The shape and size of the human cell nucleus is highly variable among cell types and tissues. Changes in nuclear morphology are associated with disease, including cancer, as well as with premature and normal aging. Despite the very fundamental nature of nuclear morphology, the cellular factors that determine nuclear shape and size are not well understood. To identify regulators of nuclear architecture in a systematic and unbiased fashion, we performed a high-throughput imaging-based siRNA screen targeting 867 nuclear proteins including chromatin-associated proteins, epigenetic regulators, and nuclear envelope components. Using multiple morphometric parameters, and eliminating cell cycle effectors, we identified a set of novel determinants of nuclear size and shape. Interestingly, most identified factors altered nuclear morphology without affecting the levels of lamin proteins, which are known prominent regulators of nuclear shape. In contrast, a major group of nuclear shape regulators were modifiers of repressive heterochromatin. Biochemical and molecular analysis uncovered a direct physical interaction of histone H3 with lamin A mediated via combinatorial histone modifications. Furthermore, disease-causing lamin A mutations that result in disruption of nuclear shape inhibited lamin A-histone H3 interactions. Oncogenic histone H3.3 mutants defective for H3K27 methylation resulted in nuclear morphology abnormalities. Altogether, our results represent a systematic exploration of cellular factors involved in determining nuclear morphology and they identify the interaction of lamin A with histone H3 as an important contributor to nuclear morphology in human cells. In physiological conditions, organelles have predictable morphologies in a cell-type and tissue-specific fashion . In contDrosophila melanogaster development, embryonic nuclei appear spherical and small while at later stages they assume a more elongated shape with an increase in overall nuclear size. Mutant studies in Drosophila identified kugelkern (kuk), a lamin-like nuclear protein, as one factor required for these nuclear morphology changes  and a micronucleus (MIC), expresses four Nup98 homologs which maintain MAC- and MIC-specific localization. Domain swapping experiments between MAC- and MIC-specific Nup98 proteins resulted in changes in nuclear size indicating that NPC composition can regulate nuclear size (One mechanism implicated in nuclear size control is transport through the nuclear pore complex (NPC) . The NPCXenopus , and inhXenopus . Furtherear size . Similarear size .LMNA, LMNB1, and LMNB2, of which LMNA produces two protein isoforms, lamin A and lamin C  of the effectors altering nuclear shape were genes whose products are involved in epigenetic modifications such as the polycomb component PCGF1, the histone acetyltransferase MYST3, the histone methyltransferase SETD2, or the ring finger protein RNF168 , lamin A/C and lamin B1 levels were unaffected indicating that the majority of shape hits did not exert their effect on shape indirectly via altering lamin levels   . Of the SLC27A3) . While Sll types .To further analyze nuclear shape and size effectors between cell lines, we employed STRING, a protein functional association network and pathway tool . In fibrSETD2, the histone H3K9 lysine demethylase KDM4D, and the histone deacetylase HDAC10, we hypothesized that histone H3-lamin interactions may contribute to maintaining nuclear size and shape.Given our identification of numerous epigenetic modulators as determinants of nuclear size and shape, combined with the lack of accompanying changes in lamins, we considered that chromatin-lamin interactions might mediate the observed size and shape effects. In particular, based on the identification in our primary screens of several post-translational modifiers of histone H3, including the histone H3K36-specific lysine methyltransferase To test this hypothesis, we first asked whether lamin A/C could bind directly to chromatin in vitro. Lamin A and lamin C consist of an N-terminal head domain followed by a long rod-like domain in the central region of the proteins and prior observations showed that the C-terminus of lamin A maintains an IgG-like fold domain and directly binds to DNA . For thaTo more precisely identify the region of the lamin A C-terminus required for the interaction with histone H3, multiple truncation mutants were generated and used in in vitro histone binding assays. GST-lamin A (aa 389–638) bound as well to histone H3 as the full-length lamin A tail , while the aa 389–626 region did not . These eBinding of lamin A to naked DNA had previously been shown to occur in the context of lamin A dimers . To ask To further identify how lamin A binds to histone H3, we utilized a histone peptide binding array to probe the effect of histone tail modifications on lamin A-histone interactions . The arrThe binding of lamin A to histone H3 is of interest since mutations in this histone variant have been implicated in disease. Mutations to histone H3.3 were first found in pediatric high-grade glioma and later in chondrosarcomas and giant cell tumors of the bone . RecentlHere, we have identified novel determinants of nuclear size and shape by utilizing an imaging-based functional genomics screen. Our findings highlight a prominent role of chromatin factors and epigenetic modifiers in the maintenance of nuclear morphology. In support of such a role, using in vitro binding assays, we find a direct interaction between lamin A and the modified tail of histone H3 and expression of disease-relevant histone H3.3 mutants altered normal nuclear morphology in human fibroblast cells.NUP205, NUP62, NUPL1, and NUP85 as well as a number of nuclear membrane proteins such as SYNE1, CHMP6, TMEM19, and GOSR2 which validate our screening method. Our results are also in line with previous screening studies using the elliptic Fourier coefficient as a distinct parameter to quantitatively identify misshapen nuclei in MCF10A breast epithelial cells which targeted 608 epigenetic gene products and found 33 determinants of nuclear shape, including a number of epigenetic factors   and MCF10AT1k.cl2  were used. Cell lines were periodically tested for mycoplasma.Previously described karyotypically normal hTERT-immortalized dermal fibroblasts cells  were maistitute)  were culFor high-throughput siRNA screening, 1200 cells were seeded into each well of a CellCarrier-384 Ultra microplate (PerkinElmer) using a Multidrop Combi Reagent Dispenser (Thermo Fisher). Cells were reverse transfected with siRNA oligos targeting specific genes at a 20 nM final concentration in 40 μL of complete media and were grown for 72 hr at 37°C. The screen used custom siRNA libraries  targeting proteins that localize to the nuclear membrane (346 genes) or proteins involved in epigenetic and chromatin regulation (521 genes) . Each geImmuno-stained plates were imaged using an Opera QEHS (PerkinElmer) dual spinning disk high-throughput confocal microscope using a 40× water immersion lens (NA 0.9). The high-throughput microscope acquired images using two CCD cameras (1.3 Megapixels) with pixel binning set at 2×2 (pixel size: 323 nm). The DAPI channel utilized the 405 nm laser for excitation and the 450/50 nm bandpass filter for acquisition, lamin B1 expression was imaged using the 488 nm laser for excitation and a 520/35 nm bandpass filter for acquisition, and lamin A/C expression was imaged using the 561 nm laser for excitation and the 600/40 nm bandpass filter for acquisition. DAPI, lamin B1, and lamin A/C images were acquired at a single focal plane. Thirty randomly selected fields of view were imaged per well, and typically >250 cells per well were analyzed.2) were calculated on a mean per well basis. To identify nuclear size hits, nuclear area was calculated on a mean per well basis. RStudio software, R, and the cellHTS2 R package (v 2.36.0) . An analysis pipeline was generated where nuclei were segmented using the DAPI staining image. Partial nuclei located at the edge of the image were excluded from subsequent steps of the analysis. To identify nuclear geometric changes, nuclear area, width, length, and circumference were measured as well as mean fluorescence intensity of lamin A/C and lamin B1. Single cell measurements were computed into mean per well values. To identify nuclear shape hits, circularity values (circularity = 4πArea/perimeter 2.36.0)  used the 2.36.0)  to norma 2.36.0)  for eachThe Z-score value for each sample (well) was calculated as the robust Z-score = /(median absolute deviation of the B-scores for the samples). The Z-score indicates how many units of variance, either above or below, a sample is from the median of all measurements for the samples. For an individual sample, the further the Z-score is from 0, the larger the degree in which the sample is from the median of all samples. A diagram explaining the Z-score calculations used to measure nuclear size and shape is presented in Escherichia coli Rosetta 2 (Novagen) in LB media. Protein expression was induced by the addition of 0.2 mM IPTG for 18 hr at 18°C. Cells were collected and suspended in lysis buffer containing 50 mM Tris pH 7.5, 150 mM NaCl, 0.05% NP-40, 1 mM PMSF, protease inhibitors, and 0.5 mg/mL lysozyme. Cells were incubated for 30 min on ice and lysed and sonicated for a duration of 20 s at 18% amplitude using a Branson Digital Sonifier 250 with a 102C converter set. Lysed cells were centrifuged at 21, 000 × g at 4°C for 15 min. The supernatant was removed and incubated with glutathione Sepharose 4B resin (GE). Beads were washed twice with lysis buffer and once with elution buffer . Recombinant GST fusion proteins were eluted by resuspending the resin in elution buffer containing 15 mg/mL reduced L-glutathione (Sigma) and incubated at 4°C for 4 hr. Recombinant proteins were run on a 4–12% BisTris gel and analyzed using colloidal staining. Plasmids are available upon request.Fragments of human lamin A, lamin C, and progerin were cloned into the pGEX4T1 vector to generate N-terminally tagged GST fusion proteins. The plasmids used for recombinant protein production are listed in Calf thymus histone binding assays were performed by incubating 50 μg of calf thymus histones (Worthington) with 10 μg of purified GST fusion proteins in binding buffer containing 50 mM Tris pH 7.5, 1 M NaCl, and 1% NP-40 overnight at 4°C. To identify histone-lamin interactions, glutathione Sepharose 4B resin (GE) was added for 1 hr. Beads were washed five times in binding buffer, resuspended in 4× Laemmli buffer, run on a 4–12% BisTris gel, and transferred to a PDVF membrane. Membranes were used for western blot analysis using the antibodies at the specified concentrations . PurificMODified histone peptide arrays (Active Motif) composed of 384 unique histone peptides representing acetylation, citrullination, methylation, and phosphorylation post-translational modifications were blocked with TBST  and 5% nonfat milk overnight at 4°C. The peptide array was washed twice with TBST, one time with interaction buffer . The array was incubated with 10 nM purified GST-lamin A (pACS37) in interaction buffer at room temperature for 1 hr. The array was then washed three times in TBST, and incubated with anti-GST  for 1 hr at room temperature in TBST with 1% nonfat dried milk. The array was further washed three times in TBST with 10 min for each wash and incubated with HRP-conjugated secondary antibody (Santa Cruz) for 1 hr at room temperature. The membrane was submerged in ECL solution (Amersham), imaged, and intensity data was quantified using Array Analyzer Software (Active Motif). Purifications of recombinant proteins and peptide binding experiments were performed at least three times.Lentiviruses containing wild-type histone H3.1 and H3.3 histone constructs were generated with the pLenti6.3/V5-TOPO TA Cloning kit (Thermo Fisher). Mutations were added to wild-type H3.1-V5 and H3.3-V5 sequences using the Quikchange II XL site directed mutagenesis kit (Agilent). Lentiviruses were transfected into HEK293T cells in combination with viral packing and envelope plasmids pSPAX (Addgene 12260) and pMD2.G (Addgene 12259), respectively, and allowed to incubate for 48 hr before virus harvesting. Viral supernatant was collected from the HEK293T cells and placed onto hTERT-immortalized fibroblast cells and incubated for 24 hr. Fibroblast cells were selected for mutant histone expression by the addition of 5 μg/mL of blasticidin for 10 days, 1200 cells were seeded onto 384-well plates, and were grown for 72 hr at 37°C. After treatment, cells were fixed in 4% PFA in PBS for 20 min at room temperature, washed three times for 5 min in PBS, permeabilized with 0.5% Triton X-100 in PBS or 15 min, washed three times for 5 min in PBS, and blocked in PBS with 0.05% Tween 20 (PBST) and 5% BSA for 30 min. For detection and measurement of nuclear morphology, cells were immuno-stained with primary antibodies against lamin A/C  and lamin B1   in PBST Scatterplots displaying the relationship between two measurements were assessed using Spearman’s coefficient analysis. Nuclear morphology features in cells expressing mutant histones were analyzed by using a two-sample Kolmogorov-Smirnov test to compare the distribution of nuclear morphology scores at the single cell level. In this manuscript the authors describe targeted, imaging-based RNAi screens to identify novel modulators of nuclear size and shape, which are established diagnostic and prognostic indicators of human diseases including cancer. This work provides new insights into the molecules that dictate nuclear morphology tied to chromatin state, the nuclear lamina, and the nuclear envelope. This resource will be broadly valuable to the nuclear cell biology and chromatin biology fields. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by and Jessica Tyler as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Dennis Discher (Reviewer #3).Thank you for submitting your article "Identification of epigenetic modulators as determinants of nuclear size and shape" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential Revisions:1. Transparency and interpretability of how the screen was performed and the use of Z-scores: The authors must provide a clear explanation of the z-score and how it is produced from the raw data to represent nuclear roundness/shape and size in the manuscript. In particular, there was concern that the measure might not address whether there are relatively few nuclei with very perturbed structures or instead a rather smaller deviation across all or most cells in a given condition – more clarity on this should be included in the revision. The authors are also encouraged to provide values or examples along the z-score axis to allow the reader to see how the direct measurements (area and perimeter) translate into the reported z-scores. Addressing these points is essential for readers to put this study in the context of others that report data as a deviation from circularity from 1 to 0, as well as allowing the reader to compare data in Figures1-3 and Figure 6. Last, the authors need to more clearly justify and communicate that they have removed hits with z-scores below -2, including stating that the focus of the work is on what amounts to genes with more subtle effects on nuclear size and shape. Given the stated criteria, it is also important for the authors to address how they have concluded that the included screen hits do not impact the cell cycle and cell viability.2 Statistics and robustness: The authors need to provide more detail on how they have justified that two replicates are sufficient for the reported data. For example, are they leveraging that they have employed 3 unique siRNAs? It would also build confidence if a subset of hits discussed in detail were tested for the knock-down efficiency and its relationship to nuclear appearance.3. Interpretation of the screen "hits": As the screen was already biased towards nuclear envelope and chromatin factors, an unbiased approach to further interpret the hits would strengthen the overall message – for example, an approach that can identify factors that interact with one another, indicating a role for a complex, etc. – many such tools are readily available. At a minimum, the authors should provide greater context, particularly for the hits that they decide to elaborate on.4. The need to temper claims about distinct sensitivities of cell types to perturbations that alter nuclear appearance or to provide additional experimental support: The conclusions made from the comparison of the hits from the two independent screens without further testing were found to be inadequately supported. The authors must test a subset of the hits from one cell line in the other (and vice versa) in order to demonstrate that there are indeed cell type specific perturbations. The authors should also compare their list to other published works.5. Interpretation of the biochemistry: There were several questions about the interpretation of the GST-lamin A biochemistry that need to be addressed. First, it is well established that GST imposes dimerization on proteins expressed as GST fusions independent of cysteines – the findings need to be reconsidered in this light, particularly as it relates to the combined lamin A/lamin C experiments. Second, even in the case that disulfide bonds between cysteines are occurring, there needs to be further consideration of whether this is possibly relevant in vivo (in the reducing environment of the nucleus) and/or requires further characterization, specifically evidence that it is indeed disulfide bonds between the lamin A and not the GST. Third, additional quantitative context for the binding of lamin A to the modified histone tails is needed  – as only relative values are provided in Figure 5, the reader currently lacks a framework for knowing what the order of magnitude is for these interactions.6. Need for more depth in the analysis of histone H3.3 variant effects : As these mutants are expressed over WT histones the authors should address how expression level impacts the measured effects on nuclear appearance. What might the ratio of H3.3-V5 to H3.3 be in these experiments . In single cells based on the immunostaining, the authors can also address whether there are trends according to the expression level. In this analysis, please also clarify if "shape" is independent of a change in area. There is also a need for statistical analysis. Last, please provide further evidence that the H3 effects manifest through lamins –  through localization, relationships to genomic data, etc.Reviewer #1 (Recommendations for the authors):1. In the introduction of evidence tying nuclear size to nuclear transport additional studies should be referenced in particular the effects of disrupting nuclear export with the small molecule leptomycin B .2. Please clarify if the lamin A fragment includes the C-terminus after processing or not.3. The introduction to experiments included in Figure 5 needs to state what form of lamin is being interrogated.4. The authors must cite explicit examples with references when suggesting that the presence of hits including nucleoporins and nuclear membrane proteins "validates their screening method".Reviewer #2 (Recommendations for the authors):This work is a fantastic advance in the field that provides many novel and deeply interesting findings. However, the paper's large and meaningful amount of data that is completely confused using Z scores (not raw data) and how to deal with variation (no error bars). These two major criticisms of the paper should be addressed before the paper is published.1. There needs to be a clear explanation of the z-score and how it is produced from data for nuclear roundness/shape and size. Overall, the paper would benefit greatly from graphing roundness and size data next to z score to allow the reader to see how the direct measurements (area and perimeter) translate into z score, the measure of change from the mean.a. The major problem with this paper lies in how the bulk data is scored and displayed. Z score is not explained anywhere causing major confusion for the reader.b. The core measurement of this paper is area 4 π divided by perimeter squared which is not shown in the paper in any capacity relative to the Z store. It would be gratefully helpful to provide at least one if not a few examples of the type of average or distribution of roundness scores relative to the output Z score. This is important as many previous papers in this field report shape measurements at 1 being a perfect circle and abnormal nuclear morphology measure along this scale from 1 to 0. While abnormal shapes are shown – there connection to the reported Z score and roundness value underlying it is hidden. Same is true for size where exact numbers would be useful to the reader.i. Please show some amount of raw comparative roundness and size measurements.ii. Why not show the > 250 nuclear measurements for scrambled, LMNA, and a few choice hits across the scale of z score?c. While the first half of the paper reports Z scores , later in the paper roundness scores are reported for Figure 6. This is confusing for the reader as measurement of change in nuclear shape and size changes in the paper. This example highlights the importance of being able to bridge the use of these two values, why they are used at different times, and overall understanding. The later histogram like distributions of roundness have no clear statistical measure from the figure.d. THE SPECIFIC question centers around how much the roundness value differs between hits and how much roundness and size vary within one hit. In diagnostic tests of human diseases abnormal nuclear morphology percentages can change by very little between non-aggressive and very aggressive illnesses.i. How much does Z score capture this vs ignore a sub population of abnormally shaped nuclei?e. It is appreciated that raw data were made available through GitHub. However, the problem with many raw data sets is that it is hard to find the numbers that matter in a meaningful way.2. The manuscript should clearly state why two replicates underscore the majority of the data and that the data graphs provide no error bars.a. The manuscript should justify why two replicates are sufficient for most of the reported data. If you can do two replicates why not have done three to provide the agreed upon triplicate measurement.i. The fact that 3 unique siRNAs are used for each KD is not discussed. Does this provide multiple replicates for the paper that are not discussed?b. For z scores, the paper clearly reports two replicates but how are those two replicates are used in the data. The variation from one experiment to the other is not clearly provided. Furthermore, with no triplicate measure should a small population of hits be verified in a more detailed manner like is common in many gene screens? Again, showing just the roundness or size measure would be highly informative.c. For binding values, there are also no error bars. How is the reader supposed to interpret the data with no idea of variance?d. Figure 6 C and D is written in a cryptic manner. Does the manuscript mean to say that compared to wild-type H3.3 all mutant forms (list them) show statistically significant changes in shape C and size D? or are there differences between different mutants? It is unclear.Reviewer #3 (Recommendations for the authors):1. The screening of Figures1-3 is fine, but I found it important that they restricted analyses "To eliminate hits due to cell death or altered cell-cycle behavior, we excluded any hits with a cell number z-score of less than -2." Some mention of this in the abstract seems important. For example: 'The most dramatic effects were found for cell cycle and death regulators, but we chose to focus on regulators of more subtle effects.' In this regard, the authors need to take great care that the targets identified have zero impact on cell cycle and death.2. The authors add "reducing agent DTT to inhibit dimerization" of GST-lamin A, but they need to tell us the specific Cys-Cys crossbridges that cause dimerization because GST also has Cys and could be contributing to non-physiological oligomers.3. In analyzing laminA-histone interactions, the authors write "For example, GST-laminA bound ~ 5-fold more efficiently to histone H3R8me2s/K9me2 than histone H3K9me2." The word "efficiently" does not seem a standard descriptor for binding, compared to terms like affinity, specificity, saturation, etc. The authors should strive to show at least one of the 'stronger' interactions is saturable and has a reasonable Kd as a basis for specificity.4. The histone-H3 mutation effects on nuclear morphology in Figure 6 are important, but some key details and insights are needed. These seem to be Lenti's that transduced a fraction of the fibroblast cultures, giving levels that are zero, low, or very high in each culture, but does that variation have any effect? Histone levels do relate to cell cycle (e.g. PMID: 35760914), and so are high expressers at later stages of cell cycle (e.g. higher DNA staining) and would that be sensible for how the construct is regulated in its expression? Does the 'shape' parameter neglect differences in 'area'. Are the histone intensities uniform, and what happens to LaminA levels or localization? Essential Revisions:1. Transparency and interpretability of how the screen was performed and the use of Z-scores: The authors must provide a clear explanation of the z-score and how it is produced from the raw data to represent nuclear roundness/shape and size in the manuscript.As requested, we now explain in a new Figure 1 —figure supplement 2 in detail how Z-scores were derived from the raw data. In addition, we further describe and clarify statistical methods in the Materials and methods and in Results sections on p. 7, 8, 9, and 22.here. Additional details are provided in the documentation of the R package , used to calculate all the statistics for the screens in this manuscript. We calculated separate Z-scores for either shape or size for each well in the screen using the geometrical measurements as described in the Materials and methods and Results.We now explicitly explain that the Z-score is a standard statistical measure used in screening datasets and describes how different an individual sample in the screen is relative to the mean of all samples in the screen PMID: 16869968. For a publicly available standard definition see 2 formula. To obtain a size parameter for the nucleus the area of each imaged nucleus was first measured. Raw per nucleus values for all measured parameters were then averaged on a per well (oligo siRNA) basis. Mean per well values were then normalized on a per plate basis. Z-scores for two biological replicates were averaged, thus providing one Z-score values per oligo siRNA. Finally, since each gene is targeted by multiple siRNA oligos in the library, we picked the median of the Z-scores for siRNA oligos against a gene . This is the Z-score value reported in the manuscript to score hits in the primary screen, and it measures the relative strength of the nuclear morphology change between the knock-out of a particular gene and the median of the population (e.g. Z-score = 0). This approach is standard in the analysis of RNAi screens (see PMID: 16869968). The analysis workflow detailed above is now presented in Figure 1 —figure supplement 2, Figure 1A and is described in detail on p. 7, 8, 9, and 22.More specifically, to obtain a roundness/shape parameter, the circularity of each imaged nucleus was measured using the 4pArea/perimeterIn particular, there was concern that the measure might not address whether there are relatively few nuclei with very perturbed structures or instead a rather smaller deviation across all or most cells in a given condition – more clarity on this should be included in the revision.As most of our studies rely on single cell analysis, we very much appreciate this point. To further analyze the data from the initial screen, we now provide, as requested, a new Figure 1 —figure supplement 5. Figure 1 —figure supplement 5 which contains single cell data for nuclear shape values of several hits and controls in histogram format and compare distributions to control cells. We find that the Z-score values on a per well/siRNA/gene basis are not driven by alterations in small subpopulations of cells, but rather reflect changes to nuclear morphology present in most cells in the population as indicated by the shift of the entire distribution. We now mention these data on p. 8.The authors are also encouraged to provide values or examples along the z-score axis to allow the reader to see how the direct measurements (area and perimeter) translate into the reported z-scores. Addressing these points is essential for readers to put this study in the context of others that report data as a deviation from circularity from 1 to 0, as well as allowing the reader to compare data in Figures1-3 and Figure 6.As requested, Z-score values and raw circularity values have now been added to figures 1. Furthermore, Figure 1 —figure supplement 4 was added to give a sense of the relationship of phenotypic measurements of area, perimeter, and circularity in comparison to Z-scores generated from the screen. In addition, a new Supplementary File 1D with the Z-score next to the raw mean per well circularity score is now included for comparison and mentioned on page 7. The reason for the use of well/siRNA/gene Z-scores in Figure 1 and 2 is because these figures represent screening data, whereas Figure 6 represents single knockdown experiments to which Z-scores cannot be applied since Z-scores compare a sample to all other samples within a screening dataset.Last, the authors need to more clearly justify and communicate that they have removed hits with z-scores below -2, including stating that the focus of the work is on what amounts to genes with more subtle effects on nuclear size and shape. Given the stated criteria, it is also important for the authors to address how they have concluded that the included screen hits do not impact the cell cycle and cell viability.AURKB, MAD2L1 and CDC2 and displayed a low cell count per well compared to control cells. To eliminate potential false positives, we considered the resulting nuclear morphology changes as secondary effects due to cellular stress, abnormal cell division, siRNA toxicity, and potential lethality as they were often accompanied by significant reduction of cell number. Therefore, we removed nuclear shape and size hits from further analysis if their cell number per well Z-score was below -2. Nuclear shape hits with Z-score below -1.5, described in Figure 1, and no significant changes in cell number, were included in our hit list. We now describe in more detail these criteria and our strategy to reduce false positives and artifacts in the Materials and methods on p. 22 and in the Results sections on p.8.We agree this is very important and we now mention this caveat in the abstract as suggested by the referee. We found that some hits in our initial shape screen were known regulators of the cell cycle and of mitosis such as 2 Statistics and robustness: The authors need to provide more detail on how they have justified that two replicates are sufficient for the reported data. For example, are they leveraging that they have employed 3 unique siRNAs? It would also build confidence if a subset of hits discussed in detail were tested for the knock-down efficiency and its relationship to nuclear appearance.As requested, we have added a more detailed description of statistical methods and robustness on p. 21 and 22. As the referee points out, we use three separate siRNAs for each target gene and hits are defined by an effect of at least 2 of the 3 siRNAs, reducing the likelihood of off target effects. It is a standard practice in the field to perform large scale screens such as the ones reported here in duplicate, especially when multiple siRNAs are used in an unpooled fashion as done here (PMID: 19644458). A comparison of the two replicates of the screen indicated strong correlation very good correspondence between hit lists  and positive hits from the primary screen were validated in secondary screens which are now shown in Figure 3 —figure supplement 6. Furthermore, the generated datasets are data-rich and based on several hundred images and involve analysis of typically 500-1000 cells per sample, generating highly robust datasets. To further address this point, we have now included, as requested, Supplementary Files 3A, 3B, and 3C, which detail the results of a validation screen using siRNAs with differing target sequences and generated using different chemistry. We find good correspondence between primary hits and our validation screen. Additionally, as requested, we now build confidence in our hit identification by inclusion of new data in Figure 3 —figure supplement 2, where we repeated knockdown experiments and used antibody staining and automated imaging-based quantification of fluorescence intensity to demonstrate high knock-down efficiency of several hits. We find knockdown efficiency to be extremely robust with typical reduction levels of 60-90% using 8 different antibodies .3. Interpretation of the screen "hits": As the screen was already biased towards nuclear envelope and chromatin factors, an unbiased approach to further interpret the hits would strengthen the overall message – for example, an approach that can identify factors that interact with one another, indicating a role for a complex, etc. – many such tools are readily available. At a minimum, the authors should provide greater context, particularly for the hits that they decide to elaborate on.As requested, we have now performed pathway analysis using STRING to assess functional protein networks and added these findings to Figure 3 —figure supplement 4 and 5. We find highly connected regions in the network corresponding to condensin and histone modifiers in fibroblast hits altering nuclear shape. In contrast, MCF10AT hits showed increased connectivity with nucleoporin proteins. Fibroblast hits displaying an increase in nuclear size identified multiple nucleoporins and MCF10AT hit analysis identified components of DNA replication. We have also expanded our description of some of the hits, especially the ones we elaborate on in the study. We describe these results on p. 11.4. The need to temper claims about distinct sensitivities of cell types to perturbations that alter nuclear appearance or to provide additional experimental support: The conclusions made from the comparison of the hits from the two independent screens without further testing were found to be inadequately supported. The authors must test a subset of the hits from one cell line in the other (and vice versa) in order to demonstrate that there are indeed cell type specific perturbations. The authors should also compare their list to other published works.As requested, we now performed side by side experiments in different cell lines to directly compare a subset of nuclear morphology hits in parallel and present the data in Supplemental Figure 3 —figure supplement 1. We find a number of hits that display strong nuclear shape abnormalities in either fibroblasts or MCF10AT cells, but not both with the exception of LMNA, which confirms our screen data. In addition, we compared the hits from our screen with previously published results finding other factors which regulate nuclear morphology to further strengthen our findings. We mention these results on p. 10-11. Despite these results, which confirm our initial claim of cell type differences, we have now toned down these conclusions considering that we have only analyzed two cell lines.5. Interpretation of the biochemistry: There were several questions about the interpretation of the GST-lamin A biochemistry that need to be addressed. First, it is well established that GST imposes dimerization on proteins expressed as GST fusions independent of cysteines – the findings need to be reconsidered in this light, particularly as it relates to the combined lamin A/lamin C experiments. Second, even in the case that disulfide bonds between cysteines are occurring, there needs to be further consideration of whether this is possibly relevant in vivo (in the reducing environment of the nucleus) and/or requires further characterization, specifically evidence that it is indeed disulfide bonds between the lamin A and not the GST. Third, additional quantitative context for the binding of lamin A to the modified histone tails is needed  – as only relative values are provided in Figure 5, the reader currently lacks a framework for knowing what the order of magnitude is for these interactions.We have now directly addressed the possibility that GST imposes dimerization regardless of the presence of disulfide bonds between cysteine residues in lamin A. To do so, we mutated GST-lamin A cysteine residues to alanine and repeated the histone binding experiments. If the observed binding were artifactually due to GST-mediated dimerization, we should not expect an effect of the cystine mutants on histone binding. We find, however, that the C522A mutation in lamin A results in increased binding of H3 in the presence of lamin C, demonstrating that the observed effects are not due to GST dimerization. We include these data in Figure 4 —figure supplement 1 and discuss the results on p. 13.We entirely agree with the referee that it is exceptionally challenging to determine the in-vivo relevance of disulfide bonds, not knowing the precise environment of the nucleus. Furthermore, since we were unable to obtain or generate dually-methylated H3-tail peptides, we are not in a position to perform quantitative binding assays between lamin A and the dual-methylation histone H3 tail. Given these concerns, we have now toned down this point on p.14 and discuss the limitations of these findings on p. 19.As requested, we now include quantitative data in a new Supplementary File 3E for the binding of lamin A to the modified peptides in Figure 5. Note that this assay is not sufficiently quantitative to generate Kds.6. Need for more depth in the analysis of histone H3.3 variant effects : As these mutants are expressed over WT histones the authors should address how expression level impacts the measured effects on nuclear appearance. What might the ratio of H3.3-V5 to H3.3 be in these experiments . In single cells based on the immunostaining, the authors can also address whether there are trends according to the expression level. In this analysis, please also clarify if "shape" is independent of a change in area. There is also a need for statistical analysis. Last, please provide further evidence that the H3 effects manifest through lamins –  through localization, relationships to genomic data, etc.As requested, we have now measured H3.3 mutant levels relative to the total H3.3 levels in the population by immunostaining and single cell analysis. We present these data in Figure 6 —figure supplement 3. By single cell imaging, we find that the mutant levels only slightly increase total H3.3 . We also quantitated, as requested, H3.3-V5 wild-type and mutant levels and compared these parameters to lamin A/C levels and to nuclear shape at the single cell level. We find a slight increase in lamin A/C expression upon expression of both WT and H3.3 mutants and we find no strong correlation between the level of H3.3 mutant expression and effect on shape and size. These data are now described in the text on p. 15 and are included in Figure 6 —figure supplement 3 and 4. We now also indicate that in these experiments shape is independent of change in area and we include statistical analysis of results as requested in Figure 6 —figure supplement 5.Reviewer #1 (Recommendations for the authors):1. In the introduction of evidence tying nuclear size to nuclear transport additional studies should be referenced in particular the effects of disrupting nuclear export with the small molecule leptomycin B .As requested, we have included numerous references and discuss on p. 4 the evidence tying nuclear size to nuclear import and export.2. Please clarify if the lamin A fragment includes the C-terminus after processing or not.As requested, we now indicate in Supplementary File 1B that the lamin A fragment is the processed mature lamin A construct.3. The introduction to experiments included in Figure 5 needs to state what form of lamin is being interrogated.As requested, we now state on p. 14 and 23 the lamin form used is recombinant GST-lamin A (a.a. 506-646).4. The authors must cite explicit examples with references when suggesting that the presence of hits including nucleoporins and nuclear membrane proteins "validates their screening method".As requested, we now include on p. 16 several references of nucleoporins and nuclear membrane proteins previously found in similar screens.Reviewer #2 (Recommendations for the authors):This work is a fantastic advance in the field that provides many novel and deeply interesting findings. However, the paper's large and meaningful amount of data that is completely confused using Z scores (not raw data) and how to deal with variation (no error bars). These two major criticisms of the paper should be addressed before the paper is published.1. There needs to be a clear explanation of the z-score and how it is produced from data for nuclear roundness/shape and size. Overall, the paper would benefit greatly from graphing roundness and size data next to z score to allow the reader to see how the direct measurements (area and perimeter) translate into z score, the measure of change from the mean.As requested, we now explain in a new Figure 1 —figure supplement 2 in detail how Z-scores were derived from the raw data. In addition, we further describe and clarify statistical methods in the Materials and methods p.22 and in Results section on p.8 and 9. We have now also added Z-scores directly to Figures 1. Furthermore, Figure 1 —figure supplement 4 was added to give a sense of the relationship of phenotypic measurements of area, perimeter, and circularity in comparison to Z-scores generated from the screen. In addition, a new Supplementary Supplementary File 1D with the Z-score next to the raw mean per well circularity score is now included for comparison and mentioned on page 7.here. Additional details are provided in the documentation of the R package , used to calculate all the statistics for the screens in this manuscript. We calculated separate Z-scores for either shape or size for each well in the screen using the geometrical measurements as described in the Materials and methods and Results.We now explicitly explain that the Z-score is a standard statistical measure used in screening datasets and describes how different an individual sample in the screen is relative to the mean of all samples in the screen PMID: 16869968. For a publicly available standard definition see 2 formula. To obtain a size parameter for the nucleus the area of each imaged nucleus was first measured. Raw per nucleus values for all measured parameters were then averaged on a per well (oligo siRNA) basis. Mean per well values were then normalized on a per plate basis. Z-scores for two biological replicates were averaged, thus providing one Z-score values per oligo siRNA. Finally, since each gene is targeted by multiple siRNA oligos in the library, we picked the median of the Z-scores for siRNA oligos against a gene . This is the Z-score value reported in the manuscript to score hits in the primary screen, and it measures the relative strength of the nuclear morphology change between the knock-out of a particular gene and the median of the population (e.g. Z-score = 0). This approach is standard in the analysis of RNAi screens (see PMID: 16869968). The analysis workflow detailed above is now presented in Figure 1 —figure supplement 2, Figure 1A and is described in detail on p. 22, and 7, 8 and 9.More specifically, to obtain a roundness/shape parameter, the circularity of each imaged nucleus was measured using the 4pArea/perimetera. The major problem with this paper lies in how the bulk data is scored and displayed. Z score is not explained anywhere causing major confusion for the reader.We now describe in a new Figure 1 —figure supplement 2 how the Z-score was generated and have expanded our explanation of the Z-score on pages 7, 8, 9 and 22. Please also see our response to the point above.b. The core measurement of this paper is area 4 π divided by perimeter squared which is not shown in the paper in any capacity relative to the Z store. It would be gratefully helpful to provide at least one if not a few examples of the type of average or distribution of roundness scores relative to the output Z score. This is important as many previous papers in this field report shape measurements at 1 being a perfect circle and abnormal nuclear morphology measure along this scale from 1 to 0. While abnormal shapes are shown – there connection to the reported Z score and roundness value underlying it is hidden. Same is true for size where exact numbers would be useful to the reader.i. Please show some amount of raw comparative roundness and size measurements.ii. Why not show the > 250 nuclear measurements for scrambled, LMNA, and a few choice hits across the scale of z score?We now expanded our description of how measurements for nuclear size and shape were made and how Z-scores were derived from the primary measurements. These methods are described in the new Figure 1 —figure supplement 2 and on p. 22.To further analyze the data from the initial screen, we now provide, as requested, in a new Figure 1 —figure supplement 5 single cell data for nuclear shape hits and controls in histogram format and compare distributions to control cells. We find that the Z-score values are not driven by small subpopulations of cells.As requested, Z-score values have now been added to figures 1 and Figure 1 —figure supplement 4 to give a sense of the relationship of phenotype and Z-scores. In addition, a new Supplementary File 1D with the Z-score next to the mean per well score was created for comparison.c. While the first half of the paper reports Z scores , later in the paper roundness scores are reported for Figure 6. This is confusing for the reader as measurement of change in nuclear shape and size changes in the paper. This example highlights the importance of being able to bridge the use of these two values, why they are used at different times, and overall understanding. The later histogram like distributions of roundness have no clear statistical measure from the figure.We apologize for the confusion. As mentioned above, robust Z-scores based on the median of the population of samples are used in analysis of screening data to score treatments/perturbations from large libraries, where most siRNA oligos in the library are expected to have negligible or no biological effect . This approach cannot be used in datasets generated by individual or small numbers of experimental conditions that are preselected for biological activity . For clarity, we have added a new Figure 1 —figure supplement 2 explaining how Z-scores are derived from morphological measurements and we are providing a histogram representation of the roundness distribution in Figure 1 —figure supplement 5 and a new Supplementary File 1D with the Z-score next to the mean per well score was created for comparison.d. THE SPECIFIC question centers around how much the roundness value differs between hits and how much roundness and size vary within one hit. In diagnostic tests of human diseases abnormal nuclear morphology percentages can change by very little between non-aggressive and very aggressive illnesses.i. How much does Z score capture this vs ignore a sub population of abnormally shaped nuclei?We appreciate these points. To further analyze the data from the initial screen, we now provide, as requested, in a new Figure 1 —figure supplement 5 single cell data for nuclear shape values of several hits and controls in histogram format and compare distributions to control cells. We find that the Z-score values are not driven by small subpopulations of cells.e. It is appreciated that raw data were made available through GitHub. However, the problem with many raw data sets is that it is hard to find the numbers that matter in a meaningful way.We have made every effort to provide all relevant data in an easily accessible way. We include GITHUB datasets so readers can evaluate the methods used to generate the data alongside the raw datasets. In addition, we have now added raw data values of the hits identified in the screen in Supplementary File 1D.2. The manuscript should clearly state why two replicates underscore the majority of the data and that the data graphs provide no error bars.a. The manuscript should justify why two replicates are sufficient for most of the reported data. If you can do two replicates why not have done three to provide the agreed upon triplicate measurement.It is a standard practice in the field to perform large scale screens such as the ones reported here in duplicate, especially when multiple siRNAs are used in an unpooled fashion (see PMID: 19644458). Our comparison of the first two repeats of the screen indicated very good correspondence between hit lists . Furthermore, the generated datasets are data-rich and based on several hundred images and analysis of typically 500-1000 cells per sample, generating statistically robust datasets. To further address this point, we have now included in Supplementary File 3A, 3B, and 3C the results of a validation screen using siRNAs with differing target sequences and generated using different chemistry. Additionally, we now build confidence in our hit identification by inclusion of new data in Figure 3 —figure supplement 1 and 2, demonstrating knock-down efficiency of several hits. We typically find reduction levels of 60-90%. We find no relationship between extent of knockdown and nuclear appearance . In summary, screening data was generated by performing the experiment in duplicate, and validation experiments were performed in triplicate such as in Figure 3 —figure supplement 1.i. The fact that 3 unique siRNAs are used for each KD is not discussed. Does this provide multiple replicates for the paper that are not discussed?We appreciate this point and have clarified on p. 8 that we indeed targeted each gene with 3 distinct siRNA in an unpooled fashion, thus essentially testing each gene in triplicate and increasing the statistical power of the two replicates of the screen. Hits were defined as showing a statistically significant effect of at least 2 of the 3 siRNAs. Please also see our response to point 2.a above.b. For z scores, the paper clearly reports two replicates but how are those two replicates are used in the data. The variation from one experiment to the other is not clearly provided. Furthermore, with no triplicate measure should a small population of hits be verified in a more detailed manner like is common in many gene screens? Again, showing just the roundness or size measure would be highly informative.nd siRNA with the strongest biological effect and reduces the chances that a positive hit gene is the result of a single siRNA oligo having strong, but off-targeteffects. To address variation from one dataset to another we have added error bars in several figures  as well as Supplementary figures. We have now also added a validation screen  which shows good concordance of Z-score in primary and validation screens.The variation from one experiment to the other is shown in Figure 1 —figure supplement 1. The Z-scores for each siRNA oligos are calculated as the mean of the two biological replicates. The Z-score on a per gene basis, which is what is reported in this manuscript, is the median of the Z-scores for the 3 oligo siRNA targeting that gene. This Z-score on a per gene basis effectively represents the value of the 2c. For binding values, there are also no error bars. How is the reader supposed to interpret the data with no idea of variance?We have added error bars for the binding data.d. Figure 6 C and D is written in a cryptic manner. Does the manuscript mean to say that compared to wild-type H3.3 all mutant forms (list them) show statistically significant changes in shape C and size D? or are there differences between different mutants? It is unclear.We have now expanded the description of Figure 6 on p. 14 and 15. We find the H3K9M, H3K27M, and H3K36M mutants alter both nuclear size and shape. While we see subtle differences in their effects, we prefer not to highlight these as they required further investigation.Reviewer #3 (Recommendations for the authors):1. The screening of Figures1-3 is fine, but I found it important that they restricted analyses "To eliminate hits due to cell death or altered cell-cycle behavior, we excluded any hits with a cell number z-score of less than -2." Some mention of this in the abstract seems important. For example: 'The most dramatic effects were found for cell cycle and death regulators, but we chose to focus on regulators of more subtle effects.' In this regard, the authors need to take great care that the targets identified have zero impact on cell cycle and death.AURKB, MAD2L1 and CDC2 and displayed a low cell count per well compared to control cells. To eliminate potential false positives, we considered the resulting nuclear morphology changes as secondary effects due to cellular stress, abnormal cell division, siRNA toxicity, and potential lethality as they were often accompanied by significant reduction of cell number. Therefore, we removed nuclear shape and size hits from further analysis if their cell number per well Z-score was below -2. Nuclear shape hits with Z-score below -1.5, described in Figure 1, and no significant changes in cell number, were included in our hit list. We now describe in more detail these criteria and our strategy to reduce false positives and artifacts in the Materials and methods on p. 22 and in the Results sections on p.8 and p. 9.We agree this is very important and we now mention this caveat in the abstract as suggested by the referee. We found that some hits in our initial shape screen were known regulators of the cell cycle and of mitosis such as 2. The authors add "reducing agent DTT to inhibit dimerization" of GST-lamin A, but they need to tell us the specific Cys-Cys crossbridges that cause dimerization because GST also has Cys and could be contributing to non-physiological oligomers.We have now directly addressed the possibility that GST imposes dimerization regardless of the presence of disulfide bonds between cysteine residues. We mutated GST-lamin A cysteine residues to alanine and repeated the histone binding experiments. If the observed binding were artifactually due to GST-mediated dimerization, we should not expect an effect of the cystine mutants on histone binding. We find, however, that the C522A mutation in lamin A results in increased binding of H3 in the presence of lamin C, demonstrating that the observed effects are not due to GST dimerization. We include these data in Figure 4 —figure supplement 1 and discuss the results on p. 13.3. In analyzing laminA-histone interactions, the authors write "For example, GST-laminA bound ~ 5-fold more efficiently to histone H3R8me2s/K9me2 than histone H3K9me2." The word "efficiently" does not seem a standard descriptor for binding, compared to terms like affinity, specificity, saturation, etc. The authors should strive to show at least one of the 'stronger' interactions is saturable and has a reasonable Kd as a basis for specificity.This is a good point. Unfortunately, to our knowledge there is no current ChIP-seq human genome map of di-methyl modifications on histone tails. In addition, we were unable to generate or procure the individual dually methylated peptides and methyl-methyl H3 antibodies are not available and we are thus not able to perform quantitative binding assays. However, to begin to address this issue, we now provide in a new Supplementary File 3E quantitative data of binding intensities. Note that this assay is not sufficiently quantitative to generate Kds. Given these limitations, we have now toned the claims regarding the methyl-binding sites.4. The histone-H3 mutation effects on nuclear morphology in Figure 6 are important, but some key details and insights are needed. These seem to be Lenti's that transduced a fraction of the fibroblast cultures, giving levels that are zero, low, or very high in each culture, but does that variation have any effect? Histone levels do relate to cell cycle (e.g. PMID: 35760914), and so are high expressers at later stages of cell cycle (e.g. higher DNA staining) and would that be sensible for how the construct is regulated in its expression? Does the 'shape' parameter neglect differences in 'area'. Are the histone intensities uniform, and what happens to LaminA levels or localization?We appreciate these points. We generally do not see cell cycle effects in these cells. However, to address some of these issues, we have now measured H3.3 mutant levels relative to the total H3.3 levels in the population by immunostaining and single cell analysis. By single cell imaging, we find that the mutant levels represent a wide range although total H3.3 levels remain relatively consistent. We also quantitated as requested H3.3-V5 wild-type and mutant levels and compared these parameters to lamin A/C levels and to nuclear shape at the single cell level. While we find a slight increase in lamin A/C expression upon expression of both WT and H3.3 mutants, we see no significant effect on lamin A localization. We also see no strong correlation between the level of H3.3 mutant expression and effect on shape and size. These data are now described in the text on p. 14 and p. 15 and are included in Figure 6 —figure supplement 3 and 4. We now also indicate that in these experiments shape is independent of change in area and we include statistical analysis of results as requested in Figure 6 —figure supplement 5.


To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn’s Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a  This sudden availability of data initially led to rapid advancements, such as the discovery of causative variants for many Mendelian disorders3 and the identification of many associated variants for complex diseases5. In time, the main bottleneck towards understanding our genome shifted from an issue of data availability to one of data interpretation. Despite the growing list of known genetic associations7 and the attempts at disease risk prediction11, models that aim to truly capture all the complexity of the underlying biological molecular mechanisms, are indeed still missing12.In the last two decadesgenotypes-in, phenotypes-out predictive paradigm falls under the umbrella term of Genome Interpretation (GI)13 and it is very recent, with some of the first GI Neural Network (NN) methods for the case/control prediction of human diseases15 and the multi-phenotypic regression of quantitative traits16 that have been just published.To fully encompass this the complexity of biology and directly model the genotype-to-phenotype relationship, the idea of applying the latest Deep Learning (DL) methods to genomic data is in principle very appealing. This 19, Natural Language Processing20, and molecular biology24 were all characterized by huge data sets used to train extremely deep architectures. To unlock similar breakthroughs in the genomics and clinical genetics context, researchers thus need a way to aggregate sufficiently large genomics and phenomics25 data sets to train data-hungry DL architectures. In a scenario in which clinical and genomics data require large memory storage and are highly privacy sensitive, this aggregation poses several multifaceted issues involving biotechnological, infrastructural, computational, statistical, and even legal and ethical aspects. Standardized, homogeneous and high-quality genomics and phenomics25 samples for training and testing ML methods need to be collected, stored, and shared in a controlled and ethical way among collaborators and medical centers26. This brings several complications, including statistical issues arising from batch effects and other systematic differences14  used in the data acquisition phase by different centers that could make the data not independent and identically distributed (iid). Moreover, sharing the data to create a large centralized data set carries infrastructural issues related to hosting and moving possibly huge amounts of highly sensitive data, that indeed require the highest privacy, protection, and data access control standards26.The recent successes of DL in several fields, such as object and image recognition28. FL was originally introduced as a distributed ML paradigm30 to allow the training of a centralized model with privacy-sensitive data coming from a large numbers of clients . Due to the similar privacy concerns that sensitive data such as medical imaging, health records and sequencing data carry, recently the FL paradigm has been applied to several life-sciences applications, showing promising results. FL-based data analysis and ML methods have been proposed for the analysis of health records and medical imaging data, such as MRI and fMRI34, the meta analysis of biomedical data35, and the analysis of genomic data36, such as gene expression37 and Genome Wide Association Studies (GWAS)38. The democratization of the access to genomic data through FL is currently being advocated39, and FL has been used for the predition of clinical outcomes during the COVID-19 outbreak40Federated Learning (FL) is a recently proposed ML paradigm that tackles these issues by allowing advanced ML methods such as DL to be trained and tested in a collaborative and parallel way that does not require the actual exchange of sensitive data between partnersin silico exome-based risk prediction of human disorders in the FL context. To do so, we extended and adapted CDkoma, our previously developed GI NN model for the discrimination of Crohn’s Disease (CD) cases from controls14, to work within the popular flower FL framework41, creating FedCrohn.In this paper we test the hypotheses that 1) FL is not only a viable framework to perform conventional genomic data analysis, but that it can also be used to train DL GI methods for disease risk prediction and that 2) it can allow multiple centers  with relatively small datasets to pool them together in a privacy-preserving way, benefitting from the resulting larger training data set. To test these hypotheses, we implemented for the first time a “genotype-in, phenotype-out” GI Neural Network (NN) method for the proof-of-concept that demonstrates the feasibility of combining NN GI methods with FL to address the data interpretation bottleneck that is hindering clinical genetics and Precision Medicine advancements, by allowing GI NN methods to be trained on larger data sets without actually centralizing the data. FL provides indeed a solution to the several infrastructural, statistical and legal issues that make large genomics data sets hard to gather, currently preventing the application of DL methods to GI.FedCrohn is a 42. Its multi-factorial nature reduces the accuracy of sequencing-based disease risk prediction approaches43, due to the susceptibility to environmental factors44, its variable severity and age of onset45. Besides our CDkoma14 approach, few similar methods have been developed, in the context of the Critical Assessment of Genome Interpretation (CAGI) challenges46.For this proof-of-concept, we focus on the exome-based risk prediction of CD, which is a subtype of Inflammatory Bowel Disease (IBD)43 to benchmark FedCrohn in different realistic FL scenarios, comparing its performance with CDkoma14, which achieves the same goal in non-FL setting . We show that NN models for GI have similar performances in FL and non-FL settings, indicating that FL can allow researchers to overcome infrastructural and data sensitivity issues without reducing the model performance. We also show that the number of collaborating FL clients , and thus the level of fragmentation of the data, do not influence negatively FL performance. The objective of this study is to provide a proof-of-concept of FL in realistic GI settings in which each client has non-iid data that show significant batch effects (they are produced in different years and with different technologies) and different case/controls ratios. Our results indicate that (1) FL can learn effective models even in these sub-optimal, but realistic settings, and that (2) performances are in line with the ones that could be obtained in non-FL settings.In this paper we use 3 CD data sets from CAGI46 to benchmark the ability of bioinformatics methods to predict CD cases from controls on WES data. The CAGI2 dataset contains 56 exomes (42 cases and 14 controls). As described in46, this data set is peculiar since cases and the controls have been sequenced in different settings, resulting in a striking batch effect between them43. The CAGI3 dataset contains 66 WES samples (51 cases and 15 controls). Twenty-eight pedigrees and two discordant twin pairs43 are recognizable with clustering46, but this stratification is less severe than in the CAGI2 dataset. The CAGI4 dataset is the largest and highest quality data set among the three. It contains 111 sequenced exomes (64 cases and 47 controls). All cases are unrelated and only two pairs of controls are related46. All the data sets are provided as VCF files listing the observed variants. CAGI3 and 4 data sets are mapped onto the hg19 Human genome build, while CAGI2 is mapped onto the hg18 version. More details on these datasets can be found in43.In this study we used 3 case-control Crohn’s Disease (CD) data sets. They have been respectively used in the 2011 (CAGI2), 2013 (CAGI3), and 2016 (CAGI4) editions of the Critical Assessment of Genome Interpretation (CAGI)14.The goal of this study is to compare the performance of NN GI models for disease risk prediction implemented in FL and non-FL  settings. To ensure this comparability, we used the same VCF annotation procedure and feature encoding we proposed in our previous non-FL GI method for exome-based CD risk predictor47, which is a widely used VCF annotation tool. Annovar identified the following 9 types of variants “exonic”, “UTR3”, “UTR5”, “ncRNA exonic”, “ncRNA intronic”, “upstream”, “downstream”, “intronic” and “splicing”.As shown in Fig. mutational damage carried by each gene  RVIS48 gene-burden score and (2) the publication weight score extracted from PhenoPedia49, obtaining a final vector of 11 dimensions for each gene  and its degree of involvement in CD (PhenoPedia49).Given the small sample size of the data sets, we condensed these annotations into the most compact possible ML-ready feature encoding, with the goal of avoiding overfitting by privileging simplicity and model robustness. To do so, we summarized the variants mapped on each gene by counting how many times each type of variant occurs on it, obtaining a histogram describing the see Fig. . We thentain the  RVIS48 g49, instead of the entire exome. Each sample is therefore represented by a  tensor, and the final shape of a tensor representing an entire data set containing N samples is , as shown in Figs. To reduce further the size of this gene-centric feature representation, we considered only 691 CD-related genes, selected from PhenoPediam  than samples n, because while sample collection is a relatively slow and complex procedure, WES and WGS data encompass respectively tens of thousands and millions of variants. This heavily underdetermined 16, we reduced the complexity of the models as much as possible by using weight sharing and modular NN structures. Sparsifying the the CDkoma architecture .CDkoma is written in PyTorch52. The only difference between LeakyReLU and ReLU is that instead of returning 0 for negative activations values (dying ReLU problem).All the hidden neurons used the LeakyReLU activation29. More formally, given K clients controlling privacy-sensitive K centers. In this paper, we focus on a centralized synchronous FL involving tens of centers.FL is a distributed inference paradigm in which multiple data-controlling clients  collaborate in training a global consensus model without sharing the possibly sensitive data between each otherk clients. Second, each client trains the model e, and then sends the parameter updates resulting from this training to the CS  and have similar size and labels balancement. The main issue in FL is that none of these assumptions necessarily hold, and the client-controlled datasets are likely to show various kinds of client-specific biases29, FedAvgM54, FedAdam, FedYogi and FedAdagrad30. FedAvg stands for Federated Averaging29, and it is the most direct translation of conventional Stochastic Gradient Descent (SGD) to FL. In FedAvg, in each FL round the CS collects the model updates (parameters) from the clients and takes their weighted average to create a new global model, which is then shared back to the clients at the beginning of the next FL round. FedAvg may suffer from slow convergence in certain data and class unbalancement scenarios54, and natural extensions such as FedAvgM (Federated Averaging with Momentum) have been proposed. FedAvgM adds a momentum term to the updates received from the clients, improving the convergence speed and reducing the impact of noisy updates54. Extending this even further, in30 the authors proposed three CS adaptive aggregation strategies  which respectively inspired by the popular Adam55, AdaGrad56 and Yogi57 optimization algorithms. These algorithms further extend the FedAvgM method by keeping track of both the first and second moments of the gradients (i.e. the running average of the gradients and the squared gradients) to adaptively change the learning rate of each individual weight30 in function of the (1) sparsity of the gradients and (2) the number of iterations. These approaches are designed to be more robust when where the data distribution across client devices is non-i.i.d30. In this paper we benchmark these aggregation strategies using their implementation in the flower library41.Different aggregation strategies have been proposed so far, and in this study we will benchmark on five of them: FedAvgWe evaluated the performance of FedCrohn using the Sensitivity (SEN), Specificity (SPE), Precision (PRE), Matthews Correlation Coefficient (MCC), Area Under the ROC curve (AUC), and the Area Under the Precision-Recall curve (AUPRC) metrics. In the cross-validations, we computed them for each folds and we averaged them to obtain the final score.16. Recently, thanks to the development of flexible Neural Networks (NN) libraries, such as PyTorch, it became possible to develop ad hoc NN architectures for different types of problems, adapting the model itself to the structure of the data. This paradigm has shown a lot of potential in several life science fields, with the most prominent example being Alphafold21 and structural biology24 in general.Genome Interpretation (GI) is the umbrella term describing computational methods aiming at modeling the genotype-phenotype relationshipTo apply the same approach to genomics, clinical genetics, and precision medicine, hoping to achieve similar breakthroughs, researchers have to face some specific challenges because of the unique privacy-sensitive nature of clinical data. Currently, infrastructural and legal issues impede the creation of sufficiently large data sets for DL. To overcome this issue, Federated Learning (FL), which is a novel distributed ML paradigm that avoids the necessity of sharing the actual data while training ML models, has been introduced.14 NN GI model , larger data sets could be available as training sets for data scientists and bioinformaticians.In Exp1, we simulate exactly this scenario. We use 3 CD case/control cohorts from the 2011 (CAGI2), 2013 (CAGI3), and 2016 (CAGI4) editions of the Critical Assessment of Genome Interpretation (see Methods). They respectively contain 56 exomes (42 cases and 14 controls), 66 exomes (51 cases and 15 controls), and 111 exomes (64 cases and 47 controls). This simulates the situation in which small data sets are scattered among different centers, since the data have been obtained with different data acquisition procedures in different years (see Methods).In the first FL GI experiment, we thus imagined 2 client nodes . See Methods for more details.Each FL round see Fig. B starts 58 p-value = 0.048), while the minimal difference in AUC with the center controlling CAGI4 data is not significant , while the center controlling CAGI2 data would have performed similarly to FedCrohn with FedAdagrad, the lowest performing FL method (17% lower AUC with respect to the best FL model). Similarly to the previous experiments, the center with the lowest quality data (CAGI2) would have thus benefited from the FL approach .In Exp2, we investigate FedCrohn performance in function of the number of clients (from 2 to 30) and the way in which the data are divided among them. To do so, we merged the 3 CAGI data sets and we split them in Figure The difference between the random and the stratified splits is that in the latter, the same proportion of positive versus negative cases is guaranteed. When the number of splits gets high, and thus the samples assigned to each fold/center gets lower, stratified splits ensure more stable results, since the random splits might, by sheer chance, assign very few  are involved, provided that the negative/positive ratio is more or less preserved.43. We also reported the AUC scores of the best performing model in the 2016 edition (CAGI4)42. This method, mentioned as “GWAS markers +ML” in Table 14 They are produced by training our model on CAGI4 data to predict CAGI2,3 and by training on CAGI3 data when predicting CAGI4.In Table 43. The “Key variants weighting” approach consists in ranking the samples in function of the number of known CD-causing SNVs present in the exomes. The “Biclustering” method is a simple K-means clustering of the data with 43. “Manual prediction” refers to the manual assessment of each sample, performed by a human expert. The “Count of SNVs in CD genes” produces a score proportional to the variants found on CD-related genes. The “Transductive SVM” approach uses transductive learning59 on a set of variants statistically significantly associated with CD43.We briefly summarize the methods listed in Table 46. In all the cases, FedCrohn performs similarly to CDkoma, in line with the results we showed so far, and outperforms most of the approaches benchmarked by CAGI. Nevertheless, we must note that CAGI performances were obtained in true blind test settings. For example, CAGI4 and CAGI3 data was not respectively available to CAGI2,3 and CAGI2 participants. On the other hand, CAGI4 scores are more directly comparable, since CAGI2,3 data were available also to CAGI4 participants.In Table The recent astonishing achievements of Deep Learning (DL) methods have been achieved both thanks to the latest developments of Neural Networks (NNs) and to the use of very large training sets. To bring the DL revolution to the Precision Medicine and clinical genetics fields, similarly large genomics and phenomics data collections should be gathered. Thanks to high-throughtput sequencing technologies, data scarcity is not the main factor hindering the creation of such large collections. Instead, due to the high privacy sensitiviy of genomics and phenomics data, several infrastructura, ethical and legal aspects need to be sorted out in order to aggregate smaller datasets into larger studies. Federated Learning (FL) is a distributed Machine Learning paradigm allowing multiple clients controlling different data sets to cooperate towards training a consensus model on the entirety of the data, without actually sharing or moving the data, and thus overcoming many of the above mentioned issues. Here we provide a proof-of-concept (PoC) that FL can be successfully applied to train NNs Genome Interpretation (GI) for the exome-based Crohn’s Disease risk prediction. We test different realistic scenarios, showing that in most cases, the medical centers collaborating towards training a consensus FL GI model, would benefit in terms of quality of the predictions, with respect to the accuracy they could get from a model trained solely on the locally controlled data set. We also show that FL can work even among tens of centers each sharing a very small data set (tens of samples). Our PoC shows that FL could be suitable to kick-start a novel GI paradigm trying to directly model the genotype-phenotype relationship using the latest DL developments.


To avoid the scalingof the number of qubits with the size of thebasis set, one can divide the molecular space into active and inactiveregions, which is also known as complete active space methods. However,selecting the active space alone is not enough to accurately describequantum mechanical effects such as correlation. This study emphasizesthe importance of optimizing the active space orbitals to describecorrelation and improve the basis-dependent Hartree–Fock energies.We will explore classical and quantum computation methods for orbitaloptimization and compare the chemically inspired ansatz, UCCSD, withthe classical full CI approach for describing the active space inboth weakly and strongly correlated molecules. Finally, we will investigatethe practical implementation of a quantum CASSCF, where hardware-efficientcircuits must be used and noise can interfere with accuracy and convergence.Additionally, we will examine the impact of using canonical and noncanonicalactive orbitals on the convergence of the quantum CASSCF routine inthe presence of noise. The original “killerapplication” was Shor’s algorithm for factorization6 but is now rather defined by computation of energylevel structures of catalyzing enzymes like FeMoCo with chemical accuracy.7 Exponential computational advantage is howeveran elusive concept that might be difficult to achieve in practice.8 In the NISQ era, we therefore need to settlefor more modest goals, programming quantum chemistry applicationson quantum computers and trying to achieve practical quantum advantage.Theoretical chemistryhas always been one of the main drivers ofthe development of high-performance computing and computers (HPC)9 and hybrid variational quantum algorithms (VQA). QPE hastwo important limitations: the first one is the need to have an initialstate with nonzero overlap with the true FCI eigenstate, which requiresa state preparation routine to increase the probability of collapsingin the ground state.11 The second limitation comes fromthe need for an increasing number of ancilla register qubits (in additionto the Hamiltonian qubits) to reach the desired accuracy.12 The iterative QPE14 requires only one register qubit,but its implementation will be limited by the coherence time in thequantum device.13 On the other hand, VQAsand, particularly, the variational quantum eigensolver (VQE)15 reduce significantly the requirements for coherenttimes in the quantum device.16 The VQEis based on the Rayleigh-Ritz variational principle:E0 is the ground state energy. Fromhere, the VQE will use a parametric version of the molecular electronicwave function r), and a†, a are the creation and annihilation operators,respectively. The MOs can be obtained from either the Hartree–Fock(HF) method or from density functional theory (DFT). 17 or Bravyi–Kitaev (BK)18 after transformation of the integrals ntegrals 3 and 4 f19 Consequently, proposals for quantum computational chemistryhave been focused on isolating the strongly correlated component ofthe molecule to be treated by the quantum device, known in chemistryas active space.20 In this study, we willfocus on several of the strategies proposed in the literature to increasethe accuracy without additional hardware requirements. We will specificallyinvestigate three strategies: First, just constraining the numberof qubits by using an active space for the qubit Hamiltonian  and the following basis sets: STO-3G,23 6-31G,24 CC-PVDZ25 and ANO-L-VDZP.27 We will restrictthe active space to 4 MOs leading to 8 qubits (28 and MultiPsi (article in preparation). Oncethe integrals are obtained, the molecular Hamiltonian will be mappedto a qubit Hamiltonian using the JW encoding, and the minimum eigenvalueis computed using VQE with a unitary coupled cluster singles-doubles(UCCSD) ansatz29 with an HF initial state;all those included in the package IBMQ Qiskit.30 The UCCSD-VQE minimum eigenvalue will account for the activespace energy, and the energy of the inactive space is described byHF. The total UCCSD-VQE energy will be the addition of active andinactive space energies. In this work we will employ the Löwdindefinition of correlation,31 i.e., differencewith respect to HF.As mentionedin the Supporting Information) but less suitable to describe correlation.Consequently, using a nonrelaxed-orbital (nonoptimized) reduced activespace (to be computed with the quantum backend) and increasing thequality of the basis set provokes the confluence of the UCCSD solutionwith the HF solution for a fixed active space. Evidently, increasingthe active space will increase the energy difference between UCCSDand HF to include more correlation, but the trend is expected to staythe same since the relative ratio of inactive/active space will increasewith the basis set size, increasing the HF contribution to the finalenergy.The results in 2.121 to study the insertion of beryllium into H2 but, in our case, we are interested in the trend of the correlationwith the basis set size. We have used the same molecule with the sameactive space as before, but, in this case, we have obtained the integralsfrom a converged CASSCF calculation computed with MultiPsi.To increase the correlationin the active space and improve the accuracy of the quantum simulation,one can bring more correlated orbitals into the molecular Hamiltonian.This strategy has been adopted before by Sugisaki et al.Supporting Information). In consequence,an orbital optimization routine is required for any meaningful calculationusing an active space to reduce the qubit requirements.In Classically optimizing theorbitals, while being promising, doesnot exclude the need to perform a CASSCF calculation, in which casethe converged energy will also be slightly better when using a fullCI (FCI) description of the active space instead of UCCSD 4. The di320 and a fullyhybrid quantum-classical CASSCF has been reported by Tilly et al.22 where the 1 and 2 body RDMs were sampled independentlyto mitigate their error and noncanonical orbitals were used. Otherquantum multi-configurational SCF implementations have been also reportedin the literature.37 Nevertheless, to our knowledge, no comparison has been made withthe results coming from an orbital-optimized wave function in thequantum measurement after VQE. We have implemented a CASSCF routine and the 4 orbitals 4 electrons active spaceused until now. In this case, only the ANO-L-VDZP basis set will beused in order to benchmark the energy obtained with other references.Also, tight convergence criteria (Δe–6 Ha higher than FCI). The final converged energy is −76.103329Ha, which is only 0.015499 Ha higher than the 20 qubits reported.19 In order to obtain a lower energy than the 20qubits reported, one could use this method with a larger CASand the expected energy would be slightly higher than or equal to−76.127864 Ha. The lack of significant difference between bothdrivers is expected to be due to the weak correlation of the watermolecule, and FCI does not offer a significant advantage with respectto UCCSD. This hypothesis will be properly tested in the next section.Also, the final energy is just 1.3e–6 Ha lower than the one obtained by using the orbital-optimized wavefunction directly in the quantum backend. This means that in accuracyterms, there is no significant gain in using a quantum CASSCF routine.Also, using a converged CASSCF wave function in the quantum backendis expected to be less sensitive to quantum noise since it relieson a single measurement. In addition, by applying the reference stateerror-mitigation proposed by Rahm and collaborators,38 the energy could be easily corrected. Nevertheless, classicalCASSCF algorithms present a factorial scaling of computational resourceswith respect to the active space size.22 Therefore, while this strategy is feasible for small active spaces,for larger active spaces a quantum CASSCF could potentially overcomethis scaling limitation.39In 3.2Supporting Information) is known for having astrong correlated transition state defined by a open-shell Dh4 symmetry, as seen in The transitionstate in the cyclobutadiene automerization , the error increases to 1.2e–3 Ha. This loss of accuracy is due tothe stronger correlation in the cyclobutadiene active space that willneed higher-order excitations than doubles to describe its correlationexactly. Nevertheless, the energy difference 1.2e–3 Ha is in the chemical accuracy (1.6e–3 Ha) range, and consequently this method is stillhighly accurate.As shown in 3.322 HO3 )and C2 ) using the CC-PVDZ basis set.All the geometries can be found in the Supporting Information. As shown in 2 with a CAS active space, and we can see how the error increaseanother order of magnitude. Even though C2 is a stronglycorrelated molecule  activespace are 1.917, 1.001, 0.999 and 0.083) similar to the cyclobutadieneautomerization transition state active space are 1.904, 1.000, 1.000 and 0.096) for the same activespace, the error is lower. The reason is the correlation in C2 has relatively strict pair structure, well reproduced byUCCSD, while the one in cyclobutadiene does not, which can be seenwhen looking at the conventional CI coefficients (see Supporting Information).Even though the error with respect toFCI is larger for the transition state of the cyclobutadiene automerizationthan for water, the error is still better than chemical accuracy.Since for strongly correlated molecules the accuracy is more sensitiveto the inclusion of higher order excitations, other strongly correlatedmolecules will be tested with the quantum CASSCF routine and comparedto classical CASSCF. In particular we will test three systems withincreasingly bigger active space: CO ),4Supporting Information).In this way, we tested convergence with 1000 shots in the IBMQ QASMSimulator using the coupling map from IBMQ Santiago . The optimizer for the VQEparameters has been replaced from the sequential least squares programming(SLSQP) to simultaneous perturbation stochastic approximation (SPSA)41 for having a better performance in the presenceof noise.42 Finally, we have also useda parity mapper with two-qubit reduction.43 To test the convergence under noisy simulations for water, we haveconsidered a smaller active space consisting of the HOMO–1and the LUMO .So far, all the results have been produced under the assumptionof ideal behavior of the quantum device. In contrast, NISQ devicesare noisy, which creates decoherence and computational errors. Wehave tested the performance of the quantum CASSCF under noisy simulations,including realistic noise coming from IBMQ Santiago version 1.3.14 for which we do not know if it mitigates or amplifieserrors. The 1-RDM transformations routine for canonical orbitals willundergo the following steps after readout from the quantum backendand prior to the orbital optimization:1-RDM evaluated in the quantum simulator Normalizationof the 1-RDM as Symmetrizationas U is the unitarymatrix of eigenvectorsCanonicalizationas Orbital optimizationroutineThe 1-RDM transformations routine fornoncanonical will justskip the canonicalization step and use Additionally, diagonalizationof the 1-RDM leads to canonical MCSCF orbitals  and are used by default in MultiPsi and Dalton,4.2In order to investigate the effect of canonicalizationin the quantum CASSCF routine, we will test the convergence with canonicaland noncanonical orbitals. In noiseless numerical simulations, thechoice of canonical or noncanonical orbitals does not change the finalconverged energy (−76.063804 Ha). Since the noise will inducerandom behavior in the energies and density matrices, the quantumCASSCF has been executed 20 times for each type of orbitals in theactive space with the aim to extract averaged tendencies.From As shown in 4.3Usingthe quantum CASSCF routine 4, strong522 By using canonical orbitals, convergence is reached innoisy simulations without error mitigation or sampling of the RDMs.22 This indicates that improvements in the orbitaloptimization routines can be done to mitigate errors without additionalcost on the quantum devices. We believe that this work can serve asa starting point to design more noise-tolerant orbital optimizationroutines.In this work, we explorestrategies to improvethe accuracy ofquantum computational chemistry simulations while keeping the numberof qubits low. Specifically, three strategies have been explored,from the simplest of just reducing the active space of the moleculeto the most complex relaxing the active orbital space through a quantumCASSCF. The choice of just selecting the active space as the moststrongly correlated orbitals without relaxation (optimization) willlead to an eminently classical HF description of the molecule unlessthe active space is large enough. On the other hand, classically relaxingthe orbitals improves significantly the results, but at the cost ofperforming an actual CASSCF calculation on the classical side. Ifthe CASSCF is to be done with the aid of quantum computers, as inthe quantum CASSCF routine presented here, the convergence is stronglyaffected by noise. Moreover, canonicalization of the active spaceorbitals appears to greatly improve the convergence in absence ofan independent sampling of the RDMs, like the one reported by Tillyand co-workers.


Huang et al., reported that four disease predisposition HLA genes, DRB1*08:03, DRB1*07:01, DRB1*14:05, and DRB1*14:54 were present in high frequency and majority had already developed cirrhosis, further confirming the link between HLA genes and liver disease severity. In addition, they reported a link between HLA genes and Lactobacillus, indicating possible role of microbiota in disease progression. Mulinacci et al. have focused their work on studying the immunogenetics of PBC, both HLA associated and non-HLA associated in the pre-genome wide association studies (GWAS) and post GWAS eras. The role of environment in increasing the risk of developing PBC was elucidated by studies in North-east of England where there was a higher prevalence in urban areas with strong coal-mining heritage and in New York city clusters of PBC were found in patients living in zip codes that contained or were adjacent to toxic waste sites. They also discuss using Antigen specific immunotolerance – tolerogenic vaccines - as well as utilizing nanotechnology to develop tolerogenic immune modifying nanopeptides as a therapeutic strategy in PBC.This Research Topic aims to establish the links between HLA and non-HLA genes in autoimmune liver disease (AILD) in a diverse population that is inclusive of different ages, races and ethnicities. This is translational with original research articles as well as state of the art mini-reviews summarizing current knowledge. In this collection, we included four laboratory-based investigations from Asia and Turkey, located Western Asia and southeastern Europe, Southwestern and Far East Asia; and 6 mini-reviews from United States, United Kingdom/Europe, South America and Canada. In a large cohort of patients with primary biliary cholangitis (PBC) from China, Yang et al., identified inosine as a metabolite with immunomodulatory effects and noted that it was highly altered between DRB1*04:05 positive and negative Chinese patients with AIH. Administration of inosine in a murine model of Con-A induced acute AIH demonstrated its protective effect by attenuating hepatocyte apoptosis, preventing oxidative stress and inhibiting the activation and glycolysis of CD4pos T effector cells. This may have potential in the development of future therapy of acute AIH. Lapierre and Alvarez discussed how the main genetic association in type 1 and type 2 AIH is with HLA class II genes and the HLA DR locus, and that a lesser association with non-HLA genes exists. Studies that implicate single nucleotide polymorphisms (SNPs) in genes including TNF-α, Vitamin D, and AIRE in increasing susceptibility to AIH have been included and they have also highlighted the use of animal models to study the effect of a particular genetic background or specific genetic mutation in the development of AIH. We learned from the investigation by Yuksel et al. that HLA DRB*11 was most prominent in Turkish patients with AIH. They report that in children with AIH, immunosuppression could severely reduce the number of regulatory B cells , which actually tended to be at a higher frequency than in healthy controls. On the other hand, immunoregulatory cells such as naïve and activated T regs (CD4posFOXP3highCD45RAneg), fluctuate over time. Tacrolimus was reported to be a B reg-sparing drug. Autoimmune B effectors played a complex role in the pathogenesis of AIH, one of the hallmarks of AIH is positivity for autoantibodies, ANA, SMA in type 1 AIH (AIH-1) and LKM1 in type 2 AIH (AIH-2). AIH-1 and AIH-2 have partially overlapping HLA gene profiling, HLA-DR3 for both subtypes of AIH, HLA-DR7 is unique for AIH-2. While most studies in AIH analyse susceptibility of HLA alleles according to AIH subtypes, Cancado et al. have given an interesting twist by exploring the relationship between genetic markers of susceptibility linked to HLA with individual serological markers of disease, using celiac disease as a prototype.Using metabolomics, Ahuja et al. described an important role played by non-HLA genes in AIH patients, not previously reported from India. They reported association of HLA DRB1*03 gene and the increased frequency of GG genotype of cytotoxic T-Lymphocyte-associated protein 4 (CTLA-4) CT60 mutation in a cohort of North Indian patients who were anti-SLA positive and poor responders to immunosuppressive therapy. Their investigation has reiterated that both HLA and non-HLA genes play a role in the disease manifestation of AIH. In the review focused on pediatric autoimmune liver diseases, Mack reports data from the largest cohort of children with AIH and autoimmune sclerosing cholangitis (ASC) from Kings College Hospital that showed that the presence of HLA DRB1*03 conferred the highest risk of AIH and ASC compared to healthy controls. Data from other large cohorts in Germany and South America are included as well as information on HLA genotypes that are protective from AILD. She has suggested several research initiatives including using modern technologies like HLA3D to isolate MHC-restricted autoantigens in AILD as well as designing novel therapies using nanotechnology.Although there is still no clear answer to the question whether HLA genes play a decisive role in disease development and severity, Beretta-Piccoli et al. and colleagues. The fact that 80% of the blood supply to the liver is from the splanchnic circulation, lends credence to the fact that gut microbiome and the many factors that influence it, be it changes in gut permeability, dysbiosis, translocation of microbial products into the circulation allowing molecular mimicry to trigger autoimmune inflammatory responses or simply changes in dietary intake, ethnicity or geography may have a profound influence in the development of autoimmune disease. This is also a fertile area for manipulation and drug discovery. There is a difference between the genetic risk and observed risk of autoimmune liver disease and in a beautifully written minireview illustrated with several tables and figures, Czaja has summarized existing knowledge on how epigenetics could influence not only the development of autoimmune liver diseases but also outcomes by altering key processes like DNA methylation and miRNA (microRNA). This has the potential to be another exciting area of new drug development. In conclusion, we hope that this collection of translational offerings will be of interest to both the clinician and the basic scientist and allow one to get updated on the current status of HLA and non-HLA in relation with AILD, with focus mainly on AIH and PBC, particularly from areas where such data was not previously available.The role of HLA and microbiome in the development of autoimmune liver disease is summarized succinctly by All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.