Records of ice-on and ice-off dates are available for lakes and rivers across the Northern Hemisphere spanning decades and in some cases centuries. This data provides an opportunity to investigate the climatic processes that may control ice phenology. Previous studies have reported a trend toward shorter ice-covered seasons with global warming, as well as links between ice phenology and several modes of natural climate variability such as the North Atlantic Oscillation, the Pacific-North American Pattern, the El Niño-Southern Oscillation, the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation. The 11-year sunspot cycle has also been proposed as a driver of ice phenology, which is somewhat surprising given that this cycle’s strongest impacts are in the stratosphere. In this study, we use a large data set of lakes and rivers across the Northern Hemisphere to test this potential link. We find little or no connection between the sunspot cycle and either ice-on or ice-off dates. We conclude that while many well-known climate cycles do impact ice phenology, we are able to rule out any strong impact of the solar cycle. The phenology of ice on inland water bodies affects the annual cycles of mixing and stratification with resulting impacts on ecosystem processes. Recent studies emphasize the importance of winter conditions and the activity of various ecological communities  during this time of the year , 2. Ice In addition to its importance for aquatic ecosystems, lake and river ice is an interesting subject from a climate science perspective because ice phenology data is often available for longer time periods than other climate data. The most detailed climate data is available for the duration of the satellite era (1979—present), and while some data does exist for considerably earlier times, its interpretation is complicated by differences in instrumentation and sparser coverage. On the other hand, freezing dates and ice breakup dates (“ice-on” and “ice-off” respectively) are easy to observe without sophisticated instruments and have been recorded at various locations across the Northern Hemisphere for decades to centuries. This data therefore provides an inviting opportunity to study the climatic drivers of lake and river ice phenology.Most obviously, anthropogenic climate change over the last several decades to a century should have driven a trend toward shorter ice-covered seasons, consistent with warming global surface temperatures. Numerous studies document this trend , 7–18, aThis remaining variability may be explained in part by natural modes of climate variability. Examples would include the North Atlantic Oscillation (NAO), Pacific-North America Pattern (PNA), El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). In contrast to centennial-scale anthropogenic climate change, these processes are internal to the climate system and operate on timescales ranging from weeks to decades. These processes can influence near-surface air temperatures in the Northern Hemisphere, which in turn would presumably change the timing of ice-on and/or ice-off. Indeed, several studies have found links between lake ice phenology and some of these natural modes of variability , 23–27, A third possible driver of ice phenology is solar variability. In contrast to the previous two categories, this driver is neither anthropogenic nor internal to the climate system, but represents a natural, cyclic change in solar forcing. Specifically, observers have for centuries noted a clearly defined 11-year cycle in the number of sunspots visible on the disk of the sun. This cycle is so clear that it can be seen even with relatively rudimentary instruments and temporally sparse data. Thus, the available time series is centuries long, and the cycle is clearly evident in the time series data . GiWork in this field has suggested that the impact of the solar cycle is strongest in the stratosphere . This isIn this study we consider six questions using six different methods to assess possible connections between ice phenology and the solar cycle: (1) based on the sensitivity of temperature to the solar cycle and the sensitivity of ice to temperature, should we expect the connection to be strong enough to be measurable? (2) Is there any detectable correlation between the 11-year sunspot cycle and ice phenology at individual locations? (3) Is there a lagged correlation between the 11-year sunspot cycle and ice phenology at individual locations? (4) Does the Fourier transform of ice data show any 11-year cycles? (5) Is there any spatial coherence in the correlations at individual locations? (6) Does the spatial pattern of ice-vs-sunspot correlations match the spatial pattern of temperature-vs-sunspot correlations? .We use ice-on and ice-off data from the Global Lake and River Ice Phenology Database maintained by the National Snow and Ice Data Center (NSIDC) . The iceTo track the solar cycle, we use monthly-mean values of the Wolf Sunspot Number from January 1818 to August 2022. This dataset is maintained by WDC-SILSO, Royal Observatory of Belgium, Brussels.We also use monthly-mean 2-meter air temperature and monthly total precipitation from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis data set  for theFor this analysis, we often average both temperatures  and sunspot numbers over two seasons, the November-December-January (NDJ) season, and the March-April-May (MAM) season. We choose those months so that the sunspot numbers are taken from a time of year that corresponds reasonably well to the freeze or melt dates for most lakes and rivers in the data set. However, we also note that the solar cycle changes so slowly that the exact choice of months used in the average is unlikely to change the results.k is labeled by Dk, the 2-meter air temperature at that location (at 1˚ resolution) by Tk, and the sunspot number by S. Then by the chain rule for derivatives,Before attempting to detect a correlation between ice phenology and the solar cycle, we ask whether we should even expect such a correlation. Specifically, if we compute the sensitivity of ice-on and ice-off dates to temperature, and the sensitivity of temperature to the solar cycle, we should be able to estimate the sensitivity of ice-on or ice-off to the solar cycle. Suppose the ice-on or ice-off date for the lake or river at location Dk on S is mediated by Tk alone. If this is not the case, then a more complicated form of the chain rule may be needed, but this simple version should suffice for an order-of-magnitude estimate. To make the result easier to interpret, we multiply by 150, which is roughly the difference in sunspot number between a typical solar maximum and solar minimum. .We multiply these two regressions at each location . We find that ice-on should change by about 0.41 days between solar maximum and solar minimum, and ice-off should change by about 0.78 days. The signs differ depending on location (not shown). Thus, the expected connection between ice phenology and the solar cycle is small and would likely be difficult—though perhaps not impossible—to detect. Notably though, the connection might be easier to detect for ice-off, and indeed this is reflected in our subsequent analyses below.It is possible that the solar cycle affects ice phenology via some variable other than temperature, so we repeat the above analysis with temperature replaced by precipitation. We then find that ice-on should change by about 0.19 days between solar maximum and solar minimum, and ice-off should change by about 0.21 days, when the effect is mediated by precipitation alone. Once again, the signs differ by location. Thus, we conclude that the effect of the solar cycle—if there is any effect at all—is likely to be mediated more by temperature than by precipitation.The next method is simple: we check the correlations  between ice phenology and the solar cycle for individual lakes and rivers. More specifically, for each body of water, we calculate the correlation between the timeseries of ice-on dates and the timeseries of sunspot numbers, using the NDJ average for the latter so that each timeseries (temperature and ice-on) has one entry per year. Separately, we calculate the timeseries of ice-off dates with the timeseries of MAM-averaged sunspot numbers.The top left panels of Figs overestimated the impact of the solar cycle. On the other hand, in some of the locations a genuine connection could be masked by short time series, in which case the analysis could be underestimating the impact. Both possibilities deserve some attention.These initial results already suggest that there is at most a weak connection between ice phenology and the solar cycle, but this rather simplistic method does not entirely answer the question, since (1) there could be stronger correlations when the sunspot data is lagged, and (2) we cannot immediately tell whether the few statistically significant correlations represent a real effect that occurs in a small number of locations or merely a result of the large sample size. The latter point could mean that the analysis has We address the first issue in two different ways—first, by considering lagged correlations of ice phenology with sunspot data; and second, by checking Fourier transforms of the ice data alone to look for any 11-year periodicity. We address the second issue using two more methods—both of which involve studying the full spatial pattern of ice phenology rather than individual locations.To address the first question above, we check correlations for individual locations as before but with the sunspot data leading the ice data by 0 to 10 years in 1-year increments.  In all 11 cases, the mean absolute value of the correlations for ice-on is about 0.11 or 0.12, and the number of statistically significant correlations is between two and five. For ice-off, the mean absolute values of lagged correlations are always between 0.09 and 0.14, with between zero and three being statistically significant. It is noteworthy that in both cases, the significant correlations are generally for short time series: when we change the minimum cutoff from 22 years to 33 years, almost no statistically significant correlations remain for any lag. The maps of lagged correlations between ice-on and the solar cycle  are agaiAs a separate check, we use power spectra generated by the fast Fourier transform of the ice-on and ice-off timeseries to search for any 11-year periodicity regardless of the lag. An advantage of this method is that we can average the Fourier transforms over many locations without being concerned about whether the signs or lags differ by location.For this analysis, we use only locations with at least 60 years of uninterrupted data (49 locations for ice-on and 142 for ice-off), and we use exactly 60 years of data for each such location, even if more is available. This allows us to avoid technical problems that arise with time series of differing lengths when we attempt to average the power spectra. Also note that while a Fourier transform is typically a function of frequency, we transform the curves so that the x-axis represents period instead.The second question posed above was whether the individual correlations (or lack thereof) were meaningful since (1) a few statistically significant correlations could occur coincidentally and (2) real relationships could be masked by the short time series in some locations. To address this, it will be helpful to consider not just individual locations, but the full dataset together. It is possible that a small impact of the solar cycle on lake/river ice that is difficult to discern at individual locations may be more clearly apparent in the aggregate data.In order to check this, we determine whether the correlations between ice-on or ice-off and the sunspot cycle are spatially coherent. If, for example, we found that some region had predominantly positive correlations, and some other region predominantly negative, then even if the correlations were individually small and statistically insignificant, this overall spatial pattern might suggest the existence of a real effect.ri and rj are the correlations between ice-off (or ice-on) and the solar cycle at the individual locations i and j, r, N is the number of locations used, wij is the weight assigned to the  pair of locations, and wij is taken to be 1 if the locations i and j are adjacent (in some suitably defined sense) and 0 otherwise. For this study, we instead define the weights with an exponentially decaying function of distance between the locations. Specifically,i,j) is the great-circle distance (shortest distance between two points on the surface of a sphere) from location i to location j in kilometers. This weighting scheme is designed to determine whether the individual correlations are themselves spatially autocorrelated over distances of roughly 1000 km and less.We use Moran’s I statistic as a measure of spatial autocorrelation. It is defined as:e weight . This isInstead of attempting to interpret the numerical value of the I statistic directly, we use a bootstrapping method  to estimate its statistical significance—in other words, to ask whether the I value is larger than would be expected by chance. To do this, we randomize the temporal order of the sunspot data while leaving the ice data as it is, and then check the I statistic for each randomized time series. After performing this procedure 1000 times, the fraction of cases in which the randomly generated I statistic is greater than the original is then taken as an estimate of the p-value of the original.p = 0.77), but interestingly, this changes when we lag the sunspot data by two years (p = 0.01) suggesting a possible lagged correlation between sunspot number and ice-off. Some researchers have reported a possible 2-year lagged correlation between sunspot number and the NAO  images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. 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Please try and answer as many of the concerns for another round of reviews.[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to Questions  Comments 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: PartlyReviewer #2: YesReviewer #3: Partly**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: NoReviewer #2: YesReviewer #3: 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: NoReviewer #2: YesReviewer #3: 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: NoReviewer #2: YesReviewer #3: Yes**********  5. Review Comments to the Author  Please 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: The manuscript predominantly relies on raw data, lacking necessary corrections or adjustments. One notable instance is the ice phenology data, which exhibits disparities in both spatial and temporal resolution. To ensure the credibility of this information, it necessitates validation through either on-site field observations or the integration of high-resolution remote sensing data. Similarly, the utilization of ECMWF data with a resolution one degree, with a lack of validation, raises concerns about its reliability for accurate assessment. Consequently, I find myself skeptical about the overall data quality as presented in its unprocessed state by the authors. Further efforts to refine and validate the data are imperative to bolster the manuscript's scientific rigor and integrity.Reviewer #2: Potential link between the 11-year sunspot cycle and Lake and River Ice Phenology was investigated. The study showed no strong impact of the solar cycle and ice phenology. The topic is very interesting, and the text was written very well, but the figures suffer from low quality. My suggestions to improve this manuscript are listed below.- No need to the given abbreviations in the Abstract.- Line 107-110. Some these cited studies are suggested to be reviewed before talking about third possible driver of ice phenology.- Introduction could be wider reviewing more recent studies. Some suggestions are:** Pourderogar, H., et al. "Modeling and technical analysis of solar tracking system to find optimal angle for maximum power generation using MOPSO algorithm." Renewable Energy Research and Applications 1.2 (2020): 211-222** Beiranvand, A., et al. "Energy, exergy, and economic analyses and optimization of solar organic Rankine cycle with multi-objective particle swarm algorithm." Renewable Energy Research and Applications 2.1 (2021): 9-23https://doi.org/10.1016/j.scib.2021.11.010** Zhou, J., Wang, L., Zhong, X., Yao, T., Qi, J., Wang, Y.,... Xue, Y. (2022). Quantifying the major drivers for the expanding lakes in the interior Tibetan Plateau. Science Bulletin, 67(5), 474-478. doi: - line 168: The way used to de-trend the values should be described.- If the years in which a lake or river did not freeze included in your study, how your results may change?- Figure 2 and 3 need legend and color bar.- What kind of correlation was calculated? linear or nonlinear? justify. How about MI and lagged MI? see: Danandeh Mehr, A. (2023). A Gene-Random Forest Model for Meteorological Drought Prediction. Pure and Applied Geophysics, 1-11.Reviewer #3: Using statistical methods such as the Fourier Transform and simple correlation coefficient, the authors investigate whether there is a connection between solar cycle and lake and river ice phenology over the Northern Hemisphere. However, I have several concerns that I would like to address:- In addition to temperature, it's essential to consider the role of precipitation in ice cover dynamics. I recommend that the authors include precipitation data in their sensitivity analyses, alongside temperature.- A dedicated Discussion section is necessary to contextualize the findings within the context of prior research. While some comparisons with previous work have been made, further discussion is warranted. It is recommended that the authors place a stronger emphasis on the key findings derived from the analyses conducted in this study. This would involve providing a more detailed and comprehensive discussion of the results obtained. By highlighting the specific outcomes and insights gained from their research, the authors can effectively underscore the significance of their work within the broader context of lake and river ice phenology and its potential relationship with solar cycle variations. This will help readers better understand the novel contributions of this study and its implications for our understanding of environmental and climatic dynamics.- It would be interesting to explore the importance of the solar cycle on ice-off variability in comparison to other primary variables, such as temperature and precipitation. The authors should provide an explanation for the weak correlation between solar cycles and ice-off time series.- In the Conclusions section (lines 386-399), the authors mention ocean-atmosphere circulations like PDO, NAO, AMO, ENSO, etc. as primary drivers of ice phenology, but the analysis and discussion related to these features and their correlation with ice variability are not presented in the manuscript. I also suggest they should improve the conclusions section by focusing on the findings obtained by the analyses made in the current work.- For Figures 1 and 4, it's crucial to conduct significant analyses to determine the significance of the dominant periodicities that have been identified.- For Figures 2 and 3, it's advisable to incorporate statistical tests to indicate where the correlations are statistically significant. One approach could be to mark points with significant correlations using cross-marks on the maps.- Regarding Figure 4, it would be beneficial to display the upper and lower bounds of the power spectrum surrounding the averaged curve by considering the curves from all the ice recording points.- In Figure 4, it would be helpful to provide additional explanations regarding the primary drivers of the observed dominant periodicities. Notably, there is a dominant mode at around 4.7 years in the ice-on data and approximately 15 years in the ice-off data. Exploring why these dominant modes differ and whether they have distinct drivers should be addressed.- In Figure 4: Addressing the discrepancy in dominant patterns between ice formation (ice-on) and ice melting (ice-off) periods is crucial. It's worth considering whether distinct factors drive each of these phases, and if so, how it is possible for the primary influencing factors of ice-on and ice-off processes to vary, despite both occurring within the same hydrological system.- The resolution of Figures 2 and 3 appears to be low and should be improved for better clarity.- Including several examples of ice-on and ice-off time series would enhance the presentation of the data and help illustrate key points.- Lines 217-218: The abbreviations NDJ and MAM should be defined to ensure clarity for readers.- Lines 316-330 should be relocated to the methodology section for better organization and clarity.**********  6. 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Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  10 Oct 2023Responses to Reviewer 1The manuscript predominantly relies on raw data, lacking necessary corrections or adjustments. One notable instance is the ice phenology data, which exhibits disparities in both spatial and temporal resolution. To ensure the credibility of this information, it necessitates validation through either on-site field observations or the integration of high-resolution remote sensing data. Similarly, the utilization of ECMWF data with a resolution one degree, with a lack of validation, raises concerns about its reliability for accurate assessment. Consequently, I find myself skeptical about the overall data quality as presented in its unprocessed state by the authors. Further efforts to refine and validate the data are imperative to bolster the manuscript's scientific rigor and integrity.We appreciate the reviewer’s concerns, and indeed, a study focused on the dynamics of individual lakes would require more attention to local detail than we have given here. Our focus, however, is different. We are considering the sunspot cycle as a potential driver of global climate variability. Hence, the signal we are looking for should be coherent across large spatial scales, and we need data from as many locations as possible in order to adequately study it. Thus, it is crucial that we have data spanning hundreds of locations over many decades (in some cases centuries). Under these circumstances, on-site field observations are clearly not plausible. However, it is also less important under these circumstances to have very high-resolution data and locally-specific detail, as the patterns we are looking for are much larger in scale. Responses to Reviewer 2Reviewer #2: Potential link between the 11-year sunspot cycle and Lake and River Ice Phenology was investigated. The study showed no strong impact of the solar cycle and ice phenology. The topic is very interesting, and the text was written very well, but the figures suffer from low quality. My suggestions to improve this manuscript are listed below.- No need to the given abbreviations in the Abstract.We have removed the abbreviations. - Line 107-110. Some these cited studies are suggested to be reviewed before talking about third possible driver of ice phenology.We have added some comments here. However, we keep these quite brief, as these other modes of variability are not the subject of the current study. - Introduction could be wider reviewing more recent studies. Some suggestions are:** Pourderogar, H., et al. "Modeling and technical analysis of solar tracking system to find optimal angle for maximum power generation using MOPSO algorithm." Renewable Energy Research and Applications 1.2 (2020): 211-222** Beiranvand, A., et al. "Energy, exergy, and economic analyses and optimization of solar organic Rankine cycle with multi-objective particle swarm algorithm." Renewable Energy Research and Applications 2.1 (2021): 9-23https://doi.org/10.1016/j.scib.2021.11.010** Zhou, J., Wang, L., Zhong, X., Yao, T., Qi, J., Wang, Y.,... Xue, Y. (2022). Quantifying the major drivers for the expanding lakes in the interior Tibetan Plateau. Science Bulletin, 67(5), 474-478. doi: The first two papers are about solar energy technologies and are not relevant to this manuscript, which is about the impact of the sunspot cycle on lake ice. The third paper is of more scientific interest, but it does not deal with ice, so again it is mostly unrelated to the topic of this manuscript. However, we have done an additional literature search and added some other new references to make sure the literature review is up to date. - line 168: The way used to de-trend the values should be described. We have added a note on the de-trending procedure.- If the years in which a lake or river did not freeze included in your study, how your results may change?This is an interesting question. Technically, in no-freeze years there simply is no ice-on or ice-off date to use in the calculations. One could perhaps treat the ice-on date as December 31 and the ice-off date as January 1 under such circumstances just to fill the gap, but that would still be somewhat arbitrary. This could be a real problem if it occurred frequently, but it is so rare in the NSIDC dataset (less than 1% of data) that it should be inconsequential to our conclusions. - Figure 2 and 3 need legend and color bar.There are only two colors in those figures, so a colorbar cannot really be generated. However, we acknowledge that the original versions of these figures did come out at somewhat marginal resolution. We have re-generated these figures with improved quality. - What kind of correlation was calculated? linear or nonlinear? justify. How about MI and lagged MI? see: Danandeh Mehr, A. (2023). A Gene-Random Forest Model for Meteorological Drought Prediction. Pure and Applied Geophysics, 1-11.The correlations referenced here are standard Pearson correlation coefficients. We have added a clarification to that effect. We do consider both lagged and unlagged correlations in considerable detail. Responses to Reviewer 3Using statistical methods such as the Fourier Transform and simple correlation coefficient, the authors investigate whether there is a connection between solar cycle and lake and river ice phenology over the Northern Hemisphere. However, I have several concerns that I would like to address:- In addition to temperature, it's essential to consider the role of precipitation in ice cover dynamics. I recommend that the authors include precipitation data in their sensitivity analyses, alongside temperature.We thank the reviewer for this suggestion. We have repeated the sensitivity analysis for precipitation, and we find that the precipitation effect is considerably weaker than the temperature effect. We have added a paragraph to the text to describe this analysis. - A dedicated Discussion section is necessary to contextualize the findings within the context of prior research. While some comparisons with previous work have been made, further discussion is warranted. It is recommended that the authors place a stronger emphasis on the key findings derived from the analyses conducted in this study. This would involve providing a more detailed and comprehensive discussion of the results obtained. By highlighting the specific outcomes and insights gained from their research, the authors can effectively underscore the significance of their work within the broader context of lake and river ice phenology and its potential relationship with solar cycle variations. This will help readers better understand the novel contributions of this study and its implications for our understanding of environmental and climatic dynamics.[WORK ON THIS]- It would be interesting to explore the importance of the solar cycle on ice-off variability in comparison to other primary variables, such as temperature and precipitation. The authors should provide an explanation for the weak correlation between solar cycles and ice-off time series.We have added some text to the conclusion to explain the likely reason for the weak connection between the solar cycle and ice phenology. - In the Conclusions section (lines 386-399), the authors mention ocean-atmosphere circulations like PDO, NAO, AMO, ENSO, etc. as primary drivers of ice phenology, but the analysis and discussion related to these features and their correlation with ice variability are not presented in the manuscript. I also suggest they should improve the conclusions section by focusing on the findings obtained by the analyses made in the current work.The point of that statement was to contrast the drivers  that do have significant impacts on ice-off with the subject of this study (the sunspot cycle) which does not. We have modified the text in an attempt to clarify that point. - For Figures 1 and 4, it's crucial to conduct significant analyses to determine the significance of the dominant periodicities that have been identified.There are indeed methods for this, but in Figure 4, for example, the point is not to identify all periodicities in the data, but simply to note that there is no peak at 11 years. Since the 11-year peak does not exist, there is really nothing whose statistical significance we would need to test. - For Figures 2 and 3, it's advisable to incorporate statistical tests to indicate where the correlations are statistically significant. One approach could be to mark points with significant correlations using cross-marks on the maps.Indeed, we would normally do this by using open circles and closed circles to distinguish between insignificant and significant correlations, respectively. However, in this case almost none of the correlations are significant , so instead we simply list the few correlations in the text. - Regarding Figure 4, it would be beneficial to display the upper and lower bounds of the power spectrum surrounding the averaged curve by considering the curves from all the ice recording points.We appreciate the suggestion. We have replaced the figure with a new version that includes the individual curves as faint blue lines and the average as a bold red line. - In Figure 4, it would be helpful to provide additional explanations regarding the primary drivers of the observed dominant periodicities. Notably, there is a dominant mode at around 4.7 years in the ice-on data and approximately 15 years in the ice-off data. Exploring why these dominant modes differ and whether they have distinct drivers should be addressed.The purpose of Figure 4 is simply to demonstrate that there is no clear 11-year peak in either power spectrum. Any other periodicities would be a subject for a different study. - In Figure 4: Addressing the discrepancy in dominant patterns between ice formation (ice-on) and ice melting (ice-off) periods is crucial. It's worth considering whether distinct factors drive each of these phases, and if so, how it is possible for the primary influencing factors of ice-on and ice-off processes to vary, despite both occurring within the same hydrological system.Previous studies have indeed found that the drivers of ice-on and ice-off differ, essentially because of differing atmospheric conditions between fall and spring seasons. [add details and reverences]. The question of this study, however, is simply whether the solar cycle has a noticeable influence. Figure 4 does not show any such influence for either season. - The resolution of Figures 2 and 3 appears to be low and should be improved for better clarity.Agreed. We have re-generated these images at considerably higher quality. - Including several examples of ice-on and ice-off time series would enhance the presentation of the data and help illustrate key points.We appreciate the recommendation. We have added a new figure with a few representative time series. - Lines 217-218: The abbreviations NDJ and MAM should be defined to ensure clarity for readers.These acronyms were defined in the previous section. They refer to the November-December-January (NDJ) season and the March-April-May (MAM) season. - Lines 316-330 should be relocated to the methodology section for better organization and clarity.We appreciate the recommendation. We did indeed have some difficulty separating the “methods” and “results” sections of the paper, as many of the methods would not make sense until the reader sees the results from previous analyses. The resulting structure is a little different, but for this particular paper it seems to make the presentation flow more naturally.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 14 Nov 2023Does the 11-Year Solar Cycle Affect Lake and River Ice Phenology?PONE-D-23-23784R1Dear Dr. Schmidt,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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I suggest the approval of the paper to the editors.Reviewers' comments: 20 Nov 2023PONE-D-23-23784R1 Does the 11-Year Solar Cycle Affect Lake and River Ice Phenology? Dear Dr. Schmidt: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 customercare@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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The concentrations of the metabolites of probe substrates were all measured by ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method. It was found no time-dependent inhibition (TDI) of vortioxetine through determination of half-maximal inhibitory concentration (IC50) shift values. The enzymes and metabolites involved in this experiment in human and rats were as follows: CYP3A4/CYP3A (midazolam); CYP2B6/CYP2B (bupropion); CYP2D6/CYP2D (dextromethorphan); CYP2C8/CYP2C-1 (amodiaquine); CYP2C9/CYP2C-2 (losartan); and CYP2C19/CYP2C-3 (mephenytoin). We found that vortioxetine competitively inhibited CYP2C19 and CYP2D6 in human liver microsomes (HLMs) with inhibition constant (Ki) values of 2.17 μM and 9.37 μM, respectively. It was noncompetitive inhibition for CYP3A4 and CYP2C8, and its Ki values were 7.26 μM and 6.96 μM, respectively. For CYP2B6 and CYP2C9, vortioxetine exhibited the mixed inhibition with Ki values were 8.55 μM and 4.17 μM, respectively. In RLMs, the type of vortioxetine inhibition was uncompetitive for CYP3A and CYP2D (Ki = 4.41 and 100.9 μM). The inhibition type was competitive inhibition, including CYP2B and CYP2C-2 (Ki = 2.87 and 0.12 μM). The inhibition types of CYP2C-1 and CYP2C-3 (Ki = 39.91 and 4.23 μM) were mixed inhibition and noncompetitive inhibition, respectively. The study of the above mechanism will provide guidance for the safe clinical use of vortioxetine so that the occurrence of DDI can be avoided.Vortioxetine is a novel anti-major depression disorder drug with a high safety profile compared with other similar drugs. However, little research has been done on drug-drug interactions (DDI) about vortioxetine. In this paper, the inhibitory effect of vortioxetine on cytochrome P450 (CYP450) and the type of inhibitory mechanism were investigated in human and rat liver microsomes. We set up an  With the rapid development of the times and fast-paced life, the number of people suffering from depression is increasing year by year . The cogIn addition to paying attention to the pharmacodynamics of the drug, we should also pay attention to its pharmacokinetics, such as what happens to vortioxetine in the body’s metabolism. Drug’s metabolism through the liver, gastrointestinal tract, lungs, skin, kidneys, etc., of which 90% of the drugs are metabolized by the liver . The reaHere we explored the type of mechanism of inhibition by vortioxetine on CYP3A4/3A, CYP2B6/2B, CYP2C8/2C-1, CYP2C9/2C-2, CYP2C19/2C-3, and CYP2D6/2D in human liver microsomes (HLMs) and rat liver microsomes (RLMs), in which the substrates corresponding to each enzyme were midazolam, bupropion, amodiaquine, losartan, mephenytoin, and detromethorphan. We used an ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) as the analytical instrument to assess the specific mechanism of inhibition of vortioxetine on CYP450 enzymes. However, no related results have been reported before.HLMs and RLMs all were purchased from iPhase Pharmaceutical Services Co., Ltd. . Metabolic probe substrates and their metabolites including midazolam, 1-hydroxymidazolam, bupropion, hydroxy bupropion, amodiaquine, N-desethyl amodiaquine, losartan, E-3174, mephenytoin, 4-hydroxy mephenytoin, dextromethorphan, and dextrorphan were purchased from Shanghai Canspec Scientific Instruments Co., Ltd. . The internal standard diazepam and vortioxetine were purchased from Beijing Sunflower Technology Development Co., Ltd. . The reduced nicotinamide adenine dinucleotide phosphate (NADPH) used in the incubation system was purchased from Roche Pharmaceutical Ltd. . Methanol and acetonitrile used for UPLC-MS/MS were purchased from Merck . Ultrapure water was obtained from Millipore  by using a Milli-Q A10 purification system.in vitro incubation system of 200 μL was established, and the incubation temperature was 37°C. The components of the incubation system were as follows: 0.20 mg/mL HLMs or 0.20 mg/mL RLMs, 0.1 M Tris-HCl, vortioxetine, and the probe substrates of each enzyme. In the study of Km’s (Michaelis–Menten constant) experiment, midazolam  corresponded to CYP3A4/3A; bupropion  corresponded to CYP2B6/2B; amodiaquine  corresponded to CYP2C8/CYP2C-1; losartan  corresponded to CYP2C9/CYP2C-2; mephenytoin  corresponded to CYP2C19/CYP2C-3; and dextromethorphan  corresponded to CYP2D6/CYP2D. After 5.0 min of preincubation in the shaking water bath, the reaction was started with the addition of 1.0 mM NADPH. The incubated temperature at 37°C for 30 min. After the incubation, the enzyme reaction was stopped by cooling at −80°C, and 200 μL of acetonitrile and 20 μL of internal standard diazepam (200 ng/mL) were added for protein precipitation. The solution was fully vortexed and centrifuged at 13,000 × rpm for 10 min, and the supernatant was collected for data analysis by UPLC-MS/MS.To study the inhibitory effect of vortioxetine on specific enzymes, 50) was the concentration of the inhibitor to cause 50% inhibition of original enzyme activity. To explore the IC50, the concentrations of the inhibitor vortioxetine were all 0.01, 0.1, 1, 10, 25 and 50 μM, while individual substrate was set according to its corresponding Km value.The significance of half-maximal inhibitory concentration (IC50-shift was used to investigate whether the drug produced time-dependent inhibition (TDI) of the enzyme. This inhibitory property belongs to the category of irreversible inhibition among the types of enzyme inhibition. The IC50-shift experiment was performed by preincubating vortioxetine with HLMs or RLMs for 30 min at 37°C in the absence and presence of 1.0 mM NADPH. The above mixture was then incubated for another 30 min. During the experiment, the individual substrates midazolam (4 μM), bupropion (40 μM), amodiaquine (3 μM), losartan (8 μM), mephenytoin (90 μM), and dextromethorphan (10 μM) were determined with vortioxetine IC50-shift in HLMs. In RLMs, the substrates midazolam (3.5 μM), bupropion (20 μM), amodiaquine (20 μM), losartan (15 μM), mephenytoin (400 μM), and dextromethorphan (20 μM) were determined with vortioxetine IC50-shift, which based on its Km value. Other experimental conditions were the same as above.IC50 and Km values, respectively.The inhibitory effect of the drug on different enzymes was different. The inhibitory mechanism of the inhibitor on the enzyme was generally used by different concentrations of the inhibitor against different concentrations of the probe substrate. The concentrations of the inhibitor and the concentrations of probe substrates in the experiment were estimated based on ICIn HLMs, the concentrations of the substrates midazolam, bupropion, amodiaquine, losartan, mephenytoin, and dextromethorphan were 1, 2, 4, 8 μM; 10, 20, 40, 80 μM; 0.8, 1.5, 3, 6 μM; 2, 4, 8, 16 μM; 20, 45, 90, 180 μM; 2.5, 5, 10, 20 μM, respectively. Corresponding to the concentration of vortioxetine were 2, 4, 9, 18 μM; 2, 4, 8, 16 μM; 4, 8, 15, 30 μM; 5, 10, 25, 50 μM; 0.6, 1.2, 2.5, 5 μM; 4.5, 9, 18, 36 μM, respectively.In RLMs, the concentrations of the substrates midazolam and dextromethorphan were 0.8, 1.6, 3.5, 7 μM and 5, 10, 20, 40 μM, respectively, vortioxetine concentrations were 4, 8, 16, 32 μM. The concentrations of mephenytoin and amodiaquine were 100, 200, 400, 800 μM and 5, 10, 20, 40 μM, respectively, vortioxetine concentrations were 5, 10, 20, 40 μM. The losartan concentrations were 4, 8, 15, 30 μM, corresponding to vortioxetine concentrations of 0.08, 0.15, 0.3, 0.6 μM. Bupropion concentrations were 5, 10, 20, 40 μM, corresponding to vortioxetine concentrations of 2, 4, 8, 16 μM. The experimental conditions were the same as above.All experiments results were detected by UPLC-MS/MS, which equipped with a Waters Acquity UPLC system  and a Waters Xevo TQ-S triple quadrupole tandem mass spectrometer with an electrospray ionization source . The chromatographic separation was performed on a Waters ACQUITY UPLC BEH C18 column  at 40°C. The mobile phase was consisted of acetonitrile (A) and 0.1% formic acid in water (B). The gradient elution at a flow rate of 0.4 mL/min was conducted as follows: 0–0.5 min, 10% A; 0.5–1.0 min, 10%–90% A; 1.0–2.0 min, 90% A; 2.0–2.1 min, 90%–10% A, and 2.1–3.0 min, 10% A. The relevant analysis parameters were shown in 50 values and enzyme kinetic parameters were calculated by Graphpad Prism 6.0 . Lineweaver-Burk Plot was used to determine inhibitor patterns. Dixon plots of Ki values were obtained from the slope of the nonlinear regression line in the Lineweaver-Burk plot as a function of different inhibitor concentrations, while secondary plots for αKi were obtained from the y-intercept of the nonlinear regression line in Lineweaver-Burk Plot versus concentration of different inhibitors. Data for each experiment were expressed as mean ± SD (n = 3).ICAccording to the results in 50-shift was used to assess whether there was a TDI in the process of enzyme inhibition. As previously reported, when IC50-shift >10, there will be obvious TDI. According to the results, we found no TDI for CYP450 by vortioxetine  of 254.72 ngh/mL ((0-t) was 10% higher and Cmax 24% lower than healthy subjects, but it is not considered to adjust the clinical dose (2022). However, the plasma protein of vortioxetine was as high as 99%, and no serious adverse reactions caused by other drugs competing with vortioxetine for plasma proteins have been found, which should be paid attention to together with vortioxetine’s inhibition of CYP450 enzymes in vivo. Other antipsychotic drugs inhibited CYP450 enzymes as well. For example, the novel atypical antipsychotic drug asenapine is mainly metabolized in the body by CYP1A2, CYP2D6 and CYP3A4. In vitro, it was found that asenapine significantly inhibited CYP1A2, CYP2D6, CYP3A4, and the data of CYP1A2 and CYP2D6 were likely to speculate that the same reaction will occur in vivo (in vitro was found, and it is very likely that it can occur in vivo (Vortioxetine at a single oral dose of 10 mg was clinically used in 14 healthy subjects with a C2 ngh/mL . Another in vivo . If inhi in vivo .in vitro on the inhibitory effect of vortioxetine on CYP450. Based on the inhibition mechanism discussed above, it is helpful to treat different conditions encountered by different patients, and it also provides an indispensable basis for the research of in vivo.In this paper, the studies we did were in vivo experimental data and provide guidance for the safe use of vortioxetine in clinical practice.In summary, we investigated the inhibitory mechanism and Ki values of vortioxetine on CYP3A4/3A, CYP2B6/CYP2B, CYP2C8/CYP2C-1, CYP2C9/CYP2C-2, CYP2C19/CYP2C-3, CYP2D6/CYP2D and found no TDI effect of vortioxetine on CYP450. In HLMs, CYP3A4 and CYP2C8 were noncompetitive inhibition, CYP2B6 and CYP2C9 were mixed inhibition, and CYP2C19 and CYP2D6 were competitive inhibition. In RLMs, CYP2C-3 was noncompetitive inhibition, CYP2C-1 was mixed inhibition, CYP2B and CYP2C-2 were competitive inhibition, and CYP3A and CYP2D were uncompetitive inhibition. All data need to be supported by future


It demonstrated complex voltage dependence with voltage-independent and voltage-dependent components, suggesting the presence of shallow and deep binding sites. At −80 mV holding voltage, the voltage-dependent component dominates, and we observed pronounced tail currents and overshoots evidencing a “foot-in-the-door” open channel block. At depolarized voltages, the voltage-independent inhibition by sepimostat was significantly attenuated by the increase of agonist concentration. However, the voltage-independent inhibition was non-competitive. We further compared the mechanisms of the action of sepimostat with those of structurally-related amidine and guanidine compounds—nafamostat, gabexate, furamidine, pentamidine, diminazene, and DAPI—investigated previously. The action of all these compounds can be described by the two-component mechanism. All compounds demonstrated similar affinity to the shallow site, which is responsible for the voltage-independent inhibition, with binding constants in the range of 3–30 µM. In contrast, affinities to the deep site differed dramatically, with nafamostat, furamidine, and pentamidine being much more active.N-methyl-D-aspartate (NMDA) receptors are inhibited by many amidine and guanidine compounds. In this work, we studied the mechanisms of their inhibition by sepimostat—an amidine-containing serine protease inhibitor with neuroprotective properties. Sepimostat inhibited native NMDA receptors in rat hippocampal CA1 pyramidal neurons with IC The NMDA receptors are a subtype of ionotropic glutamate receptors, playing important roles in synaptic plasticity, learning and memory, neuronal development, and survival . They ar50 value in submicromolar range. In our previous papers [50 value of 0.20 ± 0.04 µM at −80 mV holding voltage [In addition, NMDA receptors are inhibited by many compounds, possessing amidine or guanidine groups. Antimicrobial diarylamidine medication pentamidine ,15 is a s papers ,17, we h50 values of 4.52 and 29.8 µM, respectively. In contrast, gabexate and camostat—two other serine protease inhibitors—did not demonstrate retinal protection activities in their experiments.Sepimostat mesilate FUT-187), an orally active derivative of nafamostat, is another amidine-containing inhibitor of serine proteases 7, an ora,20. HoweIn this paper, we used patch clamping to check whether sepimostat would be able to inhibit native NMDA receptors and to describe its kinetics and molecular mechanisms of action. We compared the mechanisms of NMDA receptor inhibition by sepimostat to those of nafamostat and gabexate , furamid50 values and Hill coefficients of 1.8 ± 0.4 µM and 1.1 ± 0.2 (peak) and 3.5 ± 0.3 µM and 0.9 ± 0.1 (steady-state), respectively. The sensitivity of different CA1 pyramidal neurons to sepimostat was similar. It is worth noting that sepimostat was significantly less active than nafamostat (IC50 = 0.20 ± 0.04 µM) [Application of extracellular solutions containing NMDA 100 µM and glycine 10 µM induced desensitizing inward currents in pyramidal cells of the CA1 region of the hippocampus with a steady-state component of about 300–1000 pA at −80 mV holding voltage. Different concentrations of sepimostat were applied simultaneously with agonists leading to a reversible concentration-dependent inhibition of the peak and steady-state component of the currents through NMDA receptors A. High c0.04 µM)  in this https://chemaxon.com/, accessed on 5 September 2023) predictions, a sepimostat molecule possesses a +1 charge predominantly  at pH 7.4. Thus, the δ value can be considered to be 0.6 ± 0.2 as well. The constant for voltage-independent binding (Kvi) was 7 ± 1 µM, and the constant for voltage-dependent binding (Kvd) was ~130 µM. Thus, sepimostat has much higher affinity for the external site than for the site in the channel pore. As a result, the voltage-dependent inhibition is seen only at hyperpolarized membrane potentials, which enhance the voltage-dependent action. The concentration–inhibition curves for sepimostat action at conditions favoring binding to the external site (+30 mV) are shown in 50 values and Hill coefficients were 3.5 ± 0.8 µM and 0.9 ± 0.1 (peak) and 5.8 ± 1.5 µM and 0.9 ± 0.1 (steady-state), respectively. The IC50 value for the steady-state component (5.8 µM) is in good agreement with the Kvi value (7 µM) derived from the voltage dependence data analysis.A sepimostat molecule contains one amidine and one 4,5-dihydro-1H-imidazol-2-ylamino group B. ChargeWe performed further experiments to better understand the nature of both voltage-dependent and voltage-independent components of inhibition by sepimostat and other guanidine and diarylamidine compounds.According to interaction with channel gate the ion channel blockers are divided into two major classes. Trapping blockers can remain in the closed channels while “foot-in-the-door” compounds prevent either channel desensitization, channel closure, agonist dissociation, or all of these processes . We haven = 6). The intersection of the control tail currents and the tail currents after the agonist and the blocker coapplication  at −80 mV holding voltage demonstrated tail currents that significantly prolonged the response A,B. The lication B is a din = 4). For 10 µM sepimostat concentration, the plateau/peak ratio was significantly increased , implying that only nonblocked channels can desensitize. According to Sobolevsky and coauthors the normalized plateau/peak ratio ((IBS/IB0)/(ICS/IC0)) > 1 implies that the blocker prevents channel desensitization. This ratio was 1.8 ± 0.1 for 10 µM sepimostat (n = 4) at −80 mV holding voltage, suggesting the prevention of channel desensitization. At +30 mV holding voltage, the plateau/peak ratio in control was 0.52 ± 0.09 (n = 4), which was not significantly different from the plateau/peak ratio at −80 mV (0.6 ± 0.1). These data suggest that calcium-dependent inactivation does not play a significant role in the NMDA receptor desensitization in our experimental conditions and that the inhibitory action of sepimostat is not related to the effect on calcium-dependent inactivation.In a thorough paper, Sobolevsky and coauthors  elaboratn = 4). The kinetics of the overshoot rising phase was very fast: τrise = 62 ± 15 ms (n = 4). That of the falling phase was significantly slower . It is worth mentioning that nafamostat did not significantly change the plateau/peak ratio and did not demonstrate overshoots, evidencing the absence of its effect on NMDA receptor desensitization, in contrast to sepimostat [Another sign of the prevention of desensitization by a channel blocker is the appearance of an overshoot after the removal of a blocker in the continuous presence of the agonists, and it was also observed in the case of 30 µM sepimostat D,E. Suchpimostat .n = 5).To fully exclude the trapping effect, we tested sepimostat in the “double-pulse” protocol ,24, whicAn analysis of the voltage-independent mechanism of block requires experimental conditions that minimize the voltage-dependent component. At low voltages, the currents are too small for a precise analysis; at positive voltages, the low clamp stability complicates long complex experiments. Therefore, we tested sepimostat action at −30 mV. It is worth noting that at −30 mV holding voltage, the amplitude of the tail currents was significantly smaller than at −80 mV holding voltage, and the tail currents after the agonist and the blocker co-application did not intersect with control tail currents.One of the most important physiological properties of NMDA receptors is a voltage-dependent block by magnesium ions ,4. Becau50 values for sepimostat action in the presence and absence of magnesium (n = 7) and 3.6 ± 1.0 µM (n = 5), respectively, evidencing the idea that sepimostat does not compete with Mg2+ for a binding site in the NMDA receptor channel pore at −30 mV holding voltage . These data suggest the prevalence of a voltage-independent component of inhibition at −30 mV. It is worth mentioning that at −30 mV, in the presence of 1 mM magnesium ions, nafamostat was only two-fold more active, than sepimostat, with IC50 values 1.7 ± 0.2 and 3.6 ± 1.1 µM, respectively. The difference in activities at −80 mV in the absence of magnesium was ~20-fold (IC50 = 0.20 ± 0.04 µM for nafamostat and 3.5 ± 0.3 µM for sepimostat). These data once again emphasize the importance of measuring activities against NMDA receptors in conditions that are closer to physiological.The ICagnesium  were 3.6To reveal the mechanism of the voltage-independent inhibition, we first checked if it depended on NMDA concentration. In our experiments, the percentage of inhibition by 5 µM sepimostat significantly decreased with the increase in NMDA concentration from 30 to 1000 µM A at −80 n = 4, n = 10, p > 0.1, unpaired t-test).At −80 mV holding voltage, the action of sepimostat includes a voltage-dependent component. To avoid it, we performed an analogous analysis at −30 mV, where the voltage-independent action dominates (see above). The inhibition by sepimostat at these high NMDA concentrations was attenuated at −30 mV holding voltage but remained significant at high NMDA concentrations B, suggesSepimostat is structurally very similar to nafamostat, a “foot-in-the-door” NMDA receptor channel blocker described by us previously . They bovd) and shallow (Kvi) sites are presented in NMDA receptor inhibition by sepimostat was mostly voltage-independent with a small voltage-dependent component. Among the compounds studied, a very similar voltage dependence was demonstrated by diminazene A. Complevd values and zδ values of 0.6 and 0.9, respectively. For the voltage-independent inhibitors DAPI and gabexate, the parameters, which describe a voltage-dependent block, cannot be estimated reliably. However, the experimental data on gabexate and DAPI can be readily fitted with the consensus zδ value 1.1 for the doubly charged DAPI (taken from fitting of nafamostat and furamidine) and 0.55 for the single-charged gabexate (taken from fitting of sepimostat) (see vd). The single exception is camostat, which exhibited low activity and was not studied in detail.All parameters are very close for the voltage-dependent blockers nafamostat, furamidine, and pentamidine, including zδ values of 1.1 to 1.4. The second group includes sepimostat and diminazene with large Ktat) see . Accordin = 5, p > 0.05, paired t-test), but at −30 mV, the significant attenuation was observed . The voltage-independent action of DAPI was found to be agonist-dependent at both −80 mV  and −30 mV . However, for both diminazene and DAPI, the inhibition effect at −30 mV remained significant at 1 mM NMDA concentration. Thus, we can conclude that for all compounds belonging to the structural family, the voltage-independent component of the action is agonist-dependent but non-competitive.Finally, we compared the agonist dependence of the action of all the compounds. It has been shown previously that the voltage-independent inhibition by gabexate is agonist-dependent but non-competitive . In the In this paper, we have shown that sepimostat inhibits native rat hippocampal NMDA receptors by interacting with two sites—shallow and deep. It acts as a voltage-dependent “foot-in-the-door” NMDA receptor channel blocker and also causes voltage-independent inhibition, which is agonist-dependent but non-competitive. At −80 mV holding voltage, sepimostat inhibited peak currents stronger than plateau currents, which is typical for fast “foot-in-the-door” NMDA receptor channel blockers, for example, tetrapentylammonium . NMDA re2+, memantine, ketamine, MK-801, and others) in the NMDA receptor channel pore is well characterized [We have also systematically compared molecular mechanisms of inhibition by sepimostat to those of other serine protease inhibitors  and diarylamidine compounds . Except for DAPI, abovementioned compounds demonstrated complex voltage dependence with voltage-dependent and voltage-independent components, suggesting binding to the deep site in the pore and a superficial site. The action of DAPI was completely voltage-independent. Likely, affinity for the deep binding site is low, and a weak voltage-dependent component of the action is masked by the voltage-independent inhibition. The analysis of voltage dependencies of nafamostat, furamidine, and diminazene gave zδ values of about 1.0. That of pentamidine was higher—1.4. This subtle difference can be easily explained by the flexibility of pentamidine molecule, resulting in the interaction of both charged groups with the selectivity filter . In the cterized ,30. SuchThe flexibility of pentamidine molecule also explains why only this compound from this structurally similar group is able to stay in the closed NMDA receptor channel . The molThe location of the superficial site is not so clear. Our data suggest that the action significantly decreases with the rise in NMDA concentration, but it is non-competitive. Among non-competitive antagonists, such a decrease in activity with the increase in agonist concentration has been shown earlier for the NMDA receptor “foot-in-the-door” blocker tetrabutylammonium . It is i50 = 1.7 ± 0.2 µM) and sepimostat (3.6 ± 1.1 µM) are even slightly more active than memantine . Both sAll experimental procedures were approved by the Animal Care and Use Committee of the Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences . Outbred male Wistar rats (13–18 days old and weighing 25–35 g) were obtained from a local (IEPHB) facility. Maximum efforts were made to minimize the number of animals used and to minimize discomfort.2 1.3, MgCl2 2.0, NaHCO3 26, NaH2PO4 1.24, D-glucose 10, aerated with carbogen . All experiments were performed at room temperature.The rats were anesthetised with sevoflurane and then decapitated. The brains were brought out quickly and cooled to 2–4 °C. Transverse hippocampal slices were cut using a vibratome  and stored in a solution containing (in mM): NaCl 124, KCl 5, CaClVibrodissociation method ,33 was u2 2.5, D-glucose 18, HEPES 10 (pH adjusted to 7.4 with HCl). The pipettes with resistance of 2–5 MΩ were filled with the following solution (in mM): CsF 100, CsCl 40, NaCl 5, CaCl2 0.5, EGTA 5, HEPES 10 (pH adjusted to 7.2 with CsOH). Sepimostat (HY-136299) was obtained from MedChemExpress . Other reagents were purchased from MedChemExpress , Sigma , or Tocris Bioscience .To record membrane currents in response to applications of NMDA and glycine, the whole-cell configuration of patch clamp technique was used. Series resistance (<20 MΩ) was compensated by 70–80% and monitored during experiments. Only cells with stable holding currents were used in further analysis. The current signals were amplified using EPC-8 , filtered at 5 kHz, sampled, and stored on a personal computer. RSC-200  perfusion system was used to apply the drugs under computer control. The composition of extracellular solution (in mM) was as follows: NaCl 143, KCl 5, CaClNMDA receptors were activated with 100 µM NMDA plus 10 µM glycine unless otherwise stated. The percentages of blocking of the steady-state or peak currents by different drug concentrations were measured at −80, −30 or +30 mV holding voltages. Kinetics of transient processes of more than 20 ms duration were approximated with single or double exponential functions. In case of double exponential fitting, the weighted time constant was used.The voltage dependence of compounds’ action was analyzed with the classical Woodhull model  with theOur experimental data were not well fitted by abovementioned equation, presumably because of the presence of voltage-independent component. Thus, we deduced Equation (2), taking it into account and assuming that the binding to the deep and shallow site is independent:In this equation, Kvd is the affinity of the drug to the deep site, Kvi—the affinity of the drug to the shallow site, and other parameters are the same as in Equation (1).t-tests. Differences were considered significant at p < 0.05. Concentration dependencies were approximated with Hill equation. Voltage dependencies were approximated with Equation (2). Patch destabilization with a large number of transitions between different potential states and the limited capability of the application system (a maximum of 8 solutions) did not allow for an estimation of the entire voltage dependence and agonist dependence, respectively, in a single experiment. The data from different cells  were pooled together and fitted with Equation (2) (voltage dependence) or Hill equation (agonist dependence) using Origin 2019b 9.65  software. Approximation error values were taken as the precision measures.All experimental data are presented as the mean ± SD estimated from at least four experiments (cells). Significance of the effects was tested with


Neurospora crassa is an important model organism for circadian clock research. The Neurospora core circadian component FRQ protein has two isoforms, large FRQ (l-FRQ) and small FRQ (s-FRQ), of which l-FRQ bears an additional N-terminal 99-amino acid fragment. However, how the FRQ isoforms operate differentially in regulating the circadian clock remains elusive. Here, we show l-FRQ and s-FRQ play different roles in regulating the circadian negative feedback loop. Compared to s-FRQ, l-FRQ is less stable and undergoes hypophosphorylation and faster degradation. The phosphorylation of the C-terminal l-FRQ 794-aa fragment was markedly higher than that of s-FRQ, suggesting the l-FRQ N-terminal 99-aa region may regulate the phosphorylation of the entire FRQ protein. Quantitative label-free LC/MS analysis identified several peptides that were differentially phosphorylated between l-FRQ and s-FRQ, which were distributed in FRQ in an interlaced fashion. Furthermore, we identified two novel phosphorylation sites, S765 and T781; mutations S765A and T781A showed no significant effects on conidiation rhythmicity, although T781 conferred FRQ stability. These findings demonstrate that FRQ isoforms play differential roles in the circadian negative feedback loop and undergo different regulations of phosphorylation, structure, and stability. The l-FRQ N-terminal 99-aa region plays an important role in regulating the phosphorylation, stability, conformation, and function of the FRQ protein. As the FRQ circadian clock counterparts in other species also have isoforms or paralogues, these findings will also further our understanding of the underlying regulatory mechanisms of the circadian clock in other organisms based on the high conservation of circadian clocks in eukaryotes. Circadian clocks enable organisms to predict and adjust their physiology and behavior to the daily changing environment . It has Neurospora crassa is a classical model for circadian research, which has substantially contributed to the understanding of circadian systems in other organisms  and WC-2 are the two positive elements that form the White Collar Complex (WCC) to activate the transcription of the frequency (frq) gene; and the latter encodes the FRQ protein which is the negative element , which is called small FRQ (s-FRQ)  of ely used . Translain total , 23, 24. (s-FRQ) , 23. Sevvariants , 26, 27.nslation , 22, 23.l-frq strain) displays a shorter period while the strain expressing only s-FRQ (s-frq strain) displays a longer period, in comparison to the wild type, respectively. S-FRQ loses its function at high temperatures, while l-FRQ becomes dysfunctional at low temperatures , an inhibitor of spliceosome assembly , indicating that suppression of the splicing of frq I-6 directly affects the production of the synthesis ratio of FRQ isoforms.Both splicing and ambient temperature contribute to the production of s-FRQ and l-FRQ , 23, 27.assembly . Multiplassembly ; therefoO strain A, indicafrq and other clock-controlled genes in a circadian fashion, and FRQ represses the function of WCC in a negative feedback loop . These data support that l-FRQ and s-FRQ may function differentially to sustain circadian rhythmicity in the negative feedback loop in a temperature-sensitive manner , the levels of WC-2 and FRQ were between those of s-frq and l-frq, reflecting a mixed effect. By contrast, the change in WC-1 levels was comparable in all three strains (B and C), which is in agreement with previous reports (s-frq strain was much higher than that in the other two strains at 28 °C (D).As the ratio of FRQ isoforms differs at high and low temperatures, changed s-FRQ/l-FRQ ratios may affect the levels of WCC proteins , 23, 28. reports , 23, 28. strains , B and C reports , 23. Howat 28 °C D.wc-2 and the accumulation of WC-1 protein through different positive limbs (wc-1 and wc-2 which showed comparable expression at both 20 °C and 28 °C (B). WC-2 protein showed a faster degradation rate at 28 °C than that at 20 °C, while the degradation rates of WC-1 or WC-2 were comparable in s-frq or l-frq strains at both 20 °C and 28 °C (C–F). Therefore, together, these facts suggest that the increased degradation of WC-2 at 28 °C in s-frq may account for the difference in WC-2 expression from that in l-frq.Besides its role in the circadian negative feedback loop, FRQ also supports the transcription of ve limbs , 9, 32. nd 28 °C B. WC-2 pnd 28 °C , C–F. ThIn addition to the repression of WCC function in promoting transcription, FRQ also facilitates the phosphorylation and accumulation of WCC which constitutes a positive loop in the circadian clock negative feedback circuits, which leads to reduced WCC turnover. Therefore, we measured the levels of WCC proteins as this way the function of FRQ could be determined , 14. Andl-frq and s-frq strains to assess the association between WCC and s-FRQ and l-FRQ at 20 °C and 30 °C, respectively. The CO-IP results showed that s-FRQ bound to more WCC than l-FRQ at both temperatures (E and F), which suggests that s-FRQ exhibits higher binding to WCC in the negative feedback loop.FRQ binds to the WCC complex to repress its own transcription, which closes the negative feedback loop. Next, we conducted co-immunoprecipitation (CO-IP) with WC-2 antiserum in the eratures , E and FG), suggesting that the phosphorylation of FRQ isoforms is differentially regulated by CK1a. WCC proteins undergo phosphorylation and dephosphorylation by related kinases and phosphatases, and the WCC phosphorylation levels determine their turnover .The FRQ-CK1a association determines the prevalent phosphorylation of FRQ and the circadian period , 33. ComFig. 1turnover . We condA–C). To exclude the possibility of artificial effects caused by CHX, we also examined the degradation rates of l-FRQ and s-FRQ by LD transition experiments. FRQ protein maintains a relatively high level in constant light and undergoes gradual degradation in the first several hours after transition into darkness, and the decrease in l-FRQ level was faster at 20 °C and 25 °C (D–F).Phosphorylation and stability are two crucial aspects of FRQ protein, which are closely related to its function , 34. Hownd 25 °C , D–F.Figl-frq were loaded for electrophoresis for approximately 10 min, and then the CHX-treated protein samples of s-frq were loaded on the same gel, to allow the samples of s-frq and l-frq to run at comparable levels for better comparison of their phosphorylation. The phosphorylation of l-FRQ reached its highest level after 3 h while the phosphorylation of s-FRQ was significantly delayed (A–C). These results demonstrate the differential phosphorylation and turnover between the FRQ isoforms.We also compared the changes in phosphorylation levels of l-FRQ and s-FRQ during their degradation. The CHX-treated protein samples of  delayed , A–C. ThAs the entire sequence of s-FRQ is identical to the C-terminus of l-FRQ (100 aa–989 aa), the higher phosphorylation level of l-FRQ might be attributed to higher phosphorylation of the 890 aa C-terminus. Another possibility is that the l-FRQ N-99 fragment itself but not the rest part is more phosphorylated in l-FRQ. A third possibility is the combination of both.G). The constructs expressing l-FRQ-c-Myc-His and s-FRQ-c-Myc-His bearing were transformed into the frq10 strain, respectively, and the resultant transformants frq10, s-frq-c-myc-his, and frq10, l-frq-c-myc-his were obtained. The l-FRQ-c-Myc-His and s-FRQ-c-Myc-His proteins were purified and subjected to thrombin cleavage and λ-phosphatase for dephosphorylation. The migration of cleaved products was obviously slower in l-FRQ-c-Myc-His than that in s-FRQ-c-Myc-His (H), suggesting that at least the C-terminus of FRQ is more hyperphosphorylated in l-FRQ than that in s-FRQ and the N-99 aa fragment of l-FRQ regulates the phosphorylation of FRQ C-terminus.Sequence analysis indicates that both l-FRQ and s-FRQ harbor one common thrombin cleavage site, which is located between R195 and R196 in l-FRQ and between R96 and R97 in s-FRQ G. The co-Myc-His H, suggesA). Similarly, the results from partial trypsin digestion showed that l-FRQ decreased more rapidly than s-FRQ . Together, these results indicate that l-FRQ might possess a looser structure that is more subject to phosphorylation and degradation, and the differential conformation of FRQ isoforms might result from the N-99 aa region itself and/or the effects of N-99 aa region on the rest parts of FRQ.Phosphorylation and degradation of FRQ are tightly linked to its structure , 34. FRQFig. 3frq10 (A and B). The results of the race tube assay showed that l-FRQΔ(71–77) and l-FRQΔ(78–99) displayed arrhythmic phenotypes while other strains displayed rhythms of conidial zonation. The period of l-FRQΔ(2–33) was shorter while l-FRQΔ(34–55) was longer than l-frq (C).To map the minimal region involved in the determination of the function of the N-99 region, we generated a series of constructs with deletions in the N-99 region, which were transformed into frq10 , A and Ban l-frq C.Figure frq promoter into the l-FRQΔ(71–77) and l-FRQΔ(78–99) strains and found that, consistently, robust rhythmic luciferase activity was abolished in these two strains (D). These data suggest that the aa 71 to 99 region is required for the function of the N-99 region, and deletion of it may compromise the function of the entire FRQ protein. In contrast, the strains harboring mutations in this region, including the strain with mutation of S73A and the M2 strain with mutations of S72A, S73A and S76A, displayed conidiation rhythmicity . Deletion of these FRQ regions at its N terminus led to different influences on the phosphorylation of FRQ and WC-1. FRQ displayed overt hypophosphorylation in the strain l-FRQΔ(71–77) compared to other deletion strains; the WC-1 phosphorylation levels in l-FRQΔ(34–55) and l-FRQΔ(71–77) strains were higher than those in the strains of l-FRQΔ(2–33), l-FRQΔ(55–70) and l-FRQΔ(78–99). These data suggest that these regions differentially affect FRQ phosphorylation and contribute to the positive limb of the circadian clock (A). The turnover of l-FRQΔ(71–77) and l-FRQΔ(78–99) was significantly slower than that of l-FRQ (B and C), suggesting that although these deletion strains cannot precisely mimic the phenotype of the s-frq strain, the 71 to 99 aa region plays a critical role in regulating FRQ function and circadian rhythms.We measured the expression and phosphorylation of clock proteins in the deletion strains, and the Western blotting results showed that deletion of l-FRQ(71–77) led to a dramatic decrease in the level of hyperphosphorylated FRQ proteins compared to other deletions A. Deletian clock A. The tuof l-FRQ , B and Cfrq10, l-frq-c-myc-his, and frq10, s-frq-c-myc-his strains which were subsequently subjected to quantitative label-free LC-MS analysis (A–C).As the phosphorylation level is higher in l-FRQ than that in s-FRQ, we conducted label-free LC-MS to quantitatively detect the phosphorylation sites in l/s-FRQ proteins. L-FRQ-c-Myc-His and s-FRQ-c-Myc-His proteins were purified from analysis , A–C.FigD and D and E).The results from label-free quantitative LC/MS analysis revealed 19 peptides with differential phosphorylation patterns between s-FRQ and l-FRQ. The preferential phosphorylation at the sites in the N-99 aa region supports the validity of the present analysis. In addition, phosphorylation occurred in s-FRQ and l-FRQ in an interlaced pattern, in which even some neighboring sites showed opposite preferential phosphorylation  which showed no significant difference in phosphorylation between s-FRQ and l-FRQ. Both of these sites are located in the FRQ6 region, and especially, T781 is located in the FRQ6B region which is critical for FRQ-FRH interaction and circadian rhythmicity  and T781A (l-FRQT781A) in the l-frq strain, respectively. In addition, we also generated mutants of T782A (l-FRQT782A) and T781A,T782A  although phosphorylation of T782 was not found in this work or in previous studies . L-FRQT781A but not l-FRQS765A showed lower phosphorylation levels under constant light compared to that of l-FRQ . Co-IP was conducted to measure the association between mutated FRQ and FRH, and the results showed that mutations of the three sites, S765A, T781A, and T782A, did not abolish the FRQ–FRH interaction (D). In most cases, a higher phosphorylation level is correlated with a decreased stability of FRQ (E). The two mutations on s-FRQ, s-FRQS666A and s-FRQT682A which are counterparts to l-FRQS765A and l-FRQT781A, respectively, showed no overt effects on conidiation rhythmicity, and mutation of s-FRQT682A but not s-FRQS666A caused a slower degradation rate compared to the s-frq strain (A–C). The functional significances of phosphorylation on l-FRQT781 and s-FRQT682 remain to be further investigated.We generated mutants harboring S765A  and demonstrate that l-FRQ is more effective in recruiting CK1 which leads to its higher phosphorylation (H and 2H). L-FRQ shows faster phosphorylation and degradation than s-FRQ (A–H and A–C). All of these findings support that FRQ isoforms play differential roles in regulating Neurospora circadian rhythms.The alternative splicing of nd s-FRQ , 22, 23.nd s-FRQ , B–D andrylation H and 2H. A–H and , A–C. AlH and 6D), which might contribute to the differential turnover, conformation and function between FRQ isoforms. Bake et al. reported that FRQ mutation S73 led to an increased period length (1.8 h–2.3 h) in the WT strain , which might be attributed to differential phosphorylation of the same sites or phosphorylation of different sites in the same regions. This interlaced phosphorylation pattern suggests that some of these phosphorylated regions may have distinct functions in controlling the circadian clock, and the N-terminal region of l-FRQ may play an important role in modulating phosphorylation, stability, conformation, and accessibility to kinases of the FRQ protein. Moreover, we identified two novel phosphorylation sites in the FRQ protein, S765 and T781 (A). Based on this report and previous reports . The very high abundance of phosphorylation sites indicates that FRQ has a very intricate structure that undergoes complex regulation.Mutations of most phosphorylation sites in the middle part of FRQ lead to longer periods , 18. We Fig. 6and T781 A. Based  reports , 18, 103Neurospora circadian period. Nonetheless, there are some exceptions, for example, in the strain lacking the F-box protein FWD-1, an E3 ubiquitin ligase, FRQ oscillates with a period of ∼24 h despite its severely compromised turnover (B and E and A–C), which supports that the FRQ stability does not always correlate with the circadian period , and consistently, l-FRQ displayed higher phosphorylation and faster turnover rate (A–F and H and A–C). These data provide more evidence that the recruitment of CK1 by the circadian negative components acts in the positive and negative feedback loops.The degradation rate of FRQ is regulated by CK1, which is negatively associated with turnover . Mutation period . FRQ-boun period , 45, 46.ectively . Liu et  , suggesting that the formation of the FRQ–FRH complex is independent of the phosphorylation status of FRQ.The association between FRQ and FRH plays multiple important roles in regulating FRQ phosphorylation, stability, interaction with other partners, and clock function , 49, 50.Fig. 7Drosophila, phosphorylation of PER leads to a more open conformation than the hypophosphorylated isoforms, and phosphorylation decreases the stability of PER (A and B). We also observed lower phosphorylation of WCC proteins in the l-frq strain at 22 °C, 25 °C, and 28 °C (H), which cannot be explained by the higher l-FRQ-CK1 affinity (G). It is possible that although l-FRQ shows higher binding affinity with CK1, it may be less efficient to promote the phosphorylation of WCC due to its looser conformation. Alternatively, the phosphorylation difference in WCC proteins between s-FRQ and l-FRQ may be controlled by other kinase(s) rather than CK1. These issues remain to be investigated.FRQ is an intrinsically disordered protein , 36, andy of PER . Hyperphy of PER , A and Bnd 28 °C H, which affinity G. It is via their coiled-coil (CC) domains, which are critical for maintaining WCC expression and circadian rhythmicity (via their CC domains (F). A looser conformation in the middle part of l-FRQ would facilitate the accessibility of CK1a with FRQ-CK1a interaction domains (FCDs) which might explain the late phase of phosphorylation of the N terminus .The extensive phosphorylation of FRQ occurs in a dynamic and circadian fashion, in which phosphorylation of the N-terminus of l-FRQ occurs late in the circadian cycle . The N-tthmicity , an FRQ  domains F. A loosterminus . In contof l-FRQ F.Drosophila and mammals, PER and CRY/TIM proteins are the counterparts of FRQ in the circadian negative feedback loop, and PER and CRY contain paralogues in mammals , the frq null strain, is the host strain for all FRQ mutant constructs. Pkaj120 plasmids bearing different deletions were transformed into the frq10 host strain at the his-3 locus. L-frq and s-frq strains were generated by transforming plasmids harboring mutations at the first and the third initiation codons into frq10, respectively (Pkaj120 is an frq null strain transformed wild-type kaj120 plasmid as a control strain (301-5 (bd) strain was also used as wild-type control in indicated experiments . To construct the pkaj120 plasmids comprising different mutations or deletions, the mutations were first generated in partial frq sequence cloned in puc19 vector by site-directed mutagenesis strategy through overlap extension using PCR with primers harboring respective mutations. Next, the fragments were subcloned into pkaj120 through specific restriction enzyme sites.ectively . Pkaj120l strain . The 301eriments . The mdrNeurospora in constant darkness. In race tube assay, Neurospora is inoculated inside the race tube which allows it to grow in one-way. During growth, Neurospora releases asexual conidia in a circadian fashion, and the circadian period can be calculated according to the interval time between the conidiation bands  (The thrombin cleavage site of FRQ was predicted by the Peptidecutter online tool (cutter/) . L-FRQ-cLabel-free quantitative LC/MS analysis was used to identify FRQ peptides with differential phosphorylation patterns . The labt test in Microsoft Excel software. A p-value of <0.05 was considered significant. ∗p < 0.05, ∗∗p < 0.01, #p < 0.001.Data are the mean values ± SD or mean values ± SE as indicated. Experimental data were analyzed using Student All data in this study are available within the article, supporting information, and/or from the corresponding author on reasonable request.This article contains The authors declare that they have no conflicts of interest with the contents of this article.