Deep Mutational Scanning of FDX1 Identifies Key Structural Determinants of Lipoylation and Cuproptosis

Domain: chemistry
Published: 2025-12-21
Editorial outcome: minor_revision

Abstract
Cuproptosis is a recently described form of regulated cell death triggered by ionophore-induced copper (Cu) overload in mitochondria. It is critically dependent on ferredoxin 1 (FDX1), a mitochondrial iron-sulfur cluster containing protein that acts as an electron shuttle. FDX1 reduces ionophore-bound Cu(II) to Cu(I), thereby triggering its release, and promotes mitochondrial protein lipoylation, which is directly targeted by the released copper to drive cell death. Despite the pivotal role of FDX1 in cuproptosis, the structural determinants underlying its distinct functions remain unclear. To address this, we performed deep mutational scanning on FDX1 and find that two conserved solvent-exposed residues, D136 and D139, on alpha helix 3 are essential for both cuproptosis and lipoylation. Charge-reversal mutations at these positions abolish FDX1’s ability to induce cuproptosis and support lipoylation in cells, despite retaining full enzymatic activity in vitro. Guided by structural and genomic analyses, we further identify dihydrolipoamide dehydrogenase (DLD), the E3 subunit of lipoylated complexes as an alternative FDX1 reductase both in cells and in vitro. Together, these findings establish the acidic alpha helix 3 of FDX1 as a critical interface for its upstream regulation and suggest that FDX1’s roles in cuproptosis and in lipoylation are both structurally and functionally linked.

Introduction
Cuproptosis is a recently identified form of regulated cell death triggered by the toxic accumulation of copper (Cu) in mitochondria, a process facilitated by Cu ionophores such as elesclomol (ES)1,2. Multiple CRISPR/Cas9 whole-genome screens and genetic analyses of hundreds of cancer cell lines have identified ferredoxin 1 (FDX1) as a key regulator of ES-induced cuproptosis. FDX1 promotes cuproptosis by directly reducing ES-bound Cu(II) to Cu(I), leading to Cu release, ES recycling, and subsequent Cu-induced cytotoxicity1–3. This toxicity arises from the selective targeting of the mitochondrially localized dihydrolipoyl transacetylase (DLAT) and iron-sulfur (Fe-S) cluster proteins, resulting in lipoylated protein aggregation and Fe-S cluster destabilization, respectively2.

The crucial role of FDX1 in regulating cuproptosis highlighted the importance of establishing its natural role in human cells. In steroidogenic tissues, FDX1 regulates steroid hormone synthesis by donating electrons to various cytochrome P450 (CYP) proteins such as CYP11A14–6. Beyond steroidogenesis, FDX1 has been suggested to play a role in the biosynthesis of bile acid, heme, and vitamin A and D by donating electrons to other cytochrome P450 enzymes7. Over the years there has been some debate as to the spectrum of FDX1 downstream substrates. For instance, FDX1 was suggested to be involved in Fe-S cluster biogenesis8,9. However, in recent genetic and proteomic studies in human cells10–12, this role was largely disproved, showing that the predominant non-steroidogenic role of FDX1 in human cells is in regulating protein lipoylation2,10–12 and maintaining electron transport chain (ETC) Complex IV integrity11,13. The role of FDX1 in regulating lipoylation was also confirmed in vitro, showing that FDX1 is essential for lipoic acid synthase (LIAS) activity by promoting the reductive cleavage of S-adenosylmethionine (SAM) to a required 5’-deoxyadenosyl 5’-radical intermediate10. While some controversy remains regarding the full spectrum of endogenous, functional downstream substrates of human FDX1, there is consensus that its upstream electron donor is the mitochondrial NADPH-dependent ferredoxin reductase (FDXR)7,14,15.

FDX1 plays a dual role in cuproptosis (Fig. 1A). First, FDX1 reduces the Cu(II) bound to ES, prompting its release in mitochondria1,3. Second, FDX1 regulates protein lipoylation, the downstream target of the released Cu. The absence of protein lipoylation is sufficient to rescue ES induced cuproptosis, establishing lipoylation as a requirement to facilitate cuproptosis through a toxic gain-of-function mechanism2. Thus, to mechanistically understand the regulatory roles of FDX1 in promoting cuproptosis, there is a need to first establish the structural elements of FDX1 that are required for its distinct functions.

Results
Deep mutational scanning defines FDX1 structure-function
To identify the structural elements of FDX1 required for cuproptosis induction, we performed a DMS analysis. First, we established two cell line models lung adenocarcinoma ABC1 and human embryonic kidney 293 cells T expressing SV40 large T-antigen (HEK293T) in which FDX1 was deleted using electroporation of Cas9 ribonucleoprotein complexes, avoiding constitutive Cas9 overexpression. As expected, knockout (KO) of FDX1 conferred resistance to ES-induced cuproptosis, whereas reconstitution of FDX1 in KO cells re-sensitized them, yielding even greater sensitivity than in the wild-type control (~100-fold increase) in the ABC1 cell line (Fig. 1B; Supplementary Fig. 1A). This strong dependence on FDX1 protein levels created an ideal system for a DMS analysis.

To systematically assess the functional importance of individual residues, we generated a comprehensive library of FDX1 mutants, where each amino acid was substituted with every possible residue, including stop codons. This library was introduced into FDX1 KO ABC1 and HEK293T cells (Fig. 1C). Cells expressing FDX1 mutants were grown in the presence or absence of ES, and the ability of each mutant to restore ES sensitivity was assessed after 14 and 11 days in ABC1 and HEK293T cells, respectively. As expected, nonsense mutations failed to re-sensitize FDX1 KO cells to ES, whereas silent mutations did (Fig. 1D), confirming the specificity of our approach and establishing the confined boundaries for missense mutation analysis.

Energetic mapping reveals selective cuproptosis mutants
To quantify the impact of individual mutations, we measured the log fold-change (LFC) viability for each FDX1 mutant, centered them to the means of silent mutants and nonsense mutants (i.e., scaled LFC), and visualized these data using a heatmap for both ABC1 (Fig. 2A) and HEK293T (Supplementary Fig. 1B) cells. The effects of FDX1 mutations on ES sensitivity were highly correlated across the two lines (Supplementary Fig. 1J), and several clusters of positions with mutational intolerance emerged. First, we observed a cluster around residues 40 to 50 at the N terminus that is more pronounced in the ABC1 cell line (Fig. 2A). We hypothesized that this region contributes to mitochondrial targeting and that substitutions to this region would impair import and diminish FDX1 function (Supplementary Fig. 1C, D). Second, variants at V69 and F71 mapped to a hydrophobic β-strand core, and hydrophobic substitutions at these sites were largely tolerated (Supplementary Fig. 1E). Third, residue E134 on the third alpha helix (α-helix 3) coordinates the side chain of R149 and the backbone at L149, suggesting that its mutation disrupts the α-helix 3-stabilizing network and indicates an important structural role for this helix (Supplementary Fig. 1F). Finally, mutations of the iron-sulfur cluster coordinating cysteines produced strong loss-of-function phenotypes, as expected (Fig. 2C). It should also be noted that substitutions in the extreme C terminus (>V167) were largely tolerated, consistent with its predicted disorder and a region that is not essential for FDX1 function (Supplementary Fig. 1G). Together, these patterns indicate that mutational intolerance that attenuate FDX1-mediated cuproptosis arises from substitutions that impair mitochondrial targeting, destabilize protein structure, or disrupt cofactor binding.

We next sought to distinguish between two classes of FDX1 mutations that impair the ability to re-sensitize FDX1 KO cells to ES: (1) missense mutations that disrupt FDX1 structure, leading to enzymatically inactive forms of FDX1, and (2) mutations that selectively impair FDX1’s ability to regulate cuproptosis while preserving enzymatic function. To differentiate between these classes, we performed an in silico FoldX mutational analysis16,17 to predict the mean free-energy change (ΔΔG) for each position in FDX1. Loss-of-function (LOF) variants predicted to cause global protein instability exhibit high ΔΔG values. It should be noted that this metric represents a computational prediction rather than an experimental measurement and may therefore result in false positives/negatives18. Plotting the predicted ΔΔG against the scaled LFC in ES-induced viability (Fig. 2B and Supplementary Fig. 1H, left panels) revealed that most variations causing resistance were associated with high ΔΔG values, consistent with structural destabilization. Resistant variants that did not exhibit high ΔΔG values but did exhibit high scaled LFC values included the essential Fe-S cluster-binding cysteine residues (C106, C112, C115, C152), as the Fe-S cluster-binding properties of FDX1 were not accounted for in FoldX calculations. In addition to these expected findings, two residues—D136 and D139—stood out as critical regulators of ES-Cu sensitivity (Fig. 2C). These positions were susceptible to LOF upon mutation to positively charged residues, specifically lysine or arginine residues (Fig. 2B and Supplementary Fig. 1H, right panels; Supplementary Fig. 1I). The mutational intolerance to charge-specific substitutions at D136 and D139 while not impacting FDX1 stability suggested that these solvent-exposed residues, which sit on FDX1’s α-helix 3, play a role in mediating intermolecular, electrostatic interactions important for cuproptosis (Fig. 2C).

Charge reversal on α-helix 3 disrupts FDX1 function
To validate our findings, we generated four D136 and D139 mutants: two alanine substitutions (D136A, D139A) to create neutral charge mutants that eliminate the negative charge while maintaining overall structure, and two arginine substitutions (D136R, D139R) to introduce a charge flip by replacing the negatively charged aspartic acid with a positively charged residue. These mutants, along with wild-type (WT) FDX1, were complemented to similar levels in FDX1 knockout (KO) ABC1 and HEK293T cells, and their sensitivity to ES or ES-Cu was assessed (Fig. 3A–C).

As expected, WT FDX1 fully restored ES/ES-Cu sensitivity in FDX1 KO cells. Similarly, the neutral D136A and D139A mutants fully re-sensitized cells to ES/ES-Cu, demonstrating that the negative charge at these positions is not strictly required for FDX1 function. In contrast, cells expressing the charge-flipped D136R and D139R mutants failed to regain ES/ES-Cu sensitivity. These findings highlight that while residues D136 and D139 located on α-helix 3 (Fig. 2C) are not intrinsically essential (as some mutations are tolerated), their substitutions to positively charged residues specifically inhibit the ability of FDX1 to induce cuproptosis.

FDX1 facilitates cuproptosis by reducing Cu(II)-ES to Cu(I) and regulating protein lipoylation1–3,10–12. Therefore, we further assessed the impact of D136 and D139 substitutions on protein lipoylation levels in ABC1 and HEK293T cells. As expected, FDX1 deletion led to a complete loss of DLST and DLAT lipoylation (Fig. 3D, E). Complementation of WT, D136A, or D139A mutants completely rescued lipoylation, suggesting that these aspartic acid residues are dispensable and can be substituted by other amino acids without an overall loss of enzymatic function. However, the charge-flipped D136R and D139R mutants completely abolished FDX1’s ability to restore lipoylation (Fig. 3D, E). Previously, FDX1 was shown to regulate biogenesis of mitochondrial cytochrome c oxidase (COX) in human cells13. The effect of FDX1 KO on COX subunit II (COX-II) levels seems to be cell line specific, with dramatic loss of COX-II in HEK293T but not ABC1 FDX1 KO cells (Fig. 3D, E). In HEK293T cells, expression of wild-type FDX1 as well as the D136A and D139A mutants successfully restored COX-II levels, whereas the D136R and D139R mutants failed to rescue COX-II expression. The D136R and D139R mutants also failed to sustain growth in low-glucose conditions (Fig. 3F), whereas the D136A and D139A mutants showed no defects at 10 mM or 5 mM glucose, with only a mild reduction in D136A growth at 2 mM glucose. Collectively, these findings suggest that positive charge substitutions on D136 or D139 residues (and not the loss of the negative charge) on α-helix 3 inhibits both its ability to facilitate lipoylation and its function in cuproptosis.

FDX1 mutants are enzymatically active in vitro
The D136 and D139 residues are solvent-exposed (Fig. 2C) and are in a region not predicted to significantly impact FDX1’s overall structure. However, previous studies suggest that this evolutionarily conserved α-helix (Supplementary Fig. 2A) plays a key role in mediating interactions between FDX1 and both its upstream reductase FDXR and downstream substrate enzymes5,6,19–23 (Fig. 4A). AlphaFold324 predictions of FDX1-FDXR binding indicate that D136 and D139 of FDX1 form a network of electrostatic interactions with R242, R271, and R275 of FDXR (Fig. 4B; Supplementary Fig. 2B, C). Consistent with this, similar electrostatic interactions are observed in the crystal structures of FDX1 and FDXR from Bos taurus PDB: 1E6E20 and with an electron acceptor FNR from Zea mays PDB: 3W5U25 (Supplementary Fig. 2D). In contrast, AlphaFold3’s prediction of the binding between FDX1 double mutant DD136/139RR and FDXR fail to reach a confident score, highlighting the importance of the intricate electrostatic network between FDX1 α-helix 3 and FDXR (Supplementary Fig. 2E).

To determine the enzymatic activity of FDX1 mutants, we purified WT, D136, and D139 FDX1 mutants, where the residues were substituted with either an alanine (D136A, D139A) or an arginine (D136R, D139R). We then tested their ability to support LIAS-mediated lipoylation in-vitro. In this setting, FDX1 is reduced by FDXR (Fig. 4C). All FDX1 mutants retained activity in this FDX1-dependent lipoylation assay, although D136R exhibited slightly slower kinetics than the other variants. These results demonstrate that D136 and D139 mutations do not compromise FDX1’s structural integrity and enzymatic potential.

To examine if these variations alter FDX1 protein-protein interactions, we employed Temperature-related Intensity Change (TRIC) fluorescence assays to measure FDX1 binding to both FDXR and LIAS. The results for FDX1 WT binding to both FDXR and LIAS are shown in Fig. 4D and Fig. 4E, respectively. The summarized results (Table 1) indicate that substituting the aspartic acids at these positions with either alanine or arginine does not significantly disrupt these interactions in vitro. Additionally, these mutations did not alter FDX1’s midpoint potential (Table 1). While these findings establish that D136R and D139R are enzymatically active, they present a puzzling contradiction: despite retaining in vitro activity, these mutants fail to support lipoylation in cells. This discrepancy raises the possibility that, in human cells, an alternative upstream regulator distinct from FDXR interacts with FDX1 through electrostatic contacts that involve D136 and D139.

DLD can replace FDXR in reducing FDX1
To explore whether additional upstream regulators of FDX1 exist (Fig. 5A), we used the Cancer Dependency Map (www.depmap.org) resource that measures the viability effect of genome-wide CRISPR/Cas9 loss-of-function gene knockouts in each individual cell line across more than a thousand different cell lines. We reasoned that the viability effect of genetically ablating FDX1 should highly correlate with that of any upstream reductase or downstream substrates of FDX1. Using this gene dependency correlation analysis approach, we previously established that the primary role of FDX1 in human cells is to regulate mitochondrial protein lipoylation12. Surprisingly, correlating the FDX1 gene dependency across DepMap does not show FDXR as a top correlating dependency, whereas the FDX1 paralog FDX2 does (Fig. 5A and Supplementary Fig. 3A). The top FDX1 correlating dependencies were LIAS and GCSH (FDX1’s downstream targets), along with dihydrolipoamide dehydrogenase (DLD), the E3 component in the lipoylated mitochondrial complexes. Although DLD’s best-characterized enzymatic function is its dehydrogenase activity critical for the redox recycling of lipoic acid, it has also been shown to exhibit reductase (diaphorase) activity by oxidizing NADH in various contexts26–35 (Supplementary Fig. 3B). This suggests that it could, in theory, serve as an upstream regulator of FDX1. Unexpectedly, unbiased structural homology analysis with DALI36 and Foldseek37, completed by targeted alignment using FATCAT38, identified significant structural similarity between DLD and FDXR (Supplementary Fig. 3C, D), suggesting that DLD could be the functional upstream reductase of FDX1 in human cells (Fig. 5B).

To test this hypothesis, we used multiple complementary experimental approaches. First, we examined the interaction between FDX1 and DLD and compared it to that of FDX1 and FDXR. HEK293T cells were transfected with Strep-Tag (STII)-tagged DLD or FDXR constructs, followed by immunoprecipitation analysis for WT or mutant FDX1 binding. WT FDX1 showed substantially stronger binding with DLD than with FDXR. Notably, the D136R and D139R FDX1 mutants displayed decreased binding to DLD, while their interaction with FDXR remained unchanged (Fig. 5C). This pattern is consistent with our DMS results, which identified D136 and D139 as critical residues for FDX1 function, and suggested a specific role in mediating interactions with its upstream partner. The lack of differential binding of FDX1 and its mutants with FDXR further supports the idea that DLD, rather than FDXR, is the physiologically relevant FDX1 reductase in this context. Next, we tested whether DLD could functionally replace FDXR in an in vitro LIAS-mediated lipoylation assay. DLD supported FDX1-dependent LIAS activity at a level comparable to that of FDXR (Fig. 5D). We then examined the effect of DLD gene knockout on protein lipoylation, reasoning that if DLD functions solely as the E3 component of lipoylated complexes, its loss should not alter total lipoylation levels. In JHH7 cells, DLD knockout lowered protein lipoylation levels by approximately four-fold when normalized to DLAT levels (Fig. 5E). The effect was more subtle in HEK293T (Fig. 5F), possibly reflecting a modest compensatory increase in FDXR levels. In contrast, FDXR deletion led to a loss of lipoylation as well as of Fe–S cluster proteins, including LIAS, making it difficult to determine whether FDXR supports lipoylation primarily by maintaining LIAS protein levels or through its FDX1–LIAS regulatory axis (Fig. 5G; Supplementary Fig. 3E). Finally, we wanted to test the relative contribution of either DLD or FDXR to FDX1-mediated cuproptosis. We found that in HEK293T DLD overexpression but not FDXR increased the sensitivity by approximately two-fold to ES-induced cuproptosis (Fig. 5H). Taken together, these results provide evidence that DLD can functionally serve as an upstream electron donor for FDX1. Collectively, these results support a model in which both DLD and FDXR contribute to FDX1 activation in cells, with their relative contributions likely depending on cell type and metabolic state.

Although FDX1 and DLD interact in cells, we did not detect direct binding between them in vitro using the TRIC assay (Supplementary Fig. 3F, top panels). Moreover, FDX1 D136R and D139R mutants, which disrupted DLD binding in cells, retained full activity in the in vitro LIAS-mediated lipoylation assay with DLD as the reductant (Supplementary Fig. 3F, bottom panel). This apparent discrepancy between cellular and in vitro contexts suggests the involvement of an additional cellular factor that facilitates or stabilizes the FDX1–DLD interaction in cells but is absent in vitro. Despite this, our results clearly demonstrate that reversing negative charges on α-helix 3 of FDX1 disrupts its interaction with DLD in cells, correlating with severely abrogated ES-induced cuproptosis and a complete loss of protein lipoylation. These findings underscore the essential regulatory role of α-helix 3 and raise the possibility that DLD may function, at least in part, as the physiological reductase of FDX1 in cells (Fig. 5I).

Methods
Cell line generation
Cell lines were created as previously described12. The ribonucleoprotein (RNP) used in FDX1 KO by nucleofection was formed using a 1:1 ratio of Alt-R CRISPR-Cas9 tracrRNA (100 μM) and Alt-R CRISPR-Cas9 predesigned crRNA (100 μM) (Hs.Cas9.FDX1.1.AD IDT guide) (Integrated DNA Technologies). The mixture was incubated at 95 °C for 5 min and then brought to room temperature to form the gRNA duplex. The gRNA duplex (50 μM) was then combined with Alt-R Cas9 enzyme (61 μM) (Integrated DNA Technologies) at a ratio of 3:2 and incubated at room temperature for 10-20 min to form the RNP that was used for the nucleofection described below. Nucleofection of ABC1 (from the Genetic Perturbation Platform of the Broad Institute) and HEK293T cells (from ATCC CRL-3216) was conducted according to the manufacturer’s protocol (Lonza). 500,000 cells per sample were pelleted by centrifugation at 4 °C using a tabletop centrifuge at 800 x g for 5 min. Cell culture media supernatant was aspirated and discarded, and the cells were resuspended in 30 μL of a mixture containing 20 μL Lonza SF cell line solution, 5 μL FDX1 Cas9 RNP, 1 μL electroporation enhancer, and 4 μL PBS (Lonza Biosciences). Each cell suspension was added to individual wells of an electroporation cuvette (Lonza Biosciences), and then nucleofected using the Lonza 4D system program A549 pulse code CM-130. Cells were then transferred from the cuvette into pre-warmed media in a 6-well or 12-well culture plate and allowed to grow to 80% confluence. The nucleofected HEK 293T cells were further single-cell plated to generate FDX1 KO single-cell clones. ABC1 FDX1 KO cells could not be single cell cloned, and the bulk nucleofected population was validated for high-efficiency KO and used in subsequent experiments. For the generation of the FDX1 KO cell lines with reconstituted FDX1, pMT025 FDX1 vector (see below) was used to make lentiviral particles. These particles were produced in HEK293T cells, and the FDX1 KO cells (ABC1, HEK293T) were infected and selected with puromycin to ensure homogenous FDX1 gene expression.

Viability assays (Cell-Titer Glo and Cellcyte)
Cell-Titer Glo (CTG) assays are performed as per manufacturer’s protocol. Briefly, ABC1 and HEK293T cells were plated in 384 well plates and left to attach overnight. Drugs were then treated as indicated. After the indicated incubation time, CTG reagent was added at equal volume as the media volume. For cell proliferation assays, cells stably expressing histone 2 GFP (H2GFP), which enable accurate live cell number quantification, were plated at 5000-10,000 cells per well in 96-well plates. Then, the plates were imaged with Cellcyte Live Cell Analyzer (Cytena) at 6-h intervals for three to 6 days. Image analysis was done using the Cellcyte analysis software, quantifying each GFP+ nuclei as one cell. Proliferation rates are plotted in log 2 fold change (Fig. 3B, C) from initial count or as confluence % over time (Fig. 3F).

Immunoprecipitation (IP)
HEK293T stably expressing either DLD or FDXR are transfected with the indicated FDX1 constructs (1 ug of DNA) in six-well plates. After 2 days of transient transfection, cells were harvested and lysed in PBS containing 1% IGEPAL and HALT protease and phosphatase inhibitors. Lysates were then clarified at 10,000 x g for 10 min at 4 C; this serves as the input for the IP. Strep-Tactin Magnetic beads were then added to each sample, and the mix was incubated in the cold for 30 min rotating at low speed. Beads were then washed with the lysis buffer, incubated with 1x SDS loading dye (with reducing reagent), and boiled at 95 °C for 10 min. Samples were then separated by SDS-PAGE and immunoblotted with the indicated antibodies.

Structural Modeling
Individual AlphaFold (AF) structures were retrieved from the AlphaFold Database (DB), which was generated by the AF Monomer v2.0 piepline and was last updated on July 31st, 2025. Specifically, for FDX1 the model used is AF-P10109-F1-V6. For DLD the model used is AF-P09622-F1-V6. For FDXR the model used is AF-P22570-F1-V6. The predicted interaction between FDX1 and FDXR was generated by Alphfold3, and was performed on the AlphaFold server (alphafoldserver.com). The disordered amino acid sequences were trimmed from the input (as indicated on Supplementary Fig. 2C), as we reasoned that their high flexibility could bias the model toward non-specific contacts and depress confidence metrics. The model was generated using the default options, and the resulting .pdb files were inspected and visualized using Pymol. DALI and Foldseek are programs that offer structure alignments against large protein structure collections. DALI server analysis (ekhidna2.biocenter.helsinki.fi/dali/) was performed using the AF-DB comparison search, and the Foldseek server analysis (search.foldseek.com/search) was restricted to mammalian structures only. Flexible structure AlignmenT by Chaining Aligned fragment pairs allowing Twists (FATCAT)’s pairwise alignment function was used to align and analyze the FDXR and DLD structural similarity. Models were visualized using Pymol.

Cloning of pMT025 FDX1
Full length FDX1 (NM_004109.5) was cloned into the pMT025 backbone vector (Addgene plasmid #158579) previously described17,42. Briefly pMT025 vector was digested using NheI and BamHI and FDX1 ORF inserted using Gibson assembly protocol.

Design and generation of FDX1 saturation mutagenesis library
The FDX1 has 183 amino acids excluding the start and stop codons. For each amino acid position of the FDX1 ORF reference sequence, either 20 or 21 alternative codons were selected, one for each of the 19 possible amino acid substitutions, one for a nonsense mutation, and, in some cases, also a codon corresponding to a silent mutation. Whenever multiple choices of codon were available, we selected codons that differed from the reference sequence by 2 or 3 nucleotides rather than codons for which only 1 nucleotide differed from the reference sequence. This was done to reduce confounding contributions to variant read counts due to errors in PCR amplification or sequencing miscalls, both of which are predominantly single-nucleotide (detailed in ref. 42). Silent mutations were included only for the 81 residues for which silent mutations could be achieved with a 2- or 3-nucleotide change to the reference sequence.

In all, the FDX1 variant library consists of 3740 variants, 20 alterations at each of the 183 amino acid positions, plus 81 silent alleles, minus one missense variant that was excluded because there was no codon option that avoided conflict with the cloning sites. A library of FDX1 full-length ORF variant oligonucleotides was synthesized at Twist BioScience. The ORF sequences were flanked with adapters for cloning. The fragment library was digested with NheI and BamHI, and ligated into the pMT_025 vector (available from Addgene). The ligated construct was used to transform Stbl4 bacterial cells and plasmid DNA (pDNA) was extracted using QIAGEN Maxi Prep Kits. The resulting pooled ORF variant plasmid DNA library was sequenced via Illumina Nextera XT platform to determine the distribution of variants within the library.

DMS screen in HEK293T and ABC1 cells
Lentivirus was produced by transfecting 293T packaging cells with TransIT-LT1 (Mirus Bio) and three plasmids: the pooled FDX1 ORF pDNA library, psPAX2 (Addgene), and pMD2.G (Addgene). Medium was replaced 6–8 h post-transfection, and viral supernatant was collected ~24 h later. After titration on each target line, 3 × 10^6 HEK293T and ABC1 cells were infected at an MOI of 0.3. Following selection and expansion, 1.0 × 10^7 cells were allocated per experimental arm.

FDX1 DMS PCR amplification and deconvolution
The general screen deconvolution strategy and considerations were described in detail in ref. 42. The integrated open reading frame (ORF) in genomic DNA was amplified by PCR. The PCR products were shotgun sheared with transposon, index labeled, and sequenced with next-generation sequencing technology. The PCR primers were designed in such away that there is a ~100 bp extra sequence at each end leading up to the mutated ORF region. We used 2 primers: (Forward: 5’-ATTCTCCTTGGAATTTGCCCTT-3’; Reverse: 5’-CATAGCGTAAAAGGAGCAACA-3’). PCR reactions were performed for each gDNA sample with a reaction volume of 50 uL and with 1 ug gDNA. Q5 (New England BioLabs) was used as DNA polymerase. 1/3 of 96 PCR reactions of a gDNA sample were pooled, concentrated with Qiagen PCR cleanup kit, and then purified by 1% agarose gel. The excised bands were purified first by Qiagen Qiaquick kits, then by AMPure XP kit (Beckman Coulter). Following Illumina Nextera XT protocol, for each sample, we set up 6 Nextera reactions, each with 1 ng of purified ORF DNA. Each reaction was indexed with unique i7/i5 index pairs. After the limited-cycle PCR step, the Nextera reactions were purified with AMPure XP kit. All samples were then pooled and sequenced with Illumina NovaSeq S4 platform. NovaSeq 600 S4 data were processed with software Analyze Saturation Mutagenesis, ASM v1.0 for short, which was developed by Broad Institute as previously described40. Typically, the pair-end reads were aligned to the ORF reference sequence. Multiple filters were applied, and some reads were trimmed. The counts of detected variants were then tallied. The output files from ASM v1.0, one for each screening sample, were then parsed, annotated merged into a single .csv file that is ready for hit-calling utilizing software tools that are freely available as previously described40.

DMS analysis
Abundance of each variant was calculated by the fraction of reads compared to the total reads of all variants in each endpoint, and log 2 Fold Change (LFC) was determined between elesclomol treatment compared to DMSO control. There were two replicates per treatment group, and the duplicates were averaged for analyses. To better appreciate our variant activity relative to wild type FDX1, the DMS LFC was centered to the mean of the wildtype FDX1 (silent mutations) distribution and normalized against the mean of FDX1 non-sense variants.

In silico saturation mutagenesis with FoldX
The in silico saturation mutagenesis studies on apo-FDX1 structure PDB: 3P1M that evaluate the protein stability from the perspective of free energy change (ΔΔG) upon mutations were performed using the FoldX16. MutateX43 was used for automation to systematically mutate each available residue to all other possible residues. The RepairPDB function of FoldX was first applied for energy minimization to modify the protein system to reasonable conformations. The BuildModel function was followed for the computational mutagenesis and reporting ΔΔG values.

Protein expression and purification
Recombinant protein purification: the protein coding sequence of DLD (aa 36-509), FDX1 WT (aa 61–184), FDXR (a.a 33–491) and LIAS (aa 28–372) all lacking their respective mitochondrial transit peptides were custom synthesized at Thermo Fisher Scientific after codon optimization for recombinant protein overexpression in E. coli. The DLD, FDXR and LIAS genes were subcloned into a modified pSUMO vector (LifeSensors Inc.), (pDWSUMO), while the FDX1 WT and its D136A, D136R, D139A, and D139R mutant genes were subcloned into pET28a vector. The genes were cloned using NdeI and XhoI restriction sites of the appropriate vector. After gene sequence verification at Penn State Genomics Core Facility (University Park, PA), the DLD and FDXR plasmids were separately transformed into E. coli BL21 (DE3) cells, while LIAS, FDX1 WT, and its variants were separately transformed into BL21 (DE3) cells carrying pDB1282, the plasmid which harbors an isc operon from A. vinelandii44. DLD, FDXR, and LIAS were each overexpressed as a fusion with a ULP1 protease cleavable N-terminal SUMO tag that also carried a hexa-histidine tag at its N-terminus, while FDX1 and variants were overproduced with an N-terminal hexa-histidine tag. During purification, the SUMO tag was cleaved from the fusion protein, affording pure respective proteins with a Gly-His appendage at the N-terminus. All proteins were overexpressed and purified, with the respective iron-sulfur clusters (only for FDX1 and LIAS) reconstituted following procedures as previously published for LIAS with no further modifications45.

In vitro FDX1 dependent lipoylation assays
All in vitro activity assays were conducted as previously reported45. Assays were conducted in an anaerobic chamber. In each assay, the following components were added at their final concentrations (unless otherwise noted) in a final volume enough for all time points as needed: buffer (50 mM HEPES, pH 7.5, 250 mM KCl) containing 10 µM LIAS, 10 µM SAH nucleosidase, 15 µM FDX1, 5 µM FDXR (or DLD), 1 mM NADPH, 0.75 mM SAM and 350 µM octanoyllysyl-containing peptide substrate (Glu-(N6-octanoyl) Lys-Ala-Tyr). The reactions were incubated at 30 °C for 10 min before initiating with SAM, quenched at specific time points, and analyzed as previously reported45.

Temperature-Related Intensity Change (TRIC) Fluorescence Assays
TRIC is a biophysical technique that detects binding events between a fluorescently labeled target and a ligand by measuring fluorescence intensity changes induced by localized temperature shifts. The interactions between FDX1 and LIAS, and FDXR and DLD were measured via the TRIC method. For these experiments, a Dianthus NT.23 instrument (NanoTemper Technologies GmbH, München, Germany) was used. For the binding assays, RED-NHS 2nd generation protein labeling kit (NanoTemper Technologies GmbH) was used to label tagless FDXR for its binding measurements to N-terminus 6His-tagged FDX1 WT and its variants. Alternatively, Red-Tris-NTA 2nd generation protein labeling kit (NanoTemper Technologies GmbH) was used to label N-terminus 6His-tagged FDX1 WT and its variants for binding measurements to tagless LIAS. All the labeling and binding steps of the experiment were performed in an anaerobic chamber (Coy Laboratory products, Grass Lake, Michigan). The labeling, binding and data analysis steps were carried out following the procedures as recommended by the manufacturer of the instrument and the labeling kits. The TRIC measurements were performed in triplicate at room temperature in TRIC Buffer (25 mM MOPS, pH 7.5, 125 mM KCl, 125 mM NaCl, 5% glycerol, 0.05% Tween). For FDXR-FDX1 assays, 50 nM final concentration of labeled FDXR target was used while the FDX1 ligand was serially diluted (each with 11-point serial dilutions from: 56 µM (WT), 61 µM (D136A); 63 µM (D136R); 68 µM (D139A); and 54 µM (D139R)). For FDX1-LIAS assays, 100 nM final concentration of labeled FDX1 WT and its variants as target was used while LIAS was serially diluted (with 10-point serial dilutions in each case from 63 µM) as the ligand. The FDXR-FDX1 and LIAS-FDX1 mixtures were incubated for 30 min at room temperature. TRIC data was collected using the DI.control data collection software (NanoTemper Technologies GmbH) at 22 °C using default settings with laser on-time set to 5 s with auto-excitation. The data was plotted using GraphPad Prism v8.3.0.

Redox potential measurements
Electrochemical experiments were carried out anaerobically in a Coy glovebox using a WaveNow wireless potentiostat (Pine). A three-electrode configuration was used in a water jacketed glass cell. A platinum wire was used as the counter electrode and the reference electrode was a standard Ag:AgCl electrode; potential reported are relative to the normal hydrogen electrode. Baseline measurements were collected using a pyrolytic graphite edge (PGE) electrode polished with 600 µm alumina slurry and applied 10 uL of 2.5 mg/mL multi-walled carbon nanotube (MWCNT) in dimethylformamide (DMF). The PGE:MWCNT was rinsed and placed into a glass cell containing a RT mixed buffer solution (10 mM MES, CHES, TAPS, HEPES), pH 6.5 with 300 mM NaCl. A 5 µL aliquot of 1.5 mM protein with 13 mg/mL polymyxin B sulfate Salt (Sigma) solution co-adsorbent was applied directly to the polished PGE:MWCNT electrode surface and incubated for 4 mins. The excess protein sample was removed, and the electrode was placed back into the buffer cell solution. Non-turnover electrochemical signals were analyzed by correction of the non-Faradaic component of the current from the raw data using the QSOAS package.

Immunoblot Analysis. Cells were lysed using RIPA lysis buffer (Sigma-Aldrich) with protease inhibitor cocktail tablets (Sigma-Aldrich) and incubated on ice for 30 min. Samples were subjected to centrifugation for 10 min at 10,000 x g to pellet cell debris. The soluble fraction was collected for quantification, and the insoluble fraction was discarded. Protein was quantified using the bicinchoninic acid (BCA) method with a bovine-specific albumin standard curve for normalization. Each protein extract was added to final 1x LDS sample buffer with a 1:10 TCEP solution reducing agent and size fractionated via pre-cast SDS-PAGE Bis-Tris 4 to 12% gels (Thermo Fisher Scientific). Gels were then transferred onto nitrocellulose membranes with the trans blot turbo system (Bio Rad). Each membrane was then incubated at room temperature for 1 h in LI-COR Odyssey blocking buffer, followed by overnight incubation at 4 °C with the indicated antibody. The membranes were then washed four times with 1× TBS with 0.1% Tween 20 (TBST) and incubated with fluorophore-specific IRDye secondary antibodies (LI-COR) at 1:10,000 for an hour. The membranes were then washed again four times with TBST before being imaged on a LI-COR Odyssey machine. Antibodies used in this study include (all at 1:1000): FDX1 (Abcam ab108257), Lipoic acid (Millipore, cat# 437695 and Abcam ab58724), LIAS (Proteintech 11577-1-AP), DLAT (Cell signaling 12362S and Thermo), DLST (Cell signaling 5556S), OXPHOS (Abcam ab110411), FDXR (Proteintech 15584-1-AP), DLD (Proteintech 16431-1-AP), and StrepTag II (STII) (Invitrogen MA5-37747). All relevant antibodies were previously validated for specificity with knockouts.

Correlation analysis
CRISPR FDX1 dependency scores across 1128 cell lines from the Broad Institute’s public 24Q4 dependency map were correlated with other CRISPR dependency scores from the Broad Institute’s 24Q4 Dependency Map/CCLE release. Pearson’s correlations were performed in R using the cor.test function. P-values were then corrected for false discovery using the Benjamini-Hochberg method of the p.adjust function in R and q-values were -log10-normalized. Top 1000 correlating genes were plotted using GraphPad Prism v8.3.0.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Recent studies have highlighted critical roles of mitochondrial reductase – the Ferredoxin 1 (FDX1) in diverse biochemical processes, including steroid hormone synthesis, lipoylation, electron transport chain complex IV biogenesis, as well as copper-mediated cell death. In this context, this study by Hsiao et al., to elucidate the molecular basis of FDX1 function is an important advance. Here, the authors used deep mutational scanning to identify two key amino acids residues, D136 and D139 that are critical for mediating FDX1 function in both cuproptosis and protein lipoylation. Interestingly, despite losing function in cells, these FDX1 mutants maintained full enzymatic activity in vitro when FDXR was used as the electron donor. This suggests that FDX1 may receive electron from another donor in cells. Using data from the Cancer Dependency Map and follow-up experiments, the authors identified dihydrolipoamide dehydrogenase (DLD) as a likely upstream reductase that activates FDX1 in vivo. Firmly establishing DLD’s role in FDX1 activation will greatly strengthen the paper.\nComments:\n1. Are the redox potentials of FDX1 and DLD consistent with the proposed electron donor role of DLD?\n2. Since copper-transporting property of elesclomol has been shown to be evolutionarily conserved, are aspartate residues (D136 and D139) also conserved?\n3. Some discrepancy between the in vivo and in vitro data regarding DLD-FDX1 functional interaction (Figure 5C and 5E) prevents reaching clear conclusions about the electron donor role of DLD.\n4. There is a discrepancy in kd values depicted in Figure 4D and 4F. In one case, it is reported as 1.5 +/- 1.2 (Figure 4D) and other case it is reported as 1.5 +/- 0.2 (Figure 4F). This typographical error should be corrected.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The paper entitled “Deep Mutational Scanning of FDX1 Identifies Key Structural Determinants of Lipoylation and Cuproptosis” by Hsiao and collaborators describes the critical role of two aspartic acid residues present in the α-helix 3 of mitochondrial FDX1 for supporting lipoylation and cuproptosis in cells. Charge-reversal mutations of these residues, initially selected via a deep mutational scanning approach, do not affect FDX1 structure or enzymatic activity as assessed by in vitro experiments but have important cellular consequences on the above-mentioned processes. It is concluded that these two functionally important residues are required for favoring FDX1 reduction in cells by the dihydrolipoamide dehydrogenase (DLD) rather than by the ferredoxin reductase.\nOverall, this is an original and well executed study and the results support the conclusions. I only have a few concerns about the description of some methods and results that are detailed below.\n1. Cell viability tests after treatment with elesclomol were sometimes performed after 72 hours, as shown in Figures 1B and 3 but also after 14 and 11 days for data shown in Figure 1B. Is there a rationale for this difference? By the way, the text (lines 76-82) is written such that we understand that two cell models (HEK293T and ABC1) have been analyzed but results shown in Fig 1B are those obtained using ABC1 cells. Add results about HEK293T cells in the main figure or as supplemental figure?\n2. There is a need to better describe the cell lines used and to present all control lines and data.\nIn Figure 1: One cell line used in the study is a FDX1 KO overexpressing FDX1. It may be important to indicate to which extent the cells overexpress FDX1. It seems that the same cells have been used for immunoblots shown in Figure 3 which should allow providing quantitative data and discussing whether indeed the levels of cell viability correlates with expression levels. If this is correct, then the naming is confusing since they are referred to as KO mutant complemented with WT FDX1 in Figure 3 (and no more as FDX1 KO + FDX1 OE). Please clarify this point. On this aspect, the “methods” section also requires improvement as it is only indicated in lines 411-412 that FDX1 was cloned into the pMT025 backbone vector”. What was cloned exactly? the full-length gene? which promoter and terminator were used?\nIn Figure 3B/C why is there no KO + WT as a reference here while they are used for other experiments?\nIn Figure 3E, why is there no parental line as a reference here?\n3. in line 140-141, there is a statement “Defects in the integrity of the ETC Complex IV were similarly observed in these cells” only referring to the reference 13 but no indication of a related figure. Do we have to understand that the immunoblots shown in Figures 3D and 3E for COXII CIV support this statement? If yes, the data obtained for ABC1 and HEK293T cells are not so comparable. Some explanation/clarification is needed here.\n4. The authors mentioned in the discussion that the activity profile of DLD differs according to the oligomerization forms which can be monomers, dimers/tetramers (lines 272-274). Because several experiments have been performed using DLD (enzymatic and interaction assays, Fig 5D, S3E), it seems important to this reviewer to provide indications about the homogeneity of the sample used for the experiments.\nMinor comments:\n1. Line 78: the term “transient electroporation” does not seem appropriate. Transient expression?\n2. Line 134-135: The following sentence “To assess the impact of D136 and D139 substitutions on protein lipoylation, we expressed WT and mutant FDX1 in FDX1 KO ABC1 and HEK293T cells.” may be rephrased since this information is provided just above (lines 124-129).\n3. Line 156: The use of a maize FDX-FNR complex for structural comparison is maybe not appropriate, first because these are chloroplastic proteins and second because FNR is the electron acceptor in this case and not the electron donor.\n4. Line 185: sentence that needs a correction “Using this approach, we were previously able to determine that the primary role of….”\n5. Line 486: Recombinant protein purification - Genes encoding DLD (36-509), FDX1 WT (aa 61 – 184)… Change genes by coding sequences since there may be introns in the genes?\n6. Line 544: Define these acronyms: MWCNT in DMF.\n7. In Figure 2, replace Fe-S binding by Fe-S cluster binding.", "reviewer_4": "Hsiao et al present a comprehensive study of FDX1’s role in cuproptosis. They explore FDX1’s two main roles in reducing copper ions and regulating protein lipoylation. They use an innovative Deep Mutational Scanning (DMS) approach to explore the impact of every possible FDX1 variant on overall protein function, as revealed by a cell viability assay. Their analyses (DMS but also a computational stability analysis) revealed two conserved residues, D136 and D139, in FDX1’s α-helix 3 as essential for lipoylation and cuproptosis, though mutations at these sites preserve enzymatic activity in vitro. They further explore these two residues and several other aspects of FDX1 functon(outside our main area of expertise which is DMS). The core DMS experiments are well-performed and shed insight into FDX1 function, and we applaud the authors on using this method to gain insight into protein function. However, several important methods details are not presented and some of the conclusions are somewhat overstated.\nMajor comments:\nThe first sentence of the discussion: “...FDX1’s dual roles in cuproptosis and mitochondrial protein lipoylation are structurally and functionally inseparable” is overstated. The authors validated several FDX1 residues as being important for both roles, but it is possible that there are other mutants that do separate these two functions that were not explored in the paper. The authors main DMS assay is a general cell viability assay that does not distinguish between cuproptosis and lipoylation (which is fine—but don’t overstate conclusions).\nThe authors focus on positions D136 and D139 for further detailed analysis. These two positions come from a combination of the DMS and a computational stability analysis. There are several other interesting “hotspot” regions in the DMS map that go unmentioned or barely mentioned in the paper, including regions near residues 40, 70, 134, and 152. (It is hard to tell precisely because there is no numbering of positions in the heatmap, which should be added). Although it is not necessary for the authors to perform in-depth followup experiments on these residues as with 136+139, can the authors at least discuss these other DMS hotspots, in regards to structural elements/location in protein/hypotheses for mechanism? Other structure-function conclusions like the fact that nonsense variants post ~170 are not loss of function go undiscussed as well.\nIt is important to note that the stability metric is a computational prediction, not an experimental measurement (which could have been experimentally measured by a VAMP-seq DMS approach). We would recommend changing “Higher Global Protein Instability” in Figure 2B-C to “Higher Predicted Protein Instability” and similar edits in the text. A caveat to the singular focus on 136+139 is that other variants could have predicted loss-of-stability but actually have normal stability.\nSome DMS methods are missing or unclear. How was the DMS mutant library generated? How was it delivered into cells? How many cells were grown/transfected/harvested?\nGeneral minor comments:\nAdd “MAVE” to keywords\nIn addition to Figure 1A, it would be helpful to have a broader overview figure drawn of the cuproptosis pathway/other proteins/pic of mitochondria, etc that is the main subject of the introduction+paper.\nMore details on FDX1 knockout needed. Sanger confirmed? Homozygous or heterozygous?\nHow many total mutations were DMS scores were obtained for and what fraction of possible mutants?\nSpell out HEK293 and ABC1 at first use. It is a strength that the DMS experiment was performed in both cell lines with similar results, but it is unclear why the 2 cell lines were chosen or necessary. Was there a difference or hypothesized difference between the two lines in terms of cuproptosis or FDX1 function?\nRe: “The effects of FDX1 mutations on ES sensitivity were highly correlated between the two cell lines.” Can the authors plot the scores from the two cell lines against each other and mention the correlation coefficient in the results?\nHow many DMS replicates were performed in each cell line? What was the correlation of scores between the replicates? How were replicates (if any) handled in the analysis?\nFigure 2 minor comments:\nAdd numbers to the heatmap indicating residue numbers\nWhy isn’t deldelG calculated for the N-terminus?\nSuggest to label residue types in Figure 1A (e.g. hydrophobic, polar, charged etc)\nThere is excellent discrimination between nonsense and silent mutants, indicating that the assay+data quality is excellent. However, consider scaling the heatmap colors more gradually so that there is more white on the heatmap because the silent mutations had a bit of a spread of scores.\nWere late nonsense mutants (with normal scores) excluded in the violin plot of nonsense mutants, and excluded for the normalization of scores?\nMethods minor comments:\nRefers to “viral” particles without saying what type of virus.\nWas every variant included in the DMS analysis or were there QC criteria for excluding a variant (for example based on low abundance in the initial sample).\nData availability:\nThe standard in the DMS/MAVE community is to deposit scores to MAVE-DB before or at time of publication. In addition, depositing raw high-throughput sequence data in NCBI Sequence Read Archive before or at time of publication. I would request these from the authors.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We thank the editor and reviewers for their constructive feedback, which substantially improved the manuscript. In response, we (i) clarified the central conclusions and tempered language where appropriate; (ii) added new in-cell experiments to probe the role of DLD in FDX1 activation, including DLD knockouts/overexpression across two cell models and ES–Cu sensitivity assays; (iii) corrected typographical/consistency issues; (iv) expanded Methods; and (v) revised figures/legends. We also now discuss additional DMS hotspots and caveats regarding predicted versus measured stability. We believe these changes address each comment and strengthen the mechanistic framework. Please see detailed response below.\nOur point-to-point responses are colored in blue,\nFigure, citations, and line callouts are bolded and highlighted in yellow,\nRecited lines are quoted and italicized,\nFigure legends are colored in dark blue.\nReviewer #1 (Remarks to the Author):\nRecent studies have highlighted critical roles of mitochondrial reductase – the Ferredoxin 1 (FDX1) in diverse biochemical processes, including steroid hormone synthesis, lipoylation, electron transport chain complex IV biogenesis, as well as copper-mediated cell death. In this context, this study by Hsiao et al., to elucidate the molecular basis of FDX1 function is an important advance. Here, the authors used deep mutational scanning to identify two key amino acids residues, D136 and D139 that are critical for mediating FDX1 function in both cuproptosis and protein lipoylation. Interestingly, despite losing function in cells, these FDX1 mutants maintained full enzymatic activity in vitro when FDXR was used as the electron donor. This suggests that FDX1 may receive electron from another donor in cells. Using data from the Cancer Dependency Map and follow-up experiments, the authors identified dihydrolipoamide dehydrogenase (DLD) as a likely upstream reductase that activates FDX1 in vivo. Firmly establishing DLD’s role in FDX1 activation will greatly strengthen the paper.\nWe thank the reviewer for succinctly summarizing the paper’s main point and for the constructive feedback. As described in detail below, we have made a concerted effort to address the question of DLD’s role in FDX1 activation in cells through a series of cell-based experiments.\nComments:\n1. Are the redox potentials of FDX1 and DLD consistent with the proposed electron donor role of DLD?\nYes, the redox potentials of FDX1 and DLD are consistent with the proposed role of DLD as an electron donor to FDX1. Specifically, midpoint potentials at pH 7.0 are as follows: DLD E2 (enzyme disulfide) −0.280 V; DLD E1 (flavin) −0.346 V (Matthews & Williams Jr., 1976); FDX1 −0.240 V (Fig. 4F). Thus, electron transfer from DLD to FDX1 is thermodynamically favorable under standard conditions. We have now emphasized this point in the text (lines 368 to 370):\n“Moreover, while DLD efficiently reduces FDX1 in vitro, a property consistent with the reduction potentials of DLD (E1: -346 mV, E2: -280 mV) (PMID: 6467) and FDX1 (-240 mV), strong FDX1–DLD binding is observed only in cells.”\n2. Since copper-transporting property of elesclomol has been shown to be evolutionarily conserved, are aspartate residues (D136 and D139) also conserved?\nYes, these two aspartic acid residues are conserved across evolution, extending to bacterial ferredoxins. Please refer to Rebuttal Fig. 1 below (and the now updated Fig. S2A), which includes an amino acid sequence alignment across multiple species. We have also added this Figure call-out at line 302-204.\n3. Some discrepancy between the in vivo and in vitro data regarding DLD-FDX1 functional interaction (Figure 5C and 5E) prevents reaching clear conclusions about the electron donor role of DLD.\nWe agree with the reviewer that there is a discrepancy between our in vivo and in vitro data regarding the functional interaction between DLD and FDX1, which we have acknowledged in both the Results and Discussion sections of the manuscript. It is important for us to further emphasize that the discrepancy between the in vivo and in vitro data is true for both the D136R and D139R mutants, as they show no lipoylation in cells. Yet, they retain their function and binding to both FDXR and DLD in the in vitro setting. This point is re-emphasized in our discussion (lines 366 – 382). In this rebuttal, we focused our attention to reaching clearer conclusions about the reductase activity of DLD by conducting a series of in cell experiments, which we deem the more physiologically relevant context.\nTo recap, our experiments thus far showed that the strong binding of FDX1 to DLD in cells is disrupted upon the introduction of the D136R or D139R mutations (Fig. 5C). This finding along with others allowed us to hypothesize that DLD may be the predominant upstream regulator of FDX1 in cells. To test this hypothesis, we generated DLD knockout (KO) lines in two cellular models, HEK293T and JHH7, which we have previously used to study protein lipoylation (PMID: 37453661).\nIf DLD’s sole role were an E3 subunit of lipoylated complexes, DLD loss would not be expected to affect overall protein lipoylation. However, we observed a cell line specific loss of lipoylation: an approximately four-fold reduction of lipoylated protein levels in JHH7 when normalized to total DLAT levels, but no loss in HEK293T cells (Rebuttal Fig. 2A-B). We do, however, see a modest increase in FDXR levels in HEK293T, perhaps suggesting compensatory mechanisms. Next, we wanted to test the relative contribution of either DLD or FDXR to FDX1-mediated cuproptosis. We found that in HEK293T, DLD overexpression but not FDXR increased the sensitivity by ~2 fold to elesclomol-induced cuproptosis (Rebuttal Fig. 2C). Given that global lipoylation levels remain unchanged in the DLD overexpression cell line, this supports its direct functional impact on cuproptosis by acting on FDX1. Moreover, given that DLD drives dihydrolipoamide to lipoamide, DLD overexpression is expected to deplete lipoyl thiolates, making it unlikely that it sensitizes cells to cuproptosis by increasing copper-reactive lipoyl thiolate targets. Collectively, these results support a model in which both DLD and FDXR contribute to FDX1 activation in cells, with their relative contributions likely depending on cell type and metabolic state.\nWe also considered the reciprocal approach of knocking out FDXR. However, this experiment cannot resolve the specific contribution to FDX1 activation, as FDXR loss also disrupts Fe–S cluster biosynthesis, leading to loss of LIAS (and SDHB) and thus an indirect decrease in global lipoylation levels (Rebuttal Fig. 2D). This confounding variable therefore cannot allow us to establish FDXR’s role in reducing FDX1 in cells.\nWe believe that these results have allowed us to reach clearer conclusions about the electron role of DLD, specifically in the more physiologically relevant in-cell context. We have emphasized this point more explicitly in the revised Discussion (lines 364 - 382).\n“This is supported by our finding that DLD binds directly to FDX1 in cells, and that this interaction is severely impaired by the D136R and D139R mutations. However, this is difficult to reconcile with the mutants’ preserved activity in vitro, where they retain full enzymatic function, maintain binding to FDXR and LIAS, and support DLD-mediated LIAS activity. Moreover, while DLD efficiently reduces FDX1 in vitro… strong FDX1–DLD binding is observed only in cells. One model that could explain this discrepancy is that, in cells, the charge-flip mutants aberrantly engage an inhibitory factor which is absent in vitro and prevents FDX1 reduction by upstream partners (Fig. 5F). Alternatively, additional regulatory mechanisms such as post-translational modifications, conformational changes, or adaptor proteins may be required to facilitate WT FDX1 binding to upstream reductases in the cellular environment.\n4. There is a discrepancy in kd values depicted in Figure 4D and 4F. In one case, it is reported as 1.5 +/- 1.2 (Figure 4D) and other case it is reported as 1.5 +/- 0.2 (Figure 4F). This typographical error should be corrected.\nWe thank the reviewers for catching the typographical error. We have now fixed it to the correct value of 1.5 +/- 0.2 µM in the updated Figure 4D.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #3 (Remarks to the Author):\nThe paper entitled “Deep Mutational Scanning of FDX1 Identifies Key Structural Determinants of Lipoylation and Cuproptosis” by Hsiao and collaborators describes the critical role of two aspartic acid residues present in the α-helix 3 of mitochondrial FDX1 for supporting lipoylation and cuproptosis in cells. Charge-reversal mutations of these residues, initially selected via a deep mutational scanning approach, do not affect FDX1 structure or enzymatic activity as assessed by in vitro experiments but have important cellular consequences on the above-mentioned processes. It is concluded that these two functionally important residues are required for favoring FDX1 reduction in cells by the dihydrolipoamide dehydrogenase (DLD) rather than by the ferredoxin reductase. Overall, this is an original and well executed study and the results support the conclusions. I only have a few concerns about the description of some methods and results that are detailed below.\nWe thank the reviewer for their positive evaluation of our study. We appreciate the constructive comments, which we address point-by-point below.\n1. Cell viability tests after treatment with elesclomol were sometimes performed after 72 hours, as shown in Figures 1B and 3 but also after 14 and 11 days for data shown in Figure 1B. Is there a rationale for this difference? By the way, the text (lines 76-82) is written such that we understand that two cell models (HEK293T and ABC1) have been analyzed but results shown in Fig 1B are those obtained using ABC1 cells. Add results about HEK293T cells in the main figure or as supplemental figure?\nThe rationale for the extended 11- and 14-day treatments in the DMS screen is to ensure robust selection of cells resistant to elesclomol-induced cuproptosis. This longer exposure period is standard in most screening workflows, as it maximizes the likelihood of identifying truly resistant clones while minimizing noise. In contrast, all individual mutant validations are performed over 72 hours, during which we consistently observe the reported phenotypes.\nRegarding the calibration of our system in HEK293T, we have now added the relevant data to Fig. S1A (Rebuttal Fig. 3), and have revised the text to call out the figure in lines 84 to 87:\n“As expected, knockout (KO) of FDX1 conferred resistance to ES-induced cuproptosis, whereas reconstitution of FDX1 in KO cells re-sensitized them, yielding even greater sensitivity than in the wild-type control (~100-fold increase) in the ABC1 cell line (Fig. 1B; S1A).”\n2. There is a need to better describe the cell lines used and to present all control lines and data.\nIn Figure 1: One cell line used in the study is a FDX1 KO overexpressing FDX1. It may be important to indicate to which extent the cells overexpress FDX1.\nWe have now included immunoblots directly comparing FDX1 levels in the complemented cell line to those in the parental line (Fig. S1A; Rebuttal Fig. 3). The results show significant increase in FDX1 levels in the FDX1 KO + WT FDX1 cell line over the parental. To avoid confusion, we have now standardized the nomenclature throughout the manuscript to refer to this model consistently as “FDX1 KO complemented with FDX1 WT.” The revised text are as follows:\nLine 164 to 166: “These mutants, along with wild-type (WT) FDX1, were complemented to similar levels in FDX1 knockout (KO) ABC1 and HEK293T cells, and their sensitivity to ES or ES-Cu was assessed (Fig. 3A-C).”\nLine 177 to 187: “Complementation of WT, D136A, or D139A mutants completely rescued lipoylation, suggesting that these aspartic acid residues are dispensable and can be substituted by other amino acids without an overall loss of enzymatic function.”\nIt seems that the same cells have been used for immunoblots shown in Figure 3 which should allow providing quantitative data and discussing whether indeed the levels of cell viability correlates with expression levels. If this is correct, then the naming is confusing since they are referred to as KO mutant complemented with WT FDX1 in Figure 3 (and no more as FDX1 KO + FDX1 OE). Please clarify this point.\nBecause the relative FDX1 levels are relatively similar as indicated in Fig. 3D among the complemented cell lines (KO +WT or +mutants), the levels of cell viability are principally dependent on the biological effects of FDX1’s ability to mediate elesclomol-induced or glucose deprivation-induced toxicity.\nOn this aspect, the “methods” section also requires improvement as it is only indicated in lines 411-412 that FDX1 was cloned into the pMT025 backbone vector”. What was cloned exactly? the full-length gene? which promoter and terminator were used?\nWe have expanded the Methods section to specify that the full-length human FDX1 coding sequence (Uniprot ID: P10109) was cloned into the pMT025 lentiviral backbone under the control of the EF1α promoter, with the SV40 polyadenylation signal used as the terminator right after a WPRE signal (lines 467 to 469). We will be depositing these plasmids onto Addgene.\nIn Figure 3B/C why is there no KO + WT as a reference here while they are used for other experiments?\nWe have now included KO + WT as a reference in Fig. 3B. While performing the experiment, we did not include the KO + WT cell line in Fig. 3C, as we reasoned that the marked differential sensitivity of the alanine mutants to the arginine mutants was sufficient to emphasize the point that the former could mediate elesclomol-induced cuproptosis but not the latter.\nIn Figure 3E, why is there no parental line as a reference here?\nFig. S1A (Rebuttal Fig. 3) now show immunoblots directly comparing FDX1 levels in the complemented cell line to those in the parental line.\n3. in line 140-141, there is a statement “Defects in the integrity of the ETC Complex IV were similarly observed in these cells” only referring to the reference 13 but no indication of a related figure. Do we have to understand that the immunoblots shown in Figures 3D and 3E for COXII CIV support this statement? If yes, the data obtained for ABC1 and HEK293T cells are not so comparable. Some explanation/clarification is needed here.\nWe thank the reviewer for pointing out the lack of Figure call-out in the text and for pointing out the discrepancy between the data obtained from ABC1 and HEK293T. We agree that the extent of the defect appears much more pronounced in HEK293T cells compared to ABC1, which we have not further investigated here as we believe it is a phenomenon beyond the scope of this manuscript. We, however, speculate the difference may reflect intrinsic variations in how FDX1 contributes to the heme a/a synthesis pathway (as proposed by reference 13) between the two cell lines. We have now clarified this difference in the revised text and explicitly linked the statement to the relevant figure (lines 188 to 193).\n“Previously, FDX1 was shown to regulate biogenesis of mitochondrial cytochrome c oxidase in human cells13. The effect of FDX1 KO on COX-II levels seems to be cell line specific, with dramatic loss of COX-II in HEK293T but not ABC1 FDX1 KO cells (Fig. 3D-E). In HEK293T cells, expression of wild-type FDX1 as well as the D136A and D139A mutants successfully restored COX-II levels, whereas the D136R and D139R mutants failed to rescue COX-II expression.”\n4. The authors mentioned in the discussion that the activity profile of DLD differs according to the oligomerization forms which can be monomers, dimers/tetramers (lines 272-274). Because several experiments have been performed using DLD (enzymatic and interaction assays, Fig 5D, S3E), it seems important to this reviewer to provide indications about the homogeneity of the sample used for the experiments.\nBy using Blue-Native PAGE analysis, we have assessed the oligomerization state of the DLD used to perform the in vitro enzymatic and binding assays. The results show that DLD exists homogenously as a dimer, as shown below in Rebuttal Fig. 4. We believe that the dimeric form is the active species that donates electrons to FDX1.\nMinor comments:\n1. Line 78: the term “transient electroporation” does not seem appropriate. Transient expression?\nWe thank for reviewer for pointing out the confusing terminology. We have now fixed it by deleting the word transient to describe the electroporation process (lines 81 to 83):\n“First, we established two cell line models (ABC1 and human embryonic kidney 293 cells T expressing SV40 large T-antigen [HEK293T]) in which FDX1 was deleted using electroporation of Cas9 ribonucleoprotein complexes, avoiding constitutive Cas9 overexpression.”\n2. Line 134-135: The following sentence “To assess the impact of D136 and D139 substitutions on protein lipoylation, we expressed WT and mutant FDX1 in FDX1 KO ABC1 and HEK293T cells.” may be rephrased since this information is provided just above (lines 124-129).\nWe agree with this suggestion, and we have now fixed the text to avoid redundancy (lines 174 to 175):\n“FDX1 facilitates cuproptosis by reducing Cu(II)-ES to Cu(I) and regulating protein lipoylation 1-3,10-12. Therefore, we further assessed the impact of D136 and D139 substitutions on protein lipoylation levels in ABC1 and HEK293T cells.”\n3. Line 156: The use of a maize FDX-FNR complex for structural comparison is maybe not appropriate, first because these are chloroplastic proteins and second because FNR is the electron acceptor in this case and not the electron donor.\nWe thank for the reviewer for pointing this out, which we acknowledge we had previously missed. Considering FDX1’s conservation in prokaryotes (Rebuttal Fig. 1), we still believe that plant FDX1 is a relevant comparison due to several reasons. First, the acidic alpha helix is still observed in plant FDX1 (residues 117 to 123 in maize FDX1 [Uniprot P27787]), which demonstrates structural homology to human FDX1. Second, human FDX1 also uses the same acidic alpha helix to interface with the electron acceptor Cytochrome P450 enzymes. Lastly, plant FDX1, which contain an iron-sulfur cluster, participates in similar electron transfer reactions within plastids, providing a mechanistic parallel that supports its utility as a comparative model. That said, we agree with the reviewer that differences in subcellular localization, physiological context, and potential partner proteins between plant and human systems represent important caveats to this comparison. We have revised the text in the manuscript accordingly (lines 202 to 204).\n“However, previous studies suggest that this evolutionarily conserved α-helix plays a key role in mediating interactions between FDX1 and both its upstream reductase FDXR and downstream substrate enzymes 5,6,18-22 (Fig. 4A, Fig. S2B)… electrostatic interactions are observed in the crystal structures of FDX1 and FDXR from bovine (PDB: 1E6E) 19 and maize (PDB: 3W5U) 24 (Fig. S2A).”\n4. Line 185: sentence that needs a correction “Using this approach, we were previously able to determine that the primary role of….”\nWe have now corrected this sentence (line 256 to 257) to ensure clarity.\n“Using this gene dependency correlation analysis approach, we previously revealed and established that the primary role of FDX1 in human cells is to regulate mitochondrial protein lipoylation12.”\n5. Line 486: Recombinant protein purification - Genes encoding DLD (36-509), FDX1 WT (aa 61 – 184)… Change genes by coding sequences since there may be introns in the genes?\nWe have now corrected this sentence (line 561 to 564) to ensure clarity.\n“Recombinant protein purification: the protein coding sequence of DLD (aa 36-509), FDX1 WT (aa 61 – 184), FDXR (a.a 33–491) and LIAS (aa 28–372) all lacking their respective mitochondrial transit peptides were custom synthesized at Thermo Fisher Scientific after codon optimization for recombinant protein overexpression in E. coli.”\n6. Line 544: Define these acronyms: MWCNT in DMF.\nWe have now defined these acronyms in the text (lines 625 to 627).\n“Baseline measurements were collected using a pyrolytic graphite edge (PGE) electrode polished with 600 µm alumina slurry and applied 10 uL of 2.5 mg/mL multi-walled carbon nanotube (MWCNT) in dimethylformamide (DMF).”\n7. In Figure 2, replace Fe-S binding by Fe-S cluster binding.\nWe have now updated Fig. 2A and Fig. S1B.\nReviewer #4 (Remarks to the Author):\nHsiao et al present a comprehensive study of FDX1’s role in cuproptosis. They explore FDX1’s two main roles in reducing copper ions and regulating protein lipoylation. They use an innovative Deep Mutational Scanning (DMS) approach to explore the impact of every possible FDX1 variant on overall protein function, as revealed by a cell viability assay. Their analyses (DMS but also a computational stability analysis) revealed two conserved residues, D136 and D139, in FDX1’s α-helix 3 as essential for lipoylation and cuproptosis, though mutations at these sites preserve enzymatic activity in vitro. They further explore these two residues and several other aspects of FDX1 functon(outside our main area of expertise which is DMS). The core DMS experiments are well-performed and shed insight into FDX1 function, and we applaud the authors on using this method to gain insight into protein function. However, several important methods details are not presented and some of the conclusions are somewhat overstated.\nWe thank the reviewer for their positive evaluation of our work and for recognizing the value of our DMS approach in uncovering FDX1 function. We appreciate the constructive feedback regarding methodological details and the interpretation of our conclusions, and we have addressed these points in detail below.\nMajor comments:\nThe first sentence of the discussion: “...FDX1’s dual roles in cuproptosis and mitochondrial protein lipoylation are structurally and functionally inseparable” is overstated. The authors validated several FDX1 residues as being important for both roles, but it is possible that there are other mutants that do separate these two functions that were not explored in the paper. The authors main DMS assay is a general cell viability assay that does not distinguish between cuproptosis and lipoylation (which is fine—but don’t overstate conclusions).\nWe agree that our current data do not exclude the possibility of mutants that could separate FDX1’s roles in cuproptosis and lipoylation. We have revised the first sentence of the discussion to remove the overstatement and to clarify that our DMS assay measures overall cell viability, which may reflect contributions from both processes.\nLines 29 to 31: “Together, these findings establish α-helix 3 of FDX1 as a critical interface for its upstream regulation and suggest that FDX1’s roles in lipoylation and cuproptosis are both structurally and functionally linked.”\nLines 303 to 306: “Our study provides a comprehensive structure-function analysis of FDX1, suggesting that FDX1’s dual roles in cuproptosis and mitochondrial protein lipoylation are structurally and functionally connected…”\nThe authors focus on positions D136 and D139 for further detailed analysis. These two positions come from a combination of the DMS and a computational stability analysis. There are several other interesting “hotspot” regions in the DMS map that go unmentioned or barely mentioned in the paper, including regions near residues 40, 70, 134, and 152. (It is hard to tell precisely because there is no numbering of positions in the heatmap, which should be added). Although it is not necessary for the authors to perform in-depth follow up experiments on these residues as with 136+139, can the authors at least discuss these other DMS hotspots, in regards to structural elements/location in protein/hypotheses for mechanism? Other structure-function conclusions like the fact that nonsense variants post ~170 are not loss of function go undiscussed as well.\nWe thank the reviewer for highlighting these additional mutational hotspots in our data. In response, we have added a new section discussing these regions, including their structural context and possible functional relevance, with guidance from structural modeling and in silico analysis (Figure S1C-F). In the interest of space, we have chosen not to reproduce the figures here in this rebuttal, and we apologize for any inconvenience. All comments below are incorporated in the text (lines 103 to 130). We have also added numbering of positions in the heatmap.\nFor residues near 40 to 50, Alphafold predicts the first 65 amino acids as disordered, and the solved crystal structured (PDB 3P1M) lacks this region. We hypothesize that this region serves as a mitochondrial localization sequence. Indeed, when we performed in silico analysis on sequentially truncated versions of FDX1, the score for FDX1’s mitochondrial localization drops sharply when it is truncated at position 41 from the N-terminus (Fig. S1C). Collectively, these indicate the possibility that residues 40 to 50 form part of the mitochondrial targeting sequence, and that mutations in this region may impair mitochondrial import and thus indirectly affect FDX1’s function of cuproptosis.\nFor residues V69 and F71, we show by structural modelling that they help define a hydrophobic core with beta-strand character in this region (Fig. S1D). We hypothesize that their mutations significantly disrupt the fold of FDX1 and thus impair its enzymatic activity.\nFor residue E134, structural modelling indicates that it coordinates the side chain of R149 and the backbone of L149 (Fig. S1E). This glutamate residue is in fact highly conserved, down to bacterial FDX1 (Fig. S2B). We hypothesize that this residue is critical for stabilizing the acidic alpha helix in which D136 and D139 reside.\nAs for residue C152, it coordinates the iron-sulfur cluster on FDX1 and is therefore essential for FDX1’s enzymatic activity (Fig. 2C).\nFor residues after 167, we hypothesize that since AlphaFold predicts that the region starting from position 167 is disordered, the extreme C-terminal tail is not absolutely required for a functional FDX1 (Fig. S1F).\nIt is important to note that the stability metric is a computational prediction, not an experimental measurement (which could have been experimentally measured by a VAMP-seq DMS approach). We would recommend changing “Higher Global Protein Instability” in Figure 2B-C to “Higher Predicted Protein Instability” and similar edits in the text. A caveat to the singular focus on 136+139 is that other variants could have predicted loss-of-stability but actually have normal stability.\nWe have updated Fig. 2B-C and the corresponding text to use the term “Higher Predicted Protein Instability,” emphasizing that the stability metric is computationally derived. We have also added a statement acknowledging that predicted loss of stability may not always correspond to experimentally measured instability across all variants and have cited VAMP-seq–based DMS as a potential validation approach (lines 136 to 139).\n“Loss-of-function (LOF) variants predicted to cause global protein instability exhibit high ΔΔG values. It should be noted that this metric represents a computational prediction rather than an experimental measurement, and may therefore result in false positives/negatives (PMID: 29785012).”\nSome DMS methods are missing or unclear. How was the DMS mutant library generated? How was it delivered into cells? How many cells were grown/transfected/harvested?\nWe have incorporated the necessary details into the text (lines 482 to 500).\nA smaller excerpt of the methods section is reproduced here:\n“In all, the FDX1 variant library consists of 3740 variants, 20 alterations at each of the 183 amino acid positions, plus 81 silent alleles, minus one missense variant that was excluded because there was no codon option that avoided conflict with the cloning sites. A library of FDX1 full-length ORF variant oligonucleotides was synthesized at Twist BioScience. The ORF sequences were flanked with adapters for cloning... 293T viral packaging cells were transfected using TransIT-LT1 transfection reagent (Mirus Bio) with three plasmids: the pooled ORF pDNA library, a lentiviral packaging plasmid containing gag, pol and rev genes (psPAX2, Addgene), and an envelope plasmid containing VSV-G (pMD2.G, Addgene)…..\nDMS screen in HEK293T and ABC1 cells. Lentivirus was produced by transfecting 293T packaging cells with TransIT-LT1 (Mirus Bio) and three plasmids: the pooled FDX1 ORF pDNA library, psPAX2 (Addgene), and pMD2.G (Addgene). Medium was replaced 6–8 h post-transfection, and viral supernatant was collected ~24 h later. After titration on each target line, 3x106 HEK293T and ABC1 cells were infected at an MOI of 0.3. Following selection and expansion, 1.0x107 cells were allocated per experimental arm. ”\nGeneral minor comments:\nAdd “MAVE” to keywords\nWe have now added MAVE to keywords.\nIn addition to Figure 1A, it would be helpful to have a broader overview figure drawn of the cuproptosis pathway/other proteins/pic of mitochondria, etc that is the main subject of the introduction+paper.\nWe agree with this suggestion, and we have now added a schematic depicting our current molecular model of the cuproptosis pathway into Fig. 1A.\nMore details on FDX1 knockout needed. Sanger confirmed? Homozygous or heterozygous?\nAlthough we have not confirmed the KOs by Sanger sequencing, our immunoblots of the knockout consistently show a significant reduction in FDX1 protein levels (by one order of magnitude in ABC1s and below the detection limit in HEK293Ts), as indicated in Fig. 3D & S1A. Accordingly, we were able to able to achieve a complete or near-complete functional loss of FDX1 in both models, consistent with the reduction of FDX1 levels as detected by immunoblotting. We also note that these same knockout cell lines have been extensively characterized in our previous study establishing FDX1 as a critical regulator of de novo lipoic acid synthesis (PMID: 37453661, 35298263).\nHow many total mutations were DMS scores were obtained for and what fraction of possible mutants?\nScores were obtained for 3,740 variants (missense = 3470; nonsense = 183; silent = 81). Excluding the first methionine, FDX1 contains 183 amino acids that can be mutated to 19 other amino acids to produce 3,477 missense mutations. Our variant library of missense mutations covers 99.8% of all possible variants. We have now outlined these details in Methods Section (lines 482 to 500).\nSpell out HEK293 and ABC1 at first use. It is a strength that the DMS experiment was performed in both cell lines with similar results, but it is unclear why the 2 cell lines were chosen or necessary. Was there a difference or hypothesized difference between the two lines in terms of cuproptosis or FDX1 function?\nWe have revised the text to spell out HEK293T at its first use, but ABC1 does not appear to be an acronym (lines 81 to 83).\nThe ABC1 cell line was selected because it was identified in a Broad Institute-developed PRISM (Profiling Relative Inhibition Simultaneously in Mixtures) screen of over 700 cell lines as one of the most sensitive to elesclomol-induced cuproptosis (PMID: 31133756). HEK293T cells were included as an independent model to test the generalizability of hits from our DMS screen across different cellular contexts. As such, the DMS screen performed with ABC1 resulted in generally higher signal-to-noise ratio, but the same hits of interest were similarly observed in the HEK293Ts. It should be emphasizeFd that both cell lines were characterized previously for their FDX1 dependent regulation of protein lipoylation (PMID: 37453661, 35298263).\nRe: “The effects of FDX1 mutations on ES sensitivity were highly correlated between the two cell lines.” Can the authors plot the scores from the two cell lines against each other and mention the correlation coefficient in the results?\nWe appreciate the insightful suggestion. We have now incorporated a linear model between the two screens in Fig. S1H (Rebuttal Fig. 5). We find an overall linear regression model coefficient of R = 0.46, with a highly significant Pearson correlation p-value (< 0.001). We have now included these details within the Results section (lines 102-104).\nHow many DMS replicates were performed in each cell line? What was the correlation of scores between the replicates? How were replicates (if any) handled in the analysis?\nThere were two replicates per treatment condition. Except for the replicates of the elesclomol treatment arm in HEK293T, each replicate had good corollary with one another between replicates (Rebuttal Fig. 6). As described in Methods (lines 545-550), replicates were averaged together for the analyses.\nWhy isn’t deldelG calculated for the N-terminus?\nWe did not calculate structure-based ΔΔG values for the N-terminal region because the first amino acids are absent from the available FDX1 crystal structure (PDB 3P1M) and they are predicted to be disordered and function as the mitochondrial targeting signal. Structure-based ΔΔG methods, including the FoldX method we used here (Methods Section) requires a pdb file to base these free energy calculations. We have clarified this rationale in the revised text.\nSuggest to label residue types in Figure 1A (e.g. hydrophobic, polar, charged etc)\nWe have now added annotations for biophysical properties of each amino acid substitution to the heatmap to indicate residue numbers.\nThere is excellent discrimination between nonsense and silent mutants, indicating that the assay+data quality is excellent. However, consider scaling the heatmap colors more gradually so that there is more white on the heatmap because the silent mutations had a bit of a spread of scores.\nWe thank the reviewer for this constructive suggestion. The heatmap has been re-scaled to provide a more gradual color gradient, increasing the proportion of neutral (white) values to better visualize score variation among silent mutations. This adjustment improves resolution of subtle differences while preserving the clear discrimination observed between nonsense and silent variants, supporting the overall high assay performance and data quality.\nWere late nonsense mutants (with normal scores) excluded in the violin plot of nonsense mutants, and excluded for the normalization of scores?\nWe appreciate the reviewer’s thoughtful question regarding the treatment of late nonsense mutations. We carefully considered whether to exclude C-terminal nonsense variants that retained near-normal activity. However, we found the statistical basis for drawing an objective cutoff to be challenging. Several late nonsense positions exhibited partial or semi-loss-of-function behavior, making it difficult to consistently exclude some while retaining others without introducing subjective bias. For this reason, and after deliberate evaluation, we opted not to remove these variants from the violin plot or from the normalization process. We believe this approach avoids arbitrary exclusions and, if anything, renders our analysis more stringent, as it holds all nonsense variants to the same standard rather than selectively filtering.\nMethods minor comments:\nRefers to “viral” particles without saying what type of virus.\nWe have fixed this lack of clarification (line 428-429). Lentivirus was used.\nWas every variant included in the DMS analysis or were there QC criteria for excluding a variant (for example based on low abundance in the initial sample).\nAll variants were initially included in the DMS analysis. We carefully evaluated whether to impose quality-control thresholds based on plasmid DNA (pDNA) abundance or Day 0 representation. Specifically, we examined the relationship between plasmid counts and log fold-change (LFC) values, as well as the distribution of Day 0 counts across replicates. Our analyses (see code) showed that variants with lower plasmid representation (e.g., ≤50 counts) did not systematically bias LFC measurements, and there was no clear correlation between initial abundance and assay outcome. Accordingly, we chose not to impose an arbitrary abundance cutoff, other than removing variants where log fold-change values were undefined (e.g., −∞ when the denominator equaled zero). Variants with zero Day 0 counts or missing data at endpoints were set to NA and excluded from downstream calculations by necessity, but otherwise no additional filtering was applied. This approach maximizes transparency and inclusivity of the dataset while still ensuring stringency by discarding only values that were mathematically undefined.\nData availability:\nThe standard in the DMS/MAVE community is to deposit scores to MAVE-DB before or at time of publication. In addition, depositing raw high-throughput sequence data in NCBI Sequence Read Archive before or at time of publication. I would request these from the authors.\nWe agree with the reviewer that deposition of both processed variant scores and raw high-throughput sequencing data is an important standard for ensuring reproducibility and broad accessibility in the DMS/MAVE community. We are working on organizing our data so that we can deposit our complete variant effect score dataset in MAVE- and submit the raw FASTQ files to the NCBI Sequence Read Archive before the paper is published. Both datasets will be publicly available at the time of publication, with accompanying documentation describing file structure, processing pipelines, and relevant metadata to facilitate reuse.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nWe thank the reviewers for their helpful comments and suggestions. Following Reviewer #1’s recommendation, we have added new panels (Fig. 5E–G) and updated the manuscript text accordingly (lines 254–270). Also all raw FASTQ files have been fully deposited to NCBI (PRJNA1373349), and all processed source data are available through Figshare (https://doi.org/10.6084/m9.figshare.30606473.v1), and Github (https://github.com/jkwonbio/FDX1) these repositories provide everything necessary for full reproducibility and reanalysis. We will try our best to complete the MAVE upload within the next month. However, the MAVE deposition process is not intuitive and requires substantial time and effort, and we do not currently have the personnel or technical bandwidth to prioritize this. Given that all mandated data have already been deposited and are publicly accessible, we do not think that publication should be contingent on the MAVE accession. If the journal or reviewers can provide direct guidance or assistance with the MAVE upload, we would truly appreciate it and be happy to coordinate.\nWe also implemented the editorial revisions requested throughout the manuscript to improve clarity and accuracy."}]

Decision letter