An overexpression platform reveals the functional diversity of human KRAB-Zinc Finger Proteins in maintaining cellular homeostasis

  1. Romain Forey
  2. Charlène Raclot
  3. Arianna Dorschel
  4. Jérôme Archambeau
  5. Evarist Planet
  6. Olimpia Bompadre
  7. Sandra Offner
  8. Wayo Matsushima
  9. F. Gisou van der Goot
  10. Didier Trono

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Abstract

Kruppel associated box zinc finger proteins (KZFPs) form the largest family of transcriptional regulators in mammals, yet most remain uncharacterized. Here we established a scalable framework to probe KZFP function. An arrayed inducible overexpression screen of 366 human KZFPs in K562 cells identified factors that alter cellular proliferation, enabling functional prioritization. Integrative transcriptomic, chromatin and proteomic analyses revealed diverse mechanisms, including transposable element–linked repression (ZNF43), promoter proximal regulation (ZNF257), and SCAN domain dependent transcriptional activation (ZNF498/ZSCAN25 and ZNF18). These results highlight the functional diversity of KZFPs and provide a strategy for their annotation.

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    Reply to the reviewers

    1. __ General Statements__ We thank the reviewers for their thoughtful and constructive evaluations of our work. We are particularly encouraged that both recognize the value of this study as a scalable and systematic framework for the functional exploration of the human KZFP family and agree that the resource generated here will be of broad interest to the KZFP, transposable element, and genome regulation communities. Reviewer 1 explicitly notes that "the screening framework itself represents a potentially useful resource for prioritizing candidate KZFPs for downstream study" and that "the study may nonetheless serve as a useful starting point for future investigations into KZFP biology and transcriptional regulation." Reviewer 2 similarly emphasizes that "the authors provide an efficient and valuable screening platform that can identify promising candidates for further investigation" and that "the methodological advance represents the primary contribution of the work."

    We can only concur with these assessments. The principal goal of this study was not to elucidate the physiological roles of all or even a subset of individual KZFPs, but rather to provide a scalable framework that enables their systematic prioritization and generates experimentally testable hypotheses regarding their functions. To support our argument, we ventured into some mechanistic analyses, but these could not pretend to be complete and definitive. In that respect, we hear the reviewers when they note that the original manuscript does not always sufficiently distinguish candidate discovery from mechanistic validation. In its revised version, we will therefore more clearly frame the inducible K562 overexpression assay as a standardized and sensitive readout of regulatory potency rather than as a direct surrogate of physiological function. Within this framework, K562 fitness defects are interpreted as a quantitative measure of the extent to which ectopic KZFP expression perturbs transcriptional homeostasis in a controlled cellular context, while the direct targets and transcriptional networks identified through our integrative analyses are presented as hypotheses to be tested in more physiologically relevant systems. Accordingly, the revised manuscript preserves the broad scope and resource aspect of the study while incorporating additional experimental validation, expanded methodological descriptions, and a more cautious interpretation of the proposed biological functions of the selected KZFPs.

    __Although this document is submitted as a Revision Plan, we have already incorporated a substantial number of revisions into the transferred manuscript. In particular, we have implemented most of the presentation, methodological, and conceptual modifications requested by the reviewers, including clarification of the scope of the study, extensive revisions of the Results and Discussion, expanded Materials and Methods, and numerous figure and text corrections. These revisions are detailed in Section 3 ("Description of the revisions that have already been incorporated into the transferred manuscript"). __

    The remaining points requiring additional experimentation or more extensive analyses are described in Section 2 ("Description of the planned revisions").

    __ Description of the planned revisions__

    Reviewer 1 Major comment 1

    “Finally, several aspects of the data presentation are currently difficult to reconcile. In Fig. 1D, the meaning of the purple category is unclear, and the percentage scaling on the x-axis is difficult to reconcile with the cumulative values displayed. For instance, the sum of all the bars would not reach 100%, as the values of the bars span percentages up to 4% at most (for 105 MYO KZFPs) according to this plot. Similarly, the reported numbers of TE-binding KZFPs in Fig. 1E-F and Fig. S1D appear internally inconsistent and should be clarified. Specifically, 53+14=67 KZFPs are reported to bind TEs in total, yet a larger number of KZFPs appears associated with individual TE families (e.g., 86 for LTR.ERV1). If the values shown correspond to percentages rather than absolute counts, this should be explicitly clarified in both the figure and legend. In addition, Fig. S1D appears inconsistent with the counts reported in Fig. 1E-F, as only 5 out of the 53 toxic KZFPs displayed in the plot show no enrichment for any of the highlighted TE families.”

    We thank the reviewer for this insightful comment, which has helped us identify areas where the presentation of our data can be substantially improved. We agree that the current presentation of the TE-binding analyses could be clearer and that revising these figures will improve both their readability and the overall consistency of the manuscript. In the revised manuscript, we will clarify the apparent inconsistencies in the presentation of the TE-binding KZFP analyses and revise the corresponding figures and legends accordingly. Importantly, these inconsistencies do not arise from errors in the underlying data but rather from an insufficient explanation of the statistical enrichment analyses and the way the results are represented. We will therefore redesign the relevant figures and expand their legends to more clearly describe the analytical approach, the enrichment criteria, and the interpretation of the results. We believe that these revisions will improve the clarity, transparency, and internal consistency of the manuscript, allowing readers to more readily interpret the TE-binding analyses. Minor comments of the reviewer 1 were extremely useful to detect mistakes and we are grateful for that. All the modifications that were asked see below were included in the manuscript.

    Reviewer 1 Major comment 2

    “Finally, while the proteomics results aimed at identifying SCAN-dependent interactors are of interest, several aspects of the experimental design and data analysis remain unclear. In particular, it is not specified whether the experiment was performed in biological replicates or as a single measurement. This is important, as it directly affects how the data can be interpreted and how stringent downstream filtering can be. In the Results section, the authors state that "we identified a set of SCAN-dependent interactors, i.e., proteins that co-immunoprecipitated with the full-length construct but were absent in controls and lost upon deletion of the SCAN domain," which suggests a relatively binary, "presence/absence" filtering strategy. However, this description does not specify whether any quantitative threshold (e.g., enrichment ratio) was applied when comparing full-length constructs to deletion mutants. In contrast, the Methods section states that "proteins lacking signal above background were excluded and proteins were additionally required to show stronger signal in at least one bait condition than in GFP controls based on heatmap clustering (see script)," which instead suggests that a threshold-based criterion was used to define enrichment relative to controls and deletion mutants. If this is the case, the exact criteria and thresholds used for filtering should be clearly stated and consistently reported between the Results and Methods sections. If replicate measurements were not performed, this should be explicitly acknowledged, as peptide-level variability may substantially influence the identification of high-confidence interactors, particularly if the applied cutoffs are not highly stringent.”

    We agree that a more detailed description of the experimental design and analysis strategy, together with additional validation, will strengthen the interpretation of the proteomic data. In the revised manuscript, we expanded the Results and Materials and Methods sections to provide a clearer and more quantitative description of the filtering strategy, including the enrichment criteria and thresholds used to define SCAN-dependent interactors. To further strengthen these findings, we propose to perform an independent biological replicate of the co-immunoprecipitation mass spectrometry experiment. This additional experiment will increase confidence in the identified SCAN-dependent interactors and further support the conclusions drawn from the proteomic analysis.

    Reviewer 1 Minor comments

    • In Fig. 2A, readability could be improved by adjusting the layering of points, as the darker dots (in particular the red ones) are currently obscured by lighter ones. Alternatively, removing the outline of the points (which is not transparent) may also improve visibility, but in that case the legend for point size would need to be updated accordingly.

    Thank you for this helpful suggestion. We will revise Figure 2A to improve its readability by reworking the layering of the points in accordance with the reviewer's recommendation. We will also evaluate the point outlines and, if appropriate, remove them and update the point-size legend accordingly to ensure the figure is clear and easy to interpret.

    Reviewer 2 – Major comment

    “- The authors looked at available chromatin data in either K562 cells or HEK293 cells, which I think is a very good way of utilizing publicly available data. Since the authors showed that different KZFPs might be functionally relevant in different cell types/tissues, I was wondering if they checked if there is available ChIP Seq or CUT&RUN data in those specific cell types/tissues. If yes, that data should be included in the manuscript.”

    We agree that integrating KZFP binding data generated in biologically relevant cell types or tissues would further strengthen the proposed regulatory models. As described in the revised manuscript, we have already adopted this approach for ZNF43 by integrating chromatin landscape data from thymus and liver, where suitable datasets were available.

    To further address this point, we propose to systematically explore publicly available ChIP-seq, CUT&RUN, CUT&Tag, and related chromatin profiling datasets for the other KZFPs investigated in this study. Where suitable datasets are available, these analyses will be incorporated into the revised manuscript to further support the proposed tissue-specific regulatory models and provide additional biological context for the identified target genes.

    __ Description of the revisions that have already been incorporated in the transferred manuscript__

    Reviewer 1 Major comment 1

    “The large-scale overexpression screen represents the foundation of the manuscript and provides a potentially valuable resource for prioritizing candidate KZFPs for downstream study. However, several aspects of the experimental setup and data presentation currently limit the interpretation of the reported proliferation defects. First, key details regarding the screening workflow remain unclear. While the Methods section describes the overall procedure, it is difficult to determine when cells were seeded relative to doxycycline induction, in which plate format the cells were maintained throughout the experiment, and whether medium exchange was performed during the 9-day assay. These points are particularly relevant given the use of suspension K562 cells (which can complicate medium exchange in a 96-well plate format and make long-term culture more difficult to control) and a metabolic viability readout (PrestoBlue), as differences in nutrient depletion or overgrowth could also influence the signal independently of reduced proliferation or toxicity. Additional clarification regarding seeding density, timing of induction, plate format, culture handling throughout the assay, and whether cell morphology/density was visually monitored would substantially improve interpretability and reproducibility. Second, it is unclear whether the observed proliferation phenotypes may be influenced by differences in transgene expression levels or integration effects. Were all constructs validated for comparable expression following induction? In the absence of such controls, it remains difficult to determine whether the reported phenotypes reflect specific KZFP activities or differences in overexpression efficiency. While it may not be possible to conclusively distinguish KZFP-specific effects from toxicity associated with high transgene expression levels, this limitation should at least be acknowledged. In addition, the possibility that some phenotypes may be influenced by transgene integration effects should also be considered. Unless independent transductions were validated for the KZFPs classified as toxic, it remains difficult to exclude integration-site-specific contributions to the observed proliferation defects. Third, the normalization strategy would benefit from additional clarification. In Fig. S1A, the LacZ control appears variably affected by doxycycline treatment across plates, whereas the GFP control appears more stable. Since normalization relies on the mean behavior of both controls within each batch and condition, the authors should clarify whether this variability could influence hit calling.”

    We agree that additional methodological details improve the clarity and reproducibility of the screening assay. Accordingly, we substantially expanded the Materials and Methods section to describe the experimental workflow, quality controls, data normalization, and hit-calling criteria. The revised paragraph is reproduced below.

    Arrayed overexpression screen

    __To systematically assess the effect of human KZFP overexpression on cellular fitness, K562 cells were individually transduced with doxycycline-inducible lentiviral vectors encoding 366 human KZFPs. Lentiviral particles were produced as described above and used to transduce cells without MOI calculation. Instead, a fixed volume of viral supernatant (200µL per × 104 cells in 48 well plate filled with 200ul of RPMI) was used for all transductions to ensure comparable experimental conditions. Transduced cells were selected with puromycin before doxycycline induction. Following puromycin selection (1µg/mL for 3 days), cells were seeded at 20 000 cells per well in 24-well plates filled with 1ml of medium in technical triplicate for each KZFP. Following puromycin selection and prior to doxycycline induction, cell survival was visually assessed as a quality control metric for each KZFP construct (__Supp Table 2____). Doxycycline (1µg/mL) was added immediately after cell seeding to induce expression of the HA-tagged KZFPs. At each time point, metabolic activity was measured using PrestoBlue™ reagent according to the manufacturer's instructions (10µL reagent added to 100µL culture medium, incubated for 3h in a 96 plates). Absorbance was recorded at 570 nm and 600 nm using a plate reader (Hidex Sense Microplate Reader), GFP- and LacZ-expressing control wells were included on every plate to account for plate-to-plate and batch-to-batch variability. Peripheral wells were filled with culture medium to minimize evaporation-induced edge effects. Cells were maintained in RPMI supplemented with 10% fetal bovine serum (FBS) and 1× penicillin–streptomycin, and splited (1/10) with aspiration of the surface medium every three days throughout the assay while maintaining doxycycline at 1µg/mL. Cell proliferation was assessed after 4, 7, and 9 days of induction. and the A570/A600 ratio was used as a surrogate measure of viable cell number and proliferative capacity. For computational normalization, raw A570/A600 values were first background-corrected by subtracting the signal from medium-only controls and then normalized in two steps. First, each value was divided by the mean signal obtained from the GFP and LacZ control wells from the corresponding batch and induction condition to correct for inter-batch variability. Second, the resulting value was normalized to the corresponding −Dox condition for the same KZFP and time point to correct for seeding variability, yielding a relative proliferation score that reflects the effect of KZFP induction. KZFPs with a normalized proliferation score ≤ 0.85 at day 9 were arbitrarily classified as proliferation-impairing hits in this screening framework.

    __After doxycycline induction, dot blot analysis using anti-HA and anti-actin antibodies was systematically performed to assess KZFP expression and sample loading, respectively (Supplementary DotBlot.pdf). The HA signal following doxycycline induction (HA_Dox) and actin signal following doxycycline induction (Actin_Dox) were visually scored from the dot blot signals (__Supp Table 2).

    In addition, to strengthen the methodological description and address these concerns more directly, we will:

    1/ Include a supplementary table summarizing our experimental observations for each individual KZFP throughout the screening process (See preliminary Supp Table 2). -> See header here:

    2/ Perform and include Dot Blot analyses, to assess and compare transgene expression levels across KZFP constructs. (Supplementary File DotBlot.pdf____). __Generation of these files is in progress, with a few missing dot blots still being completed (we have done 303 over 366 already). However, preliminary versions have already been submitted. __-> See header of the .pdf here:

    In addition, we agree that a more explicit discussion of the limitations of our screening approach improves the interpretation of our findings. Accordingly, we expanded the Discussion to address the limitations associated with variable transgene integration, heterogeneous transgene expression, potential toxicity due to ectopic KZFP overexpression, and the use of K562 cells as a standardized rather than physiological cellular model.

    “Several methodological considerations should be taken into account when interpreting these results. As with any lentiviral overexpression screen, three potential sources of technical variability may influence the observed phenotypes: differences in transgene integration sites, heterogeneity in transgene expression levels, and non-specific toxicity resulting from ectopic overexpression. Variable integration sites are unlikely to represent a major source of bias in the present study because all analyses were performed on polyclonal populations of transduced cells rather than individual clones, thereby averaging integration-site effects across many independent events. In contrast, heterogeneity in transgene expression levels is expected, as the abundance of each KZFP depends not only on transduction efficiency but also on intrinsic differences in mRNA stability, translational efficiency, and protein stability. To minimize these sources of variability, all constructs underwent systematic quality control, including assessment of cell survival following puromycin selection and evaluation of transgene expression by HA dot blot after doxycycline induction. Although transgene expression levels varied across KZFPs (Supplementary File DotBlot.pdf), this variability showed no systematic relationship with the proliferation phenotypes, suggesting that differences in overexpression efficiency are unlikely to be the primary determinant of toxicity. Nevertheless, ectopic expression exposes cells to supraphysiological concentrations of KZFPs capable of generating non-physiological interactions or regulatory effects. Therefore, while the screening strategy is well suited for identifying candidate functional regulators, independent validation under endogenous expression conditions remains essential to confirm KZFP-specific functions.”

    Reviewer 1 Major comment 2:

    “A central conceptual issue throughout the manuscript is that the downstream functional analyses of the selected KZFPs remain largely disconnected from the original screening phenotype. The four candidates were prioritized based on proliferation defects observed upon overexpression in K562 cells; however, the subsequent analyses (with the only exception being a more in-depth experimental analysis of ZNF498 in ciliogenesis, which stands out as comparatively more directly supported by experimental evidence) primarily rely on correlative expression patterns and KZFP ChIP-seq datasets to infer potential biological functions in unrelated cellular contexts. As a result, it remains unclear whether the proposed transcriptional programs are mechanistically linked to the proliferation phenotypes that motivated candidate selection in the first place. This issue is evident across multiple sections of the manuscript. For example, the proposed role of ZNF43 in regulating fatty acid metabolism and detoxification pathways is primarily inferred from tissue-level expression correlations. While these analyses focus on genes identified as potential ZNF43 targets, the underlying ChIP-seq datasets were themselves generated under ZNF43 overexpression conditions. Therefore, the current analyses do not establish whether ZNF43 regulates these pathways under physiological expression levels or within a relevant cellular context, nor how such regulation relates to the proliferation defect observed in K562 cells. Moreover, several proposed target genes remain substantially expressed in tissues where ZNF43 expression is not particularly low (e.g., kidney and heart muscle), suggesting that additional regulators are likely involved. Similarly, the proposed model of ZNF257-mediated regulation of MAGEA genes during spermatogenesis is intriguing but does not fully account for the expression behavior of all MAGEA family members, particularly MAGEA2B, which displays strong expression in spermatocytes despite high ZNF257 expression. This expression pattern should be acknowledged in the main text and reflected in Fig. 3K. In addition, the labels for MAGEA6 and MAGEA2B in Fig. 3C appear to be inverted. More broadly, the proposed regulatory model is difficult to reconcile with the generally restricted expression pattern of MAGEA genes across adult tissues, as their expression does not appear to consistently correlate with ZNF257 levels outside the germline context. Related concerns also apply to the analyses of ZNF498 and ZNF18, where the proposed functions in cilium formation and sperm maturation remain disconnected from the proliferation defects identified in the initial screen.”

    We agree that this comment raises an important conceptual point and has helped us clarify the scope of the study and the interpretation of our findings. In the revised manuscript, we explicitly distinguish hypothesis generation from mechanistic validation by clarifying that the proliferation phenotype observed in K562 cells reflects the regulatory potential of ectopically expressed KZFPs rather than their physiological functions. We also adopted a more cautious interpretation of the functional analyses, emphasizing that the proposed regulatory networks are hypothesis-generating and that individual KZFPs are unlikely to act as sole regulators. More broadly, we emphasize that the primary objective of this study is to establish a scalable screening platform for prioritizing KZFPs and identifying biologically relevant contexts for future investigation, rather than to provide a comprehensive functional characterization of individual KZFPs. We agree that this comment highlights an important limitation of our proposed regulatory model. In the revised manuscript, we adopted a more nuanced interpretation by presenting ZNF257 as a contributor to, rather than the sole regulator of, the MAGEA transcriptional program, and by explicitly discussing the exceptions identified by the reviewer.

    Modification in the revised manuscript:

    1/

    “Integrative transcriptomic, chromatin and proteomic analyses reveal diverse mechanisms, including transposable element–linked repression (ZNF43), promoter-proximal regulation (ZNF257), and SCAN domain–dependent transcriptional activation (ZNF498/ZSCAN25 and ZNF18).”

    Is now:

    “Integrative transcriptomic, chromatin and proteomic analyses identify distinct regulatory properties and generate testable hypotheses regarding diverse mechanisms, including transposable element-associated repression (ZNF43), promoter-proximal regulation (ZNF257), and SCAN domain-dependent transcriptional activation (ZNF498/ZSCAN25 and ZNF18).”

    2/

    “Detailed follow-up of four such candidates, ZNF43, ZNF257, ZNF498 and ZNF18, revealed as hypothesized distinct modes of action, ranging from TE-linked transcriptional repression to promoter-proximal gene silencing and SCAN domain-mediated transcriptional activation. These findings reinforce the view that KZFPs, while often viewed as a homogeneous family of TE-repressive TFs, are rather functionally diverse regulators with wide-ranging impacts on human biology.”

    Is now:

    “Detailed follow-up of four such candidates, ZNF43, ZNF257, ZNF498 and ZNF18, identified distinct regulatory properties and generated hypotheses regarding their physiological functions. By integrating overexpression-induced transcriptional responses, chromatin occupancy, proteomic analyses and tissue-specific expression data, we propose candidate biological contexts in which these KZFPs may operate. These hypotheses now provide a framework for future mechanistic studies performed under physiological conditions. Together, these findings reinforce the view that KZFPs, while often viewed as a homogeneous family of TE-repressive transcription factors, comprise functionally diverse regulators with broad potential roles in human biology.”

    3/

    “We conclude from these data that ZNF43 regulates a transcriptional program related to fatty acid metabolism and detoxification, allowing for the preferential expression of its effectors in the liver (Fig. 2G). Interestingly, neither expression nor chromatin state followed the same pattern at the functionally unrelated DNAI4 locus, indicating that this gene is subjected to other dominant regulators.”

    Is now:

    “Together, these observations identify a small set of candidates ZNF43 target genes involved in fatty acid metabolism and detoxification and suggest that ZNF43 may contribute to the regulation of these transcriptional programmes in appropriate physiological contexts (Fig. 2G). However, these conclusions are derived from overexpression-based datasets and tissue-level expression analyses and should therefore be considered hypothesis-generating. Interestingly, neither expression nor chromatin state followed the same pattern at the functionally unrelated DNAI4 locus, indicating that additional regulatory mechanisms contribute to the control of these genes.”

    4/

    “It strongly suggests that ZNF257 contributes to initiating the transcriptional repression of these two MAGEA genes during early spermiogenesis, after which their silencing may be stabilized through stable epigenetic mechanisms such as DNA methylation.”

    Is now:

    “These observations suggest that ZNF257 may contribute to the initiation of transcriptional repression of a subset of MAGEA genes during the spermatogonia-to-spermatocyte transition, after which their silencing may be stabilized through epigenetic mechanisms such as DNA methylation.”

    5/

    “Together, these results identify ZNF498 as a transcriptional activator of gene modules controlling cytoskeleton-dependent processes and suggest that this TF may act as a regulator of neuronal cytoskeletal architecture, warranting investigation in relevant neural models.”

    Is now:

    “Together, these results indicate that ZNF498 functions as a transcriptional activator in our overexpression system and support the hypothesis that it contributes to transcriptional programmes controlling cytoskeleton-dependent processes in physiologically relevant neural contexts, warranting further investigation in dedicated neural models.”

    6/

    “The co-expression of ZNF18 and its target genes at the spermatid stage suggests that ZNF18 activates a transcriptional program supporting these processes.”

    Is now:

    “The co-expression of ZNF18 and its candidate target genes at the spermatid stage is consistent with the hypothesis that ZNF18 contributes to transcriptional programmes supporting these processes.”

    7/

    “The four KZFPs characterised here illustrate this diversity. ZNF43 represses a coherent set of genes involved in fatty acid metabolism and detoxification through binding to nearby LTR/ERV1 integrants, with its expression anticorrelating that of its targets: i.e., highly expressed in thymus and bone marrow, where these metabolic genes are silent, and lowly expressed in liver, where they are most active. This represents a clear example of host genomes coopting TE-derived sequences and shaping their regulatory activities in a cell-type specific manner by the differential expression of KZFPs. ZNF257, by contrast, acts as a promoter-proximal repressor whose targets show accelerated sequence evolution at their promoters, consistent with integration into a KZFP-orchestrated GRN through rapid promoter diversification, a feature previously described for KZFPs (Farmiloe et al., 2023). Its regulation of the MAGEA gene cluster exemplifies a distinct evolutionary mechanism: an ancestral intronic binding site, present in MAGEA6 gene body, before ZNF257 emerged, was propagated across the cluster through tandem duplication, enabling coordinated regulation of multiple paralogs. Temporal expression analysis during spermatogenesis further suggests that ZNF257 initiates MAGEA repression at the spermatogonia-to-spermatocyte transition, after which silencing may be maintained through epigenetic mechanisms such as DNA methylation. ZNF498 and ZNF18, both SCAN-containing KZFPs with variant KRAB domains, on the other hand acted as transcriptional activators. ZNF498 activates a programme centred on microtubule cytoskeleton organisation, as demonstrated by the disruption of ciliogenesis upon its overexpression, and both ZNF498 and its targets are broadly expressed in the central nervous system, particularly in excitatory neurons where microtubule dynamics are essential for axonal architecture. ZNF18 similarly activates genes involved in chromatin remodelling and cytoskeletal reorganisation at the spermatid stage, processes that are hallmarks of spermiogenesis. Together, these case studies demonstrate that even within a single screen, KZFPs with fundamentally different regulatory logics can be identified through a single unifying phenotype and then mechanistically dissected to uncover their unique properties.”

    Is now:

    “The four KZFPs characterized here illustrate the functional diversity that can be uncovered using this screening strategy. For ZNF43, integration of overexpression transcriptomics with ChIP-exo binding data identified a small set of candidate direct target genes located near LTR/ERV1 elements. Their tissue-specific expression patterns are consistent with the hypothesis that ZNF43 contributes to transcriptional programmes associated with fatty acid metabolism and detoxification, although these analyses, which rely on overexpression-derived datasets and tissue-wide correlations, do not establish physiological regulation or causality. Rather, they identify a candidate regulatory network whose functional relevance will require investigation in appropriate biological models. More generally, these observations support the concept that host genomes may exploit TE-derived regulatory sequences in a tissue-specific manner through differential KZFP expression, while recognizing that additional transcription factors almost certainly participate in controlling these gene expression programmes. Similarly, ZNF257 emerged as a promoter-associated transcriptional repressor in our overexpression system. Evolutionary analyses suggest that tandem duplication propagated an ancestral ZNF257-binding sequence across the MAGEA locus, generating the hypothesis that ZNF257 may contribute to coordinated regulation of this gene cluster during spermatogenesis. The temporal expression profiles of ZNF257 and the MAGEA genes are compatible with such a model but remain correlative and therefore require direct functional validation. ZNF498 and ZNF18, two SCAN-containing KZFPs with variant KRAB domains, displayed transcriptional activation rather than repression following overexpression. For ZNF498, the integration of transcriptomic analyses with expression profiling pointed to microtubule cytoskeleton organization as a candidate biological process, a prediction that was further supported experimentally by the marked impairment of ciliogenesis following ZNF498 overexpression in hTERT-RPE1 cells. This represents the strongest functional validation presented in this study and supports the biological relevance of the analytical framework developed here. For ZNF18, the co-expression of the KZFP and its candidate target genes during spermatogenesis is consistent with the hypothesis that it contributes to transcriptional programmes involved in chromatin remodelling and cytoskeletal reorganization during spermatid differentiation. Together, these case studies illustrate how a standardized overexpression screen can identify KZFPs with distinct regulatory properties and generate biologically coherent hypotheses regarding their physiological functions. Rather than establishing definitive functions for individual KZFPs, this framework prioritizes candidates, proposes relevant cellular contexts, and provides a foundation for future mechanistic studies performed under physiological conditions.”

    “In addition, interpretation of the SCAN-deletion experiments is complicated by the reduced expression levels of the deletion constructs relative to the corresponding full-length proteins, making it difficult to determine whether the observed proliferation phenotypes are pathway-specific or partially driven by differential expression.”

    We thank the reviewer for this important observation and agree that differences in expression levels between the full-length and ΔSCAN constructs could complicate the interpretation of the observed phenotypes. To address this concern, we performed a quantitative comparison of the expression levels of full-length and ΔSCAN proteins using both western blotting and transgene expression using RNAseq, while accounting for differences in transgene length. This result are now added in (Fig S6C, D).

    With modification of the legend:

    • HA signal after OE of HA-tagged ZNF18, ZNF18∆SCAN, ZNF498, ZNF498∆SCAN or GFP in K562 cells. Actin as control.
    • Quantification of ZNF18, ZNF18∆SCAN, ZNF498, ZNF498∆SCAN It appears that the difference is small (Minor comments of the reviewer 1

    “- In the Abstract and in the "Limitations of the study" section, the term "annotation" is used. It would be preferable to specify "functional characterization" instead of "annotation".

    Done as suggested by the reviewer.

    • In the Introduction, there may be a minor citation confusion. Following the sentence: "Characterized by an N-terminal KRAB domain and a C-terminal tandem array of C2H2 zinc fingers, KZFPs primarily target transposable element (TE)-embedded sequences," the cited references are predominantly experimental studies supporting this statement. However, the inclusion of the review "Bruno, Mahgoub and Macfarlan, 2019" appears less appropriate in this context, as it does not directly present ChIP-seq data supporting this claim. More relevant primary studies from the same research area include "Wolf et al. 2020" and "Bruno et al. 2025.".

    Done as suggested by the reviewer.

    • In Fig. 1A, "D10" appears inconsistent with the text and other figures (Fig. 1B, 1G, 1H), which refer to 9 days post-induction.

    Done as suggested by the reviewer.

    • In Fig. S1, there may be a mismatch in the highlighted plate: the zoomed image appears to correspond to the first plate from the top. The correct plate should be highlighted for consistency.

    Done as suggested by the reviewer.

    • In Fig. 1B, there is a typographical error ("K ZFPs" instead of "KZFPs").

    Done as suggested by the reviewer.

    • In Fig. S1E, it is unclear what "other" refers to. Please clarify whether this represents the mean of all remaining KZFPs or a defined subset, ideally in the figure description.

    Done as suggested by the reviewer.

    • In Fig. S2E, "SetDB1" should be corrected to "SETDB1".

    Done as suggested by the reviewer.

    • In Fig. 3B, it is unclear what distinguishes the upper and lower "Diverse REs". A brief clarification in the figure legend would improve interpretability, particularly regarding the transposable element families included.

    Done as suggested by the reviewer.

    • In Fig. S3C, the x-axis labels appear slightly misaligned and shifted to the right.

    Done as suggested by the reviewer.

    • In Fig. 3C, the labels for MAGEA6 and MAGEA2B appear to be inverted.

    Done as suggested by the reviewer.

    • In Fig. 3K, "MAGE3" should be corrected to "MAGEA3".

    Done as suggested by the reviewer.

    • In the ZNF498 section, line 4, the punctuation should be corrected so that the period appears after the figure reference ("promoters (Fig. S1E).").

    Done as suggested by the reviewer.

    • In the final sentence of the ZNF498 section, a noun appears to be missing after "cytoskeleton-dependent," possibly "processes".

    Done as suggested by the reviewer.

    • In the last section of the Results and corresponding figures and their descriptions, "SCAN dependant" should be corrected to "SCAN-dependent".”

    Done as suggested by the reviewer.

    Major comments of the reviewer 2

    “- The authors chose four KZFPs to study in detail, but why they chose these 4 candidates is unlcear to me. It would be nice to add a more detailed description of the process by which they chose the four candidates.”

    We agree that the rationale for selecting the four KZFPs should be presented more explicitly. Accordingly, we revised the manuscript to clarify the selection criteria.

    “However, a modest correlation was noted between the number of transcription start sites (TSS) bound by KZFPs and the drop in PrestoBlue signal induced by their overexpression (Fig. 1G), and SCAN-containing KZFPs (SKZFPs) tended to induce proliferation defects more frequently than family members lacking this domain (Fig. 1H).”

    Is now:

    “However, a modest correlation was noted between the number of transcription start sites (TSS) bound by KZFPs and the drop in PrestoBlue signal induced by their overexpression (Fig. 1G), and SCAN-containing KZFPs (SKZFPs) tended to induce proliferation defects more frequently than family members lacking this domain (Fig. 1H). These observations indicated that KZFPs affecting proliferation do not constitute a homogeneous functional group, prompting us to select representative candidates spanning the evolutionary, structural, and genomic diversity of the KZFP family for mechanistic characterization.____”

    “- The materials and methods part of the manuscript is not detailed enough for other researchers to reproduce the study. They should add more details to both experiments and data analysis part of this section. Below I highlight some examples for sake of clarity, but the authors should revise the whole materials and methods section and add more details keeping these examples in mind:

    The authors do not state the titer of lentiviral vectors they generate nor the MOI or amount of virus they use to transduce the cells

    In many cases, the specific softwares and the software version is not stated e.g., the analysis of the Gene Ontology Biological Processes

    It would be beneficial for the readers to get more details about the construct they used, for example a map of the plasmid.

    It is unclear how many cells were used for RNA extraction

    It is unclear which microscopes were used for imaging.

    The concentration of antibodies used for staining and the product number, and provider of the antibody is not always depicted.”

    We agree that the additional methodological details requested by the reviewer will improve the reproducibility and transparency of the study. Accordingly, we have expanded the Methods section to provide a more detailed description of the experimental procedures and data analysis workflow.

    “Lentiviral particles were produced in HEK293T cells by transient co-transfection of transfer, packaging and envelope plasmids. Cells were transfected at approximately 70–80% confluence using a standard lipid-based transfection reagent. Viral supernatants were collected 48 h after transfection, cleared by centrifugation, filtered through 0.22-µm membranes, and used fresh or stored appropriately until use. Recipient K562 or hTERT-RPE1 cells were transduced under conditions optimized for efficient gene delivery.”

    Is now:

    “Lentiviral particles were produced in HEK293T cells. 105 cells were seeded in 24 well plates filled with 1ml DMEM the day before transfection. Cells were co-transfected individually with 0.15ug of each plasmids encoding KZFPs tagged with HA (pTRE-KZFPX-HA-PGK-puro), 0.1ug of the packaging plasmid (pR8.74) and 0.07ug of the envelope plasmid (pMD2G) using TransIT®-LT1 Transfection Reagent (MIR 2306), according to the manufacturer's instructions. Viral supernatants were harvested 24h after transfection, clarified by centrifugation, filtered through 0.45-µm filters and used immediately.”

    “Coding sequences were cloned into doxycycline-inducible lentiviral transfer vectors designed to express N-terminally HA-tagged proteins.”

    Is now:

    “Coding sequences corresponding to 366 human KZFP open reading frames were codon-optimized for human expression and cloned into doxycycline-inducible lentiviral transfer vectors expressing C-terminal HA-tagged proteins under the control of a tetracycline-responsive promoter pTRE-KZFPX-HA-PGK-puro. All expression constructs used in the primary overexpression screen have been deposited and are publicly available (De Tribolet et al., 2023). A schematic representation of the lentiviral expression cassette, including the promoter, HA tag, cloning site, antibiotic resistance cassette, and regulatory elements, is provided in Supplementary file. Selected constructs encoding ZNF43, ZNF257, ZNF498 and ZNF18 were used for follow-up mechanistic studies. For SCAN-domain functional analyses, deletion constructs lacking the SCAN domain (ΔSCAN) were generated for ZNF18 and ZNF498 in the same lentiviral backbone. Deletion were done using In-Fusion cloning with specific primers. PCR was performed with high-fidelity polymerase, followed by gel purification and recombination with the linearized plasmid using the In-Fusion HD Cloning Kit (Takara Bio©) according to the manufacturer’s protocol. The product was transformed into HB101 Escherichia coli cells, and colonies were screened by PCR. Positive clones were verified by Sanger sequencing, and confirmed plasmids were propagated and purified for further use.”

    “Total RNA was extracted...”

    Is now:

    “For each biological replicate, approximately 1 × 10⁶ K562 cells were harvested 72 h after doxycycline induction. Total RNA was extracted…”

    “Images were acquired by fluorescence microscopy under identical conditions across samples.”

    Is now:

    “Images were acquired using a confocal microscope Leica-SP8 (Leica Biosystems) with an objective HC PL APO 63x/1.40 and a pinhole size of 1 AU, using identical acquisition settings for all conditions. Images were processed using Fiji/ImageJ (version 2.9.0) without nonlinear intensity adjustments.”

    “Cells were fixed and stained with antibodies against ciliary markers (ARL13B)”

    Is now:

    “Cells were fixed in 4% paraformaldehyde, permeabilized with 0.1% Triton X-100, blocked with 2% BSA, and incubated with rabbit anti-ARL13B (Proteintech, Cat. No. 17711-1-AP, 1:200) followed by Alexa Fluor 568-conjugated donkey anti-rabbit IgG (Thermo Fisher Scientific, Cat. No. A-10042, 1:1000). Nuclei were stained with Hoechst (1 µg/mL).”

    “- The authors mention that KZFPs are usually expressed at a low level in the K562 cell line they use, but there is no figure showing the expression level of KZFPs in this cell type. It would be important to see the baseline KZFP expression in these cells, the level of overexpression and compare it to the endogenous expression levels they show in different cell types/tissues, at least for the four candidates studied more in depth. This would help to understand whether this level of activity is something that could occur naturally in a physiologically relevant context.”

    We thank the reviewer for this insightful suggestion and fully agree that providing additional context regarding endogenous and ectopic KZFP expression levels will help readers better assess the physiological relevance of our findings. As suggested, we included data showing the baseline expression levels of the four selected KZFPs in K562 cells together with the expression levels achieved following doxycycline-induced overexpression. We also compared these values with publicly available transcriptomic data from cell lines. Importantly, only cell lines are assessed as we need ground through (K562) to estimate transgene expression. We modified Fig. S2, Fig. S3, Fig. S4 and Fig. S5 to add the results of these analysis. Here is ZNF43 as an example:

    With the following legend:

    “(C) Distribution of endogenous expression levels, (using GFP control cells), of all expressed genes (light grey) and all KZFPs (dark grey) in K562 cells. The solid red line indicates endogenous ZNF43 expression in GFP control cells, whereas the dashed red line indicates the corrected transgene expression following doxycycline induction.

    (D) Endogenous ZNF43 expression across Human Protein Atlas cell lines, (https://www.proteinatlas.org/about/download#cell_line), following normalization to the local RNA-seq dataset. K562 cells are highlighted in red. The dashed red line indicates the corrected transgene level measured following doxycycline-induced overexpression in K562 cells overexpressing ZNF43.”

    Modified the result section:

    “ZNF43 is a ~43-million-year-old KZFP with a canonical TRIM28-recruiting KRAB domain and 19 zinc fingers that preferentially recognize an LTR/ERV1-embedded sequence (Fig. S1F). We first verified that ZNF43 overexpression impaired the growth of K562 cells (Fig. S2A, B). Endogenous ZNF43 expression was readily detectable in K562 cells and across human cell lines (Fig. S2C, D). Following doxycycline induction, transcript abundance markedly increased and exceeded the highest endogenous expression level observed among the analyzed cell lines (Fig. S2C, D).”

    We also updated the Methods section:

    “Quantification of endogenous and transgene expression levels

    Endogenous KZFP expression in K562 cells was estimated from GFP control RNA-seq samples using normalized mean expression values obtained from the differential expression analyses. For ZNF18, whose transgene sequence is identical to the endogenous coding sequence (i.e., not codon-optimized), transgene-derived expression was estimated directly by subtracting the endogenous transcript abundance measured in GFP controls from the total transcript abundance measured following doxycycline induction (OE − GFP). For ZNF43, ZNF257 and ZNF498, the overexpression constructs were synthesized using codon-optimized coding sequences. RNA-seq reads were therefore additionally aligned against the codon-optimized transgene reference sequences to specifically quantify exogenous transcripts without interference from endogenous reads. Because these codon-specific counts are generated through an independent alignment strategy, they are not directly comparable to the endogenous RNA-seq expression values. To calibrate these measurements, a scaling factor was derived from the ZNF18 dataset by comparing the codon-specific read counts with the transgene abundance estimated from the differential expression analysis (OE − GFP). This empirically determined correction factor was subsequently applied to all codon-optimized constructs, thereby expressing transgene abundance on the same scale as the endogenous RNA-seq measurements. Corrected transgene expression values were then used for all downstream comparisons. To compare endogenous expression across physiological contexts, publicly available RNA-seq datasets from the Human Protein Atlas (cell lines) were downloaded and normalized to the local RNA-seq scale. A normalization factor was calculated from the median expression ratio of KZFPs detected in both the Human Protein Atlas K562 dataset and the local K562 GFP control RNA-seq dataset, and subsequently applied uniformly to all Human Protein Atlas datasets. This normalization enabled direct comparison of endogenous expression across biological contexts with the corrected transgene expression values. Global KZFP expression was calculated as the median normalized expression of all annotated KZFPs within each biological context. For the four KZFPs selected for detailed characterization, endogenous expression across Human Protein Atlas cell lines was compared with corrected transgene expression following doxycycline induction. Expression distributions of all genes and KZFPs were visualized using ranked expression plots and density histograms. All analyses were performed in R using the tidyverse package.”

    We fully acknowledge that the overexpression system used in this study was primarily designed as a discovery platform to identify candidate functions, targets, and interaction partners of KZFPs that are otherwise expressed at lower levels in K562 cells. As the reviewer correctly points out, determining whether these regulatory effects occur at endogenous expression levels in physiologically relevant cellular contexts represents an important next step. We Thereby also clarified this in the “Limitations to this study” paragraph:

    “To better place our experimental system into a physiological context, we compared endogenous KZFP expression in K562 cells with publicly available transcriptomic datasets from the Human Protein Atlas. These analyses showed that K562 cells do not exhibit unusually low global KZFP expression compared with other human cell lines. However, consistent with the restricted expression patterns of this protein family, KZFPs as a whole are expressed at substantially lower levels than the average human gene. For the four KZFPs characterized in detail, doxycycline induction produced transcript levels that exceeded the highest endogenous expression observed across the analyzed human cell lines. Accordingly, the overexpression system used in this study was not designed to recapitulate physiological expression levels but rather to maximize the identification of candidate target genes, interacting partners, and regulatory pathways for KZFPs that are otherwise expressed at low endogenous levels. Consequently, the molecular interactions identified here should be considered as hypotheses requiring validation under endogenous expression conditions in physiologically relevant cellular models.”

    “- RNA seq analysis: It is unclear how many cells were used in the RNA seq analysis, I would like to ask the authors to clarify that. Moreover, from my understanding the RNA seq analysis was done on day 3, while the Presto Blue analysis was done on days 4, 7 and 9. I would like to kindly ask the authors to motivate their choice for the day of the RNA sequencing analysis.”

    We agree that this information required clarification. The Methods section has been revised to specify the number of cells used for RNA-seq library preparation and to explain the rationale for performing RNA-seq after 3 days of doxycycline induction, before measurable proliferation defects emerge, in order to capture primary transcriptional responses to KZFP overexpression. The corresponding modification has also been added to the Results section when introducing the RNA-seq analyses.

    “For transcriptome profiling, K562 cells expressing the indicated inducible constructs were treated with doxycycline for 72 h before harvest. Total RNA was extracted using the NucleoSpin RNA plus kit (Macherey-Nagel) according to the manufacturer’s recommendations. RNA quantity and purity were assessed by spectrophotometry, and RNA integrity was evaluated before library preparation.”

    Is now:

    “For transcriptome profiling, 1 × 10⁶ K562 cells expressing the indicated inducible constructs were treated with doxycycline for 72 h before harvest. RNA was collected after 3 days of induction to capture the primary transcriptional responses to KZFP overexpression before substantial differences in proliferation became apparent. This early time point was chosen to minimize secondary transcriptional changes resulting from altered cell growth, cell-cycle distribution, or cellular stress, which become detectable in the proliferation assays performed after 4, 7, and 9 days of induction. Total RNA was extracted using the NucleoSpin RNA plus kit (Macherey-Nagel) according to the manufacturer’s recommendations. RNA quantity and purity were assessed by spectrophotometry, and RNA integrity was evaluated before library preparation.”

    “We then profiled the transcriptome of K562 cells overexpressing ZNF43 by deep RNA sequencing (RNA-seq)”

    Is now:

    “We then profiled the transcriptome of K562 cells overexpressing ZNF43 by deep RNA sequencing (RNA-seq) after 3 days of doxycycline induction, a time point selected to capture primary transcriptional responses before the onset of measurable proliferation defects.”

    Minor comments of the reviewer 2

    “- Figure S1D is not mentioned in the text before figure S1E. The order of the panels should be changed in the figure.

    Done as suggested by the reviewer.

    • "We selected genes that were downregulated upon ZNF43 overexpression and harboured a ZNF43 binding site within 10kb of their TSS (Fig. 1A) - don't the authors mean Fig. 2A?

    Done as suggested by the reviewer.

    • In Figure 4D, the GO terms cannot be read, as the sentences seem to be cut.

    Done as suggested by the reviewer.

    • All figures and figure legends need to be revised. In some cases, the letter size is too small, or the legend and explanation of colours is missing. Please see some examples below: Fig. S6C, Fig 6C, Fig S4C, Fig S5C (letter size too small) Fig S6G, Fig 4E (label/scale is missing)”

    Homogenized to Arial 6 by default as requested by most of journal guidelines

    __ Description of analyses that authors prefer not to carry out__

    We think that by proceeding as described above we will have addressed all major conceptual issues raised by the reviewers.

  2. Review Commons

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    Referee #2

    Evidence, reproducibility and clarity

    Summary

    Foley et al establishes a scalable framework to probe KZFP function. They performed an array of inducible overexpression screen of 366 human KZFPs in K562 cells. This screen, together with the analysis of transcriptomic and available chromatin and proteomic datasets revealed that KZFPs regulate many different mechanisms, highlighting the functional diversity of KZFPs. Understanding this functional diversity is a very interesting, timely and relevant question, but it is also challenging to study. Therefore, the approach the authors develop is promising. While the quality of the experiments and data analysis is high, the weakness I see in the manuscript is the lack of major biological insights in relevant model systems. Please see my detailed comment in the significance part.

    Major comments

    • The authors chose four KZFPs to study in detail, but why they chose these 4 candidates is unlcear to me. It would be nice to add a more detailed description of the process by which they chose the four candidates.
    • The materials and methods part of the manuscript is not detailed enough for other researchers to reproduce the study. They should add more details to both experiments and data analysis part of this section. Below I highlight some examples for sake of clarity, but the authors should revise the whole materials and methods section and add more details keeping these examples in mind:
      • The authors do not state the titer of lentiviral vectors they generate nor the MOI or amount of virus they use to transduce the cells
      • In many cases, the specific softwares and the software version is not stated e.g. the analysis of the Gene Ontology Biological Processes
      • It would be beneficial for the readers to get more details about the construct they used, for example a map of the plasmid.
      • It is unclear how many cells were used for RNA extraction
      • It is unclear which microscopes were used for imaging.
      • The concentration of antibodies used for staining and the product number, and provider of the antibody is not always depicted.
    • The authors looked at available chromatin data in either K562 cells or HEK293 cells, which I think is a very good way of utilizing publicly available data. Since the authors showed that different KZFPs might be functionally relevant in different cell types/tissues, I was wondering if they checked if there is available ChIP Seq or CUT&RUN data in those specific cell types/tissues. If yes, that data should be included in the manuscript.
    • The authors mention that KZFPs are usually expressed at a low level in the K562 cell line they use, but there is no figure showing the expression level of KZFPs in this cell type. It would be important to see the baseline KZFP expression in these cells, the level of overexpression and compare it to the endogenous expression levels they show in different cell types/tissues, at least for the four candidates studied more in depth. This would help to understand whether this level of activity is something that could occur naturally in a physiologically relevant context.
    • RNA seq analysis: It is unclear how many cells were used in the RNA seq analysis, I would like to ask the authors to clarify that. Moreover, from my understanding the RNA seq analysis was done on day 3, while the Presto Blue analysis was done on days 4, 7 and 10. I would like to kindly ask the authors to motivate their choice for the day of the RNA sequencing analysis.

    Minor comments

    • Figure S1D is not mentioned in the text before figure S1E. The order of the panels should be changed in the figure.
    • "We selected genes that were downregulated upon ZNF43 overexpression and harboured a ZNF43 binding site within 10kb of their TSS (Fig. 1A) - don't the authors mean Fig. 2A?
    • In Figure 4D, the GO terms cannot be read, as the sentences seem to be cut.
    • All figures and figure legends need to be revised. In some cases, the letter size is too small, or the legend and explanation of colours is missing. Please see some examples below: Fig. S6C, Fig 6C, Fig S4C, Fig S5C (letter size too small) Fig S6G, Fig 4E (label/scale is missing)

    Significance

    Understanding the diverse roles of KZFPs is an important and interesting research question. However, studying KZFPs is challenging, as many KZFP-mediated effects appear to be highly cell type- and tissue-specific. This complexity is also highlighted by the findings of the current manuscript.

    A major strength of this study is the development of a scalable system that enables the simultaneous investigation of the entire KZFP family. Performing such analyses on an individual basis would be extremely time-consuming. Therefore, the authors provide an efficient and valuable screening platform that can identify promising candidates for further investigation. In this regard, the methodological advance represents the primary contribution of the work.

    At the same time, the study lacks a clear biological conclusion. While the screen identifies KZFPs with potential functional effects, it would substantially increase the impact of the manuscript if the authors selected at least one candidate for in-depth characterization in a biologically relevant cellular context. The current study is still of high quality and importance without these experiments, but such follow-up analyses would greatly strengthen the biological significance of the findings.

    Another limitation is that the experiments were performed in a cell type in which many of the investigated KZFPs are not normally expressed. As a result, the forced overexpression strategy may not accurately reflect physiological conditions and could potentially generate false-positive results. This concern is particularly relevant in light of the authors' statement that "KZFPs with sufficient regulatory potency to perturb cellular fitness outside of their normal setting are strong candidates for playing important roles within it." While this may indeed be true for some KZFPs, it is also possible that certain observed phenotypes simply arise from ectopic expression in an inappropriate cellular environment.

    More generally, the observation that KZFPs can have functions beyond TE repression is already established in the literature. Therefore, the manuscript provides limited new biological insight into this concept. The authors could potentially strengthen the novelty of the study by placing greater emphasis on specific KZFP subfamilies, such as SCAN-containing zinc finger proteins, which are a novel direction and have been implicated in non-canonical regulatory roles.

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    Referee #1

    Evidence, reproducibility and clarity

    Summary

    In the manuscript "An overexpression platform reveals the functional diversity of human KRAB-Zinc Finger Proteins in maintaining cellular homeostasis", the authors describe a scalable framework aimed at prioritizing individual KZFPs for functional characterization. The study is centered on a large-scale screening approach in which human erythroleukemia K562 cells were transduced with inducible constructs enabling the overexpression of 366 individual KZFPs. The effects of KZFP overexpression on cellular proliferation were then assessed using a cell viability assay comparing induced and non-induced conditions.

    Based on the results of this primary screen, the authors selected four KZFPs for further investigation among those whose overexpression was associated with proliferation defects. Follow-up analyses integrated tissue- and cell type-specific expression patterns of these KZFPs with expression analyses of putative target genes identified from previously published ChIP-seq datasets, with the aim of inferring potential biological functions. On the basis of these analyses, the authors propose that the selected KZFPs may regulate distinct gene networks involved in processes including fatty acid metabolism, spermatogenesis, and other aspects of cellular homeostasis.

    The study spans multiple biological contexts, but the evidence supporting several of the individual conclusions remains relatively preliminary, and the breadth of the manuscript often comes at the expense of mechanistic depth.

    Major comments

    1. Interpretation and robustness of the initial overexpression screen

    The large-scale overexpression screen represents the foundation of the manuscript and provides a potentially valuable resource for prioritizing candidate KZFPs for downstream study. However, several aspects of the experimental setup and data presentation currently limit the interpretation of the reported proliferation defects. First, key details regarding the screening workflow remain unclear. While the Methods section describes the overall procedure, it is difficult to determine when cells were seeded relative to doxycycline induction, in which plate format the cells were maintained throughout the experiment, and whether medium exchange was performed during the 9-day assay. These points are particularly relevant given the use of suspension K562 cells (which can complicate medium exchange in a 96-well plate format and make long-term culture more difficult to control) and a metabolic viability readout (PrestoBlue), as differences in nutrient depletion or overgrowth could also influence the signal independently of reduced proliferation or toxicity. Additional clarification regarding seeding density, timing of induction, plate format, culture handling throughout the assay, and whether cell morphology/density was visually monitored would substantially improve interpretability and reproducibility. Second, it is unclear whether the observed proliferation phenotypes may be influenced by differences in transgene expression levels or integration effects. Were all constructs validated for comparable expression following induction? In the absence of such controls, it remains difficult to determine whether the reported phenotypes reflect specific KZFP activities or differences in overexpression efficiency. While it may not be possible to conclusively distinguish KZFP-specific effects from toxicity associated with high transgene expression levels, this limitation should at least be acknowledged. In addition, the possibility that some phenotypes may be influenced by transgene integration effects should also be considered. Unless independent transductions were validated for the KZFPs classified as toxic, it remains difficult to exclude integration-site-specific contributions to the observed proliferation defects. Third, the normalization strategy would benefit from additional clarification. In Fig. S1A, the LacZ control appears variably affected by doxycycline treatment across plates, whereas the GFP control appears more stable. Since normalization relies on the mean behavior of both controls within each batch and condition, the authors should clarify whether this variability could influence hit calling. Finally, several aspects of the data presentation are currently difficult to reconcile. In Fig. 1D, the meaning of the purple category is unclear, and the percentage scaling on the x-axis is difficult to reconcile with the cumulative values displayed. For instance, the sum of all the bars would not reach 100%, as the values of the bars span percentages up to 4% at most (for 105 MYO KZFPs) according to this plot. Similarly, the reported numbers of TE-binding KZFPs in Fig. 1E-F and Fig. S1D appear internally inconsistent and should be clarified. Specifically, 53+14=67 KZFPs are reported to bind TEs in total, yet a larger number of KZFPs appears associated with individual TE families (e.g. 86 for LTR.ERV1). If the values shown correspond to percentages rather than absolute counts, this should be explicitly clarified in both the figure and legend. In addition, Fig. S1D appears inconsistent with the counts reported in Fig. 1E-F, as only 5 out of the 53 toxic KZFPs displayed in the plot show no enrichment for any of the highlighted TE families.

    1. Relationship between the screening phenotype and the proposed biological functions of selected KZFPs

    A central conceptual issue throughout the manuscript is that the downstream functional analyses of the selected KZFPs remain largely disconnected from the original screening phenotype. The four candidates were prioritized based on proliferation defects observed upon overexpression in K562 cells; however, the subsequent analyses (with the only exception being a more in-depth experimental analysis of ZNF498 in ciliogenesis, which stands out as comparatively more directly supported by experimental evidence) primarily rely on correlative expression patterns and KZFP ChIP-seq datasets to infer potential biological functions in unrelated cellular contexts. As a result, it remains unclear whether the proposed transcriptional programs are mechanistically linked to the proliferation phenotypes that motivated candidate selection in the first place.

    This issue is evident across multiple sections of the manuscript. For example, the proposed role of ZNF43 in regulating fatty acid metabolism and detoxification pathways is primarily inferred from tissue-level expression correlations. While these analyses focus on genes identified as potential ZNF43 targets, the underlying ChIP-seq datasets were themselves generated under ZNF43 overexpression conditions. Therefore, the current analyses do not establish whether ZNF43 regulates these pathways under physiological expression levels or within a relevant cellular context, nor how such regulation relates to the proliferation defect observed in K562 cells. Moreover, several proposed target genes remain substantially expressed in tissues where ZNF43 expression is not particularly low (e.g. kidney and heart muscle), suggesting that additional regulators are likely involved.

    Similarly, the proposed model of ZNF257-mediated regulation of MAGEA genes during spermatogenesis is intriguing but does not fully account for the expression behavior of all MAGEA family members, particularly MAGEA2B, which displays strong expression in spermatocytes despite high ZNF257 expression. This expression pattern should be acknowledged in the main text and reflected in Fig. 3K. In addition, the labels for MAGEA6 and MAGEA2B in Fig. 3C appear to be inverted. More broadly, the proposed regulatory model is difficult to reconcile with the generally restricted expression pattern of MAGEA genes across adult tissues, as their expression does not appear to consistently correlate with ZNF257 levels outside the germline context.

    Related concerns also apply to the analyses of ZNF498 and ZNF18, where the proposed functions in cilium formation and sperm maturation remain disconnected from the proliferation defects identified in the initial screen. In addition, interpretation of the SCAN-deletion experiments is complicated by the reduced expression levels of the deletion constructs relative to the corresponding full-length proteins, making it difficult to determine whether the observed proliferation phenotypes are pathway-specific or partially driven by differential expression. Finally, while the proteomics results aimed at identifying SCAN-dependent interactors are of interest, several aspects of the experimental design and data analysis remain unclear. In particular, it is not specified whether the experiment was performed in biological replicates or as a single measurement. This is important, as it directly affects how the data can be interpreted and how stringent downstream filtering can be. In the Results section, the authors state that "we identified a set of SCAN-dependent interactors, i.e. proteins that co-immunoprecipitated with the full-length construct but were absent in controls and lost upon deletion of the SCAN domain," which suggests a relatively binary, "presence/absence" filtering strategy. However, this description does not specify whether any quantitative threshold (e.g. enrichment ratio) was applied when comparing full-length constructs to deletion mutants. In contrast, the Methods section states that "proteins lacking signal above background were excluded and proteins were additionally required to show stronger signal in at least one bait condition than in GFP controls based on heatmap clustering (see script)," which instead suggests that a threshold-based criterion was used to define enrichment relative to controls and deletion mutants. If this is the case, the exact criteria and thresholds used for filtering should be clearly stated and consistently reported between the Results and Methods sections. If replicate measurements were not performed, this should be explicitly acknowledged, as peptide-level variability may substantially influence the identification of high-confidence interactors, particularly if the applied cutoffs are not highly stringent.

    Overall, many of the proposed biological functions are currently supported primarily by correlative analyses and would benefit either from additional functional validation or from a more cautious framing of the conclusions.

    Minor comments

    • In the Abstract and in the "Limitations of the study" section, the term "annotation" is used. It would be preferable to specify "functional characterization" instead of "annotation".
    • In the Introduction, there may be a minor citation confusion. Following the sentence: "Characterized by an N-terminal KRAB domain and a C-terminal tandem array of C2H2 zinc fingers, KZFPs primarily target transposable element (TE)-embedded sequences," the cited references are predominantly experimental studies supporting this statement. However, the inclusion of the review "Bruno, Mahgoub and Macfarlan, 2019" appears less appropriate in this context, as it does not directly present ChIP-seq data supporting this claim. More relevant primary studies from the same research area include "Wolf et al. 2020" and "Bruno et al. 2025.".
    • In Fig. 1A, "D10" appears inconsistent with the text and other figures (Fig. 1B, 1G, 1H), which refer to 9 days post-induction.
    • In Fig. S1, there may be a mismatch in the highlighted plate: the zoomed image appears to correspond to the first plate from the top. The correct plate should be highlighted for consistency.
    • In Fig. 1B, there is a typographical error ("K ZFPs" instead of "KZFPs").
    • In Fig. S1E, it is unclear what "other" refers to. Please clarify whether this represents the mean of all remaining KZFPs or a defined subset, ideally in the figure description.
    • In Fig. 2A, readability could be improved by adjusting the layering of points, as the darker dots (in particular the red ones) are currently obscured by lighter ones. Alternatively, removing the outline of the points (which is not transparent) may also improve visibility, but in that case the legend for point size would need to be updated accordingly.
    • In Fig. S2E, "SetDB1" should be corrected to "SETDB1".
    • In Fig. 3B, it is unclear what distinguishes the upper and lower "Diverse REs". A brief clarification in the figure legend would improve interpretability, particularly regarding the transposable element families included.
    • In Fig. S3C, the x-axis labels appear slightly misaligned and shifted to the right.
    • In Fig. 3C, the labels for MAGEA6 and MAGEA2B appear to be inverted.
    • In Fig. 3K, "MAGE3" should be corrected to "MAGEA3".
    • In the ZNF498 section, line 4, the punctuation should be corrected so that the period appears after the figure reference ("promoters (Fig. S1E).").
    • In the final sentence of the ZNF498 section, a noun appears to be missing after "cytoskeleton-dependent," possibly "processes".
    • In the last section of the Results and corresponding figures and their descriptions, "SCAN dependant" should be corrected to "SCAN-dependent".

    Significance

    General assessment

    This study presents a large-scale inducible overexpression platform aimed at systematically exploring the functional diversity of human KZFPs. The screening framework itself represents a potentially useful resource for prioritizing candidate KZFPs for downstream investigation and may be of interest to researchers studying KZFPs, transcriptional regulation, and transposable element biology. A notable strength of the study is the breadth of the screening effort and the attempt to integrate multiple orthogonal datasets to generate functional hypotheses for relatively understudied KZFPs. The more in-depth experimental analysis of ZNF498-mediated ciliogenesis also provides an example of how the platform may be used to identify biologically relevant candidates for further characterization. At the same time, many of the functional conclusions currently remain preliminary and are supported primarily by correlative analyses integrating overexpression phenotypes, expression datasets, and KZFP binding preferences also determined by overexpressing KZFP constructs. In several cases, the proposed biological functions remain insufficiently connected to the original proliferation phenotype used for candidate prioritization. As a result, the current study is best viewed as an exploratory and hypothesis-generating framework rather than a definitive functional characterization of the selected KZFPs. Clarifying these limitations and moderating some of the broader conclusions would substantially strengthen the manuscript.

    Advance

    The principal advance of the work is therefore primarily technical and resource-oriented, providing a scalable experimental framework for systematic KZFP prioritization and downstream functional exploration. While the study does not yet provide extensive mechanistic validation for most proposed functions, it may nonetheless serve as a useful starting point for future investigations into KZFP biology and transcriptional regulation.

    Audience

    The manuscript will likely be of greatest interest to a specialized basic research audience working on KZFPs, transposable element regulation, epigenetic regulation, and transcriptional control. Researchers interested in large-scale functional screening approaches may also find the methodological framework useful.

    Field of expertise: transposable elements, KRAB-zinc finger proteins, epigenetic regulation, functional genomics, genome regulation.

  4. Version published to 10.64898/2026.04.20.718945 on bioRxiv