Thalasseus bergii breeding in the Benguela ecosystem. Mean modelled adult daily food intake increased from 140.9 g·d−1 of anchovy Engraulis capensis during incubation to 171.7 g·d−1 and 189.2 g·d−1 when provisioning small and large chicks, respectively. Modelled prey intake expected to be returned to chicks was 58.3 g·d−1  over the entire growth period. Based on our observations, chicks were fed 19.9 g·d−1 (17.2–23.0 g·d−1) to 45.1 g·d−1 (34.6–58.7 g·d−1) of anchovy during early and late provisioning, respectively. Greater crested terns have lower energetic requirements at the individual (range: 15–34%) and population level (range: 1–7%) than the other Benguela endemic seabirds that feed on forage fish. These modest requirements – based on a small body size and low flight costs – coupled with foraging plasticity have allowed greater crested terns to cope with changing prey availability, unlike the other seabirds species using the same exploited prey base.There is a growing desire to integrate the food requirements of predators living in marine ecosystems impacted by humans into sustainable fisheries management. We used non-invasive video-recording, photography and focal observations to build time-energy budget models and to directly estimate the fish mass delivered to chicks by adult greater crested terns  As anthropogenic activities and environmental change threaten an increasing number of habitats, there is a growing need to investigate the energy requirements of species dwelling in impacted ecosystems4 particularly when those species compete with humans for resources6. Such knowledge can facilitate the development of management plans that account for a species’ needs at the population level.The balance between energy expenditure and food consumption determines many aspects of animal ecology, including the role of species within ecosystems and the mechanisms that drive population dynamics7. They need regular access to food resources because of their high metabolic rate and energetically demanding flight9. Birds therefore offer opportunities to explore the relationships between environmental limitations (e.g. climate change), food web characteristics (e.g. trophic relationships) and energy budgets10. This requires accurate energetic estimates of individuals in the wild, but these are usually laborious and invasive to obtain. For example, they include the capture of individuals for laboratory work , the use of doubly labelled water9 or the deployment of data-loggers13. Such methods are becoming a growing ethical concern14, particularly for threatened species, making birds a challenging group to study16. Modelling approaches using time-activity budgets combined with knowledge on the energetic costs of specific behaviours offer non-invasive alternatives to estimate bird energy expenditure in the wild18, and generally provide improved estimates over allometric equations or thermodynamics modelling19.Accurately measuring energetic needs is particularly important for birds as most species operate at higher trophic levels, exerting top–down control on lower trophic levels and/or reacting to bottom–up forcing20. Today ~28% of the world’s ~350 seabird species are considered to be threatened with extinction by the International Union for Conservation of Nature21. Moreover, seabirds have high foraging costs and are greatly affected by commercial fishing activities24. In the North Sea, for example, competition with the industrial fishery for lesser sandeel Ammodytes marinus is partly responsible for the low breeding success and population decline of black-legged kittiwakes Rissa tridactyla and several other seabird populations26. Moreover, fluctuations in this key prey appeared to affect disproportionately small, surface-feeding species with high foraging costs, leading to the suggestion that such species – including terns – are sensitive indicators of deterioration in the state of marine ecosystems27. Using energetic models to better quantify the consumption of these sensitive seabird species thus offers great potential to integrate their needs into an ecosystem approach to fisheries18.Worldwide, many marine environments have been severely altered by human activity with large impacts on top predators31. In particular, the decreased access to prey is considered to be the key driver of ongoing declines of three endemic seabird species: African penguins Spheniscus demersus, Cape cormorants Phalacrocorax capensis and Cape gannets Morus capensis31. Perhaps surprisingly, numbers of greater crested terns Thalasseus bergii, which rely on the same resources and breed in the same region, have tripled over the last few decades; the reasons for these contrasting fortunes remain equivocal33. Considerable foraging plasticity34 and their ability to move breeding sites35 could have helped greater crested terns maintain high annual survivorship in the face of ecosystem-wide changes36. In addition, it is possible that their small body size (~390 g), single egg clutch, and short breeding period (68 days) reduce the greater crested tern’s overall energy requirements compared to other sympatric breeding seabirds. Thus, estimating energy budgets for the Benguela’s breeding seabirds may help us to understand why numbers of greater crested terns are increasing while the region’s threatened and endemic seabirds that rely on the same resource are decreasing. This information will also improve our knowledge of food partitioning within the Benguela ecosystem food-web, provide a baseline against which to assess the impact of future environmental change, and assist the development of conservation planning.The Benguela ecosystem off southern Africa is one of the four major eastern boundary upwelling ecosystems and one of the most productive ocean areas in the world. Over the last 70 years a combination of fishing and environmental change have altered the availability of lipid-rich forage fish forage in this system, with knock-on consequences for higher trophic level predatorsHere, we report the foraging activity budget of the southern African population of breeding greater crested terns using non-invasive methods. Based on the duration and cost of activities performed by breeding adults, we modelled the daily energy expenditure (DEE) and daily food intake (DFI) of adults during different breeding stages. To account for parameter uncertainty and propagate sources of error, we used Bayesian inference and Markov chain Monte Carlo (MCMC) estimation. We then compared our observed estimates of chick daily food intake to our model results.Over a total of 51 days, 374 greater crested tern nests were video monitored during incubation and 240 nests during early chick provisioning (hereafter “early provisioning”). These videos provided duration estimates for 1,138 incubation foraging trips and 1,747 early provisioning foraging trips. Over a 16-day period of focal observations, 31 chicks that had left the nest cup (hereafter “mobile chicks”) were monitored during late chick provisioning (hereafter “late provisioning”), which provided duration estimates for 252 foraging trips.−1  than during incubation  was similar to early provisioning , with a DFI of 140.8 g·d−1 of fish , which was similar to during incubation. However, the estimated total DFI for an adult, including that fed to the chick, was 22% more at 171.7 g·d−1  with a total modelled DFI, including that of the chick, of 189.2 g·d−1 , which results in a chick modelled DFI of 75.6 g·d−1 (58.2–98.2 g·d−1) over the pre-fledging period. Thus, the expected mean amount returned to chicks across the breeding population – assuming a breeding success of 0.59 chicks fledged per pair – would be 58.3 g·d−1 (44.9–75.8 g·d−1), or 29.2 g·d−1 (22.5–37.9 g·d−1) by each parent  returned to the nestling during early provisioning, with more fish returned during late provisioning . Chick observed DFI increased from early provisioning  to late provisioning .The mean (95% CI) mass of anchovies brought to the chick during early provisioning was 4.4 g , which was smaller than the anchovy returned during late provisioning to mobile chicks  could overcome this limitation, providing precise time-budget data on different at-sea behaviours  and estimates of their associated energy expenditure43. However, we favoured non-invasive methods as animal-borne data loggers can affect bird condition and behaviour16, and because greater crested terns are highly sensitive to human disturbance44. Furthermore, the approach used in this study can provide better population-level inference than data logger studies, which usually rely on small sample sizes45.Uncertainties in reconstructing time-energy expenditure can derive from several sources, including the inaccuracy of activity durations47. This can result in loss of prey (up to 3.2 g·d−1 of anchovies for interspecific kleptoparasitism) and/or additional energy costs to counter kleptoparasitic attacks48. Accordingly, provisioning adults may have to compensate for the food lost in this way, with implications for their energy expenditure49; however, this interaction is poorly understood and few studies can account for the energy expenditure linked to kleptoparasitism in models.Observed feeding rates in our study were limited to delivered prey. However, prior to feeding their chick, provisioning adults may be forced to perform specific behaviours which require additional energetic expenditure. Terns are often the target of inter- and intra-specific kleptoparasitism as they bring prey to the colony in their bill50. Modelling approaches are increasingly being implemented to study seabird-fishery competition23, including studies to predict the smallest forage fish biomass needed to sustain seabird productivity over the long term51. To provide an overview of seabird energetic needs, it is particularly important to account for species body size, clutch size, and number of fledging days. These needs can then be extrapolated to a broader ecosystem level by accounting for the total population breeding in the system.The recent decreases in seabird populations in the Benguela ecosystem suggest that updated estimates of food consumption are needed to account for energy partitioning in the management of the purse-seine fisheries, with which predators compete for prey52 ~10 kg for a Cape gannet chick28 and ~6 kg for a Cape cormorant chick (T. Cook unpublished data). With approximately 15,000 pairs breeding in the Benguela ecosystem, the whole population requires ~2,800 kg·d−1 of anchovy, which equates to ~133 times less than the Cape gannet population and ~37 times less than the Cape cormorant population breeding in the region , which also relies on bearded goby in Namiba54, has remained stable, suggesting an ability to cope when switching to low-quality prey56. Terns in the North Sea were found to be most vulnerable and sensitive to sandeel exploitation, presumably as a consequence of their specialized diet, small foraging range and inability to increase parental foraging effort when prey becomes scarce25. In contrast, greater crested terns breeding in the Benguela ecosystem could buffer these limitations due to their flexible diet, which includes ca. 50 different prey species34 and their low fidelity to breeding sites, which are believed to be chosen depending on the local availability of prey immediately preceding the breeding season, rather than by philopatry32. In addition, the recent major decrease of migrant tern populations to the Benguela ecosystem (e.g. common tern Sterna hirundo57) may have led to reduced interspecific competition with surface-gleaning seabirds, providing more resources for this resident tern species. In this context, the greater crested terns’ low energy requirements combined with their ability to switch to alternative prey provide a great advantage, highlighting the apparent species-specific responses to shifting foraging conditions, which seem to favour the greater crested tern in this ecosystem.In the northern Benguela, the population of sardine has been depleted since the early 1970s, and there has been little if any compensation by anchovy, forcing seabirds there to consume low-quality prey such as bearded goby 59.In conclusion, this study shows that greater crested terns have relatively low energy requirements at both the individual and population level, when compared to other seabirds breeding in the Benguela ecosystem that rely on the same resources. These low energy requirements appear to contribute to their recent increase in this exploited ecosystem. Further studies implementing detailed knowledge of the energetics, prey demands and demography of the Benguela’s endemic seabirds are needed to understand the apparent differences in their food requirements and assist the development of conservation planning for the threatened seabird species breeding in the regionForaging trip durations and offspring feeding rates of breeding greater crested terns were assessed on Robben Island , in South Africa’s Western Cape Province, using non-invasive video recordings of nest-cup activities during early provisioning Figure . All met53. Thus, we monitored individual chicks banded with engraved colour rings using binoculars and a hide (distance 10–30 m) to determine foraging trip durations and feeding rates during late provisioning. Observations and recordings were made from February to May during three breeding seasons . See Supplementary Information Greater crested tern chicks become mobile and leave the nest cup after approximately four days60, but our cameras were not always able to capture useable footage from first light or after sunset. Therefore, if birds on focal nests had already left by the start of filming at dawn, or not returned to the nest by the time our cameras could no longer operate due to low light levels, we used nautical twilight as a proxy of their departure and arrival times62. Nautical twilight is defined when the centre of the sun is 12° below the earth’s horizon63. The time of twilight on a given date at each colony was obtained from www.timeanddate.com.Video recordings were analysed using VLC media player (VideoLAN project). Three breeding stages were recognised: incubation , early provisioning (the mean week when chicks are provisioned in the nest cup), and late provisioning . Greater crested terns do not forage at night34. Prey were photographed using a non-invasive photo-sampling technique, allowing for an accurate determination of fish species and standard length64 For anchovy, we converted estimated fish lengths to mass using a yearly species-specific regression (see Supporting Information Prey carried by greater crested terns returning to the breeding colony to feed chicks were recorded as part of a program monitoring tern diet−1). Specific input values shown in Table 43. Due to their small size and sensitivity to disturbance, such data is lacking for almost all tern species. Thus, greater crested terns were assumed to be flying the entire time they were away from the colony. This assumption is supported by the fact that, while foraging, greater crested terns do not rest at the sea surface, diving events are infrequent and dives last only a few seconds at most (pers. obs.). Budgets were based on the bioenergetic model elaborated by Grémillet et al.6. By considering the duration (D) and metabolism per time unit (M) of each activity daily energy expenditure  for adults was defined as:DEE was then converted into adult DFI. Anchovy make up ~65% of the prey species consumed by greater crested terns in the Western Cape34 but since one of our aims was to compare observed estimates of chick DFI to our model results, for the purpose of the model we assume that anchovy makes up the entire diet (but see Supplementary Information Cp) of 6.22 ± 0.65 kJ·g−1 (wet mass)69 and an assimilation efficiency37 (Ea) of 0.77 ± 0.34, we calculated adult DFI (g·d−1) as:Time-energy budget models were built for adult greater crested terns to calculate the amount of food that individuals needed to consume daily to rear their progeny in a season , as derived from their time-activity budget, and the amount needed for chick maintenance and growth. Greater crested tern chicks’ energetic requirements have not been measured before. Chick energetic requirements were thus estimated by fitting an allometric regression to published data on 10 larid species37  in relation to asymptotic chick mass  and β is the distribution for the estimate of the slope parameter (posterior mean = 37.3). Mean chick daily metabolizable energy intake (MEI) (kJ) over the fledging period (40 days) was thus calculated in relation to days taken to fledge (F):We took adult 7 Figure . This re70 (Table CMR) by assuming that nests fail at random through time:We used a breeding success of 0.59 chicks fledged per pair and a fledging period of 40 days70 Table  to estimTDFI) for each of the early-provisioning (p = 1) and late-provisioning (p = 2) phases as:TDFI across the 40-day fledging period as:We then used the resulting survival function Figure  to estim−1 derived from respirometry71, 2 × BMR as an estimate of the cost of resting at the colony72 and estimated the cost of flying in greater crested terns (as 5.2 × BMR) with the software Flight 1.2573 using a wingspan of 1 m53, a wing aspect ratio of 10.4 (from the sooty tern Sterna fuscata)73 and a body mass of 390 g53. This software uses aerodynamic modelling, species-specific body mass and dimension to calculate the energetic cost of flying. Terns may use alternative flight modes to continuous flapping  and incur different flight costs depending on the flight mode or the wind field (wind speed and direction). However, we assumed that greater crested terns were flying continuously during their time away from the colony, that the time spent using alternative flight modes was marginal and that overall, greater crested terns experienced an equivalent proportion of different wind speeds and directions. Flight cost (35.6 W·kg−1) was thus calculated as the average between the minimum (31.8 W·kg−1) and maximum (39.5 W·kg−1) power to fly using continuous flapping. Food requirements for the other Benguela endemic seabirds were collected from previous studies  for means (where 10−7 is precision) and U for the residual standard error (σ), with the precision specified as σ−2.To account for the impact of the uncertainty of the different input parameters on the estimated energy budget, we used MCMC estimation in JAGS (v.4.1.0) via the ‘jagsUI’ library (v. 1.4.2)N for the estimated coefficients in the linear predictor and U for the shape parameter. The observed chick DFI was calculated by multiplying the posterior distributions for anchovy mass and number of prey delivered.To calculate chick DFI estimated from fish mass recorded by photo-sampling, we used the MCMC method described above to fit a gamma regression with a log-link function to estimate the mean (±95% CI) mass of anchovy returned to the colony by breeding stage  from n = 755 photographs. The mean (±95% CI) number of prey delivered to offspring by breeding stage from n = 274 events recorded on video or during focal observations, the mean (±95% CI) foraging trip duration, and the mean (±95% CI) number of offspring feeds per day (feeding rate) by breeding stage  were also estimated using gamma regressions with a log-link functions. For the gamma regressions, we used uninformative priors, For all parameters, we modelled means ±95% Bayesian credible intervals (CI) using three MCMC chains . All models unambiguously converged (all Supplementary Information


The use of these films in surface plasmon resonance-type biosensing is described, where they provide specific advantages versus traditional thiol-based films.The formation of organic films on gold employing N-heterocyclic carbenes (NHCs) has been previously shown to be a useful strategy for generating stable organic films. However, NHCs or NHC precursors typically require inert atmosphere and harsh conditions for their generation and use. Herein we describe the use of benzimidazolium hydrogen carbonates as bench stable solid precursors for the preparation of NHC films in solution or by vapour-phase deposition from the solid state. The ability to prepare these films by vapour-phase deposition permitted the analysis of the films by a variety of surface science techniques, resulting in the first measurement of NHC desorption energy (158±10 kJ mol Self-assembled monolayers (SAMs) have shown tremendous number of applications but can suffer from low stability. Here, the authors report air and bench stable carbene precursors allowing facile SAM formation, and furthermore demonstrate an application in biosensing These films have identical chemical and electrochemical stability to films deposited using the free carbene methodCompound ilm on Au1 by simpilm on Au1 by simp3a was also used to generate films in vacuo from the solid state by connecting a solid doser containing 3a to a ultra high vacuum chamber and warming to 325 K  chloride compound and the Au–C bond thus appears at a different location.NHC films were examined by high-resolution electron energy loss spectroscopy (HREELS) to probe NHC orientation and the C–Au bond. in vacuo have high uniformity with subtle modification of the zigzag arrangement of soliton boundaries of the Au(111) herringbone reconstruction23Film uniformity is of critical importance for biosensing applications. This was probed by scanning tunnelling microscopy (STM). Films prepared 3c to molecular density 17SH, with virtually the same number of atoms separating the gold and the ferrocene unit  was probed quantitatively using electrochemical techniques by relating the Fc redox signal from  density . Surfacek larger , an effek larger . This mak larger , where tk larger 26. The at 9 s−1; 27. The ct 9 s−1; .in vacuo, we examined these films in SPR-based biosensing   . The comip (HPA) , a largeip (HPA) , completip (HPA)  top, witAdsorption of the lytic peptide melittin was then examined . This pr32333435To determine conditions under which NHC-supported bilayers can operate, the quality of the supported bilayers was tested in the presence of different buffers at different pHs. Bovine serum albumin (BSA) absorption was employed as a benchmark for film quality, as it adsorbs at defects in the supported hybrid lipid bilayerNHC chips also showed high thermal stability, surviving heating at 65 °C in air for 24 h . In addiWith these preliminary proof-of-concept results in hand, we then prepared an NHC-linked version of a commonly employed carboxymethylated dextran and streptavidin (SA) chips, . To prepin vacuo from bench-stable, easily handled benzimidazolium hydrogen carbonates. Scanning tunnelling microscopy reveals that vapour-deposited NHC SAMs have exceptionally high quality with low pit and island density, and closely related morphological behaviour is observed following solution deposition. TPD and HREELS reveal that the NHC stands upright on the Au(111) surface and forms a very strong NHC-Au bond (∼158 kJ mol−1). Most importantly, NHC-based films provide significant advantages versus thiol analogs in SPR-based biosensing, including more reliable and reproducible bilayer formation, and more consistent protein sensing results under extremes of pH.In conclusion, NHC films can be prepared in solution under ambient conditions and 1a)  , 2.25 mmol in 0.5 ml water) was injected. Vigorous CO2 bubbling was maintained for 1 h under stirring during which time the solution turned yellow and then brown until the formation of a purple precipitate was detected. The mixture was filtered by vacuum filtration and washed with 3 ml of water resulting in a clear, colourless filtrate solution (pH 8), leaving the insoluble iodine as a violet solid precipitate. Water was removed by flushing air overnight over the surface of the solution then the product was dried under high vacuum for 2 h to give a white solid. The resulting solid was triturated and sonicated in acetone (3 × 3 ml), which was then decanted off. Subsequent drying under vacuum afforded the desired product as a white powder . It is worthwhile to note that this procedure cannot be applied in organic solvents such as methanol due to the solubility of the formed iodine and its disproportionation under basic conditions.A 50 ml round-bottom flask capped with a rubber septum and containing a needle for ventilation and a glass pipette for addition of gaseous carbon dioxide was charged with a clear, colourless solution of 1,3-diisoproplylbenzimidazolium iodide (ol) ref.  in deionTo test for complete removal of iodine, a qualitative silver nitrate test was performed where one drop from the reaction aliquot was mixed with excess aqueous silver nitrate (1 M) solution. In cases where incomplete exchange was observed, a yellow precipitate of silver iodide formed that persisted on the addition of nitric acid. When iodide was completely exchanged, a white precipitate of silver bicarbonate formed that became colourless on addition of a solution of 1 M nitric acid. Quantitatively, the removal of iodide was assessed by elemental analysis in house looking at CHN and also externally analysing for iodide content. Mp: 123–124 °C (dec.).−1) of the resin was suspended in 10 ml deionized water (pH 8) and carbon dioxide bubbled through the solution for 0.5 h . To test the conversion of the resin, aqueous KI  was added to 0.2 ml of resin before and after CO2 bubbling. The mixtures were sonicated for 10 min and to one drop of the aliquots, excess aqueous silver nitrate (1 M) was added. The fresh (hydroxide) resin gave a dark brown precipitate of silver oxide, whereas the bicarbonate resin gave a white precipitate of silver bicarbonate. Both precipitates gave a clear colourless solution after addition of nitric acid.A hydrogen carbonate exchange resin was prepared from commercial (Sigma Aldrich) Amberlyst A26 hydroxide resin. To accomplish the conversion, 10 g  and transferred to a 20 ml vial where the resin was allowed to settle and water was decanted. The resin was washed with methanol (3 × 2 ml). 1,3-Diisoproplylbenzimidazolium iodide (1a)  was dissolved in 5 ml methanol and transferred to the resin. The mixture was stirred for 30 min. The silver nitrate test indicated the completeness of the exchange reaction. The hydrogen carbonate solution was passed through a cotton plug to remove any resin beads and the resin was washed with methanol (3 × 2 ml), which was then added to the original filtrate. Solvent was evaporated and the residual solid was triturated and sonicated in acetone (3 × 3 ml), which was then decanted off via syringe and discarded. Subsequent drying of the white powder under vacuum afforded the desired product as a white powder . Mp: 123–125 °C (dec.).Resin-HCOBefore functionalization, the Au(111) films were cleaned by washing the films in 3 × 2 ml of methanol, drying them under an argon gas (4.8 Praxair) stream for 1 min, then cleaning them with plasma generated from room air at a medium radio frequency (RF) level and a pressure kept between 300 and 500 mtorr for 1 min. The films were then used immediately for functionalization.SAMs were prepared by immersion of Au(111) on mica substrates in a 1 mM solution of the corresponding free carbeneSAMs were prepared by immersion of Au(111) on mica substrates in 1 to 10 mM solutions of the corresponding benzimidazolium hydrogen carbonate salt in methanol for 24 h at room temperature in air. Substrates then were rinsed in methanol (5 × 2 ml) and dried under an argon gas (4.8 Praxair) stream for 1 min.iPr2bimy(H)[HCO3] (3a) was outgassed for 12 h at room temperature on a differentially pumped manifold separated from the ultra high vacuum system by a gate valve. Dosing was achieved by resistively heating a glass microcapillary wrapped in Ta wire containing compound 3a (β=3.5 K s−1. Coverages are reported as fractions of a full monolayer. One monolayer (1 ML) is defined as the total integrated area of the peak for the fragment m/z=41 amu (most intense) achieved on saturation exposure (adsorption beyond a chemisorbed monolayer was not observed at 300 K). Subsequent coverages are expressed as the area under the curve relative to the saturated peak area. The best fit accompanying the data was achieved by the sum of Gaussian functions and a 10–15 point baseline. HREELS spectra were collected in specular mode (θi=θf=45°) at 20 scans per spectrum, with a primary beam energy of E0=5 eV and at a resolution of 3.85 meV measured as the full width at half maximum of the elastic peak. All spectra have been normalized to the elastic peak.pound 3a . CalibraiPr2bimy(H)[HCO3] (3a) was dosed on the Au (111) crystal, which was held at room temperature. Multiplexing between 1 and 150 amu in TPD experiments revealed fragments m/z=2, 27, 39 and 41 amu desorbing from the surface. No other signal was detected that could indicate co-adsorption of the carbonate counterion  nor any other higher mass fragment.Spectral and purity data are available for all new compounds, along with original X-ray photoemission spectroscopy data, SPR data, TPD, HREELS, low-energy electron diffraction and electrochemical data. All data described in the manuscript are available from the authors upon request.How to cite this article: Crudden, C. M. et al. Simple direct formation of self-assembled N-heterocyclic carbene monolayers on gold and their application in biosensing. Nat. Commun. 7:12654 doi: 10.1038/ncomms12654 (2016).Supplementary Figures 1-10, Supplementary Tables 1-5, Supplementary Methods and Supplementary References


Mendelian-like inheritance of germline DNA methylation in cancer susceptibility genes has been previously reported. We aimed to scan the genome for heritable methylation marks associated with breast cancer susceptibility by studying 25 Australian multiple-case breast cancer families. Here we report genome-wide DNA methylation measured in 210 peripheral blood DNA samples provided by family members using the Infinium HumanMethylation450. We develop and apply a new statistical method to identify heritable methylation marks based on complex segregation analysis. We estimate carrier probabilities for the 1000 most heritable methylation marks based on family structure, and we use Cox proportional hazards survival analysis to identify 24 methylation marks with corresponding carrier probabilities significantly associated with breast cancer. We replicate an association with breast cancer risk for four of the 24 marks using an independent nested case–control study. Here, we report a novel approach for identifying heritable DNA methylation marks associated with breast cancer risk. DNA methylation is associated with breast cancer risk. Here the authors measure DNA methylation in the blood of individuals from 25 Australian families with multiple cases of breast cancer but not known mutations associated with breast cancer risk to identify possible heritable methylation markers. While increased global methylation is associated with a reduced risk, increased methylation levels within functional promoters have been associated with an increased risk of breast cancer3.DNA methylation is a breast cancer risk factor. Several genome-wide studies of DNA methylation have found evidence that global methylation levels measured in blood-derived DNA is associated with breast cancer risk for women in the general population, and for women from families at high genetic riskBRCA1 have a substantially elevated risk of breast cancer and their tumours typically have distinctive histological features6. We found that peripheral blood DNA methylation at the BRCA1 promoter was associated with an estimated 3.5-fold  increased risk of breast cancer diagnosed before the age of 40 years7. Hansmann et al.8 reported that 1.4% of 600 women from the German Consortium for Hereditary Breast and Ovarian Cancer had constitutive BRCA1 hypermethylation confined to one of the two alleles8.Candidate gene approaches have been used to assess whether methylation at CpG islands of breast cancer susceptibility genes is associated with breast cancer risk. Women carrying germline mutations in ATM are also at substantially elevated risk of breast cancer11. Flanagan et al.12 performed methylation microarray analyses of peripheral blood DNA across several genes including BRCA1, BRCA2, CHEK2, ATM, TP53, CDH1, and MLH1, and demonstrated that gene body hypermethylation of ATM was associated with an estimated threefold increased risk of breast cancer12. Brennan et al.13 combined two nested case–control studies of women at high risk of breast cancer and found evidence that methylation at an intragenic locus in ATM (ATMmvp2a) was associated with increased risk of breast cancer13.Women carrying specific rare germline mutations in 14 described promoter region methylation of PALB2 was evident in ~7% of breast and ovarian cancers, including those with germline mutations in BRCA2, using methylation-specific PCR and bisulfite sequencing14. In contrast, Mikeska et al.15 found little evidence of PALB2 methylation in high-grade serous ovarian cancers using a methylation-sensitive high-resolution melting assay15.Potapova et al.16. Changes in gene expression through altered DNA methylation or histone modifications induced from cis- or trans-acting genetic factors known as methylation Quantitative Trait Loci, (mQTL) are therefore not epimutations in this strict sense.The terminology being used to describe these observations is variable and vulnerable to misuse and misinterpretation. The term ‘epimutation’ is strictly defined as a heritable change in gene activity that is not associated with a DNA mutation but rather with gain or loss of DNA methylation or other heritable modification of chromatin8. These phenomena could therefore account for some of the familial risk of breast cancer that is not yet identified.Epimutations and mQTLs can mimic germline mutations in their effect on cancer predisposition and it is likely that their contribution has been largely underestimated due to limited research beyond the candidate gene approaches described aboveMLH1 and MSH2 in the context of Lynch Syndrome (LS), a hereditary condition in which genetic mutations in key mismatch repair genes predispose individuals to colorectal, endometrial, and other cancers17. While two thirds of LS cases carry germline genetic mutations at the DNA mismatch genes18, a small proportion of LS has been associated with epimutations20. It has since been demonstrated that some methylation marks at MLH1 and MSH2 that are transmitted transgenerationally are in fact linked to nearby cis-acting genetic variants and consequently follow Mendelian inheritance patterns22, and are thus not strictly epimutations. Other MLH1 epimutations occur sporadically and have not been linked to underlying genetic variations23; while these epimutations are often observed in a familial context, they do not follow complete Mendelian inheritance patterns23.Intergenerational transmission of epimutations  has been observed in We hypothesised that breast cancers in multiple-case breast cancer families with no known genetic susceptibility mutations are in part due to the contribution of heritable DNA methylation marks (including true epimutations and mQTLs). To test this, we assessed genome-wide DNA methylation for 25 multiple-case breast cancer families using the Infinium HumanMethylation450 K BeadArray. One or more women with breast cancer in these families had been previously screened for, and found not to carry germline mutations in known breast cancer susceptibility genes. In this study, we report a new analytic approach to identify CpG sites with Mendelian-like inheritance patterns and a set of 24 heritable methylation sites associated with breast cancer risk.p-value < 0.05), β-values and M-values were obtained from a total of 481,563 analysable CpG probes across DNA samples from 210 individuals in 25 families (20 families participating in kConFab and 5 families participating in the ABCFR). β-values denote % methylation levels obtained from the HM450K platform, where 0 indicates 0% methylation and 1 indicates 100% methylation. Due to the heteroscedastic nature of β-values, the log2 ratio of methylation intensity, known as M-values, are also calculated and used for all statistical analyses24.After removing 3949 poorly performing CpG probes  and 123 unaffected controls. In order to examine the overall genome-wide methylation similarities between samples and families, a hierarchical clustering analysis was performed according to l  are listed in Supplementary Data l above 77, which suggests that they are highly heritable. We estimated carrier probabilities for the 1000 most heritable methylation marks using family structure alone.The proportion of probes within 10 bp of known single-nucleotide polymorphisms (SNPs) increased significantly with ∆p-value threshold of 5 × 10−5  differences were substantial (Δβ > 0.30) between individuals, with most of these marks showing methylation patterns distinctly falling into hypermethylated (β > 0.80), hypomethylated (β < 0.20), or hemimethylated (β ~ 0.50) groups, indicating potential allele-specific methylation pattern at these sites  encompass a ~50 bp region 150 bp upstream from the transcription start site and overlapping a CpG island. Although these probes target 5 independent proximally located CpG sites, the 50mer probes largely overlapped with each other  and included additional siblings when possible by performing clonal bisulfite sequencing. We observed strong hypermethylation of the maternally inherited allele, confirming the maternal imprinting of this locus. We found complete loss of methylation in one child whose three other siblings retained the methylation in the maternal allele . The methylation patterns of all samples at this methylation site grouped clearly into hypomethylated, hemimethylated, or hypermethylated. Only 13 of our 210 samples were hypermethylated at this methylation site. We found three of the other methylation marks overlapping promoter regions of DUSP22, TMC3, and PPP2R5C. Nine other heritable methylation marks were located in gene body regions of MMP27, ANO10, CLGN, ZZEF1, PNKD, XYLT1, c7orf50, RASA3, and IL10RB, while six heritable methylation marks were not known to be associated with any gene : 5.0 to 13.1 years. Supplementary Fig. β-methylation value distribution for MCCS cases and controls for the 24 methylation sites showing heritable methylation patterns and associated with breast cancer in the family-based analyses. Of the 24 sites, four showed linear association with risk of breast cancer in the MCCS at the nominal significance threshold p < 0.05 : 1.18, 95% CI: 1.03–1.36), cg01741999 , cg03916490  and cg27639199 .Altogether, 433 invasive breast cancer cases and their matched controls were included in the analysisGREB1), which was trimodal, both the hypomethylated and hypermethylated peaks were associated with decreased breast cancer risk (OR = 0.60 (95% CI: 0.45–0.80), and OR = 0.56, (95% CI: 0.34–0.95), respectively). The methylation pattern at cg27639199 (TMC3) was also trimodal where the hypermethylated peak was strongly associated with breast cancer risk (OR = 2.16 (95% CI: 1.26–3.72)). At cg03916490 (C7orf50), reduced methylation was associated with the breast cancer risk (OR = 1.61 (95% CI: 1.16–2.24)). An annotated CpG probe (cg18514595) was associated with breast cancer risk when categorised into three methylation peaks.When comparing values belonging to the smaller vs. larger ‘peak’ of the methylation variable distribution, the results were consistent and more significant Table . At cg18M-values . Similar results were also found when restricting the analyses to DNA that was extracted from dried blood spots (Supplementary Table GREB1) representing 251 MCCS participates was included in the analysis. This region had eight common variants (in linkage disequilibrium) nominally associated with breast cancer risk. We found a very strong linear association between methylation at cg18584561 and the genotypes at this region (p = 1 × 10−65–1 × 10−71). The association between these genetic variants and the corresponding methylation β-value is presented graphically in Supplementary Fig. Genotyped and imputed variants from the iCOGS  = 0.03), cg01074083  vs. ER + : OR = 0.72 (0.53–0.98), p-value = 0.02) and cg23947138 (ER-: OR = 0.80 (0.68–0.94) vs. ER + : OR = 1.24 (0.93–1.65), p-value = 0.01). This result is shown in Supplementary Table We tested whether methylation levels at any of these 24 CpG sites were influenced by ER status in our nested case–control study and found evidence for three methylation marks cg06536614  vs. ER + : OR = 0.71 (95% CI: 0.53–0.96), 27. In this study, we tested whether heritable DNA methylation marks are associated with breast cancer risk in multiple-case breast cancer families that do not carry pathogenic mutations in known breast cancer susceptibility genes.Genome-wide studies of heritable DNA methylation studies in the context of familial breast cancer have not been conducted previously, although ~50% of familial breast cancer cases cannot be explained by what we currently know about genetic risk28. However, overall genome-wide methylation did not appear to segregate with affected status in any families . We validated this analytic approach by showing that it identifies probes that are known to overlap SNPs (the methylation measurements at these probes are likely to be influenced by the underlying SNPs). We then removed all SNP-overlapping probes from the analysis, screened the remaining probes for those with the most Mendelian-like inheritance patterns and tested some of the most heritable methylation marks for association with breast cancer. Note that our screening for probes with Mendelian-like inheritance patterns removed many probes that cannot be associated with familial breast cancer, so this screening step greatly increased our statistical power for detecting probes associated with familial breast cancer.VTRNA2-1  also noted that VTRNA2-1 exhibits all the hallmarks of ‘metabolic imprinting’ and is likely to be a determinant of cancer risk35. Here we have shown that methylation at the VTRNA2-1 promoter is also associated with heritable breast cancer risk that is measurable in DNA extracted from blood.We found 24 probes associated with breast cancer risk after adjusting for multiple testing Table . Five ofVTRNA2-1 locus  included in our study , the methylation at VTRNA2-1 seemed exceptionally variable in the families included in our study. Romanelli et al. also report an atypical imprinting pattern at this locus and concluded this region was a polymorphically imprinted differentially methylated region26. By performing clonal bisulfite sequencing within families, we confirmed the polymorphic imprinting of this locus as reported by above studies  underlies these methylation marks. The currently published risk-associated SNP closest to any of the identified heritable methylation marks is located ~1.5b MB from cg18584561 (GREB1). We found a strong linear association between the DNA methylation pattern at cg18584561 (GREB1) and 8 proximal common genetic variants . As discussed above, methylation at the GREB1 gene is associated with heritable breast cancer risk. This gene codes for the protein ‘growth regulation by oestrogen in breast cancer 1’ and has been shown to play a critical role in hormone dependent breast cancer38. There is currently no direct evidence of epigenetic regulation of this gene.The other 19 CpG probes were all located in different genomic regions. We showed that a single CpG overlapping the transcription start region of the GREB1, approximately half of the families appear to be methylated which is consistent with replication being possible in a population-based sample, another fraction (~10%) of the population are hypermethylated at this CpG. Interestingly, both the hypomethylated and hypermethylated profiles were associated with a decreased breast cancer risk, with similar estimated risk reduction of 40–45%. At cg03916490 (C7orf50), about 30% of the nested case–control participants were not strongly hypermethylated, which was associated with a 60% increase in risk. It is possible that the marks that did not validate in the nest case–control sample were either not present or at a very low frequency. Fig Four of the 24 methylation marks were associated with breast cancer risk in an independent nested case–control study of methylation and breast cancer risk  and new opportunities for the development of (or repurposing of) epigenetic therapeutics targeting these risk factors including chemo-prevention.40. The present study was based on samples and phenotypic data from a total of 210 family members (87 affected and 123 unaffected) from 25 families and phenotypic data on their relatives.Multiple-case breast cancer families. Subjects were members of 25 multi-generational families with multiple cases of breast cancer. The families were participants in the Kathleen Cunningham Foundation Consortium for Research into Familial Breast Cancer (kConFab) and the Australian Breast Cancer Family Registry (ABCFR)One or more members of these families had undergone previous genetic testing and were not found to carry a mutation in a known breast cancer susceptibility gene. Genomic DNA was isolated from blood samples or (if no blood specimen was available) from Epstein-Barr virus transformed cell lines  occurring between blood collection and 31 December 2007 and ascertained by record linkage to the population-based Victorian Cancer Registry (VCR), and to the Australian Cancer Database. Controls were selected through incidence density sampling and matched with cases on year of birth, year of baseline attendance, country of origin and, when possible, type of baseline blood specimen . The HM450K array was used to measure genome-wide methylation in DNA prepared from peripheral blood sample collected prior to cancer diagnosis of the cases as described by Severi et al. (2014)2. All participants provided signed informed consent to participate in the relevant research resources.Data from an independent nested case–control study of methylation as a risk factor for breast cancer within the Melbourne Collaborative Cohort Study (MCCS) were used to test the findings from the family analysis42. The Infinium HM450 K  was performed using the TECAN automated liquid handler  according to the manufacturer’s instruction.A total of 500 ng of genomic DNA per sample was bisulfite converted using Zymo Gold EZ-DNA kit . Prior to processing the bisulfite converted samples on the Infinium HM450 K BeadArray, the conversion was confirmed using bisulfite-specific PCR designed in-house43. Raw intensity signals were imported and processed using the minfi package44. All samples had an average detection p-value < 0.001, indicating good quality data. Therefore, no sample was removed from the analysis. Wherever possible, individuals from the same families were run on the same chips. Individual CpG probes with detection p-value greater than 0.05 (3949 CpG probes) were deemed unreliable and excluded from further analyses. All samples were Illumina and SWAN normalised to reduce technical bias between Type 1 and Type 2 probes45. β-values and M-values were calculated in minfi44. β-values denote relative methylation percentage calculated from the ratio of the methylated probe intensity and the overall intensity, where 0 indicates 0% methylation and 1 indicates 100% methylation24. Due to the heteroscedastic nature of β-values and unsuitable for many statistical tests, M-values, which are the log2 of β-values, are also calculated24. Methylation measures from twelve technical duplicates were used for testing the reproducibility of methylation measures and removed from subsequent analysis. No further batch correction method was performed.All bioinformatic processing was performed with R version 3.2.0VTRNA2-1 locus as previously described25. Germline DNA provided by 8 families, including 16 children were included in this analysis. All DNAs were first genotyped for rs2346019, (located at the downstream region of VTRNA2-1) using High-Resolution Melt curve analysis run on a RotorGene thermocycler . Families where the allelic-specific methylation could be discriminated using this genotype information were selected for the bisulfite sequencing analysis . A set of previously published bisulfite-specific primers were used for amplifying the VTRNA2-1 locus25. Cloning was performed using a TOPO-TA kit and at least 10 colonies per individual? were selected for Sanger Sequencing.Clonal bisulfite sequencing was performed to test for the parent-of-origin allelic methylation patterns of the 43 because existing general segregation analysis software was too slow to make the calculations feasible for almost half a million probes.Our method for identifying heritable methylation marks is based on a generalisation of the standard expectation–maximisation (EM) algorithm for Gaussian mixtures to allow for non-independent group memberships. These calculations were performed using custom code implemented in R version 3.1.1M-values: a mixture model, in which the M-values were modelled as a mixture of two normal distributions (with means and variances to be estimated); and a Mendelian model, which is the same as the mixture model except that group membership was modelled as the carrier status  at an autosomal genetic locus. Therefore, group memberships are independent under the mixture model but not under the Mendelian model. The maximised log-likelihoods, lmix and lMendel, for these models were calculated using the EM algorithm, with lmix obtained from the standard EM algorithm for Gaussian mixtures46 and lMendel calculated using the modification of this algorithm described in The EM algorithm for the Mendelian model, below. For each model, setting the means and variances for the two groups to be equal corresponds to a Gaussian model in which the M-values follow a normal distribution, so this Gaussian model is nested inside both the mixture and Mendelian models. Using the likelihood ratio test to compare these models to the Gaussian model is uninformative because many probes appear to have a bimodal distribution, so instead we compared lmix to lMendel. A maxim from the field of statistical model selection is that the maximised log-likelihood quantifies how well a model fits the observed data47. Therefore, Δl = lMendel − lmix is a measure of how Mendelian the probe’s M-values are, over and above how bimodal they are. Note also that since the mixture and Mendelian models have the same number of model parameters, Δl is the difference between the AICs for these two models, so the AIC model-selection approach would select the Mendelian model in preference to the mixture model whenever Δl > 0 (and similarly for the BIC)47.For each methylation site (CpG probe), two statistical models were fitted to the site’s l statistic to identify methylation sites with Mendelian-like inheritance patterns, we calculated Δl for all 481,563 methylation sites and used logistic regression and the likelihood ratio test to test whether or not the proportion of probes within 10 bp of a known SNP increases with Δl. This is a test on the efficacy of our statistic Δl, because the observed M-values of methylation probes with nearby SNPs are likely to have Mendelian-like inheritance patterns, just as an artefact of how the HM450 K array measures methylation48. The HM450K probes are 50mer oligonucleotides in design with the interrogated target CpGs at the last base. A technical limitation of the platform is that a large proportion of probes overlap one or more known SNPs48. As the accuracy of methylation measurements relies on the efficient hybridising of probes to target complementary DNA fragment, SNPs within probes potentially interfere with this binding and interrupt the actual methylation measurements48. The observed methylation values are therefore biased by nearby SNPs and will tend to follow Mendelian patterns of inheritance. We could therefore assess if Δl identified heritable sites by testing whether probes with higher values of Δl were more likely to have nearby SNPs. In addition to the formal test above, we also binned probes by their values of Δl and graphed the proportion of probes within 10 bp of a known SNP for each bin. Known SNPs were defined by Illumina’s HM450 K Manifest v1.2 (see Web resources).To validate the ability of the Δl. Note that this screening was based on the structure of the 25 families and did not use any data on breast cancer-affected status or age. For each of the 1000 most Mendelian sites , we calculated carrier probabilities for the hypothetical genetic variant that determines group membership in the Mendelian model. These calculations used standard techniques from segregation analysis49, in which the observed M-values played the role of the ‘phenotypes’ and the Gaussian densities  played the role of the ‘penetrance’ function. The calculation of these carrier probabilities also only used pedigree structure and M-values, not age or breast cancer data.To identify heritable methylation marks associated with breast cancer, we first excluded all methylation probes on sex chromosomes or within 10 bp of known SNPs. Then we screened the remaining 365,169 probes for those most consistent with a Mendelian pattern of inheritance, using the statistic Δ43 using the coxph function of the survival package50. To adjust for multiple testing, a Bonferroni-corrected p-value threshold of 0.05/1000 was used to determine statistical significance. Note that the effects of multiple testing were greatly reduced in our study because we screened the methylation sites for those with Mendelian inheritance patterns before testing for association with breast cancer.Cox proportional hazards survival analysis was then used to test for associations between breast cancer and the carrier probabilities for the 1000 most Mendelian methylation marks. These analyses were conducted in R version 3.1.1p-values for association with breast cancer are valid. These p-values were based on the likelihood ratio test, not the Wald test, so variances for the hazard ratios were not needed and hence were not estimated using either standard maximum likelihood or robust variance estimators.The families in this study were ascertained because they each contained multiple breast cancer cases, and no adjustment for this ascertainment criterion was made. This means that our hazard ratio estimates are biased, so we do not report these here, but since the ascertainment criterion has no effect on the test statistic under the null hypothesis, our This section gives a detailed, mathematical description of our generalization of the standard EM algorithm for Gaussian mixtures to allow for non-independent group memberships, as well as a precise description of the above statistic l for measuring how Mendelian the inheritance pattern of a given site is: for each of the methylation sites, we fitted two statistical models to the sites’ M-values x1,…,xn, where n is the number of people with epigenome-wide data and xi is the site’s M-value for person i. The first model is a mixture of two Gaussians, so under this model there are binary random variables y1,…,yn so that: the n bivariate random variables ,…, are independent; and for each j = 0 or 1, P(yi = j) = αj and (xi|yi = j) ~N(μjσj2), where θ =  is a vector of parameters to be estimated while satisfying the constraint α0 + α1 = 1. In this paper, we will also impose the additional constraint that α1 = 0.01, so α0 and α1 are fixed constants. The second model is the same as the first, except that the group membership variables y1,…,yn are modelled as the carrier status for a rare, autosomal genetic variant, with yi = 1 if individual i is a carrier and yi = 0 if he or she is a non-carrier. Note that yi and yj will generally be dependent random variables if individuals i and j belong to the same pedigree, though we still assume that x1,…,xn are conditionally independent given y1,…,yn.The statistic Δμ0 = μ1 and σ0 = σ1in either of these models gives a third model for the M-values, in which x1,…,xn are independent and follow a univariate normal distribution, that we call the Gaussian model. The maximised log-likelihoods M-values47. Since the Gaussian model is nested inside the other two models, M-values of a very large number of the sites are bimodal, so these tests very often prefer both of the other models to the Gaussian model. To discover sites whose methylation patterns are Mendelian, we therefore compare 47. Also, since M-values47, the better the Mendelian model fits the data compared to the mixture model, the larger M-values at the site are with a Mendelian pattern of inheritance within families.We will refer to these models as the mixture and Mendelian models, respectively. Setting Note that we have assumed that all familial aggregation of aberrant DNA methylation is due to a major gene, so 46, which can be iterated to rapidly converge (in most cases) to the maximum likelihood estimates. For the Mendelian model, we used a modification of this algorithm that we now describe in detail.A detailed description of the EM algorithm for the Mendelian model: since our analysis included approximately 480,000 sites, efficient algorithms were needed to maximise the likelihoods. For the mixture model this was straight-forward, because the EM algorithm for a mixture of Gaussians results in analytical update formulaeM-values x1,…,xn of a given site as the observed data and the binary carrier statuses y1,…,yn as the hidden data. For now, the reader can simply think of y1,…,yn as variables defining group memberships, as in the standard EM algorithm for Gaussian mixtures46, though with the caveat that y1,…,yn are not independent. With model parameters θ =  as above, if θt is the estimate of these parameters at iteration t then the EM algorithm chooses the estimate θt + 1 at the next iteration to be the argument which maximises the function of θ given byx = , y = , P is the likelihood of the full data at parameter value θ. More precisely, if n is the set of all binary vectors of length n, thenM-values x1,…,xn are assumed to be conditionally independent given the carrier statuses y, with the distribution of xi only a function of yi. The first sum in (1) is a function only of the parameters μ0, μ1, σ0, and σ1, while the second sum only depends on α0 and α1. So, to find θ that maximises Q, we can maximise these two functions separately. In the analysis presented in this paper, however, α0 and α1 were fixed to the values 0.99 and 0.01, respectively, so we focus on maximising the first term of (1) here.In the EM algorithm for the Mendelian model, we took the δij denote the Kronecker delta, and for each j = 0 or 1, let φ be the probability density function for the normal distribution N evaluated at xi, so that P = ϕ. Then the first sum in (1) isi corresponding to x and the parameter values θt when l = 1 . Let F be the partition of {1,…,n} into families, so that F is a set of sets of indices, with each f∈F of the form f = {i1,…,ik}, where i1,…,ik are all of the people in a given family with epigenome-wide methylation data. For any such f∈F, let xf =  and yf =  be the observed and hidden data for the family, respectively. Then since the carrier statuses and M-values of people from different families are independent,P from the right-hand side of (5), we note that, as before,To calculate these estimates, we used the definition (3) of P(yf|θt) can be calculated using standard techniques from segregation analysis49, as described in more detail in Statistical methods, above. Finally, the denominator P(xf|θt) in the right-hand side of (5), which is just a normalising constant, can be obtained by summing the numerator overall values of yf. Therefore, P can be calculated from (5), so substituting this into (3) gives Also, Improving calculation speeds: our analyses of ~480,000 sites would not be feasible without a number of techniques to improve the speed of the EM algorithm for the Mendelian model, so we briefly describe two of these techniques now.49, and for such models the most time-consuming part of the calculation is summing overall possible genotype combinations for all family members in each family. However, this part of the calculation is essentially common to all methylation sites, so we obtain considerable improvements in speed by performing this calculation once and storing the results for later use.The Mendelian model is a segregation analysis modelP(yf|θt) via  can be expressed as a sum over all genotype combinations for the family which are consistent with the genotypes yf. Evaluating these sums is usually very time-consuming, however P(yf|θt) depends on t, so P(yf|θt) does not depend on t or the M-values xf. Therefore, we calculated P(yf|θt) once for every possible combination yf of genotypes, and stored these values of P(yf|θt) for later use in the update equations  via  and 5),,P(yf|θt)yf for which we had to store values of P(yf|θt), we assumed that no more than 1 of the founders in each family is a carrier and that no founder is a homozygote carrier . This assumption is not essential, however, and it can be weakened  or entirely dispensed with (if the families are not too large and not too many family members have epigenome-wide data).We also used a simplifying assumption. To reduce the number of genotype combinations M-values in blood HM450K data set of 433 cases and their matched controls from the MCCS. The models were adjusted for body mass index, tobacco smoking, alcohol drinking, time between blood collection, and cancer diagnosis, and sample type  of case–control pairs were successfully matched on sample type). For methylation marks exhibiting a bimodal or trimodal distribution, we categorised the methylation variables into groups corresponding to the observed ‘peaks’ of hypo, hemimethylated or hypermethylated, based on visual inspection of the M-value distribution  further adjusting the models for age at menarche, menopausal status, number of live births, and use of hormonal replacement therapy; (3) restricting the analyses to DNA prepared from dried blood spots.For each of the 24 CpG sites of interest, we first estimated odds ratios (OR) for breast cancer risk using conditional logistic regression models, for a one standard deviation increase in the methylation 52. A total of 251 participants (231 cases and 20 controls) had iCOGS and HM450K data available. Association between genotype and methylation was assessed using linear regression, with beta-value as the outcome variable and allele count as the explanatory variable. The allele count was estimated by rounding the allele dose to an integer value.Data for all variants with 1 kb of the GREB1 probe that were genotyped or imputed using the iCOGS array were retrieved for MCCS participants included in the Breast Cancer Association Consortiumhttp://support.illumina.com/array/array_kits/infinium_humanmethylation450_beadchip_kit/downloads.htmlIllumina Infinium HumanMethylation450K manifest was downloaded from All DNA methylation data (HM450K array) has been deposited to GEO (Accession No. GSE104942) and all bisulfite sequencing data has been deposited into BankIt2071934 (MG686237-MG686418) and is freely available.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 , that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat. While fluorescence microscopy has been extensively used to study subcellular structures and organelles, its application to structural analysis of macromolecular complexes has been restricted by the diffraction limit of visible light (∼200–250 nm). Super-resolution microscopy has broken the diffraction barrier and, of the various super-resolution approaches, the best resolution is obtained using single molecule localization microscopy (SMLM). Based on the repeated activation (blinking) of small numbers of discrete fluorophores , whose precise localization is determined using a Gaussian fit of the point-spread function (PSF), SMLM provides ∼20 nm X-Y  resolution and, with the addition of an astigmatic cylindrical lens into the light path, ∼40–50 nm Z  resolution3.Understanding the structure of macromolecular complexes is critical to understand the function of subcellular structures and organelles. X ray crystallography and nuclear magnetic resonance spectroscopy report on protein structure at the atomic level; recent technical advances in cryo-electron microscopy have enabled structural visualization of macromolecular biological complexes at near atomic resolution4. Surface reconstruction and density plots of 3D super-resolution data assume idealistic, noise-free setting and lack quantification5. Ripley’s K, L, and H-functions and univariate/bivariate Getis and Franklin local point pattern analysis have been used to analyze super-resolution data for different applications12. While useful for global cluster analysis, these second-order statistics have limited ability to deal with localized shape and size properties of homogenous clusters. Moreover, calculating the Ripley’s function is computationally intensive making it impractical for handling millions of points13. It is also known that Ripley’s function underestimates the number of neighbors for points near the boundary of the 2D or 3D study area (known as the edge effect)14. Several correction methods were proposed to solve the edge effect problem but at the expense of even further increase in computational complexity making it unfeasible to scale to SMLM big-data.However, development of analytical tools to interpret the point distributions generated by SMLM is in its infancy16 retain the inability to deal with varying cluster densities and sensitivity to prior settings and noisy events. DBSCAN has several parameters that must be carefully set and its runtime scales quadratically with the number of points 17. Voronoi tessellation depends on Voronoi cell areas to segment clusters and has limited multiscale capability18. Griffié et al.20 proposed a parameter-free cluster analysis method that is based on Bayesian prior probabilities. Their method is sensitive to the prior settings and processing time to analyze one data set, 30 2D ROIs of size 3 × 3 μm2 each at low density, is ∼19 hours, and thus does not scale to large SMLM datasets19. Other methods focused on molecular counting by studying photoactivatable fluorescent proteins (FPs) and their blinking behavior, which is based on the photokinetic model of the FPs22. Moreover, Krüger et al.22 used the molecular count per cluster and cluster shape, size, and overlap to select a set of clusters from SMLM data for quantitative analysis. Nieuwenhuizen et al.23 proposed a method that measures the average number of localizations per fluorophore from spatial image correlation during acquisition.Density-based methods  and Bayesian approach combined with Ripley’s functionset al.24 summarize the methods used to mitigate the overcounting artifacts using temporal and spatial grouping techniques. The temporal methods require detailed information about the photokinetic rates and the dark time for the fluorophores used and not all the photoswitchable probes are photodetectable21. Annibale et al.25 uses photophysical information to combine events that are assumed to be originating from the same fluorophore. Sengupta et al.26 proposed a method that combines pair-correlation analysis with PALM to distinguish between spatial localizations from a single protein (i.e. nano-clusters) and clusters of proteins. Recently, a method adopted by two groups28 is based on determining the ratio of clustered area per image and the density of localizations per clustered area at various densities, either by changing the labeling density27 or using temporal accumulation analysis of the acquired localizations (blinks)28.The multiple blinking of a single fluorophore can cause an overcounting artifact that might bias the quantification of SMLM data and consequently lead to misinterpretations of the biological findings. Keller 31, a well-established representation used in 3D visual computing. Virtual connections between points transform the point cloud into a network modeled computationally as a graph with nodes (or vertices); edges are connections between nodes (points) that weight the distance between nodes. Such network representations have been widely and successfully adopted for analysis of brain, social and computer networks36. Using machine learning approaches, features that distinguish networks are identified and used to understand the underlying organization or architecture of the network. Here we apply point cloud network analysis to SMLM data sets to define the molecular architecture of plasma membrane-associated caveolae and caveolin-1 (Cav1) scaffolds.We modeled the SMLM data as a 3D point cloud37. Cav1 is also expressed in functional non-caveolar domains, or Cav1 scaffolds, that cannot be distinguished from caveolae by fluorescence microscopy, as both are smaller than the diffraction limit38. Metastatic PC3 prostate cancer cells express Cav1 but no CAVIN1 and no caveolae; stable transfection of CAVIN1 (generating PC3-PTRF cells) induces caveolae37. Application of machine learning to point cloud SMLM network analysis of Cav1 distribution in PC3 and PC3-PTRF prostate cancer cells has now defined Cav1 localization signatures for scaffolds and caveolae.Formation of caveolae, 50–100 nm plasma membrane invaginations, requires both the coat protein Cav1 and the adaptor protein polymerase I and transcript release factor (PTRF or CAVIN1)Indirect immunofluorescence labeling of PC3 and PC3-PTRF cells with anti-Cav1 primary and Alexa647 conjugated secondary antibodies is shown by TIRF and GSD-TIRF SMLM Fig. . Quantif40 to identify distinct clusters (or blobs) and extracting features for each cluster; and 6) Cluster characterization: Defining distinct clusters via unsupervised learning methods in PC3 and PC3-PTRF cells.The analysis pipeline Fig. , referreMultiple, temporally distinct blinks from the same fluorophore or same antibody-labeled Cav1 molecule can give rise to blinks with distinct localizations, thereby skewing the data and subsequent network analysis. To generate a robust method to remove high degree blinks and reconstruct a network of representative Cav1 localizations, we applied an algorithm that performs iterative merging of blinks within 20 nm, the reported resolution limit of the Leica GSD microscope. In short, for every blink, we find neighboring blinks within a 20 nm sphere centered around that blink. Starting with the blink with the largest number of neighbors, we replace that blink and all its neighbors by a new blink positioned at their average location. Then we continue to process the blink with the second largest number of neighbors and so on. We repeat the procedure until no pair of blinks are within 20 nm from each other. Note that we use k-d-tree search algorithm to speed up the process of finding the nearest neighbors. Running our algorithm takes <1 min to process an input point cloud of more than 1.5 million blinks. Application of the merging algorithm to PC3 and PC3-PTRF cells selectively reduces high degree (>50) nodes Fig. . Reductiμm3) and extracted 32 weighted and unweighted network features at 24 thresholds  to be significantly larger than the size of the biological structure we are studying, caveolae, of about 80–100 nm diameter and yet had manageable memory space.To identify the proximity threshold of nodes in 3D space that best discriminates between PC3 and PC3-PTRF cells, we constructed weighted and unweighted undirected networks at thresholds from 20 nm to 250 nm. To enable high speed processing of the large data sets, up to 1 million blinks per image Fig. , we divip1, p2, and p3; feature-threshold pairs that survived the Bonferroni multiple comparison correction are marked with red X’s  were combined by finding the L2-norm of X’s Fig. . We used41 machine learning classification, using a leave-one-cell-out cross-validation (ROIs from each cell are excluded from training and used only in testing) strategy, tested the feature and threshold ranking. We report the classification accuracy of the detected class of (i) each test ROI and (ii) the cell, chosen to be the detected class of the majority of the ROIs  if its degree value (δi) is greater than the average degree value of a random graph (δrand) multiplied by a scalar α, i.e. if α is user-controlled to determine the extent of removal of noisy blinks  and obtained the best filtration with α = 4  and for clustering coefficient features between 120–250 nm (average 180 nm). We therefore chose 80 and 180 nm thresholds for further analysis. To filter out noisy blinks and return clusters in the PC3 and PC3-PTRF data, we used degree features at threshold 80 nm (the mean of the significant thresholds) and compared network measures with those of a random graph Fig. . The nodnks Fig. . We compnks Fig.  and S3 w40 to segment and separate each blob. Twenty-eight measures at 80 nm threshold describing size, shape  and topology  were extracted for each individual blob  returned the optimal number of groups in each population: 2 groups  for PC3 and 4 groups  for PC3-PTRF. Unsupervised clustering methods gathered similar blobs in the same group and dissimilar blobs in different groups  and P2 most similar to PP1 (S2 scaffolds). The PC3-PTRF group most dissimilar to PC3 P1 and P2 groups is PP2, suggesting that it corresponds to caveolae. Identical group matching was obtained if the estimated number of molecules feature was not included and only the remaining 27 features were used  than S1 (P1/PP3/PP4) and S2 (PP1/P2) scaffolds Fig. . S2 scaf45 to find the optimal number of modules and extract modularity features for each blob at thresholds from 20 to 250 nm. Most of the blobs become one connected component  at thresholds >50 nm and show a stable modular structure between thresholds of 60–140 nm, with PP2 caveolae presenting 5–6 modules per blob  did not affect the result; changes between the various conditions analyzed were <5% with no statistically significant difference (i.e. p ≫ 0.05). To differentiate the single antibody blobs from the blobs derived from antibody clusters, we utilized K-means clustering (K = 2) based on the extracted blobs’ features  and increased curvature (sphericity). The presence of hemispherical S2 scaffolds in PC3 cells suggests that Cav1 oligomerization can induce membrane curvature independently of CAVIN1, as reported for Cav1 in bacteria46.Application of computational network modelling and machine learning based 3D pattern analysis tools to SMLM data sets  has identified plasma membrane-associated Cav1 scaffolds that combine to form caveolae and larger scaffolds. S1A scaffolds are equivalent to the 200–600 kD SDS-resistant, 8 S Cav1 oligomers of 14-15 Cav1 molecules46. Further, EM tomography of caveolae labeled with MINISog, reports the presence of 12 regularly spaced Cav1 densities with a half-maximum width of 8–10 nm and spacing of 10 nm51. A recent model of caveolae based on biochemical and cryoEM analysis proposed that caveolae are polygons (dodecahedrons) of Cav1 disks, equivalent to 8 S oligomers52. Our identification of 6-7 caveolae modules in the caveolar coat is consistent with this model and suggests that 8 S oligomers, or S1A scaffolds, regroup to form larger scaffolds that combine to form a caveolae  are combined into one average position. The algorithm iteratively combines all blinks within the merging threshold, in this study set at 20 nm, and converges when there is no pair of blinks within the merging threshold. As such, the merging algorithm allows the interrogation of molecule number and organization within the structure.Using blinks to define molecular architecture assumes that a blink equals a molecule. However, the same fluorophore can blink twice in succeeding acquisition frames21. Indeed, our approximation of 110 Cav1 molecules is less than the predicted 145 Cav1 proteins per caveolae or 160 Cav1’s per 70 S oligomer57. We used a fixed merge threshold for this study that did not take into consideration the differential Z resolution of cylindrical lens-based SMLM. Indeed, analysis of single Alexa647-conjugated antibodies coated on a cover slip generated point clouds with approximate XY standard deviations close to 20 nm and Z dimension close to 40 nm. Future studies should consider modulation of the merge threshold to adapt to labeling conditions, microscope acquisition parameters and the different Z resolution of standard SMLM systems.As SMLM resolution approaches molecular distances between proteins, and particularly at high protein densities, the blinking cycles of individual fluorophores overlap in time such that undersampling and undercounting is a major concern58. Consistent with our findings, EM analysis of anti-caveolin labeling is cytoplasmic and at a distance from the membrane51. In addition, our analysis of simulated SMLM data from a 60 nm sphere, resembling a caveolae, generated point cloud distributions of similar sizes to those we observed experimentally for real caveolae  of the 80,000 blobs analyzed approach the 60–80 nm caveolae diameter by EMlae Fig. . TogetheImportantly, we are the first to use machine learning algorithms to quantify specific features of blobs and identify them automatically. We equipped 3D SMLM Network Analysis with a feature extraction module that can be used to extract features for individual clusters. The clusters features can be used for further analysis  by machine learning. Network analysis, as described here, can therefore be used to reconstruct heterogeneous clusters of different shapes and sizes. Further development and refinement of this approach should allow the determination of the molecular architecture of multiple sub-diffraction limit cellular structures. As indicated, 3D SMLM Network Analysis is a fast, 3D, scalable, and multi-scale method. Moreover, SMLM Network Analysis is enabled with single cluster visualization and can be integrated with different machine learning approaches to extract and automatically identify cluster features.50 and enhanced our understanding of how Cav1 molecules organize to form a caveolae. Indeed, we argue that point cloud network analysis is particularly appropriate for the analysis of the blink distributions generated by SMLM allowing super-resolution fluorescence microscopy to define the molecular structure, stoichiometry and architecture of macromolecular complexes.Network analysis has therefore provided us with invaluable insight into the structure of sub-diffraction limited Cav1 domains. This analysis has defined three classes of non-caveolar scaffolds, including a heretofore undescribed hemispherical intermediate distinct from the biochemically defined 8 S and 70 S Cav1 complexes3D SMLM Network Analysis is, therefore, an integrated pipeline that consist of various computational modules for 3D SMLM quantification and analysis. Each module can be replaced by an equivalent module based on state-of the art methods and re-integrated into the pipeline. The current pipeline can be further improved and we are currently optimizing the implementation such that we can handle the whole cell as one big-ROI. Some other improvements that can be added to the current pipeline are the merging, denoising, and segmentation modules. The merging module may undercount/overcount. The current denoising module can remove signal (not noise). In addition, the segmentation module may over-segment or under-segment and may combine nearby structures into one. Finally, the detection of the different domains depends on the extracted network measures and features, which in turn depend on the different artifacts of the aforementioned modules. We do not believe that the current merge algorithm can distinguish between an active dye that blinks multiple times and multiple fluorophores on a single secondary antibody. Indeed, we consider that such blinks all contribute to the non-biological networks and our goal is not to distinguish these blinks but rather interpret them to determine the molecular localization that gave rise to these non-biological blinks.2 and 0.1 mM CaCl2), permeabilized with 0.2% Triton X-100 in PBS/CM, and blocked with PBS/CM containing 10% goat serum (Sigma-Aldrich Inc.) 1% bovine serum albumin . Then the cells were incubated with the primary antibody  for 12 h at 4 °C and with the secondary antibody  for 1 h at room temperature. The primary and secondary antibodies were diluted in SSC  buffer containing 1% BSA, 2% goat serum and 0.05% Triton X-100. Cells were washed extensively after each antibody incubation with SSC buffer containing 0.05% Triton X-100. Post-fixation was applied using 3% PFA for 15 min followed by extensive washing with PBS/CM. For single antibody cover slip coating, cover slips were incubated with 2.5 μg fibronectin for 12 h at 4 °C and then with Alexa647-conjugated anti-rabbit secondary antibody diluted 1:500 in PBS/CM for 1 h at room temperature.PC3 and PC3-PTRF cells were cultured in RPMI-1640 medium (Thermo-Fisher Scientific Inc.) complemented with 10% fetal bovine serum  and 2 mM L-Glutamine (Thermo-Fisher Scientific Inc.). All cells were tested for mycoplasma using PCR kit . Cells were plated on cover slips  for 24 h before fixation. Cells were fixed with 3% paraformaldehyde (PFA) for 15 min at room temperature, rinsed with PBS/CM , 0.5 mg/ml glucose oxidase (Sigma-Aldrich Inc.), 40 μg/mL catalase (Sigma-Aldrich Inc.), 50 mM Tris, 10 mM NaCl and 50 mM β-mercaptoethylamine  in double-distilled waterGSD super-resolution imaging was performed on a Leica SR GSD 3D system using a 160 × objective lens , a 642 nm laser line and a EMCCD camera . Full laser power was applied to pump the fluorophores to the dark state, and at frame correlation value 25% the imaging program auto-switched to acquisition with 50% laser power. Epi-illumination was applied for the pumping process while a TIRF illumination with 100-nm penetration depth was applied for the acquisition step. For cells, acquisition was done for 5 min with camera exposure time at 6.43 ms/frame and for single antibody coated cover slips for 13 minutes with camera exposure time at 20 ms/frame. The event lists were generated using the Leica SR GSD 3D operation software with XY pixel size 20 nm, Z pixel size 25 nm and Z acquisition range +/−400 nm.μm3. For analysis, each cell is divided (tiled) into 36 ROIs of 3 × 3 × 1 μm3. Each ROI is much greater than subcellular structures. Tiling the cell into ROIs is essential to speed up processing time for the whole cell via HPC cluster computer.Each image has dimensions of 18 × 18 × 1 31. An iterative blink merging algorithm merges blinks within 20 nm of each other, the resolution limit of the GSD approach, removing high degree (high interacting) points (i.e. from the same fluorophore or clusters of fluorophores). Virtual connections constructed between the resulting 3D points transform the Cav1 point cloud into a network modeled computationally as a graph with: nodes (representing a single Cav1 protein); edges (capturing the interaction between pairs of proteins); and weights . We calculate network measures  of Cav1 event lists generated by Leica GSD analysis software as a point cloud (a set of points in 3D space), a well-established representation used in 3D visual computing35. Using machine learning approaches, features distinguishing networks can be identified and used to understand the underlying organization or architecture of the network.Well-established, quantifiable measures can then be extracted from these networks  and divided into different classes:  global vs. local; microscopic (node-level) vs. macroscopic (graph-level) vs. mesoscopic (community-level); network integration vs. segregation; geometrical vs. topological, etc.G =  is a pair of set of vertices or (nodes) V and set of edges E, where n is the number of nodes, and m is the number of edges. An edge is connected between a pair of nodes  ∈ V if the Euclidean distance between the nodes T is the proximity threshold. The Euclidean distance i is the 3D coordinate of point i (the interaction between node i and node j is symmetric), and hence the graph is undirected. We constructed two types of undirected graphs, weighted and unweighted. In the weighted undirected graph, the edge weight is set as We represent the nodes  as graphs (networks), where a graph 45 to find the optimal number of modules (or communities) in a given network. The modules were extracted using the eigenvectors decomposition from the adjacency matrix that represents the blob’s network. The modularity is one of the network measures that we used to estimate the intra-community similarity and inter-community dissimilarity of the molecules in one blob. If the modularity measure is very high, this means that the blob consists of modules , wherein each module is a highly intra-connected set of molecules.We employed the Newman methodN × M similarity (or affinity) matrix Y, in which high similarity between the groups is encoded with small distances. N is the number of groups in the first population (PC3) and M is the number of groups in the second population (PC3-PTRF). The entry in the i − th row and j − th column of Y is set to yi and yj are the feature vectors  from the population of PC3 cells to similar groups in PC3-PTRF cell population, we first form an rs Table  describiTo decide whether or not a blink, in an ROI of a real cell, is regarded as noisy and hence must be filtered out, we resort to comparing the network properties of that node representing the blink with the corresponding properties of nodes of a purely random graph. Those nodes with properties similar to those of the purely random graph are regarded as random noise (and filtered out) and the remaining nodes are retained. The random graph is constructed with the same number of nodes as in the real cell ROI, and each spatial coordinate of the location of its nodes is generated with same distribution of the cell ROI blinks. For example, the locations of the blinks in both the X and Y dimensions followed a uniform distribution, while the distribution of the blinks in the Z dimension followed a normal (Gaussian) distribution. An example of generated random blinks that correspond to a real ROI taken from one of the PC3-PTRF cells in our data set Fig. .47 to simulate SMLM imaging for single molecule (i.e. a 10 nm rod resembling a single antibody) and a sphere with diameter of 60 nm to resemble a caveolae. We used the average standard deviation for blinks from the single molecule clusters to find the localization precision values. We set the lateral precision (XY) to be 20 nm and the axial precision (Z) to be 38 nm. We recorded 40,000 frames and we set the label epitope distance to be 20 nm and the epitope density to be 0.012821 nm−2 with labeling efficiency of 100% to match the reported 145 Cav1 molecules per caveolae57. All the SuReSim simulation experiments were performed using the direct simulation workflow. The SureSim simulated data event list was processed using 3D SMLM Network Analysis and the obtained bio-signatures were compared with real data obtained for caveolae in PC3-PTRF cells.We used the recently published SuReSim softwareSupplemental MaterialSupp. Video S1Supp. Video S2Supp. Video S3Supp. Video S4


Rainfall in the central Andes associated with the South American Monsoon and the South American Low-Level Jet results from orographic effects on atmospheric circulation exerted by the Andean Plateau and the Eastern Cordillera. However, despite its importance for South American climate, no reliable records exist that allow decoding the evolution of thresholds and interactions between Andean topography and atmospheric circulation, especially regarding the onset of humid conditions in the inherently dry southern central Andes. Here, we employ multi-proxy isotope data of lipid biomarkers, pedogenic carbonates and volcanic glass from the Eastern Cordillera of NW Argentina and present the first long-term evapotranspiration record. We find that regional eco-hydrology and vegetation changes are associated with initiation of moisture transport via the South American Low-Level Jet at 7.6 Ma, and subsequent lateral growth of the orogen at 6.5 Ma. Our results highlight that topographically induced changes in atmospheric circulation patterns, not global climate change, were responsible for late Miocene environmental change in this part of the southern hemisphere. This suggests that mountain building over time fundamentally controlled habitat evolution along the central Andes. This corresponds to weeks in deciduous plants2518Osc have also been reported to form during the rainy season, or at its end28wax as a proxy for the leaf water hydrogen isotopic composition (a function of plant transpiration), soil-carbonate δ18Osc as a proxy for soil water δ18O values (a function of soil evaporation), and volcanic glass δDvg as a proxy for precipitation δD values, and their respective isotopic differences as a measure of plant transpiration (Δ δDleaf − δ18Osoil) and soil evaporation (Δ δ18Osoil − δDprecipitation) 2530One commonly encountered impediment in stable isotope paleoaltimetry is the potential transience of stable isotope lapse rates through time, e.g. changes in moisture sources and trajectories due to topographic uplift or climate change1028P margin 23. We foOur isotope records distinguish between two stages in eco-hydrological and topographic conditions along the eastern AP: (1) a humid phase, which corresponds to a contiguous open foreland setting; and 2) an arid phase, representing an intermontane basin setting following the establishment of an orographic barrier (Sierra León Muerto) east of the present-day AB  and 2. B an arid wax and δ18Osc values decreased from ca. −110 to −150%, and 23 to 18%, respectively, indicating a trend towards wetter conditions in this inherently dry region  supports this notion as it indicates that meteoric water in the contiguous AB had a similar stable isotope composition at that time as modern meteoric waters from the undeformed foreland 83234Paleoenvironmental and sedimentologic data prior to deposition of the Palo Pintado formation indicate arid conditions within the AB32y region . For then the AB 32 likelyforeland 21. The wwax and δ18Osc records consistently display a large variability in isotope values , whose extant relatives live below 800 m in the modern Andean foreland , thick calcrete-bearing paleosols, and deposition of halite and gypsum in restricted fluvio-lacustrine environments  and used the method “interpl” with the specification “linear”. From the equidistant time series for leaf wax, soil carbonates and volcanic glass source waters we calculated the differences between each proxy representing evaporation, transpiration and evapotranspiration. The underlying individual source water reconstructions are discussed in further detail below.The multi-proxy-isotope approach presented here represents one of the first steps to reconstruct the different amounts of evaporation, transpiration and evapotranspiration from a single record on a million of year timescale. Assuming that individual δDwax values are determined by the isotopic composition of water that is available for biosynthesis of leaf wax n-alkanes representing leaf-water and the net biosynthetic fractionation called εbio that accounts for various biochemical hydrogen isotope fractionations during biosynthesis of n-alkanes -enrichment in response to plant transpiration , using the equation δDmw = 1.0343 [1000 + δDvg ] − 1000vg values are presented in After deposition rhyolitic glass incorporates large amounts of meteoric water (3 to 8 wt%) over a time frame of 5 to 10 kyHow to cite this article: Rohrmann, A. et al. Miocene orographic uplift forces rapid hydrological change in the southern central Andes. Sci. Rep.6, 35678; doi: 10.1038/srep35678 (2016).