Soil microorganisms act as gatekeepers for soil–atmosphere carbon exchange by balancing the accumulation and release of soil organic matter. However, poor understanding of the mechanisms responsible hinders the development of effective land management strategies to enhance soil carbon storage. Here we empirically test the link between microbial ecophysiological traits and topsoil carbon content across geographically distributed soils and land use contrasts. We discovered distinct pH controls on microbial mechanisms of carbon accumulation. Land use intensification in low-pH soils that increased the pH above a threshold (~6.2) leads to carbon loss through increased decomposition, following alleviation of acid retardation of microbial growth. However, loss of carbon with intensification in near-neutral pH soils was linked to decreased microbial biomass and reduced growth efficiency that was, in turn, related to trade-offs with stress alleviation and resource acquisition. Thus, less-intensive management practices in near-neutral pH soils have more potential for carbon storage through increased microbial growth efficiency, whereas in acidic soils, microbial growth is a bigger constraint on decomposition rates. Land use intensification could modify microbial activity and thus ecosystem function. Here, Malik et al. sample microbes and carbon-related functions across a land use gradient, demonstrating that microbial biomass and carbon use efficiency are reduced in human-impacted near-neutral pH soils. Land management strategies to reverse this decline are needed, but we do not sufficiently understand the detailed mechanisms underlying the accumulation and loss of SOC. This is hampering the development of better land management strategies and introduces uncertainties into models that forecast feedbacks to climatic change. Furthermore, soil is the largest carbon (C) pool in the terrestrial biosphere containing twice as much C as in the atmosphere, with the topsoil (0–30 cm) containing approximately half the amount5. By regulating the storage and release of organic C through decomposition of soil and plant organic matter, soil microorganisms play a major role in soil C fluxes6. New research suggests that better knowledge of this microbial regulation may be critical in projecting changes in SOC, thereby improving predictions of climate change feedbacks8. A new paradigm also recognizes the direct, significant contribution of microbial biomass to organic matter accumulation, whereby microbial residues are transformed into stable SOC fractions11. However, uncertainties remain over the influence of microbial physiology on its biomass production and eventual persistence in soil. This highlights the need to explore both the ecological and biochemical basis for SOC storage with a focus on the microbial traits—their phenotypic characteristics13.The need for food and energy for the growing human population has led to an immense pressure on the planet’s soil resources, with intensive land management often leading to reduced soil organic carbon (SOC) storage15. Microbial carbon use efficiency (CUE) or growth efficiency, the proportion of substrate C that microorganisms assimilate versus that lost in respiration, is a key trait that determines the fate of C in soils17. Recent theory suggests that high microbial growth efficiency may indicate increased ability of those communities to store SOC through relatively greater biomass synthesis and resultant increases in the amount of microbial residues available for stabilization19. An increased microbial investment in resource acquisition in the form of extracellular enzyme production to degrade complex substrates is thought to result in lower growth efficiency20. Similarly, physiological adaptations to alleviate stresses such as soil acidity or decreased water availability could lead to increased maintenance energy requirements, thus lowering the growth efficiency21. Yet, microbial trait trade-offs between the use and acquisition of complex resources, adaptation to stress and biomass production have hitherto been unexplored. We argue that these trade-offs are significant in determining the fate of C inputs in soils.Shifts in microbial traits due to climate change have been shown to have consequences for SOC storage12. We now have evidence that gradients of soil properties such as soil pH are strong drivers of soil microbial diversity23. Therefore, we posit that along similar gradients, there are differences in microbial ecophysiological traits that may affect CUE at a community level. Knowledge of such empirical trends could be the key to better understand the effects of land management on SOC storage. Identifying consistent effects of land management on soil microbial ecophysiology is difficult as land use intensification and their impacts on edaphic properties like soil pH, bulk density and moisture content are often site-specific and context-dependent24. Trait estimates from literature syntheses provide the theoretical and empirical basis of the CUE variability across soils and suggest that microbial efficiency is an integrative measure of stoichiometric constraints, substrate quality and quantity, soil biodiversity, and edaphic properties27. However, we still cannot confidently establish empirical relationships between the microbial CUE and environmental variables. This makes it difficult to predict the spatiotemporal patterns of CUE that are key to upscaling the microbial ecophysiological responses to anthropogenic change28.The collective traits of diverse microbial populations regulate the ecosystem functioning through their interactions with each other and the environmentThe overall aim of this study was to assess the effect of land use intensity on microbial ecophysiological traits across landscape soil gradients and evaluate the consequences of trait trade-offs on SOC accumulation. To achieve this, we analyzed the microbial traits in soils collected from 56 distributed sites across the UK Fig.  in tempe13C-labeled plant litter DOC incorporated into microbial DNA relative to that lost as respired CO2, appeared to be driven by interactions of multiple edaphic properties and could not be assembled in a single linear model. We then used recursive partitioning through regression tree analysis to disentangle the interactions; the best partitioning parameter was soil pH from among the edaphic properties that were tested, which also included soil moisture, clay content, C and N concentration, and C:N ratio , we observed clear relationships between certain microbial ecophysiological measures (biomass estimates and specific respiration) and SOC concentration. However, substrate CUE at a community level, measured as the proportion of ion Fig. . The CUEion Fig. , but theion Fig. ; both inion Fig. . Decreas37 was higher in soils with higher SOC  increased with decreasing SOC concentration Fig. , corroboSOC Fig. , highligass Fig. . Thus, aass Fig. . This vaion Fig. ; however25 and at sites below the threshold pH, acidity and wetness  to intensively managed grasslands or arable croplands (high intensity). To test the effect of land use intensification on the ecophysiology of the soil microbial community, we used 21 locally paired low- and high-intensity land use contrasts from within this survey  sites under investigation, intensification lead to a shift towards alkalinity, microbial community CUE generally decreased and growth rate increased  can be linked to SOC loss in intensive land use systems.Type 1 effect of land use intensification: Plant productivity is highest in soils at the higher end of the pH spectrum, making such soils more favorable for agriculture; evidence suggests that the land cover area under intensive management tends to increase with an increase in mean soil pHsed Fig. , suggest32. In the low pH sites in our study where intensification lead to increased soil pH crossing the threshold , microbial CUE and turnover rates were both higher in more intensive soil systems, with a decrease in microbial biomass and increase in specific respiration , where the increase in soil pH on intensification does not cross the threshold of 6.2. Microbial CUE and turnover rates in both land use contrasts were systematically lower relative to the high pH sites, and intensification leads to a small decrease in both parameters Fig. . This is43. On the contrary, lower availability of organic substrates in the intensive systems is reflected in lower relative abundance of ABC transporters and higher abundance of proteins linked to maintenance pathways, such as large and small subunit ribosomal protein synthesis, amino acid synthesis, purine metabolism (DNA-directed RNA polymerase subunits), and energy generating oxidative phosphorylation proteins like ATPase  were identified as indicators of high and low intensity land use treatments but their relative abundances were significantly higher in the more acidic low intensity soils as an acid tolerance response44. The higher investment into stress alleviation in low intensity soils along with the lower metabolic efficiency reiterates the trade-offs in microbial functional traits.The microbial metabolic constraints in stressed environments such as arable croplands are also apparent in the type 2 effect of land use intensification  but the mechanisms here are distinct. Differences in the frequency and relative abundance of protein indicators under type 2 scenarios highlights several physiological adaptations. A decrease in acidity and moisture levels in the intensive land use system leads to increased microbial turnover Fig.  that is 17. As highlighted earlier, the microbial controls on soil C cycling processes were much lower in low pH soils, as evidenced by lower microbial turnover rates. However, soil microorganisms continued to respire possibly using non-plant C sources like direct C fixation34, although we did not find any evidence from proteomic analyses about alternative C respiration redox strategies. Observations emphasizing that microbial physiological trade-offs are key determinants of the microbial contribution to soil organic matter formation thus need to be further tested to fully understand the biochemical basis of soil carbon accumulation. Nevertheless, we present strong evidence of such trade-offs in microbial traits being important in near-neutral soils, which is where the majority of productive agriculture occurs.In the type 3 intensification effect, where both contrasts were below the threshold pH value of 6.2, pairwise protein indicator analysis presented fewer functional indicators of land use intensification since the two systems were similar Fig. . Key ind39 to determine the site-specific effects of land management strategies on SOC accumulation. The mechanisms highlight the significance of microbial ecophysiological controls on soil organic matter accumulation in high pH soils. Here, less-intensive land management practices have greater potential for soil carbon storage through increased microbial growth efficiency that causes greater channeling of substrates into biomass synthesis. Intensification in low pH soils leads to alleviation of acid-related retardation of microbial growth and organic matter degradation, leading to large losses of carbon through microbial decomposition. In these systems, preserving equivalent amounts of organic C would involve managing the abiotic C-accumulating factors, like acidity and wetness, whilst enhancing plant production. We thus highlight the importance of including physiological attributes of soil microorganisms in designing restorative land management strategies aimed at mitigating losses of soil C by intensive agricultural practices.We established landscape-scale empirical links between key microbial ecophysiological traits and soil C concentration supporting the central role of microorganisms in belowground carbon cycling. Results show that an efficient microbial physiology with a greater proportion of substrate allocated to biosynthesis manifests in the increased ability of such communities to store C in near-neutral pH soils. Trade-offs in microbial physiological traits determine the proportion of microbial organic C investment into biosynthesis. Growth and biosynthesis decline in scenarios of stress and resource limitation when cellular investment is far higher in traits focussed on stress tolerance and resource acquisition. We discern two distinct mechanisms of soil carbon accumulation across a pH threshold of 6.2 for these soils: at higher pH (>6.2), an efficient substrate metabolism leads to increased SOC accumulation; and in acidic wet environments (pH < 6.2), abiotic factors limit microbial growth and decomposition causing accumulation of SOC. This evidence supports the use of soil pH as an integrated proxy of land use change, parent material and climateSoil samples were collected from 56 geographically distributed sites that differed in their edaphic properties Fig. . From thR2 = 0.93), with the only deviations being for the calcareous soils with high inorganic C. For soil from these sites, total C was predicted based on the LOI–C regression model for the non-calcareous sites. Soil pH was measured in deionised water and moisture content was determined by the gravimetric method.Loss on ignition (LOI), C, N, pH, moisture, and clay content were assessed in replicated soil samples from each site using standard protocols. Soil total C concentration strongly correlated with LOI  coupled to a PAL autosampler  with general purpose (GP) interface . Following the basal respiration measurement, vials with soils were opened and incubated in the dark for 8 h before adding 100 µL of 13C-labeled DOC solution (0.13 mgC). The filter-sterilized solution was prepared from 13C-labeled powdered plant leaf litter, containing a range of compounds varying in their decomposability. Leaf litter was produced by growing a temperate herb in a 13CO2 atmosphere (~1 atom% 13C at 400 ppm)45. Respiration measurements were repeated following the same incubation procedure as mentioned above to obtain the proportion of DO13C in respired CO2.For microbial basal respiration measurements, an aliquot (1 g) of the field-moist soil was placed in a 10-mL glass vial and incubated overnight (for ~16 h) in the dark at room temperature (21 °C) without manipulating the moisture levels. Respired CO13C solution and overnight (16 h) incubation in the dark at field moisture capacity. Both extracts were analyzed in the size-exclusion chromatographic (SEC) mode on a liquid chromatography isotope ratio mass spectrometer LC-IRMS 46. This allowed us to obtain the DNA-C content and the proportion of DO13C in microbial DNA from soils with and without substrate addition. Microbial CUE was estimated as DNA-13C/(DNA-13C + ∑CO2-13C), where ∑CO2-13C is the cumulative DO13C lost during respiration. Microbial turnover rate (synonymously referred to as growth rate) was calculated as DNA-13C/DNA-C.Soil microbial total DNA-C concentration was used as a proxy for biomass C; DNA extraction was carried out on a soil aliquot of 0.25 g using PowerSoil-htp 96-well soil DNA isolation kit following the manufacturer's instructions . Another set of identical DNA extraction was performed following addition of 25 µL of DO48. Raw sequences from amplicon sequencing were quality filtered, merged and clustered to generate OTU’s at 97% sequence similarity. Richness was calculated on rarified data (2000 reads) using the R vegan library.An aliquot of the DNA was used to perform 16 s rRNA gene- and ITS region-tagged amplicon sequencing using Illumina MiSeq platform to estimate the bacterial and fungal taxonomic diversity, respectively, using previously reported dual index methods38. Briefly, the soil washes were obtained using 1.5 g of homogenized soil shaken in 20 ml of deionized water49. The resultant slurry was used to perform esterase activity assay using 4-Methylumbelliferyl acetate esterase substrate. The reaction was performed for 3 h at 28 °C, with one fluorometric measure every 30 min (BioSpa 8 Automated Incubator). Fluorescence intensity was measured using a Cytation 5 spectrophotometer linked to the automated incubator.The potential activity of the enzyme acetyl esterase was estimated with the common assay protocol using fluorigenic substrates. The enzyme acetylxylan esterase is the most relevant acetyl esterase in soil; it belongs to the family of hydrolases that catalyzes the deacetylation of xylans  and was chosen as it is involved in the initial stages of depolymerization of complex plant substrates45. The protein extract was purified using 1D SDS-PAGE and the resultant product was subjected to tryptic digestion. Proteolytically cleaved peptides were separated prior to mass spectrometric analyses by reverse-phase nano HPLC on a nano-HPLC system  coupled online for analysis with a Q Exactive HF mass spectrometer  equipped with a nano electrospray ion source . Raw data from the MS instrument were searched using Proteome Discoverer v1.4.1.14 (Thermo Fisher Scientific) against a FASTA-formatted database  using the SEQUEST HT algorithm. Database searches were performed with carbamidomethylation on cysteine as a fixed modification and oxidation on methionine as a variable modification. Enzyme specificity was selected to trypsin with up to two missed cleavages allowed using 10 ppm peptide ion and 0.02 Da MS/MS tolerances. Only peptides with a false discovery rate (FDR) < 1% estimated by Percolator50 and only rank 1 peptides were accepted as identified. Unipept v3.251 was applied to assign proteins to their phylogenetic origin. GhostKoala52 and KEGG classifier were used for functional annotation.For proteomic analysis of microbial communities, three replicated samples were used from both high- and low-intensity land use, consisting of six sites, each contrast representing one of the three types of hypothesized land use intensification effects Table . A total53. Regression tree analysis for recursive partitioning of microbial CUE data by edaphic properties that included soil pH, moisture, total C and N concentration, C:N ratio and clay content was performed on the entire dataset (56 sites) using the rpart package. Here, all three replicates from each site were treated independently to account for spatial non-independence. Slope failure test or piecewise regression for the CUE–soil C linear regression was performed by recurrent movement of the soil pH window over a range of 1.4 units, with increments of 0.1 units. The threshold pH value was determined as the last pH unit before the R2 of the CUE–soil C regression dropped dramatically  and high , and generalized linear models of various physiological traits and soil C were run for both datasets separately.Statistical analyses were performed under the R environment software 2.14.054. The most parsimonious model was identified by non-significant X2 tests (P ≥ 0.05), low Akaike Information Criterion (AIC), low Root Mean Square Error of Approximation index (RMSEA ≤ 0.1), low Standardized Root Mean Square Residual index (SRMR ≤ 0.1) and high Comparative Fit Index (CFI ≥ 0.90).This was applied to test direct and indirect effects of microbial diversity and ecophysiology on soil carbon accumulation by organizing the dataset into a path relation network. Using a confirmatory approach, we aimed to test the maximum likelihood of data fit to the hypothesized path model inferring microbial taxonomy and function influences soil C accumulation. Microbial functional traits that were included as predictor variables were CUE and biomass, and a latent variable for microbial taxonomy was generated using bacterial and fungal alpha diversity. The fit of the path model and structural relationships with data were verified using SEM analysis conducted with the lavan R packageTo assess the effect of land use intensification, we determined the quantity and direction of change in the measured microbial traits across the 21 chosen land use contrasts (42 sites with three replicates at each site). Fold change on intensification was calculated for each contrast as the ratio of mean trait values from low- and high-intensity treatments. Following the failure to elucidate any global patterns, we segregated the data based on our mechanistic understanding of soil C cycling into categories of contrasts that fall above and below the hypothesized threshold soil pH and into a third category of contrasts where land use intensification leads to soil pH shift from below to above the threshold value Table .55. This was implemented within the R library labdsv (http://ecology.msu.montana.edu/labdsv/R). The IndVal score for each protein is the product of the relative frequency and relative average abundance within each land use treatment, and significance was calculated through random reassignment of groups (1000 permutations).Pairwise Indicator Species Analysis was used to identify the protein functions (from soil metaproteomics) that were significantly associated with low- and high-intensity land use treatment for each contrastSupplementary InformationPeer Review File


Scaffold-free prevascular endothelial–fibroblast constructs (SPECs) may serve as a modular and reshapable vascular bed in replacement tissues. SPECs, fibroblast-only spheroids (FOS), and silicone implants were implanted in 54 Sprague Dawley rats and harvested at 6, 12, and 24 h (n = 5 per time point and implant type). We hypothesized that the primary endothelial networks of the SPECs allow earlier anastomosis and increased vessel formation in the interior of the implant compared to FOS and silicone implants within a 24 h window. All constructs were encapsulated by an endothelial lining at 6 h postimplantation and SPEC internal cords inosculated with the host vascular network by this time point. SPECs had a significantly higher microvascular area fraction and branch/junction density of penetrating cords at 6–12 h compared with other constructs. In addition, SPECs demonstrated perivascular cell recruitment, lumen formation, and network remodeling consistent with vessel maturation at 12–24 h; however, these implants were poorly perfused within our observation window, suggesting poor lumen patency. FOS vascular characteristics (microvessel area and penetrating cord density) increased within the 12–24 h period to represent those of the SPEC implants, suggesting a 12 h latency in host response to avascular grafts compared to prevascular grafts. Knowledge of this temporal advantage in in vitro prevascular network self-assembly as well as an understanding of the current limitations of SPEC engraftment builds on our theoretical temporal model of tissue graft vascularization and suggests a crucial time window, during which technological improvements and vascular therapy can improve engineered tissue survival.Vascularization remains a substantial limitation to the viability of engineered tissue. By comparing  However, to realize this potential, several hurdles must be overcome. Vascularization is arguably the most important practical limitation in tissue engineering, imposing both dimensional and time constraints on the technology. For example, tissue-engineered constructs (TECs) designed to replace donor material typically have dimensions exceeding the diffusional limit of nutrients and oxygen, cited in literature as between 100 and 200 μm.4–7 These data are incomplete as they do not evaluate dynamics of vascularization at early time points (hours to a few days) following implantation. Host-derived microvessels can invade an avascular implant as part of the foreign body response, but the process has been reported to take at least a week.8 Peri-implant neovascularization in such a scenario typically proceeds shortly after formation of a fibrous capsule.8 Unfortunately, ischemic damage to tissues occurs within a time span of hours rather than days,9 necessitating a focus on processes impacting vascularization within a 24 h postimplantation window. A systematic temporal model for vascularization of TECs should focus on this time scale, informing us on the degree of vascularization attainable in the hours following implantation. This information will serve as the backbone of future efforts to improve implant vascularization rate.To sustain larger TECs, an intact vascular pedicle, consisting of an inlet, outlet, and a perfusable capillary bed, must either preexist or form rapidly. The need for rapid neovascularization or anastomosis of TECs underscores another major knowledge gap in tissue engineering. A systematic approach toward solving this problem should include an appropriate step-by-step timeline of vascularization events for these constructs. This temporal model should illustrate the rate-limiting steps in blood vessel formation that slow vascularization of tissues. Previous literature has tracked vascularization at endpoints past a few days to weeks following implantation.10 The kinetics of each phase of the vascularization pipeline can be explored by evaluating TECs that, by design, enter the pipeline at different points. The field of reconstructive surgery provides useful terminology to describe the spectrum of entry points. Restoration of damaged or dysfunctional tissue can either involve transfer of tissue lacking blood flow, termed a graft, or transfer of a tissue sustained by its own blood supply in the form of a vascular pedicle, termed a flap.11 Unique to current engineered grafts are the absence of any vascular architecture, forcing them to enter the vascularization pipeline at the earliest time point. The unrealized goal of tissue engineering is the formation of a true tissue flap, where the entire vascularization pipeline can be completed in vitro, and perfusion can be established simply through microsurgical anastomosis of mature, perfusable TEC vessels to the host vasculature.Vascularization of an avascular tissue can be modeled by a well-ordered series of events , startinin vitro assembly of primitive endothelial networks resembling a capillary bed, is one method of attaining a more flap-like TEC design. Laschke and Menger make a useful distinction between angiogenesis and inosculation that explains the utility of prevascularization: angiogenic sprouting, while potentially faster than de novo vasculogenesis, is still a slow process, whereas inosculation or merging of microvessels into larger diameter vessels occurs more rapidly.12 Specifically, in vivo microvessel growth by angiogenic sprouting occurs at a peak rate of 5 μm/h.13 Spanning an entire implant exceeding dimensions of a few hundred micrometers at this rate is too slow to prevent ischemic damage,12 as hypoxia peaks in skeletal muscle at approximately 8 h.9 Creating implants in vitro with preformed vessels can vastly reduce the distance an angiogenic sprout must travel to bridge the host and implant vascular elements or shortens the entire process to direct inosculation.14 In addition, a preformed endothelial network has essentially completed the phases of extracellular matrix (ECM) remodeling and proteolysis characterizing the branching morphogenesis of the vascular network.15 Prevascularization approaches have been used in past years by growing an implant, such as artificial skin, around a host arteriovenous fistula that has been extended to an externally located pocket, allowing angiogenic sprouts to permeate the implant.2 This surgical approach to forming a vascular pedicle ensures adequate vascularization of an implant, but has a limited practical application because the implant must remain coupled to an immobilized host during its development. More recent methods have involved coculture of human umbilical vein endothelial cells (HUVECs) and mesenchymal stem cells (MSCs) to allow spontaneous formation of primitive vascular network17; however, translational limitations exist with this technology. The difficulty of regulating differentiation of MSCs as well as their potential tumorigenicity limits the appeal of using these multipotent cells to develop a prevascular stroma.19 Adipose microvascular cell-derived microvessels appear to be more stable than HUVEC microvessels, suggesting that cell source is another means to improve engraftment potential of endothelial/stromal cell cocultures.21Prevascularization, or 5 We use primary human adipose-derived endothelial cells to ensure clinical translatability, where an autologously derived population of cells can be reimplanted in a patient with minimal morbidity due to immune compatibility complications. The adult fibroblasts create the extracellular-rich stroma necessary to support a vascular bed and provide additional proangiogenic stimuli.22 Mature endothelial cells, while capable of forming spontaneous capillary-like tubes in vitro, appear to require consistent input of proangiogenic environmental signaling. Fibroblasts, through constitutive expression of vascular endothelial factor (VEGF), basic fibroblast growth factor, and angiopoitin-1 (Ang-1), address this basic need.22 The presence of fibroblasts corresponds to an increased microvessel density within an implant and stabilization of vessels by signaling endothelial cell to express smooth muscle actin.16 Other ECM proteins deposited by the fibroblasts such as laminin, collagen type I, and collagen type IV are needed for vessel maturation.10 We have previously reported that a specific 1:4 ratio of human microvascular endothelial cells and fibroblasts maximizes the density of endothelial cords when allowed to self-assemble in a scaffold-free nonadherent environment. Increasing the density of fibroblasts resulted in endothelial clusters without cords, and increasing density of endothelial cells resulted in structures lacking avascular stromal areas consistent with a vascular bed.23 Within 3 days of in vitro culture in an agarose mold, the coculture generated an ECM containing laminin, type I collagen, and fibronectin. The interplay between ECM components, such as laminins and fibrillar collagen, and cell surface integrins plays a key role in vascular lumen formation.24 Czajka et al. noted the presence of laminin throughout the ECM rather than constrained to the basement membrane, as would be expected in mature blood vessels. This suggests a more embryonic, primitive vascular network.23 Implanted constructs show early signs of anastomosis in a rat hind limb muscle. Within 3 days, there was evidence of red blood cell perfusion in the implants with vascular structures that persisted out to 2 weeks.5We have developed and previously reported on the anastomotic potential of a scaffold-free prevascular implant model that is formed from the coculture of human adipose microvascular endothelial cells (HAMECs) and normal human dermal fibroblasts (NHDFs).25 This scalable and shapeable nature, coupled with the primary endothelial cord networks of the SPECs, makes them ideal analogs to a vascular stroma or artificial vascular bed that is inherent to the function of most tissues. The SPECs can readily incorporate renal segments26 and pancreatic islets,27 paving the way for rapidly vascularizable artificial renal grafts and bioartifical pancreas. To make these technologies a reality, however, we must first identify processes that speed or hamper anastomosis and perfusion of implanted grafts.The SPECs retain a set of properties that could have inherent therapeutic value when incorporated into replacement tissue technologies. SPEC spheroids can readily fuse to form larger constructs in unconstrained nonadherent conditions and can reshape their cytoskeletal structures to assume patterns dictated by confinements such as an agarose mold.In this study, we contrast the vascularization behavior of an avascular graft-like fibroblast spheroid and vascularization of our more flap-like prevascular implant model, offering two different points of entry into our proposed temporal model of vascularization. While previous examples of prevascular engineering demonstrated successful anastomosis in a time span of weeks, few studies have taken a systematic stepwise approach to temporally assess vascular development beginning at a few hours following implantation of engineered tissue. In this study, we examine early steps in vascular development following implantation of our novel scaffold-free, prevascular endothelial–fibroblast tissue-engineered constructs (SPECs). The SPEC model permits examination of the transition from primitive network to a complex ordered anastomosed network. Specifically, we hypothesize that the existing self-assembled primitive network of the SPECs allow earlier host-implant anastomosis and increased presence of lumen-containing vessels in the interior of the implants by 24 h compared to avascular grafts such as fibroblasts spheroids.HAMEC  were cultured in endothelial growth medium-2 (EGM2) . Normal human dermal fibroblasts-Adult (NHDF-Ad)  were cultured in fibroblast growth media-2 (FGM2) . Cells were collected between passages 7–10 for use in experiments and implant development.23 Rod-shaped troughs of 0.9 cm by 0.1 by 0.5 cm depth were constructed in 2% UltraPure™ Agarose  and high density 4:1 mixtures of 720,000 NHDF-Ad cell, and 180,000 HAMECs were pipetted into the troughs. Cells were then cultured in a 2:1 mixture of FGM2 and EGM2 for 3 days. Implants were either collected in Dulbecco's phosphate-buffered saline for use in surgical implantation or collected and fixed in 4% paraformaldehyde for histology. Fibroblast-only spheroids (FOS) were constructed similarly with 900,000 NHDF-Ad and no HAMECs. Silicone fragments of rectangular box dimensions of 0.6 cm by 0.1 cm by 0.1 cm  were autoclaved and stored in DPBS in preparation for implantation.The scaffold-free prevascular endothelial fibroblast constructs (SPECs) were modifications of a protocol established by Czajka and Drake.n = 15), FOS (n = 15 rats), and silicone implants (n = 15 rats). Nine rats were set aside for sham surgeries. Surgeries were performed as described by Calder and colleagues.5 Implants were placed in submuscular pocket, with the long axis oriented parallel to the hind limb running proximal to distal. Five rats within each implant group were sacrificed at 6, 12, and 24 h, with muscle excised from the left hind limb en bloc with implant or sham surgery, placed in O.C.T. compound , and frozen at −70°C for cryosectioning. Muscle from the right limb was harvested for comparison.Animal procedures were conducted following approval by the Institutional Care and Animal Use Committee (IACUC) of the Medical University of South Carolina. Fifty-four Sprague Dawley rats  were divided into three groups: SPECs , and Alexa Fluor™ phalloidin 488  for f-actin. Selected sections were stained with primary antibodies to von Willebrand Factor , CD31 , human CD31  , and smooth muscle actin . Primary antibodies were fluorescently tagged with the secondary antibodies Alexa Fluor goat anti-mouse 488, goat anti-rabbit 546, goat anti-mouse 546, and goat anti-rabbit 633 . Sections were mounted on Colormark Plus microscope slides in Prolong Gold antifade reagent (Molecular probes P36934).g at 4°C. Supernatant was stored in fresh tube at −20°C. Pierce™ BCA Protein Assay Kit (ThermoFisher Scientific 23227) was used to estimate protein concentration for samples as per manufacturer's instructions. Before gel electrophoresis, samples were diluted in RIPA buffer to attain 20 μg of protein in 20 μL solution, and further diluted 1:1 in 2 × Laemmli Sample buffer to attain 40 μL loading volumes. Samples were loaded onto Any kD™ Mini-PROTEAN® TGX™ Precast Protein Gels. Following protein separation and overnight transfer onto PVDF membranes, western blots were performed using antibodies toward GAPDH (loading control) , VEGFR2 , VE-Cadherin , vWF , and DLL4 .SPECs and FOS were collected after 1, 2, and 3 days of culture in 2% linear agarose molds as previously described. Samples were snap frozen and mechanically homogenized in RIPA lysis buffer with protease inhibitor cocktail. Samples were maintained in constant agitation for 2 h at 4°C and centrifugated for 20 min at 16,000 28) and binarized. Perimeter of the implants and endothelial capsule around implants were drawn freehand. Binary images were used to calculate microvessel area fractions of the implant. Binary images were skeletonized using an ImageJ plugin, provided by Arganda-Carreras et al.,29 with branches pruned by lowest intensity voxel. Resulting images were used to calculate number of junctions and vessel branch lengths.Confocal images were acquired using Leica TCS SP5 AOBS Confocal Microscope system  and collected as average projected z-stacks at 20 × and 40 × magnifications. Stitching was performed using LAS AF v2.6.3 Build 8173 and encompassed the entire visible cross section of each implant. Images were auto-enhanced and analyzed using ImageJ . To analyze endothelial cord organization, stitched 40 × (512 × 512) confocal images of sections stained with antibodies for vWF and CD31 were segmented as follows. Average projected images (10 μm depth) were autothresholded in ImageJ . Levene's test for homogeneity of variance was used to determine equality of variances (α = 0.05). For data with equal variance between groups, one-way ANOVA was performed with + endothelial cells  staining for apoptotic and necrotic cells (Cells within both FOS and SPEC continued to die between 6 and 24 h as evidenced by increased TdT dUTP Nick-End Labeling (TUNELic cells . There wic cells .in vitro development of our implant and shortly following implantation. The 6–12 h postimplantation window is particularly important as it contains the time points associated with a peak in markers of hypoxic stress found within autologous full-thickness muscle flap transplants in earlier studies by our laboratory.9The timeline of vascular events  immediat31 If the rearrangement was entirely driven by passive cell adhesion behavior rather than active vascular development processes, a single interface between endothelial cells within the core and fibroblasts on the periphery would be observed. This behavior would optimize interfacial energy based on cell type-specific expression of adhesion molecules such as cadherins.32 Further evidence of active vessel formation within the implant is provided by western blot data, through expression of vascular markers such as VEGFR2, VE-cadherin, and vWF. Dll4 expression in implants is consistent with angiogenic and anastomotic potential of endothelial cells as reported in literature.34 The upregulation of DLL4 expression in SPECS at D2 is consistent with increased vascularization of the implant; in contrast, the comparative downregulation at D3 of incubation, coinciding with when the implant finishes resolving into a solid structure, is consistent with quiescence of the prevascular networks. This period of quiescence may contribute to the latency between anastomosis and in vivo tubulogenesis. While ideally anastomosis of prevascular implants should only involve inosculation of externally located cords to the nearby host vasculature, the need to ramp up the angiogenic machinery of the construct cells might delay further morphogenesis of these tubes and delay perfusion through the resulting networks.The endothelial cord formation within the SPECs is a nonrandom process, with the initial dispersed endothelial cells coalescing into cords throughout the 3-day incubation period. This migration stands contrary to the popular theory of cellular behavior termed the differential adhesion hypothesis.in vivo inosculation or surgical anastomosis.1 While these constructs were not well perfused during the 24 h observation time (in vitro.One of the major goals of vascular tissue engineering is near instantaneous perfusion of well-organized cords either by spontaneous ion time , endotheion time , suggest5 per mm2 implant tissue, resembling the preimplantation average branching density of 1.12 × 105 per mm2. The FOS, on the contrary, show a significantly lower presence of branching endothelial structures within the implant stroma, with most of the 8% ± 3% microvessel area confined to the external capsule (In addition, the microvessel vascular area of the SPECs 6 h postimplantation is 26% ± 5%, which is comparable to the vascular density of the implant before implantation and only approximately 1.2-fold lower than the average microvessel vascular area fraction throughout the 24 h time point . The fil capsule . As such capsule .35The 6–12-h window is a period of remodeling in both the SPEC and FOS implant models. Notably, the band of host-derived endothelial structures around the implant appears to thicken with a small but significant increase of internally penetrating branches within some of the FOS. However, the mean microvessel area of the fibroblast spheroids, including the capsular components, remains 3.2-fold lower than the SPEC implants and comparable to that of the silicone implants. In other words, the lack of an existing internal endothelial network in the FOS results in a 12 h latency in vascular development of these implants compared to SPECs. The SPECs, on the contrary, maintain a nearly constant mean vascular area; however, there is a 1.5-fold decrease in junctions within the implant and a two-fold decrease in junctions within the endothelial capsule. This may be attributed to increased condensation of endothelial branches to larger structures, an example of which is seen in 1 The presence of a branching vascular architecture, however, still seems largely limited to the SPECs.By 24 h, SPECs and FOS begin to resemble each other in terms of endothelial organization and mean vascular area, with a greater preponderance of penetrating endothelial cords in the FOS than at previous time points. The advantage in anastomosis provided by the SPECs, thus, seems to lessen at the 24 h time point, as cords from the peripheral endothelial capsule appear to reach the center of the FOS. This rapid invasion of vessels in a previously avascular space is itself a surprising finding. Vascular network can invade on its own as a part of foreign body response, but the process has been cited to take a few days to a week.36 However, in the absence of consistent perfusion, the SPECs show some indication of vessel maturation. Smooth-muscle actin presenting cells, representing mural, stabilizing cells such as pericytes around capillaries, or smooth muscle cells around larger arterioles and arteries, are recruited in the latter stages of angiogenesis, involving a careful interplay between basolateral elements of endothelial cells such as Tie-2, macrophages, and pericytes.38 The SPECs, which present with an apparently disorganized SMA+ cells at the early 6 h time points, show SMA+ cells more fully organized around lumen-like structures in the SPEC at the 12 and 24 h time points (In angiogenesis, maturation of vessels follows anastomosis and usually occurs concurrent with perfusion of vascular networks.e points .Perivascular organization alone, unfortunately, does not translate directly to improved vessel patency, as evidenced by poor dye perfusion and leaky vasculature within the implant at 24 h . In addi39 vascularization dynamics in literature have not focused on early time points preceding a few days following implantation. Our study reveals that an earlier observation window informs us on the relative rapidity of endothelialization around cellular constructs and reveals that the crucial advantages to a prevascular network might be best seen within 6–12 h of implantation. By this period, the groundwork for a vascular pedicle feeding the implants has already been laid, with evidence of reorganization toward a more mature host-implant vascular network. Vascular tissue engineering strategies that proceed from this point should promote lumen formation and patency of the existing vascular architecture.As past studies have reported inosculation of prevascular implants at 2–5 days,


Charadrius alexandrinus and the enigmatic White-faced Plover C. dealbatus – which display strong plumage differentiation, yet show minimal genetic divergence based on previous mitochondrial and microsatellite work. Two scenarios may lead to this situation: (1) they represent clinal or poorly diverged populations with limited genomic differentiation despite substantial plumage variation, or (2) they are diverging taxa at the cusp of speciation, with ongoing limited gene flow obliterating signals of differentiation in traditional genetic markers. We compared the genotypes of 98 plovers  sampled in eastern Asia and Europe using ddRADSeq to harvest over 8000 genome-wide SNPs. In contrast to previous studies, our analyses revealed two well defined genomic clusters, with limited hybridization and a narrow contact zone. We also uncovered significant differences in bill length and further sex-specific differences in size, which may signal differences in mate choice between Kentish and White-faced Plovers. Our results support the hypothesis that this shorebird duo is on the verge of speciation.Genetic isolation of populations over evolutionary time leads to the formation of independent species. We examined a pair of shorebirds – the Kentish Plover  Firstly, why, if species have descended from other species by insensibly fine gradations, do we not everywhere see innumerable transitional forms? Why is not all nature in confusion, instead of the species being, as we see them, well defined?” – Darwin (1859)1“2, with allopatric barriers being its most well recognized driver3. However, there is now growing recognition that variation in intensity of sexual selection or preferences for different traits may also lead to species divergence5. Speciation can occur extremely rapidly over the course of a few generations6 or over long time scales, and may or may not be accompanied by gene flow from overlapping congenerics7. Consequently, young species can often be difficult to distinguish from divergent populations10. Biologists often conservatively use the term ‘taxon’ to refer to difficult cases in which there is no consensus on whether a lineage qualifies as a full species or is merely a divergent population.Speciation is the evolutionary process by which populations differentiate to become distinct species. The formation of barriers to gene flow plays an important role in the speciation process14. Charadrius plovers, one of the core clades and the name-sake group, have been an important study subject in evolutionary and ecological research given their unusual diversity in mating and parental care systems, even within a single species16. This variation in breeding behaviour has also been linked to population differentiation and diversification, whereby monogamous breeding systems in plovers are thought to be key promoters for population divergence whereas polygamous behaviour may instead slow down divergence5. The advent of Next-Generation Sequencing (NGS) technologies provides us with powerful tools to investigate differentiation across plovers from a genomic perspective17.Shorebirds (order Charadriiformes) are an old avian lineage with high behavioural and morphological diversityCharadrius as an extremely well-studied bird genus, the last few years have seen the discovery of novel populations and taxa18. Among these, Kennerley et al.19 documented the discovery of a distinctly-plumaged East Asian population of the Kentish Plover Charadrius alexandrinus complex that was shown to refer to the generally synonymized taxon name dealbatus. Several aspects regarding the breeding plumage of this rediscovered taxon, such as the extent of white on the face, and minor differences in morphology, render it as distinct as many universally recognized plover species20, which has led to its occasional recognition as an independent species named White-faced Plover C. dealbatus . However, a first molecular inquiry using mitochondrial DNA and microsatellites on museum specimens found no evidence of genetic differentiation between White-faced and Kentish Plovers20. While the two taxa overlap widely in their Southeast Asian winter distribution . Our taxon sampling covers a broad geographic range to investigate the magnitude of differentiation and dynamics of the contact zone, and includes a densely sampled coastal transect in China, a population from the western distribution end of Kentish Plovers in Spain, and a population from another East Asian subspecies nihonensis that inhabits island archipelagos at the northeastern end of the Kentish Plover breeding distribution. Our sampling incorporates a comprehensive dataset of body measurements to explore whether biometric changes correlate with the genomic clines. Our objective is to establish the nature of genomic connectivity between these two taxa to ascertain how and if two such divergent plumage phenotypes can be maintained in the presence of gene flow.Here we investigate the two possible scenarios by examining the patterns of genomic differentiation between plovers along the east coast of China. We used a genotyping-by-sequencing approachSTRUCTURE and PCA (see below).We recovered a total of 1,729,307,240 paired-end 150 bp reads from Illumina sequencing of 122 plover samples. Preliminary filtering of the sequencing data included manually removing seven individuals with poor sequencing coverage (less than one million reads). Each of the remaining 115 samples had a mean sequencing depth of 139x. A further nine individuals were removed in PLINK 2.0 as they exhibited more than 30% missing stack data, whilst eight additional individuals that showed evidence for first or second-order kinship with another individual were also pruned. This yielded a final dataset of 98 individuals and 8088 SNPs. The total genotyping rate was 0.990466. Of these 98 individuals, 59 were Kentish Plovers, 35 were White-faced Plovers and four consistently displayed genomic signatures intermediate between the two taxa in both STRUCTURE showed a primary division between Kentish Plovers and White-faced Plovers, and subsequent division between allopatric subspecies alexandrinus (the Chinese mainland and Taiwanese samples) and nihonensis  and the minority were Kentish Plovers . More importantly, when we considered only records from the breeding season . Conversely, in southern Zhejiang, 78% of breeders were Kentish and only 22% were White-faced Plovers  plovers sampled were Kentish Plovers Fig. . The remSTRUCTURE results. White-faced and Kentish Plovers separated along PC1, with White-faced Plovers forming a tight cloud, whereas Kentish Plovers were more broadly dispersed along both axes  were consistent with the xes Fig. . The disSTRUCTURE results, with White-faced Plovers displaying high genetic divergence and population differentiation from all Kentish Plover populations. Within the Kentish Plover populations, the Japanese population (nihonensis) consistently appeared to be the most distinct, whilst divergences of Spanish and Western Chinese populations mirrored their geographic distance from other populations.Our summary statistics also echoed the The results of all Shapiro-Wilk tests for normality of the morphological traits were non-significant . Two way ANOVAs showed that White-faced Plovers had longer bills than Kentish Plovers   hybrids among our samples of breeding birds, indicating that gene flow, while present, is rare between these two taxa. Our results favour a narrow contact zone resulting in two lineages with deep genomic differentiation, rather than a genetic cline from one form to the other Fig. .Their pronounced differences in breeding plumage coloration are matched by different bill sizes and sex-specific differences in tarsus, wing and full length measurements, hinting at differences in the intensity of sexual selection between the two taxa. Future research into differences in the reproductive behaviour of these two young taxa may shed light on the evolution of sexual traits in these two shorebirds.Charadrius alexandrinus complex , mainly in China across a coastal transect spanning the breeding distribution of the two forms (n = 87), but also including samples from Taiwan (n = 18), Japan (n = 10) and Spain (n = 7)  Table . We capt37. We used a Qubit® 2.0 Fluorometer to quantify DNA concentrations of extracts.The following procedures were approved by National University of Singapore’s Office of Safety, Health, and Environment. We extracted DNA either with DNEasy Blood & Tissue Kits (Qiagen) using the manufacturer’s recommended protocol or using an ammonium salt method38) as per Tang et al.23. We split samples into total DNA yield bands between 170–500 ng, based on post-extraction concentrations. We then calculated input volumes for the first restriction step based on these cut-off values. We used the restriction enzymes EcoRI and Msp1 (New England Biolabs Inc.) to double digest the samples for 3.5 hours at 37 °C, followed by clean up using a 1.1X ratio of Sera-Mag SpeedBead Carboxylate-Modified Magnetic Particles (Thermo Scientific). We then quantified samples again and ligated unique barcodes (PIE adaptors) to the fragments using T4 DNA Ligase (New England Biolabs Inc.) at 16 °C for 16 hours.We performed double digest restriction enzyme associated DNA sequencing  using a 1.5X bead ratio, and then carried out size selection for the seven pools using a Pippin Prep Gel Electrophoresis system (Sage Science) to isolate fragments for a sample peak of 420 base-pairs in length. After another subsequent AMPure XP clean up, we amplified size-selected fragments using a polymerase chain reaction (PCR) for 12 cycles, followed by a final AMPure XP clean up step. We screened pools for quality control on a Fragment Analyzer  and then quantified pools on a Qubit 2.0 Fluorometer before creating two final libraries of pools in equimolar proportions. We submitted the DNA libraries to Novogene  for 150 bp paired-end sequencing using the Illumina HiSeq X Ten platform.process_radtags command in STACKS v1.3439. We removed samples with less than one million reads from subsequent analysis. We aligned the sequence reads of the remaining 115 samples against the congeneric Killdeer (Charadrius vociferus) genome assembly40 using BWA-MEM41.We used FastQC (Babraham Bioinformatics) to analyze sequence quality across all base positions. Demultiplexing was then performed using the ref_map.pl in STACKS to call single nucleotide polymorphic markers (SNPs), employing a minimum stack depth of 10 in our analyses. We ran the ‘populations’ module to retain loci present in more than 90% of individuals, whilst assigning all individuals to a single population. To reduce the effects of linkage disequilibrium in subsequent analyses, we only accepted one SNP from each locus using the –write_single_snp option provided.We used the pipeline 42 to remove any individuals exhibiting more than 30% missing data, and then to further filter SNPs to account for missing data and linkage disequilibrium. We checked for SNPs under selection using BayeScan43.We ran PLINK 2.044 to estimate pairwise kinship coefficients using maximum likelihood estimation. We removed a single individual from each pair which displayed a kinship coefficient above 0.125 (second order kinship). We then re-ran the populations module in Stacks v1.34 and re-ran PLINK 2.0 without filtered individuals/loci to generate the final dataset.We used the R package, SNPRelateSTRUCTURE v2.3.445. First, we removed any singleton or doubleton alleles46 using a minimum allele count filter in PLINK 2.0. We then implemented STRUCTURE runs using the remaining 5639 SNPs without a priori hypotheses of cluster membership. We ran STRUCTURE from K = 1 to K = 10 with five iterations per K. For each iteration we implemented a burn-in of 100,000 generations and MCMC for 500,000 generations. We used STRUCTURE Harvester Web v0.6.9447 to process the output and subsequently averaged the results across replicates by evaluating individual ancestry coefficients  with CLUMPP v1.1.248. Averaged population subdivision over changing values of K is depicted in Fig. K = 3, the optimum K value as determined by the Evanno method, which calculates the most statistically likely number of genetic clusters47, are additionally incorporated into Fig. We assessed population subdivision using a model-based clustering approach implemented in 50. Different network topologies were explored by specifying different values of k, which dictates the number of genetically nearest neighbors each sample is connected to. We ran Netview for k values of 1–35, and plotted the resultant graphs using the Kamada-Kawai force-directed graph drawing algorithm51 as implemented in iGraph52. We explored population structure using a PCA in SNPRelate44 using a genetic covariance matrix calculated from genotypes.We then used Netview, a network theory-based approach, to construct networks of individuals illustrating the connectivity and information flow within and between populations based on genetic similaritySTRUCTURE and PCA analyses.Among our samples we identified hybrids between Kentish and White faced Plover based on their consistent genotypic assignment indicating mixed genotypes based on both 53 and Adegenet55. We divided our Kentish Plover samples into four populations  and retained White-faced Plovers as a single population. We excluded any hybrids we identified from this analysis. We utilized Nei’s genetic distance in Adegenet to calculate 56.We calculated pairwise population summary statistics for Kentish and White-faced Plover populations using the R packages HierfstatWe recorded morphological measurements for a total of 54 birds sampled along the Chinese coastline Table , includi57. For traits that adhered to a normal distribution we examined differences between sexes and taxa using two-way ANOVAs. We utilized the R package ggplot258 to generate boxplots comparing morphological measurements between the two genomically-prescribed groups for the measurements which showed significant interactions between taxa and sex, and plotted relevant significance bars with the R package ggsignif59. We used R version 3.4.1 for all statistical analyses60.We obtained ddRADSeq genotypes for 41 of the 54 measured individuals. These comprised 13 males and 28 females, of which we identified 11 birds based on their genotypes as Kentish Plovers, 29 as White-faced Plovers and one as a hybrid (see Results). We assessed normality of the morphological data using Shapiro-Wilk testsResearch protocols were approved by Jiangsu Normal University and National Natural Science Foundation of China. All lab work was conducted in accordance with regulations outlined by the National University of Singapore’s Office of Safety, Health, and Environment.Supplementary Information


Frailty is a common syndrome in older individuals that is associated with poor cognitive outcome. The underlying brain correlates of frailty are unclear. The aim of this study was to investigate the association between frailty and MRI features of cerebral small vessel disease in a group of non-demented older individuals. We included 170 participants who were classified as frail (n = 30), pre-frail (n = 85) or non-frail (n = 55). The association of frailty and white matter hyperintensity volume and shape features, lacunar infarcts and cerebral perfusion was investigated by regression analyses adjusted for age and sex. Frail and pre-frail participants were older, more often female and showed higher white matter hyperintensity volume  compared to non-frail participants. Frail participants showed a non-significant trend, and pre-frail participants showed a more complex shape of white matter hyperintensities  compared to non-frail participants. No between group differences were found in gray matter perfusion or in the presence of lacunar infarcts. In conclusion, increased white matter hyperintensity volume and a more complex white matter hyperintensity shape may be structural brain correlates of the frailty phenotype. Frail older individuals have a higher risk of falling, postoperative complications, dependency and cognitive decline, compared to non-frail counterparts5. The impact of these consequences of frailty on a societal and economic level in an aging population stresses the importance of investigating the pathways leading to a frail condition. To date, the biological pathways leading to the decline of multiple physiological systems remain unknown. The association between frailty and cognitive disorders suggests a structural brain correlate, but this is currently largely unknown.Frailty is a chronic condition of increased vulnerability to physiological stressors that is common in older individuals and is most often described using the physical frailty phenotype7. SVD is one of the major causes of stroke, cognitive decline and dementia in older individuals8. The most commonly used MRI features of SVD are white matter hyperintensity of presumed vascular origin (WMH) volume and the presence of lacunar infarcts9.Cerebral small vessel disease (SVD) is a syndrome that is characterized by manifestations of diseases of the small vessels in the brain, with probably different underlying pathophysiology and etiology16. Due to the heterogeneous nature of SVD, other MRI features of SVD may provide additional information20. Recent studies have shown that WMH shape is a novel promising feature related to more severe small vessel changes20. Furthermore, cerebral perfusion measured by arterial spin labeling (ASL) MRI could detect early hemodynamic changes that may be related to SVD23. Frailty may therefore be associated with both structural brain changes of SVD as well as quantitative hemodynamic changes.Previous studies on the association between of frailty with WMH volume or lacunar infarcts have shown inconsistent resultsThe aim of this study was to investigate the association between frailty and MRI features of cerebral SVD in a group of non-demented older individuals who were scheduled for major elective surgery. We assessed both commonly used structural features (WMH volume and presence of lacunar infarcts) and novel structural (WMH shape) and hemodynamic features  of cerebral SVD.Of the 178 participants that completed the MRI scanning protocol, a total of 8 participants had to be excluded from all analyses, due to extremely large ventricles that hindered accurate segmentation (n = 1), an incomplete FLAIR and ASL sequence (n = 1), or major MRI artifacts , leaving 170 participants for the current study. Demographics for frail (n = 30), pre-frail (n = 85) and non-frail (n = 55) participants are shown in Table Frail participants showed a higher total (0.69 [95% CI 0.08 to 1.31]), p = 0.03 and periventricular/confluent  natural log transformed WMH volume compared to non-frail participants  of the frail participants, 27% (n = 23) of the pre-frail participants and 22% (n = 12) of the non-frail participants had lacunar infarcts. Logistic regression analyses corrected for age and sex showed no between-group differences in the presence of lacunar infarcts (frail versus non-frail: OR (95% CI) = 1.25 [0.35 to 4.42], p = 0.73; pre-frail versus non-frail: OR (95% CI) = 1.35 [0.59 to 3.08], p = 0.48).Frail participants showed a non-significant trend for a more complex shape of periventricular and confluent WMH  compared to non-frail participants , see Supplementary Table C. The physical frailty component slowness showed an association with WMH volume  and with a more complex shape of WMH . The component exhaustion also showed a relation with WMH volume . Other physical frailty components  showed no significant association with the studied SVD features.In summary, we observed that frail and pre-frail participants had a higher WMH volume compared to non-frail participants. Furthermore, pre-frail participants showed a more complex shape of periventricular and confluent WMH compared to non-frail participants. No between group differences were found in shape features of deep WMH, cerebral perfusion or presence of lacunar infarcts.16. Direct comparison with these studies is however hindered by the use of different methods to assess frailty. The frailty assessment of these studies differed between the physical frailty phenotype by Fried et al.16, and frailty scores that included measures of cognition, such as the Edmonton Frail Scale or the frailty index24. Four of the previous studies that assessed WMH showed an association between WMH and frailty16, three other studies that were performed only found a weak or no association14. Almost all previous studies were community-based studies performed in older adults16, although the studies that did not find an association included slightly younger individuals, possibly explaining the different findings13. Our study adds to the body of evidence for an association between frailty and WMH volume15.Previous community-based studies showed an inconsistent association between frailty and WMH volume and lacunar infarcts25. One previous study found an association between frailty and cerebral infarcts >3 mm15, whereas another earlier study showed no association with a frailty score that included cognitive tests and infarcts <20 mm10. Another previous study that investigated the relation between physical performance  and lacunar infarcts in memory clinic patients showed no relation between impaired physical performance and the presence of lacunar infarcts according to the STRIVE criteria26. Our findings are in accordance with these previous findings, as we did not find an association between frailty and lacunar infarcts10. The prevalence of lacunar infarcts in our study (24% in a population with a mean age of 71 ± 5 years of age) is comparable to the prevalence in a general older population (20% in a population with a mean age of 70 ± 7 years of age)27.Comparison of previous studies regarding the association between frailty and lacunar infarcts is difficult due to use of different definitions of lacunar infarcts. Most previous studies did not conform with the size definition of lacunar infarcts (3–15 mm) according to the internationally accepted guidelines that we used on MRI markers of small vessel disease proposed in the STRIVE criteria20. A previous cohort study in patients with symptomatic atherosclerotic disease showed that the presence of lacunar infarcts was related to a more complex WMH shape20. Furthermore, WMH shape was different in patients with type 2 diabetes mellitus  compared to healthy individuals19. These investigations indicate that WMH shape may be helpful in examining the heterogeneity of cerebral SVD and that a more complex shape of WMH may be associated with more severe manifestations of cerebral SVD20. Although our study is the first to show the relation between frailty and a more complex shape of WMH, these associations were not completely independent of WMH volume. A more complex WMH shape combined with increased WMH volume could be part of the structural brain changes that underlie the physical frailty phenotype.No previous study has been performed on the association between frailty and WMH shape features, and only few studies have been performed on WMH shape features in general28. Previous cross-sectional studies on SVD in both demented and non-demented populations showed that a higher WMH volume is associated with lower total CBF, although this was partly driven by co-occurrence of neurodegenerative diseases30. Our study is, to the best of our knowledge, the first that assessed the relation between global gray matter perfusion and frailty. We did not find an association between cerebral perfusion and physical frailty. A possible explanation for not finding this association may be that altered brain hemodynamics could be a marker for a more severe type of SVD in comparison to our population29.Cerebral perfusion as measured by arterial spin labeling MRI is an indicator of brain metabolism and is one of the early markers for cognitive deterioration. Recently it has been suggested that low perfusion is a marker for SVD, although it is not yet clear whether this precedes or follows the presence of WMH31, the short physical performance battery32, mobility33, and exhaustion34. These studies all showed an association between these components and MRI markers for SVD34. In exploratory analyses in our study, all components were associated with a higher WMH volume and a more complex WMH shape, although only slowness and exhaustion reached statistical significance. For slowness, this finding is in line with previous findings on gait speed and the short physical performance battery32. The exhaustion component in our study was derived from one question of a questionnaire on anxiety and depression, and may not be accurate enough. The recommended assessment of exhaustion is by the CES-D depression scale, which could be more sensitive and specific to exhaustion35. A possible explanation for the relation between exhaustion and WMH volume might be that exhausted participants were slightly more depressed, which has previously been related to a higher WMH volume33. As this was a secondary analysis and there is not much previous evidence, these results should be carefully interpreted.No previous studies on frailty and markers of SVD additionally explored the relation between individual frailty components and features of SVD. However, some previous investigations were performed on separate components without exploring overall frailty, such as gait speedStrengths of our study include the detailed assessment of both commonly used features of cerebral SVD (WMH volume and lacunar infarcts) and more novel features of cerebral SVD  together with a detailed assessment of the physical frailty phenotype. Limitations of our study could be the limited number of physically frail individuals, causing a difference in group size between frail, pre-frail and non-frail individuals. Due to these differences, we had a lower statistical power to detect differences between frail and non-frail individuals. This may explain why we did not find a statistically significant difference in WMH shape or perfusion between frail and non-frail individuals. Another limitation is that participants from our study were all scheduled for major elective surgery and may not be comparable to the general population. This could limit the generalizability of our findings to the general population. A limitation in our assessment of slowness is that we did not consider comorbidities such as arthritis and pulmonary disease, which could have influenced the results of walking speed and exhaustion. A technical limitation could be that WMH segmentation methods in general have a limited accuracy for segmentation of especially smaller deep WMH. This may have underestimated possible associations between frailty and shape of deep WMH.In conclusion, increased WMH volume and a more complex shape of WMH may be structural correlates of the physical frailty phenotype.36. The BioCog study is performed at the Charité Universitätsmedizin Berlin and the University Medical Center Utrecht36. The present study uses patient data that were collected at the University Medical Center Utrecht only. Patients who were included in BioCog (1) were at least 65 years of age, (2) were scheduled for major elective surgery of at least 60 minutes, and (3) had a mini-mental state exam (MMSE) score of 24 or higher. The medical ethics committee of both centers approved all procedures under ethical approval number EA2/092/14 (Berlin: Ethikkommission der Charité) and 14-469 (Utrecht: Medisch Ethische Toetsingscommissie Utrecht). All participants signed informed consent. All methods were performed in accordance with all relevant guidelines and regulations that apply to research with human participants.Data were obtained from the BioCog consortium study: an international observational study that aims to identify biomarkers of postoperative cognitive disorders37) and administered the MMSE during a visit prior to surgery. A hospital anxiety and depression scale (HADS) score on the depression subscale ≥8 was seen as having depressive symptoms38. Anesthesiologists (in training) performed the classification for the American Society of Anesthesiologists score.Trained research personnel collected demographic data, data on vascular risk factors .Participants were classified based on five frailty components according to the Fried frailty phenotype3; TR/TE = 7.9/4.5 ms), a 3D fluid-attenuated inversion recovery (FLAIR) sequence , a pseudo-continuous arterial spin labeling (pCASL) sequence  and a diffusion-weighted image (DWI) . Presence of lacunar infarcts was visually rated on the T1-weighted, FLAIR and DWI images by two experienced neuro-radiologists (JB (11 years of experience) and TW (25 years of experience)) according to the standards for reporting vascular changes on neuroimaging (STRIVE) criteria25.Participants were scanned on a Philips Achieva 3T MRI scanner. The MRI scanning protocol consisted of a three-dimensional (3D) T1-weighted sequence (voxel size = 1.0 × 1.0 × 1.0 mmhttp://www.fil.ion.ucl.ac.uk/spm/doc/) for Matlab . WMH segmentations were performed on the registered 3D FLAIR images by the lesion prediction algorithm  of the lesion segmentation toolbox version 2.0.15  for SPM12. Visual quality control for the WMH segmentations was performed by a trained researcher (IK) and supervised by a neuro-radiologist (JB), see Fig. 20. The solidity, convexity, concavity index and fractal dimension of periventricular and confluent WMH were calculated by reconstruction of the convex hull, volume and surface area of all individual lesions. The eccentricity and fractal dimension were calculated of deep WMH (see20 and Supplementary Table A for more details). For all WMH shape features, mean values per feature were calculated per patient and used for further analyses.3D FLAIR images were registered to the T1-weighted images using statistical parametric mapping version 12  for SPM12. The filled T1-weighted images were segmented using CAT1245. CBF images were motion corrected and registered to the gray matter partial volume maps44. The CBF images were quantified with a single compartment model46, after which the mean CBF was obtained for a total gray matter perfusion and deep WM region-of-interest (ROI)47. The spatial coefficient of variation  was calculated within the total GM as a proxy parameter of vascular sufficiency (for more details see23). All perfusion images were rated as images containing (1) CBF contrast, (2) vascular contrast or no contrast by a trained researcher (IK), supervised by an experienced ASL researcher . Images that were classified as CBF contrast were used in the perfusion analysis. Images with vascular contrast were included in the analysis of spatial CoV, but excluded from the perfusion analysis. The no contrast images that contained noise only or large artifacts were excluded from further analysis.ASL images were processed with ExploreASLFor demographic analyses, three groups  were compared by a one-way ANOVA or chi-square test depending on the type of variable . WMH volumes, WMH shape features and cerebral perfusion were compared between the frail and non-frail group and between the pre-frail and non-frail group by linear regression analyses adjusting for age and sex. WMH volumes were natural log transformed before linear regression analysis and additionally adjusted for intracranial volume (ICV). A Pearson’s correlation coefficient was computed to assess the relation between natural log transformed total WMH volume and global cerebral gray and white matter perfusion. The presence of lacunar infarcts was compared between the frail and non-frail group and between the pre-frail and non-frail group by logistic regression analyses adjusted for age and sex.In secondary analyses, WMH shape features were compared between groups by linear regression analyses additionally adjusted for WMH volume, to test if the found associations were WMH volume independent. Exploratory post-hoc analyses of the relation between MRI features of SVD (that showed significant between group differences) and frailty components were performed by linear regression analyses adjusted for age and sex, and for the WMH volume analysis additionally adjusted for ICV. All analyses were performed in SPSS version 25. A p-value of below 0.05 was considered statistically significant.The association between frailty and MRI features of cerebral small vessel disease – supplementary material