Olive is a long-living perennial species with a wide geographical distribution, showing a large genetic and phenotypic variation in its growing area. There is an urgent need to uncover how olive phenotypic traits and plasticity can change regardless of the genetic background. A two-year study was conducted, based on the analysis of fruit and oil traits of 113 cultivars from five germplasm collections established in Mediterranean Basin countries and Argentina. Fruit and oil traits plasticity, broad‐sense heritability and genotype by environment interaction were estimated. From variance and heritability analyses, it was shown that fruit fresh weight was mainly under genetic control, whereas oleic/ acids ratio was regulated by the environment and genotype by environment interaction had the major effect on oil content. Among the studied cultivars, different level of stability was observed, which allowed ranking the cultivars based on their plasticity for oil traits. High thermal amplitude, the difference of low and high year values of temperature, negatively affected the oil content and the oleic acid percentage. Information derived from this work will help to direct the selection of cultivars with the highest global fitness averaged over the environments rather than the highest fitness in each environment separately. Olea europaea L. subsp. europaea var. europaea) covering more than eight million hectares, is one of the main oil crops worldwide. The crop, traditionally cultivated in the Mediterranean Basin, where it still represents 92% of the entire world supply , is now experiencing a great expansion to new and different areas of south-western Asia, Oceania, South Africa, and the Americas3. The Mediterranean area, considered particularly sensitive to global climate change, represents a hotspot region where the effects of new climate conditions will threaten the agro-ecosystems, with serious impacts on plant/environment interactions6. Thus, olive cultivation is going to face the challenges posed by the new climate constraints, that will benefit some olive-producing areas, and will adversely affect others7. In this context, knowledge about the olive reaction to the new climate scenarios represents relevant information for defining new adaptation strategies8.Cultivated olive  interaction results by variable phenotypes, but informative data on the correlation between genotype and phenotype and on the effectiveness of empirical selection are still missing40. G × E interaction also makes it difficult to choose cultivars well adapted to new and untested climate conditions9. Fruit size, oil content and oil quality are target objectives of the olive breeding44. Deep knowledge of the possible environmental effects on these traits would be useful to allow for an effective selection of high performing genotypes. Regarding oil quality, the unsaturated fatty acids (FAs) are the most important component of olive oil, followed by secondary metabolites, such as phenols46. Olive oil accumulates in the drupe mesocarp (more than 95%) and just a minimal percentage in the seed47. This feature determines a greater susceptibility of fatty acid composition to thermal variations concerning seed oleaginous crops48. Olive oil contains up to 83% of the monounsaturated oleic acid, followed by linoleic (3.5–21%), palmitic (7.5–20%), stearic (0.5–5%) and linolenic acid (<1%). The low content of saturated FAs in olive oil and the high percentage of monounsaturated FAs makes it an excellent source of fat, able to reduce cardiovascular diseases49.In olive, fruit oil content and composition is the result of a complex interaction between genotypic, environmental and agronomical factors52 and, therefore, variations in temperature may cause modifications in FAs composition53.Among the climatic factors that affect FAs composition, temperature plays an essential role by regulating FA desaturases55, nevertheless, it has been observed that during oil accumulation, olive varieties could have different responses to temperature in terms of FAs composition56. Moreover, other experiments, under controlled temperature conditions, demonstrated that oil content is sensitive to temperature changes. In fact, by increasing the daily mean temperature between 16 °C and 32 °C, oil content decreases23. Analyzing oil accumulation on six olive cultivars at different locations, a negative correlation was observed between oil content and mean temperature37 and between maximum daily temperature during oil accumulation within a narrow range (29–31.5 °C) of temperatures57.Several studies have evaluated the effect of climate conditions on fruit maturation and oil composition in restricted sets of cultivars and locationsA comprehensive study testing a large number of cultivars in different locations with diverse climate conditions has never been previously reported. This work represents the first extensive study on the evaluation of a very large set of olive cultivars (113) during two growing seasons from five locations geographically and climatically different in Italy, Spain, Morocco, Lebanon, and Argentina. These cultivars were assessed for FAs composition, oil content and fruit traits. Results should address the following questions: (i) how do cultivars respond to different environments? (ii) How different environments may affect fruit and oil traits in olive? (iii) How knowledge on cultivar plasticity for FAs composition may be applied to optimize orchard management and breeding strategies to improve olive adaptation under new climate constrains?A wide range of variation was observed for all analyzed traits. The traits included: fruit fresh weight (FrFW), fruit moisture (FrM), oil content in fruit dry weight (OCFrDW), palmitic acid , oleic acid (C18:1), linoleic acid (C18:2) and oleic/ acids ratio (OLP) for the common set of 14 cultivars evaluated in six environments Fig. . Among tThe highest average value of FrFW trait was related to Morocco15 (Morocco 2015) and Morocco16 , while in Morocco15 was detected the highest variability of this trait (55%) Fig. .The highest average values of FrM were found in Spain15 (63.5%), whereas its main variation was related to Morocco15 Fig. .The highest average values of OCFrDW were related to Morocco15 (45.8%), while the highest variation was related to Morocco16 Fig. .The highest average values of C16:0 were registered for Morocco15 and Spain16 , although the highest variation was registered in Morocco16 Fig. .For C18:1, the highest average value was observed in Italy16 (79.1%), while the highest variation was found for the Morocco15 (12.2%) Fig. . A generThe highest average values of C18:2 were found for Morocco15 (15.2%), whereas its highest variation was related to Spain15 Fig. .Finally, the OLP ratio had the highest values in Italy15 and Italy16, while approximately the same ratios were observed in the other four environments. The highest variation of OLP among different environments was related to Spain15 with 42.6% Fig. .The same dataset of 14 cultivars in six environments was used to investigate the cultivar variability Fig. . In thistrait, 16.8 and 17.4%, respectively)  of FrM were related to ‘Oblica’ and ‘Maurino’ cultivars , while ‘Frantoio’ and ‘Moraiolo’ cultivars displayed the lowest ones. Among the 14 cultivars, ‘Arbequina’ and ‘Moraiolo’ were the most unsteady cultivars for this character (CVly) Fig. .The highest average percentages of OCFrDW were related to cvs. ‘Bianchera’ (49.6%) and ‘Carolea’ (47.8%), while ‘Koroneiki’ showed the lowest values among all cultivars. In the six environments under study, ‘Lastovka’ and ‘Moraiolo’ cultivars displayed the highest variation for this trait Fig. .The highest amount of C16:0 was detected for ‘Salonenque’ cultivar (19.0%), while ‘Coratina’ was the cultivar which had the lowest amount (12.7%) Fig. . C16:0 h‘Picual’ and ‘Koroneiki’ cultivars showed the highest amount of C18:1, with 79.4 and 77.8% on average, respectively. It is interesting to note that ‘Picual’ not only reached the highest amount of C18:1, but also was the most stable cultivar for this trait in all environments (3.7%) Fig. . While ‘The cultivar ‘Salonenque’ had the highest average amount of C18:2 (20.0%), while the lowest values were detected in ‘Picual’ (3.2%). ‘Coratina’ cultivar showed the highest variation for C18:2 (75.4%), while ‘Carolea’ was the most stable cultivar for this trait (31.6%) Fig. .The highest OLP ratio was found in csv. ‘Picual’ (5.1) and ‘Coratina’ (4.1), while the lowest amount was observed in ‘Salonenque’ cultivar. ‘Oblica’ and ‘Empeltre’ cultivars had the highest percentage of instability for OLP , being ‘Picual’ the most stable one for this trait Fig. .m values . In contrast, OCFrFW (8.41%) and C18:1 (9.04%) were the most stable traits over six environments  and C16:0 (47.3%), σ²E for FrM (69.9%) and OLP (43.9%), and σ²GxE for OCFrDW (43.6%). The remaining FAs evaluated (C18:1 and C18: 2), both σ²G and σ²E showed similarly high values (from 35.8% to 39.1%). Consequently, broad-sense heritability (H2) ranged from 0.32 to 0.73, over the seven studied traits. FrFW had the highest H2 estimates (0.73); other traits, especially FAs and OLP, had also high values (0.59–0.64), while the lowest one was related to OCFrDW (0.32).A full dataset of all cultivars 113) from nine environments , representative of the overall observed variability, were used to study the relationships among them and to identify promising genotypes regarding these traits. A positive significant correlation was obtained between genotypic BLUP values for OCFrDW and FrFW. Some of the analyzed cultivars, such as ‘Konservolia’, ‘Chalkidikis’ and ‘Domat’ had higher OCFrDW and FrFW than the average, while others such as ‘Koroneiki’, ‘Megaritiki’, ‘Pendolino’ and ‘Vera’ showed high negative values Fig. . CorrelaBLUP values for OCFrDW, FrFW and OLP traits on the different environments were not correlated among them, indicating different best-performing environments for each of the evaluated traits , showed that the highest average of maximum temperatures (Tmax > 27 °C) was in Morocco15 and Morocco16,  and in Argentina15 and Argentina16 , especially during winter and spring months. Although Italy15 and Italy16 , with about 21 °C on average, had the lowest Tmax, and it was almost the lowest during the year Fig. . The higFor what concerns the average minimum temperatures, it is interesting to note that in Lebanon15 and Lebanon16 , minimum temperatures were higher than in other environments, particularly during spring and summer. Furthermore, the Italian environments showed the lowest average of minimum temperatures during the year. Fluctuations between the minimum and maximum daily temperatures reached the highest values in the Argentinean environment, more evident during winter, spring and autumn when the thermal amplitude was higher than 17 °C. The lowest amplitude of temperatures was observed in Lebanon15 and Lebanon16 , during the year and especially in spring and summer Fig. .The whole dataset of 113 cultivars evaluated in nine environments, were used to calculate Pearson’s correlation coefficients between ten-day temperature series and average values of OCFrDW and C18:1 Fig. . Maximum2 = 0.99). The highest percentage of this fatty acid was found in Italy15 and Italy16, with 2,000–2,100 TT °C × day and the lowest one was related to Morocco15 with 3,580 TT °C × day. On the contrary, both C18:2 and C16:0 showed positive correlations with TT °C × day . As expected, OLP had the same accumulation pattern as C18:1, with R2 = −0.99. A high positive correlation was also found between FrFW and TT °C × day from full flowering time to harvesting time (R2 = 0.93). In fact, the lowest FrFW was related to Italy15 and Italy16 and the highest FrFW was achieved in Morocco15. There was no correlation of OCFrDW and FrM with TT °C  over all environments. Among all analyzed cultivars, ‘Bianchera’, showed high stability for almost all the evaluated traits, while the opposite was observed for cv. Moraiolo. This may indicate that cv. Bianchera possesses higher adaptive capacity, for the studied traits, expressed as the highest global fitness averaged over the environments77, rather than the highest fitness in each environment separately79 respect to cv. Moraiolo.In the present study, the effect of environment and genotype on trait plasticity was studied separately on a set of 14 cultivars simultaneously present in six different environments (three locations per two seasons). The degree of variation was analyzed for seven traits, like oil content and fatty acids composition, as main characters of interest for the agronomical value of cultivars and breeding progenies80. Considering the importance and the economic impact of the olive oil and table olives production, it is becoming urgent to characterize the wide variability of this species, in order to identify those with a stable response in different environments, especially for important traits like oil content and fruit size. The complex effect of genotype, environment and their interaction on trait plasticity has been analyzed on 113 olive cultivars in five sites for two years, for a total of nine different environments. It represents the first concerted effort to gather information on the phenotype plasticity of a particularly large and representative sample of the entire variability of cultivated olive germplasm. Traits of high agricultural importance, related to fruit and olive oil composition, were taken under consideration.An important goal of crop improvement should be to minimize unfavorable G × E interaction and allow advantageous dynamic trait responses81 and G × E effect for oil content has been observed, while similar high genetic and environmental variance was observed for fatty acids composition. In accordance, G × E interaction was significant for maximum oil content and rate of accumulation, with similar or even higher variance component than the genetic variance, as reported by Navas-Lopez et al.82. Consequently, high values of broad-sense heritability were found for fruit fresh weight and low values for oil content, as previously reported from the analysis of variability between and within olive breeding progenies83. Similarly, the high heritability values obtained for fatty acids and mono-unsaturated fatty acid ratio (OLP) were also reported from studies in olive progenies84. Moreover, the correlation between BLUP values extracted from REML models suggested the possibilities for simultaneous selection for oil content and fruit fresh weight, while the opposite was found for oil content and mono-unsaturated fatty acid ratio. These results should be considered in further comparative trials and future breeding programs, to develop molecular tools useful as early selection criteria. Thus, phenotyping of large olive germplasm set under different environments should lead to a specific characterization of loci of interest with respect to classic approaches based on association or QTL mapping.A high variance in the whole dataset for fruit fresh weight due to the genetic background86, particularly if they occurred during the warmer months when fruits are developing and oil is accumulating in the mesocarp. High thermal amplitude during summer decreased the quantity of oil on dry weight and negatively affected the quantity of oleic acid during the colder months57. In fact, in the Argentinean environments, the oleic acid content was at the minimum level over all environments. Thus, olive oils arising from warm areas had consistently lower oleic acid content and higher mono-unsaturated fatty acid ratio, independently from cultivars, as previously observed by the analysis of a restricted set of samples and environments55. On the other hand, in environments with moderate minimum and maximum temperatures, without significant thermal range variability, as in Italy and Lebanon, oleic acid might go to the maximum levels, confirming other evidence obtained in some Italian environments87. The spread of olive orchards out of the Mediterranean area towards new habitats, completely different from the original ones, is limiting the ability of the species to survive and fructify3. Only few cultivars, as is the case of ‘Arbequina’ and some others used in new intensive groves, are able to yield consistently in the new environmental conditions, but often changing negatively their fatty acid profiles55. It has been widely pointed out that summer temperatures differently influence the fatty acids composition of olive oil89. It has also been shown that an increase in mean daily temperature, above 25 °C, has a negative effect on fruit dry weight, and a linear decrease both in fruit oil concentration and oleic acid proportion observed over the range of 16–32 °C23.The correlation analysis between temperature and oil content and composition demonstrated that the highest values of maximum average temperature during the colder months of the year are positively correlated with an increase of oil content, but correlation becomes negative if temperatures rise substantially during the summer. High values of maximum and minimum temperatures affected negatively the percentage of oleic acid90. Therefore, the use of cultivars with high stability, showing interesting traits such as high values of oil yield and oleic acid content, can decrease fluctuations in olive oil production and standard quality. The richness of olive germplasm should be thoroughly protected, conserving cultivars with phenotypic specializations, that would be able to buffer future environmental extremes due to climate and land-use changes91.From a breeding perspective, it has been suggested that selection criteria should consider adaptive plasticity, particularly in relation to adaptation to climate changesThe present study indicates the need to understand how much phenotypic variability can be mainly attributed to genetic control, in order to enhance the prediction of crop performance across diverse environments.The study offers useful information on the varieties best suited to each environment, depending on the desired trait. These studies on olive germplasm and comparative trials under different environments are essential to predict the behavior of specific varieties in view of widening the area of olive cultivation and future changes in climate scenarios, particularly regarding new environments not previously experienced by the olive crop.The results obtained in this work should be taken into account to evaluate both genetic and environmental variance components for the traits mainly related to the product composition. Our study revealed complex patterns of phenotypic plasticity in the most important olive traits. Genotype effect on the plasticity of traits among cultivars, in addition to variation in mean trait values, may thus form a component of the adaptation to different climates in this species. The availability of extensive genetic and traits variation among olive cultivars, the knowledge on the response of fatty acids composition to temperature, and the new information on the genomic composition of the species, will facilitate, in the near future, development of new olive genotypes well adapted to different environments through genomics-assisted breeding.93. A total of 816 trees were sampled, each accession being represented by two trees for each environment. Fourteen cultivars were in common between IOGC, WOGB-INRA and WOGB-IFAPA germplasm collections.Fruit samples were collected from trees of cultivars available in five olive germplasm collections located in Italy, Spain, Morocco, Lebanon, and Argentina. Thus, the following collections were included in the study: (i) the World Olive Germplasm Bank (WOGB-IFAPA) of IFAPA ; (ii) the World Olive Germplasm Bank of INRA (WOGB-INRA) ; (iii) the International Olive Germplasm Collection (IOGC) of Zagaria ; (iv) the Olive Collection of LARI (OC-LARI) ; and (v) the Olive Cultivar Collection of INTA (OCC-INTA) , to represent non-Mediterranean climate conditions  and fruit moisture (FrM), all others were conserved at −20 °C. The harvesting time in the Mediterranean countries was set in November, as the usual harvesting time in this hemisphere, while in Argentina fruits were collected in May.Three sub-samples of around 25 g of fruits were randomly selected to measure FrFW. They were dried in a forced-air oven at 105 °C for 42 h to ensure dehydration and to determine FrM. The same samples have been used to measure the oil content using an NMR Fat Analyzer Bruker Series NMS 100 Minispec . The results were expressed as a percentage of dry fruit weight (OCFrDW). The samples were prepared through the same protocol in each collection and all the NMR analysis was performed in one laboratory.94 with minor changes. 50–100 mg of the flesh were placed in a reaction tube. The methylation solution was prepared by methanol, toluene, 2,2-dimethoxypropane and sulfuric acid in a 39:20:5:2 proportion. In each reaction tube, 4 ml of the methylation solution and 2 ml of heptane were added. Tubes were then transferred in a water bath for two hours to 80 °C when solution temperature reached to the room temperature, 1.5 ml of supernatant were transferred to the gas-chromatograph tube. Separation of fatty acid methyl esters was carried out by Gas–Liquid Chromatography (GLC) with a Split injector and flame ionization detector, using similar equipment and conditions in the different laboratories involved in the work. Fatty acids monitored in this study include palmitic acid (C16:0), oleic acid (C18:1) and linoleic acid (C18:2) expressed as a percentage of total FAs and oleic/ acids ratio (OLP).FAs composition was analyzed by FAs methylation of fruit flesh (fruit epicarp and mesocarp)www.statgraphics.com).Statistical descriptive analyses of the different traits were performed for one balanced and for the whole unbalanced datasets. The balanced dataset includes 14 common cultivars available in the three collections IOGC, WOGB-INRA and WOGB-IFAPA located in Italy, Morocco and Spain, respectively and two seasons (six environments). For these cultivars, variability for seven traits was calculated through Violin Plot Statlet  The environmental effect on trait plasticity in a common set of cultivars was calculated through the coefficient of variation for each trait in each environment (CVtrait); 2) The genotype effect on trait plasticity in a common set of cultivars was calculated through the coefficient of variation over the environments .The quantitative estimators of phenotypic plasticityThe complex effect of genotype, environment and their interaction on trait plasticity of all studied cultivars was carried out on the unbalanced dataset, including all 113 genotypes from five collections and two seasons (nine environments). Trait variation in each environment, as well as overall environments, was calculated as previously described.Variance components were then estimated according to the statistical model:ijk was the phenotypic value of the k tree of the i genotype in the j environment; µ the overall mean of the progeny; Gi a random effect contributed by the i genotype; Ej a random effect of the j environment; (G × E)ij the interaction between the i genotype and the j environment; and εijk was the random residual error effect.where PG), among environments (σ²E), associated with the G × E interaction (σ²GE), and residual error effect for the measured samples (σ²ε), were obtained. Broad-sense heritability (H²) for all evaluated traits was estimated as the ratio between genotypic and phenotypic variances: σ²G/(σ²G + σ²GE + σ²ε). Best linear unbiased prediction (BLUP) values for genotypes and environments were also estimated from these models. Analyses were performed using R version 3.5.0 software, with REML estimation method under lme4 package for developing mixed linear models .From this model, variance components among genotypes  has been included, being 10 the total number of environments under study.97. Five environments were considered in this case as Spain15 was excluded from the analysis due to missing flowering time data. Pearson correlation coefficient was used to analyze the correlation between climate data  and average values of the studied traits in each environment.To calculate the accumulation of thermal time in each environment for the 14 common cultivars, linear or bilinear functions were fitted to the relationships between three main fatty acids , OCFrDW, FrM, FrFW and OLP with thermal time . Thermal time was registered from full flowering time to the harvesting time, using the single sine, horizontal cut-off methodSupplementary data


Molecular knots represent one of the most extraordinary topological structures in biological polymers. Creating highly knotted nanostructures with well-defined and sophisticated geometries and topologies remains challenging. Here, we demonstrate a general strategy to design and construct highly knotted nucleic acid nanostructures, each weaved from a single-stranded DNA or RNA chain by hierarchical folding in a prescribed order. Sets of DNA and RNA knots of two- or three-dimensional shapes have been designed and constructed (ranging from 1700 to 7500 nucleotides), and they exhibit complex topological features, with high crossing numbers (from 9 up to 57). These single-stranded DNA/RNA knots can be replicated and amplified enzymatically in vitro and in vivo. This work establishes a general platform for constructing nucleic acid nanostructures with complex molecular topologies. Designing nucleic acid-based nanostructures with knots remains challenging. Here the authors present a general strategy to design and construct highly knotted 2D and 3D nanostructures from single-stranded DNA or RNA Knotted DNA structures are usually formed during genomic DNA replication and transcription and are eventually resolved by DNA topoisomerases5. Although knotted DNA structures are present in some bacterial phage genomes during the packing of the genomic DNA into the phage capsid7, they are not very predictable or well-organized. The construction of molecular knots requires a high level of control over their assembly behaviors at a nanometer scale8. It is challenging to design and construct highly knotted nanostructures with well-defined topologies and geometries. The programmability of nucleic acids10 makes them excellent candidates to use for creating topological knots at a molecular level. Synthetic topological DNA nanostructures have previously been constructed by creating topological nodes based on B-form/Z-form double-stranded DNA (dsDNA) helices14, paranemic crossovers16, and DNA four-way junctions17. In contrast to the strategies that rely on the design of individual topological nodes and finding the appropriate DNA motifs/interactions with which to assemble such nodes, a different approach is to fold one long single-stranded DNA (ssDNA) chain into programmable topologies. However, this strategy presents an important yet unanswered question: can partially paired, dsDNA thread through its own entanglement to form highly knotted topological structures?Naturally existing molecular knots have been found in biopolymers, such as DNA and proteins. During protein folding, the protein molecules occasionally exhibit small amounts of crossings18. Each of these ssDNA or ssRNA knots are folded and then self-assembled from a replicable single-stranded nucleic acid molecule with sizes ranging from 1700 to 7500 nucleotides (nt). Various ssDNA knots that had different crossing numbers and that displayed two-dimensional (2D) patterns were designed, constructed, and characterized using atomic force microscope (AFM) imaging. The yield of the folded knots was optimized by programming a step-wise hierarchical folding pathway through sequence design in the paranemic cohesion regions. A series of design rules have been formulated to significantly improve the yield of well-formed target structures. Furthermore, we expanded the design strategy to construct ssRNA knots and three-dimensional (3D) ssDNA knots. The 3D knots were characterized by cryogenic transmission electronic microscopy (cryo-EM) single particle reconstruction, which confirmed their designated geometries.Here, we present a general approach to creating topological knots of ssDNA and ssRNA with high crossing numbers (up to 57). A crossing number is a knot invariant that shows the smallest number of crossings in any diagram of the knot, representing the topological complexity of a knot20, drug delivery21, cryo-EM analysis22, and DNA-based memory storage24.Our ability to construct knotted structures that can be replicated and transcribed enzymatically, with the use of designer single-stranded nucleic acids that can self-fold into molecular knots of customized shapes, allows us to significantly reduce the cost of production as compared with the cost of the multi-stranded DNA nanostructure systems. The single-stranded topology may also open up more opportunities for us to select and generate nanostructures with desired functions by using in vitro evolution. In addition, our work establishes a fundamental and general platform for constructing nucleic acid nanostructures with increased sizes and unprecedented complex molecular topologies. This ability to construct such structures may allow us to create more intricate diverse transformative applications in nanotechnology and molecular science, such as nanophotonics1 (Alexander–Briggs notation) can be assembled by either connecting nine right-handed X-shaped junction tiles together  motifs as the modular building blocks and a node-edge network as the geometric blueprint for arbitrary nanostructures, as they can be readily connected with other PX motifs to enable single-stranded routing. In comparison with the compact parallel or antiparallel helical arrangements, wireframe networks are better candidates for constructing knotted structures, as they offer more space for DNA chains to thread through during the early formation of partial structures. For example, knot 9her Fig.  or by thher Fig. . Here, wher Fig. . We arraher Fig. , and sepher Fig. . The oveher Fig. .Fig. 1DeDesigning a topologically and kinetically favorable folding pathway is a key step for the successful formation of intricate structures with high crossing numbers. We introduced a hierarchical folding strategy to guide the knotting process in a prescribed order. For example, a knot with 23 crossings could be assigned to a location on a three-column grid that is represented by a rectangle with three square cavities Fig. . To main25. Second, in the early stage of folding when the unfolded portion of the strand is still long, the folding pathway should avoid threading DNA strand through any of its own preformed structures  well-formed structures, while the branched one .Three essential rules were identified for optimizing the folding pathway. First, a linear folding path is better than a branched one, because the linear folding pathways involve two free ends that thread to form the loops in a sequentially ordered pathway, while the branched folding pathways have parallel steps that each involves a single free end to thread through the preformed loops. Based on an entropic point of view, the formation of two free ends looping with each other is expected to be possibly easier than one free end threading itself through preformed loops  and high purity. The ssDNA strand was obtained, then self-assembled (folded) in a 1x TAE-Mg buffer with a 12 h or 24 h annealing ramp from 65 °C to 25 °C (see details in Methods section). The folded products were then characterized by using AFM imaging, gel electrophoresis and/or cryo-EM imaging.Both the chemical and enzymatic synthesis of long ssDNA molecules are technically challenging, because the chain possesses a large portion of self-complementarity. As shown in the folding pathway, the ssDNA molecule will first form a long hairpin-loop structure with the 5’ and 3’ ends meeting each other. We first split the full-length ssDNA strand into two equal halves, with each strand lacking significant secondary structures, we then inserted each of them into plasmids as double-stranded genes, and then amplified them by cloning. The two dsDNA genes were obtained separately from the plasmids by restriction enzymes digestion  and were then ligated together with a linearized phagemid vector, pGEM-7zf(-)  and 58% (N = 214), respectively  and none of the resulting structures were perfectly formed with the 67 crossings (N = 389) based on single-molecule analysis by AFM imaging  and the best geometric fitting, 8 (instead of 4 or 6) bp was chosen for the length of a paranemic crossover  of crossing steps. The same strategy has also been adapted for the design and construction of complex ssRNA knots. Compared with previously reported synthetic DNA topological structures36, our ssDNA knots have the largest synthetic DNA knots (up to 7.5 k bases) and the most complicated topology  unique sequence limit: 8 nt; (2) repetition limit: 6–8 nt; (3) G repetition limit: 4 nt; (4) GC content: 0.45–0.55. Once sequences were generated, a few nucleotides were adjusted to eliminate the restriction enzyme targeting sequences  for cloning purposes. Then, the raw sequences of the paranemic cohesion regions were inspected manually and several modifications were made: the local sequences that were used to form the paranemic crossovers were checked to make sure that each of the crossovers were stable; the GC contents in each paranemic cohesion region were designed individually and inter-dependently as they needed to be compared with one another so that all of the paranemic cohesions would have a strength that was ordered sequentially according to a predetermined folding pathway; lastly, the uniqueness of the paranemic cohesions was optimized independently, so that the occurrence of mismatches and cross-talking in the second step of the folding process were minimized. For ssRNA origami sequences, a T7 promoter sequence was followed by two or three consecutive Gs that were manually incorporated onto the 5’ end of the strand in order to facilitate efficient in vitro transcription reactions during gene synthesis.DNA/RNA structures and sequences were designed using the Tiamat software (Yanlab.asu.edu/Tiamat.exe)E. coli DH5α competent cells along with a helper plasmid pSB4423, a kind gift from Dr. Stanley Brown . E. coli colonies were formed after overnight incubation at 37 °C, and a single colony was inoculated into the 2xYT medium that had been supplemented with 2 mM MgSO4 (Sigma-Aldrich) and grown at 37 °C overnight with shaking at 250 rpm. During the overnight growth, the recombinant M13 phages were continuously produced and secreted into the medium. During the next day, the culture was centrifuged at 5000 g for 15 min in order to pellet down the E. coli cells. The recombinant M13 phage was precipitated from the recovered supernatant with the addition of NaCl (to 30 g per liter) and PEG8000 (to 40 g per liter), and incubated in the 4 °C cold room for 1 h. The precipitated phage was then collected by centrifugation at 4500 g and 4 °C for 15 min and resuspended in the TE buffer (10 mM Tris-HCl pH 8.0 and 1 mM EDTA). The phage was lysed with two volumes of PPB2 buffer (0.2 M NaOH and 1% SDS) and mixed with 1.5 volumes of PPB3 buffer (3 M potassium acetate pH 5.5), and centrifuged at 5000 g for 15 min. The supernatant was then precipitated with one volume of 100% ethanol, and centrifuged for another 15 min at 5000 g. The phage DNA precipitate was then washed once with 75% ethanol and dissolved in the TE buffer. The phage ssDNA was digested by EcoRV enzyme (New England Biolabs) and resolved on a 1% agarose gel. The correct bands were sliced and purified by using a Monarch DNA Gel Extraction Kit (New England Biolabs).The ssDNA origami sequences were divided into two fragments with restriction sites added onto both ends. The first fragment contained EcoRI and XbaI restriction sites and the second one contained XbaI and HindIII restriction sites. The EcoRV sites were also manually added to both ends of the full-length sequence in order to facilitate the production of the final ssDNA. The two DNA fragments that were ordered as double-stranded genes in plasmids were from Biobasic Company (Biobasic.com) with sequences that were verified through Sanger sequencing. The two DNA fragments that were cleaved from the plasmids by the restriction enzymes were accordingly subcloned into an EcoRI and a HindIII linearized pGEM-7zf(-) vector (Promega). After sequencing verification, the pGEM-7zf(-) vector that contained the full ssDNA genes was co-transformed into For the ssRNA molecule synthesis, the DNA sequence with a T7 promoter at the 5’ end was first cloned into a pUC19 vector by using the same method as was used for the ssDNA gene cloning process that was described above. The plasmid containing the ssRNA gene was linearized by using a HindIII enzyme (New England Biolabs) and the plasmid was purified by using a Phenol/chloroform extraction and ethanol precipitation. The in vitro transcription reaction was carried out by using the T7 RiboMAX Express Large Scale RNA Production System (Promega), following the manufacturer’s instructions. The RNA molecules were then purified via a RNA Clean & Concentrator-25 kit (Zymo Research).The purified DNA and RNA molecules were diluted to 5–10 nM in 1× TAE-Mg buffer . The resulting solution was annealed from 65 °C to 25 °C with a cooling ramp of 1 °C per 20 min to form the desired structures.2 solution was added onto the mica, and 40 µl of the same buffer was deposited onto the microscope tip. The samples were then scanned by following the manufacturer’s instructions.All samples were imaged in “ScanAsyst mode in fluid,” using a Dimension FastScan microscope with PEAKFORCE-HiRs-F-A tips (Bruker Corporation). After annealing, 2 µl of each sample was deposited onto a freshly cleaved mica surface , and left to adsorb for 1 minute. Then, 80 µl of 1× TAE-Mg buffer and 2 µl 100 mM of a NiClThe Biobasic company (Biobasic.com) synthesized four ssDNAs with the customized sequences and then cloned these sequences into a pBluescript SK( + ) vector (Biobasic), with the gene sequences flanked by two BtsCI restriction sites. The final plasmids were then co-transformed with pSB4423 to produce recombinant M13 phages. The phage particles and phage DNAs were then purified by using the same methods as described in the ssDNA synthesis section. The ssDNAs were cleaved off from the recombinant phage DNAs by using a BtsCI restriction enzyme (New England Biolabs), and were gel purified by using a 4% urea denaturing PAGE gel. The four ssDNAs were annealed into two sets of dsDNAs  in the 1x annealing buffer (50 mM Tris-HCl pH 8.0 and 100 mM NaCl). The two linear dsDNAs were mixed in a 20 nM concentration in a 1× TAE-Mg buffer, annealed from 65 °C to 25 °C at 1 °C per 20 min to form the desired paranemic cohesion interactions, and were then characterized by AFM imaging. The sticky ends on the two sets of the dsDNAs were able to close the ring structure without ligation. In the second control experiment, the two linear dsDNAs were ligated separately with T4 DNA ligase in a 1x ligation buffer (Thermo Fisher Scientific) at room temperature for 1 h to enablel them to form the two circular dsDNAs. The ligation products were treated with exonuclease I and exonuclease III (New England Biolabs) to remove any linear DNA. A Monarch PCR & DNA Cleanup Kit (New England Biolabs) was used to further purify the solution. The two circular dsDNAs were then mixed at 20 nM in a 1× TAE-Mg buffer, annealed from 65 °C to 25 °C at 1 °C per 20 min and characterized by AFM imaging.2 with the defocus ranging from 1.5 to 3 μm.In the cryo-EM experiments, 2 μL of the aforementioned ssDNA nanostructure samples  were applied onto a 200 mesh R1.2/1.3 holey carbon Quantifoil grid (Quantifoil) that was cleaned with acetone (Sigma-Aldrich) for 12 h and glow discharged for 40 s before use. The grid was blotted for 3.5 s and immediately frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific) with a constant temperature of 6 °C and humidity at 100%. The grid was stored in liquid nitrogen until the imaging session. All grids were examined on a JEM2200FS cryo-electron microscope (JEOL) that was operated under the following parameters: 200 kV, spot size 2, condenser aperture 70 μm, objective aperture 60 μm, 30,000 × magnification . The images were recorded under a low-dose condition on a direct detection device (DDD)  while operating in movie mode at a recording rate of 24 raw frames per second. The total dose was 40 electrons/Å39 Approximately 50 particles were used to generate a de novo initial model in EMAN2. The final 3D reconstruction with tetrahedron symmetry applied in EMAN2, resulted a cryo-EM density map at 17 Å resolution, calculated using the 0.143 criterion of the Fourier shell correlation (FSC) curve with a mask. A Gaussian low-pass filter was applied to the final 3D maps and displayed in the Chimera UCSF software package.40For the ssDNA tetrahedron sample, a total of 47 images were manually recorded on the DE-20 detector. Motion correction was performed by running the averages of three consecutive frames with the use of the DE_process_frames.py script . A total of 533 particle images were manually boxed, CTF corrected, and extracted in EMAN2.Tilt-pair validation for the cryo-EM map was performed by collecting data at two goniometer angles, 0° and 10°, for each region of the grid. The test was performed using the e2tiltvalidate.py program in EMAN2. Additional details on the tilt-pair validation is provided in Table A total of 53, 75, and 46 micrographs of ssDNAs that folded into triangular prisms, square pyramids, and pentagonal pyramids were collected. Subsequently, 226, 330, and 238 particles were extracted, respectively. The initial models were generated as mentioned above and the final reconstructions were applied with the corresponding C3, C4, and C5 symmetries that yielded EM density maps at resolutions of 32, 25, and 26 Å. These resolutions were calculated using the 0.143 criterion of the Fourier shell correlation (FSC) curve with mask.Supplementary InformationPeer Review File


Human milk delivers an array of bioactive components that safeguard infant growth and development and maintain healthy gut microbiota. Milk fat globule membrane (MFGM) is a biologically functional fraction of milk increasingly linked to beneficial outcomes in infants through protection from pathogens, modulation of the immune system and improved neurodevelopment. In the present study, we characterized the fecal microbiome and metabolome of infants fed a bovine MFGM supplemented experimental formula (EF) and compared to infants fed standard formula (SF) and a breast-fed reference group. The impact of MFGM on the fecal microbiome was moderate; however, the fecal metabolome of EF-fed infants showed a significant reduction of several metabolites including lactate, succinate, amino acids and their derivatives from that of infants fed SF. Introduction of weaning food with either human milk or infant formula reduces the distinct characteristics of breast-fed- or formula-fed- like infant fecal microbiome and metabolome profiles. Our findings support the hypothesis that higher levels of protein in infant formula and the lack of human milk oligosaccharides promote a shift toward amino acid fermentation in the gut. MFGM may play a role in shaping gut microbial activity and function. This is at least partly due to the presence of antimicrobial proteins such as secretory immunoglobulin A, lactoferrin and lysozyme in human milk as well as oligosaccharides that function to selectively enhance colonization of specific groups of gut microbes including Bifidobacterium spp.5. Through selection of specific microbes, microbial richness and diversity are ultimately reduced2 as they influence the growth of other bacteria7, which results in modulation of the luminal pH and metabolic profile9.Breastfeeding influences the development of the gut microbiota according to the degree of exclusivity13, and has been associated with increased risk of childhood obesity15. The difference in serum metabolic profiles may in part be due to differences in the intestinal microbiota, but are also likely due to the substantially higher protein concentration of infant formula as compared to human milk. Specifically, formula-fed (FF) infants have been shown to have higher levels of circulating amino acids, amino acid derivatives and urea than BF infants12.Feeding infant formula has been shown to alter the fecal microbiota from that of breast-fed (BF) infants, induce a different serum metabolic profile than in BF infants16 to provide substrates for microbial fermentation. Amino acids produced by intestinal microbiota are then absorbed and contribute to the host nitrogen pool17. Circulating lysine and threonine, that we previously found to be higher in FF infants13, are considered metabolites with a gut microbial origin20, as modification of intestinal microbiota via prebiotic supplementation has been shown to reduce free amino acids in cecum, colon, serum and liver of adult mice21, and to slightly lower the blood urea level of infants22. These studies support the importance of exploring gut microbiota as contributing factors to infant metabolism.There is a substantial quantity of nitrogenous compounds from dietary origin that escape digestion, and these compounds are retained in the lumen of the terminal ileum24. Indeed, we found that providing MFGM to young infants in formula reduces the incidence of acute otitis media25, and to older infants as a supplement in weaning food reduces the prevalence of diarrhea26 and decreases circulating IL-227 as well as trimethylamine-N-oxide27, an oxidized microbial byproduct. These studies led to the hypothesis that MFGM may act as a substrate that selectively promotes growth of healthy infant gut microbiota, and that supplementing an MFGM concentrate may alter gut microbial composition and by-products to a profile that is more similar to an exclusively BF reference group.The milk fat globule membrane (MFGM) has historically been discarded with the milk fat in the manufacturing of bovine milk based infant formula. However, protective activity of MFGM against pathogens and viral infections has been shown29, where FF infants were randomly assigned to receive a standard infant formula (SF) or a bovine MFGM isolate-supplemented, low-energy, low-protein experimental formula (EF) from ~2 until 6 months of age. An exclusively BF reference group was included in this study. The influence of feeding on the composition and metabolic activity of intestinal microbiota was evaluated through secondary analyses of feces collected from these infants at baseline (~2 months), 4, 6 and 12 months of age via 16S rRNA amplicon sequencing and quantitative metabolomic profiling.To provide a mechanistic explanation regarding the influence of MFGM on gut microbiota, we evaluated the fecal microbiome and metabolome of a subgroup of 90 infants who participated in a prospective, double-blind, randomized controlled study29 a subset of 90 infants  were randomly selected for fecal microbiome and metabolome analysis at 2, 4, 6, and 12 months. 96.2%, 96.5% and 82.1% of infants in the BF, SF, and EF group respectively were born vaginally. Inclusion criteria for the original study were: exclusively formula-fed at <2 months of age or intention to exclusively breastfeed until 6 months of age; gestational age at birth: 37–42 weeks; birth weight 2.5–4.5 kg and absence of chronic illness. After data generation, a few samples were further excluded, including infants who stopped consumption of study formula (n = 2), infants who had no record of a food diary (n = 1), infants from the BF group that were heavily mixed-fed (n = 3), infants from the FF group who consumed another formula (n = 4), and infants who had a record of antibiotics use (n = 3). For fecal metabolome data, samples that contained urea were suspected of being contaminated by urine since normal fecal samples do not contain urea due to bacterial urease activity. Therefore, fecal samples containing urea were excluded from analysis. The number of samples from each intervention and time point are summarized in SI Tables Out of a total of 240 infants who participated in a clinical trial concerning the outcomes of feeding a formula supplemented with a bovine MFGM isolate, which initially consisted of BF, n = 80; SF-fed, n = 80, and EF-fed, n = 80 , following exclusions28. In this randomized subgroup, the intake of formula was only slightly higher in the EF group , Veillonella, Enterococcus and Bacteroides from 2 to 6 months of age. Over time, as more weaning food was introduced, the relative abundance of Blautia in the stool increased , no significant difference was detected between the two FF groups or between BF and FF infants, which may be due to the fact that some of the FF-infants were likely breast-fed after delivery for some time before enrolment at ~2 months of age.In comparison to BF infants, FF infants showed a slightly higher microbial richness (observed species) at 2 months , but not statistically different  at other time points examined. When applying “Tail” statistics index  and fermentation byproducts from carbohydrate metabolism  were observed. In contrast, the concentration of microbial by-products from amino acid degradation  were associated with lower fecal water % Fig. .Starting from 2 months of age, a profound difference in the fecal metabolome between BF and FF was observed and continued to 4 and 6 months when no or small amounts of complementary food were introduced. The effect of diet on the fecal metabolome was reduced in the group with complementary food consumption and became indistinguishable at 12 months of age Fig. , BF infa36, supplementing a bovine MFGM concentrate to the EF did not drive the fecal metabolome to the extent expected for human oligosaccharides (HMOs), thus the differences observed were likely derived from other active ingredients.The major difference between the two FF groups was revealed during the exclusive feeding period, with lower levels of amino acids , and 2-hydroxyisovalerate (branched-chain amino acid degradation product), cadaverine (lysine breakdown product), and 4-hydroxyphenyllactate (tyrosine breakdown product) in the EF group Fig. , were alN-acetylglucosamine, fucose, galactose and glucose. In comparison, the major sugar monomers in the stool of FF infants were mostly galactose and glucose, sugar monomers from lactose. By 12 months of age, glucose was the major sugar monomer in infant stool. Only a few BF infants at 2 months of age had high levels of detectable HMOs in the stool, and the majority of these infants had lower concentrations of by-products from complex carbohydrate degradation  become substrates for gut microbial fermentation. Introduction of complementary food to either BF or FF infants disrupts the BF- or FF- specific fecal profile Figs , 4a. As ood Fig. . Howeverood Fig. .36, which could provide a substrate for microbial fermentation, the amount of bovine MFGM supplemented in the current study is lower (approximately 0.48 g/L) compared to a recent study on rat pups (1.2 g/L) that showed a significant change in colon microbial composition and diversity with MFGM supplementation37. In comparison to the difference in the serum metabolome and other biochemical, clinical and cognitive measurements, the influence of MFGM supplementation on the overall oral microbiota38 and fecal microbiota  harbor α-fucosidases and demonstrate the ability to produce 1,2-propanediol from fucose and fucosyllactose45. Bifidobacterium has been shown to produce a considerable amount of 4-hydroxyphenyllactate in vitro47, providing an explanation for the high 4-hydroxyphenyllactate found in the stool of BF infants.In comparison to formula-feeding, human milk consumption yielded higher levels of lactate, pyruvate, 1,2-propanediol, ool Fig. . The larin vitro incubation of intestinal content49, colon content from rodents, and in stool from individuals consuming high protein diets52. The higher fecal levels of propionate and butyrate in FF infants  may be due to degradation of certain amino acids55 or production by species from the Eubacterium genus  and phenylacetate Fig. , which anus Fig. , which d58 which can be explained by excess dietary protein that escapes digestion59. This further implies that the intestinal microbiota in FF infants is likely to participate in significant catabolism of amino acids in the intestinal lumen. To an extent, the presence of ammonia reduces the uptake and utilization of short chain fatty acids (SCFAs) by colonocytes, and subsequently decreases the production of ketone bodies and CO2 62; therefore, the elevated butyrate level in the stool of FF infants  had faster transit times and softer and more frequent stools87. Here, we observed that a watery stool is associated with an excess amount of sugar monomers and by-products from oligosaccharide fermentation  during the study intervention period. Although the relative abundance of Lactobacillus in stool was low (<3%), we cannot rule out its potential contribution to the infant fecal metabolome90.One strength of the present study is the double-blind randomized design among FF infants. Comparisons between FF and BF infants in the present study should be made with caution since these two groups were not randomized and potential confounding factors involving feeding choice cannot be ruled out. Another limitation of this study is the lack of information on the volume of human milk consumed and mother’s milk composition. It is well established that the composition of HMOs is driven by maternal secretor status and Lewis Blood group status91 on this cohort, this study provides information on the effect of feeding on gut microbiota in the first year of life. The different fecal microbiomes are reflected by differences in fecal water % and concentration of microbial by-products in the stool. With limited oligosaccharides present and high protein levels in infant formula, we speculate that the difference observed between the BF and FF infant fecal microbiome and metabolome can be explained by an alteration in the availability and microbial utilization of carbohydrates and proteins as energy substrates. The difference between the fecal microbial taxonomic profiles of MFGM supplemented EF and SF was moderate in comparison to the distinct difference between BF and FF infants. However, an influence of MFGM on the fecal metabolome was observed, and may be associated with a change in microbial activity and function. Further studies are needed to explore the linkage between the potential antimicrobial property of MFGM proteins and the specific alteration we observed in the fecal metabolome.The results obtained in the present study clearly show the importance of early life nutrition on the community profile and functional characterization of intestinal microbiota. Breastmilk or formula-feeding as well as complementary food influence gut microbiota and the luminal environment. Building on previous workThis double-blinded, parallel randomized controlled trial took place at Umeå University Hospital, Umeå, Sweden after approval by the Regional Ethical Review Board in Umeå (Dnr 07-083 M) for all experimental protocols, and obtaining both oral and written informed consent from the parents/caregivers before inclusion. The study was registered at clinicaltrials.gov , and was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines.SF infants consumed BabySemp1  and EF infants consumed a formula modified from BabySemp1 with the addition of bovine MFGM-enriched whey protein concentrate  to account for 4% of the total protein in the EF. The macronutrient composition of the EF and SF has been described previously and also available in SI Table 3-day food diary was recorded by parents every month over 3 consecutive days at 2, 3, 4, 5, 6 and 12 months of age. All food and drink that a child consumed were recorded either by weight (grams) or volume  by the parents using household measures and kitchen utensils. Taste portions were noted as if the intake volume was <15 mL. Brand name and manufacturer name were recorded if the food/drink consumed was a commercial product. The nutrient intake of each complementary food was calculated according to the Swedish National Food Agency Database and food labels. Use of medications, vitamins or dietary supplementations was documented. Persistence of breastfeeding was reported but the volume was not.Before or at the time of each visit, stool samples were collected by parents into a plastic container. The samples were immediately frozen at −80 °C or, if the sample was collected at home, frozen at −20 °C in a home freezer before transportation to the clinic and storage at −80 °C. The maximum storage time at −20 °C was 3 days.2O) was added to 207 µL filtrate. The pH of each sample was adjusted to 6.8 ± 0.1 by adding small amounts of NaOH and HCl to minimize pH-based peak movement. 180 µL aliquots were subsequently transferred to 3 mm Bruker NMR tubes  and stored at 4 °C until spectral acquisition.After manual homogenization with a sterile microspatula, avoiding undigested food, approximately 250 mg of fecal material were weighted and then extracted by vigorously vortexing in 1.5 ml ice-cold Dulbecco’s phosphate buffered saline  followed by centrifugation. The pellet was collected, frozen at −80 °C, and subsequently lyophilized to determine fecal dry weight . The supernatants were carefully collected and sequentially filtered through a syringe filter with 0.22 µm pore size  and an ultracentrifugal filter with a 3k Da molecular weight cut-off  to remove microbes and excess proteins. 23 µl of internal standard  on Bruker Avance 600 MHz NMR spectrometer . Spectral acquisition and processing parameters are identical to our previous work from this cohort13. Spectra were manually processed and profiled using Chenomx NMR Suite v8.3 . The concentration of fecal metabolites were estimated as:92, a scale from category 1 to 7 that indicates from hard to watery stool, was documented by one laboratory technician to ensure consistency.Stool water % was calculated as (wet − dry)/wet stool weight * 100. Bristol stool score93. The HMP protocol was modified from the manufacturer’s instruction as follows: (1) samples were heated at 65 °C for 10 min and then 95 °C for 10 min after adding C1 solution; (2) bead-beating of the samples was performed at 6.5 m/s for 1 min followed by 5 min rest, and bead-beating again for 1 min ; and (3) after bead beating, samples were centrifuged for 3 min to ensure pellets fully formed. The DNA purity was determined spectrophotometrically .To minimize the freeze-thaw cycle, the fecal metabolite and microbial DNA were extracted on the same day for each sample. Approximately 250 mg of fecal material was extracted according to the HMP protocol using MoBio PowerLyzer PowerSoil DNA isolation kit 94 modified according to Bokulich et al.95 to contain an illumina adapter sequence on the forward primer. An 8 bp Hamming error-correcting barcode was attached to the 3′ end of the forward primer to enable sample multiplexing. PCR reactions were performed in 20 µL reaction volumes following the protocol developed by Gohl et al.96. PCR reactions contained DNA template, DMSO , 5X KAPA HiFi Buffer , dNTP Mix (10 mM), KAPA HiFi HotStart Polymerase , PCR-grade water , DNA template and primers. The ideal volume of DNA template in the PCR reaction was estimated using a dilution series experiment followed by a quality check via gel electrophoresis of the amplicon product. The PCR reaction consisted of an initial 95 °C for 5 min followed by a 25-cycle program of 20 s at 98 °C for denaturation, 15 s at 55 °C for annealing, 60 s at 72 °C for primer extension and a final extension of 72 °C for 10 min. All reactions were amplified in duplicate and combined prior to purification.The V4 region of the 16S rRNA gene was targeted using F515/R806 primersAll amplified PCR products were quality checked by gel electrophoresis. The band intensity (around 380 bp) was visualized using SYBR safe DNA stain (Invitrogen) and its quantity (in ng/µL) was estimated using a molecular ladder with known concentration (BioRad EZ ladder 1 kb) through ImageLab software . Amplicons were pooled at equimolar ratio and purified using the QIAquick PCR Purification Kit  using a modified protocol from the manufacturer’s instruction. To maximize DNA yield and purity in the final library, amplicons were first combined into 12 separate libraries (not exceeding the column DNA capacity) and each were cleaned according to the manufacturer’s instruction. The final elusion was made by eluting the first column with 60 µL of elusion buffer and all subsequent column were eluted from the filtrate from the previous column. A Purified amplicon library was quality checked by Bioanalyzer and submitted to the UC Davis Genome Center DNA Technologies Core for 250 bp paired-end sequencing on the Illumina MiSeq platform.et al.97, the DNA extraction was performed using PCR clean and sterile filter pipette tips and in a cleaned biosafety cabinet sanitized by UV for at least 30 min prior to the experiment followed by surface decontamination by DNAaway (Thermo Scientific). One negative control “blank sample” from extraction of PCR-grade water  was included for each batch. This batch sample was subsequently amplified via PCR, visually inspected using gel electrophoresis and included in the final DNA library.To avoid airborne contamination during extraction as described in Salter 98 (version 0.33) at the end of both forward and reverse sequences. About 0.5% poor quality sequences were excluded. The subsequent sequence data was analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) platform99 (version 1.9.0). Forward and reverse reads were joined using Fastq-join100 via join_paired_end.py where at least 50 bp of overlapping was required and the maximum difference was below 5%. About 74.16% of the sequences remained after joining paired end reads. The subsequent joined sequences were preprocessed and demultiplexed according to the following criteria: (1) removal of primer sequences; (2) minimum acceptable Phred quality score Q > 20: and (3) no barcode mismatch allowed. Closed OTU picking at 97% identity was done using sortmerna101 against the greengenes database (greengenes 13_8)102. After removing singletons , the OTU table was normalized by the copy number using normalize_by_copy_number.py function developed in PICRUSt (version 1.1.3)103.A sliding window trimming of 4 bases wide on the low quality bases  was performed using Trimmomaticphyloseq and the microbiome package. α-diversity and β-diversity distance matrices were computed using DiversitySeq and the vegan package, respectively. Generalized log transformation (defined as log(y + sqrt(y2 + lambda))) was applied to all metabolomics data where lambda is 1. Principal coordinate analysis was computed using the pcoa function from the ape package. Adonis, a nonparametric implementation of a permutational analysis of variance, was computed using the adonis function from vegan with permutation set at 999. The spread (homogeneity) within a group was determined as 1 - cor where cor is the average spearman correlation between samples and the overall group-wise average. This feature is implemented in the divergence function in the microbiome package.Statistical computing and graphical generation were performed using the R programing environment. OTU table handling was achieved using 104 and controls for False Discovery Rate (FDR). A pseudo count value of 0.001 was added for the relative abundance data. For other data, significance between groups was evaluated using the Kruskal-Wallis test  and p-values from the Kruskall-Wallis test were further adjusted by FDR ). The overall level of significance was set at p < 0.05.The differential analysis for the microbiome was computed at the genus level using ANCOMcliff.delta function from effsize package. The 95% confidence interval of each computed Cliff’s delta was estimated assuming a normal distribution . The magnitude was assessed using the thresholds suggested by Romano et al.105, where |δ| < 0.147 corresponds to negligible, |δ| < 0.33 corresponds to small, |δ| < 0.474 corresponds to medium, otherwise large.Effect size between BF and FF and between SF and EF was evaluated using Cliff’s delta (δ) statistics using the 2) was computed using cor, (method = “pearson”) to evaluate the strength of the correlation. All plots were generated using ggplot2.The square of Pearson’s correction coefficient (rCONSORT ChecklistStudy ProtocolSupplementary Information


We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB.Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity.Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity.An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands. •AD and DLB patients spend more time in sparse connectivity states than controls.•DLB patients spend less time in strongly connected states compared to controls.•DLB patients have reduced variability of global brain network efficiency. To date22.1The study involved 102 participants over 60 years of age: 33 were diagnosed with probable DLB, 36 with probable AD, and 33 were age-matched healthy controls (HCs) with no history of psychiatric or neurological illness. Participants from two contemporary and independent dementia studies conducted at one research center (Newcastle) were combined for this analysis. Both studies recruited patients from the local community-dwelling population who had been referred to old age psychiatry and neurology services. Dementia diagnoses were performed independently by two experienced old age psychiatrists; AD was diagnosed using the National Institute on Ageing-Alzheimer's Association (NIA-AA) criteria for probable AD  while DLExclusion criteria for all participants included co-morbid severe or unstable medical illnesses and contraindications for MRI such as pacemakers, cochlear implants, and metal body clips. Written informed consent was obtained from all participants and both studies were approved by the local ethics committee.2.22 with slice thickness 1.2 mm, yielding a voxel size of 0.93 × 0.93 × 1.2 mm3 (study 1) and in-plane field of view 240 × 240 mm2 with slice thickness 1.0 mm, yielding a voxel size of 1.0 × 1.0 × 1.0 mm3 (study 2). Resting state scans were obtained with a gradient echo echo-planar imaging sequence with 25 contiguous axial slices, 128 volumes, anterior-posterior acquisition, in plane resolution = 2.0 × 2.0 mm, slice thickness = 6 mm, repetition time (TR) = 3000 ms, echo time = 40 ms, and field of view = 260 × 260 mm2. Participants were asked to keep their eyes open and stay awake for the duration of the resting state scan. DLB patients who were taking dopaminergic medication were scanned in the ON state.Imaging for both studies was performed on the same 3 T Philips Intera Achieva scanner. The imaging protocol was the same in both studies except for a different resolution of the structural scans. Structural images were acquired with a magnetization prepared rapid gradient echo (MPRAGE) sequence, sagittal acquisition, echo time 4.6 ms, repetition time 8.3 ms, inversion time 1250 ms, flip angle = 8°, SENSE factor = 2, and in-plane field of view 256 × 256 mm2.3www.fmrib.ox.ac.uk/fsl) was used to perform motion correction with MCFLIRT, slice-timing correction, and spatial smoothing with a 6.0 mm full width at half maximum Gaussian kernel. Participants were excluded if the MCFLIRT motion parameters exceeded 2 mm translation and/or 2° rotation. To ensure that there were no group differences in motion between the groups, motion was compared between the groups using the formula introduced in  which is part of the FMRIB's software library  to remove motion components from each participant's functional data . Subsequ2.4Resting state networks (RSNs) were estimated with an independent set of 42 healthy control participants from two previous studies that were conducted on the same MR scanner using a similar imaging protocol (see Supplementary Table S1). Data from all 42 HCs were temporally concatenated and subjected to a group-ICA using FSL's MELODIC. A meta ICA approach was adopted to obtain reliable components  using a Subsequently, FSL-dual regression was run with all 27 identified RSNs concatenated in a single 4D image, to obtain subject-specific representations of the RSN spatial maps and associated subject-specific time courses A. Resulthttp://mialab.mrn.org/software/gift/index.html) to remove remaining noise sources. Postprocessing included (1) detrending to remove linear, quadratic, and cubic trends, (2) outlier detection based on AFNI's 3dDespike function (http://afni. nimh.nih.gov/afni) and interpolation of outliers using a third-order spline fit to the clean parts of the time courses, and (3) low-pass filtering using a fifth-order Butterworth filter with a cutoff frequency of 0.15 Hz.The subject-specific time courses resulting from dual regression were further processed in Matlab (R2016b) using functions from the Group ICA of fMRI toolbox  was assessed by calculating the standard deviation (SD) of the RSN-to-RSN correlations across time windows. To assess whole-brain dynamics the mean SD across all connections between RSN pairs was computed. Additionally, the mean SD for each network across all other networks was considered and each RSN-to-RSN connection was also tested separately.2.6To assess patterns of functional connectivity that reoccur over time across different participants, k-means clustering was applied to the windowed covariance matrices from all windows and all participants using the Manhattan (L1) distance function D. The opGroup differences were assessed with respect to (1) frequency: proportion of windows assigned to a state, (2) mean dwell time: average number of consecutive windows assigned to a state, (3) intertransition interval: average number of consecutive windows before a state transition occurs, (4) number of transitions: overall number of transitions between different states .2.7We also considered a graph-theoretic approach to study the dynamics of global and local efficiency using the Brain Connectivity Toolbox . For eac2.8https://www.R-project.org/). The variability of functional connectivity of each network and each connection was compared between the groups using a non-parametric multivariate ANOVA (MANOVA (2 (see Supplementary Tables S3 and S5). Spearman's rank correlations between dynamic functional connectivity measures that showed significant group differences and clinical variables in the DLB group were assessed for the CAF total score as a measure of cognitive fluctuation severity, the UPDRS motor subscale as a measure of the severity of Parkinsonism, the NPI hallucination subscale which was specifically scored for the severity and frequency of visual hallucinations, and Mini Mental State Examination (MMSE) and Cambridge Cognitive Examination (CAMCOG) as measures of global cognition. In the AD group, correlations with MMSE and CAMCOG were calculated.All statistical analyses were performed in R ( (MANOVA ) with diU-tests .To assess the effect of dopaminergic medication on dynamic connectivity measures, DLB patients were divided into those patients who were taking dopaminergic medication and those who were not on these medications and all dynamic connectivity measures were compared between the two groups using Mann-Whitney Additionally, to investigate the possible influence of motion artefacts on the dynamic connectivity measures, we calculated correlations between mean framewise displacement and the dynamic connectivity measures across all participants .2.9To study differences in regional grey matter (GM) volume between the three groups, a voxel-based morphometry (VBM) analysis was conducted using DARTEL in SPM12 using age, gender, and total intracranial volume as covariates. Additionally, total grey matter volume and total intracranial volume (TIV) were compared between the groups. Total GM volume and TIV were also considered as covariates when residualizing the windowed correlation matrices see .32 = 0.79, p = 0.67; translation, H2 = 0.67, p = 0.71). Furthermore, mean framewise displacement was not different between the groups  = 0.24 (0.11); AD, mean(SD) = 0.25 (0.15); DLB, mean(SD) = 0.24 (0.09); Kruskal-Wallis ANOVA, H2 = 1.22, p = 0.54).One AD patient had to be excluded due to coregistration errors. Additionally, two controls, six AD, and two DLB participants were excluded because of excessive motion. This resulted in 31 DLB, 29 AD patients, and 31 HCs for further analysis. The overall motion for all included participants did not differ between the groups  and dementia duration . As expe3.22 = 0.06,p = 0.97).The subject-specific values for the regularization parameter λ that resulted from the optimization procedure did not differ between the three groups . Furthermore, repeating the analysis of connectivity variability for different window sizes did not show any significant group differences .3.3An optimal number of k = 3 clusters was determined by the elbow criterion . State 1 was characterized by relatively strong positive and negative between-network correlations . EspeciaThere were no significant differences between the groups in the number of state transitions or the intertransition interval (Supplementary Tables S4 and S5). The frequency of occurrence of the three states was not correlated with time, i.e. we did not observe an increase or decrease in the occurrence of any state over the duration of the scan (Supplementary Table S4).Non-parametric MANOVAs revealed that there was a significant effect of diagnosis on frequency and mean dwell time across all three states (Supplementary Tables S4 and S5). Follow-up univariate Kruskal-Wallis ANOVAs and pairwise post-hoc tests demonstrated that state 1 occurred less frequently in AD and DLB compared to controls with no difference between the dementia groups G. There Several further analyses were performed to assess the robustness of the k-means analysis. Supplementary Fig. S4 shows results for different numbers of clusters demonstrating that the main result of differences in frequency and dwell time of state 1 and 2 persisted when using a higher k. Additionally, repeating the k-means analysis with k = 3 for different window sizes confirmed that the specific choice of window length did not influence the state identification . We also performed split-half and bootstrap resampling which showed that states 1 and 2 were consistently identified in both split-half and all bootstrap resamples, while state 3 failed to be identified in some of the bootstrap resamples .3.4There was no difference between the groups in terms of variability of local efficiency .3.5Table S6 shows correlations between clinical variables and all dynamic connectivity measures that showed significant group differences in the DLB and AD group separately. There were no significant correlations between cognitive fluctuation or visual hallucination severity and the dynamic connectivity measures. Frequency of state 2 was positively correlated with the UPDRS in DLB. However, this correlation did not survive correction for multiple comparisons. There were no significant clinical correlations in the AD group.Comparing DLB patients who were on dopaminergic medication to those who were not, did not reveal any significant differences between the two groups (Supplementary Table S7).There were no significant correlations between the dynamic connectivity measures that showed group differences and mean framewise displacement (Supplementary Table S8).3.6The AD group showed clusters of reduced GM volume compared to controls, mainly in right and left hippocampus . No regions showed increased GM volume in AD compared to controls. The DLB group had reduced GM volume in a small cluster in right cingulate cortex . Again, there were no areas of increased GM volume in DLB compared to controls. There were no significant differences in regional GM volume between the two dementia groups.2 = 6.09, p = 0.048). However, post-hoc Dunn's tests showed that there were no significant differences between any groups  = 0.06, p = 0.18, p = 1.0). The same analysis was repeated for TIV which did not show any differences between the groups .Mean GM volume (in litres) was 0.56 (SD = 0.06) in healthy controls, 0.53 (SD = 0.06) in AD patients, and 0.53 (SD = 0.06) in the LBD group. Since this was not normally distributed in the AD group, a Kruskal-Wallis ANOVA was performed to compare total GM volume between the groups. This test revealed a marginally significant overall effect of group .4In this study we investigated differences in functional connectivity dynamics and dynamic brain network topology between patients with DLB, patients with AD, and healthy controls. In terms of dynamic changes in overall network structure, we found reduced variability of global efficiency in the DLB group compared to controls which was not observed in the AD group. Using a state-based analysis it became evident that both dementia groups spent less time in a state of strong internetwork connectivity than controls. Additionally, DLB patients spent more time in a more sparsely connected state characterized by the relative loss of strong anti-correlations and an isolation of motor networks relative to other networks. While dynamic connectivity measures of the AD group were often between those of the control and DLB groups, we did not see significant differences in the direct comparison of both dementia groups.4.1While the number of states visited and the number of state changes was not altered in the dementia groups, there was a significant difference in the type of state changes in dementia patients compared to controls. The frequency with which the control participants visited each of the three states was relatively balanced, i.e. they spent about a third of their time in each state. In contrast, the distribution of states in the AD and DLB groups was more out of balance compared to controls with a clear decrease in frequency of state 1 in both dementia groups accompanied by an increased frequency of state 2 in DLB. In addition to visiting state 1 less often, the dementia patients also switched out of this state more rapidly and DLB patients stayed in state 2 for longer consecutive periods of time. In accordance with previous reports in healthy participants , develop4.2Regarding dynamic network topology, we found less variable global efficiency in DLB compared to controls. Global efficiency is a measure of communication efficiency across the whole brain network . In gene4.3Given the transient nature of clinical DLB symptoms such as visual hallucinations and cognitive fluctuations, we expected to find relations between symptom severity and dynamic connectivity measures. However, we did not observe any relation with respect to visual hallucinations and cognitive fluctuations, even before correcting for multiple comparisons. A possible reason for this might be the difference in time-scales: while our data only allowed the characterization of dynamics during a 6-minute resting state scan, the time-scale of cognitive fluctuations and visual hallucinations can be on the order of minutes to hours and even days. Performing repeated scans with DLB patients at different times of the day or over several days might thus help to understand more about the relation between functional connectivity dynamics and clinical symptom severity. There was a trend for an association between frequency of state 2 and severity of Parkinsonism in DLB, i.e. an increased time spent in this sparsely connected state might relate to more severe Parkinsonism. Relative to state 1, this state was characterized by a disconnection of motor networks from other networks and the observed correlation might thus indicate that the isolation of motor networks might contribute to the severity of clinical motor symptoms. However, this is only an exploratory result that did not survive multiple comparison correction and further research will be needed to confirm this conjecture.4.4The interpretation, functional significance, and origin of dynamic functional connectivity have been the subject of an extensive debate . HoweverAlthough the sliding window approach has been widely applied to study dynamic functional connectivity  its valiA potential limitation in the interpretation of dynamic connectivity results is that dynamic connectivity measures are sensitive to changes in other features of the data such as variance and signal-to-noise ratio of the fMRI time series and non-stationarities in mean and variance. In theory, this problem can be overcome by generating data under the null hypothesis of static connectivity and showing that the probability of finding group differences in dynamic connectivity from these data is low. However, while for Gaussian data this can be achieved by using coherent phase randomization  or vectoAnother factor that might influence dynamic connectivity measures is GM atrophy and group differences in GM atrophy have been shown to potentially lead to spurious results in a group comparison of functional connectivity . However4.5A limitation of this study is that over half of the DLB patients were on dopaminergic medication and scanned in the ON state which might have influenced their functional connectivity measures. However, dopaminergic medication has been shown to normalize connectivity towards more healthy levels  suggesti4.6The loss of variability of global efficiency in DLB indicates an abnormally rigid brain network. This might be associated with less economical dynamics that can prevent specific and effective responses of the brain network to situational demands. This loss of dynamics was not observed in AD patients and seems to represent a DLB-specific abnormality that might relate to the cognitive slowing observed in DLB patients. In contrast, the inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations was observed in both dementia groups and might thus be related to the presence of dementia in general rather than any symptoms that are specific to AD or DLB.