Northern peatlands are experiencing more frequent and severe fire events as a result of changing climate conditions. Recent studies show that such a fire-regime change imposes a direct climate-warming impact by emitting large amounts of carbon into the atmosphere. However, the fires also convert parts of the burnt biomass into pyrogenic carbon. Here, we show a potential climate-cooling impact induced by fire-derived pyrogenic carbon in laboratory incubations. We found that the accumulation of pyrogenic carbon reduced post-fire methane production from warm (32 °C) incubated peatland soils by 13–24%. The redox-cycling, capacitive, and conductive electron transfer mechanisms in pyrogenic carbon functioned as an electron snorkel, which facilitated extracellular electron transfer and stimulated soil alternative microbial respiration to suppress methane production. Our results highlight an important, but overlooked, function of pyrogenic carbon in neutralizing forest fire emissions and call for its consideration in the global carbon budget estimation. Warmer and drier conditions are increasing the frequency of forest fires, which in turn produce pyrogenic carbon. Here the authors show that accumulation of pyrogenic carbon can suppress post-fire methane production in northern peatlands and can effectively buffer fire-derived greenhouse gas emissions. Increasing fires threaten to shift the soil carbon regime from a sink to a source by releasing large amounts of carbon into the atmosphere3, but they also convert parts of the burnt biomass into pyrogenic carbon through the incomplete combustion of biomass4. Due to its carbon enrichment and environmental persistence, pyrogenic carbon has shown the ability to buffer carbon emission during the fire5 and store carbon in peat soils from centuries to millennia after the fire8. In this context, current studies are striving to quantify the production of pyrogenic carbon caused by fires9 and fill up the storage gap in the global carbon budget8. However, thus far, it is still unclear how this long-term stored pyrogenic carbon interacts with the indigenous methane (CH4) production in peat soils.Multiple lines of evidence show that forest fires become increasingly frequent in northern peatlands  due to their vulnerability to a warming and drying climate2) but a source of atmospheric CH410. Even though methanogens in peat soils are phylogenetically diverse, they produce CH4 mainly through hydrogenotrophic (∆G0 = −33 kJ mol−1 hydrogen) and aceticlastic (∆G0 = −36 kJ mol−1 acetate) methanogenesis11. The prevalence of either methanogenesis pathways depends on the nutrient level and feeding type  of specific peat soil, but they all compete with anaerobic respiration that utilizes alternative (to oxygen) terminal electron acceptors  for substrate11. Alternative respiration has been widely studied and demonstrated its favorability (∆G0 = −40 to −234 kJ mol−1 hydrogen and −69 to −841 kJ mol−1 acetate) in substrate competition with methanogenesis, and thus suppressing CH4 production13. Alternative terminal electron acceptors that have been reported in peat soils include manganese oxide, nitrate, iron minerals, sulfate, and dissolved and particulate organic matter in the order of high to low reduction potential (averagely from 0.8 to −0.2 V14). Specialized anaerobic microbes, such as Geobacter and Shewanella species, can perform alternative respiration by transferring electrons out of the cell  to solid-phase alternative terminal electron acceptors and gain energy for their growth15. These microbes can also use pyrogenic carbon as an intermediate electron acceptor and conduit through redox-active quinone and hydroquinone functional groups and electrically conductive carbon matrices in pyrogenic carbon17.Northern peatlands have historically been a sink for atmospheric carbon dioxide  pyrogenic carbon can function as a microbial electron-snorkel20 in anaerobic peat soil, which stimulates alternative respiration by facilitating extracellular electron transfer to reach a larger electron-accepting pool; and (2) the enhanced alternative respiration can outcompete methanogenesis and potentially reduces peatland net CH4 emissions. To verify these hypotheses, we conducted 6 large-group microcosm and bioelectrochemical peat soil as well as pure-culture incubations, which in total contained 144 individual incubations to probe the functions of pyrogenic carbon in electron snorkeling and CH4 suppression. This systematic study helped to better understand the post-fire interactions between pyrogenic carbon and methanogens as well as the underlying mechanisms controlling CH4 production, which might improve the estimates, and more importantly, the prediction of peatland gas emissions for future climate change scenarios.Electron accepting and donating cycles of the pyrogenic carbon quinone and hydroquinone functional groups have been shown to mediate biogeochemical redox reactions in facilitating microbial mineral reduction4, with a field production rate of 3.5 Mg ha−1 yr−1 ref. 21, projecting to 119 Mg CO2equivalent ha−1 yr−1 . Hydrogenotrophic methanogens  were found to dominate the peat soil CH4 production. Geobacter spp.  was determined as the major microbial member that performed alternative respiration  that lie in the lower range of its naturally occurring concentration in peat soils8 to conservatively assess the effect of electron snorkeling on CH4 suppression. Even though the added concentration varied among incubations, we normalized the number of snorkeled electrons to a unit mass of either pyrogenic carbon or soil carbon to focus on the electron snorkeling efficiency instead of the effect of different addition rates. We found that three electron transfer mechanisms contributed to electron snorkeling of pyrogenic carbon and caused significant decreases in CH4 production: (1) redox-cycling electron transfer by the quinone and hydroquinone functional groups; (2) capacitive electron transfer through the carbon matrices; and (3) conductive electron transfer through the carbon matrices. While redox-cycling electron transfer was the kinetically preferred mechanism for the electron snorkeling of pyrogenic carbon that was produced at low-temperature range, capacitive and conductive electron transfer mechanisms dominated the electron snorkeling of pyrogenic carbon that was produced at high-temperature range  was added to the peat soil to recreate the natural accumulation of pyrogenic carbon after vegetation fires. To distinguish pyrogenic carbon from native soil carbon and investigate the effect of biodegradation of pyrogenic carbon on gas production, the original biomass was isotopically labeled with 4 production in the microcosm peat-soil incubations  lower total production  in the pyrogenic carbon-amended peat soil in comparison with the pyrogenic carbon-free control treatment  in the peat-soil organic matter was eightfold  higher than the electron-accepting capacity  demonstrated a slow kinetics in transferring electrons to soil organic matter  respiring on the carbon matrices. The number of capacitively accumulated electrons (0.61 ± 0.31 mmol e− g−1 pyrogenic carbon) was fourfold less  than the electrons (2.5 ± 0.7 mmol e− g−1 pyrogenic carbon) that were accumulated in soil organic matter through the redox cycling of pyrogenic carbon during the same incubation period. Therefore, only at the late stage of the incubation did we observe a significant  decrease of CH4 production , which switched the electron snorkeling mechanism from the capacitive electron transfer to the conductive electron transfer  of Geobacter species29 but several orders of magnitude faster than the reported rate (0.1–1 µA cm−2) of methanotrophs during anaerobic methane oxidation31.After the capacitance of the carbon matrices would be fully charged, a local electric fieldfer Fig. . The car− g−1 soil carbon day−1 to stimulate alternative respiration. This rate was eightfold  faster than that (21 ± 7.7 µmol e− g−1 soil carbon day−1) caused by redox-cycling electron transfer. By accumulating electrons to a number of 785 ± 120 µmol e− g−1 soil carbon, which was in the range of the reported maximum electron-accepting capacities (a few hundred to thousand µmol e− g−1 soil carbon) of soil organic matter in several northern peat soils32, we obtained a 24%  CH4 suppression ) is enriched in the pyrogenic carbon that was produced at low (400–500 °C) pyrolysis temperatures33, which was most likely the available carbon source for microbial metabolization34. Although the pyrogenic carbon-derived gas production only accounted for <2% of the total gas production, the metabolism of pyrogenic carbon could induce a shift of the peat soil microbial structure and function19, which contributed to the suppression of the methanogenesis activity in addition to the redox-cycling induced electron snorkeling process. However, no detectable metabolism of pyrogenic carbon  incubations. Compared to the multi-phase composition and complexed microbiota in the peat-soil incubations, the pure-culture incubations provided a highly defined system for evaluating the kinetics across a single and identical microbe–pyrogenic carbon interface.Natural fires generate a highly heterogeneous pyrolysis temperature, which normally covers a range from 400 to 800 °C depending on the size of fires and the types of fuelG. sulfurreducens. This was evidenced by the increased number of transferred electrons as a result of redox-cycling  through the carbon matrices that were produced at high-temperature range (700–800 °C) than low to intermediate temperature range (400–650 °C)  to the conductive electron transfer rate through the carbon matrices produced at intermediate pyrolysis temperature 650 °C (0.14 ± 0.03 mmol e− g−1 pyrogenic carbon day−1), but slower  than that of the capacitive and conductive  electron transfer through the carbon matrices produced at high pyrolysis temperatures (700–800 °C)  quinone-hydroquinone couple at 0.16 ± 0.02 mmol ease Fig. . This ra39, caused a 90%  decrease of the electron transfer kinetics  burnt peatland sites (see: http://peatland-es-uk.york.ac.uk/field-measurements/ghg-emissions). By showing the links between fire-derived pyrogenic carbon and methanogens’ activity, this study provides a new insight in explaining the mechanisms of fire-induced methane reduction. In contrast to the traditional ideas that rationalize the legacy effect of pyrogenic carbon mainly from the perspective of its persistence46, the CH4 suppression observed in this study was a result of strong redox and bioelectrochemical interactions between pyrogenic carbon and microbial respiration, which provides a new direction of re-evaluating the legacy effect of pyrogenic carbon.Here, we presented a post-fire CH47 or co-culture (Geobacter and Methanogen) incubations48 have reported increased CH4 production after addition of carbonaceous materials . However, these engineered systems are different from naturally occurring peat soils. To achieve high CH4 yield, there are usually very limited amount of alternative electron acceptors available in anaerobic digestors and co-culture incubations, which creates a thermodynamically favorable environment for methanogens’ metabolism . In contrast, peat soils generally contain a variety of alternative electron acceptors, for instance the iron oxides13 and quinone molecules of soil organic matter49. It has been shown that continuous CH4 production can only start after the electron-accepting capacity of soil organic matter being consumed to an extent that drives a slower alternative respiration than methanogenesis23. We observed similar phenomenon in the pyrogenic carbon-free control treatment that slower alternative respiration appeared after evolution of CH4 production . We used shrub willow for the production of pyrogenic carbon because it is a common plant in shrubby peatland and its electron transfer mechanisms can be representative of woody biomass-derived pyrogenic carbon due to the similar formation and composition of carbon matrices and functional groups during the pyrolysis of woody materials24. We applied pyrolysis temperatures of 400–800 °C, which covered the temperature range of naturally occurring peatland fires36 and created a spectrum of aromaticity and surface functionality25 that represented the structural transition of naturally occurring pyrogenic carbon55. We used a dwell time of 60 min for all pyrolysis temperatures. Before pyrolyzing, the biomass carbon had been labeled in growth chambers using 13C labeled CO2 as the source for willow growth (a detailed labeling process can be found in ref. 33). The bulk δ13C in pyrogenic carbon after biomass pyrolysis was on average 774 ± 2.3‰ (vs. VPDB). More information on the physicochemical properties  of pyrogenic carbon can be found in our previous study33. Even though lab-made pyrogenic carbon is structurally more homogeneous than naturally occurring pyrogenic carbon produced by vegetation fires, the revealed electron transfer mechanisms are applicable to both cases as they follow the same carbonization and functionalization trend in response to the change of pyrolysis temperatures55.Pyrogenic carbon samples were produced under controlled conditions in the laboratory by anoxic pyrolysis of woody biomass from shrub willow . Detailed description of the peat soil was given in Supplementary Method 4 production was very slow (<1 µmol g−1 soil carbon day−1) in the first few days of incubation. It started to rise after day 6. The CH4 production rate, thereafter, was in the range of 4.7–5.9 µmol g−1 soil carbon day−1 (or 0.08–0.1 mmol L−1 soil suspension day−1)  from the same peat soil56. Accordingly, pyrogenic carbon was added to the peat soil after 6 days of pre-incubation.We sampled the peat soil from the Mclean Bog located in Dryden, New York . The Mclean Bog is a national natural landmark with known methanogenesis activity and CHG. sulfurreducens strain PCA (1 mL of stock culture at 0.1 OD) was inoculated in all pure-culture incubations. From all electron-donating bacteria, we chose G. sulfurreducens because of its mediator-free electron transfer mechanisms that facilitated the mechanism study by only crossing one electron transfer interface  and avoided potential interference caused by the mass transport of mediators. Further, G. sulfurreducens is an important and abundant alternative-respiring bacterium that coexists with methanogens in many anaerobic environments57. The average abundance of Geobacter species accounted for 1% of 16S rRNA gene sequences, which dominated the alternative respiratory bacteria in the studied peat soil  during the investigation of pyrogenic carbon matrices electron snorkeling to facilitate the incorporation of pyrogenic carbon (as a working electrode) in the bioelectrochemical circuit. This range of pyrogenic carbon particle size falls into the sizes of pyrogenic carbon that has been found in soils58.The microcosm and bioelectrochemical incubations were performed to investigate the electron snorkeling induced by the pyrogenic carbon functional groups and carbon matrices, respectively. For studying electron snorkeling of pyrogenic carbon functional groups, we used small pyrogenic carbon particles to reach a homogeneous mix with peat soil and electrochemical quantification of electron-accepting capacities of pyrogenic carbon and peat soil mixture. We prepared small pyrogenic carbon particles by following a published ball milling method, which yielded a particle size of ~40 µm−1 soil in the microcosm peat-soil incubations and 0 and 1 mg pyrogenic carbon mL−1 growth medium in the microcosm pure-culture incubations. The application rate was 0 and 10 mg pyrogenic carbon g−1 soil and 0 and 6.7 mg pyrogenic carbon mL−1 growth medium for bioelectrochemical peat-soil and pure-culture incubations, respectively. We monitored daily production rates of CO2 and CH4 and δ13C partitioning in the gas phase of all bioelectrochemical and microcosm incubations, using a Picarro stable isotope analyzer . Detailed information about the design, operation, and data acquisition of microcosm and bioelectrochemical incubations could be found in Supplementary Method P < 0.05 (n = 3). Sub-samples for triplicated incubations were from the same peat soil.We performed peat-soil and pure-culture incubations in parallel together with pyrogenic carbon in microcosm and bioelectrochemical incubations at 32 °C). The application rate was 0, 0.03, and 3 mg pyrogenic carbon g °C. The Further information on research design is available in the Supplementary InformationPeer Review FileDescription of additional supplementary filesDataset 1Dataset 2Dataset 3Dataset4Reporting Summary


In a simulated clinical trial, a screening algorithm combining all three plasma biomarkers would reduce the required sample size by 70%  with cognition as trial endpoint, and by 63%  with subsequent AD dementia as trial endpoint. Plasma ATN biomarkers show usefulness in cognitively unimpaired populations and could make large clinical trials more feasible and cost-effective.Plasma biomarkers of amyloid, tau, and neurodegeneration (ATN) need to be characterized in cognitively unimpaired (CU) elderly individuals. We therefore tested if plasma measurements of amyloid-β (Aβ)42/40, phospho-tau217 (P-tau217), and neurofilament light (NfL) together predict clinical deterioration in 435 CU individuals followed for an average of 4.8 ± 1.7 years in the BioFINDER study. A combination of all three plasma biomarkers and basic demographics best predicted change in cognition  need to be characterized in cognitively unimpaired (CU) elderly individuals. Here, the authors show plasma ATN biomarkers predict clinical deterioration and cognitive decline and show in a simulated clinical trial combining all three biomarkers reduced the required sample size. Specifically, Aβ is thought to potentiate the spread of neocortical tau pathology which in turn drives neurodegeneration and cognitive decline2. Accruing evidence indicates, however, that these changes are slow and protracted and precede the initial symptoms of AD by many years or decades4. A prerequisite for the testing of therapeutic interventions is the identification of individuals at risk for progression to AD dementia. Biomarkers have been explored for this5. The National Institute on Aging and Alzheimer’s Association has proposed a framework for research wherein cerebrospinal fluid (CSF) and imaging (magnetic resonance and positron emission tomography (PET)) based measures of Aβ (A), tau (T), and neurodegeneration (N) can be compiled into an ATN classification system6. CSF measures include the Aβ42/Aβ40 ratio, tau phosphorylated at threonine 181 (P-tau181) and neurofilament light (NfL). Continued assay development now makes it possible to measure ATN biomarkers in blood, including the Aβ42/Aβ40 ratio8, P-tau11 and NfL13. With lower cost and higher accessibility, blood-based AD biomarkers may circumvent the limitations inherent to CSF and imaging. While recent work on plasma biomarkers has primarily focused on individualized risk assessment in patients with mild cognitive impairment (MCI)16, it has been noted that substantial irreversible neuronal loss is already seen by this stage, which may reduce the likelihood of disease-modifying therapies to prevent dementia onset17. This has led to an increasing focus on cognitively unimpaired (CU) older individuals at risk for progression to AD dementia on the basis of biomarker evidence of brain AD pathology18. Studies on combinations of blood-based Aβ42/Aβ40, P-tau and NfL have reported findings on their diagnostic accuracy for the separation of AD dementia from CU individuals and patients with non-AD disorders22, but data are lacking on their performance in predicting cognitive decline and clinical progression in CU individuals. We therefore tested the usefulness of plasma Aβ42/Aβ40, P-tau217, and NfL (separately and combined) for predicting longitudinal cognitive decline and clinical outcomes in a CU population comprised of both cognitively normal (CN) healthy controls and individuals with subjective cognitive decline (SCD) who did not meet the diagnostic criteria for MCI. We hypothesized that a combination of ATN plasma biomarkers could improve the prediction of cognitive decline and risk of AD dementia compared to using only basic demographic information. We also investigated the extent to which plasma biomarkers could reduce the sample size required to adequately run AD clinical trials in a CU elderly population.Alzheimer’s disease (AD) is characterized by the presence of amyloid-β (Aβ) plaques and tau tanglesP < 0.001) and Preclinical Alzheimer’s Cognitive Composite  scores, a significantly higher risk of AD dementia (P < 0.001), and significantly higher baseline plasma NfL levels (P = 0.04). However, there was no difference in longitudinal change in MMSE (P = 0.41) or PACC (P = 0.10), nor in baseline plasma Aβ42/Aβ40 (P = 0.72) or P-tau217 (P = 0.28) levels, between CN and SCD participants. Study population characteristics are provided fully in Table A total of 435 CU individuals were included of which 167 (38.4%) had SCD. During follow-up, a total of 28 individuals converted to AD dementia (6.4%) and 39 individuals converted to all-cause dementia (9.0%). SCD participants had significantly lower baseline Mini-Mental State Examination , between P-tau217 and NfL , and between NfL and Aβ42/Aβ40 .When testing the associations between unadjusted plasma biomarker values using Spearman correlation  were plasma Aβ42/Aβ40-positive, 162 participants (37.2%) were plasma P-tau217-positive, and 165 participants (37.9%) were plasma NfL-positive.β = −0.20 points per year per SD increase in biomarker value, P < 0.0001), followed by plasma Aβ42/Aβ40 , and plasma NfL . When combining all three plasma biomarkers together in the same model . Adding APOE ε4 status to the basic model did not affect the significance of any biomarkers in either the combined or individual models .We first tested whether plasma biomarkers were significantly associated with cognitive decline as measured by the PACC. When added individually to a basic model consisting of age, sex, and education Fig. , we founR2 = 0.07, CI ; see Table Δ = −28 versus basic model; R2 = 0.14 , P < 0.0001), followed by plasma Aβ42/Aβ40 only , plasma P-tau217 only , and finally plasma NfL only .All plasma biomarker models significantly improved prediction of PACC change compared to the basic model , while this did not affect the significance of any plasma biomarkers nor the significant improvement in model fit found by adding plasma biomarkers to the basic model , followed by plasma Aβ42/Aβ40 , while plasma NfL on its own did not add significant information to the basic model . When combining all three plasma biomarkers together in the same model (Table P = 0.0004) and Aβ42/Aβ40  remained significant. When adding APOE ε4 status to the set of demographic predictors, AUC values increased for all models, but all plasma biomarkers which were significant in the previous model remained significant when APOE ε4 status was added , followed by plasma P-tau217 only , plasma Aβ42/Aβ40 only . The model with plasma NfL alone was not significantly better than the basic model .All plasma biomarker-based models significantly improved prediction of 4-year conversion to AD compared to the basic model , plasma Aβ42/Aβ40  and plasma NfL . Moreover, combining all three biomarkers in a multivariable enrichment model led to the largest reduction in sample size . All plasma biomarkers were robust to up to 20% change in inclusion threshold and a stricter inclusion threshold led to larger reduction in required sample size  and plasma Aβ42/Aβ40 , while plasma NfL did not significantly enrich such a clinical trial . An enrichment model which combined all three biomarkers achieved the greatest reduction in sample size . Again, all plasma biomarkers besides plasma NfL appeared robust to larger variation in the inclusion threshold , but did lead to a significant reduction in sample size with conversion to AD dementia as trial outcome . For PACC, we found an increased effect of plasma Aβ42/Aβ40 , plasma NfL , and the combined model , but a decreased effect of plasma P-tau217 . With AD dementia as outcome, the plasma Aβ42/Aβ40, plasma P-tau217, and combined models remained significant while the plasma NfL model remained non-significant.Because SCD participants had a significantly higher risk of AD dementia  previously established in AD research improve prediction of both cognitive and clinical outcomes for elderly CU individuals. Our primary findings were that plasma biomarkers add significant prognostic information to a basic model consisting only of demographic information  and can significantly reduce the number of participants required to run AD-related clinical trials in this population.All three plasma biomarkers were significantly associated with the primary outcomes on their own, but we showed here that they also provide some degree of overlapping information with each other. Plasma P-tau217 and Aβ42/Aβ40 contributed significantly to the prediction of both PACC and risk of AD dementia, while plasma NfL added significant prognostic information for PACC and appeared more useful for predicting general cognitive decline (MMSE) and risk of all-cause dementia. Together, our results demonstrate the wide applicability of this plasma biomarker panel in a CU elderly population.All three plasma biomarkers also significantly reduced sample sizes needed to run AD clinical trials in a CU population when used for pre-trial inclusion screening. This is a promising result given that clinical trials of AD are increasingly focused on elderly individuals without existing cognitive impairment. Running such a clinical trial in a cost-effective way likely requires the ability to easily and affordably  identify CU individuals at risk of developing AD. Our results suggest that these plasma biomarkers are in fact an effective way to enrich clinical trials in a CU population, and that they even add additional value in a population which has already been enriched for SCD status. While the biomarkers showed promise for stand-alone use for trial enrichment, combining them in a multivariable model led to the greatest reduction in required trial sample size and the lowest variability in estimated sample size reduction.Interestingly, the combined multivariate model and individual plasma biomarkers were robust to variation in the inclusion threshold, indicating that random shifts in the demographics of a clinical trial screening population or in the analytical variability of the plasma assay may not have a significant negative impact on the effectiveness of such an enrichment scheme. Still, this result should be interpreted with caution since it is not easy to determine how much of the variability is controllable  and how much is due to inherent biological variability. It was also not possible to account for the possibility that enrichment may increase study attrition rates or otherwise alter trial dynamics since this type of data is not available to us.APOE ε4 status, although including such information did generally raise the prognostic accuracy. Also, plasma Aβ42/Aβ40 had a stronger effect with PACC as outcome rather than MMSE; this might be expected given that PACC was created specifically to pick up early AD-related changes in non-demented individuals23 and has been shown to have greater sensitivity to Aβ-related cognitive decline24, while the MMSE is a measure of global cognition originally created to differentiate dementia patients from those with psychiatric syndromes26. This highlights the idea that selection of cognitive scale impacts which plasma biomarkers are most relevant.With regards to cognitive outcomes, we found that all three plasma biomarkers added independent information for predicting change in PACC even when combined in the same model. This indicates that if only one or two of the three biomarkers have already been collected in a specific individual, a more accurate prediction can be obtained by additionally measuring the other, unknown biomarker(s). This result was not dependent on the inclusion of 28. The contribution of plasma Aβ42/Aβ40 was greatly affected when APOE ε4 status was included, but the same effect was not seen for CSF Aβ42/Aβ40, indicating that APOE ε4 status may contain prognostic information related to Aβ accumulation which is not picked up by plasma Aβ42/Aβ4029. This finding aligns with studies showing a link between APOE ε4 and alterations in Aβ-related processes31. Our results further suggest that APOE ε4 status may be informative in a CU population even when plasma biomarker values are available, as prediction of subsequent dementia in particular was significantly better when including APOE ε4 as a covariate. The effect of plasma NfL greatly increased when analyzing conversion to all-cause dementia, thereby strengthening previous findings of plasma NfL as a general biomarker of neurodegeneration that detects non-AD-related neurodegenerative changes34.With regards to clinical outcomes, we found that both plasma Aβ42/Aβ40 and plasma P-tau217 added independent information for prediction of conversion to AD dementia, even when combined in the same model, further adding to the existing evidence of plasma Aβ42/Aβ40 and P-tau217 as AD-specific markersR2 between models. This indicates that CSF and plasma may provide similar predictive accuracy overall but that CSF provides a much more confident prediction with smaller confidence intervals. We hypothesize that this difference is largely based on the observation that CSF Aβ42/Aβ40 is a much stronger prognostic marker than plasma Aβ42/Aβ40. However, the discrepancy between plasma and CSF Aβ42/Aβ40 may not be biological but may instead be related to the performance of the specific plasma assays used here (Elecsys immunoassay). Our work in MCI patients showed a greater effect of plasma Aβ42/Aβ40 by using a mass-spectrometry method, indicating that a more effective plasma assay could lead to greater value35. However, such data were not widely available in this cohort. Further work is needed to compare performance of CSF and plasma biomarkers in CU individuals using different assays, as recent studies suggest that the difference between CSF and plasma biomarkers is less pronounced in MCI 35.We also compared plasma biomarkers directly against their corresponding CSF biomarkers. CSF biomarkers still appear to be superior to plasma biomarkers in terms of cognitive and clinical prognosis in elderly individuals without cognitive impairment, although this comparison was based on Akaike Information Criterion (AIC) values and there was in fact significant overlap in 6. However, SCD participants in our study were followed in a memory clinic setting and had higher risk of developing AD dementia. Including this diagnostic distinction improved performance of the basic (demographics-only) model but did not alter the finding that plasma biomarkers provide significant prognostic information. Also, enriching for SCD status led to a significant reduction in trial sample size with AD dementia as outcome, but not with cognition as outcome. Since cognition is the main target of current AD trials, this indicates that enriching for SCD alone should not be considered as an adequate replacement for plasma biomarker-based enrichment. In all, our results show that plasma biomarkers are effective in both sub-groups and that it was not simply SCD participants that were driving model performance.Importantly, our results in terms of biomarker effect size did not change when we further took into account the main distinction of cognitively unimpairment—CN versus SCD. To note, both CN and SCD participants are both considered to be CU according to NIA-AA criteria36. Understanding the overlapping contributions of biomarkers is therefore a major contribution of our results. Regarding biomarker contributions, however, we note that confidence intervals of individual biomarker effect sizes overlapped greatly within models, indicating that it is not quite possible to determine whether one biomarker was more or less effective in any given model. Therefore, we cannot comment on whether biomarkers should be weighted differently within a prognostic model. Rather, our analysis helps make the decision as to whether a biomarker should be included in such a predictive model at all.The strengths of this study include the standardized collection and measurement of relevant biomarkers in both plasma and CSF—which allowed for direct comparison between biomarkers and across the two modalities—and the availability of multi-year follow-up data in a large number of participants. Our study improves in particular on previous studies linking plasma P-tau levels on their own with cognitive decline and neurodegeneration in similar populationsNot having a state-of-the-art mass-spectrometry plasma Aβ42/Aβ40 assay is a weakness, although plasma Aβ42/Aβ40 was still important to the prognostic algorithms. In addition, since evidence of abnormal CSF biomarker levels was required for setting the diagnosis of AD dementia at the memory clinic, it was not possible to fairly compare the performance of plasma and CSF biomarkers in this manner. The evidence we did provide for cognition, however, indicates that CSF biomarkers are still superior to plasma biomarkers in a CU elderly population at least in terms of individual prediction accuracy. Factoring in cost and ease of collection may make plasma more beneficial at a population level. In the future, we hope to validate our findings in an independent cohort as soon as other studies with sufficient longitudinal cognitive follow-up and measurements of plasma P-tau217 become available.To summarize, our results show that plasma biomarkers relate to AD-related changes in the CU elderly and can significantly reduce sample sizes needed to run clinical trials in such a population. Further work is needed to validate the optimal panel of biomarkers in an eldery CU population, although our preliminary results suggest that all three core plasma biomarkers could be useful.www.biofinder.se) cohort used in the present analysis consisted of CN participants with no objective evidence of cognitive impairment at baseline  and SCD participants who were referred to the memory clinic for investigation but deemed to not have cognitive impairment. The inclusion criteria for CN participants were the following: (i) age ≥65 years; (ii) absence of cognitive symptoms as assessed by a physician with a special interest in cognitive disorders; (iii) MMSE score between 26 and 30 at screening visit; (iv) do not fulfil the criteria for MCI or dementia according to DSM-5 ; (v) fluent in Swedish. Exclusion criteria include (i) significant unstable systemic illness that makes it difficult to participate in the study; (ii) current significant alcohol or substance misuse; (iii) refusing lumbar puncture or MRI. SCD participants were followed in a memory clinic setting due to cognitive symptoms experienced by the patient and/or informant. These symptoms did not have to be memory complaints, but could also be executive, visuospatial, language, praxis, or psychomotor complaints. After inclusion, patients were categorized as having SCD or MCI based on an extensive neuropsychological battery performed at baseline, examining verbal, episodic memory, visuospatial ability and attention/executive domains. Patients with domain z-score > –1.5 in all domains were classified as SCD and only these participants were included in the present analysis37. All data were collected between July 2008 and June 2019.The Swedish BioFINDER  was evaluated based on physician’s follow-up assessments and reviewed by a consensus group that included memory clinical physicians and a senior neuropsychologist. The diagnosis was made according to the diagnostic and statistical manual of mental disorders version 5 (DSM-5) criteria and required a positive indication of abnormal amyloid accumulation based on CSF Aβ42/Aβ40 levels All participants had available plasma Aβ42/Aβ40, P-tau217, and NfL measurements and CSF Aβ42/Aβ40, P-tau181, and NfL measurements. All participants gave written informed consent and ethical approval was given by the Regional Ethical Committee in Lund, Sweden.23. The PACC score used for the present study was comprised of the MMSE, delayed word recall from the Alzheimer’s Disease Assessment Scale-Cognitive Subscale , animal fluency, and trail-making B tests and was calculated as described previously38. The secondary cognitive outcome was the MMSE collected longitudinally during all available follow-up visits. The primary clinical outcome was conversion to AD dementia at any time during longitudinal follow-up. The secondary clinical outcome was conversion to all-cause dementia.The primary cognitive outcome was the PACC, as this scale was developed specifically to identify early cognitive changes in individuals without dementia7. Plasma P-tau217 was measured on a Meso-Scale Discovery platform , using an assay developed by Eli Lilly39. Plasma NfL was analyzed using a Simoa-based assay13. CSF levels of Aβ42/Aβ40 and P-tau181 were measured using Elecsys assays (Roche Diagnostics GmbH), and CSF NfL was measured using the ELISA method . All biomarker values were treated as continuous measures in all statistical analyses after being transformed using the natural log function and any biomarker values more than four standard deviations away from the mean were excluded.Biomarkers included in the present study were Aβ42/Aβ40, P-tau217, and NfL measured in plasma, along with Aβ42/Aβ40, P-tau181 (P-tau217 was not available in CSF), and NfL measured in CSF. Plasma Aβ42/Aβ40 was measured using an Elecsys immunoassay on a Cobas e601 analyzer n = 350) who did not convert to AD dementia versus MCI (n = 111) and CU (n = 30) individuals who did convert to AD dementia). For plasma Aβ42/Aβ40, the resulting cutoff was 0.066 pg/mL. For Plasma P-tau217, the resulting cutoff was 0.199 pg/mL. For plasma NfL, the resulting cutoff was 22.3 pg/mL. To note, binarized biomarker status was used for visualization purposes only to demonstrate theoretical differences in outcome trajectories.Biomarker cutoffs were derived from levels at baseline using the Youden index procedure to maximize separation of CU individuals without a positive CSF Aβ42/Aβ40 (Linear mixed-effects (LME) modeling was used to model longitudinal PACC, with five models tested in total: a basic model , the basic model plus each of the three plasma biomarkers added separately , and the basic model plus all three plasma biomarkers together. LME models had random intercepts and random slopes with an unstructured covariance matrix. Normality of model residuals was confirmed to ensure appropariateness of LME modeling. Cox regression was used to model clinical conversion to AD dementia with the same models fit as above; all participants were right censored at last follow-up visit or at clinical conversion. A power analysis was performed in which the reduction in sample size needed to achieve 80% power to observe a 30% reduction in cognitive decline or clinical conversion was calculated in a scenario where each biomarker was used individually (based on univariate thresholds) or combined (based on a fitted multivariate model) to screen individuals for trial inclusion and in order to enrich the trial population for the relevant outcome. All biomarkers were treated as continuous variables after being log-transformed prior to any analysis.42. The risk of AD dementia was not analyzed using CSF biomarkers because these biomarkers were consulted by neurologists during diagnosis, leading to potential circular reasoning.The primary cognitive outcome was also analyzed using CSF biomarkers on the same subjects and results were compared between CSF and plasma models using the AIC. The larger the difference in AIC is between two models, the less plausible it is that the model with higher AIC provides the best fitAPOE ε4 status (one or more ε4 copy versus zero ε4 copies) included as an additional predictor in the basic model.Sensitivity analyses were also performed with (a) MMSE as the cognitive outcome instead of PACC, (b) all-cause dementia as the clinical outcome instead of AD dementia, and (c) ABA  package (v1.0.1) written in the R programming language (v4.0.0). All statistical tests were two-sided with a significance level of 0.05.All analyses were performed using the Further information on research design is available in the Supplementary InformationReporting Summary


Scientific Reports 10.1038/s41598-021-88024-x, published online 20 April 2021Correction to: The original version of this Article contained errors in the in-text citations.Firstly, in the Materials and methods section under the subheading ‘Confocal imaging and analyses of GFP expression, migration and numbers of PGCs in Adamts9 knockout’,adamts9−/−) were crossed with Tg(vasa:EGFP)30”“Adamts9 knockout male fish (now reads:adamts9−/−) were crossed with Tg(vasa:EGFP)29”“Adamts9 knockout male fish  are well known for their involvements in cell motility such as stem cell migration or cancer cell invasionnow reads:30–34”“Matrix metalloproteinases (MMPs) are well known for their involvements in cell motility such as stem cell migration or cancer cell invasionAlso in the Discussion section,40,41,43. A threshold number of germ cells is required for ovarian development. The depletion of germ cells in development or the adult will lead to the development of males40,41,42,43”“It is well established that the number of germ cells is important for gonadal development and sex determination in zebrafishnow reads:41,42,43. A threshold number of germ cells is required for ovarian development. The depletion of germ cells in development or the adult will lead to the development of males41,42,43”“It is well established that the number of germ cells is important for gonadal development and sex determination in zebrafishThese errors have now been corrected in the HTML and PDF versions of the Article.


Purpose: To analyze the key factors and decision-making behaviors affecting overall satisfaction based on perceptual data of outpatients.Methods: The official satisfaction questionnaire developed by the National Health Commission of the People's Republic of China was used. Rough set theory was used to identify the perception patterns between condition attributes  and a decision attribute  and to express them in rule form .Results: The four minimal-coverage rules, with strength exceeding 10% in the good class, and six crucial condition attributes were obtained: “Ease of registration (C1),” “Respected by registered staff (C2),” “Registered staff's listening (C3),” “Respected by doctor (C9),” “Signpost (C12),” and “Privacy (C16).” In addition, the average hit rate for 5-fold cross-validation was 90.86%.Conclusions: A series of decision rules could help decision-makers easily understand outpatients' situations and propose more suitable programs for improving hospital service quality because these decision rules are based on actual outpatient experiences. Hospitals are a service industry whose medical service directly affects patient's lives, medical treatment, and health care . OutpatiHospital service quality involves both qualitative and quantitative factors, which belong to multi-attribute decision-making (MADM). For example, Altuntas et al. developed a MADM method based on a weighted SERVQUAL scale for measuring the perceived service quality of hospitals . Shieh eTo fill the gap, this study applied a data-mining method to establish behavior patterns of outpatient patient satisfaction from hospital service quality survey data. First, the questionnaire developed by the National Health Commission of the People's Republic of China was used to investigate the service quality for a third-level first-class hospital. Then, based on the survey data, the decision model between service factors and overall service level was established using rough set theory (RST). Finally, this decision-making model  is helpful for hospital quality management departments to propose various improvement directions and measures.This paper is structured as follows: Section 2 introduces the questionnaire, RST, and data collection. Section 3 describes the case study results based on RST. Section 4 discusses management practices, model stability, and research limitations. Section 5 presents the conclusions.D = 1]”), the middle third (marked as “Medium class [D = 2]”), and the bottom third (marked as “Poor class [D = 3]”). The public hospital satisfaction questionnaire in this study is shown in In China, to improve the quality of medical services in nationwide public hospitals, the National Health Commission set up a national satisfaction survey management platform and designed a series of satisfaction questionnaires. The questionnaire includes outpatient, inpatient, and medical staff versions. Subsequently, a large medical satisfaction survey was conducted in all two- and three-level public hospitals in the country. The outpatient satisfaction questionnaire was used as the purpose of this study is to improve the satisfaction of outpatient medical services from the perspective of outpatient behavior patterns. The questionnaire consists of two parts: service quality factors  and overall service level . In addition, the overall satisfaction value originates from the average value of all service quality factors  and is divided equally into three levels: the first third . The questionnaire survey was conducted in March 2019. This study collected 536 questionnaires detailing women's perspectives. After excluding questionnaires with incomplete data , 525 questionnaires remained as data for this study. Finally, this study uses the rose2 software to do the rough set analysis. The detailed information regarding the background of respondents is shown in To further determine the reliability of the decision rules generated in this study, a 5-fold cross-validation method was applied to the dataset. First, 80% of the dataset was randomly selected as training samples to generate decision rules. Next, the remaining 20% of the data was used as a test sample to verify the click rate of the generated decision rules . Finally, these processes were repeated five times through RST modeling; their average hit rate is shown in D = 1),” the lower and upper approximations are 175 objects each for an approximation quality of 1.000 (175/175). Then, for “Medium class (D = 2),” the lower approximation is 166 objects, and the upper approximation is 180 objects for an approximation quality of 0.9222 (166/180). Last, for “Poor class (D = 3),” the lower approximation is 170 objects, and the upper approximation is 184 objects for an approximation quality of 0.9239 (170/184). From the approximation accuracy perspective, the approximate boundary of class 1 is clear and has no ambiguity . In addition, the approximate boundaries of classes 2 and 3 are fuzzy—that is, there is an ambiguous phenomenon .The overall quality of the decision class approximation boundary is 0.9733, and the approximation accuracy for each class is shown in D = 1),” 28 apply to “Medium class (D = 2),” and 16 apply to “Poor class (D = 3).” To better achieve the goal of improving outpatient's satisfaction with hospital service quality, this study focused only on “Good class (D = 1),” and the data percentage threshold for this class was set at 10%. The four rules included indicate that, if the requirements of conditional attributes are met, the hospital's overall service satisfaction has a certain probability of reaching “Good class (D = 1).” The rules also represent four ways to improve the quality of hospital services and thus achieve the service levels expected by outpatients. The four rules were selected as shown in RST can produce minimal-coverage rules, 52 rules in this dataset. Among these, 8 rules apply to “Good class (D = 1),” which has the condition attributes of “Registered staff's listening (C3) = {4},” “Signpost (C12) = {4},” “Hardware (C13) = {4},” and “Timely response (C17) = {4}.” Rule 2 indicated that 68.00% of hospital outpatients associated the “Good class (D = 1)” service rating with “Ease of registration (C1) = {4},” “Registered staff's listening (C3) = {4},” “Respected by nurse (C6) = {4},” “Respected by doctors (C9) = {4},” “Doctor's speaking (C11) = {4},” and “Privacy (C16) = {4}.” Rule 3 indicated that 58.29% of hospital outpatients associated the “Good class (D = 1)” service rating with “Ease of registration (C1) = {4},” “Respected by registered staff (C2) = {4},” “Respected by doctors (C9) = {4},” “Space allocation (C15) = {4},” and “Privacy (C16) = {4}.” Finally, Rule 4 indicated that 21.71% of hospital outpatients associated the “Good class (D = 1)” service rating with “Ease of registration (C1) = {4},” “Respected by registered staff (C2) = {4},” “Pharmacy staff's listening (C5) = {4},” “Doctor's listening (C10) = {4},” “Signpost (C12) = {4},” and “Toilet clean (C14) = {4}.”Rule 1 indicated that 73.14% of hospital outpatients rated the overall service evaluation of the hospital as “Good class (D = 1),” the frequency of these condition attributes was as follows: “Ease of registration (C1),” three times; “Respected by registered staff (C2),” “Registered staff's listening (C3),” “Respected by doctor (C9),” “Signpost (C12),” and “Privacy (C16),” two times; “Pharmacy staff's listening (C5),” “Respected by nurse (C6),” “Doctor's listening (C10),” “Doctor's speaking (C11),” “Hardware (C13),” “Toilet clean (C14),” “Space allocation (C15),” and “Timely response (C17),” one time. These results indicate that “Ease of registration (C1),” “Respected by registered staff (C2),” “Registered staff's listening (C3),” “Respected by doctor (C9),” “Signpost (C12),” and “Privacy (C16)” are several major condition attributes that outpatients recognize in hospitals with good medical quality.To further determine the relative importance of the condition attributes related to “Good (In order to further understand the classification accuracy of age groups-based data. The 525 data set is divided into two groups according to age: (i) under 30 years old ; and (ii) over 30 years old . Based on the results of 5-fold cross-validation, the classification accuracy of the former is 83.32%; The latter is 86.4%. Compared with the original classification accuracy , the data according to different age groups will really affect the accuracy of classification. Since the interviewees are all women, there are no men. Therefore, this study did not make a comparative analysis of gender on the results. The results are shown in This study indicates the six most important attributes clinical practice: ease of registration, respected by registered staff, registered staff's listening, respected by doctor, signpost, and privacy. This indicates that patients would like to complete the outpatient process smoothly. First, the online reservation system should be easy to use, and telephone reservation personnel need to be empathetic and listen carefully to the patient's needs so that they can successfully complete the reservation process. This is because, once patients miss their appointment opportunity, they must spend more time waiting for an appointment. In addition, physical condition is a very private issue. Patients hope that clinicians can respect their wishes or opinions in the diagnosis or treatment process and protect their privacy. Finally, the hospital should have a clear movement route with signposts to help patients find the visiting area quickly and save time, especially for pregnant women for whom moving is not convenient. As mentioned above, these six service quality factors are critical and foundational to overall satisfaction with hospitals. In addition, hospital decision-makers can choose rules 1 to 4 to improve the overall service quality of outpatient service according to the actual situation.The results indicate that the difference in modeling quality between the five methods is not significantly based on the same data. However, the RST used in this study can demonstrate the pattern between condition attributes and a decision attribute through the regular expression. This way of expressing rules can assist hospital decision-makers or managers to more easily understand the decision rules for each decision class and their probability of occurrence. Compared with RST, the BPANN method requires more training time , 20, 21.The limitations of this study were as follows. (i) The questionnaire items were used by the National Health Commission of the People's Republic of China, without considering other questionnaire scales. (ii) The questionnaire survey was conducted in March 2019, and the analysis of the results was limited to the satisfaction analysis from the female perspective, without considering other factors such as related events, gender, grade, and admission time. The reason is that other factors may affect the change of classification accuracy so they are not within the scope of this study. (iii) The results of RST analysis are limited to the behaviors of the investigated participants and should not be extended to other hospitals.This is the first time to study the public hospital service quality scale based on the National Health Commission of the People's Republic of China, and apply rough set theory to explore the key attributes and behavior rules that affect the overall service quality from a group of patient behavior data. The key attributes can help decision makers better understand the factors that patients care most about; Behavior rules can help decision-makers put forward specific improvement strategies. As a beginning, this study provides different thinking of data behavior decision-making from expert experience. Chinese public hospitals can take this research model as the research basis, and consider other factors such as related events, gender, grade, and admission time, so as to provide more in-depth results and analysis in the future.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Guangdong Nursing Association (No. hdfyhlbgdhlxueh2019zx113). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.M-LD and PT collected the data and wrote a draft article. Y-CC calculated the RST results, interpreted the results, and revised the article. T-HT and C-WC designed the research process and content and reviewed the final version. All authors have read and agreed to the published version of the manuscript.This research was funded by a study on nurse-patient relationship and patient satisfactions in a women and children's hospital, Grant No. gdhlxueh2019zx113.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by genetic and epigenetic aberrations that alter the differentiation capacity of myeloid progenitor cells. The transcription factorCEBPα is frequently mutated in AML patients leading to an increase in DNA methylation in many genomic locations. Previously, it has been shown thatecCEBPα (extra coding CEBPα) - a lncRNA transcribed in the same direction asCEBPα gene - regulates DNA methylation ofCEBPα promoter incis. Here, we hypothesize thatecCEBPα could participate in the regulation of DNA methylation intrans.Method: First, we retrieved the methylation profile of AML patients with mutatedCEBPα locus from The Cancer Genome Atlas (TCGA). We then predicted theecCEBPα secondary structure in order to check the potential ofecCEBPα to form triplexes around CpG loci and checked if triplex formation influenced CpG methylation, genome-wide.Results: Using DNA methylation profiles of AML patients with a mutatedCEBPα locus, we show thatecCEBPα could interact with DNA by forming DNA:RNA triple helices and protect regions near its binding sites from global DNA methylation. Further analysis revealed that triplex-forming oligonucleotides inecCEBPα are structurally unpaired supporting the DNA-binding potential of these regions.ecCEBPα triplexes supported with the RNA-chromatin co-localization data are located in the promoters of leukemia-linked transcriptional factors such as MLF2.Discussion: Overall, these results suggest a novel regulatory mechanism forecCEBPα as a genome-wide epigenetic modulator through triple-helix formation which may provide a foundation for sequence-specific engineering of RNA for regulating methylation of specific genes. It is the most prevalent form of leukemia in older adults (>60 years) with an annual mortality rate of 50% and a 5-year survival rate of 24%3. With the combined effects of the global increase in average life expectancy and AML drug inefficiency, the number of patients is expected to significantly increase in the coming years5.Acute myeloid leukemia (AML) is a malignant tumor characterized by the proliferation of undifferentiated myeloblastsPML-RARA], t [BCR-ABL], inv(16) [CBFB-MYH11], t [RUNX1-ETO] are called cytogenetically abnormal (CA-AML), while cases with genetic abnormalities  are called cytogenetically normal (CN-AML)4. The former accounts for 50–55%, while the latter accounts for 45–50% of diagnosed AML cases7. Even though these mutations and chromosomal alterations are crucial for initiating AML, they are not sufficient to explain AML progression, heterogeneity, and relapse8.The current understanding of the molecular interplay in AML has been defined under two distinct categories; (i) genetic abnormalities and (ii) non-random chromosomal rearrangements. Cases of AML with chromosomal rearrangements as t [. LncRNAs are emerging functional transcriptional products of at least 200 nucleotides lacking an open reading frame. Although they account for a large proportion of transcriptional products in mammals 12, only a small number of lncRNAs have been well characterized. Although lncRNAs are mostly not conserved evolutionarily, they are heavily regulated suggesting their functional role13. They may function either as signal transducers, protein guides, or molecular scaffolds to regulate transcriptional and epigenetic events. Some lncRNAs perform these functions incis- by modulating transcription of nearby genes (Dum)17 or act intrans-, by modulating genes at multiple distant loci (MALAT1)18, while some can do both (HOTAIR)19.Recently, studies have identified the role of non-coding RNAs, especially long non-coding RNAs (lncRNA) in the initiation and progression of cancersDNMT1, DNMT3A, andDNMT3B20. LncRNAs have been identified in recent studies as important agents that can modulate DNA methylation, either by activating or repressingDNMTs. For example, the lncRNADum was discovered to repress a nearby geneDppa2 by recruiting multipleDNMTs leading to methylation of a promoter region, thus promoting myoblast differentiation17. Conversely, the lncRNAH19 represses the activity ofDNMT3B by interacting withSAHH which hydrolyzesSAH, a step required forDNMT3B activation21.ecCEBPα, which is transcribed from theCEBPα locus, directly blocksDNMT1 to prevent methylation of proximal and distal located promoters, thus promotingCEBPα-mediated granulocyte differentiation20. Mechanistic studies via reduced representation bisulfite sequencing and RNA immunoprecipitation sequencing shows thatecCEBPα suppresses DNA methylation incis- by acting as a shield that sequestersDNMT1 from theCEBPα promoter. We speculate thatecCEBPα could regulateDNMT1 activities in distant DNA regions (in trans-) as well. The mechanism of this potential interaction is to be determined.Recent studies have identified lncRNAs for their remarkable role in regulating major epigenetic processes such as DNA methylation and chromatin remodeling. DNA methylation in mammals is coordinated by one of the three DNA methyl-transferases (DNMT);ecCEBPα is not annotated most likely due to an overlap with a protein-coding geneCEBPα on the same strand. We retrieved the completeecCEBPα sequence from the human genome  based on information reported in the work of Di Ruscioet al.20.ecCEBPα is approximately 4.8kb and it overlaps with the intronlessCEBPα gene (~2.6kb) on the same strand.ecCEBPα does not share either the same transcription start site (TSS) or transcription end site (TES) withCEBPα, starting ~0.89kb upstream and ending ~1.46kb downstream of theCEBPα gene.In the recent version of GENCODE,CEBPα mutation data for 186 AML patients were retrieved from the Cancer Genome Atlas (TCGA:http://firebrowse.org). CpG methylation levels were measured in 307796 unique loci. We split all the AML patients into two groups based on CEBPα mutation status (13 patients with a CEBPα mutation and 173 patients without a mutation). We classified a CpG position as hypermethylated (HM) in patients with a CEBPα locus mutation if DNA methylation level was significantly increased in the case of a CEBPα mutation  and all non-hypermethylated (NHM) CpG in the case of a CEBPα mutation were classified as non-hypermethylated CpGs . As a result, we obtained 11955 HM and 261433 NHM CpGs.CpG methylation (Illumina 450K array) and22, unpaired RNA nucleotides are more likely to form triplexes with DNA. We predicted RNA secondary structure using RNAplfold (V 2.4.14), from the Vienna suite using a cut-off for pairing probability (-c) of 0.124. To search for potential interactions between ecCEBPα and DNA target regions we used only unpaired nucleotides, while the nucleotides predicted to pair were replaced with ‘N’.As suggested in a previous studyecCEBPα triplex formation with DNA target regions, we used Triplexator (V 1.3.2)25, since it has higher accuracy of prediction14, with the following optimization parameters suggested inDNA target regions were defined as 100 nucleotides centered at each CpG. To predict26. Briefly, all 272131 CpG loci with at least one predicted triplex were taken as input target regions. By predicting triplexes in the background regions, TDF is capable of estimating the statistical significance ofecCEBPα binding between target regions and other non-target CpGs regions. Since TDF allows only to mask regions in the genomic background rather than to select the background explicitly we had to prepare a special mask for the non-target regions. To do so we removed 35715 CpG loci with zero triplex predictions from the human genome using BEDtools subtract (BEDTools v2.29.2). TDF was implemented with a minimum triplex length (-l) of 10 nucleotides, an error rate (-e) of 20%, and (-f) to mask background loci in 100 random samplings (-n).To estimate the statistical significance of predicted triplexes we used Triplex domain finder (TDF v 0.12.3), which clusters RNA triplex-forming oligonucleotide (TFO) into DNA binding domains (DBD)27. The data was downloaded from GEO (GSM3478205). The iMARGI dataset was mapped to the hg38 genome assembly. We used UCSC Liftover to convertecCEBPα sequence coordinates from hg19 to hg38 sequences28. We expanded the DNA coordinates of CpGs by 3.0kb nucleotides upstream and downstream. IntersectBed from BEDTools was used to check the co-location of predicted triplexes and experimentally validated interactions ofecCEBPα29. Fisher’s exact test was calculated for the number of confirmedecCEBPα interactions between TDF and iMARGI data.To validate the predicted interactions we used RNA:chromatin interactome obtained with iMARGI method capturing chromatin-associated RNA (caRNA) and their genomic interaction loci30 to infer the biological significance of the genes potentially affected byecCEBPα binding.Finally, since Illumina 450K array probes are located close to genes, we performed functional enrichment using BiNGO (v 3.0.3) 31 and Python 3.7. Code is available athttps://zenodo.org/record/438525932.All statistical analyses were performed using R 4.0 or SciPy v1.5.1 library. Visualization was done in Cytoscape 3.2.0ecCEBPα in the modulation of global CpG methylation status intrans via direct interaction with DNA regions.ecCEBPα (extra codingCEBPα), reported in work by Di Russioet al.20, is located on chromosome 19 and transcribed from theCEBPα locus  with its binding sites and if such interactions affect DNA methylation. We observed that regions capable of forming triplexes withecCEBPα remain protected from global DNA hypermethylation observed in case of a mutation in aCEBPα locus  also have a protective effect on ecCEBPA’s binding sites in comparison to the rest of the genomic CpGs . The DBD region is located downstream from theCEBPα gene suggesting thatecCEBPα binding region is not affected by the mutation in theCEBPα locus. The predicted secondary structure of theecCEBPα sequence showed that more than 95% of sequence positions from 4087-4987 (~0.5kb fromCEBPα TES)  - a triplex prediction tool that refines the resolution of predicted TFOs in RNA into DNA binding domains (DBD) and calculates the significance of the number of predicted triplexes for each DBD. Overall, 17 significant DBDs were identified within lncRNA . These DPα TES)  were unpPα TES) .ecCEBPα contactswithin the iMARGI dataset and 29 of them contained predicted triplexes. Altogether, these 29 iMARGI interactions were made up of 182 predicted TTS  locatedin cis and in trans on 14 chromosomes (ecCEBPα) are accounted for by all predictions. Since these ecCEBPA contacts have been experimentally validated, we suggested that they are the most reliable regions where ecCEBPA might have a protective effect from DNA methylation. Overall, a mean methylation delta score of experimentally validated binding sites is significantly lower than that of the non-binding sites   .ecCEBPα targets  suggests that triplex formation may represent a possible mechanism employed byecCEBPα to regulate TF methylation and as a consequence, their expression. Furthermore, out of the 33 genes we identified to be targets of ecCEBPA, 16 genes  are clearly related to hematopoiesis and various leukemias. A summary of the genes and their function is provided inWe performed gene ontology analysis on the genes located nearby 182 ecCEBPα triplexes supported by iMARGI. Key gene ontology categories such as nucleic acid binding and transcription factor activities were enriched among putativetargets . Represe8. In this study, we elucidate a putative mechanism for the regulation of DNA methylation byecCEBPα. In a previous study,ecCEBPα, which accompanies the transcription ofCEBPα on the same locus, was shown to protect the promoter ofCEBPα from DNA methylation leading to active expression20. We speculated thatecCEBPα might perform a similar function in trans. We demonstrated thatecCEBPα-DNA triplex formation might provide the molecular basis of this interaction.ecCEBPα binding presumably protects the region from genome-wide hypermethylation induced byCEBPα mutation in AML patients. Di ruscioet al. confirmed that the most mutations in CEBPα do not influence the expression of ecCEBPα but rather, its ability of the RNA to fold properly. We suggest that the inability to fold properly even in overexpressed cases may affect its structural ability to bind its targets.ecCEBPα contains a TFO/DBD-rich region at its 3’ end, with low pairing probability, suggesting that it is capable of triplex formation. Several of the predictedecCEBPα binding sites, which include transcription factors such as MLF2, SUV39H2, RBM5, UBTF, and sequence-specific DNA binding proteins includePOU2F2, MED12L, and DNASE1L1, were supported by experimental iMARGI RNA:chromatin interactions data.Unlike other forms of cancers, AML progression is often mutation-independent but may be explained by altered epigenetic regulation, DNA methylation specifically33. As thoroughly reviewed previously, lncRNAs such asecCEBPα, Dum, Dali, Dacor1, and LINCRNA-P21 interact with DNA in trans to regulate DNA methylation34. The results presented herein further demonstrate that triplex formation betweenecCEBPα and CpG containing DNA regions could indeed be regulatory and protect CpG sites from DNMT activity.Currently, the understanding of lncRNA function and mechanisms of action is limited to a few dozen well-annotated lncRNA transcripts. A few functional characterization attempts are based on the ‘guilt by association’ hypothesis, which may not resonate well with the ability of lncRNA to interact in trans35 and lowly expressed, it is not surprising that we could validate only a few of the predicted interactions. Nevertheless, based on our results, we suggest a model of potentialecCEBPα chromatin interactionin trans , an oncogene in breast cancer and myeloid leukemia39 as well as UBTF which controls rDNA expression41 contributes significantly to cancers upon promoter hypermethylation42. The suppressor of variegation 3-9 homolog 2 (SUV39H2), a histone-lysine-N-methyltransferase which regulates the hypermethylation H3K9 has also been reported to indirectly influence over 450 promoters in AML43. Having in mind thatecCEBPα is transcribed from CEBPα locus - a key transcription factor of hematopoiesis - this lncRNA could participate in the formation of a hub in the hematopoiesis regulatory network.Recent studies have observed that promoter or transcription start sites (TSS) regions, which tend to be rich in CpG dinucleotides, are TTS-rich and potential triplex-forming hotspotsIn conclusion, we have shown that ecCEBPα could serve as a trans-acting regulatory agent protecting its binding sites from genome-wide CpG methylation, and its dysregulation could contribute to aberrant methylation profile in AML patients. These results suggest a novel regulatory mechanism for ecCEBPα as a modulator of DNA methylation through triplex formation providing a foundation for sequence-specific engineering of RNA for regulating methylation of specific genes.https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.13/Complete ecCEBPα sequence retrieved from the human genome :CEBPα mutation data for 186 AML patients retrieved from the Cancer Genome Atlas (TCGA):http://firebrowse.org.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3478205GEO: Embryonic kidney that expresses SV40 large T antigen, Accession number GSM3478205:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM980576GEO: DNA Methylation by Reduced Representation Bisulfite Seq from ENCODE/HudsonAlpha GSM980576:https://doi.org/10.5281/zenodo.438525932Zenodo: josoga2/eccebp-alpha-project: F1000 Code Release,This project contains the following underlying data:-DNA_BINDING_DOMAINS_ID.tsv-predicted_secondary_structure_of_ecCEBPA.fa-probes.csv (main data)https://github.com/josoga2/eccebp-alpha-project/tree/f1000Code used for analysis available from:https://doi.org/10.5281/zenodo.438525932Archived code as at time of publication:http://doi.org/10.5281/zenodo.443322244.Zenodo: Supplementary Data for Secondary structure and DNA binding domain prediction,This project contains the following extended data:Supplementary Table 1: Summary table of DNA binding domains (DBD), the counts of target regions within the genome and statistical analysis.ecCEBPα secondary structure prediction with RNAplfoldCreative Commons Attribution 4.0 International license (CC-BY 4.0).Data and code are available under the terms of the All the major comments raised by the reviewer have been answered thoroughly by the authors. The additional methylation analysis of HL-60 datasets in both normal and overexpressed ecCEPBα is highly appreciated. Moreover, re-labelling of the plots as highlighted previously has made it easier to comprehend them. However, there are few minor technical remarks, both figures 1 and 2 do not have the legend for the updated figure 1(d) and 2 (c), and accordingly, the subsequent legends labelling needs to be modified.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Structure function relationship of noncoding RNA, with major focus on LncRNA function via triple helical structures.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The manuscript by Ogunleye AF and co-authors describes a novel phenomenon of lncRNA ecCEBPα -dependent regulation of methylation in several genes. The authors analyzed the open datasets and showed that mutations in CEBPA gene  changed the global CpG methylation status in AML cells. The manuscript is nicely written, conclusions are logically justified. This work will be interesting to the readers in the epigenetic regulation and cancer research field. Still, I have several concerns about the analysis. There are a lot of predicted triplex target regions for ecCEBPA genome-wide. Some of them could be false-positive predictions. Is there any way to confirm the observed effect only of experimentally validated contacts? Or in regions where predictions are most reliable? Also, it is unclear other genes, not only transcription factors, predicted to be regulated by ecCEBPA are related to hematopoiesis. It would be helpful to have at least some hypotheses in the discussion. If there is some link found between ecCEBPA targets and regulation of hematopoiesis, it would really strengthen the conclusions of the work. Probably, KEGG pathway enrichment analysis may help with that.Minor comments: It is unclear on the figure with a secondary structure of ecCEBPA where the triplex-forming region is located.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Models of human diseases, cancer research, fibrosis, pagingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Response to Reviewer Two Comment one: There are a lot of predicted triplex target regions for ecCEBPA genome-wide. Some of them could be false-positive predictions. Is there any way to confirm the observed effect only of experimentally validated contacts? Or in regions where predictions are most reliable?Response one: We are grateful to the reviewer for this suggestion. We performed a validation for the most reliable predicted triplexes using only experimentally validated ecCEBPA:DNA contacts. Overall, a mean methylation delta score of only 0.01  was observed which further emphasizes the protective effect of ecCEBPA.  The following text and images were added to the main body of the paper:“Since these ecCEBPA contacts have been experimentally validated, we suggested that they are the most reliable regions where ecCEBPA might have a protective effect from DNA methylation. Overall, a mean methylation delta score of experimentally validated binding sites is significantly lower than that of the non-binding sites  .”https://drive.google.com/file/d/1iynm-t0DfGkxLF5vsumG4iCcfo2u2xCJ/view?usp=sharing  New figure panels (1&2) here:Comment two: Also, it is unclear if other genes, not only transcription factors, predicted to be regulated by ecCEBPA are related to hematopoiesis. It would be helpful to have at least some hypotheses in the discussion. If there is some link found between ecCEBPA targets and regulation of hematopoiesis, it would really strengthen the conclusions of the work. Probably, KEGG pathway enrichment analysis may help with that.Response two: We thank the reviewer for this important comment. Indeed, we discovered that other non-transcription factor genes have links to acute myeloid leukemia via literature mining. Unfortunately, this could not be enriched through KEGG. We suggest including a supplementary table that details the different roles of the identified genes. In the main text, we added:“Out of the 33 genes we identified to be targets of ecCEBPA, 16 genes  are clearly related to hematopoiesis and various leukemias. A summary of the genes and their function is provided in Table 1.” Table 1: AML specific function of nearby genes that are protected by ecCEBPA (added to the paper). In Figure 1(b), a global DNA hypermethylation has been represented in AML patients with CEBPα mutation, but the numbers of the HM and NHM sites in the methods section do not reflect the same. It shows higher sites for NHM, than the HM sites, kindly check if some mislabeling is there at the authors part.Also, the reviewer has failed to understand Figure 1(c). In both the x-axis and the side legend, binding and non-binding have been depicted, but which one is the AML patients with and without mutation isn't clear from the figure. There are few other key points that were not clear; (a) what is the level of ecCEBPα expression in AML patients with and without mutation. (b) Additionally, if the expression of the lncRNA remains the same in both cases, how does one explain the increase in binding of the lncRNA and subsequent higher NHM sites, in the case of AML patients with CEBPα mutation?1)et.al on ecCEBPα had also analyzed genome-wide methylation, in which they report that methylation levels remain unchanged even when ecCEBPα was overexpressed. This is in contrast with the major theme of the paper i.e., methylation of genes in trans is affected by the ecCEBPα. The authors should comment on this in the discussion part.  (c) Moreover, a previous report , a global DNA hypermethylation has been represented in AML patients with CEBPα mutation, but the numbers of the HM and NHM sites in the methods section do not reflect the same. It shows higher sites for NHM, than the HM sites, kindly check if some mislabeling is there at the authors part.Response Two: We believe that there is a confusion here. The plot was correct and reflected the change in DNA methylation in all CpG positions including those that were not statistically differentially methylated. We counted hypermethylated CpG  and those that were not hypermethylated . In this way, we refer to non-hypermethylated (NHM) to any CpG with DNA methylation change less than 0.1, which includes either not changing or hypomethylated and not only hypomethylated. To avoid this confusion we suggest replacing the plot with the one below. We specified a 0.1 mid-point to aid visualization. .Comment Two: Also, the reviewer has failed to understand Figure 1(c). In both the x-axis and the side legend, binding and non-binding have been depicted, but which one is the AML patients with and without mutation isn't clear from the figure.Response Two: Each violin in Figure 1(c) is a representation of the differential methylation score between CpGs of AML patients with and without CEBPa mutation. The blue violin represents all CpGs that are located in ecCEBPA binding sites, while the orange violin represents CpGs that do not bind with ecCEBPA. To provide clarity, we corrected the labels on the X and Y axes and added explicit explanation in the legend of the figure . Difference in methylation = Mean methylation of CpGs in AML patients with CEBPA mutation - Mean methylation of CpGs in AML patients without CEBPA mutation.Comment 3: There are few other key points that were not clear; (a) what is the level of ecCEBPα expression in AML patients with and without mutation. (b) Additionally, if the expression of the lncRNA remains the same in both cases, how does one explain the increase in binding of the lncRNA and subsequent higher NHM sites, in the case of AML patients with CEBPα mutation? (c) Moreover, a previous report  on ecCEBPα had also analyzed genome-wide methylation, in which they report that methylation levels remain unchanged even when ecCEBPα was overexpressed. This is in contrast with the major theme of the paper i.e., methylation of genes in trans is affected by the ecCEBPα. The authors should comment on this in the discussion part. Response: (a&b): It is relatively difficult to estimate the expression level of ecCEBPA since this gene is not in the annotation used by TCGA. The raw data is not freely available in TCGA. ecCEBPA gene also overlaps with the CEBPA gene making the estimation of expression of each of the genes even more complicated. Since the mutation in AML patients happens in the body of CEBPA gene, we do not expect a significant change in the expression of either CEBPA or ecCEBPA.  We explained the relationship between ecCEBPA binding and methylation in the first result section . We suggested that the mutation(s) in CEBPA does not affect the expression of the lncRNA, but rather the ability to bind its targets.  We further emphasize this point in the paper with this line (discussion):“Di Ruscio et al. confirmed that most mutations in CEBPα do not influence the expression of ecCEBPα but rather, its ability of the RNA to fold properly. We suggest that the inability to fold properly even in overexpressed cases may affect its structural ability to bind its targets.” (c): We are very grateful to the reviewer for this valuable comment. In response to this, we retrieved the DNA methylation data (RRBS) for wild-type HL-60 cells (ENCODE Dataset) and overexpressed HL-60 cells . We then calculated the methylation difference between the two cell states and compared it between the ecCEBPA binding sites versus the non-binding sites. Non ecCEBPA binding sites were more methylated in comparison to ecCEBPA binding sites . Bearing in mind that ecCEBPA targets are located genome-wide, it is tempting to suggest that “enforced overexpression”  of ecCEBPA strongly protects local CpGs from methylation while the rest of the genome gain some methylation.  We added the following statement to the results in the main text:"Furthermore, the overexpression of ecCEBPA in HL-60 cells  lead to ecCEBPA binding sites stay unmethylated while non ecCEBPA binding sites gain methylation  , suggesting a protective effect on ecCEBPA’s binding to its target locations ."