Boys engaged in more light PA (LPA) when on foot and in school, compared with girls (p = 0.003). KS3 children engaged in significantly more moderate PA (MPA) at school than KS2 children (p = 0.006). Focus groups revealed fun, enjoyment, friends, and family to be associated with PA, and technology, costs, and weather to be barriers to PA. This mixed methodological study highlights differences in the PA patterns and perceptions of children according to age and gender. Future studies should utilize a multi-method approach to gain a greater insight into children’s PA patterns and inform future health policies that differentiate among a range of demographic groups of children.The school environment is ideally placed to facilitate physical activity (PA) with numerous windows of opportunity from break and lunch times, to lesson times and extracurricular clubs. However, little is known about how children interact with the school environment to engage in PA and the other locations they visit daily, including time spent outside of the school environment i.e., evening and weekend locations. Moreover, there has been little research incorporating a mixed-methods approach that captures children’s voices alongside objectively tracking children’s PA patterns. The aim of this study was to explore children’s PA behaviours according to different locations. Sixty children —35 key stage 2 (aged 9–11) and 25 key stage 3 (aged 11–13)—wore an integrated global positioning systems (GPS) and heart rate (HR) monitor over four consecutive days. A subsample of children (n = 32) were invited to take part in one of six focus groups to further explore PA behaviours and identify barriers and facilitators to PA. Children also completed a PA diary. The KS2 children spent significantly more time outdoors than KS3 children ( Regular physical activity (PA) is associated with wide-ranging health benefits ,2, and tWith the current generation of children reporting lower levels of PA, concerns are being raised on the impact this will have on their growth, lifestyle, and physical and mental development . ResearcReducing this specific decline in age-related PA behaviours has been identified as an important health priority, as inactivity tends to track into adulthood . ResearcPA behaviour can be influenced by the surrounding environment, and adapting this environment could result in improved health-related behaviours . Time spGlobal positioning systems (GPS) is a tool that is used to measure location and is increasingly popular in health research, with many studies combining GPS with other measures such as heart rate (HR) monitors and accelerometers ,25,26,27Identifying barriers and facilitators to PA is key in order to understand children’s PA, but it is also important to inform future interventions aiming to increase PA in this group . PreviouCurrent literature indicates that there are a multitude of factors that affect levels of PA. However, there appears to be a lack of studies that have applied a mixed-methods approach to objectively measure children’s PA behaviours according to location, specifically using GPS, and to establish the reasons behind their PA participation . This stSchool children (9–13 years) from one middle school in the West Midlands, United Kingdom were invited to participate in this study . FollowDetails of how to use and wear GPS and HR equipment were explained to children, which included specifics on wearing the heart rate monitor chest strap, and how to operate and recharge the GPS watch. Instructions were also provided on when not to wear the equipment . Children wore GPS watches  and synchronised HR monitors daily over a four-day period, which has been supported by previous literature . In the Children also completed a modified version of an original ‘PA Diary’ . The PA A random stratified sample (according to gender and key stage) of 32 children who provided a minimum of one hour of combined GPS and HR data over the four days were invited to take part in one of six focus groups. Focus groups have been identified as being a popular method of qualitative data collection with children . Focus gThe social–ecological model was used as a framework to underpin questions/topics for discussion in the focus groups . Focus gGPS and HR data were downloaded via the Garmin Connect website  which individually stored children’s data. GPS Utility software was used to extract the raw GPS and HR-matched data. The GPS location points were manually entered into Google Maps to establish location. An index of locations was categorised as follows based on the visited locations in which children spent time: ‘Home’, ‘School’, ‘Other indoor location’, ‘Motorised transport’, ‘On foot’, ‘Outdoors’, and ‘Outside’ (combining ‘on foot’ and ‘outdoors’). Then, data were cleaned by analysing each child’s profile of data and removing time fields where GPS and HR data were missing. Following the cleaning of the GPS and HR data, 60 out of the original 119 children’s profiles met the one-hour minimum data inclusion criteria, and were eligible for analysis , and 80 children returned PA diaries , which was inclusive of the 60 meeting GPS and HR inclusion criteria. PA diaries were useful in providing greater context to the PA data from the HR monitors and location data from the GPS monitors. A total of 32 children who provided a minimum of one hour combined GPS and HR data were invited to attend one of six focus groups .PA intensity thresholds were established by calculating each child’s heart-rate reserve. The heart-rate reserve is the difference between a children’s resting and maximum HR  recordedd = 0.45, p = 0.09). Univariate ANOVA tests were used to explore data for key stage and gender interactions between MVPA and time spent in different locations. Furthermore, Pearson’s product moment correlations were used to establish relationships between daily MVPA and location. Independent samples t-tests were used to explore gender and key stage differences, with effect sizes calculated for statistically significant findings. A two-tailed significance value of p < 0.05 was considered as being statistically significant in all statistical analyses. Descriptive statistics were provided for the analyses, including mean MVPA, sedentary behaviour (SB), light PA (LPA), moderate PA (MPA), and vigorous PA (VPA) according to different locations, and reports of meeting recommended daily PA guidelines. SPSS version 25  was used for all statistical analyses.A range of statistical procedures were carried out to explore GPS and HR data using 95% confidence intervals throughout. Results from a Priori sample size calculation exploring MVPA exceeded the sample, which limited statistically significant findings , and children meeting PA guidelines (n = 31) are provided in p = 0.016; KS3 boys = 260.9 min, KS3 girls = 153 min; [CI = 22.4:193.5], large effect size: d = 1.04). Therefore, the following results are based on descriptives.Analyses of children’s’ PA behaviours revealed one statistically significant result, where KS3 boys were more sedentary than KS3 girls  longer in MVPA than KS2 children. Additionally, girls demonstrated 38.4 min more mean daily MVPA (7.1%) than boys. After investigating gender differences according to key stages, MVPA was similar between KS2 boys and girls; however, KS3 girls engaged in more MVPA than KS3 boys .The percentage of time spent in SB was similar across genders and key stages, and LPA was similar between key stages . However, boys spent a higher proportion of their time in LPA (39.6%) compared to girls (33.6%). Further gender differences revealed girls to engage in more MPA (17.9%) than boys (11.7%). MPA behaviour between key stages showed older children to engage in more MPA time than younger children . VPA was similar across both genders and key stages .When exploring data for gender differences according to each key stage, KS2 girls were more sedentary than KS2 boys . KS2 boys engaged in a higher percentage of LPA compared with KS2 girls ; however, this pattern was reversed at KS3: KS3 girls engaged in more LPA than KS3 boys . MPA was similar between KS2 boys and girls; however, KS3 girls engaged in more MPA than KS3 boys . VPA was similar between boys and girls in KS2, and boys and girls in KS3 . r = 0.354, p < 0.01; weekday 2: r = 0.439, p < 0.01; weekend day 1: r = 0.550, p < 0.01). Weekend day 2 (Sunday) was the only day to report a stronger relationship between device wear time and MVPA ; however, this was due to a reduced number of children engaging in MVPA (n = 4) on this specific day, and therefore cannot be used as a generalisable finding.Investigation into any potential impact device wear time had on MVPA was carried out. Results from this study revealed a weak relationship when exploring weekday 1 and 2 (Thursday and Friday), and weekend day 1 (Saturday) total wear time with MVPA . This indicates that children who wore devices for greater durations did not necessarily report greater amounts of MVPA. Additionally, similar findings were reported when exploring wear time and maximum HR . This suggests that a higher maximum HR was not associated with children who reported greater device wear time.To further support this, mean wear time (over the four-day period) and MVPA time reported weak correlations , boys = 8.3% (±12.4); (CI = −17.26: 1.43), medium effect size: d = 0.61), and boys also spent significantly more time in school than girls , girls = 23.4 (±26.4); (CI = 5.29:30.83), medium effect size: d = 0.73). KS3 children spent significantly more time at home compared with their KS2 counterparts , KS2 = 24.8 (±21.2); (CI = −29.38:4.12), medium effect size: d = 0.68); however, those in KS2 spent significantly more time in motorised transport than KS3 children , KS3 = 8.4 (±10); (CI = 0.16:16.38), medium effect size: d = 0.56). KS2 children also spent significantly more time outdoors than KS3 children , KS3 = 10 (±13.1); (CI = 2.35: 15.84), medium effect size: d = 0.71), and a similar statistically significant finding was found when analysing time spent ‘outside’, with KS2 children spending more time ‘outside’ than their KS3 counterparts , KS3 = 12.4 (±8.8); (CI = 1:13.14), medium effect size: d = 0.63). When investigating gender within each key stage, KS2 girls spent significantly more time in motorised transport than KS2 boys , KS2 boys = 11.3 (±13.9); (CI = −24.54: −0.14), medium effect size: d = 0.68). This trend was repeated by KS3 children, as KS3 girls also engaged in significantly more time in motorised transport than KS3 boys , KS3 boys = 1.7 (±2.4); (CI = −17.99: −2.75), large effect size: d = 1.32). KS2 boys also spent significantly more time at school than KS2 girls , KS2 girls = 22.5 (± 23.8); (CI = 1.1:34.12), medium effect size: d = 0.74).When exploring percentage of time spent according to location, girls spent significantly more time in motorised transport than boys , medium effect size: d = 0.64) (see p = 0.05) was revealed in the school environment with KS3 children engaging in more MPA than KS2 children .When investigating HR according to location, a paired-samples t-test revealed no statistically significant differences between MVPA at school and outside of school. Children spent 9.7% of their time out of school in MVPA, which was 2.5% more than that in school. An independent t-test revealed significant gender differences, with boys engaging in significantly more LPA in motorised transport compared with girls (.64) see . A findip = 0.01; KS3 boys = 50.8% ± 27.7, KS3 girls = 24.2% ± 16.2; (CI = 7.16:46.01), large effect size: d = 1.17, see p = 0.014; KS3 boys = 73% ± 29.4, KS3 girls = 36.5% ± 34.4; (CI = 8.27:64.68), large effect size: d = 1.14, see p = 0.05) was KS2 boys engaging in more VPA than KS2 girls when at school .Gender by key stage results showed KS3 boys to engage in significantly more LPA than KS3 girls when in motorised transport  compared with girls and KS2 children. This would support safety concerns and environmental patterns previously reported in the literature suggesting that boys and older children have greater freedom than girls and younger children  you having their social time with their friends and going out.” This also suggests that KS3 children may have additional family responsibilities that mean they are less likely to engage in outdoor PA and may be confined to the home.While older children in the current study appear to have more freedom, those in key stage 3 spent more time within the home and less time outdoors in comparison with key stage 2 children. Access to parks and open green spaces have been identified as being destinations for PA; however, studies show that these locations have been underused ,62,63,64“People might not like what the variety is.” Further investigation found that PA clubs that were currently being provided were dictated by older children’s preferences; therefore, there was a need to consult younger children in KS2.When investigating the school environment, boys spent significantly more time in school than girls; however, girls engaged in greater school MVPA than boys . KS3 children spent more of their school time in MVPA (25.3%) than KS2 children (15.1%), which is in contrast to previous literature ,49,50. T“It’s a chance to meet up with friends.” Whereas a KS3 child stated: “… because that’s where the club [is], it’s what I do and I’m a part of that team.” This indicates how the outside environment is contextualised according to two different purposes, with a KS3 perspective referring to a PA club/team, and KS2 referring to more social aspects. Focus group data showed key stage differences relating to the use of the outside environment, as when describing outside locations, a KS2 child stated: Motorised transport was further investigated as this revealed significant gender differences, with boys engaging in significantly more LPA compared with girls , and there were also elevated levels of MVPA see . These fPA diaries provided an insight and a greater context into the different types of PA behaviours in which children engaged. Eighty out of the original 119 children returned PA diaries . These were of interest when investigating times where children indicated they were physically working hard, but didn’t wear the HR equipment, or no connectivity was reported due to movement of the HR sensors. The PA diaries in these instances meant that the researcher was provided with the self-reported PA of the children. Previous literature supports the use of PA diaries alongside other measures as it enables for data to be cross-referenced or collected when another source of data collection fails . Example“They might not like it, so they might not want to do it” and “… if they don’t have like the right equipment to do it.” Despite factors such as fun, enjoyment, and wanting to keep fit and healthy as being identified as motives for PA participation on an individual level, children identified time, poor diet/food intake, and the cost of equipment as restrictions to PA participation [The use of focus groups to explore PA behaviours in this study supports previous literature ,67. Chilcipation ,67.“It’s a chance to meet up with friends.” This is in accordance with previous literature, which indicates that the social aspect of PA induces peer support [“I do runs with my dad, I go on my bike.” Therefore, within this context, both family and friends were found to promote positive PA behaviours, and this supports previously published literature [The social environment surrounding the children was also identified as being a facilitator to taking part in PA. PA was often associated with a chance to ‘catch up’ or socialise with friends in a fun environment. Children stated:  support . This waterature ,69. “I go up to the college car park because it’s big and loads of my friends just play there.” Themes from previously published literature were evidenced in this study with the proximity and location of environments highlighted, describing that they visited such areas because they were the closest for friends to meet up, and also that there were no costs involved [The physical environment has been described as having a direct association with children’s participation in PA, and this has been supported by previous research . In the involved .“If it’s in the winter, some people don’t have motivation because it’s quite cold and dark, and if it’s muddy.” This is a common theme within the literature, as many studies have reported children to be more active in warmer weather, which also allows for greater outdoor PA [“When you’ve got a phone or like a console, you don’t think as much about getting active, you just want to play on them.” Children identified a need to limit the amount of time spent on technological devices: “You could reduce your time down on a tablet or computer so that you’re not always on it.” This suggests that children are aware of the negative impact that technology has on PA behaviours. Other responses from children included the weather being a factor that either supported or limited PA participation. Some children described poor and ‘muddy’ conditions as a barrier to participating in regular PA, which prompted a greater demand for indoor PA clubs: tdoor PA ,71. Howetdoor PA ,69, chilPrior to data collection, children received a tutorial on how to use all the relevant PA measurement equipment, and were also provided with guidance on how to complete the PA diaries. Despite this, there were still instances where HR data was not collected. This may have been due to the HR monitors moving/slipping whilst being worn, consequently meaning that the HR sensors would not detect the children’s heartbeat. HR monitors may have been worn correctly, but the movement/slipping of the sensors is an acknowledged limitation of their use. To overcome this, the researcher provided additional tutorials on how to tighten HR straps using safety pins, or changed the HR strap for a smaller size. In addition to this, equipment was removed for some activities due to the water-based activities or for health and safety reasons, which previous research has also reported . TherefoAs indicated by previous literature, GPS data loss may be attributable to problems with the GPS device  and/or children’ handling i.e., forgetting to wear or switch on the device . Within PA diaries are subject to the children’s perception of intensity as well as the accuracy of their memory when completing the diary. Therefore, from the diaries that were collected, there may have been some underestimation or overestimation regarding the duration and intensity of PA participation by the children. However, the objective PA measures used limited the impact of the inaccurate reporting of PA. Children’s abilities to accurately self-report PA has previously been outlined in the literature .Future research exploring PA behaviours should adopt a mixed-methods approach to enhance the context and meaning of the gathered data. Using HR, GPS, PA diaries, or focus groups in isolation would have limited the ‘richness’ of this study. A deeper understanding is provided when combining these measures. In addition to this, the nature of this study was cross-sectional, and details of PA behaviours were only representative of one particular time period within the school academic year. Future research may look to explore PA behaviours longitudinally across the school year.The mixed-methods approach to PA data collection this study implemented provides an insightful understanding of children’s PA behaviours by exploring children’s PA locations, PA according to gender, school key stage, and also by exploring barriers and facilitators to children’s PA behaviour. Fifty-two percent of children  met the current UK recommended PA guidelines  of at leBoys and KS3 spend greater time on foot, which includes time on the street and commuting to/from school, with girls in both KS2 and KS3 spending significantly more time in motorised transport than their male counterparts. This supports previously discussed literature regarding girls and younger children ,20 havinThis study reveals that in school versus out of school MVPA levels were similar; however, the school environment showed KS3 children to engage in significantly more MPA than KS2 children, and KS2 boys engaged in more VPA than KS2 girls. These differences could be explained due to school PA clubs being shaped according to KS3 children’s preferences. Therefore, there may be a greater need for schools to acknowledge younger children’s PA preferences, and schools should stage PA clubs that encompass the wider range of abilities. The gender differences in activity at home reveals KS3 boys to engage in significantly more SB than KS3 girls. These behaviours could be partly explained by qualitative data, which highlighted the use of technology by KS3 boys. This highlights how children are aware of the SB associated with technology, particularly in the home environment.There has been limited research that adopts a mixed-method design incorporating the use of GPS technology, and these measures continue to show promise for exploring PA according to location. Focus groups and PA diaries proved to be important in establishing greater context for PA levels, particularly exploring children’s voice in a deductive approach to follow quantitative data collection. Children took part in PA for enjoyment, socialising with friends/family and also because many of the PA promoting environments are local to them. However, KS2 children reported a greater need for their voices to be heard when deciding on which activities to be staged as part of the school’s extracurricular programme. Barriers such as technology, time, cost of equipment, and poor weather conditions were identified as limiting factors for children’s engagement in PA, which indicates that there is a need to develop programmes that encompass children’s preferences, and consider weather conditions to promote a more encouraging and appealing PA environment. The study extended knowledge of how children’s PA behaviours according to gender and age can be explored using a mixed-methods research design in a school-based sample.


We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort  of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort . These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package “FunSPU”.Despite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on the ENCODE, Epigenomics Roadmap and GTEx projects, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations  In recent years, large-scale whole-genome sequencing (WGS) data have been generated, such as those in the UK10K project and the ongoing NIH Trans-Omics for Precision Medicine (TOPMed) WGS program, providing unprecedented opportunities to investigate low-frequency variants and rare variants in association with complex diseases and traits. However, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on the ENCODE, Epigenomics Roadmap and GTEx projects, can be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. To this end, we have proposed a versatile and adaptive association test, FunSPU, to exploit multiple sources of biological knowledge in the analysis of WGS data. We illustrate our proposed test using the TWINSUK cohort of UK10K WGS data based on six functional annotations. We identified genome-wide significant genetic loci associated with low-density lipoprotein, which are replicated in the UK10K ALSPAC cohort. These replicated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. Moreover, to further increase the statistical power, we propose to incorporate a trait-specific global weight for each annotation based on partitioning the heritability.To address this analytical challenge, we propose a family of versatile and powerful tests called “FunSPU” that allow for incorporating multiple ramework . The funTOMM40 and APOC4-APOC2 that are associated with LDL, which are replicated in the UK10K ALSPAC WGS cohort . These replicated LDL-associated loci were missed by existing RV association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package “FunSPU”.Using extensive simulations and application to the UK10K WGS data , we compi = 1, …, n, Y =  is the vector of a trait, and Xi = ’ is the vector of the genotype scores of k RVs, for example, from a gene or some genomic region. Here, we use additive coding for each RV; that is, Xij is the count of the minor allele at RV j for subject i. For simplicity, we ignore other covariates in our model. We consider a generalized linear model (GLM):Yi, g is the identity link g(μ) = μ and the GLM is reduced to a linear model, whereas g is the logit link Yi. For the purpose of exposition, we introduce our proposed tests in the linear model framework with a quantitative trait and no covariates, though the methods can be similarly extended to binary traits, and adjusted for covariates in the GLM and score function framework [Suppose that for subject ramework , 22, 23.H0: β = ’ = 0, that is, there is no association between any of the RVs and the trait under H0. Our proposed tests are based on the score vector U = ’ for β and its covariance matrix V,Yi’s and Xi’s, respectively.We test the null hypothesis SPU(γ) test puts more weights on the larger component of U while gradually ignoring the remaining component. In particular, we have γ→∞. The SPU(∞) is closely related to the minP test (but ignores possibly varying variances of Uj’s); the two tests often perform similarly [SPU(γ) test depends on the choice of γ while the optimal choice of γ depends on the unknown true association pattern of the RVs to be tested, it would be desirable to data-adaptively choose the value of γ. To this end, the aSPU test takes the minimum p-value of the SPU(γ) tests as its test statistic: TaSPU = minγ∈ΓpSPU’ for k RVs from a gene region or sliding window based on a linear regression model. Let 0≤wlj≤1 denote the functional score from the lth of m properly scaled annotations for the jth of k RVs. The proposed functional annotation-based SPU test isa≥1 respectively control the individual variants’ and annotations’ relative contributions to the overall test statistic; e.g., γa = 1 treats all annotations equally, while γa = ∞ only considers the most significant annotation. The inner sum of weighted Uj with power γ is the weighted SPU, and they are normalized to the power of 1/γ before being subjected to the outer sum with power γa. Since the number of the RVs in this test statistic is identical across all m annotations, it is not necessary to further normalize the weighted SPU test by the number of RVs.Our proposed test is in the data-adaptive aSPU test framework. Importantly, the proposed test is adaptive at both the annotation and SNV levels. Suppose that we have the score vector a as the powers of the weighted SPU is similar to that for γ. In general, a smaller γa, e.g., γa = 1, is more effective when there are more informative annotations, each of which is roughly equally discriminative regarding the deleteriousness of the RVs for the trait of interest. In contrast, a larger γa is preferred if there is only one or fewer informative annotations that can well distinguish causal variants from neutral ones for the trait. As γa→∞, only the most significant weighted SPU is considered.The intuition to use γa priori knowledge about choosing γ and γa, we need to conduct a grid search over a set of possible values of both γ and γa. However, searching too many values will introduce extra variability and lead to reduced power. This effect was later confirmed when we used γa∈{1,2,3,…,8,∞} and γ∈{1,2,3,…,8,∞} in some preliminary simulations. Based on the results of aSPU tests [a∈Γa = {1,2,4,8,∞} and γ∈Γ = {1,2,3,…,6} for the rest of the study. We retained γa = ∞ as an approximation to the minP test and ignored some higher values of γ since the results tend to be similar to γ = 6.We aim to perform powerful tests when there are unknown association patterns of RVs and unknown informativeness of functional annotations. In practice, since we have no PU tests  and the a, e.g., γ∈Γ = {1,2,3,…,6} and γa∈Γa = {1,2,4,8,∞}, the proposed data-adaptive FunSPU test statistic is defined asTFunSPU. Therefore, we propose using a single layer of permutations (without covariates) or residual permutations (with covariates) to obtain p-values as done in aSPU [Y to obtain a new set of Y(b) for b = 1, …, B. Then, we calculate the null score vector U(b) and the corresponding test statistic p-values p-value of the FunSPU test Given a set of γ and γ in aSPU , 22. SpeUj, for example, due to varying MAF of the RVs. On the other hand, previous research has shown that it may be beneficial to account for the heterogeneity of variances in the SPU framework [Vjj is the jth diagonal element of V = Cov(U|H0) as given before.In the FunSPU test above, we ignored the possibly different variances of the score function component ramework . TherefoU weighted by each functional annotation, i.e., l = 1, …, m. We can obtain the genuine p-value p-values, m modified aSPU tests. This aSPU_minP test is similar, but not exactly equivalent to the case of FunSPU with γa = ∞: the latter chooses the maximum p-value directly, while the aSPU_minP test calculates the empirical p-value p-value TaSPU_minP as the new test statistic and resampling to calculate the final p-value.We considered alternative approaches to incorporate multiple functional annotations into the aSPU test. In contrast to the two-level FunSPU approach, we can obtain modified aSPU tests via the score vector p-values is Fisher’s meta-analysis approach, i.e., m p-values were independent, TaSPU_Fisher would follow a chi-squared distribution with 2m degrees of freedom. However, our U. Hence, we also use resampling approaches to calculate the final p-value. We can similarly apply the inverse-variance weighted method to aSPU_minP and aSPU_Fisher tests, respectively denoted as aSPUw_minP and aSPUw_Fisher.Another common method for combining Of note, aSPU_minP is closely related to the FST test . Specifim functional annotations equally a priori and completely relied on the data to adaptively combine multiple annotations in each test unit, for example, a sliding window. This may be less efficient in the presence of overall inferior or superior annotations for a trait of interest, in which case it would be desirable to globally down-weight inferior annotations (and up-weight superior annotations). To this end, we propose to modify the FunSPU test by introducing an annotation-level weight ρ = ′ and denote the modified test as wtFunSPU:In our proposed FunSPU test, we treated all a priori knowledge regarding the informativeness of a functional annotation for a given trait, we propose to estimate ρl based on some global correlation measure between the annotation weights, genotypes and phenotype. A promising approach is based on partitioning the heritability h2 by functional annotations [q = 1,…, Q, using the GCTA tool [q and regress β is used as the global weight ρ for the corresponding functional annotation in the wtFunSPU test. Prior to this calculation, we transform the functional annotation to positive integers q = 1,…, Q such that larger q corresponds to a more likely functional category. If a functional category has a very small number of SNVs or Since we assume no otations : a functCTA tool . We nextWe conducted extensive simulations to evaluate and compare the performance of our proposed functional annotation-based tests with existing association tests for RVs. To make the simulation study representative of real RV data, we randomly selected 200 RVs from chr16:56.8M~57.1M of the UK10K TWINSUK genotype data of 1,718 unrelated individuals. MAFs of the selected RVs were no larger than 1%.wj1,wj2,wj3) and 3 sets of random annotations  independently . We designated the first 100 RVs as causal variants  and the remaining 100 RVs as neutral variants . The informative annotations were generated from a uniform distribution U corresponding to causal variants and from U corresponding to neutral variants. All of the random annotations were generated from U. Second, we randomly selected k = k1+ k2 RVs: k1 causal RVs from j = 1, 2, …, 100 and k2 neutral RVs from j = 101, 102, …, 200. Third, we used only informative annotations to calculate the effect size βj = cβ for each causal RV. Fourth, the simulated phenotype was obtained from εi followed N and i = 1,2,…,1718. Furthermore, to evaluate the globally weighted wtFunSPU test, we calculated the correlations between the sum of the genotypes weighted by each annotation and simulated phenotypes for each of the 1,000 simulation replications, and used the mean of the 1,000 correlations as the global weight of each annotation, i.e., l = 1, 2, …,6), where Yi and Xij in each replication correspondingly.To evaluate power, we generated the simulated phenotypes as follows. First, we simulated 3 sets of informative annotations  in functional annotation-based tests . To test the effect of more “noisy” annotations, we implemented scenario B, which used only one informative annotation, all three random annotations and one dummy annotation in the tests. In both scenarios A and B, we used identical procedure as above to generate simulated phenotypes Yi~N , independent of k neutral RVs and 6 random annotations all from U in each replication. We set increasing numbers of neutral RVs with k = {8, 16, 32, 64, 128}.To evaluate the type I error rate, we simulated The empirical type I error rate was calculated based on 50,000 replications with the significance level α = 0.005, while the empirical power was calculated based on 1,000 replications for each scenario with α = 0.05. For permutation-based tests, 10,000 and 1,000 resamplings were conducted for each replication to evaluate type I error and power, respectively.As shown in U and U, respectively, the tests incorporating functional annotations, such as FunSPU, wtFunSPU, aSPU_minP and FST, always had higher power than tests that ignored functional annotations, such as aSPU, SKAT and T1. The FunSPU test appeared to be less powerful than aSPU_minP, suggesting a lack of efficiency in the former’s complete data-adaptive strategy to combine multiple annotations. On the other hand, wtFunSPU and wtFunSPUw outperformed aSPU_minP and FST, supporting the effectiveness of the global weighting scheme. Between the latter two, aSPU_minP had an increasing edge over FST in the presence of larger number of neural variants, due to its going beyond burden (SPU(γ = 1)) and variance-component (SPU(γ = 2)) tests with additional γ parameters. We also observed that the inverse-variance weighted tests always outperformed the original tests, e.g., wtFunSPUw versus wtFunSPU, and this advantage became more obvious with a higher proportion of neutral RVs. Lastly, the power of the aSPU_Fisher test was similar to that of the aSPU_minP test until the number of neural variants increased to 64 and 128, when the former became less powerful than the latter.Regarding power, we first considered scenario A , which wNext, we considered a weaker scenario for our proposed tests. In scenario B , we usedWe also compared the computational time needed for different methods. As shown in −6, and sample-level QC to exclude samples in poor concordance with their corresponding GWAS data[To further evaluate the performance of our proposed tests on real data, we applied FunSPU and other state-of-the-art tests, including SKAT, T5 burden test and FST (combined test), to assoGWAS data. Since ts =  were transformed into  by f(s) = (7-s)/6, whereas the Funseq categories s =  were transformed by f(s) = (1+s)/7. We substituted the missing values or zero values with 0.01  or 0.0001 (GERP++). There was no missing value in CADD and GenoSkyline for the RVs considered here. We considered six types of functional annotations for RVs. CADD, FunSeq, FunSeq210 scale. To achieve this genome-wide significance level, we used a step-up permutation strategy [B = 10,000 permutations for all sliding windows and gradually increased B; if those sliding windows with estimated p-values <10/B, we increased B to 10 times the current value and re-estimated the p-values for these sliding windows. The number of permutations in the final stage was B = 108. Of note, the variant-specific score functions in aSPU, aSPU_minP, FunSPU and wtFunSPU were not weighted by MAF, while those in aSPUw, aSPUw_minP, FunSPUw, and wtFunSPUw were inverse-variance weighted, where variants with lower MAF were up-weighted. By default, SKAT and FST used Beta weights to up-weight variants with lower MAF [We employed a sliding window approach to group RVs with a window length of 10k base pairs (bp) and a step size of 8.75k bp, resulting in 319,306 windows in total. Using the conservative Bonferroni procedure, we set the family-wise error rate at 0.05 with a significance level = 0.05/319306 = 1.56e-07, which equals 6.81 on the -logstrategy , 28. We ower MAF , 17.p-value inflation, suggesting that the FunSPU tests could control the type I error rate well in genome-wide scans. p-value in the TWINSUK discovery cohort by any of the association tests under consideration. To confirm our findings in the TWINSUK cohort, we performed replication analysis of the genome-wide significant sliding windows in the ALSPAC cohort. As shown in p-value < 0.05/24 = 2.1e-3 based on the Bonferroni correction for 24 sliding windows: 3 by at least one of the functional annotation-based tests  and one by the aSPU test. In contrast, none of the 6 sliding windows identified by the FST test in the discovery cohort was replicated; neither did SKAT nor T5 replicate any sliding window.As shown in TOMM40, APOE and APOC4-APOC2 genes. These loci have been previously identified and replicated to be associated with LDL by large-scale meta-analysis of GWAS common variants [APOE plays a central role in lipoprotein metabolism and neurodegeneration [APOE has three isoforms, 2, 3, and 4: APOE2 is associated with elevated plasma LDL level and increased cardiovascular disease risk, whereas APOE4 is associated with increased risk of Alzheimer’s disease[APOE[TOMM40/APOE locus and additionally APOC4-APOC2 locus that harbor LDL-associated RVs with fewer than a couple of thousand samples, suggesting that the power of the FunSPU test was boosted by incorporating external biological knowledge.Three of the four replicated sliding windows were close to each other on chromosome 19 around variants –31. Numeneration –34. Specs disease, 36, a rease[APOE. Here weTOMM40 and APOC4-APOC2 gene regions for Funseq2, Funseq, RegulomeDB and GenoSkyline  and applied the aSPU test to each sliding window which had at least two RVs. For a sliding window with a single RV, we merged it with its neighboring sliding window. As shown in To further investigate whether the We have proposed a versatile and adaptive association test, FunSPU, to exploit multiple sources of biological knowledge in the analysis of WGS data. It is adaptive at both the annotation and variant levels, and thus maintains high statistical power, even in the presence of noninformative annotations and a larger number of neutral variants. We have further proposed a globally weighted wtFunSPU test to more effectively down-weight less informative functional annotations in a trait-specific manner. Using the UK10K WGS data, we demonstrated that our proposed FunSPU test and its extensions, including the wtFunSPU and aSPU_minP tests, are more powerful tools to identify genome-wide significant loci than existing RV association tests that either ignore external biological information or rely on a single source of biological knowledge. The FunSPU family of tests would thus serve as a powerful and complementary tool for ongoing and future large-scale WGS studies, such as the NHLBI TOPMed project  of over The six functional annotations we considered here are diverse in terms of resources and features. For example, GERP++ is a seq2, based on which we derived a single global weight. This is in line with the “sieve principle”, which justifies using aggregated data to estimate a much smaller number of weights and then using them in subsequent hypothesis testing of small units of data  with controlled family-wise error rate [To further de-noise noninformative annotations, we proposed a novel trait-specific measure based on partitioning the heritability and used it as a global weight for each annotation in the wtFunSPU test. Interestingly, our proposal is along the line of estimating group-specific weights in the context of weighted hypothesis testing , 42, thoror rate . Our proror rate  to calcu8 residual permutations for the top sliding windows to reach the genome-wide significance threshold. We expect that further implementation of the core functions in the C language should reduce the computational burden to a more affordable level. In addition, it would be desirable to develop some asymptotic theory and test to save the computational time. Of note, some asymptotic theory has been developed for the aSPU test in the context of testing two high-dimensional means for common variants [We have some practical considerations for our proposed tests. First, some functional annotations are not well-defined across the whole genome, resulting in relatively high missing data rates, for example, 68% for Funseq; the missing scores may reduce the reliability of annotation-based association tests. On the other hand, considering multiple complementary functional annotations simultaneously may at least partially remedy the problem of missing information. Second, by employing parallel computing and a step-up residual permutation strategy for the FunSPU family of tests, we are able to perform computationally feasible genome-wide scans for WGS data. For example, in the UK10K TWINSUK WGS data application, it took 24 hours for 500 computing cores to complete the sliding window-based FunSPU scan in R, including 10variants ; howeverhttps://github.com/sputnik1985/FunSPU, and to be posted to R/CRAN.We have implemented the proposed FunSPU test and its extensions in an R package “FunSPU”, available at S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 FigGlobal quantile-quantile (QQ) plots for association analysis of rare variants with HDL in the UK10K TWINSUK cohort: (A) FunSPU (genomic control λ = 1.004), (B) FunSPUw (λ = 1.076), (C) wtFunSPU with global weights (λ = 1.004), and (D) wtFunSPUw with global weights (λ = 1.047).(PDF)Click here for additional data file.S8 FigGlobal QQ plots for association analysis of rare variants with LDL in the UK10K TWINSUK cohort: (A) FunSPU (genomic control λ = 0.611), (B) FunSPUw (λ = 0.805), (C) wtFunSPU with global weights (λ = 0.642), and (D) wtFunSPUw with global weights (λ = 0.789).(PDF)Click here for additional data file.S9 FigGlobal QQ plots for association analysis of rare variants with BMI in the UK10K TWINSUK cohort: (A) FunSPU (genomic control λ = 0.604), (B) FunSPUw (λ = 0.825), (C) wtFunSPU with global weights (λ = 0.635), and (D) wtFunSPUw with global weights (λ = 0.825).(PDF)Click here for additional data file.S10 FigGlobal QQ plots for association analysis of rare variants with SBP in the UK10K TWINSUK cohort: (A) FunSPU (genomic control λ = 0.601), (B) FunSPUw (λ = 0.821), (C) wtFunSPU with global weights (λ = 0.631), and (D) wt-FunSPUw with global weights (λ = 0.793).(PDF)Click here for additional data file.S11 FigLocusZoom plots of association test results for LDL at the locus around TOMM40 and APOC4-APOC2 in the UK10K TWINSUK cohort: (A) FunSPU, (B) wtFunSPU incorporating global weights, (C) aSPU, and (D)-(I) aSPU incorporating a single functional annotation: (D) GERP++, (E) Funseq2, (F) CADD, (G) Funseq, (H) RegulomeDB, and (I) GenoSkyline (blood).(PDF)Click here for additional data file.S12 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 TableThe corresponding phenotypes: (A) HDL, (B) LDL, (C) BMI, and (D) SBP.(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.


Yet, many serpentinitic systems are also physiologically challenging environments due to highly alkaline conditions (pH > 10) and elevated temperatures (>80°C). To elucidate the possibility of microbial life in deep serpentinitic crustal environments, International Ocean Discovery Program (IODP) Expedition 366 drilled into the Yinazao, Fantangisña, and Asùt Tesoru serpentinite mud volcanoes on the Mariana Forearc. These mud volcanoes differ in temperature  of the underlying subducting slab, and in the porewater pH  of the serpentinite mud. Increases in formate and acetate concentrations across the three mud volcanoes, which are positively correlated with temperature in the subducting slab and coincide with strong increases in H2 concentrations, indicate a serpentinization-related origin. Thermodynamic calculations suggest that formate is produced by equilibrium reactions with dissolved inorganic carbon (DIC) + H2, and that equilibration continues during fluid ascent at temperatures below 80°C. By contrast, the mechanism(s) of acetate production are not clear. Besides formate, acetate, and H2 data, we present concentrations of other SCOAs, methane, carbon monoxide, and sulfate, δ13C-data on bulk carbon pools, and microbial cell counts. Even though calculations indicate a wide range of microbial catabolic reactions to be thermodynamically favorable, concentration profiles of potential energy substrates, and very low cell numbers suggest that microbial life is scarce or absent. We discuss the potential roles of temperature, pH, pressure, and dispersal in limiting the occurrence of microbial life in deep serpentinitic environments.Serpentinitic systems are potential habitats for microbial life due to frequently high concentrations of microbial energy substrates, such as hydrogen (H Since the 1950s, advances in sampling techniques have extended explorations of subseafloor life from ~8 meters below seafloor (mbsf)  and CH4 involving nitrate, manganese(IV), iron(III), sulfate, and CO2 as electron acceptors  and s Stams, . In addi2, and CH4  indicates the presence of Lokiarchaeota with close relatives in methane hydrates, methane seeps, and anoxic subseafloor sediments, and of aerobic nitrifying Marine Group I Thaumarchaeota with close relatives in diverse deep sea benthic surface habitats. Cultivation studies have resulted in the isolation of alkaliphilic bacteria , which grow at a pH of 6.5 to 10.8–11.4 using organic substrates as electron donors, and O2, nitrate, or fumarate as electron acceptors , and electron acceptors  of the Mariana forearc system based on mud and extracted pore fluids that were obtained during IODP Expedition 366 in 2016/2017. We compare depth-related trends in SCOA concentrations to those observed for other microbial energy substrates (HR/V JOIDES Resolution drilled into the Yinazao (previously known as Blue Moon), Fantangisña , and Asùt Tesoru (previously known as Big Blue) serpentinite mud volcanoes on the Mariana forearc  onboard the −1, respectively ], sulfate, DIC and DOC were determined onboard as described in Fryer et al.  immediately after retrieval and were quantified in the home laboratory using two-dimensional ion chromatography as described in Glombitza et al. .2 analyzer. Porewater samples (1 mL per injection) were acidified with 0.2 mL of 2 M HCl. The CO2 released during this acid addition step was stripped and injected into the CO2 analyzer. Subsequently, any remaining carbon in the sample was combusted, and the DOC was obtained by difference. The CO2 Beer-Lambert absorption law was integrated to determine the total CO2 released from the sample . Isotopic compositions were measured after wet-chemical oxidation of DOC with persulfate (1 h at 100°C) on decarbonized subsamples (acidification to pH < 3 with 85% H3PO4). Two to four ml of headspace were then transferred into He flushed vials and analyzed using isotope-ratio mass spectrometry as described in Lang et al. , 50% Nycodenz (1.25 g/cm3), 80% Nycodenz (1.42 g/cm3), and 67% sodium polytungstate (2.08 g/cm3), and centrifugation at 10,000 × g for 1 h at 25°C with swinging rotors. The light density layer was collected, whereas the heavy fraction was subjected to a second round of separation after washing with 5 mL of 2.5% NaCl. The recovered supernatants were then pooled and passed through a 0.22 μm polycarbonate membrane filter. Cells on the membrane filter were stained with SYBR Green I staining solution (1/40 of SYBR Green I in Tris-EDTA [TE] buffer). The number of SYBR Green I–stained cells was enumerated by automated epifluorescence microscopic counting , which corresponds to a minimum quantification limit of 24 cells/cm3 (average plus three times the standard deviation of blank counts).To determine cell abundances, 2 cm2, CH4, and CO, 1 cm3 of mud was collected from cut ends of core sections immediately after core arrival. Samples were placed in 20 cm3 glass vials with 3 mL of distilled water and a small amount of HgCl2 to prevent microbial activity. Vials were sealed with Teflon-coated butyl rubber septa and crimped aluminum caps and then placed in an oven at 80°C for 30 min. A 0.5 cm3 aliquot of the headspace was sampled with a standard gas syringe and automatically injected into a GL Science GC4000 GC equipped with a helium ionization detector set at 250°C. The column  was packed with carbosieve (Agilent/Hewlett Packard). The GC oven program was set to 40°C during the initial 5 min with a subsequent rise to 250°C at 20°C/min. A second 0.5 cm3 aliquot of the headspace was then automatically injected into an Agilent/Hewlett Packard 6890 Series II gas chromatograph (GC) equipped with a flame ionization detector set at 250°C. The column  was packed with 80/100 mesh HayeSep (Restek). The GC oven program was set to 80°C for 8.25 min with a subsequent rise to 150°C at 40°C/min. All measurements were calibrated using two different gas standards.To quantify concentrations of H13Corg were analyzed using an elemental analyzer  coupled to a Delta V+ isotope ratio mass spectrometer (Thermo Scientific) at the Pôle de Spectrométrie Océan . Approximately 25 mg of decarbonated samples were loaded into tin capsules and introduced into an autosampler. Flash combustion was performed using an 8 s injection time of dioxygen at a flux of 240 mL min−1. Carbon isotope ratios were obtained against reference standards (SED-IVA reference number: 33802151) and in-house standards  and LIPG (yeast) from Institut de Physique du Globe de Paris, France). δ13C-TOC values are given as the per mil (‰) difference from the PDB standard. TOC was measured using the thermal conductivity detector of the Flash 2000 instrument. Routine replicate measurements had internal deviations of 0.15‰ for δ13C-TOC and <5% for TOC.Analyses were performed on solid residue samples after porewater squeezing. TOC and δGr) of potential microbial, thermogenic, and abiotic reactions were calculated based on the equationGibbs energy yields (Δ−1 of reaction) at standard concentrations  corrected for in situ temperature T (K) and pressure p (bar), R is the universal gas constant (0.008314 kJ mol−1 K−1), and Qr the quotient of product and reactant activities. To obtain in situ slab temperature and pressure as outlined in Stumm and Morgan  and cell counts on high-pH subsurface serpentinite mud fluids (pH 10.5–12.5) with shallow and adjacent samples, that are under stronger seawater or sedimentary influence and have moderate pH values concentrations are mostly below or close to the detection limit of 0.1 mM in subsurface high-pH mud fluids of all three mud volcanoes. In a few high-pH samples, concentrations of up to ~5 mM are reached, which correspond to ~10% of seawater values (~54 mM). Drilling fluid (seawater) contamination is a likely source of these elevated Mg2+ values .Similar to formate and CHtrations  in high-Carbon monoxide (CO) concentrations were barely detectable in high-pH fluids of Yinazao, but were clearly above detection in high-pH fluids of the other two mud volcanoes , killed controls (100 μM 13C-formate + sodium azide), and negative controls (no formate).To check for measurable microbial activity, we incubated high-pH muds of all three mud volcanoes at The very low cell numbers, the nearly constant concentration profiles of SCOAs in high-pH fluids, and the absence of measurable formate turnover in incubations raise the possibility that microbial activity is absent from high-pH subsurface muds. If so, then this raises the possibility that SCOAs measured in high-pH muds were thermogenically released from organic matter in the subducting slab and its sediments and/or produced by abiotic synthesis reactions linked to serpentinization of mantle rock of the Philippine Plate, and subsequently preserved over thousands of years during ascent. Under such a scenario, where all SCOAs in high-pH muds would be thermogenic or abiotic in origin, the changes in SCOA concentrations across mud volcanoes would reflect different environmental conditions deep within the subduction zone.not shown).Our data are consistent with the possibility of SCOAs in high-pH fluids having deep origins. Cross-plots, in which previously measured formate and acetate concentrations from Conical Seamount , and thus do not differ significantly between mud volcanoes or between pH-muds within mud volcanoes .Subsurface δ13C-DOC data also fall within a narrow range, with the exception of high-pH fluids from Asùt Tesoru, where the average δ13C-DOC is 11.1–12.1‰ higher than in all other mud volcano fluids . This difference in δ13C-DOC between Asùt Tesoru high-pH fluids and all other mud volcano fluids is highly significant (p < 0.01).All subsurface δ13C-DIC data show a clear division between Asùt Tesoru and the other two mud volcanoes. At Asùt Tesoru, moderate-pH and high-pH fluids have highly similar δ13C-DIC ranges . These values are significantly higher than in high-pH fluids of Yinazao (−16.3 ± 1.9‰) and Fantangisña , which did not differ significantly from each other.Despite considerable scatter, subsurface δ13C-isotope data to each other reveals several trends. Overall, the mean δ13C-DOC is consistently higher than the mean δ13C-TOC. This difference is small (1.5–2.3‰) in muds of Yinazao and Fantangisña, and moderate-pH muds of Asùt Tesoru, but comparatively large (12.8‰) in high-pH muds of Asùt Tesoru (−14.1 ± 1.6‰ vs. −26.9 ± 2.0‰). Comparing δ13C-DIC to δ13C-TOC and δ13C-DOC within each location and pH category shows that the δ13C-DIC is consistently higher than the δ13C-TOC and δ13C -DOC. The average difference between δ13C-DIC and δ13C-TOC is lower in high-pH muds of Yinazao (−11.2‰) and Fantangisña (−12.3‰) compared to moderate-pH (−20.5‰) and high-pH muds (−19.3‰) at Asùt Tesoru. The average differences between δ13C-DIC and δ13C-DOC are in a similar range in high-pH muds of Yinazao (8.9‰) and Fantangisña (10.9‰) and in moderate-pH muds of Asùt Tesoru (18.1‰). The only exception is again high-pH mud of Asùt Tesoru, where the average δ13C-DIC is only 6.6‰ higher than the average δ13C-DOC.Comparing subsurface δ4, H2, and DOC, in deeply sourced high-pH fluids of Mariana forearc serpentinite mud volcanoes increase systematically with distance to the Mariana Trench and with underlying slab temperature. Yet, despite these increases, there are no clear indications of metabolically active microbial populations. Downward SCOA concentrations in high-pH subsurface muds of all three mud volcanoes show no clear changes, and neither do concentrations of other potential metabolites, such as sulfate, DIC, and DOC. Though an interpretation of the dissolved CH4 and H2 data is confounded by potential outgassing during core retrieval, these gases also show no clear depth-related production or consumption profiles. Microbial populations, quantified by microscopic counting, are mainly around the minimum quantification limit of 20 cells cm−3. Local cell population peaks in subsurface high-pH fluids have to be interpreted with caution, given the local detection of millimolar concentrations of seawater-derived Mg2+, and evidence of significant contamination of sediment porewater by drilling fluid (surface seawater) containing cell concentrations of 105-106 cells cm−3 . Yet, even if the local peaks in cell counts accurately reflect in situ cell populations, these cells may not be metabolically active due to the highly alkaline in situ pH.Concentrations of potential microbial energy sources, such as formate, acetate, CH4, H2, and CO, and electron acceptors, such as sulfate and DIC, in high-pH fluids could provide useful insights into deep, non-biological processes in the subducting slab and the overlying forearc mantle. For these high-pH fluids to be indeed informative, non-biological alterations of electron donor and acceptor compositions during the tens of thousands of years of mud fluid ascent would need to be absent or sufficiently slow to not overprint original trends. In the following sections we first investigate possible explanations for the absence of detectable microbial activity in this subseafloor environment, focusing on free energy yields of catabolic reactions and the environmental variables pressure, temperature, pH, and fluid mixing during ascent. Afterward we discuss possible deep sources and production mechanisms of measured SCOAs, as well as sulfate, methane, and bulk carbon pools, across the Mariana forearc.If significant microbial activity is absent, then this would mean that concentrations and compositions of potential microbial electron donors, such as SCOAs, CHin situ temperature, pressure, and pH in cores, as well as at estimated temperature, and pressure in the subducting slab using the in situ pH in the cores , especially at slab temperatures. Methanogenesis from formate, acetate, and CO is also always exergonic. By contrast, methanogenesis from H2 . On the other hand, the reverse reaction is clearly exergonic, underscoring the potential for CO oxidation as a catabolic pathway in serpentinitic fluids  and Asùt Tesoru (250°C) are above the known temperature limit of life , has been estimated to be significantly higher  and for the underlying subducting slab  and extreme pH can explain the (near) absence of microbial life in ascending serpentinite mud fluids of the Yinazao, Fantangisña, and Asùt Tesoru mud volcanoes. If temperature functions as a natural sterilizing agent in the subducting slab, then this would indicate minimal recolonization of fluids during their long ascent , during which their temperatures drop within the known limits of life . By contrast, formate and acetate concentrations increase exponentially by ~3 and ~2 orders of magnitude, respectively, from Yinazao to Conical Seamount  exceed those in typical marine sediments  are clearly higher than in typical marine sediments  are consistent with temperature-driven increases in serpentinization rates. By contrast, a significant stimulation of thermogenic processes, which would be expected to clearly increase concentrations of others SCOAs, is not evident.As slab temperatures increase, the sources of individual SCOAs and/or the balance between production and consumption of individual SCOAs appear to change. Concentrations of formate, acetate, butyrate, and CH2 concentrations increase ~200- and 4-fold, respectively (A big shift in SCOA compositions occurs from Fantangisña (150°C) to Asùt Tesoru (250°C), where formate and acetate concentrations increase 15- and 7-fold, and methane and Hectively . The obs2 and 2, produced during reactions of iron(II)-rich minerals with water, with +. This reaction is equivalent to the reversal of biological formate oxidation, which is shown in 2++ takes place at 175–300°C  support an abiotic serpentinization-related production of acetate (and formate), as δ13C-values of both vary from ~0 to ~5‰, which is in the range of DIC, but considerably higher than the δ13C values of TOC and DOC  at Yinazao to <5 mM at Fantangisña. This decrease is consistent with anhydrite (CaSO4) precipitation, which has been proposed to remove most or all seawater sulfate from fluids during subduction in the Mariana forearc, and is only partially reversed by re-dissolution during fluid ascent  and similar, for the most part very low (0.01 wt. %) TOC contents indicate similar origins of TOC across all three mud volcanoes, including high-pH and moderate-pH muds , local layers with potentially significant terrestrial contributions (−24 to −25‰) were noted previously (Luo et al., 13C-enriched marine TOC fractions are selectively enriched during subduction, as a result of greater resistance to microbial and heat-driven degradation processes. Alternatively, given that the TOC contents and δ13C values are in the same range as those from other oceanic basement rocks [mostly 0–0.02 wt. %, −25 to −30‰; compiled in (Delacour et al., 13C-isotopic values in the range of DIC at Asùt Tesoru, can only explain an isotopic offset of ~+2‰. Thus, the origin of a major component of the DOC pool in high-pH fluids of Asùt Tesoru remains unknown.The TOC, DOC, and DIC isotopic compositions provide general insights into the sources of organic and inorganic carbon at Yinazao, Fantangisña, and Asùt Tesoru. Similar δ-pH muds . These v4, are produced, which unknown sources contribute to the strongly elevated DOC concentrations in high-temperature, high-pH fluids, and what the origins of the still enigmatic moderate-pH fluids are.Our study produces novel insights into the controls on the production of microbial energy substrates, in particular SCOAs, in deeply buried subducting slab environments. Despite the presence of high microbial energy substrate concentrations and significant Gibbs energy yields of a wide range of catabolic reactions, microorganisms are rare or absent, and no unequivocal evidence for microbial activity could be detected. This has implications for our understanding of deeply buried serpentinitic environments as potential habitats or even deep hotspots of microbial life, and suggests that the combination of temperature, highly alkaline pH, and dispersal limitation may strongly limit microbial population size in these environments. Instead, due to the absence of strong diagenetic alteration during fluid ascent over tens of millennia, high-pH fluids from mud volcanoes offer a unique window into the abiotic production mechanisms of microbial energy substrates by serpentinization and by serpentinization-related processes within subduction zones. Future studies will reveal the mechanisms by which some of these energy compounds, e.g., acetate, CHThe datasets generated for this study are available on request to the corresponding author.PE and ML designed the study. PE, KT, OS, SSu, CM, SSa, PS, ET, and ML took samples, performed measurements, and/or contributed data analyses. SB, CG, BJ, and YM provided technical advice and support with the analyses. PE and ML wrote the manuscript with input from all co-authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Fluoranthene and pyrene were the main contributors to the PAH deposition fluxes, accounting on average for 19% each, while deposition fluxes of PCBs and OCPs were dominated by PCB153 (26%) and γ-hexachlorobenzene (30%), respectively. The highest deposition flux of Σ15PAHs was generally found in spring, while no seasonality was found for PCB deposition. For deposition fluxes for Σ12OCPs, no clear spatial trend was found, confirming the perception of long-lived regional pollutants. Although most OCPs and PCBs hardly partition to the particulate phase in ambient air, on average, 42% of their deposition fluxes were found on filters, confirming the perception that particle deposition is more efficient than dry gaseous deposition. Due to methodological constraints, fluxes derived from bulk deposition samplers should be understood as lower estimates, in particular with regard to those substances which in ambient aerosols mostly partition to the particulate phase.Polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) are ubiquitous and toxic contaminants. Their atmospheric deposition fluxes on the regional scale were quantified based on simultaneous sampling during 1 to 5 years at 1 to 6 background/rural sites in the Czech Republic and Austria. The samples were extracted and analysed by means of gas chromatography coupled to mass spectrometry. For all seasons and sites, total deposition fluxes for ΣThe online version of this article (10.1007/s11356-019-05464-9) contains supplementary material, which is available to authorized users. Polycyclic aromatic hydrocarbons (PAHs) and many halogenated substances, such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs), are ubiquitous contaminants in the global environment UNEP . PCBs anPAHs are products of incomplete combustion and have both anthropogenic  and natural sources (e.g. crude oil and wild fires) (Dat and Chang Persistent organic pollutants (POPs) and PAHs can be found in every compartment of the environment and their physical and chemical characteristics allow them to be transported from one compartment to another   deposition samples were simultaneously collected near the Czech–Austrian border at three sites in the Czech Republic, Kuchařovice (KUC), Košetice (KOS) and Churáňov (CHU), and at three sites in Austria, Wolkersdorf (WOL), Unterbergern (UNT) and Grünbach (GRU). All sites are considered as background with limited anthropogenic sources with the exception of KUC. KUC is a rural site, located in the vicinity of agricultural fields and affected by emissions from residential area (e.g. domestic heating) located near the sampling site (around 100 m). A map of the sampling sites is provided in Figure From September 2011 to August 2012, deposition samplers were deployed at each site. Additional deposition samples were also collected at the same three Czech sites during 2012–2015. Exact sampling periods are provided in the Table The deposition sampler used .GFFs and XAD samples were extracted with DCM using an automatic extractor . Surrogate recovery standards  were spiked onto each GFF and XAD before extraction. The extracts were then concentrated using a gentle nitrogen stream. In 2011–2012, one sampler was dedicated to PAH analysis while the second one was for PCBs and OCPs. For the remaining years, PAHs, PCBs and OCPs were analysed from a single deposition sampler and the extracts were divided with 10% used for PAHs and the remaining for PCBs and OCPs. PAHs extracts were transferred to a silica column consisting of 1 g of anhydrous sodium sulphate and 5 g of activated silica and were eluted with 10 mL of −1 to 180 °C, 5 °C min−1 to 310 °C (20 min). The inlet temperature was 280 °C. The carrier gas was He with a flow rate of 1.5 mL min−1. The temperature of the transfer line was 310 °C and 320 °C for the ion source. The used regime was selected ion monitoring. PCBs and OCPs were analysed using a 7890 GC  coupled to Waters Quattro Micro GC  equipped with SGE Analytical Science HT-8 (8% Ph) column  coupled with MS/MS . The temperature programme was 80 °C, 40 °C min−1 to 200 °C, 5 °C min−1 to 305 °C. The inlet temperature was 280 °C. The carrier gas was helium with a flow rate of 1.5 mL min−1. The temperature of the transfer line was 310 °C and of the ion source was 250 °C. The used regime was multiple reaction monitoring. The target compounds in this study were 15 PAHs, acenaphthylene (ACY), acenaphthene (ACE), fluorene (FLN), phenanthrene (PHE), anthracene (ANT), fluoranthene (FLT), pyrene (PYR), benzo(a)anthracene (BAA), chrysene (CHR), benzo(b)fluoranthene (BBF), benzo(k)fluoranthene (BKF), BAP, indenopyrene (IPY), dibenzanthracene (DHA) and benzo(ghi)perylene (BPE); 6 PCBs, PCB28, PCB52, PCB101, PCB153, PCB138 and PCB180; and 12 OCPs, namely 4 HCH isomers , 6 DDX compounds i.e. o,p'- and p,p’-DDT, dichlorodiphenyldichloroethene (DDE) and dichlorodiphenyldichloroethane (DDD) and penta- and hexachlorobenzene (PeCB and HCB).The PAHs were analysed using gas chromatography 6890 GC  equipped with a 60 m × 0.25 mm × 0.25 μm DB5-MS column  coupled to a mass spectrometer . The temperature programme was 80 °C (1 min), 15 °C minα-HCH and γ-HCH, which had levels lower than the instrumental limit of quantification (iLOQ). iLOQs were defined from the instruments as a signal to noise ratio of ten for the lowest point of the calibration curve and are presented in Table No field blanks were collected within this study, but two field blanks, each consisting of XAD and GFF, from a following, methodologically identical study, were used instead. These blank levels of individual PAHs, PCBs and OCPs were below the limit of detection or low otherwise, suggesting minor contamination during sampling, transport and analysis. Mean blank values with standard deviations are provided in Table The recoveries of individual samples were ranging from 55.6 to 117.2% for PAHs and from 63.1 to 109.1% for PCBs and OCPs. The reported fluxes have not been adjusted for recoveries but were blank corrected, by subtracting the mean concentrations of SOCs in the field blanks. To this end, blank values < iLOQ were replaced by 0. For derivation of temporal averages, values were replaced by iLOQ/2 whenever the determined concentrations in samples were lower than iLOQ.15PAHs) total deposition fluxes ranged at 23 to 1100 ng m−2 d−1 with an average value of 190 pg m−2 d−1 for all seasons and sites investigated .The total deposition fluxes for 15 PAHs  and 12 OCPs (Σ12OCPs) measured were 64–4400  and 410–7800 pg m−2 d−1 (average 1900 pg m−2 d−1), respectively  between the total deposition mass flux and precipitation amount is found only for the two HCH isomers. This test was applied only for the KOS data subset, because of its size . The result supports the perception of influence of water solubility/air-water equilibrium .Wet deposition was expected to be most relevant for substances partitioning to the particulate phase or gaseous, but with significant water solubility. At KOS, a significant correlation  higher PAH deposition fluxes were observed at KUC than at the other sites. The spatial variation across the other sites, apart from KUC, was lower for PAHs than for PCBs. The maximum at the rural site KUC was expected, because of local emissions, namely domestic heating from a nearby village with road traffic and agricultural machinery, unlike at the other sites. The PAH deposition flux at KUC site ranged from 49 to 1100 ng m−2 d−1 with an average of 140 ng m−2 d−1, i.e. 2–6 times higher than for the other sites  higher deposition flux of Σ6PCB was found at KUC, where it ranged from 64 to 4400 pg m−2 d−1 with an average of 140 pg m−2 d−1, i.e. which is 1.4–4.5 times higher than for the other sites  than at the Czech sites  and smaller than 1 in all seasons at all sites. This suggests using lindane rather than technical HCH. The fraction of 5 ring PAHs among all measured PAHs was not significantly different (p < 0.05) across sites with one exception, comparing GRU and WOL. These two ratios suggest that pollutants are distributed equally in Central Europe. The ratio between sum of all DDT isomers over all isomers of DDX compounds was significantly different (p < 0.05) between KOS and all other sites with one exception, GRU. This ratio was highest at KOS, suggesting most recent usage of DDT in that area. The ratio between o,p isomers and all isomers of DDX compounds was significantly different (p < 0.05) between KOS and all sites, also, between KUC and CHU, GRU and then between UNT and GRU. These results must be viewed with caution for the Austrian sites, because of the low number (N = 4) of samples.The ratio α-HCH/γ-HCH was not significantly different across sites (θdep), which should not be confused with the particulate mass fraction in aerosols (θ). It is known that for apolar or mid-polar compounds particle, scavenging is more efficient than gas scavenging  are too short, to address long-term trends. The results suggest that atmospheric deposition in 2010 is an important pathway of pollution transfer to ecosystems in the Central European background. The substance patterns are quite similar across sites .Koa, besides other; Wania and Westgate For substances which deposition is dominated by dry particle deposition, and because of the significance of surface roughness for dry particle deposition (McLachlan and Horstmann ESM 1(PDF 798 kb)


To determine the association of lymphatic vessel density (LVD) with the prognosis of Asian non-small cell lung cancer (NSCLC) patients via a meta-analysis.Eligible studies were selected by searching PubMed and EMBASE from inception to July 25, 2017. The reference lists of the retrieved articles were also consulted. The information was independently screened by two authors. When heterogeneity was significant, a random-effects model was used to determine overall pooled risk estimates.p = 0.000, I2 = 73.2%). Subgroup analyses were performed on 5 VEGFR-3 groups , 3 LYVE-1 groups , 5 D2–40 groups , and 2 podoplanin groups . Sensitivity analysis indicated robust results. There was no publication bias.A total of 15 studies with 1075 patients were finally included in the meta-analysis. LVD was positively associated with the prognosis of NSCLC in the overall analysis (hazard ratio (HR) 1.14, 95% confidence interval (95% CI): 1.02–1.27, LVD is an indicator of poor prognosis in Asian NSCLC patients. Lung cancer is a malignant disease associated with the highest mortality rate 18.2%) among all types of cancer worldwide 8.2% amon. ApproxiCancer relapse and metastasis lead to poor prognosis. The most common mode of metastasis is lymph node metastasis. During the early stages of tumor dissemination, malignant cells spread from primary sites to regional lymph nodes. Therefore, the lymphatic system plays an important role in cancer biology . The fordrosophila homeobox gene product that is involved in the regulation of early lymphatic development [Previous studies have identified novel molecular markers of the lymphatic endothelium that have been used to study tumor-associated lymphangiogenesis via immunochemistry. These markers include VEGFR-3, Lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1), D2–40, podoplanin, Prox-1 and desmoplakin, among others –16. VEGFelopment . Desmoplelopment –24, but elopment –27. TherPubMed and EMBASE were searched from inception to July 25, 2017, to find related studies. The search terms used were 1) “Non-small cell lung cancer”, “Non-small cell lung carcinoma”, “NSCLC”, “lung adenocarcinoma”, “adenocarcinoma of lung”, “lung squamous cell cancer”, “squamous cell cancer of the lung”, “lung squamous cell carcinoma”, “squamous cell carcinoma of the lung”, “lung large cell cancer”, “large cell cancer of the lung”, “lung large cell carcinoma”, and “lung large cell carcinoma”; 2) “Lymphangiogenesis”, “Lymphangiogeneses”, “Lymphatic microvessel density”, “Lymphatic vessel density”, “Lymphatic microvessel”, and “Lymphatic vessel”; and 3) “prognostic”, “prognosis”, and “survival”.The inclusion criteria were as follows: 1) a cohort study; 2) an Asian study population; 3) diagnosis of NSCLC based on lung histology, with the most important histological types being ADC, SCC and large cell cancer (LCC); 4) evaluation of the association between LVD and the prognosis of NSCLC patients; 5) analysis of lymph microvessel markers by immunohistochemistry; and 6) the presence of sufficient data to calculate the adjusted hazard ratio (HR) or risk ratio (RR) and the corresponding 95% confidence intervals (CIs). Studies were excluded if they had non-human study subjects. If the data were duplicated or the same population was used in more than one study, we chose the most recent or complete study.The eligible studies selected for our meta-analysis were independently evaluated by two reviewers (XXQ and XSL) based on the aforementioned selection criteria. The following information was extracted from the eligible studies: the name of the first author, publication year, study period, country, sample number, sex of patients, median follow-up period, mean age or age range of patients, histology, histological type, TNM stage, and lymphatic endothelium markers in Table . In addi2 test were used to assess heterogeneity among the studies [P values for the Q-test, which represented heterogeneity; heterogeneity was present if the P value was less than 0.10. The random-effects model was applied when I2 > 50% [To compute a pooled HR with a 95% CI, the Q-test and the I studies . We alsoI2 > 50% ; otherwiI2 > 50% . SubgrouI2 > 50% . FinallyI2 > 50% . The datUsing the predefined search strategy and inclusion criteria, 15 studies , 33–41 iThe characteristics of the 15 eligible studies are shown in Table 2 = 73.2%; P = 0.000).LVD was positively associated with the prognosis of NSCLC in the overall analysis  . No statistically significant heterogeneity was observed in the 5 VEGFR-3 group ; however, there was considerable heterogeneity in the 3 LYVE-1 groups , the 5 D2–40 groups  and the 2 podoplanin groups   or Egger’s linear regression test (p > |z| = 0.133)  Figs.  and 6.FiNSCLC is the most common subtype of lung cancer, with a high incidence, high mortality, low survival rate, low diagnosis rate and treatment rate. It has been challenging to improve survival rates due to the lack of precise prognostic markers. To overcome this problem, a comprehensive understanding of lymphatic endothelium markers is needed. It is important to examine whether LVD can be an indicator of the prognosis in Asian NSCLC patients.In our present meta-analysis, LVD was positively associated with the prognosis of NSCLC , indicating that high LVD indeed predicts poor survival in Asian NSCLC populations. To date, only Wang and colleagues  have desThe role of LVD as a prognostic predictor in NSCLC remains controversial. Kajita and colleagues were the first to report VEGFR-3 expression in lung cancer cells, but they did not evaluate its impact on the prognosis or the correlation of VEGFR-3 expression with clinicopathologic features in patients with NSCLC . Later, VEGFR-3, D2–40, LYVE-1 and podoplanin are widely used and extremely valuable markers of lymphatic vessels. However, one study has reported that lymphatic endothelium markers are not only expressed on lymphatic vessels but also expressed on blood vessels, tumor cells or in normal tissues . One of Irrespective of its strengths, the meta-analysis also has certain limitations. First, although we searched all retrospective studies for the association between LVD and the prognosis of NSCLC, the eligible studies were restricted to those published in English or Chinese. Because of linguistic barrier, some non-English or non-Chinese studies were excluded. In addition, we also missed some studies that may have been published in books or journals that were not available in the online databases. Additionally, studies with negative data may not have been submitted by investigators, or studies with nonsignificant results may have been rejected by journals. Nevertheless, there was no significant publication bias in our study, although we could not completely rule out publication bias. Second, few studies did not present clear or complete data, making data analysis difficult. When we could not obtain original data from the authors via email or other means, we had to exclude those studies. Third, because of the small number of eligible articles, our study was not the most comprehensive. Fourth, our results cannot be generalized to populations worldwide, especially non-Asian populations. Thus, more comprehensive and higher quality analyses are still required in the future.In summary, this meta-analysis indicated that LVD is an indicator of the prognosis of Asian NSCLC patients. However, higher quality and more comprehensive analyses are still needed as more data are published in the future.


Nitrospira is considered to be the most widespread and abundant group of nitrite-oxidizing bacteria in many natural and man-made ecosystems. However, the ecophysiological versatility within this phylogenetic group remains highly understudied, mainly due to the lack of pure cultures and genomic data. To further expand our understanding of this biotechnologically important genus, we analyzed the high quality draft genome of “Nitrospira lenta” strain BS10, a sublineage II Nitrospira that was isolated from a municipal wastewater treatment plant in Hamburg, Germany. The genome of “N. lenta” has a size of 3,756,190 bp and contains 3968 genomic objects, of which 3907 are predicted protein-coding sequences. Thorough genome annotation allowed the reconstruction of the “N. lenta” core metabolism for energy conservation and carbon fixation. Comparative analyses indicated that most metabolic features are shared with N. moscoviensis and “N. defluvii”, despite their ecological niche differentiation and phylogenetic distance. In conclusion, the genome of “N. lenta” provides important insights into the genomic diversity of the genus Nitrospira and provides a foundation for future comparative genomic studies that will generate a better understanding of the nitrification process.The genus  Nitrification, the two-step oxidation of ammonia to nitrate via nitrite, is a key process of the biogeochemical nitrogen cycle. Nitrite-oxidizing bacteria (NOB) are chemolithoautotrophic microorganisms that catalyze the oxidation of nitrite to nitrate, the second step of the nitrification process. For decades NOB where considered as metabolically restricted microorganisms solely associated with nitrification. However, experimental findings contradict this opinion, indicating a versatile ecophysiology of many NOB –4 and hiNitrospira is the most diverse known NOB genus and is divided in six different phylogenetic sublineages [Nitrospira sublineages, have been obtained in enrichment or pure culture so far [Nitrospira are considered to be the dominant NOB in most wastewater treatment plants (WWTPs), playing a key role in the efficient removal of nitrogen via nitrification [Nitrospira species is highly understudied, mainly due to the lack of pure cultures and genomic data [Nitrospira [The genus lineages –8. Membelineages , 9–11. De so far –15. Sublfication , 16. Besmic data , 17, 18.trospira  not onlyNitrospira lenta” strain BS10 was isolated from a municipal WWTP [Nitrospira, besides N. moscoviensis [N. japonica” [N. inopinata” [N. lenta” genome will shed light onto the genomic flexibility and metabolic diversity of the genus Nitrospira and aid further comparative studies between Nitrospira species.“pal WWTP  and it ioviensis , “N. japaponica” , and theopinata” . Thus, iN. lenta” strain BS10 is a Gram negative, aerobic NOB isolated from activated sludge of a municipal WWTP in Hamburg, Germany  with an optical tweezer system for the sorting of single cells [N. lenta” grows mainly planktonic and forms helical-shaped cells. Cells are 1.0–2.3 μm in length and 0.2–0.3 μm in diameter (Table N. lenta” is able to grow at lower temperatures (10 °C) than N. moscoviensis. Interestingly, while “N. lenta” is not able to tolerate high concentrations of nitrite, it exhibits a lower affinity for nitrite compared to N. moscoviensis and “N. defluvii”, which indicates a clear niche differentiation among these Nitrospira species [A pure culture of “le cells . “N. len species .Fig. 2ElN. lenta” was selected for whole genome sequencing on the basis of its relevance within the nitrogen cycle as well as due to the general lack of genomic information for Nitrospira species. Furthermore, because “N. lenta” was isolated from activated sludge, its genome was expected to yield insights that allow optimization and stabilization of the nitrification process in wastewater treatment. The draft genome sequence of “N. lenta” BS10 was completed on 27/07/2013. The high-quality draft genome is available in the European Nucleotide Archive (ENA) under study accession number PRJEB26290. An overview of the genome sequencing project is given in Table “N. lenta” was cultivated as described by Nowka et al. [− 1 NaNO2− as energy source. The cultures were incubated in the dark at 28 °C, with moderate stirring (100–300 rpm). The genomic DNA was extracted following the hexadecyltrimethylammoniumbromide (CTAB) protocol provided by the DOE Joint Genome Institute  as described elsewhere [“a et al.  in minerlsewhere .N. lenta” was obtained using the GS De Novo Assembler (Newbler) and comprised 3.8 Mb on 22 contigs. Genome completeness was evaluated with CheckM [N. defluvii”  and N. moscoviensis , the “N. lenta” genome was estimated to be 96% complete with 3.2% contamination.High-throughput sequencing was performed at GATC Biotech  using Roche GS FLX technology. The final draft genome of “h CheckM . SimilarN. lenta” was annotated using the MicroScope platform [N. lenta” were compared to N. moscoviensis and “N. defluvii”, the type strains of the Nitrospira sublineages II and I, respectively, using the OrthoVenn web service [The draft genome of “platform  as descrplatform . The autplatform . The gen service  for the N. lenta” draft genome consists of 22 contigs and has a total size of 3,756,190 bp with an overall G + C content of 57.9% . Most of these unique genes lack any function prediction , as well as a complete gene set (urtABCDE) for a high affinity urea ABC transporter. This implies that “N. lenta” is able to hydrolyse urea to ammonium and CO2, facilitating nitrogen and carbon assimilation from urea and reciprocal feeding between “N. lenta” and urease-negative ammonia-oxidizing bacteria [N. lenta” urease is closely related to the enzyme of N. moscoviensis, but significantly differs from known ureases of ammonia-oxidizing bacteria [In accordance with its ability to oxidize nitrite to nitrate , “N. lenion Fig. , for whirobacter . Moreoveeductase , 18, or,pathways , 19, 31.bacteria . The “N.N. lenta” conserves energy by nitrite oxidation with oxygen as terminal electron acceptor. During nitrite oxidation catalyzed by NXR, two electrons are shuttled (putatively via cytochrome c) towards a putative novel bd-like terminal oxidase [OF1-type ATPase (complex V). The other respiratory complexes (complexes I to III) will not contribute to energy conservation during lithoautotrophic growth on nitrite, but will operate in reverse to provide reducing equivalents for carbon fixation [N. lenta” for pyruvate oxidation via acetyl-CoA and CO2 fixation, respectively. Moreover, the complete glycolysis/gluconeogenesis and pentose phosphate pathways were identified. The observed presence of glycolysis and the oxidative TCA cycle might indicate that “N. lenta” can benefit from a mixotrophic lifestyle in the presence of organic carbon, as has been reported for other Nitrospira representatives [“ oxidase . The profixation . Moreoventatives , 16, 32.N. lenta” genome contains various defense mechanisms against heavy metals, antibiotics, and the antibacterial agent acriflavine. “N. lenta” encodes a superoxide dismutase for defense mechanisms against oxidative stress, as well as several bacterioferritins, which can participate in oxidative stress resistance mechanisms [Finally, the “chanisms .N. moscoviensis and “N. japonica”, “N. lenta” represents only the third cultured species of canonical nitrite-oxidizing Nitrospira from sublineage II. In this study, the genome of “N. lenta” was analyzed, demonstrating that “N. lenta” shares a significant amount of genomic features with other representatives of the genus. However, physiological differences observed by Nowka et al. regarding growth conditions and nitrite affinities [N. lenta” genome will facilitate a better understanding of the metabolic versatility of the genus Nitrospira and will be useful for future comparative studies, especially those with a focus on species obtained from engineered systems.Together with finities , 21 clea