Mean BMI (SD) at baseline was 22.17 (3.76) and 22.18 (3.93) in girls and boys, respectively. Through multiple linear regression, baseline BW and BMI were positively associated with ∆aBMD over 2 years of follow‐up at all skeletal sites in boys (p < 0.05), but not in girls. ∆BW and ∆BMI predicted ∆aBMD and ∆BMC in both sexes, but the strength of the associations was moderate. Individuals who lost weight during follow‐up demonstrated a slowed progression of aBMD accretion compared with those gaining weight, but loss of BW or reduction of BMI during 2 years was not associated with net loss of aBMD. In conclusion, our results confirm that adequate BW for height in late adolescence is important for bone health. Associations between change in weight status and bone accretion during follow‐up were moderate and unlikely to have any clinical implication on adolescents of normal weight. Underweight individuals, particularly boys, are at risk of not reaching optimal peak bone mass and could benefit from an increase in BMI. © 2019 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.Determinants of bone acquisition in late adolescence and early adulthood are not well‐described. This 2‐year follow‐up study explored the associations of body weight (BW), body mass index (BMI), and changes in weight status with adolescent bone accretion in a sample of 651 adolescents (355 girls and 296 boys) between 15 and 19 years of age from The Tromsø Study: Fit Futures. This Norwegian population‐based cohort study was conducted from 2010 to 2011 and was repeated from 2012 to 2013. We measured femoral neck, total hip, and total body bone mineral content and areal bone mineral density (aBMD) by dual‐energy X‐ray absorptiometry. We measured height, BW, calculated BMI (kg/m Osteoporosis is a major public health concern and a frequent cause of disability in Western societies.It has long been established that there is an association between BW and bone mineral parameters in the adult population.Associations and interplay between anthropometric traits, aBMD levels, and bone accretion in late adolescence are not yet fully described and understood at a population level. The mechanisms behind the weight and bone relationship are not clear as both direct and indirect effects related to mechanical forces, nutrition, age, and hormonal status could be involved. The objectives of this 2‐year follow‐up population‐based study were to explore the associations between baseline BW, baseline BMI, changes in BW (∆BW), and changes in BMI (∆BMI) on changes in bone mineral parameters in a Norwegian population from 15 to 19 years of age. We hypothesized that higher baseline BW and BMI, as well as ∆BW and ∆BMI would be positively associated with changes in bone parameters, and that negative ∆BW and ∆BMI could be detrimental to bone accrual in adolescents entering young adulthood.Detailed information on the Fit Futures Study participants and study procedures has been published previously.2) and aBMD (g/cm²) were measured by the same instrument  by DXA and analyzed with enCORE pediatric software 2), and participants were stratified into BMI quartiles. To explore if relationships changed with various BMI cut‐off points, we also categorized participants into underweight, normal weight, overweight, or obese. Participants <18 years of age were stratified based on their sex‐ and age‐specific BMI according to half‐year cut‐off points described by Cole and Lobstein.Femoral neck (FN), total hip (TH), and total body (TB) bone mineral content , bone area , the presence of acute and chronic disease, and the use of medication and hormonal contraceptives was elicited by clinical interviews. We collected pubertal maturation information, perceived physical activity level, alcohol consumption, and tobacco use by electronic self‐administered questionnaires. Pubertal status for girls was determined based on age at menarche and answers were categorized into “Early” (<12.5 years at menarche), “Intermediate” (12.5 to 13.9 years), or “Late” (>14 years) pubertal maturation. We used the Pubertal Developmental Scale (PDS) to assess pubertal maturation in boys. Secondary pubertal characteristics such as growth spurt, pubic hair growth, changes in voice, and facial hair growth were rated on a scale from 1 (Have Not Begun) to 4 (Completed), were summarized, and then were divided by 4. We categorized a score <2 as “Have Not Begun”, 2 to 2.9 as “Barely Started”, 3 to 3.9 as “Underway,” and a score of 4 as “Completed.”2 test. Welch's ANOVA with Games‐Howell post hoc procedure was used if equal variances assumption was violated. We computed annual bone‐ and anthropometric‐change variables to account for differences in time between baseline and follow‐up measures when describing change and in crude comparisons of groups. Student's t test was used to compare BMI losers and BMI gainers.All analyses were sex‐specific. Population characteristics are presented by BMI quartiles at baseline. Continuous variables are presented by means (SDs) and categorical variables by count (percentages). We compared BMI quartile groups by using one‐way ANOVA with Bonferroni correction and χ2 = β0 + β1Y1 + β2XBW + β3…). Initially we conducted a crude univariate analysis. We then compared the results using change‐score analysis (Y2 – Y1 = β0 + β1XBW) and checking for consistency because baseline adjustments in change‐score analysis may introduce bias.p = 0.10 were used as cut‐off to enter or leave the model. Any covariate with p ≤ 0.10 in a final model was included in all final models. Based on this procedure, alcohol consumption and diagnosis known to affect bone were excluded. We fitted separate models for baseline‐ and change‐exposure variables. Models with ∆BW were adjusted for ∆height. We checked for confounding and plausible 2‐way interactions related to age, pubertal maturation, and baseline weight versus weight change relationships. Because of statistical significance (p < 0.05) we added interaction terms BW * menarche age and BMI * menarche age in corresponding baseline ∆aBMD FN models in girls. In boys, a significant interaction between ∆BMI * BMI was detected and included in three ∆BMI models: FN ∆aBMD, FN ∆BMC, and TB ∆BMC; ∆BW * BW was added to the ∆BW TB ∆BMC model. Interactions were further explored and visualized by graphs.Associations between the exposure variables baseline BW, baseline BMI, ∆BW, and ∆BMI and outcomes FN, TH, and TB ∆aBMD and ∆BMC during follow‐up were assessed by multiple linear regression using the bone mineral follow‐up score as outcome and baseline score as a covariate (Yn = 53 (17.9%). Other missing covariates were menarche age in seven girls and physical activity in one girl and three boys. Multiple imputations based on predictors and outcome variables were performed to predict missing values. We assumed missing at random and 20 imputations were conducted,p = 0.05 and all procedures were performed in IBM SPSS Statistics for Windows, version 24 . Figures were made in RStudio Late introduction of the PDS questions in TFF1 may be the reason for a relatively high percentage of missing puberty values for boys: 2, respectively. In boys, means were 18.14, 20.50, 22.64, and 27.45 kg/m2, respectively.We included 651 adolescents with repeated measurements in the analyses, 355 girls and 296 boys (45.2% boys). At baseline, mean age was 16.6 years , and 18.6 years  at follow‐up. Average follow‐up time was 1.94 years (SD 0.2). Table p = 0.002). In boys, physical activity at baseline differed significantly (p = 0.004) with a higher prevalence of sedentary behavior for the upper quartile (39.7%) compared with the other quartiles, and there was a low proportion of snuff users in the second quartile compared with the three other groups (p = 0.003).One‐way ANOVA analyses showed that cross‐sectional anthropometric, aBMD, and BMC measures differed significantly with a positive linear trend between BMI quartiles at baseline, except body height. These cross‐sectional differences persisted at follow‐up 2 years later (not shown). In girls, menarche age differed significantly with higher prevalence of early menarche at the two upper BMI quartiles compared with the bottom quartile .Among girls, 5.9% of participants were classified as underweight, 75.4% normal weight, 14.0% overweight, and 4.7% obese according to Cole's weight classification at baseline with a mean group BMI of 17.6, 21.9, 26.2, and 33.9 kg/m2 . Boys gained 2.70 kg  and 0.61 kg/m2 , respectively. Eighty‐eight girls (24.6%) and 48 boys (16.2%) lost BW with an average annual loss of –1.60  and –1.97  kg. One‐hundred eleven girls (31.3%) and 62 boys (20.9%) reduced their BMI during follow‐up, with a mean annual decrease of −0.56  and –0.66  kg/m2. We observed a clear difference in longitudinal growth between girls and boys. In girls, 280 (78.9%) of the participants had an increment in height between measurements with an annual mean of 0.053 cm . Almost all the boys  grew taller during the 2 years of follow‐up. Annual mean change was 1.024 cm .In girls, mean annual BW and BMI change was 1.38 kg  and 0.41 kg/mp < 0.05). There were no significant differences in aBMD status between second and third quartiles in any of the three skeletal sites, neither at baseline nor at follow‐up. In girls, the pattern appeared similar to boys, but less polarized in the lower BMI quartiles. The aBMD levels in girls in the first quartile did not differ significantly from the two middle quartiles at the femoral sites.Cross‐sectional measures and the individual aBMD trajectories from TFF1 to TFF2 and unadjusted means within baseline BMI quartiles are illustrated in Fig. p = 0.001, TH: p = 0.005, TB: p < 0.001) and this pattern persisted during the 2 years of follow‐up in crude analyses.When participants were stratified into BMI categories, the relationships slightly changed. Figure Changes in anthropometry, ∆aBMD, and ∆BMC during follow‐up according to baseline BMI quartiles are presented in Table A) and ∆BMC  among BMI losers and BMI gainers between TFF1 and TFF2. Reduction of BMI seemed to induce a slower bone accretion rate, especially in boys, but no mean bone loss was observed in any BMI loser group in either girls or boys. Among girls, statistically significant differences between the two groups were found only at TB ∆BMC (p < 0.001). Among boys, TH ∆aBMD (p = 0.027), TB ∆aBMD (p = 0.011), FN ∆BMC (p = 0.033), TH ∆BMC (p < 0.001), and TB ∆BMC (p < 0.001) were significant. The same pattern was observed with loss of BW. In boys, the BW loser group (n = 48) had a mean annual increment in TH aBMD of 0.006 g/cm2 ; the BW gainers had a mean of 0.012 g/cm2 .Figure BMC Fig. B among p = 0.009) and baseline BMI (p = 0.021) were significantly associated with ∆BMC in the adjusted TH models. In boys, baseline BW and BMI were statistically significant predictors of both ∆aBMD and ∆BMC in most models. Exceptions were crude FN ∆aBMD/∆BMC and TB ∆BMC. ∆BW and ∆BMI had a consistent positive association with both ∆aBMD and ∆BMC in all adjusted models, except ∆BMI ∆aBMD TH (p = 0.086). The influence on ∆aBMD was strongest at femoral sites in boys, but overall changes in aBMD were moderate considering the size of the units of exposure. A baseline BMI difference of 1 SD (3.93 kg/m2) was associated with a 0.008 g/cm2 difference in TH ∆aBMD over 2 years (p = 0.002), whereas 1 SD ∆BMI (1.89 kg/m2) during follow‐up was associated with 0.004 g/cm2 ∆aBMD (p = 0.086). Statistically significant interactions were detected in six models. Pubertal maturation moderated the relationship of baseline BW/BMI and FN ∆aBMD in girls, whereas initial BW and BMI appeared to influence some of the change in weight–bone accretion associations in FN and TB among boys. The relationships between bone accretion and weight change were strongest among boys with low BMI/BW at baseline .The crude and adjusted associations from multiple linear regression models between baseline BW, baseline BMI, ∆BW, ∆BMI, and ∆aBMD and ∆BMC are presented in Table  of 1 SD .93 kg/m2In this population‐based study we explored the associations between BW, BMI, ∆BW, and ∆BMI with changes in bone parameters in adolescents entering young adulthood. Underweight boys had significantly lower mean aBMD at baseline and this disadvantage persisted during 2 years of follow‐up. Change in BW and BMI appeared to be a significant predictor of aBMD change for both girls and boys in the adjusted models, but the increments of aBMD for each unit change in exposure were relatively modest. Findings suggest that the influence of weight change might be strongest among boys with low BMI. Loss of BW or reduction of BMI was not associated with net loss of aBMD; however, our results indicate that the bone accretion rate slowed down whenever weight was lost or BMI reduced during follow‐up in both sexes. In the present study, more than one of five adolescents was classified as overweight or obese at baseline; the prevalence increased during follow‐up for both girls and boys.The results supported our initial hypothesis with a few exceptions. In girls, the influence of baseline weight status on ∆aBMD was limited compared with the results in boys. This may be caused by gender differences in maturation. Cessation of longitudinal growth in girls and strong genetic control reduce the accumulation of bone mass. Previously published results indicate that girls reach a femoral aBMD plateau between 17 and 19 years of age.Z‐score in femoral sites over 2 years in boys have previously been shown in the Fit Futures cohort.2) than the fourth BMI quartile (27.1 kg/m2), representing the tail of the distribution. In girls, the associations between baseline BMI categories and measured bone traits were positive and had a linear trend.A positive cross‐sectional association between BMI and aBMD and a positive association between baseline BW and increased 2 among girls and −0.66 kg/cm2 among boys) over 2 years in our study. To investigate more extreme cases of weight loss, an elaborate analysis stratifying ∆BMI in deciles was conducted , but a significant loss of aBMD was still not detected (not shown). The association between weight loss and loss of bone is more consistent in older compared with younger individuals.Bone loss during weight reduction is well‐documented in older individuals, but not yet demonstrated in younger populations.The determinants of bone acquisition in the period of late adolescence to early adulthood are understudied,The effect of weight reduction on bone depends on whether it is voluntary or involuntary, the rate of change, age, sex, and initial weight.BMI reflects both muscle and adiposity; the mechanisms behind the relationship between weight status and bone are complex and multifactorial. Excess weight may have both negative and positive influences on bone health through different mechanisms. The process of bone modeling is sensitive to mechanical loading: It has been stated that high BW improves bone mineralization by increasing the forces applied on weight‐bearing bones.p = 0.083) when these participants were excluded in a sensitivity analysis.On the other hand, weight‐bearing activity is essential during growth and excess BW may be associated with sedentary behavior . In addition to the mechanical‐loading factors, adipose tissue may exert an impact on bone homeostasis and bone turnover through various adipokines like leptin and estrogen.2 – Y1) and follow‐up measurement (Y2) as outcome using baseline (Y1) as a covariate are two frequently used methods. Authors recommend a comparison of methods for agreement because in some situations these two approaches can lead to a different conclusion in nonrandomized studies based on the statistical phenomenon regression to the mean and Lord's paradox.The population‐based design and repeated measures from a well‐described representative sample of both sexes from different municipalities gave strengths to the present study. The sample size provided an opportunity to analyze the results in smaller subsamples, and explorations of the tails of the distribution are of clinical interest. Using a dedicated research unit at the University Hospital of North Norway ensured the high quality of the data acquisition. We used the same densitometer through both surveys, with continuous validations following a standardized common protocol. The main limitations of this study were the short follow‐up period of 2 years and that individuals were only measured twice. Short follow‐up periods increase the risk of being obscured by variability in DXA measurements. On the other hand, the recommended minimum interval between DXA scans is 6 to 12 months.p = 0.053). This could lead to underestimation of the association between BMI and bone accretion found in this study.The 2D areal DXA measures have a tendency of overestimating BMC of larger bone because wider bones are also thicker; hence, the interpretation of measures of growing skeletons must be done with caution because of this size dependency.In conclusion, our results indicate that weight status during late adolescence could play a part in the concept of maximizing bone mass and density during growth for prevention of future fractures. ∆BW and ∆BMI predicted ∆aBMD and ∆BMC in both sexes. Although statistically significant, the magnitude of these changes in aBMD during follow‐up was moderate and unlikely to have significant clinical implication on peak bone mass for adolescents with an adequate BW. Loss of BW or reduction of BMI was not associated with net loss of aBMD, but individuals who lost weight during follow‐up, demonstrated a slowed progression of aBMD accretion compared with those gaining weight, especially among boys. Considering that more than one of five adolescents was classified as overweight or obese at baseline and with an increasing prevalence during follow‐up for girls and boys, the bone health perspective must be compared with other health benefits. However, adequate weight is important for bone and our results indicate that underweight adolescent boys may benefit from a BMI increase. Particularly underweight individuals losing weight during this critical period of bone accretion could be at risk of a less than optimal peak bone mass acquisition, thus not achieving their full genetic potential for skeletal mass. Because of the short follow‐up of 2 years, results must be interpreted with caution. Further analyses should also examine the effect of lifestyle factors present at baseline. Moreover, the cohort should be followed into adulthood to further explore factors that can alter the bone mass trajectory.All authors state that they have no conflicts of interest.Supporting Information.Click here for additional data file.Supporting Information.Click here for additional data file.


The potential biological relationship between type‐2 diabetes mellitus (T2DM) has been focused in numerous studies. To investigate the molecular associations among T2DM, prostate cancer (PCa), and chronic myeloid leukemia (CML), using a biomolecular network enrichment analysis. We obtained a list of disease‐related genes and constructed disease networks. Then, GO enrichment analysis was performed to identify the significant functions and pathways of overlapping modules in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. More than 75% of these overlapping genes were found to be consistent with the findings of previous studies. In the three diseases, we found that Sarcoglycan delta (SGCD) and Rho family GTPase 3 (RND3) were the overlapping genes and identified negative regulation of apoptotic process and negative regulation of transcription from RNA polymerase II promoter RNA as the two overlapping biological functions. CML and PCa were the most closely related, with 34 overlapping genes, five overlapping modules, 27 overlapping biological functions, and nine overlapping pathways. There were 13 overlapping genes, one overlapping modules, four overlapping biological functions and one overlapping pathway  were found in T2DM and CML.And T2DM and PCa were the least related pair in our study, with only six overlapping genes, five overlapping modules, and one overlapping biological function. SGCD and RND3 were the main gene‐to‐gene relationship among T2DM, CML, and PCa; apoptosis, development, and transcription from RNA polymerase II promote processes were the main functional connections among T2DM, CML, and PCa by network enrichment analysis. There is a “scalene” relationship among T2DM, CML, and PCa at gene, pathway, biological process, and module levels: CML and PCa were the most closely related, the second were T2DM and PCa, and T2DM and PCa were the least related pair in our study. Our study provides a new avenue for further studies on T2DM and cancers, which may promote the discovery and development of novel therapeutic and can be used to treat multiple diseases. According to the International Diabetes Federation (IDF), more than 640 million people worldwide will suffer from diabetes by 2040.Finding gene‐disease and disease‐disease associations play important roles in the biomedical area, and many prioritization methods have been proposed for this goal.In our study, firstly, we obtained a list of disease‐related genes form the Disease‐Connect website and we constructed disease networks by Agilent Literature Search software. Then, the disease networks were divided into modules by MCODE. GO enrichment analysis was performed to identify the significant functions and pathways of overlapping modules in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Based on molecular network construction and enrichment analysis, our study aimed to systemically explore the molecular associations among T2DM, CML, and PCa, which provided a novel and feasible idea for the study the association in T2DM and different cancers.22.1https://disease-connect.org/), a freely available web database for mechanism‐based disease‐disease connections. We analyzed the functions of the disease‐related genes by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.abcc.ncifcrf.gov/), which provides a comprehensive set of functional annotation tools for understanding the biological significances of many genes.P < 0.05 was used as the cutoff criterion for GO and KEGG pathway enrichment analyses, and P‐values were ranked.The terms “type 2 diabetes mellitus (T2DM),” “chronic myeloid leukemia (CML),” and “malignant prostate cancer (PCa)” were entered into the “Disease View” search box of the Disease‐Connect website (2.2https://www.cytoscape.org/), and an overview network of gene/protein associations was obtained. Cytoscape is an open source tool for visualizing complex networks and integrating biomolecular interaction networks, especially for large data sets of gene‐gene, gene‐protein, and protein‐protein interactions.Then, the disease‐related genes were submitted to Agilent Literature Search software, version 3.20 (2.3https://www.cytoscape.org) to visualize the three disease‐related networks and analyze the properties of these networks. Network parameters  were calculated and illustrated.We used the Cytoscape software version 3.20 (2.4https://baderlab.org/Software/MCODE) were performed for network module division.K = 2, Node Score Threshold = 0.2, Max. Depth = 100) were used as the criteria for network module screening.After constructing the networks for the three diseases, centrality analysis and a complex molecular algorithm MCODE 1.32 , we obtained 233 T2DM‐related, 733 CML‐related, and 511 PCa‐related genes Table . SGCD an0, 2016, 3.2All of the 233 T2DM‐related genes, 733 CML‐related genes, and 511 PCa‐related genes were submitted to DAVID for GO functional enrichment analysis. And we obtained the biological functions and pathways of these disease‐related genes. A total of 43 biological functions and 12 KEGG pathways were identified for the T2DM‐related genes, 174 biological functions and 34 KEGG pathways for the CML‐related genes, and 97 biological functions and 18 KEGG pathways for the PCa‐related genes, respectively Figure  and S4.In T2DM, insulin process (13.95%), transport process (9.3%), glucose process (9.3%), development process (9.3%), cell differentiation process (6.98%), and metabolic process (6.98%) were the main functional biological processes  were found among T2DM, CML, and PCa Table . Four ovNo overlapping pathways were identified for the three diseases. FoxO signaling pathway was the overlapping pathway between T2DM and CML; nine overlapping pathways were found between CML and PCa, mainly related to cancer and cell adhesion Table . And the3.3The disease‐related genes were submitted to the Agilent Literature Search 3.2, and we obtained the T2DM‐related  of the three diseases, we identified the modules for RND3 and SGCD by MCODE software, respectively. Functional analysis of the RND3 and SGCD modules by GO annotation was shown in Figure 3.5.2Proteolysis, transcription from RNA polymerase II promoter, cullin deneddylation, negative regulation of cell proliferation, nucleotide‐excision repair, DNA damage recognition, and neurogenesis were obtained by the GO functional enrichment analysis for the five overlapping modules between T2DM and CML Table .3.5.3Regulation of ion transmembrane transport, white fat cell differentiation, viral genome replication, and membrane depolarization during action potential were the statistically significant functions of the five overlapping modules between CML and PCa Table .4In our study, several new common biological backgrounds and overlapping genes of T2DM, CML, and PCa were discovered by network and enrichment analysis. In order to reveal and verify the genetic connections among T2DM, CML, and PCa, literature‐based text mining was performed. We discovered that T2DM and cancers have a certain molecular association at gene, pathway, biological process, and module levels according to network and enrichment analysis. More than 75% of the overlapping genes were found to be consistent with the findings of previous studies Figure B. HematoRND3 and SGCD were the overlapping genes of the three diseases, which has also been reported in previous studies.Tyrosine kinase inhibitors (TKIs), such as imatinib and dasatinib, have been proven to be effective in the treatment of CML and PCa, and in most situations can improve the pre‐existing diabetes or lower the average blood glucose levels to a certain degree.According to our findings, CML and PCa were the most closely related, with 34 overlapping genes, five overlapping modules, 27 overlapping biological functions and nine overlapping pathways. For example, development, cell proliferation, and cell adhesion processes were the main biological processes both in CML and PCa. Cancer, cell adhesion, and PI3K‐Akt signaling pathways were the common pathways in CML and PCa. Prior studies have revealed that NF‐κB, FAK, JAK1/2, ETK, JNK, MAPK, and PI3K‐Akt signaling pathways were involved in processes: cell growth, migration, invasion, angiogenesis, and apoptosis in CML and PCa Figure B.26, 30,As for the relationship between T2DM and CML, 13 overlapping genes, one overlapping modules, four overlapping biological functions and one overlapping pathway  were found. It has been shown that response to glucose process was related to CML.And T2DM and PCa were the least related pair in our study, with only six overlapping genes, five overlapping modules, and one overlapping biological function (endocrine pancreas development). Endocrine pancreas development has been associated with the risk of prostate cancer as well as insulin release from β cells in patients with T2DM.In summary, SGCD and RND3 were the main gene‐to‐gene relationship among T2DM, CML, and PCa; apoptosis, development, and transcription from RNA polymerase II promote processes were the main functional connections among T2DM, CML, and PCa by network enrichment analysis. There is a “scalene” relationship among T2DM, CML, and PCa at gene, pathway, biological process, and module levels: CML and PCa were the most closely related, the second were T2DM and PCa, and T2DM and PCa were the least related pair in our study Figure A. Our sthttps://disease-connect.org/) was on 31 December 2015, which may lead to incomplete data on disease‐related genes, and this needs to be addressed in our further studies.We also acknowledge some limitations of the present study. The last update of the database (Disease‐Connect website, The authors declare that they have no competing financial interests. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Non‐small cell lung cancer (NSCLC) patients carrying EGFR activating mutations treated with gefitinib, a tyrosine kinase inhibitor, will develop drug resistance. Ubiquitylation is one of major posttranslational modifications of proteins affecting the stability or function of proteins. However, the role of protein ubiquitylation in gefitinib resistance is poorly understood. To systematically identify the global change in protein expression and ubiquitylation during gefitinib resistance, a quantitative global proteome and ubiquitylome study in a pair of gefitinib‐resistant and sensitive NSCLC cells is carried out. Altogether, changes in expression of 3773 proteins are quantified, and changes in ubiquitylation of 2893 lysine sites in 1415 proteins are measured in both cells. Interestingly, lysosomal and endocytic pathways, which are involved in autophagy regulation, are enriched with upregulated proteins or ubiquitylated proteins in gefitinib‐resistant cells. In addition, HMGA2 overexpression or ALOX5 knockdown suppresses gefitinib resistance in NSCLC cells by inhibiting autophagy. Overall, these results reveal the previously unknown global ubiquitylome and proteomic features associated with gefitinib resistance, uncover the opposing roles of HMGA2 or ALOX5 in regulating gefitinib resistance and autophagy, and will help to identify new therapeutic targets in overcoming gefitinib resistance. However, most of these patients will develop gefitinib resistance eventually. Many mechanisms have been reported to regulate gefitinib resistance.Protein ubiquitylation is a type of protein posttranslational modification, in which ubiquitin is activated and transferred to substrates via an E1–E2–E3 cascade.Ubiquitylation plays important roles in many biological processes including cell cycle, proliferation, and apoptosis. Ubiquitin–proteasome system is responsible for intracellular protein degradation, which serves as the quality control system of the cell.In this study, we carried out quantitative proteome and ubiquitylome studies to investigate the global changes in protein expression and ubiquitylation associated with gefitinib resistance in non‐small cell lung cancer (NSCLC) cells. Our results identified thousands of proteins with differential regulation in expression and ubiquitylation associated with gefitinib resistance. In particular, lysosomal and endocytic pathways, which are involved in regulating autophagy, are enriched with proteins with upregulated expression and ubiquitylation. Finally, we found that HMGA2 overexpression or ALOX5 knockdown or inactivation suppressed gefitinib resistance by inhibiting autophagy in these cells. Thus, our study reveals the unique features of gefitinib resistance in both protein expression and ubiquitylation, which will help to identify new therapeutic targets in treating gefitinib resistance in NSCLC.Lysosomal and endocytic pathways play important roles in the physiology of cells.Here, to identify proteome‐wide changes in protein expression and ubiquitylated proteins associated with gefitinib resistance, we employed antibody‐based capture of endogenous diGly‐containing peptides from cells treated with stable isotope labeling with amino acids in cell culture (SILAC), coupled with liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) analysis, to interrogate the differences in proteome and ubiquitylome between gefitinib‐resistant and sensitive NSCLC cells.We found that many cellular pathways, especially, lysosomal and endocytic pathways are enriched with upregulated proteins and protein ubiquitylation. In addition, HMGA2 overexpression or ALOX5 knockdown reduces gefitinib resistance by suppressing autophagy. Overall, this study is the first large‐scale mapping of ubiquitylome in gefitinib‐resistant and sensitive NSCLC cells, and will help to identify new therapeutic targets in overcoming gefitinib resistance.22.1−1 gefitinib for 2 months, the surviving PC9 cells were isolated and expanded in the presence of 3 μm gefitinib in vitro. To show that these PC9/GR cells are free of contaminant cells from the microenvironment of mouse‐resistant cells, we carried out the STR (short tandem repeat) profiling analysis of both PC9 and PC9/GR cells, and showed that they are derived from the common ancestry (PC9 cells). Next, PC9/GR and PC9 cells, were grown to 80% confluence in high glucose (4.5 g L−1) Dulbecco's modified Eagle's medium (with glutamine and sodium pyruvate) containing 10% fetal bovine serum and 1% penicillin–streptomycin at 37 °C with 95% air and 5% CO2. PC9/GR cells were labeled with “light isotopic lysine” (12C‐lysine), and PC9 cells were labeled with “heavy isotopic lysine” (13C‐lysine) using a SILAC protein quantitation kit  according to manufacturer's instructions. Briefly, cells were grown in Dulbecco's modified Eagle's medium (the “light” labeled PC9/GR cells were treated with 3 μm gefitinib) supplemented with 10% fetal bovine serum and either the “heavy” form of [U‐13C6]‐ L‐lysine or “light” [U‐12C6]‐ L‐lysine for more than six generations before being harvested, to achieve more than 97% labeling efficiency. After that, the cells were further expanded in SILAC media to desired cell number (≈5 × 108) in fifteen 150 cm2 flasks. The cells were then harvested and washed twice with ice‐cold PBS. After snap freezing in liquid nitrogen, cell pellets were stored in −80 °C freezer for future use.Gefitinib‐resistant PC9 cells (PC9/GR) were established by the following procedure: nude mice carrying subcutaneous tumor xenografts derived from gefitinib sensitive PC9 cells were intraperitoneally injected with 100 mg kg2.2m Urea, 5 mm DTT (Dithiothreitol), 2 mm EDTA, 1.0% cocktail III, 10 mm N‐ethylmaleimide [NEM]). The remaining debris was removed by centrifugation at 20 000 g at 4 °C for 10 min. After concentration measurement, equal amounts of crude proteins in supernatant labeled “heavy” or “light” were mixed and the crude proteins were precipitated by adding TCA with 15% final concentration v/v (soluble fraction). After washing twice with −20 °C acetone, the protein pellets were dissolved in 100 mm NH4HCO3 (pH 8.0) for trypsin digestion.The harvested “heavy” and “light” labeled cells were sonicated three times on ice using a high intensity ultrasonic processor (Scientz) in lysis buffer  at room temperature for another 30 min. Trypsin was then added again with trypsin to protein ratio at 1:100 w/w for digestion at 37 °C for 4 h to complete the digestion cycle.Trypsin (Promega) was added into protein solution with ratio of trypsin to protein at 1:50 w/w for digestion at 37 °C for 16 h. DTT was then added to the final concentration of 5 m2.4m particles, 4.6 mm ID, 250 mm length). Briefly, peptides were first separated with a gradient of 2–60% acetonitrile in 10 mm ammonium bicarbonate pH 10 over 80 min into 80 fractions. Then, the peptides were combined into eight fractions and dried by vacuum centrifuging.The sample was then fractionated into fractions by high pH reverse‐phase high performance liquid chromatography (HPLC) using Agilent 300 Extend C18 column  were incubated with prewashed antibody beads (PTM Biolabs) at 4 °C overnight with gentle shaking. The beads were washed four times with NETN buffer and twice with ddH2O. The bound peptides were eluted from the beads with 0.1% TFA. The eluted fractions were combined and vacuum dried. The resulting peptides were cleaned with C18 ZipTips (Millipore) according to the manufacturer's instructions, followed by LC‐MS/MS analysis.To enrich Kub (lysine (K) ubiquitylated) peptides, tryptic peptides dissolved in NETN buffer .Peptides were dissolved in 0.1% FA, directly loaded onto a reversed‐phase pre‐column . Peptide separation was performed using a reversed‐phase analytical column . The gradient comprised an increase from 6% to 22% of solvent B (0.1% FA in 98% ACN) for 26 min, 22–35% for 8 min, and climbing to 80% in 3 min then holding at 80% for the last 3 min, all at a constant flow rate of 300 nL minm/z scan range was 350 to 1800.The peptides were subjected to NSI source followed by MS/MS in Q Exactive Plus (Thermo) coupled online to the UPLC. Intact peptides were detected in the Orbitrap at a resolution of 70 000. Peptides were selected for MS/MS using NCE setting as 30; ion fragments were detected in the Orbitrap at a resolution of 17 500. A data dependent procedure that alternated between 1 MS scan followed by 20 MS/MS scans was applied for the top 20 precursor ions above a threshold ion count of 1.0E4 in the MS survey scan with 30.0 s dynamic exclusion. The electrospray voltage applied was 2.0 kV. Automatic gain control (AGC) was used to prevent overfilling of the ion trap; 5E4 ions were accumulated for generation of MS/MS spectra. For MS scans, the http://www.ebi.ac.uk/pride/archive/) with the dataset identifier PXD004941.The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE2.8LC‐MS/MS analysis data are further analyzed using the MaxQuant software. Based on the MS/MS spectra, the peptides are identified while the ratios of the according SILAC pairs are used for relative quantification. In each LC‐MS run, we normalize peptide ratios so that the median of their logarithms is zero, which corrects for unequal protein loading, assuming that the majority of proteins show no differential regulation. Protein ratios are calculated as the median of all SILAC peptide ratios, minimizing the effect of outliers. We normalize the protein ratios to correct for unequal protein amounts. Whenever the set of identified peptides in one protein is equal to or completely contained in the set of identified peptides of another protein these two proteins are joined in a protein group. Shared peptides are most parsimoniously associated with the group with the highest number of identified peptides (“razor” peptides) but remain in all groups where they occur. Peptide identification information from the proteomic study is provided .2.9The resulting MS/MS data was processed using MaxQuant with integrated Andromeda search engine (v.1.4.1.2). Tandem mass spectra were searched against Swissprot_human (20 274 sequences) database concatenated with reverse decoy database. Trypsin/P was specified as cleavage enzyme allowing up to four missing cleavages, four modifications per peptide, and five charges. Mass error was set to 10 ppm for precursor ions and 0.02 Da for fragment ions. Carbamidomethylation on Cys was specified as fixed modification and oxidation on Met, ubiquitylation on Lys and acetylation on protein N‐terminal were specified as variable modifications. False discovery rate (FDR) thresholds for protein, peptide and modification site were specified at 1%. Minimum peptide length was set at 7. All the other parameters in MaxQuant were set to default values. The site localization probability was set as >0.75.2.10http://www.ebi.ac.uk/GOA/). Firstly, converting identified protein ID to UniProt ID and then mapping to GO IDs by protein ID. If some identified proteins were not annotated by UniProt‐GOA database, the InterProScan soft would be used to annotate protein's GO functional based on protein sequence alignment method. Then proteins were classified by Gene Ontology annotation based on three categories: biological process, cellular component, and molecular function.Gene Ontology (GO) annotation proteome was derived from the UniProt‐GOA database (2.11http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains, and functional sites, and makes it freely available to the public via Web‐based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large‐scale analysis of whole genomes and meta‐genomes, as well as in characterizing individual protein sequences.Identified proteins domain functional description were annotated by InterProScan  based on protein sequence alignment method, and the InterPro domain database was used. InterPro  in all protein sequences.2.15Homo sapiens. A two‐tailed Fisher's exact test was employed to test the enrichment of the protein‐containing IPI entries against all IPI proteins. Correction for multiple hypothesis testing was carried out using standard FDR control methods. The GO with a corrected p‐value < 0.05 is considered significant.Proteins were classified by GO annotation into three categories: biological process, cellular compartment, and molecular function. For each category, we used the functional annotation tool of DAVID Bioinformatics Resources 6.7 to identify enriched GO against the background of 2.16H. sapiens. A two‐tailed Fisher's exact test was employed to test the enrichment of the protein‐containing IPI entries against all IPI proteins. Correction for multiple hypothesis testing was carried out using standard FDR control methods. The pathway with a corrected p‐value < 0.05 was considered significant. These pathways were classified into hierarchical categories according to the KEGG website.Encyclopedia of Genes and Genomes (KEGG) database was used to identify enriched pathways by the functional annotation tool of DAVID against the background of 2.17p values, and then filtered for those categories which were at least enriched in one of the clusters with p value < 0.05. This filtered p value matrix was transformed by the function x = −log10 . Finally these x values were z‐transformed for each category. These z scores were then clustered by one‐way hierarchical clustering  in Genesis. Cluster membership was visualized by a heat map using the “heatmap.2” function from the “gplots” R‐package.All the protein categories obtained after enrichment were collated along with their 2.18p values, and then filtered for those categories which were at least enriched in one of the clusters with p value < 0.05. This filtered p value matrix was transformed by the function x = −log10 . Finally, these x values were z‐transformed for each functional category. These z scores were then clustered by one‐way hierarchical clustering  in Genesis. Cluster membership was visualized by a heat map using the “heatmap.2” function from the “gplots” R‐package.For further hierarchical clustering based on different protein functional classification . We first collated all the categories obtained after enrichment along with their 2.19x) and standard deviation (y) of the log2 ratio (L/H) of all quantified peptides was calculated. The L/H ratio of each peptide was then transformed to an z score based on z = (log2ratio – x) / y, where the ratio is the L/H ratio. The cutoff z scores was set according to cumulative density function of normal distribution at three different percentiles — 25, 50 and 75%. Each peptide was then allocated to the quantiles based on the transformed z score. In this way, four quantiles were generated: Q1 (0–25%), Q2 (25–50%), Q3 (50–75%), and Q4 (75–100%).The quantified proteins in this study were divided into four quantiles. The average . Finally, these x values were z‐transformed for each category. These z scores were then clustered by one‐way hierarchical clustering  in Genesis. Cluster membership was visualized by a heat map using the “heatmap.2” function from the “gplots” R‐package.All the substrate categories obtained after enrichment were collated along with their 2.21Total RNA extraction from cells and real‐time RT‐PCR were performed as previously described.2.22−1 cycloheximide (CHX)  for 0, 2, 4, and 8 h before harvesting. The following primary antibodies were used: HMGA2 , ALDH1A1 , CHK2 , FAS , TGFBR1 , Flag , LC3B , NUCB2 , GAPDH . GAPDH was used as a loading control. The following secondary antibodies were used: goat anti‐rabbit IgG  and goat polyclonal anti‐mouse IgG .Western blotting was performed as previously described.2.23Colony formation assays were performed as previously described.2.24PC9/GR cells were infected with the lentiviral supernatant containing constructs overexpressing the control plasmid, HMGA2 or ALOX5 cDNA, or constructs expressing the control shRNA, shRNAs targeting HMGA2 or ALOX5. Puromycin was added to the cells for killing uninfected cells. Multiple single colonies were selected and expanded. The detailed procedure was described as previously described.2.25Zileuton (# HY‐14164) was purchased from MedChemExpress Inc. .2.26t‐test was used. For comparisons among multiple groups, one‐way ANOVA was used. For all analyses, a p value of < 0.05 was considered statistically significant. *p < 0.05, **p < 0.01, ***p < 0.001.For comparisons between two groups, Student's 33.1To systematically identify the global change in the ubiquitylated targets of gefitinib‐resistant cells (PC9/GR) and its gefitinib sensitive parental cells (PC9), we carried out both proteome and ubiquitylome studies at the same time Figure A. In thiOur ubiquitylome study is essentially a quantitative diGly proteomics. It combines antibody‐based capture of “diGly remnant”‐containing peptides (marking ubiquitylated proteins after trypsinolysis) and SILAC. PC9/GR cells were labeled with light (L) isotopic 12C‐lysine, while PC9 cells were labeled with heavy (H) isotopic 13C‐lysine. These were followed by HPLC and LC‐MS/MS analysis. The normalized ratio of the peptide intensity from these two types of cells (ratio (L:H)) was used to determine the relative peptide abundance in PC9/GR versus PC9 cells. QC validation of the MS data was carried out to determine whether the experimental condition in each study was optimal .For global proteome analysis, 4646 proteins were identified and 3773 proteins were quantified in both PC9/GR and PC9 cells. Among them, 404 proteins were upregulated, and 148 were downregulated in PC9/GR cells, when using the normalized log2 ratio (L:H) either ≥1 or ≤−1  as the threshold . Quantiles‐based analysis divides all protein into four groups  when ranked by log2 ratio (L:H), and shows that 15.8% of proteins with log2 ratio (L:H) either ≥1 or ≤−1  either ≥1 or ≤−1 as the threshold . A typical secondary MS result was presented here using NBR1 protein as an example . Quantile‐based analysis divides all protein into four groups  when ranked by log2 ratio (L:H), and shows 41.1% of lysine sites with log2 ratio (L:H) either ≥1 or ≤−1  have upregulated protein level, but downregulated ubiquitylation level, and two proteins (EPHA2 and CNNM3) have downregulated protein level, but upregulated ubiquitylation level in PC9/GR cells. In addition, 12 proteins have both up‐ and downregulated ubiquitylated sites in PC9/GR cells , 1026 peptides were found to be enriched in four conserved motifs, within 20 amino acid residues surrounding the ubiquitylated lysine (from the −10 to the +10 positions). These motifs (motif 1–4) are illustrated as RXXXXXXKub, KubE, EKub, and KubXXXXXXXR, respectively  and a negatively charged residue (glutamate [E]). These motifs can be sorted into two categories: the position +6 or −7 is R, and the position +1 or −1 is E. Notably, the positively charged residue  is rarely found near the ubiquitylated lysine. These results suggest that ubiquitylation system prefers to modify the lysine residue surrounded with negatively changed residues from position −5 to +5 and without the positively charged residues Figure B.All the motifs differed in their abundances in the ubiquitylated peptides, and motif 3 has the most extensive distribution, while motif 1 has the least distribution Figure C. Furthe3.3To understand the functions or pathways of proteins differentially expressed or regulated by ubiquitylation associated with gefitinib resistance, all these proteins were annotated using Gene Ontology (GO). According to molecular function, proteins with increased expression in PC9/GR cells are enriched in catalytic, transporter, structural molecular, receptor, and electron carrier activities, while proteins with downregulated expression are enriched in binding activity in general Figure A,B. By cBased on subcellular location, proteins with increased expression in PC9/GR cells are enriched in nuclear, mitochondria, and extracellular compartment, while proteins with downregulated expression in PC9/GR cells are enriched in plasma membrane, cytosol, and nuclear compartments . In addition, proteins with increased ubiquitylated lysine sites in PC9/GR cells are enriched in peroxisome, nuclear, and mitochondria, but proteins with decreased ubiquitylated lysine sites are enriched in plasma membrane, cytosol, and cytoskeleton .In addition, all differentially expressed proteins were subjected to the enrichment analysis with KEGG pathways, which are a collection of pathway maps representing updated knowledge on the molecular interaction and reaction networks.Proteins containing differential regulation in ubiquitylation were also subjected to the enrichment analysis with KEGG pathways. The analysis showed that 12 pathways were significantly enriched in proteins with increased ubiquitylation, including SNARE interaction in vesicular transport, endocytosis, phagosome, ABC transporters, lysosome in PC9/GR cells, while 8 pathways were significantly enriched in proteins with decreased ubiquitylation, including metabolic pathways, gap junction, and biosynthesis of amino acids in PC9/GR cells  in the ubiquitylome study was carried out . Proteins in the Q1 group, which contain lysine sites with decreased ubiquitylation, are enriched in the following protein domains: Zinc finger C6HC‐type, glutathione S‐transferase, the conserved sequence in the N‐terminal section of class I aminoacyl‐tRNA synthetases, etc. However, proteins in the Q4 group, which contain lysine sites with increased ubiquitylation, are enriched in the following protein domains: transmembrane receptor, E3 ubiquitin–protein ligase SMURF1 type, EGF‐like calcium binding domain, etc. .KEGG pathway clustering for four protein groups Q1, Q2, Q3, and Q4) in the ubiquitylome study was also carried out . Proteins in the Q1 group are enriched in the following KEGG pathways: cell cycle, metabolic pathway, DNA replication, etc. However, proteins in the Q4 group are enriched in the following pathways: SNARE interaction in vesicle transport, endocytosis, lysosome, ABC transporters, etc., which is consistent with previous KEGG enrichment analysis using all proteins with upregulated ubiquitylation in PC9/GR cells  that mediates acidification of intracellular compartments of eukaryotic cells important for protein sorting and endocytosis. CLTA is the major protein of the polyhedral coat of coated pits and vesicles during endocytosis ) in PC9/GR cells compared to PC9 cells are listed in Table S6, Supporting Information. In addition, the schematic drawings of these proteins carrying ubiquitylated lysine sites within these pathways are also presented in Figure We also found that many lysine sites in E3 ubiquitin ligases are differentially regulated by ubiquitylation in PC9/GR cells . Protein ubiquitylation in most of these enzymes is not associated with changes in the protein expression level, except DZIP3, TRAF3, and RNF149, RBBP4 proteins. Interestingly, TRIM32 and COPA have different lysine sites undergoing both up‐ and downregulation in ubiquitylation at the same time in PC9/GR cells. However, it is possible that some of the differentially expressed protein were not detected by the current LC‐MS/MS method.Furthermore, many of autophagy related proteins have either differentially regulated protein ubiquitylation or protein expression . For example, NBR1 protein has the most significant increase in the ubiquitylation level, while HIF1A protein has the most significant decrease in the ubiquitylation level. LAMP2, GABARAPL2 have upregulated protein expression level, while ATG7, RB1CC1 have downregulated protein expression level.Taken together, our study reveals hundreds of lysine sites in proteins that are differentially regulated by ubiquitylation in gefitinib‐resistant cells, which may regulate gefitinib resistance.3.6To validate whether differentially expressed proteins identified in our proteomic study are important in regulating gefitinib resistance, we chose HMGA2 and ALOX5 for subsequent validation, because HMGA2 is a known driver promoting tumor metastasis and a molecular target in many cancers,m gefitinib concentration , and found that HMGA2 is downregulated in erlotinib‐resistant head and neck squamous cell carcinoma cells in GSE62061 dataset . In addition, HMGA2 overexpression or ALOX5 knockdown suppresses gefitinib resistance possibly by inhibiting autophagy. Therefore, these results not only validate our proteomics findings, but also indicate that ALOX5 may serve as a potential therapeutic target for overcoming gefitinib resistance. More detailed study of how ALOX5 inhibition suppresses gefitinib resistance is currently under investigation.Overall, our study reveals the previously unknown global landscape of changes in protein expression and ubiquitylation, especially in lysosomal and endocytic pathways, during gefitinib resistance, and further demonstrates that HMGA2 overexpression or ALOX5 knockdown can suppress gefitinib resistance possibly by inhibiting autophagy. Therefore, this study may help to identify new therapeutic targets to overcome gefitinib resistance in NSCLC.The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


In Experiment 2, younger and older adults also viewed emotional and neutral faces, but switched between two non-emotional tasks (i.e. responding to the face's age vs. gender). Data from Experiment 1 demonstrated that switching from an emotional to a non-emotional task was slower when the expression of the new face was emotional rather than neutral. This impairment was observed in both age groups. In contrast, Experiment 2 revealed that neither younger nor older adults were affected by block-wise irrelevant emotion when switching between two non-emotional tasks. Overall, the findings suggest that task-irrelevant emotion can impair task switching through reactivation of the competing emotional task set. They also suggest that this effect and the ability to shield task-switching performance from block-wise irrelevant emotion are preserved in ageing.In our everyday life, we frequently switch between different tasks, a faculty that changes with age. However, it is still not understood how emotion impacts on age-related changes in task switching. Using faces with emotional and neutral expressions, Experiment 1 investigated younger ( Switching between different tasks is so common in everyday life that we are often not even aware that we perform task switching. For instance, putting together a shopping list requires switching between thinking of ingredients you need, checking for them in cupboards, and writing them down. The ability to switch between tasks is a core executive function that plays an important role in everyday functioning  and more errors compared to repeating a task, which is referred to as the switch cost  repeat or (slower) switch responses. As the experimental design of previous studies allowed for response repetition on repeat trials, this might have contributed to particularly fast repeat responses on average and thus, to higher switch costs. Furthermore, processing of emotional items is known to be particularly efficient  and 32 older adults (61–80 years old) participated in Experiment 1 see . Three yAs can be seen in M = 27.80, SD = 3.12) and ten older adults  rated the valence and arousal of 234 preselected faces. Sixteen happy, 16 angry and 16 neutral expressions with the highest agreement between younger and older raters were then selected for the main experiment. The age group  and sex  of the faces were balanced in each emotion category and each picture showed a unique individual. For counterbalancing purposes, two face sets with similar arousal and valence levels  < 1.30, ps < .208) were created.Stimuli consisted of 48 images of faces from the FACES database  when the face was presented above (or below) the horizontal line and to the emotion of the face by pressing one of two buttons  when the face was presented below (or above) a horizontal line. Button presses initiated the next trial after the presentation of a blank screen  for 200 ms. Task assignment to the top or bottom half of the screen was counterbalanced across participants, as was the labelling of the keys. Task switching was associated with hand switching: responses to the task on the top half of the screen were assigned to the left hand and responses to the task on the bottom half of the screen were assigned to the right hand to avoid conflict at the response level. On a regular PC computer keyboard, the buttons “S” and “D” as well as “K” and “L” were used. An example of a trial sequence is presented in After the computer task, participants completed the Digit Symbol Substitution Test . Accuracy and RTs in the happy vs. neutral and the angry vs. neutral blocks were analysed separately. Data were analysed by a 2 × 2 × 2 × 2 mixed factors ANOVA with the within-subjects factors task (age vs. emotion), target emotion  and previous emotion  and the between-subjects factor age (younger vs. older). Post-hoc F = 11.12, MSE = .005, p = .001, partial 2η = .16, as older adults were more accurate  than younger adults . No further significant main effects or interactions were observed for accuracy for happy vs. neutral faces .Accuracy for performance in the happy vs. neutral task block in younger and older adults is presented in F = 17.40, MSE = 89530, p < .001, partial 2η = .23, with overall slower RTs in the emotion task  compared to the age task . This main effect was qualified by a task × target emotion interaction, F = 4.14, MSE = 21865, p = .046, partial 2η = .07. Follow-up t-tests revealed that in the age task, RTs were slower for happy targets  than for neutral targets , t(60) = 2.50, p = .015. In the emotion task, there was no significant difference between RTs for neutral or happy targets (p = .650). Finally, there was also a main effect of age, F = 11.81, MSE = 676967, p = .001, partial 2η = .17, as older adults were overall slower  than younger adults . No further significant main effects or interactions were observed for RTs to happy vs. neutral faces .RTs for performance in the happy vs. neutral task block in younger and older adults are presented in F = 14.99, MSE = .007, p < .001, partial 2η = .20, as accuracy scores were higher in the age task  than in the emotion task . There was also a main effect of target emotion, F = 7.58, MSE = .006, p = .008, partial 2η = .11, with lower accuracy scores for angry targets  than for neutral targets . This main effect was qualified by a task × target emotion interaction, F = 7.58, MSE = .006, p = .008, partial 2η = .11. Follow-up t-tests revealed that in the emotion task, accuracy was lower for angry targets  than for neutral targets , t(60) = 3.32, p = .002, whereas in the age task, accuracy scores for neutral and angry targets did not differ (p = .909). No further significant main effects or interaction were observed for accuracy scores for angry vs. neutral faces .Accuracy scores for performance in the angry vs. neutral task block in younger and older adults are presented in F = 50.09, MSE = 49662, p < .001, partial 2η = .46, as participants were slower in the emotion task  than in the age task . There was also a main effect of target emotion, F = 8.15, MSE = 38061, p = .006, partial 2η = .12, with slower RTs for angry targets  relative to neutral targets . This main effect was qualified by a task × target emotion interaction, F = 4.18, MSE = 32855, p = .045, partial 2η = .07. Separate analyses for the age task and the emotion task revealed that in the age task, RTs were slower for angry targets  than for neutral targets , t(60) = 4.08, p < .001. In contrast, there was no significant difference in RTs for neutral and angry targets in the emotion task (p = .567). There was also a main effect of age, F = 8.43, MSE = 601596, p = .005, partial 2η = .13. However, this effect became non-significant (p = .102) when processing speed was included as a covariate in the analysis. No further significant main effects or interactions were observed for RTs for angry vs. neutral faces .RTs for performance in the angry vs. neutral task block in younger and older adults are presented in The aim of Experiment 1 was to investigate the effects of emotion on switching between emotional and non-emotional tasks in younger and older adults. The main finding was that both age groups were slower when switching to emotional rather than neutral faces in the non-emotional age task, whereas no differences in RTs were observed for switches to neutral and emotional faces in the emotional expression task. Moreover, although the emotion of the target, which they had to switch to, modulated responses in both age groups, the emotion of the item, which they had to switch away from, did not. Lastly, there were no age-related differences in task switching besides evidence of a speed-accuracy trade-off in older adults.The finding that participants were slower when having to switch to an emotional face in the age task suggests that trial-irrelevant emotion was processed and impaired performance in both age groups. One explanation for this effect could be that having just processed and responded to emotional information in the emotional task made it difficult for participants to switch to the non-emotional task (option 1). This would be in accordance with the suggestion that proactive interference from the no longer relevant task affects task switching  to rule out some of these explanations. The analyses showed that RTs were slower for emotional compared to neutral target items for repeat trials in the age task just as it was the case for switch trials. Although this does not allow evaluating the validity of options 2, 3, and 4, it helps to eliminate option 1, according to which RTs for emotional trials were slower relative to neutral trials due to the difficulty to disengage from the emotion task of the previous trial. Given that RTs for repeat trials were faster than for switch trials, it can be assumed that participants were (at least to some degree) able to reconfigure the new non-emotional task set. Thus, it seems unlikely that the difficulty to disengage from the previously performed emotion task was responsible for the similar pattern of longer RTs for emotional relative to neutral trials in the age task for both switch and repeat trials. Instead, it appears that emotion cues were still effective in the non-emotional task even on repeat trials. To further explore the remaining explanations for a slowdown in switches to emotional relative to neutral items in the non-emotional task, Experiment 2 was conducted.In Experiment 1, participants had to switch constantly between an emotional and a non-emotional task. As emotion was relevant for one of the competing tasks, it is possible that it affected performance in the non-emotional task due to its relevance for the task block . In other words, trial-irrelevant emotion affected performance as it was block-relevant. Alternatively, emotion might have been processed in an automatic way due to the salience of emotion  when the face was presented above (or below) the horizontal line and to the gender of the face by pressing one of two buttons  when the face was presented below (or above) the horizontal line.t-tests with a Bonferroni adjustment to the 5% alpha level were performed to follow up significant main effects and interactions. Due to significant differences in the two age groups’ verbal knowledge and processing speed, all analyses were repeated with NART verbal IQ and Digit Symbol as centred covariates as in Experiment 1. The results with age as a factor reported here were qualitatively the same and significant in the analysis including covariates unless stated otherwise. It should also be noted that the order of experiments interacted with task and with target emotion in the analysis of accuracy scores in Experiment 1 as well as with task in the analysis of RTs in Experiment 2 . Due to these interactions, all analyses reported in this paper were repeated with order of experiments as a covariate. The results reported here were qualitatively the same and significant in the analysis including order of experiments as a covariate.Responses and RTs were recorded for each trial and the same exclusion criteria were applied as in Experiment 1, resulting in an exclusion of 2.28% of data points in the younger age group and 2.65% in the older age group. Accuracy and median RTs for correct trials were calculated for each condition. As in Experiment 1, only analyses for switch trials are reported below. Analyses for repeat trials are reported in the Supplemental Material. As in Experiment 1, accuracy and RTs were analysed separately for the happy vs. neutral and the angry vs. neutral blocks. Accuracy and RTs were analysed by 2 × 2 × 2 × 2 mixed factors ANOVA including the within-subjects factors task (age vs. gender), target emotion  and previous emotion  as well as the between-subjects factor age (younger vs. older). Post-hoc Accuracy. Accuracy scores for performance in the happy vs. neutral task block in younger and older adults are presented in F = 4.67, MSE = .003, p = .035, partial 2η = .07, as accuracy was higher for happy  than for neutral trials . This main effect was qualified by a target emotion × age interaction, F = 6.51, MSE = .002, p = .013, partial 2η = .10. Follow-up t-tests revealed that in younger adults, accuracy was higher for trials with happy faces  than with neutral faces , t(28) = 2.64, p = .013, whereas in older adults, there was no difference in accuracy for trials with happy and neutral faces (p = .699). There was also a main effect of age, F = 12.82, MSE = .004, p = .001, partial 2η = .18, as accuracy was higher in older adults  than in younger adults . No further significant main effects or interactions were observed for accuracy scores for happy vs. neutral faces.Reaction times. RTs for performance in the happy vs. neutral task block in younger and older adults are presented in F = 4.49, MSE = 38279, p = .038, partial 2η = .07, as RTs were longer in the age task  than in the gender task . There was also a main effect of age, F = 13.42, MSE = 581514, p = .001, partial 2η = .19, driven by slower RTs in older  than in younger adults . However, when processing speed was included as a covariate, this effect became non-significant (p = .199). There were no further significant main effects or interactions for RTs for happy vs. neutral faces.Accuracy. Accuracy scores for performance in the angry vs. neutral task block in younger and older adults are presented in F = 6.72, MSE = .017, p = .012, partial 2η = .10, as older adults were more accurate  than younger adults . No further significant main effects or interactions were observed for accuracy for angry vs. neutral faces.Reaction times. RTs for performance in the angry vs. neutral task block in younger and older adults are presented in F = 3.48, MSE = 23123, p = .067, partial 2η = .06. Separate analyses for the two tasks revealed a trend for an age × target emotion interaction in the age task, F = 3.34, MSE = 28387, p = .073, partial 2η = .06, but not in the gender task (p = .439). In the age task, the interaction was driven by older adults’ slower RTs for angry  rather than neutral targets , t(31) = 2.56, p = .016, whereas the difference was non-significant in younger adults (p = .016). There was also a main effect of age, F = 9.14, MSE = 594156, p = .004, partial 2η = .13, driven by slower RTs in older  than in younger adults . However, when processing speed was included as a covariate, the effect became non-significant (p = .477). No further significant main effects or interactions were observed for RTs for angry vs. neutral faces.The aim of Experiment 2 was to assess switching between two non-emotional tasks in the presence of block-wise irrelevant emotion in two age groups. The results revealed that there was no overall effect of block-wise irrelevant emotion on performance in either of the two non-emotional tasks. This can help to interpret findings from Experiment 1, in which both age groups showed a slowdown for emotional relative to neutral items when switching away from an emotional to a non-emotional task. Given that no such effect of emotion was observed when participants had to switch between two non-emotional tasks in Experiment 2, the data suggest that emotion has to be task-relevant for one of the competing tasks in order to affect performance in the non-emotional task. This is in accordance with research showing that consistently irrelevant emotional distractors do not interfere with working memory performance to a greater extent than neutral distractors  and does not extend to cases in which emotion is consistently irrelevant (i.e. block-irrelevant emotion). This suggests that emotional cues can delay the reconfiguration of a non-emotional task set through the reactivation of the competing emotional task set.The present study is the first to show that task-irrelevant emotion can contribute to slower switches compared to neutral task-irrelevant material, but only when it is relevant for one of the competing tasks. Importantly, the impairment emerged for the comparison between emotional and neutral items in the non-emotional task, despite the fact that switches to the non-emotional task set were faster than to the emotional task set. This pattern of results might help to explain the inconsistent findings of previous studies, which have exclusively focused on switches between emotional and non-emotional task sets without taking into account the valence of individual items in their analyses . The former would speak in favour of a short-term effect, whereas the latter would suggest that there is sustained reactivation of the competing task set. Furthermore, future studies could investigate whether higher flexibility of emotional relative to non-emotional task sets as reported by Schuch et al.  is assocThis research was the first to examine the effects of emotion on task switching in ageing and no age-related differences were observed. Contrary to the hypotheses, happy faces did not facilitate engagement with or the disengagement from a task in older relative to younger adults. Moreover, there was no evidence of age-related impairments in performance, which is in line with research showing that local task switching is largely unaffected in ageing (for a meta-analysis, see Wasylyshyn et al., Older adults’ very high accuracy rates in the two experiments suggest that they were not functioning at the limit of their cognitive capacity and that additional cognitive resources were likely available. Despite the availability of cognitive resources, emotion did not affect older and younger adults differently. This does not directly support the SST (Carstensen, To conclude, the present study was the first to investigate the effects of emotion on task switching in younger and older adults. The study revealed that task-irrelevant emotion impaired switching in both age groups, but only when emotion was relevant for one of the competing tasks. The results suggest that the impairing effect of task-irrelevant emotion in a non-emotional task was due to the reactivation of the competing emotional task. They also suggest that local task switching and the ability to shield task-switching performance from block-wise irrelevant emotion are preserved in ageing.


Prochlorococcus, and high microbial community alpha diversity. Seawater from the less protected system of Los Canarreos, Cuba had elevated microbial community beta‐diversity whereas waters from the most impacted nearshore reefs in the Florida Keys contained high organic carbon and nitrogen concentrations and potential microbial functions characteristic of microbialized reefs. Each reef system had distinct microbial signatures and within this context, we propose that the protection and offshore nature of Jardines de la Reina may preserve the oligotrophic paradigm and the metabolic dependence of the community on primary production by picocyanobacteria.There are a few baseline reef‐systems available for understanding the microbiology of healthy coral reefs and their surrounding seawater. Here, we examined the seawater microbial ecology of 25 Northern Caribbean reefs varying in human impact and protection in Cuba and the Florida Keys, USA, by measuring nutrient concentrations, microbial abundances, and respiration rates as well as sequencing bacterial and archaeal amplicons and community functional genes. Overall, seawater microbial composition and biogeochemistry were influenced by reef location and hydrogeography. Seawater from the highly protected ‘crown jewel’ offshore reefs in Jardines de la Reina, Cuba had low concentrations of nutrients and organic carbon, abundant  The collective impacts of climate change, overfishing, and coastal development have caused shifts in functioning and energy transfer in coral reef ecosystems and these changes have been documented at the level of macro‐organisms  is a protected reef‐system in Cuba that may provide useful insights into the microbial ecology of relatively healthy Caribbean coral reefs. The reefs of JR were historically protected from human activities due to their remote nature and are now further protected because maritime traffic, fishing, and recreational diving and tourism are limited within the boundary of a Marine National Park (est. in 1996) that encapsulates most of the archipelago. Additionally, Cuba does not currently have large‐scale industrialized agriculture or extensive development along most of its coastline , Cuba lies ~230 km to the west of JR. CAN encompasses three important keys that have less stringent protection compared to JR: Cayo Largo is an Ecological Reserve and the Rosario and Cantiles Keys are Faunal Refuges. Due to increased accessibility, reefs within CAN are more impacted by humans through subsistence and illegal fishing, tourism, and the diving industry compared to the remote and protected reefs within JR. Fishing has resulted in overexploitation of important finfish and invertebrates in most of Cuba's waters with the exception of the central area within JR Baisre, . The proet al., et al., et al., et al., et al., et al., The reef‐system of the Florida Keys (FK) is located in close proximity to JR and CAN, but has experienced more anthropogenic impacts relative to the Cuban reef‐systems. Reefs within FK (spanning ~570 km) are situated close to developed land within FK and South Florida and development activities have influenced the water quality in these waters  gradients across each reef‐system. At each reef, divers surveyed the reef composition and then sampled surface (<1 m) and reef‐depth seawater (within 1 m of the reef).Water sampling and reef surveys were conducted at reef locations across the three reef‐systems: Jardines de la Reina, Cuba , Los Canarreos, Cuba  and Florida Keys, USA  Fig. . The samF = 4.89, p = 0.025] in JR and FK compared to CAN and was similar between JR and FK  = 5.82, p = 0.014) in CAN compared to JR and similar to the algal cover in FK , F,14 = 4.8F,14 = 4.8F,14 = 4.8F,14 = 4.8F,14 = 4.8F,14 = 4.8F = 78.19, p < 0.05), total organic nitrogen (TON)  = 53.44, p < 0.05), and dissolved inorganic nitrogen   = 4.21, p < 0.05) were significantly higher within the JR gulf as well as nearshore FK compared to the offshore JR reefs  = 7.38, p < 0.05) and nitrate  = 3.39, p < 0.05) were significantly higher in CAN and FK compared to JR  = 14.11, p < 0.05) were significantly higher  in FK nearshore seawater compared to seawater from other reefs  in JR, 5.2 m (1–14 m) in CAN, and 3.8 m (1–6 m) in FK  within JR offshore reef‐depth and surface seawater compared to JR gulf reef‐depth seawater, CAN surface and reef‐depth seawater, and nearshore FK seawater, but not significantly different from abundances in FK offshore reef‐depth and surface seawater  in offshore JR (surface and reef‐depth) as well as JR gulf surface seawater compared to JR gulf reef‐depth seawater   = 15.98, p = 3.38E‐5; Tukey multiple comparisons of means, p < 0.05)  = 4.032, p = 0.030) in FK (16%) compared to JR (5%)   Fig.  but chanχ2 = 21.41, df = 4, p = 2.6E‐4) higher alpha diversity compared to CAN , as indicated by the number of minimum entropy decomposed (MED) nodes of bacterial and archaeal SSU rRNA gene amplicons [median of 333.5 (range: 185–359) MED nodes] Fig. . Offshory‐axis (NMDS2) and separated from JR and FK microbial communities  test on the Bray–Curtis dissimilarity index of reef seawater bacterial and archaeal SSU rRNA gene amplicons grouped into MED nodes indicated that region , subregion, reef location, and sampling depth influenced the composition of reef seawater communities, with reef location contributing the most to variation in community dissimilarity Table . Additioies Fig. A. Surpriies Fig. B.R2 = 0.11, p = 0.040) and nitrite  and silicate  concentrations were significantly correlated with the ordination of microbial communities in the NMDS.Within the NMDS, microbial communities sampled within JR ordinated together by location; microbial compositions from reefs JR 1 and 2 were more similar to each other than to the other communities sampled from sites located within the JR gulf Figs . CompareProchlorococcus sequences were 99.2%–99.6% identical to MIT9313, a low‐light ecotype of Prochlorococcus, and this was the only ecotype identified within the amplicon‐based survey. Bacteroidetes was most represented in FK reef seawater, with an average relative abundance of 16.8 ± 8.3%, and less abundant in JR (12.7 ± 5.7%) and CAN (8.6 ± 6.4%)  Fig. . Verruco4%) Fig. . Euryarc4%) Fig. .p‐adjusted <0.05, Benjamini–Hochberg correction for multiple testing)   Fig. . The 34 ng) Fig. . Cyanoba JR Fig. .Metagenomic sequencing and analysis of the whole microbial community  in reef‐depth seawater from JR and FK resulted in 163 significantly different functional genes Fig. . These g2 consumed L−1 day−1  had positive community respiration rates that ranged from 0.3 to 16.7 μmol of Oy−1 Fig. . The hig 24 Fig. . These v 23 Fig. .R2 = 0.33, p = 0.010, Fig. R2 = −0.02, p = 0.49, Fig. Prochlorococcus and Synechococcus cell abundances) and coral cover across the reef‐systems  and the hydrodynamic regime created by the complex array of cays and channels. The variability in FK may be mostly impacted by increased concentrations of organic and inorganic macronutrients, higher productivity, and/or terrestrial sources of sediments from developed land. The nearshore reefs in FK exhibited a few signs of microbialization, but this process was not as evident on the FK offshore reefs surveyed in this study. The microbial regimes observed across the reef‐systems appear to be determined by the intersection of local anthropogenic impacts as well as oceanographic processes.et al., et al., A majority of the macronutrient concentrations were low or barely detectable in JR and were similar to concentrations measured in other oligotrophic systems including the Sargasso Sea, the North Subtropical Pacific Gyre, the Red Sea, and other reefs in the Caribbean and Pacific, suggesting rapid turnover of these nutrient pools by microorganisms  have fewer data points compared to the other categories due to sampling limitations and that care needs to be taken when interpreting the statistical differences between subregions. That being said, measurements from these locations were similar within each subregion and are likely representative of the environmental conditions. However, future studies would benefit from collecting samples at a higher spatial or temporal resolution in order to unravel the process of microbialization on reefs. Additionally, there are other examples of reefs that are subjected to high loads of organic and inorganic nutrients as well as pollution, like Vardero reef in Colombia , enhanced community respiration, higher concentrations of inorganic and organic macronutrients, and shifts from coral to algal dominance on the reefs along the gradient of human impact. However, we did not observe significant changes in most of these parameters. Overall, hydrogeography and subregion were the largest influences contributing to reef similarity. Offshore reefs in both JR and FK were oligotrophic, had high abundances of picocyanobacteria, high microbial alpha diversity, and more constrained microbial beta diversity, although the magnitudes of the contrasts were different within each reef‐system. The only reefs that supported some of the predictions of the DDAM model were the nearshore reefs in FK. These two nearshore reefs in FK had significantly higher concentrations of organic macronutrients, very low abundances of et al., et al., et al., et al., et al., et al., et al., et al., Coral and algal coverage varied locally  and CAN/FK (April/May 2015) during the Caribbean dry season. Due to sampling limitations within JR, we were only able to survey and collect samples from two reef sites in the JR offshore subregion and four reefs within the JR gulf subregion. Scuba divers conducted reef surveys at all JR and five CAN reef locations to assess the percent cover of different reef organisms and substrates (Supporting Information Methods). Coverage of a wider diversity of biotypes was assessed on FK reefs using the same methods but with a different research team (Supporting Information Methods).At each reef location, hydrographic profiles of the water column were obtained and seawater from surface (<1 m depth) and reef‐depth (~ 1 m above reef) was collected for a variety of different analyses  were analysed using a continuous segmented flow system  concentrations for each sample were calculated by subtracting the NO2− concentration from the NO2− + NO3− concentration. TON concentrations were calculated by subtracting the sum of the inorganic nitrogen species concentrations (NO2− + NO3− and NH4+) from the TN concentrations for each sample.Total non‐purgeable organic carbon (TOC) and total nitrogen  concentrations were measured using a Shimadzu TOC‐Vet al., et al., et al. et al. (2015). The converged initial pigment ratio matrix was used because the phytoplankton assemblages in these samples were not determined with a microscope.Pigment analysis was conducted using high‐performance liquid chromatography , picoeukaryotes, and unpigmented cells (proxy for heterotrophic bacteria and archaea)  and a laser excitation wavelength of 488 nm. Unstained and stained  subsamples were analysed to estimate the abundance of picocyanobacteria . Briefly, V4 hypervariable regions of the 16S rRNA gene were amplified using the Fluidigm® microfluidics quantitative PCR platform and prepared for 2 × 250 bp paired‐end Illumina MiSeq sequencing (Supporting Information Methods). The Fluidgim V4 primer set 515F‐Y: 5′‐GTGYCAGCMGCCGCGGTAA (Parada n = 4) and FK (n = 5) using a modified cetyl‐trimethylammonium bromide—phenol:chloroform:isoamyl alcohol extraction (Supporting Information Methods). No samples from CAN were chosen because the DNA extracted from CAN yielded 16S rDNA sequences that were highly variable between sites. Genomic libraries were prepared using the Hyper Library construction kit  and sequenced at the W. M. Keck Center using 2 × 150 bp paired‐end Illumina HiSeq 4000 sequencing. Fastq files were demultiplexed, and library adaptors were trimmed from the 3′ ends of the reads (Supporting Information Methods). BBTools . Raw files can also be found in the NCBI SRA under bioProject PRJNA517146.Total genomic DNA was extracted from nine reef‐depth seawater samples from JR (in situ temperatures as possible in the remotefield work locations. The incubation temperatures were 26.6 ± 0.5 °C (JR), 25.0 ± 0.2 °C (CAN), and 26.5 ± 0.5 °C (FK). Initial oxygen measurements were taken every hour (h) for the first 4 h and then approximately every 4 h after that. Ten oxygen measurement readings were taken for each incubation bottle at each timepoint. Prior to calculating respiration rates, oxygen data were quality controlled to remove any individual readings greater than one standard deviation from the mean value at a given timepoint. Linear fitting to the time course oxygen data was done in matlab  using the ‘polyfit’ function.Seawater samples (4–6 per site) were collected from reef‐depth using a submersible groundwater pump and kept in the dark. Respiration rate incubations (~24 h) were conducted with ~5× replication with seawater collected from 19 reefs in acid‐cleaned 60 ml glass Biological Oxygen Demand bottles with glass stoppers. Incubations using seawater from sites 2, 12, and 24 were conducted twice. Acid‐washed bottles were equipped with oxygen optode ‘dot’ sensors (PreSens) affixed to the glass using food‐grade silicone adhesive. In the laboratory, the perimeter of the glass stopper was filled with water using a squeeze bottle to prevent gas exchange between the water in the bottle and the atmosphere. The concentration of oxygen in the bottles was measured over time using a handheld Fibox 4 (PreSens). Incubations were conducted in a static water bath in a darkened cooler located inside a darkened room at as close to p value <0.05). For data that were not normally distributed, Kruskal–Wallis rank‐sum tests, followed by either Dunn's or Conover–Iman tests using Bonferroni corrections were used to assess significant differences . We recognize that JR offshore and JR gulf subregions have fewer data points compared to the other categories, but measurements from these locations were similar within each subregion and are likely representative of the environmental conditions. We performed linear regressions using ggplot2, geom_smooth, and the method = ‘lm’  and tested for significance across different qualitative  and quantitative (e.g. TOC concentrations) parameters. To compare differences in reef cover, macronutrient concentrations, and cell abundances, data were inspected for normality using Shapiro–Wilk normality tests. Normally distributed data were tested using ANOVA followed by post hoc Tukey multiple comparisons of means tests using a 95% family‐wise confidence level . Reef‐depth seawater collected from site FK 23 was omitted from the analysis because reef‐depth TOC was not collected from this site. Nested PERMANOVA (Adonis) tests, also available within ‘vegan’, were conducted using the Bray–Curtis dissimilarity index (999 permutations) (p < 0.05). To conduct this test, the factors of reef‐depth, reef location, and subregion were nested within the region (reef‐system). The package ‘phyloseq’ (McMurdie and Holmes, et al., Amplicon‐based microbial community statistical analyses were completed using R studio (R Core Development Team, Appendix S1: Supporting InformationClick here for additional data file.