The deep biosphere is an energy constrained ecosystem yet fosters diverse microbial communities that are key in biogeochemical cycling. Whether microbial communities in deep biosphere groundwaters are shaped by infiltration of allochthonous surface microorganisms or the evolution of autochthonous species remains unresolved. In this study, 16S rRNA gene amplicon analyses showed that few groups of surface microbes infiltrated deep biosphere groundwaters at the Äspö Hard Rock Laboratory, Sweden, but that such populations constituted up to 49% of the microbial abundance. The dominant persisting phyla included Patescibacteria, Proteobacteria, and Epsilonbacteraeota. Despite the hydrological connection of the Baltic Sea with the studied groundwaters, infiltrating microbes predominantly originated from deep soil groundwater. Most deep biosphere groundwater populations lacked surface representatives, suggesting that they have evolved from ancient autochthonous populations. We propose that deep biosphere groundwater communities in the Fennoscandian Shield consist of selected infiltrated and indigenous populations adapted to the prevailing conditions. Westmeijer et al. employ high-throughput sequencing to investigate the connection between deep biosphere groundwaters and surface microbial communities. They suggest that the microbial communities of deep biosphere groundwaters in the Fennoscandian Shield are mostly comprised of autochthonous species, rather than migratory surface representatives. Despite limitations imposed by the predominantly extreme low carbon and energy conditions, subsurface microbial communities are alive6, active9, and metabolically versatile; exhibiting energy conservation strategies that include nitrate and sulfate reduction, fermentation, methanogenesis, and acetogenesis12. Life in the terrestrial subsurface can be dictated by infiltration of organic carbon13, while communities at greater depths are largely separated from surface input and are sustained by alternative sources of energy such as the abiotically produced “geogases” carbon dioxide and hydrogen15 and iron and manganese-rich minerals16. However, the limited study sites compared to the vast volume of the deep biosphere biome results in largely undescribed communities including how the microbial assemblages are shaped.The terrestrial deep biosphere is temporally and spatially separated from the Earth’s photosynthesis-driven surface, yet accommodates a diverse microbial community with an estimated number of 2 to 6 × 1010, streamlined genomes18, and a high functional interactivity such as syntrophy20. For instance, Patescibacteria are suggested to be ubiquitous in shallow groundwaters due to their ease of mobilization from soils, small genomes, and ability to survive in low carbon and energy conditions22. In contrast to the paucity of deep terrestrial biosphere studies, the marine sub-seafloor sediment microbial diversity has been shown to decrease with depth and consequently age of the sediment25 in response to gradients in both temperature and the availability of organic carbon26. Starnawski et al.27 also identified a sub-seafloor community derived by selection of a small group of surface sediment taxa with the ability to persist under severe energy limitation for >5000 years. In addition, analysis of marine sulfate reducing microbes has shown that the sediment surface community influences sub-seafloor populations through the process of species sorting whereby the geochemical conditions shape the microbial communities by favoring distinct populations28. Additional work on Indian Ocean and up to 1.3 Ma old Bering Sea sediments obtained from depths down to 332 m below the seafloor shows community dependence on the relative abundance of the population in the near-seafloor sediment29. Yet, for the terrestrial deep subsurface, the question remains whether surface microbes infiltrate the deep biosphere and possess traits that allow them to survive in this predominantly low energy and nutrient poor ecosystem or if an indigenous deep groundwater community has developed that is independent of the surface world.Deep subsurface microbial communities exhibit distinct traits to cope with low energy flux, including small cell size30, and benthic waters plus sediments33 in the south-western part of the Baltic Sea region. Microbial communities in the deep biosphere were investigated in fractures intersected by boreholes emanating from the Äspö Hard Rock Laboratory (HRL) that is built partly under the land and partly under the sea. Äspö HRL, operated by the Swedish Nuclear Fuel and Waste Management Company, is a 3.6 km long tunnel extending 460 m below sea level into ~1.8 Ga years old fractured bedrock consisting of Fennoscandian Shield Paleoproterozoic granitoids36 that bear groundwaters with contrasting depths, chemical compositions, and residence times37. Water from the terrestrial landscape and the Baltic Sea are transported deep into the bedrock via vertical to subvertical fractures and thus, provide an ideal situation to investigate how the microbial communities in the deep biosphere groundwaters are influenced by different surface communities39. The groundwaters are distinguished according to their origin with “meteoric groundwater” derived from the land surface, “modern marine groundwater” from the overlying Baltic Sea, and “old saline groundwater” thought to be completely separated from surface energy influx41. Many microbiological studies have been carried out at Äspö HRL including ‘omics’ investigations into community structure and dynamics37. These microbial communities survive in the deep biosphere by syntrophy and symbiotic associations that alleviates the ‘tragedy of the commons’42 that is aided by biofilm/aggregate formation43.Previous studies have explored the microbial diversity of surface waters42 drive species sorting of surface microbes, thereby resulting in reduced diversities.Here we set out to elucidate the potential role of infiltration into deep biosphere groundwaters of surface and near-surface microbes for the structuring of the deep microbiota. 16S rRNA gene amplicon datasets from nearby aquatic, sediment, soil, and terrestrial deep biosphere environments were collected to unravel potential similarities between their microbial communities. We hypothesized that the fixed niches harboring a common core microbial deep biosphere biome18O values , chloride concentration, and δn = 19) while the old saline groundwater samples had 108 ± 72 thousand reads  were analyzed from a variety of environments including surface and benthic seawater; upper (depth 0–1 cm) and lower sediments (6 and 20 cm); terrestrial upper (2–3 m) and lower (5 m) soil groundwaters; and meteoric, modern marine, and old saline deep groundwaters residing in bedrock fractures at depths between 70 and 460 m below the surface. The samples from the surface seawater, lower sediment, upper and lower soil groundwaters, and meteoric groundwaters were sequenced for this study while the data from the remaining environments have been previously published. An overview figure of the various sampling sites is provided in Fig. 46. The Patescibacteria dominated the upper soil and meteoric groundwaters (49 and 43%) after which the abundance of this clade decreased with depth in the modern marine (28%) and old saline (21%) groundwaters. Abundant representatives from the Patescibacteria included “Candidatus Falkowbacteria” and “Ca. Kaiserbacteria”  and modern marine (0.02%) groundwaters and was not detected in the old saline groundwater.On phylum level, the Baltic surface seawater community had a similar composition to the Baltic benthic seawater community. Likewise, the composition of sediment communities (upper and lower) resembled each other along with the soil versus deep biosphere groundwaters communities Fig. . The Actia” Fig.  that boters Fig. . On genup value = 6.3e−3, all pairwise tests in Table p = 0.22) that contrasts with the general notion that diversity decreases with sediment depth49. This incongruence was potentially caused by sampling a larger part of the deeper sediment column (i.e. top 1 cm for upper sediment compared to 6 plus 20 cm depth for lower sediment), thereby capturing more fine-scale variation e.g., local diversity hotspots in redox transition zones50, that would positively affect the overall diversity. Within the groundwaters, a negative correlation between alpha diversity and depth was observed  while the correlation of alpha diversity with DOC was positive . In addition, depth correlated with DOC  that in general showed the deeper groundwaters contained less DOC and have reduced diversities. These correlations help explain why the old saline groundwater had a reduced diversity compared to e.g., the meteoric groundwater as the former is at greater depth and has a very low organic carbon content  surface representative and these taxa accounted for 49% of the abundance in the modern marine groundwater community. 572 out of 2836 ASVs in the old saline groundwater had a (near) surface representative that accounted for 36% of this groundwater community , long retention time (up to thousands of years), and very high chloride content (12–16 g L−1). In contrast, the modern marine groundwater community shared relatively few taxa with e.g., the Baltic benthic seawater and upper sediment communities  along with “Ca. Levybacteria” (5.7%) and “Ca. Kaiserbacteria” (2.1%) in the meteoric groundwater. Nine of these core ASVs were affiliated with the genus Syntrophus (class Deltaproteobacteria) of which representatives have been identified in groundwater from a 1.34 km deep former mine55. In addition to the core community, the meteoric, modern marine, and old saline groundwaters harbored an accessory community of 1324 (67% of the community), 4895 (6.0%), and 978 (2.1%) unique ASVs, respectively that were not retrieved in any other environment. 29% of the ASVs unique to the meteoric groundwater were affiliated with the Patescibacteria  surface was the lower soil groundwater. This supported the concept of species sorting whereby in this case, microbial populations migrate, but the restrictive geochemical conditions in the deep biosphere selects. The abundant persistent ASVs aligned with the phyla Patescibacteria, Proteobacteria, and Epsilonbacteraeota. Finally, the absence of a surface representative for the majority of abundant deep biosphere taxa suggested that a large portion of the deep biosphere microbiome were from resident autochthonous populations that live isolated from the photosynthetic driven surface.33. Modern marine and old saline groundwaters from the Äspö HRL were collected under in situ pressure using a 0.1 μm filter connected to the respective boreholes as previously described13. The microbial communities in Baltic benthic seawater plus upper sediment33 and modern marine plus old saline groundwaters13 have been previously described.A total of 29 previously published 16S rRNA gene amplicon (V3–V4 region) samples, each sampled in replicates were included in this study Table . Baltic n = 93) were captured as part of a time-series between October 2011 and December 2013 at the Linnaeus Microbial Observatory (LMO), located 11 km off the northeast coast of Öland using a Ruttner sampler at a depth of 2 m below the water surface56. The lower sediment (n = 14) was sampled in June 2018 with a gravity corer in a bay near Äspö HRL (Borholmsfjärden) according to Broman et al.33 except that the sediment core was cut at 6 and 20 cm below the sediment-water interface. To account for spatial heterogeneity, seven sites within Borholmsfjärden were sampled at two depths, yielding 14 samples. With salinity as the major driver for microbial community composition in the Baltic Sea57, salinity concentrations were measured for the surface seawater samples in LMO (range 6.6–7.6%), the benthic water in Loftahammar (6.5%)32, and the water overlying the sediment in Borholmsfjärden (range 6.1–6.5%). A one-way ANOVA showed no significant differences in salinity regimes among the three environments (p = 0.15). Furthermore, soil groundwaters were sampled in October 2019 using soil tubes available on Äspö Island designated as upper  and lower  soil groundwater. Finally, meteoric groundwater  was sampled in October 2019. Both soil and meteoric groundwaters were sampled in triplicates as described for the modern marine and old saline groundwaters in Lopez-Fernandez et al.13. Briefly, to avoid contamination from stagnant water in the sampling connections and boreholes, three section volumes were flushed and discarded before connecting a high-pressure filter holder (Millipore) containing a sterile 0.1 µm pore size polyvinylidene fluoride Durapore Membrane filter and the water allowed to pass through the filter. The filters were aseptically collected in cryotubes and immediately frozen in liquid nitrogen for transport to the laboratory where the samples were kept at −80 °C.In addition to the published amplicon data, a further 122 samples were collected from various environments  as close as possible to the microbiology sampling effort during October 2019.The chemistry data was part of SKB’s extensive geochemical monitoring program that is publicly available as the Sicada database. For the modern marine and old saline groundwaters, this data has been published in Lopez-Fernandez, et al.58 for the Baltic surface seawater samples; the Qiagen DNeasy PowerSoil Kit for the upper and lower sediment samples; and the Qiagen DNeasy PowerWater Kit for both soil and meteoric groundwater samples. Regarding the soil and meteoric groundwater samples, the manufacturer’s protocol was followed apart from re-suspending the extracted DNA in 50 μL instead of 100 μL elution buffer. 16S rRNA gene fragment sequences (V3–V4 region) were PCR amplified using the primer set 341F and 805R57 according to the protocol described in Hugerth et al.59. DNA concentration was analyzed using a Qubit 2.0 Fluorometer (Life Technologies) and amplicon specificity by gel electrophoresis. Sequencing was carried out at the Science for Life Laboratory, Sweden on the Illumina MiSeq platform, producing 2 × 300 bp paired-end reads13.DNA was extracted using a phenol-chloroform based method60 that relied on FastQC (v0.11.8), Cutadapt (v2.8)61, MultiQC (v1.9)62, QIIME2 (v2019.10.0)63, DADA2 (v1.14.1)64, and the SILVA reference database (v138.1)65. Samples with less than 1000 reads were excluded from downstream analysis, thereby removing three from the 96 samples from the Baltic surface seawater environment. To test for DNA extraction kit contaminants that can be an issue in low biomass environments, four negative control DNA extracts were sequenced and processed using identical parameters. One sample yielded a total of 86 ASVs that were removed from the dataset prior to downstream analysis.Raw sequencing reads were processed using the Ampliseq pipeline (v1.2.0)66. Alpha diversity was estimated using the Shannon-Weaver index, taking the mean over replicates, followed by statistical testing of the diversity between environments by a one-way ANOVA and post-hoc Tukey’s HSD test while correcting for multiple comparisons. Correlation between alpha diversity, depth, and DOC was quantified according to Pearson correlation and tested for significance using the Pearson’s product moment correlation coefficient. Normality was checked prior to statistical testing with the Shapiro-Wilk test, Levene’s test, and quantile-quantile plots. Beta diversity was estimated by the Bray-Curtis dissimilarity index and tested for significance between groups using a permutational analysis of variance (PERMANOVA). Prior to PERMANOVA, the homogeneity of within-group variation was assessed using PERMDISP67. The null hypothesis tested with this procedure was that the average within-group variation was equivalent among groups. Beta diversity was estimated according to Bray-Curtis dissimilarities and visualized using the nonmetric multidimensional scaling (NMDS) function on default settings in the R Vegan (v2.5)68 package. Statistics and plot generation were performed in R Studio (version 3.6.3.)69. A compiled version of the R script, generated using knitr70 (v1.33), is uploaded to a public repository with the link provided in the code availability statement below.Absolute counts were standardized according to relative abundance by dividing an ASV’s count by the total number of counts within a sample as this has been reported to be more accurate than rarefying microbiome dataFurther information on research design is available in the Transparent Peer Review FileSupplementary MaterialsReporting Summary


Quantifying this early O2 has proven difficult. Here, we use the distribution and isotopic composition of molybdenum in the ancient sedimentary record to quantify Archean Mo cycling, which allows us to calculate lower limits for atmospheric O2 partial pressures (PO2) and O2 production fluxes during the Archean. We consider two end-member scenarios. First, if O2 was evenly distributed throughout the atmosphere, then PO2 > 10–6.9 present atmospheric level was required for large periods of time during the Archean eon. Alternatively, if O2 accumulation was instead spatially restricted , then O2 production fluxes >0.01 Tmol O2/year were required. Archean O2 levels were vanishingly low according to our calculations but substantially above those predicted for an abiotic Earth system.Evidence continues to emerge for the production and low-level accumulation of molecular oxygen (O Quantification of this initial rise of O2 remains elusive (2 (PO2) in the Archean atmosphere are upper limits, such as those derived from mass-independent fractionation of sulfur isotopes (MIF-S) ; (ii) sulfidic at depth (SAD) sediments, which are typically deposited beneath suboxic bottom waters and begin to accumulate H2S in the sediment porewater ; and (iii) euxinic sediments, which are deposited beneath sulfidic bottom waters .To find viable mass balance solutions to Archean Mo cycling, we use a Monte Carlo model to generate hypothetical data, given a series of source and sink constraints, and use Mo concentration and isotope values from ancient shales to “filter” out implausible results. Our model balances Mo input from rivers and hydrothermal sources against Mo burial in three sedimentary sinks sw is the concentration of Mo in seawater (M); Ai is the depositional area of that sedimentary environment (km2) ; Rin and Rout are the total input and output rates of Mo to and from the oceans, respectively (mol year−1); fi is the fraction of the total Mo output represented by each particular sedimentary environment; and δ98Mo (‰) are the isotope compositions of each sedimentary environment (where δ98Mo = (98/95Mosample/98/95Mostandard − 1) × 1000).We consider a steady-state mass balance model of Mo concentrations in seawater as a function of Mo inputs and outputs [modified from  and Fe oxide–bearing sediments (∆sw-Fe). This step is additionally important to account for the possibility of sediments deposited beneath ferruginous [Fe(II)-rich] water columns, which may preserve Fe oxides in the absence of O2 and H2S. For Fe oxide–bearing sediments, we select a fractionation factor of 1.25‰, a median value between the fractionation factors of magnetite (Δ98Mo = 0.83 ± 0.60‰), ferrihydrite (ferrihydrite (Δ98Mo = 1.11 ± 0.15‰), goethite (Δ98Mo = 1.40 ± 0.48‰), and hematite (Δ98Mo = 2.19 ± 0.54‰) , and [Mo]i = ai × [Mo]sw, where ai is a distribution coefficient relating sediment and seawater concentrations (liter kg−1). Hence, Rout,i = ri × ai × [Mo]sw × Ai = bi × [Mo]sw × Ai, where bi = ri × ai.The sw can be calculated from derived parameters when combined with key measurements such as δ98Moeuxinic in ancient black shales. However, the derived parameters, particularly Ai and bi, encompass considerable uncertainties, and the system of equations is underconstrained so that there are many nonunique solutions. There are also uncertainties about overall ocean mass balance because Rin is not well constrained.Using this framework, [Mo]bi, [Mo]sw, δ98Moin, and Ai are varied, assuming either a uniform probability distribution or a normal probability distribution across all plausible ranges (sw is varied from extremely low concentrations (0.01 × 10−9 M) to modern marine concentrations (105 × 10−9 M) using a uniform probability distribution that reflects our lack of constraints on ancient seawater [Mo]. For compositions of sediment that had a low likelihood of extending beyond continental margins (euxinic and Mn oxide–bearing sediments), we limit AD ranges to <5% and <10% of the total ocean floor, respectively. Because the deep ocean was likely ferruginous and may have contained SAD sediments or Fe oxide–bearing sediments, depending on S availability, we explore the full range of depositional area for these sediment compositions. Burial rate constants (b) were sampled using a normal distribution because they are measured values. For b value ranges, uncertainty was set at ~±25% of the measured modern value to represent not only analytical uncertainty but also uncertainty with respect to how these values apply to Archean marine sediments. This analysis produces several million solutions to the mass balance equations, which we then filter using constraints from the modern environment and geologic record to eliminate implausible results.To explore this parameter space, we use a Monte Carlo analysis whereby e ranges . For ins2 at 2.5 Ga comprises a third category due to its anomalous nature and is not included with other Neoarchean sediments. For comparative purposes, we also include a fourth category for the Paleoproterozoic. Compiled data can be found in the Supplementary Materials.For modeling purposes, the Archean is subdivided into two categories based on geologic eras of distinct shale-hosted Mo enrichment concentrations and isotopic values: the Paleo and Mesoarchean, and the Neoarchean. The whiff of O2, we assume that crustal Mo delivery could only be less than the modern.Filtering of mass balance solutions for each time period varied according to available constraints . The fir98/95MoSW values at the time of deposition [although this is not always the case; see Kendall et al. (98MoSW values outside the analytical uncertainty of the heaviest shale δ98/95Mo values. The reasoning here is that the Mo contained within shales is a mix of detritally hosted Mo and authigenic Mo, meaning that the δ98/95Mo values of these shales are always equal to or lighter than contemporaneous δ98/95MoSW (98/95Mo in Archean euxinic shales are thus those with the least crustal influence and are the most likely to have captured ancient δ98/95MoSW.The second filter removed all scenarios that did not produce an isotopic composition in euxinic sediments within the heaviest range typically observed for a given time period . We assuADSAD > ADE.A third filter removed scenarios where the depositional area of euxinic sediments exceeded the depositional area of SAD sediments. This logic has been applied before to Mo mass balance solutions [i.e., (−1 because enrichments <2 may simply reflect Mo in detrital grains. The range of sedimentation rates was modified from those used by Reinhard et al. (Last, a fourth filter removed all scenarios that did not reproduce the concentrations of Mo within the range observed in ancient euxinic shales . For eacPO2. First, we subtract the potential hydrothermal Mo input to prevent overestimating Mo input from oxidative weathering. Where data have been collected in the modern environment, it appears that both high-temperature and low-temperature fluid alterations of the lithosphere can result in Mo-bearing fluids (Rin values would overestimate the role of oxidative weathering in supplying Mo to the ocean.With the lower limits on Mo input to the ocean from Rin values to estimate the contribution of hydrothermal sources, based on the values of Wheat et al. (PO2 and O2 flux calculations.To account for the possibility that Archean hydrothermal sources of Mo were important, we subtract 1% of the modern Mo input from our


The mean (± SD) WID score of 13.42 (± 4.9) in current smokers was significantly lower compared to the WID score of 20.38 (± 5.3) in never smokers (p = 0.001). No significant differences were observed between current and former smokers and between former smokers and former smokers. Dental shade measurements by digital spectrophotometry were highly reproducible and showed that teeth whiteness of current smokers is substantially inferior compared to never smokers. Objective discrimination of dental shade can be a valuable regulatory science endpoint for investigating oral hygiene and dental aesthetics of consumer care products, smoking cessation medications, and tar-free tobacco products  for cigarette substitution.Cigarette smoking contributes to poor oral health and dental discoloration. Therefore, stopping smoking may translate into measurable amelioration of dental shade indices. We compared dental shade parameters by digital spectrophotometry among current, former, and never smokers and verified their repeatability at 7 and 30 days. Dental shade parameters (CIE L*a*b* and corresponding whiteness index for dentistry-WID) were measured in current, former, and never smokers with a digital spectrophotometer (Vita Easyshade V) on three separate study visits: at baseline (day 0), at day 7, and day 30. Dental shade parameters were analyzed in 18 current, 18 former, and 20 never smokers. The repeatability of shade parameters was consistent in current, former, and never smokers. L*, a*, b*, and WID show significant short and long-term repeatability (Clinical trial registration: the study was not registered in ClinicalTrials.gov considering that it is a pilot study, parts of a larger project with ID: NCT04649645 Cigarette smoking is known to contribute to poor oral health and tooth discoloration , 2. The Teeth color and appearance vary widely ; human eInnovative technologies for color measurement have been recently introduced to allow more accurate measurements of dental shade. Digital spectrophotometers are considered the most reliable, practical and versatile color matching system , with muThe*a*b* color space principle, developed in 1976 by the Commission International de l’Eclairage (CIE), is used in most studies on tooth color determinations with digital spectrophotometers . L* exprTo the best of our knowledge, changes in the dental shade in smokers who quit smoking have never been reported. Our research hypothesis is that stopping smoking can translate into measurable amelioration of dental shade indices. However, smoking-induced dental discoloration may be permanent, with limited whiteness restoration after quitting smoking. If this is true, former smokers’ dental shade values should not be much different from current smokers. Moreover, more information is needed to validate within subjects repeatability of digital spectrophotometry in current and former smokers. This is mandatory to increase confidence in the value of this technology for future clinical research.The objective of the study is to: (a) verify the short (7 days) and long term (30 days) repeatability of dental shade indices obtained by digital spectrophotometry; and (b) compare these measurements between current, former, and never smokers. CIE L*a*b* color space parameters and corresponding WID values were considered for the analyses.The study population consists of three study groups identified among a pool of subjects who attended a smoking cessation clinic  in the previous 2 years or contacted among hospital staff.Study group 1 consisted of current smokers, defined as smokers of > 10 cigarettes per day with an exhaled carbon monoxide (eCO) level of ≥ 7 ppm.Study group 2 consisted of former smokers, defined as quitters of at least 12 months and who were still abstinent when contacted for enrollment, with an eCO level of < 7 ppm.Study group 3 consisted of never smokers, defined as having never smoked or who reported having smoked less than 100 cigarettes in their lifetime . Their eHealthy adult subjects (age 18–50 yrs)Presence of at least ten natural anterior teeth , with no composite restorations and no prosthetics or crownCurrent, former, and never smokers had to satisfy the following inclusion criteria:Regular daily use of mouth rinse containing essential oil (EO), cetylpyridinium chloride (CPC), or chlorhexidine (CHX) for at least the preceding seven days before screening visitSubjects wearing fixed or removable orthodontic appliances or prostheses Any conditions that could interfere with dental shade measurements, including:Significant exposure to passive smoking (excludes current smokers)Had undergone professional dental cleaning within six months before screeningPregnancyFurthermore, they had to satisfy the following exclusion criteria:The study was conducted according to the Principles of Good Clinical Practice (GCP) and Declaration of Helsinki. The local Ethics Committee reviewed and approved the study.Not to change their habitual oral hygiene  pattern for the whole duration of the studyAvoid scaling and polishing procedures for the entire period of the studyNot to daily use mouth rinse for the whole of the course of the studyNot to smoke for at least 2 h before each study visitNot to toothbrush for at least 2 h before each study visitNot to eat and drink (except water) for at least 2 h before each study visitThis is an observational study to compare digital spectrophotometric parameters for dental shade evaluation by Vita Easyshade V among three study populations  and to assess their repeatability. The study consists of a total of four visits: screening visit, baseline visit at day 0 Visit 1), short-term follow-up visit at day 7 (± 1 days) (Visit 2), and a long-term follow-up visit at day 30 (± 3 days) (Visit 3)  receive information about the rationale and objectives of the research; (2) verify eligibility criteria by reviewing their inclusion and exclusion criteria; (3) assess smoking status and oral hygiene habit ; and (4) record general socio-demographic characteristics . All eligible subjects were invited to participate in Baseline Visit (Visit 1).Carried out within ten days of the Screening Visit. Subjects were asked to go over a patient information sheet and sign a consent form. After re-checking inclusion/exclusion criteria and reviewing study restrictions, eCO measurement and dental shade assessment were carried out, and baseline data were recorded. Subjects were instructed not to change their habitual oral hygiene pattern and invited to attend the next study visit (Visit 2).Carried out 7 (± 1) days after Visit 1. Eligibility criteria were verified again. Dental shade assessment was repeated for short-term repeatability. Subjects were instructed not to change their habitual oral hygiene pattern and invited to attend the next study visit (Visit 3).Carried out 30 (± 3) days after Visit 1. After re-checking eligibility criteria, a dental shade assessment was repeated for long-term repeatability.The smoking status was objectively verified by measuring exhaled carbon monoxide (eCO) levels (eCO > 7 ppm indicating smoking status) with a portable CO monitor . Subjects were asked not to smoke cigarettes for at least 2 h before eCO measurements. Subjects were invited to exhale slowly into a disposable mouthpiece attached to the eCO monitor per the manufacturer's recommendations. The value of eCO readings was noted.Before dental shade assessment, participants were asked to rinse their mouths with water. They were subjected to gentle flushing and drying by triple syringe to remove any food debris.All measurements were performed in the same examination room, under the exact ambient illumination condition, and by the same operator (GC). The digital spectrophotometer (Vita Easyshade V) was calibrated and used according to the manufacturer’s instructions.Subjects were instructed to open their mouths with the tongue away from the anterior teeth.The shade was measured at the central tooth area of the vestibular surface in “base shade determination” mode, with the measuring tip staying at 90° on the tooth surface to achieve an accurate measurement. Subjects were asked to withhold breathing for a few seconds during measurements, to avoid fogging of the measuring tip as this could cause inaccurate shade readings.CIE L*a*b* color parameters were measured for the vestibular aspect of each anterior tooth .The coordinate L* measures lightness, ranging from 0 (black) to 100 (white); the a* and b* coordinates express chromaticity measures of green/red and blue/yellow, respectively. For each subject, total CIE L*a*b* scores were obtained by adding the individual values of each tested anterior tooth and dividing by the number of teeth examined.The whiteness index for dentistry (WID), which is based on CIE L*a*b* coordinates, was calculated as the following equation :\documenEach subject’s total WID score was obtained by adding the value of each tested anterior tooth and dividing by the number of teeth examined. High WID values indicate whiter teeth, while low WID values indicate discolored or less white teeth. Differences in the WID index were evaluated in consideration of the whiteness 50:50% acceptability threshold (WAT = 2.90 ΔWID units) ; someonet test was performed to assess the mean difference between two measurements from zero.Short-term repeatability of CIE L*a*b* and WID scores was evaluated by linear regression analysis of measurements obtained at V1 and at V2 for each study group. Long-term repeatability was assessed by linear regression analysis of measurements at V1 and at V3. Scatter plots of linear regression analyses summarized repeatability results. Moreover, “Bland and Altman” plots were created to illustrate the level of agreement between V1 vs. V2 and V1 vs. V3 for each study group. A 1-tailed sample Normality was assessed using Shapiro–Wilk’s normality test. Counts and percentages summarized categorical data; continuously distributed data, with symmetrical distribution, were summarized using the mean ; continuously distributed data, with skewed distribution, was summarized using the median . Clinical data comparisons among the groups were carried out by Chi-square test for categorical data and Kruskal–Wallis test for continuously skewed data. Comparison of L* was performed using the Kruskal–Wallis test followed by Wilcoxon pairwise test with Bonferroni’s adjustment for multiple comparisons. Comparisons of a*, b*, and WID scores were performed by one-way ANOVA followed by Tukey's adjustment for multiple comparisons. Moreover, multi-way ANOVA was performed to assess the interaction of age, gender, daily toothbrushing frequency, weekly mouth-washing frequency, and weekly dental flossing frequency on WID score among the study groups.P value < 0.05. R version 3.4.3 (2017-11-30) was utilized for data analysis and generation of graphs.All analyses were considered significant with a We screened a total of 70 subjects: five failed eligibility screening and four failed to attend their baseline visit. Therefore, 61 subjects were enrolled in this study, of which 5 participants were excluded from statistical analysis after failing to attend their short- or long-term follow-up visit.Complete analysis of dental shade assessment was carried out in 56 subjects  18 current, 18 former, and 20 never smokers  and long term (30 days) repeatability of dental shade parameters for never smokers. Analyses are summarized in Table 2 = 0.9287; p = 0.0001). No subjects had the L* difference between V1 and V2 outside the 95% confidence interval, and the mean of differences between L* at V2 and V1 was not different from zero (p = 0.080). Also, for the parameters a* and b* we observed significant regression between study visits . Only two subjects had the * difference between V1 and V2 outside the 95% confidence interval. Moreover, the mean differences between a* at V2 and V1 were not different from zero (p = 0.890). Only one subject had the b* difference between V1 and V2 outside the 95% confidence interval. Moreover, the mean differences between b* at V2 and V1 were not different from zero (p = 0.233).For L*, a significant regression was observed between study visits  (Table p = 0.884).For WID scores, a significant regression was observed between study visits . Only one subject had the L* difference between V1 and V3 outside the 95% confidence interval. The mean differences between L* at V3 and V1 were not different from zero (p = 0.169). Also, for the parameters a* and b* we observed significant regression between study visits . Only one subject had the * difference between V1 and V3 outside the 95% confidence interval. Moreover, the mean differences between a* at V3 and V1 were not different from zero (p = 0.443). Only one subject had the b* difference between V1 and V3 outside the 95% confidence interval. However, the mean differences between b* at V3and V1 were significantly different from zero (p = 0.004).For L*, a significant regression was observed between study visits  (Table p = 0.338).For WID scores, a significant regression was observed between study visits (R1) Table A. Only o1) Table B. MoreovLinear regression analyses were performed to assess short term (7 days) and long term 30 days) repeatability of dental shade parameters for current smokers. Analyses are summarized in Table 0 days re2 = 0.9495; p = 0.0001). Only one subject had the L* difference between V1 and V2 outside the 95% confidence interval, and the mean of differences between L* at V2 and V1 was not different from zero (p = 0.110). Also, for the parameters a* and b* we observed significant regression between study visits . One subject had the * difference between V1 and V2 outside the 95% confidence interval. Moreover, the mean differences between a* at V2 and V1 were not different from zero (p = 0.660). One subject had the b* difference between V1 and V2 outside the 95% confidence interval. Moreover, the mean differences between b* at V2 and V1 were not different from zero (p = 0.446).For L*, a significant regression was observed between study visits  (Table p = 0.773).For WID scores, a significant regression was observed between study visits (R1) Table , Fig. 4A2 = 0.9038; p = 0.0001).For L*, a significant regression was observed between study visits (Rp = 0.712). Also, for the parameters a* and b* we observed significant regression between study visits . One subject had the * difference between V1 and V3 outside the 95% confidence interval. Moreover, the mean differences between a* at V3 and V1 were not different from zero (p = 0.394). Two subjects had the b* difference between V1 and V3 outside the 95% confidence interval, and the mean of the differences between b* at V3 and V1 was significantly different from zero (p = 0.012).No subjects had the L* difference between V1 and V3 outside the 95% confidence interval. Moreover, L* the mean differences between L* at V3 and V1 were not different from zero   Table , Fig. 5ALinear regression analyses were performed to assess short- and long-term repeatability of dental shade parameters for former smokers. Analyses are summarized in Table 2 = 0.9256; p = 0.0001).For L*, a significant regression was observed between study visits (Rp = 0.927). Also for the parameters a* and b* we observed significant regression between study visits . No subjects had the * difference between V1 and V2 outside the 95% confidence interval, and the mean of differences between a* at V2 and V1 was not different from zero (p = 0.278). Only one subject had the b* difference between V1 and V2 outside the 95% confidence interval. However, the mean differences between b* at V2 and V1 were significantly different from zero (p = 0.012).No subjects had the L* difference between V1 and V2 outside the 95% confidence interval, and the mean of differences between L* at V2 and V1 was not different from zero  (Table p = 0.038).For WID scores, a significant regression was observed between study visits (R1) Table , Fig. 6A2 = 0.9215; p = 0.0001).For L*, a significant regression was observed between study visits (Rp = 0.548). Also, for the parameters a* and b* we observed significant regression between study visits .No subjects had the L* difference between V1 and V3 outside the 95% confidence interval. Moreover, the mean differences between L* at V3 and V1 were not different from zero (p = 0.921). No subjects had the b* difference between V1 and V3 outside the 95% confidence interval, and the mean of differences between b* at V3 and V1 was not different from zero (p = 0.102).Only one subject had the * difference between V1 and V3 outside the 95% confidence interval. Moreover, the mean differences between a* at V3 and V1 were not different from zero  (Table p = 0.408).For WID scores, a significant regression was observed between study visits (R1) Table A. No sub1) Table B. MoreovIn all study groups , dental shade parameters L*, a*, b*, and WID showed significant short- and long-term repeatability. Validation of the repeatability of digital spectrophotometry can increase confidence in the value of this technology for future clinical research.p = 0.475). Moreover, no significant difference in the L* score was observed when cross-comparisons were performed . This means that tooth lightness (L*) seems not to be affected by smoking habits. Significant differences were observed between study groups for both a* (p = 0.016) and b* (p < 0.001), meaning that smoking cigarettes could alter tooth color, increasing yellowness (b*) and redness (a*) perception and causing tooth discoloration.Median (IQR) and Mean (± SD) values of L*, a*, b*, and WID scores for the three study groups are summarized in Table p = 0.002) , indicating discolored or much darker teeth. No significant differences in WID scores were observed between current smokers and former smokers (p = 0.153) and between never smokers and former smokers (p = 0.167).A significant difference was observed for WID scores among the study groups (02) Fig. . The meaA statistically significant simple main effect of age for WID score (p = 0.042). No other meaningful interactions were observed for gender, daily toothbrushing frequency, weekly mouth-washing frequency, and weekly dental flossing frequency on WID score among the study groups.Avoiding cigarette smoke toxicants may translate into measurable amelioration of teeth appearance. Information about changes in dental shade indexes in smokers who quit smoking is not available. This study is the first to investigate the impact of smoking and smoking abstinence on teeth appearance by comparing digital spectrophotometry measurements of dental shade in current, former, and never smokers. Dental shade measurements showed that the teeth whiteness of current smokers is substantially inferior compared to never smokers.Former and current smokers had lower WID values (indicating discolored or less white teeth), with a statistically significant difference being observed only between current and never smokers, but not between current and former smokers. Therefore, dental discolorations caused by smoking may be persistent, with only negligible improvement after stopping smoking. An alternative explanation for the lack of significant difference between current and former smokers is that the relatively short (2 years on average) duration of smoking abstinence in our sample of former smokers was not long enough to allow a complete appreciable reversal of dental discoloration. Moreover, it is possible that the observed trend could have become statistically significant with a larger sample size.The reported difference of teeth whiteness between current smokers and never smokers were not only statistically significant but also of practical/clinical relevance because it largely exceeded the 2.90 ΔWID units’ threshold, above which a visual difference in whiteness between two teeth starts to be apparent . The obsA few factors and limitations need to be considered when interpreting these study findings. First, the study populations consisted of relatively young subjects, and their dental shade measurements may not be representative of the general population. This is particularly important, considering that age was the only factor causing a statistically significant interaction with WID scores. Consequently, additional studies with more representative age groups are needed to confirm our findings. Second, dental shade measurements were performed only on the vestibular aspect of each anterior tooth . However, it is unlikely that study findings would have changed significantly by extending measurements to all existing natural teeth . Third, COVID-19 restrictions had only a minimal impact on the study conduct because it was carried out between the first and second wave of the pandemic in Italy (July 2020–October 2020) when clear dental settings guidelines were already set in place and most restrictions to hospital access were lifted.p = 0.001). Therefore, a sample of 18–20 subjects is adequate to detect significant and aesthetically relevant differences . However, it is possible that the absence of significant differences with former smokers  could be due to a small sample size and that a much larger sample could have detected significant differences. When considering within-subject repeatability, the narrow confidence intervals indicate very low measurement variability suggesting that a small group of 18–20 subjects can be informative.Last but not least, this is a proof-of-concept pilot study, which has been conducted to validate the repeatability of a new methodology and increase researchers’ confidence in the value of this measurement for clinical research, including our ongoing large multicentre randomized controlled trial . No prevThis study also confirms the good reproducibility of dental shade indices measured by VITA Easyshade V, both short term and long term; our findings are in agreement with those obtained with the same digital spectrophotometer in several studies –27.This is the first study to investigate dental shade repeatability in current, former, and never smokers. Study findings show significant short- and long-term repeatability in all study groups for all dental shade indices (except L*). In particular, a high level of repeatability was found for WID score with significant regression between study visits (both short and long term) in all study groups.The issue of test variability is significant when investigating dental outcomes in clinical trials of subjects with significant exposure to tobacco smoke. We believe that the good reproducibility of dental shade measurements in this study was due to (1) optimization of environmental conditions with standardization of ambient light; (2) careful consideration of factors that could significantly affect study measurements ; (3) well-trained operators performing the test correctly and accurately, and (4) using an accurate and reliable digital spectrophotometer.Dental shade measurements by digital spectrophotometry were highly reproducible and showed that teeth whiteness of current smokers is substantially inferior compared to never smokers. These findings may have important implications for smoking cessation, especially for those smokers for which bad breath and teeth appearance (due to tooth discoloration and “tar”/tobacco stains) are often perceived as a significant problem –30. More


Despite the presence of the graphene interlayer, the Heusler films have epitaxial registry to the underlying sapphire, as revealed by x-ray diffraction, reflection high energy electron diffraction, and transmission electron microscopy. The weak Van der Waals interactions of graphene enable mechanical exfoliation to yield free-standing GdPtSb membranes, which form ripples when transferred to a flexible polymer handle. Whereas unstrained GdPtSb is antiferromagnetic, measurements on rippled membranes show a spontaneous magnetic moment at room temperature, with a saturation magnetization of 5.2 bohr magneton per Gd. First-principles calculations show that the coupling to homogeneous strain is too small to induce ferromagnetism, suggesting a dominant role for strain gradients. Our membranes provide a novel platform for tuning the magnetic properties of intermetallic compounds via strain (piezomagnetism and magnetostriction) and strain gradients (flexomagnetism).Single-crystalline membranes of functional materials enable the tuning of properties via extreme strain states; however, conventional routes for producing membranes require the use of sacrificial layers and chemical etchants, which can both damage the membrane and limit the ability to make them ultrathin. Here we demonstrate the epitaxial growth of the cubic Heusler compound GdPtSb on graphene-terminated Al Single crystalline membranes enable the tuning of materials properties via strain states that are not accessible to bulk crystals or epitaxially clamped films. Here, the authors demonstrate the synthesis and strain gradient-induced magnetism in membranes of the Heusler compound GdPtSb. In contrast to the uniform strain of epitaxial films, strain in membranes can be applied dynamically, anisotropically, in gradient form, and at larger magnitudes. For example, recent experiments on ultrathin oxide membranes demonstrate the application of extreme uniaxial strain of 8%1, whereas the maximum strain possible in an epitaxial thin film is typically no more than 3% before plastic deformation. In addition, membranes enable the application of strain gradients, which are difficult to control for a film that is rigidly clamped to a substrate  and magnetostriction (M2 ∝ ϵ), is widely used to tune the magnetic properties of thin films11. In contrast, the magnetic coupling to strain gradients, i.e., flexomagnetism (M ∝ ∇ ϵ), has been theoretically predicted13, but to our knowledge has not experimentally demonstrated. This is due, in part, to difficulties in synthesizing and controlling strain gradients in nanostructures and membranes. Conventional techniques for fabricating single-crystalline membranes require etching of a sacrificial layer14, which requires a detailed knowledge of etch chemistry and limits the ability to make ultrathin membranes of air-sensitive materials.Magnetism is particularly attractive for tuning via strain in the membrane form. The coupling between magnetism and strain 15, topological states18, and novel superconductivity20. In contrast with conventional membrane synthesis techniques, which require etching of a sacrificial buffer layer14, our use of a monolayer graphene decoupling layer allows GdPtSb membranes to be mechanically exfoliated, bypassing the need for detailed knowledge of etch chemistries. Our approach is akin to “remote epitaxy,” which has recently been demonstrated for the growth of compound semiconductor22, transition metal oxide7, halide perovskite23, and elemental metal films24. Here, we show that similar approaches apply to intermetallic quantum materials. We find that the large strains and strain gradients in rippled GdPtSb membranes drive an antiferromagnet to ferri- or ferromagnet transition. First-principles calculations show that the coupling to homogeneous strain is too small to induce ferromagnetism, suggesting a dominant role for strain gradients, which would make this system the first experimental example of flexomagnetic coupling. Our work opens a new platform for driving ferroic phase transitions via complex strained geometries.We demonstrate the etch-free epitaxial synthesis and exfoliation of Heusler membranes and show that rippled membranes induce magnetic ordering, turning the antiferromagnetic half Heusler compound GdPtSb into a ferro- or ferrimagnet. Heusler compounds are a broad class of intermetallic compounds with tunable magnetic textures2O3 substrate in (0001) orientation. GdPtSb crystallizes in the cubic half Heusler structure, the same structure as the antiferromagnetic Weyl semimetal GdPtBi26. Since the layer-transferred graphene has randomly oriented polycrystalline domains, if the primary interactions are between GdPtSb film and graphene, then a polycrystalline film is expected. If, on the other hand, the primary interactions are between GdPtSb film and the underlying substrate, then an epitaxial film is expected. Given the recent demonstration of semi-lattice transparency of graphene during GaAs/graphene/GaAs “remote epitaxy”21, we expect epitaxial registry between GdPtSb and the underlying sapphire to dominate. Single-crystalline GdPtSb membranes can then be exfoliated and strained in complex geometries.Our concept relies on the weak van der Waals interactions of monolayer graphene to enable epitaxial growth and exfoliation of a membrane from a graphene-terminated single-crystalline substrate. Figure 2O3 (0001) heterostructures, we first transfer polycrystalline monolayer graphene onto a pre-annealed Al2O3 (0001) substrate using standard wet transfer techniques (“Methods”). The graphene is grown on copper foil by chemical vapor deposition. Raman spectroscopy and atomic force microscopy indicate clean transfers with long-range coverage and minimal point defects or tears in the graphene  to clean the surface. At this stage, the reflection high-energy electron diffraction (RHEED) pattern shows a bright but diffuse specular reflection compared to bare sapphire27, which we attribute to diffuse scattering from the randomly oriented top graphene layer . The streaky RHEED patterns for GdPtSb indicate growth with the epitaxial registry to the underlying sapphire substrate  by a factor of 2O3 [0001] axis with the epitaxial relationship GdPtSb films were grown by molecular beam epitaxy (MBE) on the graphene/Al2O3 1022 and for Cu films growth on monolayer graphene/Al2O3 (0001)24. However, in those cases, the presence of the second domain was attributed to an epitaxial relationship between the film (Cu or GaN) and the 2D monolayer (h-BN or graphene), while the primary domain results from an epitaxial relationship between the film and the substrate. In the present case of ±30° domains of GdPtSb, we rule out a long-range epitaxial relationship to the graphene because our graphene barrier is polycrystalline, and thus if there were an epitaxial relationship between GdPtSb and polycrystalline graphene, a large distribution of GdPtSb domain orientations would result.XRD pole figure and cross-sectional TEM measurements confirm the presence of these two epitaxial domains. Figure ϕ = 0 domain consists of arrays of tilted spindle-shaped dark spots, each of which represents a combination of one Gd atomic column, one Pt atomic column, and one Sb atomic column. For the STEM image of the ±30° rotated GdPtSb, the spindle-shaped spots are aligned along the vertical direction corresponds to clusters of Gd-Pt-Sb atomic columns, as shown by the inset cartoon.Annular bright-field (ABF) scanning transmission electron microscopy (STEM) images in Fig. 2O3 can be mechanically exfoliated to yield free-standing membranes, without the need for a metal stressor release layer. Figure θ − 2θ scans of GdPtSb, before and after exfoliation. Before exfoliation, we observe all of the expected 111, 222, 333, and 444 reflections. The rocking curve width of the 111 reflections before exfoliation is 15.4 arc second, indicating a high-quality film  and for rippled membranes on two different polymer handles (blue and red). The unstrained film shows a weak linear M(H) dependence. Temperature-dependent susceptibility measurements indicate that the films become antiferromagnetic at a Néel temperature of ~12 K  in which local Gd moments are aligned ferromagnetically within (111) planes and the planes are aligned antiferromagnetically with neighboring planes29. Our first-principles calculations are consistent with an antiferromagnetic ground state for GdPtSb  magnetic rippled membranes at room temperature. Figure μB per Gd atom, approaching the ~ 7μB/Gd ferromagnetic limit . Melting the crystalbond on a hot plate releases the membrane/polymer bilayer. Ripples form upon release and cooling  and polyurethane (red) layers show a spontaneous magnetization and hysteresis loops characteristic of ferrimagnetic (FiM) or ferromagnetic (FM) order. The saturation magnetization in the most rippled membrane on polyurethane is 5.2 30 or by bending in polycrystalline membranes on a flexible substrate31. In this study, we observe not a tunable anisotropy, but a strain-induced magnetic ordering. Other studies have investigated magnetoelastic coupling in ferromagnetic shape memory alloys33, where the magnetic phase transition is coupled with a structural martensitic phase transition.Our observation of ripple-induced magnetism in Heusler lies in contrast with previous studies of strain coupling in magnetic Heusler compounds. Previous studies have focused on the strain tuning of magnetic anisotropy, either in epitaxial films35. The RKKY interaction oscillates in sign and magnitude with the distance between magnetic atoms, exhibiting oscillation between ferromagnetic (FM) and antiferromagnetic (AFM) ordering38. However, the atomic-scale mechanism for ripple-induced magnetism in GdPtSb is not fully understood. A key question is whether the AFM to ferro- or ferrimagnetic ordering in our rippled membranes is driven by strain (piezomagnetism and magnetostriction) or strain gradients (flexomagnetism). Both strain and strain gradients are present in our rippled membranes, and both increase with the ripple aspect ratio. While piezomagnetism and magnetostriction have been widely studied, to our knowledge flexomagnetism has only been predicted13, but not experimentally observed.Due to the short-range nature of exchange interactions, subtle changes in bond length and local symmetry are expected to modify the magnetic ground state, consistent with our observed AFM to FM (FiM) transition. For example, long-range and oscillatory Ruderman–Kittel–Kasuya–Yosida (RKKY) interactions were detected in some Mn-based Heuslersϵs along the sinusoidal paths are modest, with peak values of ±0.17%. In our first-principles calculations, ±0.17% is much too small a strain to induce a ferromagnetic or ferrimagnetic state, with a much larger strain of ~ 5% being required  were cut into 1-inch-by-1-inch pieces and soaked in dilute nitric acid (5.7%) for 40 s followed by 3× DI water rinse followed by soaking in acetone and IPA to remove water from the surface. Dilute nitric acid helps remove the oxide and impurity particles from the surface. Foils were then dried under a gentle stream of air. Foils were subsequently loaded into a horizontal quartz tube furnace in which the furnace can slide over the length of the tube. Prior to monolayer graphene synthesis, the CVD chamber was evacuated to < 10−2 Torr using a scroll pump. The system was then back-filled with Ar and H2, and a steady flow  monitored by mass flow controllers was maintained at ambient pressure. The furnace was then slid to surround the samples, and annealed for 30 min. Then 0.3 sccm of P-5 gas (5% CH4 in Ar) was flowed for 45 min so that a monolayer of graphene formed on the surface. To terminate the growth, the furnace was slid away from the samples, and the portion of the quartz tube containing the samples was cooled to room temperature.Graphene was grown using thermal chemical vapor deposition of ultra-high purity CH40. (0001)-oriented Al2O3 substrates were prepared by annealing at 1400 °C for 10 h at atmospheric pressure in order to obtain a smooth, terrace-step morphology. To perform the graphene transfer, the graphene/Cu foils were cut into 6-mm-by-6-mm pieces and flattened using clean glass slides. Approximately 300 nm of 495K C2 PMMA  was spin-coated on the graphene/Cu foil substrate at 2000 RPM for 2 min and left to cure at room temperature for 24 h. Graphene on the backside of the Cu foil was removed via reactive ion etching using 90 W O2 plasma at a pressure of 100 mTorr for 30 s. The Cu foil was then etched by placing the PMMA/graphene/Cu foil on the surface of an etch solution containing 1-part ammonium persulfate  and 3-parts H2O. After 10 h of etching at room temperature, the floating PMMA/graphene membrane was scooped up with a clean glass slide and sequentially transferred into three 30-min water baths to rinse the etch residuals. The PMMA/graphene membrane was then scooped out of the final water bath using the annealed sapphire substrate, to yield a PMMA/graphene/Al2O3 stack.Our graphene transfer procedure is a modified polymer-assisted wet transfer, similar to the transfer recipes studied in previous works2O3 interface, samples were baked in the air at 50 °C for 5 min, then slowly ramped to 150 °C and baked for another 10 min. The PMMA is removed by submerging the sample in an acetone bath at 80 °C for 3 h. This is followed by an isopropanol and water rinse. The sample is indium bonded onto a molybdenum puck and outgassed at 150 °C for 1 h in a loadloack at a pressure P < 5 × 10−7 Torr before introduction to the MBE growth chamber. Finally, the graphene/Al2O3 sample is annealed at 400 °C for 1 h in the MBE chamber to desorb remaining organic residues and then annealed up to 700 °C immediately prior to the growth of GdPtSb.To remove water at the graphene/Al2O3 by molecular beam epitaxy (MBE) at a sample temperature of 600 °C, using conditions similar to ref. 27. Gd flux was supplied by a thermal effusion cell. A mixture of Sb2 and Sb1 was supplied by a thermal cracker cell with a cracker zone operated at 1200 °C. The Pt flux was supplied by an electron beam evaporator. Fluxes were measured in situ using a quartz crystal microbalance (QCM) and calibrated with Rutherford Backscattering Spectroscopy. The Gd to Pt atomic flux ratio was maintained to be 1:1. Due to the high relative volatility of Sb, GdPtSb films were grown in an Sb adsorption-controlled regime with a 30% excess Sb flux. After the growth, films were capped in situ with 75 nm amorphous Ge to protect the surfaces from oxidation.GdPtSb films were grown on graphene/AlGraphene transfer coverage and cleanliness is studied using field emission scanning electron microscopy (SEM) (Zeiss LEO 1530 Gemini). The graphene quality after the transfer is assessed via Raman spectroscopy using a 532 nm wavelength laser (Thermo Scientific DXR Raman Microscope). The laser power is kept below 5 mW in order to prevent damage to the graphene. The terrace-step morphology of the annealed sapphire substrates with and without graphene termination is analyzed by AFM (Bruker Multimode 8 SPM) in tapping mode.2O3 sample was prepared for TEM using a Zeiss Auriga focused ion beam (FIB) with a final FIB polishing step with a 5 kV 100 pA Ga-ion beam. The sample surface was further polished for a higher smoothness in a Fishione 1040 Nanomill with a 900 eV Ar ion beam, to a thickness of ~80 nm. We did not seek to get the thinnest possible sample, as the film layer could peel off from the substrate when the sample is too thin. The TEM sample was kept under vacuum and cleaned in an Ibss GV10x DS Asher plasma cleaner for 10 min under 20 W to remove contaminations before being inserted into the TEM column.The GdPtSb/graphene/AlA Thermo-Fisher Titan STEM equipped with a CEOS probe aberration corrector operated at 200 kV was used to collect STEM data. A 24.5 mrad semi convergence angle and an 18.9 pA current probe were used. A Gatan BF/ABF detector with 5.7 mrad and 22.8 mrad inner and outer collection angles was used to collect the annular bright-field (ABF) images.2O3 substrate. For the membrane samples, we subtract a background measurement of the polymer tape (polyimide or polyurethane) +  Crystalbond.Magnetic properties were measured using a Quantum Design MPMS SQUID (Superconducting Quantum Interference Device) Magnetometer. For the film data, we subtract a background measurement of the Al41 within the local density approximation. Calculations were done on a conventional unit cell (simple cubic with four formula units per cell) with 6 × 6 × 6 Monkhorst-Pak k-point mesh, a plane-wave cutoff of 25 Hartree, with spin–orbit coupling included. The criterion for convergence was a potential residual of <1.0 × 10−8.Density-functional theory (DFT) calculations were carried out with the ABINIT package. The pseudopotentials used were PAW JTH v1.1Supplementary InformationPeer Review File


Gkn1−/− mice also exhibited increased expression of the lipid-regulating hormone ANGPTL4 in the small bowel. The microbiome of Gkn1−/− mice exhibited reduced populations of microbes implicated in obesity, namely Firmicutes of the class Erysipelotrichia. Altered metabolism consistent with use of fat as an energy source was evident in Gkn1−/− mice during the sleep period. GKN1 may contribute to the effects of the stomach on the microbiome and obesity. Inhibition of GKN1 may be a means to prevent obesity.Obesity and its sequelae have a major impact on human health. The stomach contributes to obesity in ways that extend beyond its role in digestion, including through effects on the microbiome. Gastrokine-1 (GKN1) is an anti-amyloidogenic protein abundantly and specifically secreted into the stomach lumen. We examined whether GKN1 plays a role in the development of obesity and regulation of the gut microbiome. Gkn1 Obesity is a risk factor for a range of diseases including nonalcoholic steatohepatitis, cancer, diabetes, cardiovascular disease, hypertension, arthritis and others2. The cure for obesity is diet and exercise and in some cases bariatric surgery. These interventions can lead to weight loss but many patients find it very difficult to successfully lose weight or to maintain a successful weight reduction. Thus, therapies to promote or maintain successful weight loss are needed. One barrier to the development of weight-loss therapies is our incomplete understanding of the physiological regulators of diet-induced obesity. A better understanding of factors that regulate metabolism and accumulation of body fat may help in the development of drugs to fight obesity.The number of obese and overweight Americans over the age of 20 exceeded 70% in 2013–2014 with obesity rates exceeding 37% at that time3. The stomach can directly impact appetite through the production of the orexigenic hormone ghrelin4. The effectiveness of bariatric surgery in weight loss and metabolic syndrome, which can occur independent of mechanical restriction of food intake or malabsorption of calories, suggest that the stomach plays important roles in obesity and metabolism beyond its role in digestion5. The benefits of bariatric surgery can occur independent of ghrelin, suggesting that other additional stomach functions may contribute to the regulation of metabolism and obesity6.The stomach, which is a hollow muscular organ that initiates the second stage of digestion, regulates body weight and metabolism7. How the stomach contributes to regulation of the gut microbiome is not known.Bariatric surgery results in short- and long-term changes in the gut microbiome of patients and this altered microbiome promotes reduced fat accumulation when transplanted into mice9. GKN1 is not a circulating hormone but is instead secreted into the gut lumen from gastric foveolar epithelial cells10. GKN1 is protease-resistant and very stable, refolding at elevated temperatures and high concentrations of urea11, suggesting that this secreted protein may be resistant to digestion and have effects in the intestine beyond the stomach. GKN1 is highly conserved in mammals and has a single domain called a BRICHOS domain14. Like other BRICHOS domain containing proteins, GKN1 is anti-amyloidogenic and can prevent formation of model amyloid fibers in vitro15. The physiological function of GKN1 is not known. Since it is abundantly and exclusively made in the stomach and secreted into the lumen where it can potentially interact with microbes, we hypothesized that GKN1 may play a role mediating the stomach’s effects on the microbiome, metabolism or body weight gain. To test this we generated GKN1−/− mice and assessed their body composition and sensitivity to diet-induced obesity.Gastrokine-1 (GKN1) is a small protease-resistant protein that is made exclusively and abundantly in the stomachAll methods were carried out in accordance with relevant guidelines and regulations.+/− mice on a C57Bl/6 genetic background were generated by the trans-NIH Knockout-Out Mouse Project (KOMP) and bred and maintained in our facility on that background. Mice were provided food and water ad libitum and kept on a 12/12-h light/dark cycle. All cages contained at least one enrichment device in the form of a cardboard tube or “house”. To test the effects of a thermoneutral environment mice were maintained in a room kept at 30 °C. For normal chow diet, mice were maintained on Teklad diet 2919 global 19% protein-extruded rodent diet . For high-fat diet, mice were fed research diet D12492 .All animal protocols were approved by the University of Notre Dame (Boone) or the University of Cincinnati (Seeley) Institutional Animal Care and Use Committees. All experiments were carried out in accordance with the ARRIVE  guidelines and in accordance with the NIH Guidelines for the Care and Use of Laboratory Animals. GKN1RNA was extracted from tissues using TRIZOL (Thermo Fisher) and cDNA was generated using the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems). GKN1 was amplified by 30 cycles of PCR using the primers listed in supplementary Table Proteins were extracted from tissues using RIPA buffer and 30 μg total protein was resolved by SDS-PAGE using NuPAGE 4–12% precast gels and transferred to PVDF membranes (Thermo Fisher). Membranes were blocked with 5% non-fat milk, immunoblotted with rabbit anti-GKN1 antibody (1:2000 in 5% non-fat milk) (Proteintech) overnight at 4 °C, and developed with anti-rabbit IgG-HRP antibody. Actin immunoblotting and Ponceau red staining of membranes were used as controls. To assess Gkn1 protein in the gut lumen, contents were flushed in PBS, mechanically dissociated and filtered through nylon mesh and filtrates were precipitated in cold acetone, resuspended in Laemmli buffer and assessed by Western blotting.Formalin fixed paraffin embedded (FFPE) 5 μm sections were deparaffinized to water and blocked in 2% horse serum before overnight incubation with rabbit anti-GKN1 antibody (Proteintech) (1:250 in 2% horse serum). Sections were washed and developed with anti-rabbit antibody using the ImmPRESS-HRP polymer detection kit (Vector Labs). Standard protocols were followed for H&E staining of FFPE tissues.18F) fluorodeoxyglucose  and scanned 30 min later with a trimodal ALBIRA PET/SPECT/CT image station  and analyzed as described16.Magnetic resonance imaging was used to assess whole body composition of live subjects using the EchoMRI system . To assess the formation of tumors, mice were injected i.v. with 200 μCi of (Liver triglycerides were measured in approximately 50 mg of fresh frozen tissue using Pointe Scientific Triglyceride reagent set (#T7532-120) according to the manufacturer’s specifications. Histological assessment of fat in liver was performed on FFPE tissues using H&E staining. In addition, to visualize fat accumulation, OCT-embedded frozen sections were stained with oil red O. Sections of 8 mm were dried, fixed in 4% paraformaldehyde, pretreated with 60% isopropanol, stained in 0.3% oil red O (w/v in 60% isopropanol), de-stained in 60% isopropanol, counterstained in hematoxylin and photographed in aqueous media.Liver RNA was collected in TRIZOL and qPCR was performed to assess the expression of gluconeogenesis genes (phosphoenolpyruvate carboxykinase (PepcK), glucose-6-phosphatase (G6P), fructose-1,6-bisphophatase (FBP), pyruvate carboxylase (PC)) and to assess inflammation (interleukin-1 beta (IL1β) and interleukin-6 (IL6)) using primers listed in supplementary Table For assessment of food caloric extraction, food intake was measured by weighing food daily and total stool was collected, dried and analyzed by bomb calorimetry . Efficiency of calorie absorption was determined by the percent of calories consumed vs calorie content of stool.17. Briefly, diet was laced with 2.5% sucrose polybehenate and animals allowed to feed for 2 days. Fecal samples were collected 24 h later. Fecal lipid content was assayed by gas chromatography of fatty acid methyl esters. Dietary lipid absorption was estimated using a ratio of total fecal fatty acids to sucrose polybehenate.Fecal lipid content was assessed as previously describedBlood glucose was measured with the Accucheck meter and strips  following an i.p. injection of 1.5 g/kg dextrose in un-anesthetized mice previously fasted for 4 h. Fasting plasma insulin was measured by ELISA .For measurements of CCK, GIP, and ANGPLT4, tissue scrapings were collected from washed intestinal samples and processed for RNA isolation and assessment of gene expression by qPCR. Ghrelin mRNA levels were assessed from mucosal scrapings of the stomach. Leptin and GLP-1 levels in serum were assessed by ELISA .For immunolocalization of ghrelin and leptin in stomach tissue FFPE tissue sections were treated for antigen retrieval by boiling 10 min in sodium citrate buffer  and stained with monoclonal anti-Gkn1 (R&D Systems), and polyclonal anti-ghrelin or anti-leptin antibodies , washed and stained with FITC or PE-conjugated secondary antibodies, mounted anti-fade media (prolong gold with DAPI) and photographed using fluorescence microscopy.2 produced to O2 consumed. Energy expenditure was calculated as total EE (kJ h − 1)/kg body weight = [16.3 × VO2 (L h − 1) + 4.57 × VCO2 (L h − 1)]/body weight (kg).Mice were individually housed in metabolic cages  for indirect calorimetry. Mice were acclimated for 48 h prior to measurements and assessed for 48 h of oxygen consumption and carbon dioxide production and RER was calculated by the ratio of CO18.Gross morphological examination of secondary lymphoid organs and histology of tissues was performed to identify signs of overt inflammation. Flow cytometry was used to analyze lymphocyte activation from spleens and the intestinal lamina propria, as described19. Reads were quality filtered and demultiplexed using a PHRED score of 30. Quality filtering resulted in 544,839 high-quality sequences total. The quality filtered reads were clustered using the open reference OTU picking workflow against the April 2018 release of the SILVA database with a sequence identity of 97% and uclust used as the clustering algorithm. Chimeras were removed using ChimeraSlayer. Low confidence OTUs were removed. Samples with less that 5,000 reads were removed from the OTU table. Statistical analysis was performed using the PhyloSeq package in R.Sequencing of the 16S ribosomal RNA gene V4-V5 region was performed at Argonne National Laboratories  using the Illumina MiSeq instrument . Sequences were analyzed with the Quantitative Insights into Microbial Ecology (QIIME) toolkit version 1.9.1t test. A P value of < 0.05 was considered significant.Statistical analyses were performed with Prism software version 7 . ANOVA was used for multiple comparisons with post-hoc Tukey’s test. Paired comparisons were performed using the Welch 8. We confirmed the stomach specific expression of GKN1 mRNA and protein by PCR, immunoblotting and immunolocalization . To understand the function of GKN1, GKN1+/− mice were bred and maintained on a C57BL/6 N background. GKN1−/− mice were born at Mendelian frequencies with no overt health conditions.GKN1 is a small (18 kDa) protein made exclusively and abundantly in the stomach21. When fed normal chow diet , male GKN1−/− mice initially exhibited body weights comparable to WT littermates at 6 weeks of age -induced obesity, mice were switched from NCD to HFD  for 8 weeks. WT mice gained significant amounts of both weight and fat mass on this diet compared to GKN1−/− mice for alterations in a variety of potential lean phenotype inducing features. Although loss of GKN1 has been associated with gastric cancer28, we observed no increased prevalence of gastric or other cancer in GKN1−/− mice up to 12 months of age (data not shown). This was confirmed by PET scans of GKN1−/− mice indicating no cancers present, and by histology of the stomach and other tissues . These results indicate that GKN1−/− mice are not diabetic  is calculated using the amount of carbon dioxide produced compared to the amount of oxygen consumed. When carbohydrates are being used as the energy source, the RER approaches 1 whereas when fat is being used the RER is approximately 0.7. As expected, both WT and GKN1−/− mice exhibited a diurnal pattern of RER such that during waking (feeding) hours the RER of mice on NCD was higher whereas during rest (less feeding) the RER decreased   in GKN1−/− mice. This suggests that loss of GKN1 alone is not sufficient to result in increased gastric cancer. It is possible that, since GKN1 is expressed in differentiated gastric epithelial cells, the loss of GKN1 in gastric cancer reflects the loss of epithelial cell differentiation. Indeed, at foci of gastric metaplasia in intestinal tumors and other sites, GKN1 is re-expressed9. Although we did not observe spontaneous gastric cancers in GKN1−/− mice we cannot rule out the possibility that loss of GKN1 predisposes to gastric cancer in the context of other additional inducers such as inflammation or infection. In addition, the lifespan of mice extends to two years of age and so we cannot rule out the possibility that Gkn1−/− mice might develop gastric cancer later in life.The loss of GKN1 expression is associated with gastric cancer leading to the suggestion that GKN1 may be a tumor suppressor gene11. From an evolutionary perspective, a protein that increases deposition of energy in fat depots would be advantageous. Further studies of the requirement for GKN1 in diets more comparable to human consumption will be informative. There are no described human patients with GKN1 deficiency or notable genetic associations of GKN1 with obesity. However, some conditions such as Helicobacter pylori infection or use of nonsteroidal anti-inflammatory drugs suppress GKN1 expression and it will be of interest to determine whether these impact metabolism or adiposity in ways consistent with those observed in GKN1−/− mice38. Although controversial, H. pylori eradication is associated with increased BMI41 but whether this is related to restoration of GKN1 expression is not known. Other factors might explain increased BMI following H. pylori eradication, such as resolution of dyspepsia or normalization of gastric ghrelin expression resulting increased appetite. An association of NSAID use and BMI has not been reported, but high dose salicylates can result in lower serum insulin and glucose in diet induced obesity models, which phenocopies the insulin and glucose profile of GKN1−/− mice42. Whether suppression of GKN1 by salicylates plays a role in the reversal of diet- and obesity-related insulin resistance remains to be determined. It is likely that bariatric surgery restricts access of GKN1 to the gut lumen and it will be of interest to determine if this plays some role in the beneficial effects of these procedures and in the changes in the gut microbiome that occur following bariatric surgery. Lastly, agents that suppress GKN1 expression or block its actions in the gut may have the potential to prevent diet-induced obesity, improving quality of life and reducing the burdens of obesity on health care.GKN1 is highly conserved across evolution, suggesting that a similar role for GKN1 (and thus the stomach) in human obesity is possibleSupplementary Information.


In vitro protein studies have identified assembly kinetics and the structural motifs involved in binding of adjacent LN domains. The physiological importance of these interactions has been identified through the study of pathogenic point mutations in LN domains that lead to syndromic disorders presenting with phenotypes dependent on which laminin gene is mutated. Genotype-phenotype comparison between knockout and LN domain missense mutations of the same laminin allows inferences to be drawn about the roles of laminin network assembly in terms of tissue function. In this review, we will discuss these comparisons in terms of laminin disorders, and the therapeutic options that understanding these processes have allowed. We will also discuss recent findings of non-laminin mediators of laminin network assembly and their implications in terms of basement membrane structure and function.The laminins (LM) are a family of basement membranes glycoproteins with essential structural roles in supporting epithelia, endothelia, nerves and muscle adhesion, and signaling roles in regulating cell migration, proliferation, stem cell maintenance and differentiation. Laminins are obligate heterotrimers comprised of α, β and γ chains that assemble intracellularly. However, extracellularly these heterotrimers then assemble into higher-order networks  Most BMeceptors , 2009. ALaminins are an obligatory feature of every BM. Each LM is an αβγ heterotrimer comprised of one of five α chains (encoded by LAMA1-5), one of four α chains (LAMB1-4) and one of three γ (LAMC1-3) chains . Use of The LM family has arisen by gene duplication and rearrangement, and common structural motifs are shared between members . ArchetyLaminin N-terminal domain are 252–264 amino acid globular domains found not only in LMs but also other ECM proteins including netrins. 14% of all residues in LN domains are conserved with strict conservation of six key cysteines. There is 72% homology between LMα1 and LMα2, 77% between LMα3B and LMα5, 72% between LMβ1 and LMβ2, and 64% between LMγ1 and LMγ3 LN domains. Lowest conservation is between LMβ3 and the β1 and β2 LN domains  .In vitro analyses have further shown that the αβγ ternary node assembly involves rapid but unstable formation of βγ pairs that are then stabilized through integration of an αLN domain , caused by mutations to LAMA2 (LMα2) . This afIn LMα2 knockout conditions, MDC1A presents with disabilities of the proximal and distal limb muscles, with patients unable to walk more than a few steps unaided . Weaknes1  has been associated with late onset Alzheimer’s disease , and dysAlthough the standard gene and protein therapy toolbox are available to treat LN domain disorders, the large size of LM genes and associated challenges of producing and delivering recombinant therapy-grade LM protein presents challenges. However, promising results have been obtained recently from delivering the 800 kDa LM521 to the blood stream of LAMB2-null mice which rescues some aspects of Pierson syndrome. The delivered LM521 accumulated in the glomerular basement membrane in the correct orientation and led to reduced expression of the podocyte injury markers, and delayed the onset of proteinuria. However, the exogenous LM521 did not migrate to the podocytes nor fully restore the glomerular filtration barrier. Smaller, or hybrid proteins, may be a solution to overcome these challenges . For somvia the nidogen region and provide the missing αLN domain needed to allow LM411 polymerization (A particularly innovative solution exploiting the knowledge gained from studying LM polymerization and counteracting the inherent LM size problems is using protein chimeras to act as linkers . Three srization . The minrization . Where pComparison between knockout and missense mutation associated phenotypes in LM genes has provided valuable information to identify which LMs are essential for individual tissues, but also which domains are involved. Rather than a binary outcome caused by ability or inability to polymerize, we see system-wide differences highlighting the multifaceted roles of LN domains. The variety of pathologies arising from mutations within a stretch of ∼250 amino acids illustrate the importance of LN domains to tissue function.LS, CS, and KH wrote and edited the manuscript. All authors read and approved the final version of the manuscript for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Biol., 2020, 1, 148–155, DOI: 10.1039/d0cb00045k.Correction for ‘Glycosylated cyclophellitol-derived activity-based probes and inhibitors for cellulases’ by Casper de Boer  Humicola insolens Cel7B with β–1,4 glucosyl cyclophellitol was given in the Data deposition section of the original article. The correct PDB code is 6YOZ.The authors regret that an incorrect PDB code for the structure of the The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.