence context of the study gene and highlight the inherent uncertainties associated with intermediate SpliceAI scores (0.20 to 0.80). This study contributes to the field by being the first to prospectively interpret all potential coding SNVs in a disease-associated gene with a high degree of accuracy, representing a meaningful attempt at shifting from retrospective to prospective variant analysis in the era of exome and genome sequencing., (© 2024. The Author(s).)