Cerebrovascular stroke (CVS) is a potentially fatal disease. The most common risk factor for CVS is hypertension.While most studies in the field have focused on the functional roles of long noncoding RNAs (lncRNAs) NEAT1, GAS5, and HOTAIR in CVS, less attention has been paid to their clinical relevance to stroke incidence and prognosis. Also, a link has not yet been made between these lncRNAs and hypertension, our study aim was to investigate whether the expression of these lncRNAs differed between CVS with and without hypertension, as well as to compare each group to controls.In total, 181 CVS patients were enrolled, including 91 chronic hypertensive patients with stroke, 90 stroke patients without hypertension, and 51 control subjects. blood samples were collected on the day of recruitment from patients with CVS and controls. Real-time qRT-PCR was used to detect the expression of target lncRNAs in serum.GAS5 was able to differentiate patients with CVS hypertensive from patients with CVS non-hypertensive.When compared to controls, there was a statistically higher level of lncNEAT1 in each case group (median (IQR) = 3.68 (1.35–7.35) and 3.05 (0.95–6.45) for the hypertensive and non-hypertensive groups, respectively, with a significantly higher level in the hypertensive group (P = 0.04). When compared to controls, lncHOTAIR was significantly downregulated in all case groups , with a significantly lower level in the hypertensive group (P = 0.05). LncGAS5 levels in patients were significantly lower (median (IQR) = 0.16 (0.02–0.55) and 0.25 (0.03–0.99) for the hypertensive and non-hypertensive groups, respectively) compared to controls, with a significantly lower level in the hypertensive group (P = 0.02). There was a significant positive correlation between NEAT1 and GAS5, but a significant negative correlation between each with HOTAIR in both patients' groups. We also detected a significant negative correlation between each NEAT1 or GAS5 and NIHSS score while a significant positive correlation between HOTAIR and NIHSS. ROC curve analysis for Patients in each case group had statistically higher levels of NEAT1 and lower levels of HOTAIR and GAS5 compared to control levels, with higher significant NEAT1 but lower significant HOTAIR and GAS5 in the hypertensive group. Therefore, lncRNAs NEAT1, HOTAIR, and GAS5 could be used as diagnostic and prognostic biomarkers of CVS that correlate with NIHSS score and could produce a novel target for CVS therapy. Ischemic stroke is one of the major causes of physical disability and mortality worldwide . DiseaseLong non-coding RNAs (lncRNAs) are defined as transcripts of more than 200 nucleotides that have been identified as important regulators of several biochemical functions . Recent The lncRNA nuclear enriched abundant transcript 1 (NEAT1) was recognized as a modulator of the stability of paraspeckle nuclear bodies. NEAT1 has been extensively studied as a cancer regulator , and a nThe lncRNA growth arrest-specific 5 (GAS5) has been proposed as a tumor-inhibiting factor in different types of cancers . Recent The lncRNA HOX transcript antisense intergenic RNA (HOTAIR) has been shown to play a role in the development and progression of several complex disorders . HOTAIR While most studies in the field have focused on the functional roles of these lncRNAs in CVS, less attention has been paid to their clinical relevance to stroke incidence and prognosis. Also, a link has not yet been made between these lncRNAs and hypertension, which is the leading risk factor for stroke. As a result, the aim of this study was to clarify whether the expression of these LncRNAs differs in patients with hypertensive vs. non-hypertensive CVS, and if there are differences between each group when compared with a control group, in order to explore whether they could be used as a therapeutic target in CVS patients, particularly those with hypertension and to assess their utility as diagnostic and prognostic biomarkers for stroke, as well as their relationship with clinical, laboratory characteristics, and severity scores.22.1The current case-control study included a total of 181 ischemic stroke patients . They weOur patients are classified as chronic hypertensive patients with stroke and stroke patients without hypertension based on the history of known chronic hypertensive patients on long-term anti-hypertensive medications  . Fifty-o2.2Following recruitment, a complete history of risk factors and concurrent illness was recorded including age, gender, hypertension, history of diabetes mellitus (DM), smoking, rheumatic heart disease (RHD), atrial fibrillation (AF), ischemic heart disease (IHD), valve replacement procedure (VR), current therapy, history of previous stroke, or any other illness. All participants' blood pressure was measured three times and the mean was calculated. All participants also underwent laboratory tests such as hemoglobin concentration (HB), thyroid profile tests , lipid profile , triglycerides (TG), LDL, HDL), kidney function tests , fasting blood glucose (FBG), and 2 h postprandial blood glucose (2hsPPBG). All patients underwent CT and echocardiography. ECG and carotid doppler were used to rule out heart disease, atherosclerosis, and carotid artery stenosis, respectively.2.3Severity scores include The National Institutes of Health Stroke Scale (NIHSS), a measure used by healthcare practitioners to objectively evaluate the disability caused by a stroke, which consists of 11 items with the highest possible score being 42, while the lowest conceivable score is 0 . NIHSS w2.4NEAT1, GAS5, and HOTAIR) and hypothesized that they might play an important role in the therapeutic plan of CVS especially if they are related to hypertension (a major risk factor for stroke).Because the three target lncRNAs  are present in detectable amounts in circulation ,13,14, t2.5g for 10 min. Serum samples were immediately stored at −80 °C until use. We extracted total RNA from the sera using the MiRNeasy Serum/Plasma extraction kit  after adding the QIAzol lysis reagent according to the manufacturer's instructions; in a new collection tube, we pipetted 200 μl serum to 1000 μl QIAzol Reagent (about 5 vol of samples amount). After mixing well by vortexing, we added an equivalent volume of chloroform (200 μl) to the initial sample in the tube and vortexed for 15 s. Following centrifugation, the specimen separated into three parts: an uppermost aqueous solution containing RNA, whitish interphase, and a bottom, pink organic layer. We transferred the topmost aqueous solution  to a clean collecting tube, added 1.5 vol of 100% ethanol (900 μl), and carefully mixed it by pipetting up and down numerous times. We used the RNeasy spin column (supplied by the kit) to purify RNA from other debris in two phases; for each we used 700 μl of the sample, centrifuged at 8000×g for 15 s, and discarded the flow-through. We used the washing buffers  to wash the RNA as follows: first, we added 700 μl RWT to the spin column, closed the lid, centrifuged at 8000×g for 15 s, discarded the flow-through, then repeated this step with 500 μl RPE followed by 500 μl 80% ethanol, after discarding the flow-through, we put the spin column to new 2 ml tube, opened the lid, and centrifuged at full speed for 2 min for dehydration, we used RNase-free water for elution.Peripheral blood (PB) samples were collected on the day of recruitment from patients with CVS and controls. A venous blood sample of 5 mL was delivered to a plain tube. After 15 min of coagulation, the serum was separated by centrifugation at 4000×The NanoDrop® (ND)-1000 spectrophotometer  was used to determine the extracted RNA purity and concentration.For the long noncoding RNA analysis, total RNA was reverse-transcribed in a total volume of 20 μL/reaction using the RT2 first strand kit  following the manufacturer's recommendations.2.6−ΔΔCt equation was used to calculate the serum fold changes of NEAT1, GAS5, and HOTAIR. Non-coding RNAs with a fold change (FC) less than one were downregulated, whereas those with an FC more than one were upregulated [Previously, target lncRNAs in serum were measured . The levegulated . The con2.7We used a sample size of 90 cases, and we examined the power of the sample by G∗power software for the different tests of two tails used in the statistical analysis  using the medium effect of Cohen, the power of sample ranged from 0.889 to 0.999), the critical F was 3.85.2.8NEAT1, HOTAIR, and GAS5 and the clinical parameters. Multivariable linear regression analysis with NIHSS score as a dependent factor was done. The receiver operating characteristic (ROC) curve analysis was done to detect the sensitivity and specificity of NEAT1, HOTAIR, and GAS5 regarding the discrimination between CVS cases with or without hypertension and differentiation between CVS patients with healthy control subjects. All the results were interpreted its significance by considering p ≤ 0.05 is significant.Data was presented in numbers and percentages, median and interquartile ranges; (IQR), and mean ±SD (standard Deviation), The SPSS version 22 (SPSS Inc) was used to analyze data. Median and range were calculated for the quantitative data. when variables were not normally distributed, the Mann–Whitney-U test (2 groups) or Kruskal Wallis test (more than 2 groups) was used in comparing groups. Otherwise, the one-way ANOVA (for comparing the three groups) or the independent-T test (comparing the hypertensive and non-hypertensive CVS patients’ groups) was used. Chi-square (χ2) was performed to detect the significance of the qualitative data, if the expected frequency is < 5, the exact test was used instead. Spearman correlation was done to explore the association between 33.1, P = 0.037) scores in the non-hypertensive group.This case-control study included 181 patients who presented with acute cerebrovascular stroke (CVS), 91 of them known to be hypertensive (CVS + HTN) and 90 were non-hypertensive patients (CVS + NHTN). Fifty-one participants volunteers were involved as a control group. Patients with CVS and HTN had a mean age of 59.4 ± 8.55 years, CVS patients and NHTN had mean age of 56.71 ± 11.53 years and 51 controls had a mean age of 55.66 ± 10.08 years . There w3.2By comparing the two patient groups, it was found that the hypertensive group had a significantly higher incidence of IHD than the non-hypertensive group (P = 0.003). There were no significant differences between the two groups regards diabetes, smoking, H/O stroke, atherosclerosis, stenosis, AF, RHD, or valve replacement .Table 3S3.3NEAT1, HOTAIR, and GAS5 levels among the three studied groups  we found that there was a higher level of NEAT1 in each case group (median (IQR) = 3.68 (1.35–7.35) for hypertensive group and median (IQR) = 3.05 (0.95–6.45) for the non-hypertensive group when compared to controls, and stroke patients with hypertension had significant higher NEAT1 when compared to stroke patients without hypertension (P = 0.04). Regarding HOTAIR, it was significantly downregulated in all case groups  when compared to controls with a significantly lower level in the hypertensive group (P = 0.05). Serum GAS5 was significantly lower in patients’ groups (median (IQR) = 0.16 (0.02–0.55) for hypertensive group and median (IQR) = 0.25 (0.03–0.99) for non-hypertensive group compared to controls with significant lower level in hypertensive group (p = 0.02)  , Fig. 2A3.4NEAT1 was associated with the presence of diabetes mellitus and history of stroke (in both patient groups), but lower HOTAIR was significantly associated with patients with a history of stroke in the hypertensive group (P = 0.05) as well as lower GAS5 was significantly associated with diabetic patients in hypertensive group (P = 0.04).Results reported in  showed t3.5NEAT1 and GAS5 . and the significant negative correlation of each with HOTAIR in both patients’ groups  Also, the detected significant negative correlation of each NEAT1  or GAS5  with NIHSS score while the significant positive correlation between HOTAIR and NIHSS score r = 0.286, P = 0.010 in HTN, and r = 0.432, P < 0.001 in NHTN). Besides the reported significant negative correlation between NEAT1 with SBP while a significant negative correlation between HOTAIR and SBP or DBP. Moreover, there was a significant positive correlation between NEAT1 and HDL in both groups  and between GAS5 and HDL in NHTN , but, a significant negative correlation between HOTAIR and HDL in NHTN . Taken together, these results suggest that NEAT1 and GAS5 are defensive lncRNAs while lncHOTAIR is a risky lncRNA.The results of the correlational analysis are shown in , Fig. 4,3.6On Multi-variant Analysis , Fig. 6 By testing the fold change in NEAT1, HOTAIR, and GAS5 (Constant) probability to be predictors for cerebrovascular stroke, in hypertensive cerebrovascular stroke patients the Standardized Coefficients (Beta) are −0.265, 0.419, and 0.073 respectively and the P value is 0.050, <0.001 and 0.579 respectively. On testing the fold change in NEAT1, HOTAIR, and GAS5 (Constant) probability to be predictors for cerebrovascular stroke, in non-hypertensive cerebrovascular stroke patients, the Standardized Coefficients (Beta) are −0.220, 0342, and 0.048 respectively with a P value of 0.091, 0.02 and 0.675 respectively.3.7NEAT1, HOTAIR, and GAS5 showed statistically significant differences in CVS hypertensive patients and CVS non-hypertensive patients compared to control, with cut-off points of 1.39, 0.68, and 0.82, respectively, with a sensitivity of 94.6, 81.5, and 96.8%, respectively, and specificity of 100% for the three markers in CVS hypertensive patients, and cut-off points 1.33, 0.75 0.84, respectively, sensitivity 91.2, 75.8, and 91.9%, respectively, specificity of 100% for the three markers in CVS non-hypertensive patients, as shown in  with a cut-off point of 0.37 with 81.68% sensitivity and 69.77% specificity. while, ROC curve analysis for NEAT1, HOTAIR in CVS hypertensive patients compared with CVS non-hypertensive patients revealed a statistically nonsignificant difference with a cut-off point of 3.83 and 0.47, with a sensitivity of 86.4 and 65.3%, respectively, and specificity of 55.95 and 59.14%, respectively  between three groups , as well as their association with clinicopathological data and disease severity score.In reviewing the literature, no study was found to have explored these lncRNAs in patients with CVS with hypertension versus patients with CVS without hypertension. Therefore, the present study set out with the aim to compare expressions of three lncRNAs  In two 2022 studies that documented the beneficial role of NEAT1 in the induced mice injury model, they revealed that induced NEAT1 expression inhibits inflammasome activation by NLRP3 in microglia, alleviating the negative outcomes of ischemic stroke . Also, b(b) Our findings broadly support the findings of another study in this area linking GAS5 to myocardial infarction (MI), which found reduced GAS5 transcript levels in the hearts of MI-modeled mice , and thac) Regarding HOTAIR, elevated HOTAIR in permanent middle cerebral artery occlusion (pMCAO) mice brain tissues were significantly related to the larger infarcted area and worse neurological deficits and motor balance scores via the miR-148a-3p/KLF6 axis  or promoBased on the information presented above, we can conclude that by regulating the expression levels of these lncRNAs, we can target a critical pathway underlying both hypertension and hypertension-induced vascular occlusive diseases; a) Induction of NEAT1 can be beneficial by reducing oxidative stress (by activating Sirt3), relieving inflammation, (inhibiting inflammasome activation by NLRP3), enhancing angiogenesis and restoring blood flow , and decreasing vascular endothelial cell apoptosis (via activating miR181d-5p/CDKN3 axis) thus preventing hypertension-related vascular changes b) GAS5 induction can aid in the resolution of vascular inflammation (by downregulating sema3a) and the prevention of hypertensive vascular remodeling . c) Conversely, silencing HOTAIR reduced ROS formation, vascular inflammation, and intimal apoptosis (via the miR-148a-3p/KLF6 axis and/or the miR-130b-3p/PPARα axis). These findings suggested that target lncRNAs could be used as a therapeutic target in hypertensive stroke patients .Fig. 7PrNEAT1 level and NIHSS score [HOTAIR, no previous study explores its expression in stroke patients. While Huang et al, 2021 reported a significant increase of lnc-HOTAIR in brain tissues of (pMCAO) mice with unfavorable outcomes and increasing neurological deficits and motor balance scores [NEAT1 and GAS5 supporting the suggested protective roles, while a significant negative correlation between each of them and HOTAIR enforced their suggested opposite functions. In line with our findings, a previous study demonstrated a positive correlation between NEAT1 and GAS5 in breast cancer patients [Limited clinical studies have demonstrated expressions of target lncRNAs in peripheral blood of CVS patients and linked their levels to clinical features of the disease as follows; a) For NEAT1, previous two studies have demonstrated increased NEAT1 in stroke patients compared to controls and not related to clinical categorical variables including hypertension but they found a positive correlation between SS score ,34. HoweSS score . b) OnlySS score . c) Respe scores . The corpatients . While Kpatients . We alsopatients .. Hence ROC curve analysis for NEAT, HOTAIR, and GAS5 in CVS hypertensive patients versus CVS non-hypertensive patients revealed a statistically insignificant difference, with the exception of GAS5, which significantly can distinguish between stroke patients with hypertension and stroke patients without hypertension . ROC curve analysis for the NEAT1, HOTAIR, and GAS5 revealed statistically significant differences in CVS patients, whether they had hypertension or not, when compared to controls, with fair sensitivity and high specificity, implying their diagnostic values.NEAT1, HOTAIR, and GAS5 are novel diagnostic and prognostic markers for stroke associated with hypertension. They could be used as novel therapeutic targets for hypertensive stroke patients by breaking the vicious cycle of oxidative stress, inflammation, and hypertension by inducing the production of NEAT1 and GAS5 while silencing HOTAIR could be a new therapeutic and preventive strategy aiding in the treatment of hypertension and preventing stroke formation in hypertensive patients. According to previous research, traditional antioxidative and anti-inflammatory drugs are of limited value in this case, implying that more specific interventions targeting the underlying mechanism of hypertension-induced stroke would be most beneficial [For the first time, the findings presented here suggest that the neficial .The study's limitations include a relatively small sample of patients collected from the same area, so there is potential for patient selection bias. Due to the lack of a long-term follow-up program to evaluate the prognostic value of target lncRNAs and their relevance to recurrence probability, we recommend that future large-scaled multicentric studies be conducted to validate the current study's findings. Also, different polymorphisms in target lncRNAs and their relation to serum expression levels should be considered. In addition, to highlight the relevance of the current study's results to negative lncRNAs control whose circulating levels are not impacted by hypertension.5Patients in each case group had statistically higher levels of NEAT1 and lower levels of HOTAIR and GAS5 compared to control levels, with higher significant NEAT1 but lower significant HOTAIR and GAS5 in the hypertensive group. Furthermore, NEAT1 and GAS5 expression was significantly negatively correlated with NIHSS score, while HOTAIR was significantly positively correlated with NIHSS score. Another significant finding was that NEAT1 was significantly negatively correlated with DBP, whereas HOTAIR was significantly positively correlated with SBP and DBP. Therefore, lncRNAs NEAT1, HOTAIR, and GAS5 could be used as diagnostic and prognostic biomarkers of CVS that correlate with NIHSS score and could produce a novel target for CVS therapy.Marwa A. Ali: Methodology, Investigation, Writing- original draft. Olfat G. Shaker: Conceptualization. Abeer A. Khalifa: Formal analysis. Eman M. Ezzat: Methodology, Investigation, Writing- original draft. Hany Ahmed Elghobary: Writing-original draft, Software, Methodology. Tamer Sayed Abdel Mawla: Writing, Review editing, Methodology, Investigation, and Supervision. Ahmed Fathy Elkhateeb: Writing, Review editing, Methodology, Investigation, and Supervision. Ahmed Magdy Abdelrahman Elebiary: Writing- review & editing, and Investigations. Azza Mohamed Elamir: Methodology, Investigation, Writing- original draft.The authors have no conflicts of interest.


Many studies have showed that climate and soil variables influence the change of soil δ15N. However, most of the existing studies focused on the overall impact of factor on soil δ15N, without distinguishing between the direct and indirect effect. Although scholars have studied the relationships among temperature, precipitation, soil N, soil pH, and soil δ15N rather than estimating all the causal relationships simultaneously. To answer the above-mentioned questions, a regional-scale soil collection was conducted across a temperate grassland in northern China. Meanwhile, a PLS-PATH analysis was utilized to evaluate the direct and indirect effects of various factors on soil δ15N and to explore the causal relationships among variables. The results showed that along the transect, mean annual precipitation (MAP) and mean annual temperature (MAT) directly and significantly reduced soil δ15N, and indirectly affected soil δ15N through their effects on soil pH, soil clay, soil N and soil C/N. Soil C/N ratio has a significant direct impact on soil δ15N with a negative correlation. Soil clay, soil N content, and soil pH have a total positive effect on soil δ15N, but the total positive impact of soil pH is very weak because it has a negative indirect impact on soil δ15N by affecting soil clay, soil N and soil C/N ratio. The total influence is, in order, MAP > MAT > soil C/N > soil clay > soil N > soil pH . The above results will provide valuable information about ecosystem N cycle in temperate grassland of northern China.Identifying the impact path of climate and soil factors on soil δ Soil δ15N can potentially provide valuable information about N cycle in soil ecosystem, because it is mainly based on overall isotopic fractionation during microbial degradation and transformation or the preferential decomposition of the substrates depleted in 15N [15N [14N is preferentially lost from the ecosystem, resulting in the enrichment of soil 15N in the ecosystem with high N cycle openness, soil δ15N can be used as a valid indicator of ecosystem N cycle openness [15N value is often negatively correlated with precipitation on different spatial scales. Additionally, without considering the inputs of anthropogenic N and the influence of plant residues, the variation in soil δ15N in natural ecosystems is largely controlled by climate and soil factors during microbial decomposition and nitrogen turnover [15N in soil integrates a variety of information about the ecosystem N cycling [15N values in terrestrial ecosystem has not yet been fully understood, which affects the interpretation of global or regional patterns of N-cycling.With the rapid development of isotope measurement technology, the analysis of soil nitrogen isotope  for determining the physicochemical properties of the soil , and the other was air-dried and ground to uniformity with a ball mill  for measurement of soil δ15N value. Additionally, the altitude, longitude and latitude of sampling sites were determined by Global Positioning System. The MAT and MAP of each sampling site were obtained from Data Center for Resource and Environment Science in China (https://www.resdc.cn/Default.aspx), China Meteorological Data Service Centre (http://data.cma.cn), and from local weather stations. Further detailed information about the sampling sites was summarized in Our studies were approved by the Forestry and Grassland Bureau of Inner Mongolia Autonomous Region and by the Institute of Grassland Research of CAAS. During the period from July to August in 2017, a total of 40 undisturbed sites were chosen to collect soil samples along this transect. At every site, five 1 m 15N value of soil sample was measured by a Finnigan DeltaPlusXP  coupled with an automatic elemental analyzer . The isotope result of soil N was calculated with the following equation: δ15N (‰) = (Rsample/Rstandard -1)×1000, where Rsample and Rstandard represent the ratios of 15N/14N in the sample standard. The standard of δ15N is atmospheric N2. The standard deviation of above isotopic repeated measurements was ±0.15‰.The δ15N, were listed in Soil pH value was determined using a pH meter  with a dry soil-water ratio of 1:2.5. Total organic carbon (TOC) and nitrogen (TN) concentrations of soil samples were measured by a TOC/TN analyzer . Soil C/N ratio was expressed by the quotient of TOC and TN content. Another subsample was separated into clay (<2μm) by ultrasonic energy method. The result of particle size analysis was expressed as a percentage of the weight of oven-dried soil. The values of all the above observed variables, which includes the MAP and MAT of sampling locations, soil N, soil pH, soil C: N, soil clay and soil δδ15N was analyzed by bivariate correlations and linear regressions. The normality test results of the variables showed that the absolute values of critical ratios were greater than 0.05, indicating that the kurtosis (or skewness) coefficients of all variables were significant , and the relationship between each explanatory variable xi and y is linear. Second, PLS regression is used to establish the optimal regression equation between explanatory variables and explained variable. Thereupon, the path coefficient xi to the explained variable y in the PLS regression equation can be decomposed into direct path coefficient  and soil nificant . This alnificant , which m PLS-SEM . Becausexi and y; xi and xj; Sy are the standard deviations of the explanatory variable xi and the explained variable y, respectively. Basic statistics of the data was conducted with SPSS 18.0, and path coefficients were estimated via the maximum likelihood method using SmartPLS V3.2.8 software [Where software .15N values. MAP and MAT together with soil C/N directly determined soil δ15N values, and the three had a significant negative effect on soil δ15N values. On the contrary, soil clay, soil pH and soil N content had a positive effect. PLS-PATH analyses further indicated that among all independent variables, MAP, MAT and soil pH could significantly and directly alter other climate and/ or soil variables   were, in proper order, MAP > MAT > soil C/N > soil clay > soil N > soil pH. Meanwhile, the MAP, MAT and soil C/N had a direct negative influence on soil δ15N, and their standardized direct effects were -0.337, -0.219 and -0.209, respectively. The sequence of direct influence of all factors on soil δ15N values was MAP > MAT > soil C/N > soil pH >soil clay > soil N, and the indirect influence of all factors on soil δ15N values was, in order, MAP >soil pH > soil clay > soil N > MAT> soil C/N.A path diagram that displays the variable relationships was established based on the PLS-PATH analysis. oil C/N) . As can oil C/N) , MAP, MA15N. As could be seen from 15N was very sensitive to both direct and indirect impacts of MAP. When MAP increased by 1 standard deviation, the soil δ15N directly decreased by 0.337 standard deviation. Meanwhile, the indirect effect of MAP on soil δ15N through MAT, soil clay, soil N, soil pH and soil C: N was -0.340 . Among them, the indirect influence of soil clay on soil δ15N through soil N was 0.013, while the indirect influence through soil C/N ratio was 0.050. The direct contribution of soil pH increasing soil δ15N was 0.208, and the indirect impacts of soil pH through soil C/N, clay content and soil N were -0.054, -0.053 and -0.029 respectively .The soil C/N ratio had no indirect effect on soil δ15N . The incby 0.209 . As show15N. The study used the PLS-PATH method to estimate the direct and indirect impact of various factors on soil δ15N and their causal relationships. Some of our results confirmed previous conclusions. On the whole, climatic factors had stronger effects than those of edaphic factors on soil δ15N values ; whereas the total impact intensity of edaphic factors on soil δ15N was 0.574 . This indicated that the soil δ15N value was more susceptible to changes of climatic factors than to those of edaphic factors. The following was an analysis of how various climatic and edaphic factors control soil δ15N.Many scholars have studied how climate and soil factors influence on soil δN values . The tot15N is mainly determined by the isotopic values of input N and output N. Since the δ15N of input N is close to zero and usually lower than that of soil, the N output process may leave more 15N in soil compared with the input [15N decreased significantly with increasing MAP along the transect  during nitrification process, which causes a greater 15N-enrichment in soil organic N and an increase of soil δ15N [15N-depleted in the soil pool. Meanwhile, as precipitation increase, the incomplete decomposition of plant residue may bring more enriched 14N organic matter to soil N pool, thus causing soil δ15N values to be lower [15N through its effects on other factors  losses from soil with high clay concentrations. As a result, the large amount of gaseous N loss may bring about higher δ15N values in fine-textured soils because they have a great potential impact on 15N/14N fractionation [15N with clay content is also inseparable from the indirect effect of soil clay . Among them, soil pH, soil clay, and soil N have a positive direct effect on soil δ15N. In addition, the indirect impact of all factors on soil δ15N is, in order, MAP > soil pH > soil clay > soil N > MAT > soil C/N. (2) Across the transect, MAP exerted the first-order controls on soil δ15N with negative correlation, and directly or indirectly influenced soil δ15N value through affecting MAT, soil clay, soil pH, soil N and C/N ratio. Soil δ15N values significantly decreased as MAT increased. The negative soil δ15N-MAT relationship, which is in contrast with previous research, may be due to the narrow temperature range and simultaneous effect of rain and heat in the study area. (3) Soil C: N ratio imposed a significant negative direct effect on soil δ15N. This result indicates that in temperate grassland, soil C/N ratio can regulate soil δ15N values by gaseous N loss processes. Soil clay, soil N content, and soil pH have a total positive effect on soil δ15N. However, soil pH has a significant negative indirect influence on soil δ15N through its effects on soil clay, soil N and C: N ratio. Consequently, the total positive impact of soil pH on soil δ15N is weak. (4) These results suggest that climatic and edaphic factors are to some extent coupled in controlling soil 15N abundance. It should be noted that the current research did not consider plant species and microbial activities as the factors affecting soil δ15N.(1) Soil N, soil pH, and soil clay have positive total effect on soil δS1 Appendix15N.All observed data are available, which includes the longitude and latitude of sampling locations, mean annual precipitation (MAP), mean annual temperature (MAT), altitude, soil N, soil pH, soil C:N, soil clay and soil δ(PDF)Click here for additional data file.


S, meeting only legal requirements consisting of a plastic toy and softwood) or enriched (E) treatment (n = 14 groups/treatment). At regrouping during the grower to finisher transition, pigs were either kept in the same treatment  or switched from enriched to standard (ES) and vice versa (SE); each treatment was replicated on five groups. Pigs were weighted at the start and end of weaner, and finisher stage, and feed intake was recorded. Occurrence of scouring, respiratory problems, locomotor disorders, tail, ear, and body lesions were recorded twice a week. Ten males per treatment were sampled for saliva on days 1, 2 and 4, either postweaning or after the housing switch. Saliva samples were analysed for cortisol, alpha-amylase, haptoglobin (Hp), and adenosine deaminase. Additionally, these pigs were sampled for hair at the start and end of weaner, and end of finisher stage to analyse for hair cortisol and cortisone. We found that E weaners consumed less feed (P = 0.04), had better FCR  and less ear lesions for two weeks postweaning (P = 0.04), and tended to have lower occurrence of scouring (P = 0.07) and higher salivary cortisol concentrations (P = 0.09) than S weaners. Effects of enrichment treatment during weaner stage on performance were carried through to finisher stage, with EE and ES pigs having better FCR (P = 0.0009) and higher BW (P = 0.0001) compared to SS and SE pigs. E treatment during finisher stage decreased feed intake (P = 0.04) and tended to decrease Hp levels (P = 0.07). There was a significant interaction between enrichment treatments during weaner and finisher stages on finisher body lesions: EE finishers had less lesions than SS, ES, and SE finishers (P = 0.04). There were no other significant differences caused either by enrichment treatment during weaner/finisher stage or their interaction. We conclude that the novel enrichment applied at weaner stage had positive effects on ear lesions and performance, which were carried through to finisher stage. Body lesions were affected by its application during both stages, with finishers receiving the enrichment treatment throughout (EE) having reduced body lesions than the rest of the finishers.Most pigs in slatted systems are provided with enrichment meeting only minimum legal requirements. We aimed to explore the effects of a novel enrichment treatment consisting of daily provided fodder beet and jute bags for pigs in slatted systems, and investigate the timing of enrichment provision on performance, health and stress resilience. We used 280 weaners allocated into standard ( There is a need to find efficient and suitable enrichment for pigs in slatted systems. We tested fodder beet in combination with jute bags as novel enrichment for pigs in slatted systems and investigated at which stage of growth additional enrichment provision was more important. We found positive effects of novel enrichment during weaner stage on ear lesions and performance during both weaner and finisher stages. Additional enrichment during finisher stage alone was associated with less positive effects, but its provision throughout reduced body lesions.EU) legislation, farmed pigs must have permanent access to a sufficient quantity of material to enable proper manipulation and investigation , Hillsborough, Northern Ireland from March to July 2021. We focused on the weaner – grower (42 days) and finisher stage (76 days) of rearing. For the experiment, we used two batches, each with 140 Duroc × (Large White × Landrace) pigs weaned at four weeks of age; batches 1 and 2 were separated by 3 weeks. Pigs in batches 1 and 2 originated from 12 and 15 l, respectively. The only selection criterion for piglets was good health  and BW ≥ 6 kg at weaning. All pigs were tail docked, with approximately 50 % of the tail removed within 24 h of birth.S) or enriched (E) housing. S enrichment met minimal legal requirements  and water via a bowl drinker with a nipple inside .Each treatment was replicated on 14 pens (10 weaners/group). Each group was balanced for BW and gender. All pigs were housed in the same building with six rooms, and each room contained six pens. Each pen measured 2.7 × 1.4 m. Pens were assigned to a treatment randomly, while ensuring to have seven enriched pens per batch distributed across three rooms. The rooms and pens did not differ in any variables, which could have confounded the results . Pens had plastic fully slatted floor and non-transparent walls. Feed was delivered via a multispace dry feeder taking 0.23 mSS and EE treatments) or were switched from enriched to standard (ES) and vice versa (SE) enrichment treatments. Enrichment for SS and ES finishers consisted of the same items as for weaners, but the amount was doubled, i.e., two suspended toys and two pieces of softwood were provided per pen. EE and SE pigs in addition had access to fodder beet in a rack (mounted at a height of 0.5 m) and two suspended jute bags. Each pen was provided with 2.5–3.5 kg of beet per day  for 41 days and 3.5–4.5 kg  for the remaining period. Beet offered to finishers was previously stored in a freezer but was defrosted before being offered to pigs. If there were any leftovers on the racks, they were reused. Two suspended jute bags per pen were provided and replaced when almost consumed.On day 42 postweaning, i.e., at the beginning of grower – finisher stage, pigs were transported to a finishing unit located approximately 1 km from the previous accommodation. This part of the study was designed as a 2 × 2, i.e., where pigs were kept either in the same enrichment treatment . A pen measured either 3.25 × 2.00 m (if equipped with a dry feeder) or 3.30 × 3.90 m (if equipped with a wet feeder). Pigs from batch 2 were housed in two rooms (five pens per room) of House 2. A pen measured 3.3 × 3.9 m. All pens in House 2 were equipped with a MLP pro feeder , each taking 1.43 m2. Pens in both houses had a wall nipple drinker, concrete fully slatted floor. Pens were assigned to treatment randomly but ensuring to have 2/3 pens per treatment per batch. Finishers were enrolled in the experiment for 76 days . Thus, the total length of the experimental period per batch was 118 days. Feed and water were provided ad libitum during both stages, with all pigs receiving the same diets. Weaners were fed the following commercial diets: starter 1 (∼12 days), starter 2 (∼8 days), link (7 days) and grower diet (∼15 days). Finishers were fed commercial grower (15 days) and finisher diet (62 days). Enrichment treatments are shown in Each treatment was replicated on five groups. Each group consisted of 14 finishers and was balanced for previous group, BW, and gender. Both experimental batches were housed in two houses  within the same building. Pigs from batch 1 were housed in 10 pens of the same room of House 3. Five pens were equipped with a single space dry feeder taking 0.114 mDuring the weaner stage, feed intake was measured at a pen level at the end of each diet offered. During the finisher stage, feed intake was measured daily at a pen (over 73 days) and individual level (over 77 days) for batch 1 and batch 2, respectively. Beet consumption was measured every morning  by weighing leftovers. Pigs were weighted three days preweaning, on day 40 postweaning  and 76 days after housing switch (at the end of the finisher stage).Pig health was assessed twice a week (mostly Mondays and Fridays), which resulted in 22 observations per batch. The health scoring system was based on Welfare Quality assessment protocol  and is pHp) as a biomarker of inflammation, and adenosine deaminase (ADA) as an immune system biomarker. The same animals were sampled at finisher stage 1, 2 and 4 days after the housing switch. These days were chosen to assess acute stress response to transport, housing switch and regrouping. Detailed description of the saliva sampling procedure is provided in the One male pig per pen (10 sampled pens/treatment) chosen randomly was sampled for saliva one day preweaning and on days 1, 2, 4 and 40 postweaning. Days 1, 2 and 4 were chosen to assess the effect of the enrichment on acute stress response to weaning and mixing. Saliva samples were analysed for levels of cortisol and alpha-amylase as biomarkers of stress, haptoglobin  and the isopropanol discarded. The samples were washed again with isopropanol and left at room temperature until completely dry. Next, the hair samples were cut into small pieces placed in tubes with balls and pulverised to a fine powder in a homogeniser . The pulverised hair was incubated with 1 ml of methanol for 18 h at RT with continuous gentle agitation for steroid extraction. Samples were then centrifuged , and 0.6 ml of each methanol extract was aliquoted into a new Eppendorf tube. The samples were evaporated to dryness in a Speed Vac Concentrator . The dry extracts were reconstituted with 0.1 ml of phosphate buffer saline and stored at –80 °C until measuring in all samples  or at weaning (batch 1). A hair sample was also taken 76 days after housing switch, i.e., at the end of finisher stage. Hair was shaved by an electric hair clipper from the rump in preweaning piglets. However, there was not enough re-grown hair on the rump at the end of weaner stage. Therefore, we took a hair sample from the right flank in weaners and finishers. Each hair sample was put in a plastic zip-lock bag and stored in a fridge at 8 °C until analysis. The protocol used for cortisol and cortisone extraction from hair was based on a previously described method . The hai samples .Plots of predicted values against residuals and distribution histograms of residuals were visually inspected to check the homoscedasticity and normality assumptions of models and transformed if needed. Data were inspected for outliers by the boxplot method . Any outADFI) per animal. ADFI was analysed by general linear models (PROC GLM in SAS). The dependent variable was ADFI (kg) per animal in a group. In the weaner model, enrichment treatment , and average pen preweaning BW were included as fixed effects. In the finisher model, enrichment treatment during weaner stage , enrichment treatment during finisher stage , their interaction, preweaning BW and BW measured 2 days before housing switch were included as fixed effects. The correlation between average pen preweaning BW and average pen BW measured 2 days before housing switch calculated by Pearson correlation coefficient was moderate (r = 0.44), so both BWs were included in the finisher model.The sums of total pen feed intake were calculated and then divided by the number of pigs in a pen, and the number of days to get the average daily feed intake (BW (kg) measured at the end of weaner and finisher stages were analysed by generalised linear mixed models (PROC MIXED in SAS). In the weaner model, enrichment treatment , preweaning BW and gender were included as fixed effects. The BW of one pig which was euthanised at the beginning of the study, as well as the BW of three other animals experiencing poor health, were not included in the analysis. In the finisher model, enrichment treatment during weaner stage , enrichment treatment during finisher stage , their interaction, BW measured 2 days before housing switch, and gender were included as fixed effects. Thirteen pigs were not included in the analysis as they did not remain in the study until the end of the finisher stage . The correlation between preweaning BW and BW measured 2 days before housing switch calculated by Pearson correlation coefficient was high (r = 0.69). Therefore, only BW measured 2 days before the housing switch was included in the finisher model. Pig nested within a group was a random effect in both above models.FCR) was calculated as the total amount of feed eaten per animal divided by BW gain (BW measured 2 days before housing switch minus preweaning BW). FCR (log transformed) was analysed by generalised linear mixed models. In the weaner model, enrichment treatment  and gender were included as fixed effects. FCR of finishers was calculated as total amount of feed eaten per animal during finisher stage divided by BW gain . In the finisher model, enrichment treatment during the weaner stage , enrichment treatment during the finisher stage , their interaction, and gender were included as fixed effects. Pig nested within a group was a random effect in both above models. The same pigs excluded for the BW analysis were not included for the FCR analysis.Weaner feed conversion ratio , and four in finishers . Data were analysed by repeated measures mixed models (PROC MIXED with repeated statement in SAS). Data on finisher body lesions achieved normal distribution after square root transformation, but other health measurements did not. Therefore, PROC GLIMMIX with Poisson distribution and log link was used to analyse these variables. In weaners, respiratory problems, locomotor disorders and tail lesions were recorded rarely so they were not statistically analysed. After removing outliers, scouring and body lesions were not present in weaners during periods 3–4 and 5–6 weeks, respectively, and these time periods were not included in the analysis. The final weaner data set consisted of values obtained from 11 to 14 groups per treatment per week period. Number of excluded values for weaner health parameters is given in Supplementary Concentrations of cortisol (μg/dl), salivary alpha-amylase (IU/l), Hp (IU/l), and ADA (IU/l) were analysed by repeated measures mixed models. Weaner amylase values were square root transformed before analysis. Finisher cortisol, alpha-amylase and Hp values were log transformed before analysis. In the weaner models, the fixed effects were enrichment treatment, day (1/2/4), preweaning BW, preweaning concentration of the marker, and the interaction between enrichment treatment and day. Day was included as a repeated measure. The final data set consisted of values obtained from 9 to 10 weaners per treatment per day. Number of missing/excluded values for weaner salivary biomarkers is given in Supplementary Hair cortisol and corticosterone concentrations (pg/mg) from samples taken on day 37 postweaning and 76 days after housing switch were analysed by general linear models. In the weaner cortisol model, the fixed effects were enrichment treatment , preweaning BW, preweaning hair cortisol concentration and colour score (0/1/2/3). In the weaner cortisone model, the fixed effects were enrichment treatment , preweaning BW, preweaning hair cortisone concentration and colour score. In the finisher cortisol model, the fixed effects were enrichment treatment during weaner stage , enrichment treatment during finisher stage , their interaction, hair cortisol concentrations measured on day 37, and colour score. The correlation between preweaning BW and BW measured 2 days before regrouping calculated by Pearson correlation coefficient was weak (r = 0.19). Therefore, both BWs were included in the finisher hair cortisol model as fixed effects. The correlation between hair cortisol before weaning and regrouping was high (r = 0.97), so we included only the latter one in the analysis. In the finisher cortisone model, the fixed effects were enrichment treatment during weaner stage , enrichment treatment during finisher stage , their interaction, hair cortisone concentrations measured on day 37, and colour score. The correlation between preweaning BW and BW measured 2 days before regrouping calculated by Pearson correlation coefficient was weak (r = 0.21), so both BWs were included in the finisher hair cortisone model as fixed effects. The correlation between hair cortisone concentration before weaning and regrouping was also weak (r = 0.26), so both concentrations were included in the analysis.One S pig was euthanised at the beginning of the study due to lameness. Furthermore, one lame E pig was not transported to the finishing unit, four pigs died during the finisher stage (two SS and two ES), and seven finishers were euthanised due to health disorders during the finisher stage . This resulted in 65 SS, 68 EE, 67 ES, and 67 SE finishers.1,24 = 4.72, P = 0.04; 1,270 = 337.11, P < 0.0001). Enrichment treatment had a significant effect on FCR during weaner stage, as E weaners had improved FCR compared to those housed on S .The results on weaner and finisher performance are presented in 1,10 = 5.64, P = 0.04). Beet average daily pen intake was 2.99 ± 0.64 kg (mean ± SD) for 41 days and 3.55 ± 0.76 kg (mean ± SD) for the remaining finisher period.In finishers, there was no significant effect of enrichment treatment during weaner stage or the interaction between enrichment treatment during weaner and finisher stages on ADFI. However, there was a significant effect of enrichment treatment on ADFI during finisher stage, as S pigs consumed more feed than pigs on E . There was a significant effect of gender and initial BW on finisher BW. Males and pigs with higher initial BW were heavier . Neither enrichment treatment during finisher stage nor the interaction between weaner and finisher enrichment treatment significantly affected finisher FCR. Enrichment treatment during weaner stage had a significant effect on FCR, as EE and ES finishers had better FCR than SS and SE finishers . Males had better FCR .Neither enrichment treatment during the finisher stage, nor the interaction between housing during weaner and finisher stages affected BW measured at the end of finisher. Enrichment treatment during the weaner stage had a significant effect on BW at the end of finisher stage, as EE and ES pigs were heavier than SS and SE pigs , but there was no significant effect of enrichment treatment on ear or body lesions. However, the interaction between enrichment treatment and week tended to be significant for ear lesions . Posthoc analysis revealed that E weaners had less ear lesions than S weaners during weeks 1–2 . Ear lesions occurred more in weeks 1–2 compared to weeks 3–4 and 5–6, and body lesions occurred more in weeks 1–2 than in weeks 5–6.E weaners tended to have a decreased occurrence of scouring compared to S pigs : scouring occurred less in weeks 1–2 compared to weeks 3–5, and tended to occur less in weeks 1–2 than in weeks 5–6. It also occurred less in weeks 3–5 compared to weeks 9–11. There was no significant effect of enrichment treatment on respiratory problems. However, there was a significant effect of week : respiratory problems occurred less in weeks 1–2 than in weeks 3–5 and 9–11. They also occurred less in weeks 6–8 than in weeks 3–5 and 9–11. Similarly, enrichment treatment did not significantly affect locomotor disorders, but there was a significant effect of week : the occurrence of locomotor disorders was less frequent in weeks 1–2 than in weeks 3–5, 6–8 and 9–11. Locomotor disorders were also less frequent in weeks 6–8 than 9–11 and tended to be less frequent in weeks 3–5 compared to weeks 9–11. There was no significant effect of enrichment treatment on tail lesions. However, there was a significant effect of week : tail lesions were less frequent in weeks 3–5 than in weeks 6–8 and 9–11. Moreover, they occurred less in weeks 6–8 than in weeks 9–11. There was no significant effect of enrichment treatment on ear lesions, but there was a significant effect of week : finishers had more ear lesions in weeks 1–2 than in the rest of the weeks. They also had more ear lesions in weeks 9–11 than in weeks 3–5 and 6–8. Enrichment treatment during weaner and finisher stages did not have a significant effect on body lesions. However, the interaction between enrichment treatment during weaner and finisher stages was significant , which was caused by the decreased occurrence of body lesions in EE pigs compared to SS (P = 0.05), ES (P = 0.02) and SE (P = 0.01) pigs . Finishers had decreased occurrence of body lesions in weeks 3–5 compared to weeks 1–2, 6–8 and 9–11.In finishers, enrichment treatment either during weaner or finisher stage did not have significant effect on scouring. However, there was a significant effect of week (F01) pigs . There w1,15 = 3.23, P = 0.09; 1,15 = 5.22, P = 0.04). Enrichment treatment did not have a significant effect either on hair cortisol or cortisone concentrations at the end of weaner stage. There was no significant effect of enrichment treatment on weaner alpha-amylase concentrations, but the interaction between enrichment treatment and day tended to be significant : E weaners tended to have higher Hp concentrations than S weaners on day 1 and pigs on S treatment had lower Hp concentrations on day 1 than on day 2. Enrichment treatment did not have a significant effect on ADA concentrations, but the interaction between enrichment treatment and day tended to be significant : ADA concentrations were significantly lower on day 4 compared to day 1 and tended to be lower on day 4 than on day 2. ADA concentrations also tended to be lower on day 2 than on day 1.In weaners, enrichment treatment tended to have a significant effect on salivary cortisol concentrations. E weaners tended to have higher salivary cortisol concentrations than S pigs , inflammation (Hp) and immunity (ADA) in pig saliva after weaning and after regrouping at finisher stage. Hair cortisol and cortisone concentrations were also measured at the end of weaner and finisher stages. We found that the interaction between enrichment and time (day) tended to have significant effects on weaner alpha-amylase, Hp, and ADA, as they were higher in E rather than S weaners. We also found that enrichment treatment tended to affect weaner saliva cortisol concentrations, with E weaners tending to have increased concentrations compared to S weaners. This finding supports our observations on competition for the beet, at least when it was first introduced. However, this is not consistent either with our results on decreased ear lesions in E weaners or with the results of In finishers, pigs which were provided with the enrichment after the housing switch tended to have decreased Hp concentrations that could be associated with the reduced body lesions these pigs have had, since Hp is an acute phase protein which increases when there is any inflammatory stimulus . ResearcWe investigated the consequences of provision of a novel enrichment treatment on performance, health variables, and salivary and hair biomarkers of weaner and finisher pigs kept in a slatted system. The enrichment consisting of daily provided fodder beet and jute bags, in addition to the minimal legal requirements, resulted in effects on performance both during the weaner and the finisher stage, seen mainly as improvement in FCR. Enrichment resulted in a lower number of ear lesions for two weeks postweaning, and finishers receiving the enrichment treatment throughout have had reduced body lesions.We also investigated the effect of the timing of enrichment provision, by providing the additional enrichment during the weaner, the finisher or both stages of production. Additional enrichment had longer term effects, as finisher pigs with previous experience with the enrichment had better FCR and increased BW at the end of finisher stage. We conclude that fodder beet (in combination with jute bags) may be a promising functional enrichment for pigs, and that the timing of the enrichment provision may have long-term effects on their performance.https://doi.org/10.1016/j.animal.2022.100637.Supplementary data to this article can be found online at The experiment was carried out at the experimental farm of the Agri-Food and Biosciences Institute (AFBI), Hillsborough, Northern Ireland from March to July 2021. All experimental procedures, complied with the UK Animals (Scientific Procedures) Act 1986, were approved by the AFBI Animal Welfare and Ethical Review Body (AWERB) and carried out under Home Office authorization Project Licence Number PPL2851.The data and models that support the study findings are available from the authors upon request.K.B. 0002-4537-7341.R.M. 0002-8139-8668.J.C. 0002-8654-1793.I.K. 0001-7703-3626.K.B. developed the methodology, managed the experiment, collected data, analysed data, wrote the first draft, and modified it accordingly. R.M. provided advice on the methodology, managed the experimental facilities, assisted with data collection on stress biomarkers and provided comments on the paper. J.C. provided advice on the methodology related to stress biomarkers, analysed the stress biomarkers and provided comments on the paper. I.K. developed the initial idea for the research, obtained and managed funding and the overall project, developed the methodology, provided advice on data analysis, and was involved in subsequent drafts of the paper.None.We would like to thank the AFBI farm staff involved in this study. We especially thank Declan Armstrong and Richard Little for trial delivery and management, and Alexandra Strain, Ryan Bradley, Ryan Murray and Peter Ffrench Mullen for assistance with data collection. Professor Sandra Edwards of Newcastle University was involved in the funding application that supported this project.This research was part of the EU-China HealthyLivestock project. The authors wish to acknowledge that HealthyLivestock is funded by the European Union H2020 research and innovation programme under grant agreement number 773436. The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.


Recently proposed biomarker-only diagnostic frameworks propose that amyloid-beta is necessary for placement on the Alzheimer’s disease continuum, whereas tau in the absence of amyloid-beta is considered to be a non-Alzheimer’s disease pathologic change. Similarly, the pathologic designation of tau in the absence of amyloid-beta is characterized as primary age-related tauopathy and separable from Alzheimer’s disease. Our study sought to identify an early-to-moderate tau stage with minimal amyloid-beta using PET imaging and characterize these individuals in terms of clinical, cognitive and biological features. Seven hundred and three participants from the Alzheimer’s Disease Neuroimaging Initiative were classified into one of the four groups  based on PET positivity or negativity for cortical amyloid-beta (A−/A+) and early-to-moderate stage  tau (T−/T+). These groups were then compared on demographic and clinical features, vascular risk, multi-domain neuropsychological performance, multi-domain subjective cognitive complaints, apolipoprotein E epsilon-4 carrier status and cortical thickness across Alzheimer’s disease-vulnerable regions. The proportion of participants classified in each group was as follows: 47.23% A−/T−, 13.51% A−/T+, 12.23% A+/T− and 27.03% A+/T+. Results indicated that the A−/T+ and A+/T+ groups did not statistically differ on age, sex, depression levels, vascular risk and cortical thickness across temporal and parietal regions. Additionally, both A−/T+ and A+/T+ groups showed significant associations between memory performance and cortical thickness of temporal regions. Despite the different pathologic terminology used for A−/T+ and A+/T+, these groups did not statistically differ on a number of clinical, cognitive and biomarker features. Although it remains unclear whether A−/T+ reflects a pathologic construct separable from Alzheimer’s disease, our results provide evidence that this group typically characterized as ‘non-Alzheimer’s pathologic change’ or ‘primary age-related tauopathy’ should be given increased attention, given some similarities in cognitive and biomarker characteristics to groups traditionally considered to be on the Alzheimer’s continuum. et al. report that a group of older adults with PET evidence of tau in the absence of amyloid-beta demonstrate clinical, cognitive and biomarker characteristics that are consistent with groups traditionally considered to be on the Alzheimer’s disease continuum.Weigand   Additionally, PART theory would suggest that, in terms of cognition, A−/T+ would look most similar to A−/T− whereas A+/T− would look most similar to A+/T+. Whereas the previous studies have compared the features of the A−/T+ group with the ‘biomarker normal’ (i.e. A−/T−) and ‘AD’ group (i.e. A+/T+), a direct comparison with the other discrepant group (i.e. A+/T−) may yield additional insight. Investigating the patterns of clinical, cognitive and biomarker characteristics that emerge across these groups is an important step in clarifying the nature of the A−/T+ profile in relation to Alzheimer’s disease.The nomenclature for the two discrepant Aβ and tau positivity groups, conversely labelled as ‘Alzheimer’s pathologic change’ (A+/T−) and ‘non-Alzheimer’s pathologic change’ (A−/T+),8 Similarly, a recent study by Yoon et al.9 defined an A−/T+ group using cortical Aβ and medial temporal (i.e. Braak stage I/II) and/or inferolateral temporal (i.e. Braak stage III/IV) tau PET imaging and similarly found intermediate global cognitive deficits as well as intermediate hippocampal volumes relative to A−/T− and A+/T+ groups, concluding that this heterogeneous group may represent a combination of individuals with PART and individuals with Alzheimer’s disease.In contrast to PART theory and the AT(N) framework, our prior preliminary study defined an A−/T+ group using cortical Aβ and medial temporal (i.e. Braak stage I/II) tau PET imaging and demonstrated that this group had intermediate cognitive performance between the biomarker normal (A−/T−) and Alzheimer’s disease (A+/T+) groups, suggesting the possibility that the A−/T+ group may represent an early stage on the Alzheimer’s disease continuum.9 more research is needed to comprehensively characterize this group. Specifically, examination of both objective and subjective cognitive measures across multiple domains may provide insight into the clinical stage that corresponds to these A/T biomarker stages. In this study, we will assess sensitive neuropsychological measures (i.e. objective cognition) and self-reported measures (i.e. subjective cognition) across the domains of memory, language and executive function. We hypothesize that the neuropsychological performance across all domains will follow the order of A−/T− > A−/T+ = A+/T− > A+/T+, where > indicates better performance. For subjective cognitive complaints across all domains, we hypothesize that A−/T− < A−/T+ = A+/T− < A+/T+, where < indicates fewer cognitive complaints.Given increasing evidence that the A−/T+ biomarker group may be confluent with the Alzheimer’s disease continuum,In addition to investigating objective and subjective cognitive abilities, we will also characterize brain structure across these A/T groups. Specifically, we will examine cortical thickness across widespread regions of temporal, parietal and frontal cortex, as well as global white matter lesion burden as a brain-based index of vascular risk to assess whether any observed cognitive deficits may be attributable to vascular pathology. We hypothesize that cortical thickness across temporal regions will follow the pattern of A−/T− > A+/T− = A−/T + > A+/T+, where > indicates higher thickness. However, for parietal and frontal regions, we hypothesize that the A−/T+ group will be equivalent to the A−/T− and A+/T− groups, given the relatively circumscribed nature of tau pathology within temporal regions in this group, whereas the A+/T+ group will have lower thickness values due to their more widespread distribution of pathology. In terms of vascular risk, we hypothesize that vascular risk will be lowest in the A−/T− group and equal across other groups. Finally, we will assess for associations between objective memory scores and temporal cortical regions to investigate the presence and strength of biomarker–cognition associations across A/T groups. We predict that only A−/T+ and A+/T+ groups will have significant positive associations between memory and temporal cortical thickness.This study will use PET imaging to designate A/T groups based on cortical Aβ and a meta-temporal tau region, with a particular focus on the A−/T+ or ‘PART’ group as it relates to the A+/T− and A+/T+ groups who are traditionally considered to be on the Alzheimer’s disease continuum. This A−/T+ group will be comprehensively characterized using demographic, clinical, neuropsychological, subjective cognition, vascular risk and cortical thickness variables. We expect that, in general, the A−/T+ group will look most similar to the A+/T− group on cognitive and biomarker variables.www.adni-info.org.10The data sets used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease. For up-to-date information, see This study included 703 participants without dementia from ADNI who had concurrent Aβ PET, tau PET and demographic data. The data were accessed on 20 January 2022. All participants had concurrent subjective and cognitive complaint data. A subset of 660/703 participants had apolipoprotein E (APOE) ε4 data. A subset of 619/703 participants had concurrent complete neuropsychological data. A subset of 450/703 participants had concurrent MRI data with FreeSurfer parcellation. A subset of 265/703 participants had concurrent white matter hyperintensity imaging data. Participants at different clinical stages were included . Overall, the sample was predominately white (90%) and female (51%), with a mean (standard deviation) age of 73.3 (7.5) years and an education level of 16.4 (2.5) years.Demographic data included participant age, sex, level of education and race. Depression was assessed using the Geriatric Depression Scale (GDS). Notably, there was a limited range for this measure as participants were excluded from ADNI if they had a baseline GDS > 5.Neuropsychological measures were obtained across domains of memory [Auditory Verbal Learning Test (AVLT) delayed recall and recognition], language {animal fluency, confrontation naming [i.e. Boston Naming Test (BNT) or Multilingual Naming Test (MiNT)]} and executive function [Trail Making Test (TMT) parts A and B]. Note that participants either had the BNT or the MiNT as a measure of naming; these scores were converted to percent correct to place them on the same scale and create one single ‘naming’ measure. Z-scores were calculated for individual neuropsychological measures using predicted values relative to a robust normal control group (e.g. remained cognitively intact throughout the duration of their participation) that adjusted for age, sex and education level such that higher scores indicated better performance.11 Demographically adjusted z-scores from the above neuropsychological measures were used for diagnostic classification. Participants who did not meet MCI criteria were considered cognitively unimpaired.MCI was classified using comprehensive neuropsychological criteria based on the presence of (i) two impaired scores in one cognitive domain or (ii) one impaired score across all three cognitive domains.The Everyday Cognition (ECog) questionnaire assessed self-reported subjective cognitive complaints. Composite scores averaged across memory, language and executive function domains were assessed such that higher scores indicated worse subjective cognition.13 For tau PET, a composite meta-temporal ROI representative of early-to-moderate tau pathology was used that included the amygdala, entorhinal cortex, fusiform gyrus, inferior temporal gyrus and middle temporal gyrus.14 Standardized uptake value ratios (SUVRs) were calculated by dividing the SUV for each ROI by the whole cerebellum SUV (Aβ PET) or the inferior cerebellar grey SUV (tau PET). Tau PET values were not corrected for partial volume effects. The Aβ SUVR values were converted to a centiloid scale to standardize across the two PET tracers.15 Positivity thresholds were determined based on previously published cut-offs: Aβ positivity was defined as cortical summary centiloid > 28.8 to indicate established pathology (maximizing Youden’s index based on CSF Aβ/p-tau positive or negative),16 and tau positivity was defined as meta-temporal SUVR > 1.23 to indicate established pathology (maximizing accuracy based on younger controls or Aβ positive cognitively impaired older adults).17Biomarkers of Aβ (Florbetapir or Florbetaben) and tau (Flortaucipir) were assessed using PET imaging. For Aβ PET, a cortical summary measure called a region of interest (ROI) was used that included regions vulnerable to early Aβ deposition.1-weighted anatomical MRI processed cross-sectionally using FreeSurfer version 5.1 or 6.0 .Cortical thickness measurements for the entorhinal cortex, inferior temporal gyrus, precuneus and superior frontal gyrus were obtained from T18 and to be consistent with regions included in the cortical summary Aβ PET and meta-temporal tau PET measures.These regions were chosen for their vulnerability to early Alzheimer’s disease pathologyAPOE positivity was defined by the presence of one or more ε4 alleles.The pulse pressure values were obtained from blood pressure measurements as an index of arterial stiffening, where pulse pressure = (systolic blood pressure − diastolic blood pressure)/systolic blood pressure. Modified Hachinski ischaemia scale  scores were assessed for 10-year stroke risk with scores dichotomized as low risk (score = 0) or high risk (score > 0). White matter hyperintensity values were obtained from MRI data as a measure of cerebrovascular small vessel disease (ADNI file: UCD—white matter hyperintensity volumes).2tests for independence. Groups were compared on age and education level using ANOVAs. Groups were compared on depression level, Aβ PET levels and tau PET Braak meta-temporal ROI levels, pulse pressure, white matter hyperintensity levels and regional cortical thickness/volume using ANCOVAs adjusting for age, sex and APOE ε4 positivity. Groups were compared on neuropsychological performance and subjective cognitive complaints using ANCOVAs adjusting for age, sex, education and APOE ε4 positivity. Multiple linear regression models adjusting for age, sex, education and APOE ε4 positivity assessed for associations between memory scores  and associated cortical thickness regions  across the sample and separately within A/T groups. Data with non-normal distributions underwent Box–Cox transformation  with the exception of GDS score due to the interval nature of this variable , race (White/non-White), clinical classification (cognitively unimpaired/MCI), APOE ε4 (negative/positive) and Hachinski risk level (low/high) using χhttp://adni.loni.usc.edu/.Data used in this study are available through the ADNI and can be found at F = 153.3, P < 0.001], such that the T− groups were not statistically different , the A−/T+ group had significantly higher tau than the T− groups (median = 1.29) and the A+/T+ group had significantly higher tau than all other groups (median = 1.59). Groups also differed on levels of cortical Aβ PET  such that the A−/T− group was significantly younger than all other groups, who did not differ from one another; education levels  such that the A+/T+ group had significantly lower education levels than all other groups and the A−/T+ group had significantly higher education levels than the A−/T− group; and depression levels  such that the A+/T+ group had significantly higher GDS scores than the A−/T− and A+/T− groups, but not the A−/T+ group. Groups did not significantly differ on race distribution (P = 0.83) or sex distribution (P = 0.08).Demographic characteristics across these A/T groups are presented in 2 = 39.3, P < 0.001) with the highest rate of MCI in the A+/T+ group (50.8%) and similar rates across the other groups (22.0–29.5%). Groups significantly differed on all neuropsychological measures: AVLT delayed recall , AVLT recognition , naming , animal fluency , TMT A  and TMT B . On AVLT delayed recall and naming, the A+/T+ group had lower scores than all other groups and the A−/T+ group had lower scores than the A−/T− group. On TMT B, the A+/T+ group had lower scores than all other groups and the A+/T− group had lower scores than the A−/T− group. On AVLT recognition, animal fluency and TMT A, the A+/T+ group had lower scores than all other groups, who did not differ from one another. See When examining clinical classification (i.e. cognitively unimpaired or MCI), there was a significant difference across groups  = 14.0, P < 0.001], language  and executive function . For subjective memory, the A+/T+ group had higher scores (i.e. worse subjective memory) than all other groups and the A+/T− group had higher scores than the A−/T+ group. For subjective language, the A+/T+ group had higher scores (i.e. worse subjective language) than the A−/T− and A−/T+ groups and the A+/T− group had higher scores than the A−/T− group. For subjective executive function, the A+/T+ group (i.e. worse subjective executive function) than all other groups, who did not differ from one another. See Groups significantly differed on all domains of subjective cognitive complaints: memory  such that the A−/T+ and A+/T+ groups, who did not differ from each other, had lower cortical thickness than the A−/T− and A+/T− groups, who did not differ from each other. For the inferior temporal gyrus, there was a significant difference across groups  such that the A+/T + group had lower cortical thickness than the A−/T− and A+/T− groups, but not the A−/T+ group. For the precuneus, there was a significant difference across groups  such that the A+/T+ group had lower cortical thickness than the A−/T− and A+/T− groups, but not the A−/T+ group. For the superior frontal gyrus, there was no significant difference across groups . See Groups were compared on regional cortical thickness of the entorhinal cortex, inferior temporal gyrus, precuneus and superior frontal gyrus. For the entorhinal cortex, there was a significant difference across groups , global white matter hyperintensity values  or Hachinski score .Groups were compared on several indices of vascular risk and did not differ on pulse pressure [t = 5.5, P < 0.001; recognition t = 7.5, P < 0.001). Within A/T groups, AVLT delayed recall was significantly associated with entorhinal thickness only in the A+/T+ group , whereas AVLT recognition was significantly associated with entorhinal thickness in the A−/T− , A−/T+  and A+/T+  groups but not the A+/T− group. Both AVLT delayed recall and recognition scores were also significantly associated with inferior temporal thickness across the entire sample . Within A/T groups, AVLT delayed recall was significantly associated with inferior temporal thickness only in the A+/T + group , whereas AVLT recognition was significantly associated with inferior temporal thickness in the A−/T+  and A+/T+  groups but not the A−/T− or A+/T− group. See Both AVLT delayed recall and recognition scores were significantly associated with entorhinal cortical thickness across the entire sample  group; specifically, these groups had similar levels of depression, vascular risk and cortical thickness across temporal and parietal regions. Although the A+/T+ group had a worse neuropsychological performance than the A−/T+ group, it should be noted that the A−/T+ group demonstrated worse delayed recall and naming than the A−/T− group, suggesting intermediate cognitive deficits. Additionally, this group demonstrated associations between memory and medial/inferior temporal thickness that was not observed in the A+/T− group. Our results extend prior studies examining this A−/T+ group in ADNI9 to suggest that their neuropsychological and neurodegenerative profiles may be indicative of a potential Alzheimer’s disease pathologic process. Interestingly, our study demonstrated that the A−/T+ group may have marginally fewer subjective cognitive complaints in domains of memory and language relative to the A+/T− group, which, in the context of poorer neuropsychological performance in the A−/T+ group relative to the A−/T− group, may reflect a degree of anosognosia often seen in the MCI stage.20 Taken together, these findings suggest that the A−/T+ biomarker designation should be given equal attention as A+/T− and A+/T+ groups, who are more often included in research studies and clinical trials.Our study identified a group of individuals comprising 13.5% of an ADNI older adult sample that fell into the biomarker category of meta-temporal tau PET positivity in the context of Aβ PET negativity, which has historically been referred to as ‘PART’ or ‘non-Alzheimer’s pathologic change’.22 in which case this group may be better explained as an alternate pathologic process separable from Alzheimer’s disease, or otherwise as an atypical Alzheimer’s disease variant. Notably, however, even in the absence of high levels of Aβ, the A−/T+ group exhibited pathognomonic features characteristic of Alzheimer’s disease including specific deficits in memory recall and object naming, both common early cognitive features of Alzheimer’s disease23 and lower cortical thickness in the entorhinal cortex, a region affected early in Alzheimer’s disease as well as PART.18 The relative sparing of other cognitive scores in the context of lower cortical thickness across Alzheimer’s disease signature regions may be explained by a higher degree of cognitive reserve in the A−/T+ group, given their lower APOE genetic risk and a marginally higher level of education. However, our results did demonstrate an association between memory performance and temporal regions within the A−/T+ group, suggesting that the lower cortical thickness may indicate early neurodegeneration that is impacting memory. Research examining the longitudinal trajectories of this A−/T+ group is necessary to determine if they follow a declining trajectory across cognitive domains and cortical regions, as well as a transition from A−/T+ to A+/T+, which would suggest they may be on the Alzheimer’s disease continuum despite some initial cognitive resilience. Such a longitudinal examination is not possible at this time in the current data set due to low power across cells at follow-up visits but is a planned future direction of the current study when more data become available. It should be noted that individuals with PART may also demonstrate circumscribed cognitive deficits and medial temporal lobe atrophy as demonstrated by our findings;7 therefore, until longitudinal trajectories of these groups and pathologic confirmation are available, it remains unclear whether the A−/T+ group represents an early stage on the Alzheimer’s disease continuum, PART, an atypical Alzheimer’s disease variant, or another pathologic process.Questions may remain as to whether the A−/T+ group represents a stage on the Alzheimer’s disease continuum or that of a separate pathologic process (e.g. PART). There is a possibility that the tau PET tracer used (Flortaucipir) may have been binding to different tau isoforms or other pathologies,24 Most notably, one of these four profiles comprising 19% of their sample was somewhat similar to our A−/T+ group with prominent lateral temporal tau deposition (in addition to MTL deposition) and specific deficits in language, although it should be noted that, unlike our A−/T+ group, the vast majority of individuals with this tau profile were Aβ positive. Vogel et al.24 also characterized their tau profiles longitudinally and demonstrated that the lateral temporal profile had the highest rate of global cognitive decline over time and eventual extension of tau deposition to parietal and frontal regions, suggesting that this profile may be on an Alzheimer’s disease trajectory. This recent study highlights the notion that Alzheimer’s disease pathologic and clinical presentations are heterogeneous and may not always progress through a singular prescribed series of stages. The emergence of an A−/T+ group in our study with certain ‘AD-like’ characteristics  and other dissimilar features  highlights the possibility that there may exist ‘non-traditional’  pathways to Alzheimer’s disease.Interestingly, a recent study also conducted in ADNI identified four distinct profiles of tau deposition and associated cognitive deficits to demonstrate atypical Alzheimer’s disease presentations among individuals who are primarily A+.25 The questions as to whether aducanumab is truly ‘disease-modifying’ again depend on the nosological consideration of what it means to have Alzheimer’s disease and whether we consider it to be a purely pathologic construct or a combinatorial pathologic-syndromal construct. The International Working Group on the diagnosis of Alzheimer’s disease recommended that its diagnosis ‘be restricted to people who have positive biomarkers together with specific Alzheimer’s disease phenotypes, whereas biomarker positive cognitively unimpaired individuals should be considered only at-risk for progression to Alzheimer’s disease’.26 The results of our study argue that the incorporation of cognitive and clinical features into the definition of Alzheimer’s disease is critical, even as the field has shifted towards a biological perspective that largely ignores the ‘C’  in the AT(N)X classification system.27 However, regardless of the classification of this A−/T+ group as a non-Alzheimer’s process or as a stage on the Alzheimer’s disease continuum, the exclusion of this sizeable contingent of A−/T+ individuals from Alzheimer’s disease research will only perpetuate their exclusion from theoretical models of Alzheimer’s disease and reinforce the limited perspective on what is and is not Alzheimer’s disease.The debate as to whether the A−/T+ group reflects a non-Alzheimer’s pathologic process or an Alzheimer’s disease variant ultimately comes down to the nosology of AD. From a purely pathologic perspective, if one considers the presence of Aβ not only sufficient but also necessary in the definition of Alzheimer’s disease, that would negate the possibility of an A−/T+ subtype to be characterized as Alzheimer’s disease unless and until they progress to A+/T+. This reductionist view of Alzheimer’s disease, however, fails to consider the cognitive and clinical syndromal contributions to the definition of Alzheimer’s disease. Ultimately, the presence or absence of Aβ is inconsequential if there is no manifestation of cognitive and clinical deficits. This argument has recently been borne out in the FDA approval of aducanumab, a disease-modifying Alzheimer’s disease treatment that has largely failed to impart any clinical benefit despite its success in targeted reductions of Aβ.29 patterns that were not observable in our study design and sample. An extension of our study with a more representative sample and inclusion of other pathologic markers  would help to further characterize our A−/T+ group and identify additional non-traditional pathologic profiles. Additionally, the use of different PET cut-points may have yielded different proportions of A/T groups. That said, the cut-points used in this study were selected based on well-established and validated methods previously reported in the literature. Further, an examination of A significant limitation of this study is the highly homogenous nature of the ADNI sample in terms of race/ethnicity, educational attainment, psychiatric well-being and overall physical health (including limited vascular risk). The limited variance in these characteristics makes it difficult to (i) ascertain group differences and (ii) generalize findings from a non-representative sample to the overall older adult population. Neuropathologic studies have demonstrated significant differences in pathologic profiles across racial/ethnic groups as well as a general likelihood of polypathology beyond Aβ and tau,31 and research increasingly suggests the need for a shift in theoretical perspective that considers other pathologic processes and drug targets. Our findings highlight the importance of investigating non-traditional pathways to Alzheimer’s disease and the inclusion of individuals with tau biomarker positivity in the absence of Aβ biomarker positivity in studies of Alzheimer’s disease. Consideration and further characterization of these A−/T+ individuals are critical to advance our understanding of the heterogeneous nature of Alzheimer’s disease, including its neuropathologic substrates and clinical manifestations.The notion that Alzheimer’s disease is synonymous with Aβ has come under scrutiny, given the failure of Aβ-modifying treatments to slow cognitive and clinical decline,fcac272_Supplementary_DataClick here for additional data file.


Glutathione transferases (GSTs) are a family of Phase II detoxification enzymes that are involved in the development of multi-drug resistance (MDR) phenomena toward chemotherapeutic agents. GST inhibitors are considered candidate compounds able to chemomodulate and reverse MDR. The natural flavonoid myricetin (MYR) has been shown to exhibit a wide range of pharmacological functions, including antitumor activity. In the present work, the interaction of MYR with human glutathione transferase A1–1 (hGSTA1–1) was investigated by kinetics inhibition analysis and molecular modeling studies. The results showed that MYR binds with high affinity to hGSTA1–1 (IC50 2.1 ± 0.2 μΜ). It functions as a non-competitive inhibitor towards the electrophile substrate 1-chloro−2,4-dinitrobenzene (CDNB) and as a competitive inhibitor towards glutathione (GSH). Chemical modification studies with the irreversible inhibitor phenethyl isothiocyanate (PEITC), in combination with in silico molecular docking studies allowed the prediction of the MYR binding site. MYR appears to bind at a distinct location, partially overlapping the GSH binding site (G-site). The results of the present study show that MYR is a potent inhibitor of hGSTA1–1 that can be further exploited towards the development of natural, safe, and effective GST-targeted cancer chemosensitizers. Glutathione transferases (GSTs) are multifunctional enzymes that have evolved from thioredoxins/glutaredoxins family ,2,3,4. TIntense research over the past two decades has established that GSTs are key enzymes that are directly connected to the therapeutic response to chemotherapy. High expression levels of GSTs are connected with an increased resistance of tumors to a variety of anticancer drugs ,5,6,11. Polyphenols are natural compounds with variable structures that are classified into several subclasses including catechins, flavonoids , anthocyanins, chalcones, curcuminoids, and phenolic acids. Flavonoids are being increasingly studied for their diverse biological activities, including anticancer activity, and negligible side effects ,18,19. TThe present work aims to investigate the interaction of myricetin (MYR) with hGSTP1–1. MYR  is a polβ-catenin signaling, and by stimulating ROS-mediated stress, endoplasmic reticulum stress, and DNA damage [MYR is one of the most common flavonoids in fruits, vegetables, herbs, tea, and red wine and displays a range of diverse bioactivities in different cell processes ,22, suchA damage ,22,23,24A damage . MYR hasA damage ,26. SeveA damage  showed tA damage  demonstrConsidering that MYR contributes to chemoresistance and taking into account the key role of hGSTA1–1 in MDR mechanism, the present work was undertaken in order to evaluate whether the activity of hGSTA1–1 can be manipulated by MYR. The results of the present study shine light on the role of MYR as potent inhibitor towards the MDR-involved hGSTA1–1. The outcome of the work provides new insights into the drug design effort towards MDR targets that can facilitate the rational development of GST-targeted chemosensitizers.Reduced glutathione (GSH), 1-chloro−2,4-dinitrobenzene (CDNB), bovine serum albumin (BSA), MYR, and all other buffers, media, and salts were purchased from Sigma–Aldrich .E. coli BL21 (DE3) cells and purified by affinity chromatography on immobilized GSH as described elsewhere [Recombinant hGSTA1–1 was expressed in lsewhere ,32,33. PThe activity of hGSTA1–1 was assessed by measuring the conjugating activity of the 1-chloro−2,4-dinitrobenzene (CDNB)/GSH system as described previously ,32,33. FMeasurement of the dependence of the initial rates of catalytic activity on CDNB concentration was carried out in 0.1 M potassium phosphate, pH 6.5, (37 °C), using 0.02–1 mM CDNB in the presence of 2.5 mM GSH in the presence or in the absence of MYR (0–2 μΜ). Measurement of the dependence of the initial rates of catalytic activity on GSH concentration was achieved using 0.035–2.5 mM GSH and 1.5 mM CDNB, in the presence or in the absence of MYR (0–8 μΜ). Measurements of initial rates were carried out in triplicate. The kinetic data were analyzed by nonlinear regression analysis using the computer program Graph PadPrism 5 .Irreversible inactivation of hGSTA1–1 by PEITC  was perfhttp://www.pymol.org/, accessed on 9 August 2022) [Docking calculations for the MYR and hGSTA1–1 interaction were carried out by AutoDock Vina , with dest 2022)  and UCSFst 2022)  were useThe presented data were obtained from three independent measurements. Graph PadPrism 5  were used for data analysis and evaluation.n-arylimines, and benzophenones and their carbonyl n-analogues were in the range 22–71 μΜ [The interaction of MYR with hGSTA1–1 was studied by employing inhibition studies. The GST substrate system GSH/CDNB was selected for measuring the effect of MYR on enzyme activity. hGSTA1–1 is highly efficient in catalyzing the conjugation of GSH to CDNB, allowing the determination of kinetic constants with high accuracy and reproducibility ,41. Figu22–71 μΜ . However22–71 μΜ . Ethacry22–71 μΜ . Further22–71 μΜ . However22–71 μΜ .n-terminal domain I. The second, “H-site”, is found at the C-terminal domain II, which binds the hydrophobic electrophilic acceptor substrates [Kinetic inhibition studies were carried out aiming to obtain kinetics data for shining light on the type of inhibition as well as a roughly prediction of the location of its binding site on hGSTA1–1. The enzyme possesses three discrete ligand binding sites. The first is the conserved GSH binding site, “G-site”, which is located at the enzyme’s bstrates ,34,36,38bstrates ,51,52. hi = 1.1 ± 0.1 μΜ) (i = 2.1 ± 0.2 μΜ), as the lines of the Lineweaver–Burk plot intersect the velocity axes (The inhibition of hGSTA1–1 by MYR in the form of the Lineweaver–Burk plots is illustrated in  0.1 μΜ) A against 0.1 μΜ) B, suggesity axes C. The deity axes D, suggesTo further validate the inhibition pattern obtained by kinetics inhibition studies chemical modification and inactivation experiments were employed. Phenethyl isothiocyanate (PEITC) A is an iobs)  . The preAs we have already mentioned, PEITC specifically reacts with Cys112 of hGSTA1–1  Figure B. From tPrediction of the binding more of MYR to the hGSTA1–1 was carried out using in silico molecular docking performed by AutoVina . The worThe predicted binding of MYR appears to be in agreement with the results obtained by chemical modification studies, and confirmed the competition observed between PEITC and MYR or S-hexyl-GSH C. FigureThis study expands the role and function of MYR, a widespread flavonoid, and establishes its role as a potent inhibitor towards hGSTA1–1. MYR is able to bind specifically to hGSTA1–1 at a distinct location at the solvent channel partially occupying the entrance of the G-site. The results of the present work suggest that MYR holds the potential for application as adjuvant in combination with drugs known to be substrates of hGSTA1–1 enzyme. The combination of MYR and a drug can affect cancer cells, overexpressing hGSTA1–1, in two discrete ways: firstly by preventing the drug’s metabolism and detoxification through the direct inhibition of hGSTA1–1 catalytic activity, and secondly, MYR can act as an anticancer compound. The outcome of the study can accelerate the development of GST-targeted cancer chemosensitizers. Future work involving in vivo and clinical studies will further evaluate and assess the efficiency, safety, and clinical use of MYR.


We derive a theoretical model that showshow the superselectivity of multivalent interactions is modulatedby the presence of cofactors or competitors. We find that the roleof these participating moieties can be fully captured by a simplerescaling of the affinity constant of the individual ligand–receptorbonds. Theoretical predictions are supported by experimental dataof the membrane repair protein annexin A5 binding to anionic lipidmembranes in the presence of Ca The effect of cofactors  to anionic lipid membranes7 and the effect of competing oligosaccharideson the recognition of the extracellular matrix polysaccharide hyaluronan(HA) by cell surface receptors.19Here, we demonstrate based on simpletheoretical arguments thatcofactors and monovalent competitors impact superselective bindingby effectively rescaling the ligand–receptor affinity. We applythis insight to two important yet distinct examples of biomolecularinteractions, namely, the Ca6 predicts that the strength of the multivalent interaction,or avidity constant Kav, depends supralinearlyon the receptor density, ΓR, and the ligand–receptordissociation constant, Kd .When binding to a surface, this equation can be used as an input tothe Langmuir isotherm, which predicts the surface density of adsorbedprobes to bemax and the concentration of unbound probes cP. The binding is said to be superselectiveif the surface density increases faster than linearly with the receptordensity, i.e., if the selectivity parameterccf (αcf = dlog Γp/d log ccf) and competitor concentration cmc . The fulltheoretical derivation that considers the distribution of all possiblebinding states in equilibrium is provided in the Supporting Information, with only the main results being shownhere.The theory of multivalent bindingtant, Kd 1A, as1whan unity 1A. Here ccf that bind to ligandsand receptors with thedissociation constants Kd,L–cf and Kd,R–cf, while the ligand–cofactor(or receptor–cofactor) complex binds to the receptors (or ligands)with constant Kd,Lcf–R  concentration d,Rcf–L) 1B. The eccf < Kd,L–cf and ccf < Kd,R–cf, wecan approximate Kd(cf) ≈ Kd,L–cf–R/ccf, and thus changing the cofactorconcentration has the same effect as changing the receptor density nR  ≈ max. Lastly, at very highconcentrations, ccf > Kd,L–cf and ccf > Kd,R–cf, the oversaturation with cofactorsweakens the effective binding: Kd(cf) ≈ ccfKd,L–cf–R/, and thus changing ccf has the sameeffect as changing the inverse receptor density nR–1  leaflet of the plasma membrane, whereas Ca2+ ions are virtually absent in the cytoplasm but present (in mM concentrations)outside the cell. AnxA5 thus binds to the cell membrane only uponmembrane damage leading to an influx of Ca2+ ions intothe cell and possibly also to interleaflet lipid content mixing nearthe damage site.A salient biological example of how cofactors influence multivalentinteractions is AnxA5 binding to lipid membranes 2B. AnxA52+ concentrations  = Kd,L–cf–R/ccf, which implies that AnxA5 binding depends only on theproduct nRccf (nR = fPS(a/l)2, with the protein cross-section a2 = 25 nm2, the lipid cross-section l2 = 0.7 nm2, and the PS lipid fraction fPS. Indeed, when the AnxA5 binding data areplotted as a function of fPSccf, all experimental data collapse onto a single mastercurve  in a backgroundof zwitterionic phosphatidyl choline (PC) lipids, and our theoreticalmodel predicts well AnxA5 binding over 4 orders of magnitude of Catrations 2C. Moreoct nRccf 1, where ercurve 2D,thus 2+ as wellas the receptor PS lipids, with maximal α values αcf,max ≈ αR,max ≈ 4 .Our analysis identifies membranerecognition by AnxA5 as a strikingexample of superselective binding, demonstrating that binding is stronglysuperselective with respect to the cofactor Ca,max ≈ 4 2D. This Kd,R–mc. As shown in the full derivationof our analytical model, competitors at (unbound) concentration cmc effectively rescale the ligand–receptoraffinity Kd toSimilar to the theoretical treatment of cofactors,monovalent competitorsare assumed to bind to surface receptors with the affinity constant 29 we here establish that it also applies to multivalentinteractions and can be generalized to multiple competitor types (see Supporting Information).The impact of this rescaling on superselective binding isillustratedin 19 on theinhibition of HA polysaccharide bindingto CD44 cell surface receptors by HA oligosaccharides  into smalloligosaccharides. HA octasaccharides (HA8) just about fillthe binding groove in a CD44 receptor24 and thus are effective monovalent competitors.We tested our simple model ondata reported by Lesley et al.charides 3B. That Kd(mc) (nL = 500 distinct sites for binding to CD44 receptors (consistentwith an HA molecular mass of ∼1 MDa and a decasaccharide “footprint”per receptor), a coil volume of a3 = 4πRg3/3 , and Kd,R–mc ≈ 50 μM. As the only fitting parameter, we determined nR/(Kdveff) ≈ 0.03, a value that is consistent with typicalCD44 cell surface densities (see Supporting Information); that is, the simple model makes reasonable quantitative predictions.The simpleanalytical model 1 and 2 wKd(mc) 5 reproduKd(mc) 3C, illuscmc  and its receptors  modulate HA binding and downstream physiological processes.The above reanalysis of data from theliterature demonstrates thetangible benefits of superselectivity concepts. It is well known thatsmall vs large HA can exert opposing functional effects,28 and for superselective discrimination of cofactor concentrations.In conclusion, we have developed a new mechanistic understandingof multivalent recognition with cofactors and competitors. Ratherthan modifying the multivalent probe itself, the addition of monovalentbinders as competitors or cofactors is a simple, and thus attractive,avenue to modulate superselective binding. This effect can be exploited,for example, to tune the threshold receptor density Γ* of agiven probe 3A, to tatrations2D. Our ttrations1 and 2 atrations4 and 5 a