NFE2L2 gene is a transcription factor critical for protecting cells from chemically-induced oxidative stress. We developed computational procedures to identify chemical modulators of NRF2 in a large database of human microarray data. A gene expression biomarker was built from statistically-filtered gene lists derived from microarray experiments in primary human hepatocytes and cancer cell lines exposed to NRF2-activating chemicals  or in which the NRF2 suppressor Keap1 was knocked down by siRNA. Directionally consistent biomarker genes were further filtered for those dependent on NRF2 using a microarray dataset from cells after NFE2L2 siRNA knockdown. The resulting 143-gene biomarker was evaluated as a predictive tool using the correlation-based Running Fisher algorithm. Using 59 gene expression comparisons from chemically-treated cells with known NRF2 activating potential, the biomarker gave a balanced accuracy of 93%. The biomarker was comprised of many well-known NRF2 target genes , 69% of which were found to be bound directly by NRF2 using ChIP-Seq. NRF2 activity was assessed across ~9840 microarray comparisons from ~1460 studies examining the effects of ~2260 chemicals in human cell lines. A total of 260 and 43 chemicals were found to activate or suppress NRF2, respectively, most of which have not been previously reported to modulate NRF2 activity. Using a NRF2-responsive reporter gene in HepG2 cells, we confirmed the activity of a set of chemicals predicted using the biomarker. The biomarker will be useful for future gene expression screening studies of environmentally-relevant chemicals.Nuclear factor erythroid-2 related factor 2 (NRF2) encoded by the  Great strides have been made by United States governmental chemical screening programs including the EPA’s ToxCast program and the cross-agency Tox21 program in this regard, screening over 1800 chemicals in as many as 700 assays . Coordinates were annotated to bi-directional promoter features with 10 Kb using the “ChIPpeakAnno” function in the R Bioconductor package (version 3.19.3) [https://www.rdocumentation.org/packages/Biostrings/versions/2.40.2/topics/matchPWM]. Matches were based on a minimum of 80% relatedness. It should be noted that ARE binding in association with target genes was a post hoc analysis and served to support, but not develop, the composition of the biomarker  data was carried out to identify genes directly regulated by NRF2. Briefly, ChIP-seq data from experiments with activated NRF2 in human cells were obtained from Gene Expression Omnibus  treated human lymphoid cell line ; 2 biological replicates from SFN-treated human bronchial epithelial cell line BEAS-2B ; and 2 biological replicates from a human adenocarcinomic alveolar basal epithelial cell line A549 ). Raw sequencing reads downloaded from NCBI Sequence Read Archive (SRA) were mapped to the human genome (hg19) using the BWA (Burrows-Wheeler Aligner) . Replica 3.19.3) . These riomarker .To screen for chemicals that modulate NRF2, the biomarker was compared to each of the human biosets in the BSCE database. The p-values and directions of correlation were exported, p-values were converted to–log, and those with negative correlations were given negative values. Our master experimental database was then populated with the–log for each bioset, which were classified as NRF2 activating  ≥ 4), NRF2 suppressing  ≤ -4) or having no effect on NRF2  between -4 and 4). Using this database, several strategies were implemented to identify chemicals that consistently activated NRF2. First, a Fisher’s exact test was used to determine which chemicals were enriched in the group of NRF2 active biosets . For example, there are 239 biosets with “Tobacco Smoke” as the specific perturbant, 83 of which resulted in NRF2 activation and 155 of which did not, whereas only 1429 biosets of the 45,163 total biosets result in activation.  Thus, NRF2 active biosets were significantly enriched for the perturbant “Tobacco Smoke.” The same approach was taken with NRF2-suppressive biosets. This approach works well if there are a sufficient number (≥ 3) of biosets for each specific chemical; however, it does not take dose or exposure time into account.In another approach to characterize chemicals that activate NRF2, the maximum–log for each specific chemical generated in HepG2 cells or primary human hepatocytes was determined, without accounting for dose or exposure time, and used to make an active or inactive call. The resulting list of active and inactive chemicals was then compared with the active and inactive chemicals from the ToxCast and Tox21 HTS assays described above. Perturbants that suppressed NRF2 could not be compared with the HTS assays because the assays were designed to measure NRF2 activation and, therefore, were considered inactive for activation in these comparisons.Dose-response, time-course, and chemical-induced effects on datasets with listed oncogenic mutations were examined in a limited set of chemicals as proof of concept. The limited number and specific choices of chemicals was driven mainly by available data for those chemicals. Where possible, data from multiple studies were combined to construct a–log vs. dose or time relationship. In these cases, all data for a perturbant were considered, and individual dose- or time-response curves were constructed from studies carried out in a single cell type.33342 to visualize the nuclei. The time interval was ~40 min for both SRXN1-GFP and propidium iodide (PI) staining. All chemicals came from the ToxCast chemical inventory (kindly provided by Dr. Ann Richard).To investigate specific predictions based on the biomarker and differences between biomarker and HTS predictions, we employed a real-time, SRXN1-GFP fluorescence based NRF2-activation reporter system . Thirty Single-cell SRXN1-GFP intensity levels were transformed by log(intensity + 0.001) to attain a more symmetrical distribution. GFP-positive cell counts (GFP_pos) were defined as the fraction of cells per treatment-concentration that were above the 0.75 quantile + 0.25* of the inter quartile range of the matching vehicle control single-cell population GFP levels. Concentration-response fits of the GFP positive fraction were modeled with the lm function in R as function of the concentration using piece wise natural splines with 3 degrees of freedom: lm). The point-of-departure was calculated as the concentration where the fit intersects the threshold of 0.1 fraction of GFP positive cells + the upper 90% confidence limit of the regression at each matching concentration to account for replicate variability. The PI positive cells (PI_pos) represent the fraction of cells with at least 4 pixels of propidium iodide segmentation overlapping the cell segmentation. The cell counts (cell_count) represent the log fold-change in the number of cells compared to the matched vehicle controls and was defined by log10(cell count plate i/ vehicle control cell count_plate i).The strategy for constructing the NRF2 biomarker, testing predictive accuracy, and screening for modulators is summarized in The ability of the biomarker to accurately predict NRF2 activation was assessed by comparing the results of the biomarker predictions with those from two high-throughput screening (HTS) assays carried out as part of ToxCast and Tox21 screening programs. These assays measured the activity at antioxidant response element (ARE)-linked reporters in HepG2 cells. In order to increase the confidence of these classifications, only chemicals that were called positives or negatives in both assays were used. There were 36 chemicals screened in these assays that overlapped with microarray studies in HepG2 or human primary hepatocytes in our compendium. The biosets used to create the biomarker  were excluded from the test. The biomarker was compared to the microarray profiles using the Running Fisher test. Each pair-wise correlation resulted in a p-value which was converted to–Log, and those with negative correlation were converted to negative numbers. The balanced accuracy for the biomarker was calculated based on the 59 unique comparisons of biosets with corresponding HTS data . InformaHMOX1, NQO1, TXNRD1, SXRN1, GPX2, and AKR1B10 had a balanced accuracy of only 66% and a sensitivity of 36%. This analysis indicates that a biomarker with a more comprehensive set of NRF2-dependent genes is more predictive than shorter lists of hand-picked genes.Attempts were made to evaluate shorter lists of NRF2-regulated genes for prediction. A six gene biomarker based on some of the most frequently cited NRF2 targets 3KEAP1 knockdown perturbations . The bioset in which NFE2L2 was knocked down exhibited significant negative correlation to the biomarker .The 143 NRF2 biomarker genes exhibited consistent expression across the chemical and rbations . As expeAKR1B10, AKR1C1, NQO1, TXNRD1, SRXN1, GCLC and GCLM) Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't KnowReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Gene expression data:Is it possible to make the expression data available for your biomarker gene set, or does PLOS One require the publication of this data?A short explanation of the commercial database used here or how this database ensures the integration of the different gene expression data  would improve the transparency of the data analysis. May be the authors can add a short paragraph instead of citing an article. My answer to the question: "Has the statistical analysis been performed appropriately and rigorously? -> I don't know" is related to this part.Characterization of the NRF2 biomarker genes :The authors analyzed and verified the final biomarker gene set in HEPG2 cells. To what extent are the used ChIP-seq data suitable to make statements about regulatory areas in HEPG2 cells? To what extent can direct (motif-defined) or indirect binding of NRF2 be expected?How do the authors linked the experimental ChIP-seq regions to the biomarker gene set? May be a motif analysis of a "defined" promoter-region is more meaningful here. Perhaps this analysis was done already  but may be the authors can add more details here.In this paper the ChIP-seq analysis was done based on Ensembl database version 86 (Okt 2016). Why did the authors use this older version of the database in this actual paper?In figure 4 the term 'bioset' is used in the legend and in the paper. In the paper the term 'biomarker gene set' is used before. Do these two terms refer to the same gene set? A minor point but I was just confused about this two terms. May be it is a frequently used term, which I just do not know.Dose-, time-, and mutation-dependent NRF2 modulation:The authors compare different chemicals screened in the ToxCast and/or Tox21 studies and relate these results to NRF2. First liver-cells are shown but also fibroblast and breast cancer cell line data are shown. The authors validate their findings using ARE-linked reporter system in HepG2 cells. The comparison with non-liver-related results is confusing. How comparable the findings between different cell types. Is there a possibility to estimate the completeness of this gene set?-----Figure 1: Biomarker construction and screening strategy.-> In all other Figure description the authors use Figure X. Description of the figure.May be the ":" is a typo.short comment to Figure 6:May be the used red color should be defined in the legend of this figure.Reviewer #2: The manuscript describes a very reasonable approach to using chemical perturbation transcriptional signatures to identify chemicals altering the activity of the transcription factor NRF2. I have several comments that I would like to see addressed by the authors.Procedure used to construct the list of differentially expressed genes used as NRF2 biomarkers - Relevant sections: Methods (162-177), Results (334-338):- I think some key details are missing from the methods. In general, the level of detail and tone of the methods section seems more adequate to the results section. Specifically:- can you be more specific, and provide the logical rules that were used to construct these gene-sets, e.g. fold change ≥ 1.5 and adjusted p-value < ... in at least N/M NRF2 activation data-sets, and fold change < ... and adjusted p-value < ... in ... NRF2 activation data-set(s)?- how was gene differential expression calculated? Please state what method was used (e.g. limma) and if a linear model was used, how were the experimental factors modelled? And what multiple test correction method. This applies to the analysis of other differential expression data-sets- can you please specify if all genes were probed or only a subset? (some older microarray platforms only probed a subset of genes)- how different were these biomarker gene-sets from each other? E.g. can you show a reciprocal overlap matrix, and report their sizes..?Performance assessment:- I would appreciate a bit more detail on the performance for all ten biomarker gene-sets. You used ToxCast and Tox21 data to pick the best biomarker gene-set, therefore these data-sets were in a sense part of your training set. You don't have a completely separate test set, and so the performance may be a bit inflated. In general, it is a good practice to use cross-validation in this type of situation. However, if the performances of the ten biomarker gene-sets are all quite similar, I would not be too worried about this . For the same reason, it would be useful to know how different were these biomarker gene-sets from each other (as commented above). You could use the ChIP-seq data to argue that the selection of the best biomarker set is robust (see comment below).- Is this PPV really general? Usually the PPV can be different from sensitivity and specificity if the number of real positives is swamped by the number of real negatives (e.g. a test detecting a disorder that's present in 0.1% of the population). You have 14 real positives and 45 real negatives, does this reflect the general distribution in reality, can you comment on this?ChIP-seq and PWM analysis:- the percentage of signature gene with a ChIP-seq mark is impressive (68.5%). I would like a bit more details. For the annotation using ChIPpeakAnno, how far from a gene (transcription start or end) could a peak be? What fraction of genes had a peak before performing any intersection with the biomarker gene set? Can you show that this is much better as e.g. taking genes that were upregulated in at least one NRF2 activation data-set? Can you show that the top biomarker gene-set had also better ChIP-seq overlap - similar considerations for the PWM analysisIngenuity pathway analysis- can you please use multiple-test adjusted p-values? - can you provide more details on how this analysis is performed "top upstream regulating transcription factor that regulated the biomarker genes"Screening for chemicals that modulate NRF2- can you explicitly comment on the anticipated false discovery rate? My back of the envelope calculation is: 9840 biosets * 1e-4 = 0.984 biosets, which is pretty stringent - for chemicals represents in multiple data-sets, how about using the median fold-change to collapse redundancy..?Follow-up screening using the SRXN1-GFP assay- in my mind, this experiment should be used as follows: from the results of 'Screening for chemicals that modulate NRF2', pick some that induce a positive effect, some that induce a negative effect, some that don't induce an effect; this selection should be unbiased, and it should exclude chemicals whose differential expression was used to construct and optimize the biomarker set . Testing genes used to construct the biomarker gene-set would make sense only to validate this assay (e.g. it is known that quercetin activates NRF2). Doses should also be matched. Instead, it appears this experiment was designed to answer multiple questions , and it's harder to draw firmer conclusions; for instance, several of the chemicals use to construct and optimize the best biomarker gene-set were included.- for these reasons, I am not supportive of this statement in the discussion << In an independent studyof 29 chemicals, the predictions based on the NRF2 biomarker were independently validated for most (81%) of the chemicals in HepG2 cells encoding a NRF2-responsive GFP reporter. >>Discussion- it is quite long-winded and should be shortened/summarized a bitOther / General- is it possible to have both effect size and p-value for biomarker signature correlations using Running Fisher..?Table 1- Is it possible to indicate the number of differentially expressed genes?Figure 2- figure 2A has resolution issues, please remake it**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  12 Jun 2020Response to reviewer comments forMining a human transcriptome database for chemical modulators of Nrf2John P. Rooney1,2, Brian Chorley2, Steven Hiemstra3, Steven Wink3, Xuting Wang4, Douglas A. Bell4, Bob van de Water3, and J. Christopher Corton2,5We thank the two reviewers for their suggestions that greatly improve the clarity and impact of the manuscript. Our responses to each comment are found below in bold.5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Gene expression data:Is it possible to make the expression data available for your biomarker gene set, or does PLOS One require the publication of this data?We have provided the list of the biomarker genes and their associated fold-change values in Supplemental File 1. Also in this file, we provide a master table of the different microarray comparisons (biosets) examined in this study including the GEO or ArrayExpress numbers of the publicly available datasets. We hope this addresses your question.A short explanation of the commercial database used here or how this database ensures the integration of the different gene expression data  would improve the transparency of the data analysis. May be the authors can add a short paragraph instead of citing an article. My answer to the question: "Has the statistical analysis been performed appropriately and rigorously? -> I don't know" is related to this part.The reviewer makes an important point about the data analysis procedures that should be clear in the paper. We have added text in the first paragraph in the Methods section to describe the gene expression workflow used in BaseSpace Correlation Engine including the criteria for selecting significantly altered genes. It should be noted that our group has used this database for the last 5 years resulting in >20 publications. The data is rigorously quality controlled. In fact, BSCE notes that they reject ~30% of all studies, because they do not meet their quality control criteria. We have great confidence that the methods that are used are rigorous and allow for appropriate comparisons between gene lists.Characterization of the NRF2 biomarker genes :The authors analyzed and verified the final biomarker gene set in HEPG2 cells. To what extent are the used ChIP-seq data suitable to make statements about regulatory areas in HEPG2 cells? To what extent can direct (motif-defined) or indirect binding of NRF2 be expected?There are no available NRF2 ChIP datasets that have been performed in NRF2 activated human hepatocyte or liver samples, therefore we had to rely on the limited published datasets that were currently available. Our previous studies in LCL  demonstrated that in basal conditions, NRF2 has very low activation levels without an electrophilic or pro-oxidant stimulation, therefore we attempted to tip the balance using only “NRF2 activated” datasets. The three cell lines included NRF2 activated  human airway cells  and lymphoblastoid cells (LCL). Although all human based, there will be cell type or tissue specific differences for NRF2 targets in lung, blood cells, and liver, however we cast the widest net possible to include all detected ChIP-based targets in this analysis. The resultant comparison to liver-centric NRF2 targets may result in a higher rate of “false negatives”, and may have seen a higher validation of genes using a liver-based model. This point is now mentioned in the results, page 11, lines 405-408. Given our resource limited environment, we had to use previously published data. The additional analysis of detecting an ARE in these regions signifies that these are likely bone fide target genes. Indeed, some concerns of genome pull down due to complex protein interactions and 3D conformational chromatin interactions may result in NRF2 enrichment in genomic regions that NRF2 is not directly mediating transcription. To more clearly define likely cis-acting transcriptional mediation, we re-ran these datasets, limiting the NRF2-bound loci to just 10K bp with the location of bi-directional promotors using a new feature in “ChIPpeakAnno”. The combination of ARE identification + ChIP-identified loci within in promoter regions give the best possible evidence for a gene to be directly targeted (and presumably transactivated) by NRF2. How do the authors linked the experimental ChIP-seq regions to the biomarker gene set? May be a motif analysis of a "defined" promoter-region is more meaningful here. Perhaps this analysis was done already  but may be the authors can add more details here. The original analysis simply looked for the closest gene region to the annotated peaks. Based on you and Reviewer 2’s suggestions, we limited the loci to those that are likely cis-acting . The combination of NRF2 bound peaks close to annotated gene promoters and the identification of bone fide ARE sequences within these peaks gave the best available evidence that these gene promoters are likely directly bound (and presumably regulated) by NRF2. This change is now noted on pg. 7, line 262.In this paper the ChIP-seq analysis was done based on Ensembl database version 86 (Okt 2016). Why did the authors use this older version of the database in this actual paper?For this re-run of the data, the current UCSC transcriptional database, last updated Oct 2019, was used to annotate the ChIP-seq peaks. This is noted on pg. 7, lines 263-264.In figure 4 the term 'bioset' is used in the legend and in the paper. In the paper the term 'biomarker gene set' is used before. Do these two terms refer to the same gene set? A minor point but I was just confused about this two terms. May be it is a frequently used term, which I just do not know.We are sorry this was not made clear. A bioset refers to a list of differentially expressed genes resulting from a specific comparison in the BSCE such as a chemical treatment vs control treatment. Text has been added to the first paragraph of the Methods to define this term.Dose-, time-, and mutation-dependent NRF2 modulation:The authors compare different chemicals screened in the ToxCast and/or Tox21 studies and relate these results to NRF2. First liver-cells are shown but also fibroblast and breast cancer cell line data are shown. The authors validate their findings using ARE-linked reporter system in HepG2 cells. The comparison with non-liver-related results is confusing. How comparable the findings between different cell types. Is there a possibility to estimate the completeness of this gene set?The reviewer brings up some important points. The biomarker was constructed in a way to use in evaluating liver cell line data but also to use with transcript profiles derived from other cell lines that express Nrf2. Thus, we used a number of biosets from liver and non-liver cells to construct the biomarker to find common genes. Non-liver cells were used in exploratory data analysis when liver cell data was not available. Lung fibroblasts and MCF cells are known to express Nrf2 and respond to oxidative stress, and while the biomarker was not formally tested for accuracy in lung cells, it is not surprising to see activation in these comparisons. The inclusion of the breast cancer cell line was based in part on the fact that suppression of Nrf2 signaling by siRNA was carried out in this cell line. In answer to the question about the completeness of the gene set, we do not have an answer to that. It is very likely that there are other Nrf2 regulated genes that were not identified but that they may be more context specific. For example, there may be genes that are regulated by Nrf2 in chemical-specific and cell-specific manners as we note in the manuscript. However, the goal of the project was not to evaluate the Nrf2 regulon in multiple tissues but to find a set of genes consistently altered when Nrf2 is perturbed that can be used for prediction. We are confident based on our findings that the biomarker that we constructed is able to predict Nrf2 activation in many cell types. We hope this addresses the questions.-----Figure 1: Biomarker construction and screening strategy.-> In all other Figure description the authors use Figure X. Description of the figure.May be the ":" is a typo.Thank you for noticing this. This has been fixed.short comment to Figure 6:May be the used red color should be defined in the legend of this figure.A sentence has been added to the figure legend to describe the red shaded results.Reviewer #2: The manuscript describes a very reasonable approach to using chemical perturbation transcriptional signatures to identify chemicals altering the activity of the transcription factor NRF2. I have several comments that I would like to see addressed by the authors.Procedure used to construct the list of differentially expressed genes used as NRF2 biomarkers - Relevant sections: Methods (162-177), Results (334-338):- I think some key details are missing from the methods. In general, the level of detail and tone of the methods section seems more adequate to the results section. Specifically:- can you be more specific, and provide the logical rules that were used to construct these gene-sets, e.g. fold change ≥ 1.5 and adjusted p-value < ... in at least N/M NRF2 activation data-sets, and fold change < ... and adjusted p-value < ... in ... NRF2 activation data-set(s)?Thank you for pointing out that this area needs further clarification. We have now added details in the Methods for describing how we filtered the genes that ultimately ended up in the biomarker. These include a fold change cutoff of >=1.5 averaged across all 7 biosets used to construct the biomarker in which Nrf2 is activated. The unadjusted p-value cutoff of p<0.05 that is used is inherent to the BaseSpace Correlation Engine data processing (see comments from reviewer #1). For genes to be included in the biomarker, expression changes had to be in the same direction in 4 of the 7 activation biosets, and none could include changes in the opposite direction. Lastly, the direction of regulation had to be in the opposite direction in the Nrf2 siRNA bioset. We have now added text in the Methods and the Results to better describe the criteria used to build the final biomarker used in the study.- how was gene differential expression calculated? Please state what method was used (e.g. limma) and if a linear model was used, how were the experimental factors modelled? And what multiple test correction method. This applies to the analysis of other differential expression data-setsA paragraph has been added to the methods section describing the gene expression workflow used in BaseSpace Correlation Engine. The methods used by BSCE to identify genes are deliberately less stringent than other approaches in that no multiple test correction is implemented. However, this works to our advantage when building a biomarker as genes that would be filtered out by the MTC are still available to be considered as candidates for incorporation into the biomarker. Our stringent cutoffs implemented in the biomarker construction procedures greatly reduce the number of possible Nrf2 regulated genes to find those that are cell line and chemical agnostic allowing the identification of a set of genes that are predictive of Nrf2 modulation.- can you please specify if all genes were probed or only a subset? (some older microarray platforms only probed a subset of genes) This is an important point. BSCE only incorporates data from studies that use genome-level gene expression microarrays or RNA-Seq. With that said, there are examples of microarrays that do not cover the entire genome including the U133A Affy chip. Each GEO or ArrayExpress number for each bioset is provided in Supplemental File 1 to allow answering further questions about each of the biosets if the reader so desires.- how different were these biomarker gene-sets from each other? E.g. can you show a reciprocal overlap matrix, and report their sizes.?Performance assessment:- I would appreciate a bit more detail on the performance for all ten biomarker gene-sets. You used ToxCast and Tox21 data to pick the best biomarker gene-set, therefore these data-sets were in a sense part of your training set. You don't have a completely separate test set, and so the performance may be a bit inflated. In general, it is a good practice to use cross-validation in this type of situation. However, if the performances of the ten biomarker gene-sets are all quite similar, I would not be too worried about this . For the same reason, it would be useful to know how different were these biomarker gene-sets from each other (as commented above). You could use the ChIP-seq data to argue that the selection of the best biomarker set is robust (see comment below).- Is this PPV really general? Usually the PPV can be different from sensitivity and specificity if the number of real positives is swamped by the number of real negatives (e.g. a test detecting a disorder that's present in 0.1% of the population). You have 14 real positives and 45 real negatives, does this reflect the general distribution in reality, can you comment on this?The reviewer asks some very relevant questions about our analysis. We realize that this is confusing and detracts from the presentation of the value of the final biomarker used. In response to these concerns, we have deleted the text mentioning the other biomarkers as these do not add to the paper. We feel that the manuscript is long enough to describe the process of choosing the biomarker studied. We have also changed Figure 1 accordingly. These modifications now make the paper consistent with our past publications including one in PLOS ONE in which we focus on the final biomarker studied, the criteria for filtering genes and accuracy determination as we have done for the biomarkers for the estrogen receptor, the androgen receptor, and MTF1. In regards to the questions about prediction. First, we did not use the biosets used in making the biomarker in our prediction study. That is we did include the “training set”. Second, we had to use the Tox21/ToxCast data to provide a list of the true positives and negatives and we feel that this was the best dataset that could be used. These assays were trans-activation assays and did not involve gene expression profiling so including these identified chemicals examined in the same cell line but in different labs and likely under different exposure conditions provided a good test set for testing predictive accuracy. The reviewer makes a good point about the test was not balanced. Even in cases where the true positives and true negatives are not approximately equal, we decided to use the information we had to carry out the predictive accuracy.Regarding the point about using the ChIP-Seq data to help guide the selection of the genes, it could be possible, but we have not tried it. We feel that if we use the ChIP-Seq data to guide selection of the genes, we may end up with genes that are not necessarily good for prediction of Nrf2 activation when you query microarray profiles. However, looking for overlap after selection of the genes helps to validate that we have selected a set of genes enriched in those that are directly regulated by Nrf2.Regarding the comment about the distribution of Nrf2 positives and negatives in the chemical universe. Not sure anybody can answer that question with available data as it may likely be dependent on cellular context. However, screens for Nrf2 activation in the trans-activation studies could be used to answer that question. Our database is not set up to systematically answer that question due to the inherent heterogeneity of the dataset. Interesting question though.ChIP-seq and PWM analysis:- the percentage of signature gene with a ChIP-seq mark is impressive (68.5%). I would like a bit more details. For the annotation using ChIPpeakAnno, how far from a gene (transcription start or end) could a peak be? What fraction of genes had a peak before performing any intersection with the biomarker gene set? Can you show that this is much better as e.g. taking genes that were upregulated in at least one NRF2 activation data-set? Can you show that the top biomarker gene-set had also better ChIP-seq overlap  - similar considerations for the PWM analysisThank you for raising these concerns of potentially inflating the gene matches to the ChIP-seq datasets. As a result of this concern, we reevaluated these data to just annotate peaks within 10Kb of bi-directional promoter regions. This distance was dictated from our previous findings that many of NRF2 peaks with ARE regions fell within this range of gene transcriptional start sites . Using this cut-off, we enrich the possibility of finding NRF2-bound loci that are potentially cis-acting. The previous analysis identified features that were closest to the peak, however these distances could be 100kB + away. Although it is possible that conformational changes in the chromatin could create enhancer regions at these distances, we do not have the information to confirm this. Therefore, this re-evaluation enriched those loci near annotated human promoter regions. Disregarding the intersect with the biomarker gene set and using this new filter, this current analysis identified peaks 2819, 301 and 5212 transcriptional features near peaks. Combined, this is 7633 unique features. Note this is out of a possible 197,782 transcriptional features identified in the database. Of these features, 38 genes annotated with peaks were in common with the biomarker set. This lowered our match appreciably to 26.6%, however we more likely captured the genes that are being directly regulated by NRF2. Regarding enrichment of these peak-matched genes with other NRF2 data-sets, the purpose was not to validate that NRF2 was involved in the alteration of these genes , but to show some supportive evidence that some of these biomarker genes were likely directly regulated by NRF2. While not of primary concern for the gene biomarker list generation , it does support that we are likely capturing some direct target genes of NRF2. Further refinement is achieved by demonstrating the presence of an ARE. With our new analysis we show that 19 of 38 genes with NRF2 peaks contain AREs (50%). This is a much better match than with the previous analysis that only found 21 ARE associated with 98 peak-associated genes (21.4%), supporting the idea that we are enriching regions with potential of direct NRF2 binding. These changes are now reflected in the updated Supplemental File 1 and on pg. 11, lines 403-412 and pg.15, lines 604-608.Ingenuity pathway analysis- can you please use multiple-test adjusted p-values? - can you provide more details on how this analysis is performed "top upstream regulating transcription factor that regulated the biomarker genes"We have now performed a MTC using a Benjamini Hochberg analysis on the IPA results. While the more stringent cutoff resulted in some of the canonical pathways becoming insignificant, the upstream analysis results did not change. As such we have now reconstructed one of the figures and indicate in the Methods and legend that q-values were used.Screening for chemicals that modulate NRF2- can you explicitly comment on the anticipated false discovery rate? My back of the envelope calculation is: 9840 biosets * 1e-4 = 0.984 biosets, which is pretty stringent - for chemicals represents in multiple data-sets, how about using the median fold-change to collapse redundancy..?The reviewer makes an interesting point here about the false discovery rate. In past studies with other biomarkers we tried to determine what p-value cutoff to use for the Running Fisher test. We started with a p-value of 0.001 and after a BH MTC the p-value cutoff was set at 0.0001 which we have used in all of our biomarker studies. Using this as our threshold for determining activation, our predictive accuracy has always been above 90% and ranges up to 98%. Importantly we are able to identify known activators which have correlation p-values close to 0.0001 for all of our biomarkers.Regarding the point about collapsing the fold change values, we have not thought about that. We are not sure we understand what the reason would be for doing that. We feel that each bioset represents a unique exposure situation even if the same chemical is used. By not collapsing, we identify chemical exposure conditions that lead to activation. By collapsing gene sets from the same chemical, we could increase the false positive rate and false negative rates due to changes in the expression of different sets of genes within the biomarker. However, we can only speculate but we appreciate the idea that may be used in future studies if we can somehow develop code that could do this automated fashion.Follow-up screening using the SRXN1-GFP assay- in my mind, this experiment should be used as follows: from the results of 'Screening for chemicals that modulate NRF2', pick some that induce a positive effect, some that induce a negative effect, some that don't induce an effect; this selection should be unbiased, and it should exclude chemicals whose differential expression was used to construct and optimize the biomarker set . Testing genes used to construct the biomarker gene-set would make sense only to validate this assay (e.g. it is known that quercetin activates NRF2). Doses should also be matched. Instead, it appears this experiment was designed to answer multiple questions , and it's harder to draw firmer conclusions; for instance, several of the chemicals use to construct and optimize the best biomarker gene-set were included.- for these reasons, I am not supportive of this statement in the discussion << In an independent studyof 29 chemicals, the predictions based on the NRF2 biomarker were independently validated for most (81%) of the chemicals in HepG2 cells encoding a NRF2-responsive GFP reporter. >>The reviewer brings up some important points here for this validation exercise. We see the point about including the two chemicals used to make the biomarker in the analysis. We have now recategorized them as positive controls and left them out of the analysis. Using the new list of chemicals, we have performed a predictive accuracy determination to find that the balanced accuracy is 78%. We have changed the text in the Results section and the Discussion regarding this analysis. Discussion- it is quite long-winded and should be shortened/summarized a bitWe agree that there was some redundancy with the text in the Results. We have now shortened the discussion as suggested by cutting two paragraphs and removing additional text in other paragraphs. Other / General- is it possible to have both effect size and p-value for biomarker signature correlations using Running Fisher..?The reviewer brings up an intriguing point. If by effect size you mean in this case the number of overlapping genes that the correlation is calculated on or the fold-change of the genes, we have not determined that. The problem with fold-change is that the different microarray platforms have more compressed fold-change values compared to the profiles generated from RNA-Seq datasets. So the method used in CE, the fold-change rank based method compensates for these differences in fold-change between platforms. We have determined the effect of removal of different sets of biomarker genes in other studies (the estrogen receptor biomarker and TGx-DDI biomarker) and found that fewer genes generally results in lower sensitivity especially for genes that have higher fold-change (higher rank).We have considered the -Log as an effect in itself, a concept not universally appreciated. In a paper that was accepted in Toxicological Sciences this week from our lab, we have determined whether there is a -Log that is associated with liver tumor induction in rats for a set of 6 gene expression biomarkers. In fact, we can identify these thresholds and use these for prediction. The basis for this is that as the factor is first activated, the number of genes altered that overlap with the biomarker is low and thus the correlation is low with the associated p-value close to 0.0001. As the factor is activated to a greater extent, there are more genes altered that overlap with the biomarker, a greater correlation and lower p-value, etc. So in our hands, with all of our biomarkers we see a relationship over and over again of greater activation leads to greater correlation and lower p-value. Thus, we determined that the p-value can be used to get an estimate of the level of activation of the factor and in the mentioned study, we found that when the p-value is low enough there is a level of activation of a factor (say AhR or CAR) that in a chronic situation would lead to liver tumors.Table 1- Is it possible to indicate the number of differentially expressed genes?We have now added a column for the number of differentially expressed genes in each of the biosets.Figure 2- figure 2A has resolution issues, please remake itThe reviewer is correct in that the resolution is not optimal. We have remade the figure to improve resolution.AttachmentResponseToReviewers_Rooney_06_10_20.docxSubmitted filename: Click here for additional data file. 11 Aug 2020PONE-D-20-07833R1Mining a human transcriptome database for chemical modulators of NRF2PLOS ONEDear Dr. Corton,Thank you for submitting your manuscript to PLOS ONE. After further consideration, we feel that minor revisions are required before the manuscript fully meet PLOS ONE’s publication criteria. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised  by one of the Reviewers during the second round of the process.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Sep 25 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). 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For instructions see: We look forward to receiving your revised manuscript.Kind regards,Roberto MantovaniAcademic EditorPLOS ONE[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #2: (No Response)**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #2: Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #2: Yes**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #2: The manuscript is much clearer. Only minor revisions required.OVERALLThe official gene symbol is 'NFE2L2'; but the synonym 'NRF2' has probably been used broadly. To avoid confusion, can you state this at the beginning of the manuscript, and use only one of these two names in the figures and tables? For instance, it is confusing to have in figure 3C a title 'Nrf2 upstream regulators' and then 'NFE2L2' listed as one METHODS<< The average fold-change across the 7 biosets in which NRF2 was activated had to be > |+/- 1.5-fold|. >> -- is this a log2 (FC)..? Please state explicitly<< The p-value is the probability of the overlap between the NRF2 biomarker gene list and the IPA pathway gene list. The smaller the p-value the less likely that the association is random. >> -- can you adopt a more rigorous explanation?RESULTSBuilding the NRF2 biomarker and assessment of predictive accuracyIn the response to one of the reviewer comments the authors state << First, we did not use the biosets used in making the biomarker in our prediction study. That is we did include the “training set”. >> -- I think the authors meant "we did *not* include the “training set”"; can you make this clear in the results and methods?Fig 2A: can the authors add labels with the name of the chemicals, or add a table with this information?Characterization of the NRF2 biomarker genes.<< Of the 143 NRF2 biomarker genes, 38 (26.6% ) of these were associated with the NRF2- bound ChIP-Seq loci near gene promoter regions (within 10 Kb) in at least one dataset derived from cell lines treated with NRF2-activating isothiocyanate, SFN, or have constitutively active NRF2 >> -- can you report what's the background rate, when considering all genes?**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  19 Aug 2020Reviewer's Responses to QuestionsComments to the AuthorReviewer #2: The manuscript is much clearer. Only minor revisions required.We thank the reviewer for providing suggestions that helped to make this a better paper.OVERALLThe official gene symbol is 'NFE2L2'; but the synonym 'NRF2' has probably been used broadly. To avoid confusion, can you state this at the beginning of the manuscript, and use only one of these two names in the figures and tables? For instance, it is confusing to have in figure 3C a title 'Nrf2 upstream regulators' and then 'NFE2L2' listed as one We have now made a number of changes suggested by the reviewer. These include stating that Nrf2 is encoded by the NFE2L2 gene in both the abstract and introduction when Nrf2 is first introduced, changing NFE2L2 to Nrf2 in Fig 3C, and adding two changes in Table 1 to indicate that either the Keap1 or NFE2L2 gene are knocked down.METHODS<< The average fold-change across the 7 biosets in which NRF2 was activated had to be > |+/- 1.5-fold|. >> -- is this a log2 (FC)..? Please state explicitlyWe have now changed the sentence to read “The average fold-change across the 7 biosets in which NRF2 was activated had to be ≥ |+/- 1.5-fold| (not Log2(fold-change).”. We hope this suffices.<< The p-value is the probability of the overlap between the NRF2 biomarker gene list and the IPA pathway gene list. The smaller the p-value the less likely that the association is random. >> -- can you adopt a more rigorous explanation?We removed the last sentence mentioned and added “Significant reported pathways have q-values < 1E-3.”.RESULTSBuilding the NRF2 biomarker and assessment of predictive accuracyIn the response to one of the reviewer comments the authors state << First, we did not use the biosets used in making the biomarker in our prediction study. That is we did include the “training set”. >> -- I think the authors meant "we did *not* include the “training set”"; can you make this clear in the results and methods?Thanks for picking up this mistake. We have now changed the sentence on line 258 to read “The biosets used to create the biomarker  were excluded from the test.” As suggested, we added the same sentence to the Results on line 409.Fig 2A: can the authors add labels with the name of the chemicals, or add a table with this information?We have now provided this information in Table S1. We now state on line 414 “Information about the biosets used in the test are provided in Table S1.”Characterization of the NRF2 biomarker genes.<< Of the 143 NRF2 biomarker genes, 38 (26.6% ) of these were associated with the NRF2- bound ChIP-Seq loci near gene promoter regions (within 10 Kb) in at least one dataset derived from cell lines treated with NRF2-activating isothiocyanate, SFN, or have constitutively active NRF2 >> -- can you report what's the background rate, when considering all genes?We have now reported the background rate in the Results. The text now reads: “Of the 143 NRF2 biomarker genes, 38 (26.6% ) of these were associated with the NRF2-bound ChIP-Seq loci near gene promoter regions (within 10 Kb) in at least one dataset derived from cell lines treated with NRF2-activating isothiocyanate, SFN, or have constitutively active NRF2 . This is in comparison to the background rate of 13.7%, where a total of 3553 genes contained NRF2-ChIP bound regions within 10 Kb of the TSS of the approximately 26,000 identified transcribed genes in the human genome (hg38). Therefore, the NRF2 biomarker genes are more significantly represented with NRF2-bound regions .”AttachmentResponse to reviewer comments hNrf2 08_19_20.docxSubmitted filename: Click here for additional data file. 7 Sep 2020Mining a human transcriptome database for chemical modulators of NRF2PONE-D-20-07833R2Dear Dr. Corton,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Roberto MantovaniAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 15 Sep 2020PONE-D-20-07833R2 Mining a human transcriptome database for chemical modulators of NRF2 Dear Dr. Corton:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. 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Depression is common among cardiac patients and associated with adverse cardiovascular outcomes. Bright light therapy has emerged as a promising treatment for depressive symptoms, however it has not yet been investigated in this population. We conducted a double-blind, randomized, placebo-controlled pilot trial to assess the feasibility of a larger-scale trial testing bright light therapy for depressive symptoms in cardiac patients. Patients hospitalized for an acute coronary syndrome or undergoing cardiac surgery were randomized to either bright light  or dim light placebo (500 lux) lamps for 30 minutes each day over 4 weeks, beginning in-hospital. Depression was quantified using the Patient Health Questionnaire 9 (PHQ-9) and Depression Anxiety and Stress Scales (DASS-21). The Short-Form Health Survey 36 (SF-36) was used to measure quality of life. A total of 175 patients were screened and 15 were randomized  (8.6%) over 10 months. Despite protocol amendments which broadened the inclusion criteria, the trial was terminated early for infeasibility based on the rate of enrollment (1–2 participants/month), with 39.5% of the target sample (38 participants) enrolled. Future trials should take into account the timing of the onset of depressive symptoms in these patients, and consider a less conservative approach to eligibility as well as ways to increase the acceptability of bright light therapy in hospitalized cardiac patients. Once enrolled, our findings suggest that most participants will adhere to the assigned treatment and complete follow-up. As many as 15–20% of patients hospitalized for an acute coronary syndrome (ACS) or cardiac surgery may experience major depressive disorder . Depresshttps://clinicaltrials.gov/ct2/show/NCT02621567). Ethics approval was obtained from the research ethics board of the Jewish General Hospital in Montreal, Canada, prior to patient randomization. The protocol for this study and supporting CONSORT checklist are available as supporting information; see The “Bright Light Therapy Efficacy for Depressive Symptoms Following Cardiac Surgery or Acute Coronary Syndrome: Pilot Trial (BEAM-P)” was a double-blind, randomized, placebo-controlled pilot trial aimed at assessing the feasibility of a larger trial  or undergoing cardiac surgery . We excluded patients with a medical condition potentially contraindicating the use of bright light therapy, including ocular or retinal pathology, and those with conditions or taking medications increasing photosensitivity. Patients with diabetes were excluded based on their risk for eye disease . The original protocol required participants to have depressive symptoms at baseline . However, this criterion was removed to increase the enrollment rate.Participants randomized to the treatment arm received lamps that emit bright light . Based on the previous literature, we elected to use 10,000 lux  for a shorter duration (30 minutes) rather than lower lux for a longer duration ; the longer duration of use would be more difficult for patients to adhere to and both have comparable response rates . ParticiTreatment was for a period of 4 weeks. Clinic visits took place at baseline, week 4, and week 12. A visit at the day of discharge was added to the amended protocol , however this visit was logistically problematic , and only two participants completed the added visit. Participants were instructed to use the lamp for 30 minutes every morning, preferably within an hour of waking up and before 1 pm. They were instructed to sit 12 inches away from the lamp and to keep their eyes open for the duration of the 30 minutes, looking at a surface that the light reflected onto, but not directly into the light. Participants were allowed to use the lamps while reading, eating, or doing other seated activities.We collected data at baseline and at follow-ups at 4 and 12 weeks. To assess depression, we used the PHQ-9  and the Participants were asked about their use of other interventions for depression, as well as any side-effects, adverse events (AEs), or serious adverse events (SAEs) they may have experienced since the last follow-up. An AE was defined as any unfavorable or unwanted sign, symptom, or disease temporally associated with the use of the trial product, whether or not considered related to the trial product. A SAE was defined as an AE that required in-patient hospitalization or prolongation or hospitalization that resulted in persistent or significant disability or incapacity, was life-threatening, or resulted in death. Participants were asked to fill log-of-use forms every day of the 4-week treatment period to assess their adherence to protocol.In this pilot trial, the primary endpoint was recruitment rate. Secondary endpoints included retention at week 12 and adherence through week 4 (percentage of participants that used their lamp on ≥60% of mornings within an hour of waking for at least 25 minutes). Tertiary endpoints included other aspects of feasibility .Participants were enrolled over a period of 10 months (average of 1–2 enrollments per month). A total of 175 patients were screened , of whomA total of 15 participants were enrolled in the pilot trial . Eight (Most participants (73.3%) were adherent to therapy (use on ≥60% of mornings for at least 25 minutes). Participants used their lamps for a median of 24 days, interquartile range  for a median of 30 minutes  per day over the course of the 28 day treatment period. Of the 15 participants, 2 participants (one from each treatment arm) decided not to use the lamp after enrollment, prior to any lamp use. One additional participant from the treatment arm discontinued use after one week due to fatigue and re-hospitalization. All participants completed the week 4 and week 12 follow-ups (100% retention).This pilot trial was not powered to detect differences in depressive symptoms or quality of life between the bright light therapy and dim light therapy arms. According to the PHQ-9 and DASS-21 depression subscale, no participant had more than mild depressive symptoms at baseline  . QualityThree participants in total (20%) scored above “mild” on the PHQ-9 or above “moderate” on the DASS-21 at least once during follow-up. These were also the only participants who reported experiencing a SAE during the 12 week follow-up period. Participant number 8, from the bright light arm, suffered from pericardial effusion and discontinued lamp use after 8 days. This participant scored “severe” and “extremely severe” on the PHQ-9 and DASS-21, respectively, at week 4. Participant number 12, from the treatment arm, suffered from postoperative complications and did not use the lamp. This participant scored “moderate” on the PHQ-9 at week 4. Finally, participant number 14, from the control arm, suffered from retrosternal chest pain and did not use the lamp. This participant scored “moderate” on the PHQ-9 and “extremely severe” on the DASS-21 at week 12. All 3 participants also had relatively lower scores than others on the Emotional Wellbeing subscale of the SF-36 . All SAEs were considered unrelated to the use of the lamps.Our pilot study was designed to assess the feasibility of a large-scale trial of bright light therapy for depression in hospitalized cardiac patients. The trial was terminated early for slow enrollment after 10 months, with 15 participants out the target sample size of 38 enrolled. Nearly 60% of screened patients were ineligible, and 4 out of every 5 eligible patients refused to participate. No participant had more than mild depressive symptoms at baseline. However, enrolled participants were largely adherent to their assigned treatment, and retention in the study was 100% at the end of follow-up. Overall, the findings from this small, single center pilot trial suggest that a large-scale trial would be challenging to conduct.Several trials have investigated the potential of bright light therapy as a treatment modality for non-seasonal depression. In 2005, Golden et al.  conducteThe results of our feasibility study suggest that it would be difficult to study the use of bright light therapy for the treatment of depression following hospitalization for ACS or cardiac surgery. Very few patients scored higher than “normal” on either depression scale at any time during the study. Our original protocol included only patients with a baseline PHQ-9 score ≥8. If the protocol had not been amended to remove this criterion, only 4 participants would have been enrolled. This suggests that future trials need to consider the timing of the occurrence of depressive symptoms following a cardiac hospitalization, in order to determine the optimal timing of interventions to treat depression. Likewise, it remains unclear which patients would benefit the most from bright light therapy.Large-scale studies could consider a less conservative approach to eligibility. As a preventative measure, experts recommend that patients with retinal diseases avoid bright lights . TherefoFuture studies should also consider the acceptability of bright light therapy in patients with cardiovascular disease. Refusals to participate among eligible patients were high, however the reasons for refusal were not recorded. It seems plausible that patients who are have poorer physical and/or mental health were less likely to participate. Among our enrolled participants, the only patients who reported higher than “normal” depression scores were the same patients that experienced SAEs and discontinued lamp use before the end of the treatment period. It is possible that sustained health problems in these patients caused higher depressive symptoms and lower adherence to the study. If this were the case, the importance of adherence would need to be reinforced for bright light therapy to be effective in this population. Trials evaluating the efficacy of bright light therapy for non-seasonal depression in patients following cardiac hospitalization should also take seasonality into account. A questionnaire such as the Seasonal Pattern Assessment Questionnaire (SPAQ) could be used as part of a baseline assessment, and adjusted for in analyses of efficacy.This pilot study was designed to evaluate the feasibility of a larger-scale trial testing the efficacy of bright light therapy for depressive symptoms among hospitalized cardiac patients. Overall, the findings from this small, single center trial suggest that a large-scale trial would be challenging to conduct. Future trial designs should take into account the timing of the onset of depressive symptoms around hospitalization and the impact of seasonality in these patients. Researchers should also consider a less conservative approach to eligibility , as well as ways to increase the acceptability of bright light therapy in hospitalized cardiac patients. Enrollment may be feasible if these factors are addressed. Once enrolled, our findings suggest that most participants will adhere to the assigned treatment and complete follow-up.S1 CONSORT Checklist(DOC)Click here for additional data file.S1 Protocol(PDF)Click here for additional data file.S2 Protocol(PDF)Click here for additional data file.


We demonstrate that the pH has a crucial effect on the LAA-assisted conversion of graphene oxide to reduced GO (rGO): alkaline reduction at higher GO loadings leads to doubled heat production of the composite. Spectrophotometry proved that neither the moderately acidic nor alkaline conditions promote the iron dissolution of the magnetic core. Although the treatment with NaBH4 also increased the hyperthermic efficiency of aqueous GO/MNP nanocomposite suspensions, it caused a drastic decline in their colloidal stability. However, considering the enhanced heat production and the slightly improved stability of the rGO/MNP samples, the reduction with LAA under alkaline condition is a more feasible way to improve the hyperthermic efficiency of magnetically modified graphene oxides.We present a study on the magnetic hyperthermia properties of graphene oxide/magnetite (GO/MNP) nanocomposites to investigate their heat production behavior upon the modification of the oxidation degree of the carbonaceous host. Avoiding the harsh chemical conditions of the regular in situ co-precipitation-based routes, the oppositely charged MNPs and GO nanosheets were combined by the heterocoagulation process at pH ~ 5.5, which is a mild way to synthesize composite nanostructures at room temperature. Nanocomposites prepared at 1/5 and 1/10 GO/MNP mass ratios were reduced by NaBH Magnetic nanomaterials attract rising attention in biomedical applications, such as easily controllable targeted drug-delivery vehicles for cancer therapy ,2,3. Sup4 and concentrated H2SO4, and this synthesis method is industrially scalable 2+ was measured in acidic media at λ = 510 nm.The amount of dissolved iron in the presence of organic acid (LAA) was determined by spectrophotometry applying the protocol recommended by Mykhalyk et al. . BrieflyThe crystalline structure of the synthesized nanocomposites and their starting compounds was studied by X-ray diffraction. A characteristic region of diffraction patterns of the original GO, the magnetite nanoparticles and the 1/5 GO/MNP nanocomposite is presented in Transmission electron microscopy images of some selected samples  are presented in The pH-dependent surface charging of GO and magnetite nanoparticles was investigated by electrophoretic mobility measurements in order to find the optimal pH range for the composite formation by heterocoagulation process. The calculated zeta potential (ZP) values of the nanomagnets and GO particles are presented in Regarding the possibility to monitor the progress of the reduction process, we considered using two main methods to provide data that would allow comparison of each stages of reduction for the individual carbon phases and their composites. In the case of pure graphene oxide, the C/O ratio has long been used to follow and compare the efficiency of the reduction . UnfortuThe heat production of the pristine and the reduced GO/MNP nanocomposites with various compositions was studied by calorimetric measurements. Time-resolved temperature measurements were performed, and the original magnetite (MNP) and the reduced graphene oxide (rGO) dispersions were used as reference. In our previous work , we demoT between pure magnetite and the composite samples indicates that the GO sheets do not attenuate the heat production on a significant scale. As the pure GO sample also produces some moderate temperature rise in 300 s (less than 3 °C), two possible processes can be considered. One may originate from the electrolyte content of the aqueous dispersion of the GO and the second one is likely due to the resistance heating effect caused by Joule-losses of graphene oxide particles as we suggested in our previous work [First, we determined the heat production of the pristine composites at 1/5 and 1/10 GO/MNP compositions, and, as the left panel of ous work . A GO/MNous work . This maous work . Besides4, which is a frequently used compound for GO reduction [T is less only by 0.2 °C after treating the sample with NaBH4. We note that the reduced composites were not stable: the solid material started to sediment almost immediately after addition of the reducing agent. Although the borohydride reduction of GO/MNP composites apparently improved their magnetic hyperthermia applicability, it also raises stability issues. This problem poses the argument for favoring milder reducing agents, such as the frequently used ascorbic acid [On the right side of eduction ,37. The bic acid ,41.It is known that the magnetic properties of nanoparticles play an important role in their behavior in an alternating magnetic field . An inveAccording to the method applied earlier , we carr3 mixture.Under acidic conditions, the heat production of rGO and 1/5 rGO/MNP samples becomes worse than that of pristine MNP and only the sample of 1/10 composition shows the same efficiency. The calculated SAR value was 11.8 W/g for the 1/5 rGO/MNP, while the original MNP exhibited 11.6 W/g. The difference between 1/5 and 1/10 samples may be attributed to the low pH, where the LAA is partially protonated, and its amount is likely not enough to reach the same degree of reduction for the 1/5 sample with double GO content. Contrary to this, the alkaline conditions significantly improved the temperature rise measured for the composites, especially in the case of 1/5 rGO/MNP, where the SAR value increased from 9.2 up to 20.3 W/g after reducing it by LAA/NHThe heat dissipation efficiency in the AC magnetic field strongly reflects not only the propensity of magnetic dipoles, but also their different types interactions. Although SAR values decrease with aggregation at first glance, Ovejero et al.  have rev4, due to the overly strong reduction and the large excess of reductant, the treatment resulted in weaker heat producing samples, which is contrary to the aim of this study. Thus, we investigated the effect of the added amount of LAA on the hyperthermic efficiency of the reduced composites next.The 1/10 rGO/MNP sample also presented slightly higher heat production, but the SAR increase was only 2.3 W/g compared to the pristine 1/10 GO/MNP sample, reaching 11.6 W/g for the reduced composite. It is interesting to note, that the amount of added LAA was equal in all cases, but the mass of the GO changed, since the MNP concentration was kept constantly at 5 g/L. In the 1/10 composite, the amount of the carbonaceous host was half compared to the 1/5 sample, so the LAA could reduce the GO to a greater extent, leading to almost complete transformation from GO to graphitic carbon. As in the case of NaBH4, DLS was inadequate for providing a reliable estimate of the average size of aggregates in that system. Therefore, a meaningful comparison can only be provided by visual observation of the composite dispersions. These substantiate that the LAA-reduced samples remain dispersed in water for a much longer time as compared to borohydride-reduced composites, which form large flocculated particles reminiscent of light carbon soot materials obtained, e.g., upon pyrolysis of graphite oxide. It is clear then that the added LAA behaves not only as a reducing agent, but it contributes to the particle stabilization as well.The heating curves recorded for the 1/10 rGO/MNP composites treated by LAA in various concentrations at pH 9.3 are presented in Based on our previous experience  and due f = 340 kHz, H = 21.0 kA/m) of the graphene-based yolk-shell magnetic nanoparticles functionalized with copolymer Pluronic F-127 and loaded with doxorubicin drug molecules. Although this nanostructure is really promising for cancer treatment, the studied composite material was prepared by a complex multistep synthesis method unlike our composite material, which involves no functionalizing agents or loaded molecules. GO/MNP nanocomposites of various compositions were synthesized in situ for in vitro apatite mineralization by Miyazaki et al. [3O4 content of the nanocomposite. Albeit reduced GO/MNP was discussed throughout the paper, none of the broad classes of reducing agents were directly applied, only the possible decomposition  of GO during the co-precipitation of magnetic nanoparticles was assumed. A solvent evaporation method was applied by Sugumaran et al. to reside iron oxide nanoparticles of various core sizes in the GO sheet based host [Relatively few papers ,56,57 wesed host  using hy4, was found to be too aggressive of a compound for this purpose, although the heat production apparently slightly increased but the colloidal stability of the reduced samples diminished substantially. Depending on the reaction conditions , LAA revealed excellent ability for partial reduction of GO sheets into rGO form leading to increased thermal response in moderate AC magnetic field . For fine tuning of the hyperthermic efficiency by changing the oxidation state of the carbonaceous host, the LAA proved to be a good candidate, due not only to the enhanced thermal response of 1/5 rGO/MNP obtained by alkaline reduction, but also to the improvement of the colloidal stability. Since this latter property, among other criteria , is crucial for biomedical application but aggregation properties have yet barely been studied in relation to magnetic hyperthermia of MNPs and their composite materials, we believe that the further optimization of this method to afford high-stability efficient nanocomposites has high potential for cancer theranostics.Compared to the aforementioned studies, the present is unique in terms of the fabrication of the bare rGO/MNP composites directly employed for magnetic hyperthermia. Contrary to the exclusively used in situ co-precipitation, we successfully applied heterocoagulation to prepare GO/MNP nanocomposites from separately synthesized and purified GO and magnetite dispersions. The mild method allows us to use only electrostatic attraction to reside the MNPs on the carbonaceous host without applying harsh chemical circumstances. Aiming to achieve only partial reduction of GO content in the composites, the strong reducing agent, the NaBH