Cephaloscyllium isabellum) during periods ostensibly thought to be sleep, along with restful and actively swimming sharks across a 24 h period. We also investigated behaviours that often characterize sleep in other animals, including eye closure and postural recumbency, to establish relationships between physiology and behaviour. Overall, lower metabolic rate and a flat body posture reflect sleep in draughtsboard sharks, whereas eye closure is a poorer indication of sleep. Our results support the idea for the conservation of energy as a function of sleep in these basal vertebrates.Sharks represent the earliest group of jawed vertebrates and as such, they may provide original insight for understanding the evolution of sleep in more derived animals. Unfortunately, beyond a single behavioural investigation, very little is known about sleep in these ancient predators. As such, recordings of physiological indicators of sleep in sharks have never been reported. Reduced energy expenditure arising from sustained restfulness and lowered metabolic rate during sleep have given rise to the hypothesis that sleep plays an important role for energy conservation. To determine whether this idea applies also to sharks, we compared metabolic rates of draughtsboard sharks ( DespiteExtant sharks represent the earliest group of jawed vertebrates and, therefore, may provide original insight into the evolution of sleep in vertebrates . This raHeterodontus portusjacksoni) and draughtsboard  sharks are nocturnal with a reduced responsiveness to stimulation while asleep [Recent studies have found that Port Jackson , via intermittent-flow respirometry, and behaviours associated with sleep in other animals: eye state (open/closed), and body posture (upright/flat) over a 24 h period to determine whether sleep plays a role in energy conservation in sharks.Here, in draughtsboard sharks, we assessed changes in metabolic rate (mass-specific oxygen uptake rate or ṀO. 2 (a)Seven draughtsboard sharks (766–2705 g in weight) were collected from Hauraki Gulf, north-eastern New Zealand, and were housed in outdoor aquaria under natural light conditions. Animals were fed a diet of pilchards and held for a minimum of two weeks before the commencement of experiments. Food was withheld for at least 48 h prior to the start of experiments to ensure animals reached a post-absorptive state ,21. (b)2) meter with a contactless sensor spot  logged oxygen levels. ṀO2 measurement cycles were interspersed with flush cycles to ensure a high quality of water (per cent O2 range 84–98%).For detailed respirometry methods, see electronic supplementary material . In shor (c)et al. [Continuous, infrared illumination and overhead video recordings of animal behaviour during the 24 h measurement period were achieved following the methods detailed in Kelly et al. . For furet al. . (d)Animals were individually placed into the sealed respirometry chamber. Automated, intermittent-flow respirometry and video recordings began a minimum of 48 h later to allow each animal to acclimate to their new conditions before data collection began. Each protocol then lasted 24 h under a 12 : 12 light : dark photoperiod regime. (e)2 decline and the associated residual sum of squares (R2). ṀO2 (mgO2 h−1) was then calculated from the decline in oxygen saturation. Metabolic rate and behaviour  data were manually scored second-by-second using the video recordings. Eye states were scored as open or closed and body postures of inactive sharks were scored as flat (lying flat on the bottom of the tank) or upright  (a). Activity states were scored as swimming, rest (inactive less than 5 min) or sleep (inactive more than 5 min); the latter has been shown to be associated with reduced responsiveness, and, therefore, a demonstrated reflection of sleep [Custom-written software calculated the gradient of the per cent Oal fins) a. Activiof sleep . For detof sleep .Figure . 3 (a)2 levels of draughtsboard sharks with an R2 > 0.95  were significantly higher during the night  (b). However, from these data alone, it remained unclear whether restful sharks were sleeping sharks. To address this question, we sampled the ṀO2 data based on activity state (using the criteria of R2 > 0.8 and bout length of greater than 90 s) to account for varying bout lengths found within each activity state. Shark mean ṀO2 levels were significantly lower during sleep (i.e. inactive for at least 5 min) and the highest during swimming (c).Our previous studies showed that draughtsboard sharks are nocturnal ,17. Ther < 0.01) b. Howeveswimming c.R2 > 0.8), therefore, no daytime ṀO2 data were available for this state. The level of ṀO2 varied between activity states  and between day and night (2 level recordings again highest during night swimming (d). Metabolic rates were low and similar irrespective of whether sharks slept during the day or night. Variability in ṀO2 during night rest was similar to the variability observed during night swimming, suggesting that at least some night rest reflects quiet wakefulness. Accordingly, night rest ṀO2 was significantly higher than day rest  and during sleep . Conversely, day rest ṀO2 was similar to ṀO2 during both day sleep  and night sleep , which might indicate that sharks fell asleep quicker during the day than during the night . Cube-root transformed residual ṀO2 values did not vary with changes in measured rest or sleep duration (e). However, for a given measurement period, the cube-root of residual ṀO2 during sleep was between 0.19 and 1.07 less than that observed during rest. These data, therefore, reinforce the results found by Kelly et al. [These data were then parsed further to include the expression of each activity state during day and night to reveal whether sleeping animals consistently showed a lower metabolic rate while asleep. During the day, sharks never swam for more than 90 s 6,13 = 161.40, p < 0.01) (a). Conversely, during sleep, the eyes were most often closed . Postural changes were also associated with sleep as sleeping animals adopted a flat body posture  (b) whereas resting animals sat upright . This might, at first, suggest that closed eyes and a flat posture reflect sleep, and both are behaviours commonly associated with mammalian sleep [6,12 = 241.74, p < 0.01) and day rest  (c), a behavioural pattern that has also been observed in the large-spotted dogfish (Scyliorhinus stellaris) [6,11 = 122.49, p < 0.01) and sleep states  throughout the day (d). This supports our ṀO2 data that animals inactive for at least 5 min are sleeping. The fact that animals engaged in rest (inactive less than 5 min) during the day spent more time flat also supports the idea that some daytime rest might represent sleep. This might suggest that animals fell asleep faster during the day. It is important to note that night and day ṀO2 data partitioned by posture showed a similar pattern to the data partitioned by activity (e). This suggests that both the amount of time spent inactive and body posture are good predictors for sleep in this species.While swimming, sharks always had their eyes open. When resting, the eyes were also more likely to be open (z < 0.01) a. Conver < 0.01) b whereasan sleep . However < 0.01) c, a behaellaris) . However the day d. This sactivity e. This s. 4per se. This study highlights that, like in many vertebrates [The collection of metabolic data via intermittent-flow respirometry in marine fishes, including sharks, is well explored ,26–31. Utebrates  is suppotebrates . Sleep i


DEMETER (DME) and DRE2 genes in common and durum wheat. Lines with mutations in these genes were obtained that displayed reduced content of immunogenic gluten proteins while retaining essential baking properties. Although our data at first glance suggest new possibilities for treating celiac disease and are therefore of medical and agronomical interest, it also shows that inducing mutations in the DME and DRE2 genes analyzed here affected pollen viability and germination. Hence there is a need to develop other approaches in the future to overcome this undesired effect.Wheat is a major source of energy and nutrition worldwide, but it is also a primary cause of frequent diet-induced health issues, specifically celiac disease, for which the only effective therapy so far is strict dietary abstinence from gluten-containing grains. Wheat gluten proteins are grouped into two major categories: high-molecular-weight glutenin subunits (HMWgs), vital for mixing and baking properties, and gliadins plus low-molecular-weight glutenin subunits (LMWgs) that contain the overwhelming majority of celiac-causing epitopes. We put forth a hypothesis that eliminating gliadins and LMWgs while retaining HMWgs might allow the development of reduced-immunogenicity wheat genotypes relevant to most gluten-sensitive individuals. This hypothesis stems from the knowledge that the molecular structures and regulatory mechanisms of the genes encoding the two groups of gluten proteins are quite different, and blocking one group's transcription, without affecting the other's, is possible. The genes for gliadins and LMWgs have to be de-methylated by 5-methylcytosine DNA glycosylase/lyase (DEMETER) and an iron-sulfur (Fe-S) cluster biogenesis enzyme (DRE2) early during endosperm development to permit their transcription. In this study, a TILLING  approach was undertaken to identify mutations in the homoeologous  Wheat is the primary staple or source of energy to more than one-third of the world population , 2. CarbOther than the outlined benefits, wheat and derived products are also the elicitors of several frequent diet-induced health issues, specifically celiac disease (CD), wheat allergy, and non-celiac wheat sensitivity (NCWS), which collectively affect more than 7.5% of the US population , 8–10. CThe seed storage proteins of wheat, specifically prolamins , are known to possess both antigenic and allergenic properties. There are 630 prolamin sequences identified; 351 of these sequences correspond to gliadins and 229 to glutenins . An updaAnalysis of celiac causing epitopes indicated that gliadins and glutenins differ in their abilities to trigger the immunogenic reaction(s) in susceptible individuals. Also, gliadins showed great diversity and a high density of epitopes in sequences relative to LMWgs and HMWgs, which exhibited the lowest epitope density among other prolamins –15. ThisDEMETER (DME) and Derepressed for Ribosomal protein S14 Expression (DRE2) genes are responsible for the transcriptional activation of genes encoding gliadins and LMWgs in the developing wheat endosperm  approach.The above information suggested the possibility to develop wheat genotypes with significantly reduced immunogenicity by targeted silencing of gliadin and LMWgs genes. Moreover, studies have shown that eliminating these proteins with imbalanced amino acid profiles  rather indosperm –23. GiveTILLING is a reverse genetic approach to identifying chemically induced mutations in known genomes –26. In tDME and DRE2 genes using common and durum wheat populations. To pursue this goal, the following specific objectives were undertaken: (i) identification of mutations in the homoeologous wheat DME and DRE2 genes from durum wheat “Kronos” and common wheat “Express” TILLING libraries; (ii) characterization of wheat TILLING mutants for their gluten content and composition; and (iii) pyramiding/stacking of mutants identified in individual DME or DRE2 homoeologues to develop double mutants in durum wheat and triple mutants in common wheat with complete DME or DRE2 activity suppression.For all of these reasons, this research aimed at isolating and characterizing TILLING mutations in the homoeologous wheat DRE2 TILLING mutants were obtained from the University of California, Davis, and seeds of the common wheat cultivar “Chinese Spring” nullisomic-tetrasomic lines lacking chromosome 5A, 5B, or 5D were obtained from the Wheat Genetics Resource Center . The seeds of the diploid wheat progenitors  and “Chinese Spring” [Triticum aestivum (AABBDD)] were obtained from the National Small Grains Collection (NSGC), Aberdeen, ID.Seeds of the selected 2 plants of each population were sampled for DNA extraction and screening for mutations in the homoeologous wheat DME and DRE2 genes. Two to 20 M3 seeds (depending on the M2 plant zygosity) from selected genotypes were cultivated till maturity in 20 × 25 cm pots using Sunshine #1 potting mixture  in a greenhouse maintained at a daytime temperature of 20–23°C and a night time temperature of 14–16°C with a photoperiod of 18 h. During this period, plants were fertilized weekly with nutrient water containing 200–250 ppm of water-soluble fertilizer . One-month-old M3 plant leaves were sampled for DNA extraction to determine the zygosity of mutant plants, and M4 seeds were harvested for protein profiling and determination of other end-use quality parameters.Seeds of common wheat cultivar “Express” (WestBred) and durum wheat cultivar “Kronos” (Arizona Plant Breeders) were used to develop wheat TILLING populations. Wheat TILLING populations were generated by exposing Express and Kronos seeds to the mutagen ethyl methanesulfonate (EMS). Kronos seeds were treated with 0.75 or 1% EMS and Express seeds with 0.75, 1, or 1.2% EMS for 18 h  was added to the tube. Since DTT is unstable at room temperature, a fresh solution was made each time and added to the extraction buffer. The pellet was resuspended in the buffer by vortexing the tubes for 45–60 s or until a homogeneous mixture was achieved and incubated at 60°C with constant shaking at 200 rpm for 2.5 h in an orbital shaker. Subsequently, the mixture was centrifuged at 8,000 rpm for 20 min, then at 13,000 rpm for 2 min at room temperature. The higher centrifugation speed used in the second step allowed the pellet to stick to the tube wall and facilitated supernatant collection. The supernatant was collected (carrying glutenins) to a new tube and stored at 4°C until used.DRE2 TILLING mutants and wild type Kronos) and glutenins extracted from mutant and wildtype lines  were resolved on a 10% SDS-PAGE gel following Mejías et al.  to 15 μL of gliadin extract before loading. Electrophoresis was performed at 500 volts (40 mA/gel) for 120 min using the SE 600 Ruby system  in the lactate buffer, pH 3.1 at 15°C. After electrophoresis, the gels were equilibrated for 30 min in 10% trichloroacetic acid (TCA). A 0.5% solution Coomassie Brilliant Blue R-250  in ethanol was then added. The gels were stained with gentle shaking for 18 h at room temperature, and then each gel was destained in 450 mL of distilled water with 4 drops of Triton X-100 for 30 min. After staining, the gels were visualized on a lightbox and documented with a scanner.Aluminum lactate polyacrylamide gels were prepared following Mejías et al.  with minDME TILLING mutants and wild type Kronos and Express were passed through 0.45 μm Durapore® 13 mm membrane filters. The filtered samples were then injected onto a reversed-phase C8 analytical column  with 5 μm particle size and 30 nm microporous silica diameter , using a 1200 Series Quaternary LC-System liquid chromatograph (Agilent Technologies), with a diode array UV-V detector. The column temperature was maintained at 60°C, and a linear elution gradient was implemented using two mobile solvents. The polar solvent A consisted of a mixture of 0.1% trifluoroacetic acid (TFA) (v/v) and type-I ultrapure water (18 MΩ·cm specific resistance), and the non-polar solvent B contained 0.1% TFA (v/v) and acetonitrile (ACN). Absorbance was monitored at a detection wavelength of 210 nm. The injection volumes for both the gliadin and the glutenin fractions were 30 μL, and the flow rate was adjusted to 1.0 mL min−1. The elution gradient conditions were as described in Mejías et al.  were loaded onto the C8 reversed-phase analytical column  and resolved following Mejías et al. . Two peaGel images were captured on a lightbox using a Canon DS127721 DSLR camera. Gel images were all set to the same brightness and contrast setting using Adobe Photoshop ver. 20.0.06. Densitometric quantification of the protein bands was performed using ImageJ software  followinDME TILLING mutant lines were used for studying the total grain protein content (GPC) using the LECO FP-228 Combustion N analyzer . The thousand-kernel weight was determined as an estimation of the grain yield.Seven seeds each of the wild type control and the DME TILLING mutants. (i) 500 mg of ground seeds from each genotype were used in the SDS sedimentation test as described by Carter et al.  20DME genes on the “A” and “B” sub-genome of bread and durum wheat were crossed in combinations to obtain DME double mutants. All crosses were made reciprocally and in duplicates. The F1 and later F2 seeds were obtained for 3 and 4 different mutant combinations, respectively, in Kronos and Express backgrounds. The mutations for stacking were selected based on the type , locations in the DME active site, and effect on gluten accumulation in the wheat endosperm. The F2 grains obtained from the aforementioned crosses were propagated in 48 well-flats and checked for zygosity of double mutations by PCR followed by DNA sequencing.Mutations in the single homoeologous DME-specific primers  covering nucleotide positions 13,263–13,618 on barley DME sequence FM164415.1 was designed and used to amplify fragment(s) from wheat genomic DNA. The PCR product was used to screen the common wheat (Triticum aestivum L.) cv. “Chinese Spring” BAC library, with ca. 1.3 million clones. Macroarray hybridizations led to the identification of three unique BAC clones, namely 1946D08, 2106P11, and 2159B03 (DME sequence (accession numbers JF683316-JF683318). However, the three DME sequences differed from each other in length and composition. The observed differences in the length of DME homoeologues were mostly due to insertions and deletions (InDels) in the introns. Many point mutations [135 Homoeologous Sequence Variants (HSVs)] and small InDels observed among the DME homoeologous belonged to exons. These HSVs were used to design homoeologue-specific primers by tagging their 3′-ends at HSVs, and these primers were used to assign the DME homoeologues to specific sub-genomes (T. urartu (AA), Aegilops speltoides (BB), and Ae. tauschii (DD)] with Chinese Spring [T. aestivum (AABBDD)] as control. These primers allowed unambiguous assignment of 2159B03 to chromosome 5A, 1946D08 to chromosome 5B, and 2106P11 to chromosome 5D  did not yield a wheat sequence with apparent homology. Therefore, first, a putative Arabidopsis DRE2 homolog was identified in rice and Brachypodium, then these sequences were used to identify homologous sequence(s) in common wheat. These searches allowed the identification of two homologs in rice (OS04G0674400 and OS04G0682050) and two homologs in Brachypodium (BRADI1G25576 and BRADI5G25970). When rice and Brachypodium sequences were blasted against the wheat reference genome sequence, the results suggested two homologs, one on group 2 chromosomes with a copy each on 2AL (TraesCS2A02G553500), 2BL (TraesCS2B02G585100), and 2DL (TraesCS2D02G555000), and another on group 5 chromosomes with a copy each on 5AL (TraesCS5A02G262300) and 5DL (TraesCS5D02G269900) , and 20 clones per fragment were sequenced to have a fair representation of each homoeologue in the sequences. Obtained sequences were aligned with each other and the reference sequence. The sequence analysis revealed that we obtained the full-length sequence of the wheat DRE2 homoeologues.The DRE2 or Derepressed for Ribosomal protein S14 Expression gene facilitates the deposition of the iron-sulfur (Fe-S) cluster into the DME apoenzyme, which is vital for its interaction with genomic DNA and the DNA's subsequent demethylation. Given DRE2's role in DME activation, we decided to test its effect on prolamin accumulation. As a first step, we obtained the full-length DNA sequences of the wheat G269900) . The copthe gene . The amphttp://www.wheat-expression.com) to understand the transcriptional interplay among the wheat DME and DRE2 genes and genes encoding immunogenic gluten proteins . This data is largely based on the study of Ramírez-González et al.   . The DMEne layer . On the rolamins . For inst 30 DPA . In contgliadins . The γ- gliadins . In the ndosperm .in-silico expression analysis. For the sake of precision about the anthesis date, wheat cv. “Express” spikes were emasculated and artificially pollinated with its pollen. Later, we collected the developing kernels in liquid nitrogen for expression analysis and protein profiling. For expression profiling of the homoeologous wheat DME genes, a set of primers  were used to amplify products from cDNA, and the amplified product was cloned in a TA-cloning plasmid. Thirty colonies were sequenced via Sangers sequencing, and the expression level of different homoeologues was determined via sequence comparison with the genomic DNA sequences of the wheat DME homoeologues. At 5 and 8 DPA, the three homoeologous DME genes were expressed equally, but at the rest of the studied time points, the “A” and “D” subgenome DME homoeologues showed opposite expression patterns. In contrast to the “A” and “D” homoeologues, the “B” subgenome DME homoeologue maintained a constant expression level until 10 DPA  were detected. Among 42 mutations detected in the “A” homoeologue and 35 mutations detected in the “B” homoeologue, 4 and 5 mutations, respectively, coincided with the conserved motifs in the DNA glycosylase domain . A mutation resulting in a premature stop codon falling between the two conserved domains was also detected in the “A” homoeologue of TdDME. Similarly, 42 mutations in “A” homoeologue and 56 mutations in “B” homoeologue of the common wheat DME genes (TaDME) were detected  mutations in “Kronos” and “Express” backgrounds, underlying the conserved domains of “B” subgenome DME homoeologue, and 214 plants representing 5 and 9 mutants, respectively, in “Kronos” and “Express” backgrounds underlying the conserved domains of “A” subgenome DME homoeologue, were planted in the greenhouse. Similarly, a total of 44 M3 plants representing 9 (either in homozygous or heterozygous state) mutations in the “Express” background, underlying the conserved domains of “D” subgenome DME homoeologue, were propagated in the greenhouse. DNA was extracted from the leaves of 2 weeks old plants, used to amplify subgenome-specific DME products, and to determine the zygosity of plants by sequencing the PCR products , 42773 (V29M), and 33412 (splice site variant) in the “Kronos” background and 50324 (M89I) and 21599 (G93R) in the “Express” background exhibited transcriptional suppression (DRE2 mutant lines exhibited any difference in their DRE2 transcript levels.Immature seeds (17 ± 3 DPA) from the homozygous Mpression . On the TaDME and TdDME, were extracted from mature seeds and profiled using SDS-PAGE followed by densitometry. All genotypes showed reduced accumulation of one or more immunogenic gluten proteins. In particular, lines carrying premature stop codons (“Express” mutant “20396” in the “B”-subgenome and “12680” in the “A”-subgenome), splice site variant (“Kronos” mutant “33412” in the “B”-subgenome), or a non-synonymous substitution  showed significant reductions in gliadin and glutenin content  resolved under similar conditions on RP-HPLC. The analysis suggested that in comparison to the wildtype “Express,” the single mutant “12680” (stop gained in “A” subgenome DME copy) accumulated significantly reduced amounts of HMWgs, LMWgs, and α/β-gliadins . A similar great reduction in ω-gliadin content was observed in “Express” mutant “50322” (an M89I substitution in the “B”-subgenome DME copy) and γ-gliadins in “Express” mutant “50449” (a D30N substitution in the “D”-subgenome DME copy) (DME copy)  . LikewisME copy) .DRE2 gene) were used to extract gliadins and glutenins, and the proteins were profiled using SDS-PAGE followed by densitometry. Protein profiles of mutants were compared with wild-type “Kronos” to study the effects of mutations in the individual homoeologous DRE2 genes on chromosome arm 2AL and 2BL. Two types of differences were observed: qualitative differences and quantitative differences, where qualitative differences are the presence or absence of specific protein bands, and quantitative differences are under-accumulation or over-accumulation of the specific protein(s) (visible as reduced or high-intensity bands). Compared to “Kronos”, nine of 16 mutants showed quantitative differences, and seven mutants showed qualitative differences. Most if not all DRE2 mutants exhibited reduced accumulation of ω- (27% reduction to complete lack) and γ-gliadins (40–93% reduction) and over-accumulation (range from 1.1 to 2.7 fold) of α/β-gliadins (except a non-synonymous T121I substitution in the DRE2 2AL homoeologue). Further, some mutants exhibited over-accumulation of HMWgs (range from 1.3 to 5.5 fold) and LMWgs (range from 1.1 to 2.2 fold), whereas some showed reduced HMWgs (14–78%) and LMWgs (6–44%) accumulation  from 16 mutant lines ; it ranged from 0.17 g in “Express” mutant “12680” to 0.31 g in “Express” mutant “20396”, which is significantly less or more than 0.29g in “Express”. The protein content expressed in percentage ranged from 13.17% in “Express” mutant “50449” to 26.45% in “Kronos” mutant “33412”, which is, respectively, less and more than “Express” (17.72%) and “Kronos” (22.68%). The components for diagnostic SRC solvents  were calculated using standard formula and assuming 14% water content of the flour. Values obtained using 50% sucrose ranged between 136% in “Express” mutant “50449” (similar to “Express”) to 178% in “Express” mutant “12680” and 143% in “Kronos” mutant “33412” (significantly <168% in “Kronos”), indicating higher water retention capacity in the finished bread. The results of the sodium carbonate test of the lines ranged from 129% in “Express” mutant “20396” to 196% in “Express” mutant “12680” (139% in “Express”) —using the LECO FP-228 Combustion N analyzer —of reduced-gluten xpress”) . The valxpress”) .DME homoeologues of common and durum wheat were crossed in combinations to obtain DME double mutants to stack their gluten phenotypes. Crosses were made reciprocally to account for possible maternal influence, which is a prerequisite, specifically when dealing with epigenetic regulators  in the greenhouse and checked for zygosity at two DME loci via DNA sequencing. Sequence analysis allowed the identification of 20 double mutations in the “Kronos” background and 14 double mutants in the “Express” background  × “20548”  in the “Express” background, exhibited reduced accumulation of gliadins and LMWgs  (DME-5B) × “29375”  in the “Kronos” background exhibited a gluten profile reduced in gliadin and glutenin content more than any other single mutant (Protein profiles of the mature Fliadins) . Howeverliadins) . The HPLliadins) . On the e mutant . These re mutant . Prelimie mutant .DME homoeologues showed distorted segregation in most of the combinations except for the cases where crosses were made between genotypes carrying amino acid substitutions (with mild effects on the gluten profiles). To determine the cause of the observed segregation distortion, we studied anther morphology, anatomy, number of viable pollen grains, and pollen germination   . Interesion rate . The redDME homoeologues. When mutants carrying splice site variants and/or premature stop codons in different DME homoeologues were crossed in combinations, a limited number of grains were obtained. Unfortunately, none of the tested genotypes carried double mutations either in the homozygous or the heterozygous state. Similarly, the DRE2 mutants (except four mutants) in either the 2AL copy or the 2BL copy due to their effect on pollen viability and anther size were maintained in the heterozygous state, making it challenging to obtain homozygous lines to make genetic crosses to stack their gluten phenotypes.Crosses between mutants belonging to other homoeologous groups were attempted; for instance, crosses were made between “A” and “D” and “B” and “D” subgenome Wheat is an all-round and affordable source of nutrition (including micronutrients) for the global population. Fiber in wheat promote gut health as these are fermented by the gut microbiota into health-promoting phenolics and antioxidants , 45. TheGlu-1 locus that maps to the long arms of the group-1 chromosomes  electrophoresis, the numbers of gluten proteins present in wheat cultivars are estimated to be between 50 and 100 . Whereasomosomes , where eomosomes . Since n 3 and 5  and in d 3 and 5  identifi 3 and 5  reported 3 and 5  estimate 3 and 5  identifiCis- and trans-regulators of gluten genes were identified in the past [for a review, see . The DME homoeologue on 5DL exhibited a dominant expression pattern throughout grain development in the meta-expression analysis and the expression analysis we performed via PCR-cloning and DNA sequencing  gene provides an example of differential expression and function of homoeologous genes. The three homoeologous copies of this gene, Rht-1A, Rht-1B, and Rht1-D, that map to the wheat chromosomes 4A, 4B, and 4D, to some extent showed differential expression at the stem base and peduncles during the developmental process. Further, the mutations in these genes exhibit some overlapping and some unique functions  is less than DRE2 (a Fe/S cluster protein), and DRE2's expression is less than the prolamin genes', which also made sense as the regulatory genes generally exhibit low expression compared to the structural genes . Th. Thvia gl stages . Our anand HMWgs . The expnd HMWgs , who demnd HMWgs , who shond HMWgs . Howevernd HMWgs . Interesm grains .In our analysis, we did not study the subgenome-specific expression of prolamin genes. However, in the past, 68% of the total gene expression of LMWgs was determined to occur from the “B” subgenome . SimilarDME and DRE2 homoeologues tend to show reduced accumulation of specific gluten proteins, suggesting that different homoeologues are sub-functionalized to some extent   , DME mution rate . Zhu et ion rate . Congrueion rate  identifi factors . Further factors . Indeed,eologues . Furthern plants . These fbidopsis  and ricebidopsis . In partbidopsis . Similarg et al.  demonstrg et al. .2s in the DME 5A and 5B copies in durum wheat “Kronos” were one mutation per 86 and 115 kb, respectively, and common wheat “Express” were one mutation per 86 and 53 kb, respectively  is used for leavened or unleavened bread preparation, and durum wheat (“Kronos”) is used to manufacture spaghetti and pasta. The present study did not suggest any major effects of eliminating specific gluten proteins on end-use quality based on the micro SRC analysis and SDS sedimentation test  can be found below: SRu, CM, and DW contributed to the conception and design of the study. NW, CM, CO, RB-A, JM, TA, SK, DW, and SRu performed the experiments. NW, CO, and SRu wrote the first draft of the manuscript. SRu, CR, SRe, and CM edited the manuscript. All authors contributed to the article and approved the submitted version.Financial support by NIH grant 2R42DK072721-02 and Life Sciences Discovery Fund grant 3143956-01, and Clemson Faculty Succeeds grant 1502211 is gratefully acknowledged.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In addition, we successfully demonstrated effective methylcyclohexane dehydrogenation using a flange-type membrane reactor module, which was installed with 6 silica membranes. The results indicated that conversion of methylcyclohexane was around 85% at 573 K, whereas the equilibrium conversion was 42%.Methylcyclohexane-toluene system is one of the most promising methods for hydrogen transport/storage. The methylcyclohexane dehydrogenation can be exceeded by the equilibrium conversion using membrane reactor. However, the modularization of the membrane reactor and manufacturing longer silica membranes than 100 mm are little developed. Herein, we have developed silica membrane with practical length by a counter-diffusion chemical vapor deposition method, and membrane reactor module bundled multiple silica membranes. The developed 500 mm-length silica membrane had high hydrogen permselective performance (H In order to create a hydrogen energy–based society, transport and storage of hydrogen in one of the most important issues. Recently, the development of liquid hydrogen, ammonia, and organic chemical hydrides methods for hydrogen transport/storage have progressed in the world. Above all, organic chemical hydride methods such as the methylcyclohexane (MCH)–toluene system can be expected as the most practical ones, because they are liquid at normal temperature and pressure, and the existing infrastructure can be utilized.The dehydrogenation of MCH is the equilibrium reaction. Its conversion ratio can exceed the equilibrium by removing hydrogen from the reaction system using the membrane reactor with hydrogen permselective membrane. Palladium-based or silica membranes which show hydrogen selective permeation performance are developed for MCH dehydrogenation membrane reactors. Palladium-based membranes are mainly prepared by electroless plating ,2 and elFrom the view point of practical use of membrane reactors, developing silica membranes with practical length, approximately 500 mm, and modular structures of the membranes, are very important. However, there are very few researchers who have attempted to develop such silica membranes or module structures. Since MCH dehydrogenation is an endothermic reaction, a method and module structure for efficiently supplying the reaction heat is important.2 permselective performance prepared by counter-diffusion chemical vapor deposition method, and module of six silica membranes. We also evaluated the performance of bench–scale membrane reactor including multiple silica membranes for methylcyclohexane dehydrogenation. In addition, improvement of heat transfer to catalyst layer was also investigated.In this study, to advance social implementation, we have developed long-scale DMDPS–derived silica membrane having high HLonger silica membranes having practical length were prepared and evaluated. We employed counter–diffusion chemical vapor deposition method for silica membranes preparation up to 500 mm long.2, as carrier gas, and the vapor concentration was regulated at 0.1 mol/m3 by controlling the temperature of a bubbler. O2, at rate of 250 mL/min, was introduced to the interior of the support. An amorphous silica layer derived from DMDPS was deposited into the pores and surface of the support. Reaction temperature and time for CVD were 873 K and 60 min, respectively. Permeation performance of obtained silica membrane was evaluated at 573, 473, 373, and 298 K, using single component H2, N2 and SF6 gases. Flow rate of H2 and N2 in permeation side was measured by soap film flow meter, and permeances were calculated from these rates. Determination of SF6 permeance was performed with a pressure difference method.Bench–scale membrane reactor tests using a module in which multiple silica membranes are bundled were conducted. The experimental apparatus for methylcyclohexane dehydrogenation membrane reactor is shown in 2 permeances of 70, 200, and 500 mm-length membranes were 1.20 × 10−6, 1.00 × 10−6, and 1.30 × 10−6 mol m−2 s−1 Pa−1, respectively. H2/SF6 selectivity of these membranes was 12,000, 10,000, and 12,000, respectively. Here, the H2/SF6 selectivity is used as an indicator for H2/toluene selectivity, because the kinetic diameter of toluene is similar to that of SF6. All membranes showed approximately the same H2 permselective performance despite different membrane lengths. From these results, we successfully formed long–scale silica membrane.Comparative permselective performances of DMDPS–derived silica membranes formed on different effective membrane lengths as 70, 200, and 500 mm–length are shown in 2, N2, and SF6 permeation through 500 mm–length silica membrane in the temperature range of 573–298 K. Permeances of H2, N2, and SF6 showed an approximately constant value regardless of this temperature range. At 573 K, the permeances of H2, N2, and SF6 were 1.30 × 10−6 mol m−2 s−1 Pa−1, 1.31 × 10−7 mol m−2 s−1 Pa−1, and 1.07 × 10−10 mol m−2 s−1 Pa−1, respectively. The selectivity of H2/N2 was 10, and that of H2/SF6 was 12,000 at 573 K. The temperature dependence and permselectivity of this 500 mm-length silica membrane showed approximately similar performance as in a previous report of developed 100 mm-length DMDPS–derived silica membrane [membrane ,20.First, dehydrogenation membrane reactor, which was included a single silica membrane having 70 mm–length, was evaluated under 0.2 MPaA to compare between performance of packed-bed reactor and membrane reactor, as shown in −6 mol m−2 s−1 Pa−1 of hydrogen permeance. On the other hand, the H2/SF6 selectivity of 6 silica membranes was slightly different. The SF6 permeance showed an extremely low value (around 1 × 10−10 mol m−2 s−1 Pa−1), which is closed to the measurement limit; therefore, it was considered that there are few significant differences in the H2/SF6 selectivity between 6 silica membranes. 2 permeance was around 2 times higher because of changing to tubular alumina support having relatively high gas permeation performance. These membranes were applied to the flange–type membrane module. In addition, this membrane module structure had confirmed hydrogen tightness under 573 K, 0.5 MPa.A novel membrane module in which 6 silica membranes having 200 mm–length were directly bonded with glass sealant to a metal flat plate was developed as one of the modularization technologies with mass production possibility, as shown in The MCH dehydrogenation is an endothermic reaction. When the reaction is conducted with a membrane reactor, the conversion ratio is decreased owing to reaction temperature reduction. Therefore, we evaluated the effect of heat conductive fins to improve the temperature uniformity of catalyst layer. We tried two different structures of heat conductive fins, as shown in −2 K−1, respectively. These results suggested that the installation of heat conductive fins effectively transferred heat to the catalyst layer, and promoted the MCH dehydrogenation reaction.Results of bench–scale membrane reactor tests using different structure of heat conductive fins are shown in −6 mol m−2 s−1 Pa−1 of hydrogen permeance, and over 10,000 of H2/SF6 selectivity. The membrane reactor module was evaluated at 573 K, 0.4 MPaA. The MCH conversion using membrane reactor exceeded the equilibrium conversion owing to hydrogen extraction effect. In addition, we clarified that improvement of heat transfer to catalyst layer was an important issue by using the membrane reactor module improved overall heat transfer coefficient, because MCH dehydrogenation is an endothermic reaction.We successfully demonstrated the preparation of practical long silica membranes with high hydrogen permselective performance, and effective MCH dehydrogenation using a novel membrane reactor module bundled with 6 silica membranes. Developed 500 mm-length silica membrane had over 1 × 10


Correction to: BMC Microbiol 21, 222 (2021)https://doi.org/10.1186/s12866-021-02280-5Following the publication of the original article , we were‘isolate 40,816,739 (with the slowest extrusion), and QC strain ’ should read ‘isolate 40816739 (with the slowest extrusion), and QC strain ’.In the Results section:‘The isolates 10,800,294 and 21,914,232 showed resistance to cefazolin and cefotaxime without ESBL’ should read ‘The isolates 10800294 and 21914232 showed resistance cefazolin and cefotaxime without ESBL’‘Isolate 10,800,294 was a strong biofilm former and had an active AcrAB-TolC’ should read ‘Isolate 10800294 was a strong biofilm former and had an active AcrAB-TolC’.In the Discussions section:‘E. coli ATCC 25,922, S. aureus ATCC 25,923, and P. aeruginosa ATCC 27,853 ’ should read ‘E. coli ATCC 25922, S. aureus ATCC 25923, and P. aeruginosa ATCC 27853 ’.‘PPB without cell suspension was used as blank and E. coli ATCC 25,922 was used as a control’ should read ‘PPB without cell suspension was used as blank and E. coli ATCC 25922 was used as a control’.‘E. coli ATCC 25,922 was used as a control strain to check the difference in biofilm formation’ should read ‘E. coli ATCC 25922 was used as a control strain to check the difference in biofilm formation’.In the Materials and Methods section:The original article has been corrected.


The results showed a positive link between impulsivity and Internet addiction  and a negative link between restraint and Internet addiction . The moderation analysis indicated that the correlation between impulsivity indicators and greater Internet addiction was stronger among undergraduates (18–22 years old) than among adolescents (10–17 years old). Furthermore, the negative link between a restraint indicator and Internet addiction was greater (a) among students in East Asia than those in Western Europe/North America, (b) among males than females and (c) when using the Internet addiction measures GPIUS or IAT rather than CIAS. Hence, these results indicate a negative link between self-control and Internet addiction, and this link is moderated by age, culture, gender, and Internet addiction measure.As past studies of self-control and Internet addiction showed mixed results, this meta-analysis of 83 primary studies with 80,681 participants determined whether (a) these students with less self-control had greater Internet addiction, and (b) age, culture, gender, Internet addiction measures, or year moderated these relations. We used a random-effects meta-analysis of Pearson product-moment coefficients  The Internet has become an indispensable part in people’s daily life, which brought us both positive and negative effects. One of the typical negative phenomena concerning excessive use and abuse of computers or the Internet is Internet addiction. A meta-analysis has found that an estimated 6% of adolescents worldwide have suffered from Internet addiction . It is aSelf-control is defined as an inhibition capacity to regulate one’s thoughts, emotions, and behaviors in the face of external demands  are negatively related to Internet addiction . Also, oHowever, many other studies have shown no significant relationship. Some studies have found no significant link between restraint indicators  and Internet addiction . LikewisAs many studies include information on the participants’ culture, age, gender, internet addiction measures and year, our meta-analysis can test for their moderation effects. Hence, we discuss the potential moderation of each variable.A society’s cultural values might moderate the link between self-control and Internet addiction. While some societies highlight short-term goals , others encourage attention to long-term goals  and friends  synthesize the results of past studies on the relation between self-control and Internet addiction among students (10–22 years old) and (b) identify factors that influence this relationship. Specifically, this meta-analysis (a) calculates the overall effect size of the link between self-control and Internet addiction and (b) determines whether culture, age, gender, Internet addiction measures or publication year moderated this link.This meta-analysis included Chinese- and English-language publications during January 2002 to September 2020. The Chinese-language articles were retrieved from the China Academic Journals Full-text Database, the China Selected Doctoral Dissertations and Masters’ Thesis Full-text Database, and Wanfang Data. We used the keywords ‘self-control (自我控制),’ ‘self-regulation (自我调节),’ and ‘impulse control (冲动控制).’ For Internet addiction, we used ‘Internet addiction (网络成瘾),’ ‘addiction (成瘾),’ ‘Internet abuse (网络滥用),’ and ‘Internet dependence (网络依赖).’The English-language articles were retrieved from the Google Scholar, ProQuest Dissertations, Taylor Francis, Springer, Web of Science, PsycINFO, EBSCO, Elsevier SDOL. For self-control, we used the keywords ‘self-control,’ ‘self-regulation,’ ‘impulse control,’ and ‘impulse.’ For Internet addiction, we used ‘Internet addiction,’ ‘addiction,’ ‘Internet abuse,’ and ‘Internet dependence.’ These searches initially retrieved 279 articles.r, β, T, or F-values ; (c) it reported the sample size; (d) the sample predominantly comprised adolescents or university students; (e) when multiple publications use a data set, the result that used the complete data was used ; (f) if multiple publications used the same data set, journal articles are preferred over conference proceedings, which were preferred over dissertations. The exclusion criteria were: (g) editorials, letters to the editor, opinion pieces, commentaries, personal views, and abstracts only, (h) review papers, (i) studies not written in English or Chinese.The articles were screened according to inclusion and exclusion criteria . The incWe trained two reviewers to separately apply the inclusion and exclusion criteria to the studies. After the initial screening, 98 studies were identified by both reviewers. One reviewer also identified 6 additional studies. Both reviewers discussed the 6 studies, agreed to keep 4 of them, yielding a final set of 83 studies.r effect size (see r). Comparison of the results using each encoding method showed high consistency.The collected articles were coded for the following features: internet addiction measures, author, publication year, sample size, proportion of female students, culture, ages, and size see . The stusize see , IAT  . AlthougIn this standard meta-analysis, effect sizes between affective self-control and Internet addiction were calculated for each sample . Then, wr yielded the effect size /(1 − r)], where the variance of Z is VZ = 1/n − 3 and the standard deviation of Z is SEZ = (1/n − 3)0.5. The effect size is rz = (ez2 ×  − 1)/(ez2 ×  + 1). The confidence interval is computed as follows:Meta-analysis of Pearson’s product-moment coefficients ect size . Fisher’rU = Z(1 − a) × SEesZ(1 − a) × SEeses = √1/ΣWiSEWi = ni − 3The homogeneity calculation formula is computed as follows:Qw = ΣWi (rZi − 2I2 = [Q − (K − 1)]/Q × 100%.Moreover, this study follows the normative guidelines for interpreting the magnitude of a correlation proposed by 1. To test our hypotheses, we calculated sample sizes (k), weighted effect sizes (r), and 95% confidence intervals. We tested whether the mean effect sizes of the studies differed significantly (homogeneity test) via Cochrane’s Q and I2 . Averaged weighted correlation coefficients (within- and between-inverse-variance weights) of independent samples were used to compute mean effect sizes. If the homogeneity test showed significant variance in effect sizes between different samples’ characteristics, we tested for moderators. When the homogeneity test was significant (QBET > 0.05), post hoc analysis confirmed the different groups statistically. To examine moderator effects, we used meta-ANOVA for categorical variables and meta-regression analysis for continuous variables.We used the comprehensive meta-analysis software CMA 2.0Q and I2 . If I2 eAfter filtering the literature, we used 83 independent samples and calculated 85 effect sizes . In all, 80,681 students participated in the studies reviewed; sample sizes of individual studies ranged from 21 to 18,709.r = 0.371) and restraint was negatively related to Internet addiction (r = −0.362). These effect sizes were sufficiently large for moderator analysis . See We used funnel plots and Egger’s regressions  to test tLSCI(46) = 1.106, p = 0.273; tHSCI(39) = 1.702, p = 0.097). Together, these findings suggested that the overall correlation between self-control and Internet addiction was stable, and provided no evidence of publication bias for these studies.Egger’s regressions for Internet addiction with both low self-control and high self-control indicators revealed no publication bias  = 0.090, p > 0.05, see QModel  = 0.031, p > 0.05).Year was not a significant moderator. Year moderated neither the impulsivity indicator’s link with Internet addiction  and within countries (Korean Americans vs. Irish Americans). Hence, future studies can test for culture effects more rigorously by asking participants to respond to culture questions. As the cross-sectional data in these studies cannot determine causation, future studies can collect more longitudinal data, including intervention studies that test possible solutions. As the current study only tested three types of moderating factors , future studies can test other moderating factors, such as family attributes and peer relations. Moreover, only studies published in English and Chinese were used. As artificial intelligence improves translation software , future meta-analyses can include published studies in more languages. Finally, this study that covers many factors, so it is not possible to focus in depth on any one of the aspects studied, we will depth explore the relationship between specific people’s self-control and Internet addiction in the future and focusing on one of the variables in order to analyze it in greater depth.Past studies of self-control and Internet addiction showed mixed results, so this meta-analysis synthesized 83 studies with 80,681 students to show that self-control was negatively linked to Internet addiction. Specifically, there was a positive link between impulsivity and Internet addiction, while there was a negative link between restraint and Internet addiction.Furthermore, culture, age, gender, and Internet addiction measures moderated these links between self-control and Internet addiction. The negative link between a restraint indicator and Internet addiction was stronger in East Asia than in Western Europe/North America. Compared with adolescents, university students showed a greater positive link between impulsivity indicators and Internet addiction. Also, the negative link between a restraint indicator and Internet addiction was stronger among males than among females. Lastly, the relationship was weaker among CIAS than other Internet addiction measures.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.SL and HL provided the idea, designed the study, wrote the manuscript, and contributed to data collection. PR and MC provided the idea, designed the study, wrote the manuscript, and contributed to data analysis. CW contributed to design the study, analysed the data, and revised the manuscript. All authors approval of the version to be published and agreement to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


ID3, a macular retinal pigment epithelium marker. The other cluster highly expressed CRYAB, a peripheral RPE marker. Our results also showed that the genes associated with oxidative stress and endoplasmic reticulum stress were more enriched in the macular RPE. The genes related to light perception, oxidative stress and lipid metabolism were more enriched in the peripheral RPE. Additionally, we provided a map of disease-related genes in the hRPE and highlighted the importance of the macular RPE and peripheral RPE clusters P4 and P6 as potential therapeutic targets for retinal diseases. Our study provides a transcriptional landscape for the human retinal pigment epithelium that is critical to understanding retinal biology and disease.Human retinal pigment epithelium cells are arranged in a monolayer that plays an important supporting role in the retina. Although the heterogeneity of specific retinal cells has been well studied, the diversity of hRPE cells has not been reported. Here, we performed a single-cell RNA sequencing on 9,302 hRPE cells from three donors and profiled a transcriptome atlas. Our results identified two subpopulations that exhibit substantial differences in gene expression patterns and functions. One of the clusters specifically expressed  Generally, the retina is a complex structure and contains 10 layers of tissue that are responsible for detecting and converting light into neurochemical information that is ultimately transmitted to the brain, resulting in vision. Near the posterior pole of the human and primate retina is a small shallow funnel-shaped depression 2.5–3 mm in diameter known as the yellow spot or macula. The central depression of the macula is called the fovea, where the retina is the thinnest and has only two layers of cells: the retinal pigment epithelium (RPE) and cone cells. Cone cells and bipolar cells are arranged one-to-one in the fovea, so the fovea, which is the most sensitive and accurate region in the retina, provides a sharp and clear image of central vision. Many ocular diseases that cause blindness, such as age-related macular degeneration (AMD), mainly affect this area, indicating that it is of great significance to study the cellular function of the retina and, particularly, the macula.The visual formation process requires many types of neurons and supporting cell types. Among these neurons and cells, photoreceptor (PR) cells (rods and cones) convert light into an electrical signal that is then transferred to interneurons, including horizontal cells (HCs), bipolar cells (BCs), and amacrine cells (ACs). Interneurons deliver information to retinal ganglion cells (RGCs) and then input it into the brain. In addition, RPE cells, astrocytes, Müller glia and microglial cells mainly support the metabolism of the retina and play an important role in homeostasis of the retina.Some retinal cell types have been studied for their gene expression patterns and gene functions by bulk sequencing, even at the single-cell level. Recent research found that RGCs are divided into 40 cell types by single-cell RNA sequencing (scRNA-seq) . AdditioThe RPE is a monolayer tissue layer that is fundamentally important for retinal development and function. The RPE also plays critical roles in supporting the retina, including transepithelial transport, phagocytosis, blood-retina barrier function, metabolism, oxidative stress (OS), growth factor secretion, visual cycle processes  and retiPAX6 and BEST1  and that IGFBP5 was highly enriched in the other population . Thus, we hypothesized that one cluster was the macular RPE and the other was the peripheral cluster. In addition to the two specifically expressed genes, we found that CTGF, FST, KCNMA1, and ADIRF were highly expressed in the macular RPE, and RARRES2, CRYAB, FBX O 32, and CHCHD10 were specifically expressed in the peripheral RPE  according to the gene expression profile . After dTo study the heterogeneity of peripheral RPE cells, we mapped their atlas and showed that peripheral RPE cells could be classified into eight populations with distinct gene expression . We calcIGFBP5 in the data  and macular RPE marker gene (ID3) by RT-PCR analysis   to analyransport . IRX3, FEGFR receptors account for a large proportion of the macular RPE and mainly bind to VEGFA and EFEMP1  database to identify genes associated with retinal diseases and to determine the cluster distribution of these genes in our data. Studies have reported that USH2A, EYS and CRB1 are the top three genes responsible for inherited retinal dystrophy . These genes were mainly expressed in the P3, P4, P5 and P7 clusters. The genes LRAT, RPE65 and RLBP1 are associated with the visual cycle   . In prevectively . Interes2 and P7 . In addi cluster .PTPN22 was specifically expressed in macular RPE cells (From the genome-wide association study (GWAS) results , we founPE cells . BD-relaPE cells . Next, wPE cells . In summID3 and the other is peripheral RPE cluster expressing CRYAB. A recent study of single-cell sequencing of the RPE found that the expression of ID3 and IGFBP5 was enriched in the macular and peripheral RPE, respectively ; however, the other population (M2) highly expressed several genes, such as unctions . These aTYRP1 has been reported to be involved in melanin synthesis  and adhered to the tenets of the Declaration of Helsinki. All eye tissue samples used in this study were collected from Chongqing Eye Bank, the First Affiliated Hospital of Chongqing Medical University. Written informed consent was obtained from the patients prior to the study.All the primers used in this study were synthesized by Shanghai Sangon Biological Engineering Technology and Services. The primers were ID3-F:5′ GAGAGGCACTCAGCTTAGCC3’, ID3-R:5′TCCTTTTGTCGTTGGAGATGAC3’; RARRES2-F: 5′GCATCAAACTGGGCTCTGAG3′, RARRES2-R: 5′AGGGAAGTAGAAGCTGTGGG3’; CRYAB-F: 5′GGGAGATGTGATTGAGGTGC3′, CRYAB-R: 5′TTCACAGTGAGGACCCCATC3’; FBXO32-F: 5′TGTGGGTGTATCGGATGGAG3′, FBXO32-R: 5′GAGTTTCTTCCACAGCAGCC3’; CHCHD10-F: 5′CAGAGTGACCTGTCCCTGTG3′, and CHCHD10-R: 5′CCTCCTCACTTCCAATCCCA3’. Cy3-labeled goat anti-rabbit IgG (H + L) , Cy3-labeled goat anti-mouse IgG (H + L) , Alexa Fluor 488-labeled goat anti-mouse IgG (H + L)  and DAPI were purchased from Beyotime. RPE-65 , ID3 , and crystallin antibodies  were used for immunofluorescence visualization .Adult hRPE samples were obtained from three donors (age 29–64 years old) within 24 h of death . After dissecting the cornea, whole RPE tissues were collected from the remaining ocular tissue. The RPE tissues were rinsed three times with phosphate-buffered saline (PBS). First, the tissues were digested using 0.25% trypsin for approximately 30 min at 37°C until a single-cell suspension was made, and then complete Dulbecco’s Modified Eagle Mediummodified Eagle’s medium (DMEM) was used to neutralize the cell suspension. The single-cell samples were passed through a 40 μm cell strainer. Second, the cell suspension was rinsed three times with PBS and centrifuged for 5 min . Next, a hemocytometer with trypan blue was used to count the number of cells and ensure that the level of cell viability was approximately 90%. Finally, single-cell samples were subjected to scRNA-seq analysis with a high-throughput droplet-mediated scRNA-seq platform (10× Genomics Chromium).https://support.10xgenomics.com/single-cell-gene-expression/index/doc/user-guide-chromium-single-cell-3-reagent-kits-user-guide-v2-chemistry).The appropriate volume of each sample was diluted to contain approximately 5,000 hRPE cells. Subsequently, the single-cell suspension, gel beads and oils were all added to the 10× Genomics single-cell A chip. After droplet generation, samples were transferred into 200 μL PCR tubes, and reverse transcription was performed using a T100 Thermal Cycler . After reverse transcription, cDNA was recovered using a recovery agent provided by 10× Genomics. SPRIselect beads were used for cDNA clean-up, and then the cDNA was amplified for 10 cycles. The concentration of cDNA was detected by a Qubit2.0 fluorometer . Single-cell cDNA libraries were prepared according to the Chromium Single Cell 3’ Reagent Kit v2 user guidance . For RT-PCR, total RNA was reverse-transcribed using RT Primer Mix and oligo dT primers . cDNA was quantified using primers specific for mice by the ABI 7500 real-time PCR system . The sequences of the primers are provided above. PCR amplification was performed in a volume of 20 μL using RT-PCR Mix . The results were analyzed based on group assignment.Single cells were assessed using a 10× Chromium system (10× Genomics) and V2 single-cell reagent kits. After the library was built, the reads were aligned to the GRch38 human genome using the 10× analysis tool Cellranger Toolkit (Version 2.1.1) , and a uThen, the obtained matrix, barcode and gene files were imported into R software (version 3.6.1) for further analysis by Seurat (version 3.1.5) . The celThe FindVariableFeatures function was used to find the top 2000 highly variable genes for variable genetic parameters. Then, we used canonical correlation analysis (CCA) to correct the batch effect of the samples.We constructed a shared nearest neighbors (SNN) diagram for the data using principal component analysis (PCA)-reduced expression data for the top 49 principal components and then used modularity optimization through the SNN clustering algorithm to identify cell clusters. We first calculated the k-nearest neighbor and performed SNN analysis; then, we optimized the modular function. Finally, the number of clusters based on the parameter resolution was determined. Next, our clusters were displayed on a 2-dimensional diagram by nonlinear dimensional reduction. We used the Seurat function RunUMAP to achieve dimension reduction. Seurat function Findmarkers were used to identify marker genes for each cluster .We used SCENIC software to select the specific transcription factors and their corresponding target genes of the hRPE clusters. Transcription factors that were active among more than 50% of cells in the macular and peripheral RPE clusters were retained by SCENIC .iTALK (version 0.1.0) was used to analyze cell-cell interactions among hRPE clusters . We usedThe trajectory landscape of hRPE cluster development was constructed by Monocle2 (version 2.16.0) . The higp value was corrected by the Benjamini & Hochberg method.The R package ClusterProfiler (version 3.16.0)  was used


Betsch and Sprengholz  address In this study , severalhttps://pollenscience.eu/, using “allergy-relevant” pollen thresholds (30 and 100 m−3), whose own creator has highlighted that such thresholds may be disputable  (https://pollenscience.eu/).Third, based on their data and analysis code (/j5g7n/) , the var/j5g7n/) , as wellhttps://www.augsburg.de/umwelt-soziales/gesundheit/coronavirus/fallzahlen; last access: June 17, 2021), as well as pollen measurements . We found that while the number of tests per week remained relatively stable, the percentage of positive tests peaked between weeks 13 and 19 , with a minor decrease in R2 = 0.16.We do acknowledge with Betsch and Sprengholz  that it 3 and 19 . The peaOverall, we agree that the testing is a significant parameter to be taken into account when modeling the spreading of COVID-19, but when integrated into other more significant variables, like airborne pollen concentrations, the magnitude of difference in the explained variability is minimal, only 1%.


Xenopus laevis oocytes. In this study, we found that the neonicotinoid insecticide imidacloprid is unable to activate the cockroach Pameα7 receptor, although thiacloprid induces low inward currents, suggesting that it is a partial agonist. In addition, the co-application or 5 min pretreatment with 10 µM imidacloprid increased nicotine current amplitudes, while the co-application or 5 min pretreatment with 10 µM thiacloprid decreased nicotine-evoked current amplitudes by 54% and 28%, respectively. This suggesting that these two representatives of neonicotinoid insecticides bind differently to the cockroach Pameα7 receptor. Interestingly, the docking models demonstrate that the orientation and interactions of the two insecticides in the cockroach Pameα7 nAChR binding pocket are very similar. Electrophysiological results have provided evidence to suggest that imidacloprid and thiacloprid could act as modulators of the cockroach Pameα7 receptors.The functional expression of the cockroach Pameα7 nicotinic acetylcholine receptor subunit has been previously studied, and was found to be able to form a homomeric receptor when expressed in  Nicotinic acetylcholine receptors (nAChRs) are representative members of the Cys-loop ligand-gated ion channels (LGICs) superfamily, alongside GABA, glycine, and 5-HT3 receptors ,2,3. ThePeriplaneta americana [Periplaneta americana α5 (Pameα5) subunit was identified in the cockroach nervous system, suggesting that the cockroach lacked this subunit. Pameα6 and Pameα7 form a cluster with drosophila Dα7 while Pameα8 is close to drosophila Dβ2, suggesting that it corresponds more with a β rather than to an α subunit [In the aim of characterizing the nAChR subunits involved in the pharmacological properties of cockroach native nAChR subtypes, eight nAChR subunits (seven α and one β subunits) were cloned in the cockroach mericana , while americana ,21,22,23 subunit . The inh subunit . Moreove subunit . These d subunit . Based o subunit , this stWe examined the voltage-clamp recordings and molecular docking studies of the interactions of two relevant representatives of neonicotinoid insecticides , IMI andα7 nAChR [α7 nAChRs, we have studied the effect of neonicotinoid insecticides, since they have been understood to be agonists of cockroach nAChRs [α7 nAChRs. No macroscopic currents were detected within the recorded limits of detection .We proposed that IMI and THI showed distinct effects on the cockroach Pamep < 0.05, n = 6, p < 0.05, n = 6, α7 receptor.The co-application of 10 µM THI with 10 mM nicotine significantly decreased nicotine-induced current amplitudes to 54% of the control currents when they were co-applied   show 28.The generated models were energy minimized using the OPLS-2005 force field . The quaIn order to determine the binding interactions of neonicotinoids to the homology models of the cockroach Pameα7 nAChR at the atomic level, we carried out docking studies with relevant neonicotinoids. We selected IMI and THI for these investigations, given that; (i) they are forerunners of two chemical classes of neonicotinoid, namely nitro- and cyano-substituted neonicotinoids; (ii) their behavior was notably different from an electrophysiological point of view. The same trends were observed in all five interfaces, and only the results obtained for one of the subunits (CD) will be presented and discussed here.The geometric and docking parameters are reported in −1 for docking score and glide energy, respectively) must not be used to draw conclusions and should be considered cautiously. Nevertheless, such studies allow the key interactions established between the IMI and THI insecticides and their target to be delineated. The docking scores and glide energies of the top ranked poses show very similar trends for both IMI and THI insecticides . Indeed,Using electrophysiological measurements based on voltage-clamp recordings, we found that, as opposed to THI, IMI could not activate cockroach Pameα7 nAChRs, as no macroscopic currents were detected in our recording conditions. Our results are compatible with the neonicotinoids’ modulatory effect on nicotine-induced currents. Indeed, we found that THI decreases nicotine evoked current amplitudes, while IMI induces an increase. Nevertheless, we cannot exclude that IMI has a weak partial agonist action, which was not detected in our experimental conditions. In the same way, the decrease in nicotine-evoked currents under bath application of THI could be associated to a competitive effect on the binding site. Indeed, we found a strong modulatory effect under co-application, which was not present with pre-treatment. Complementary molecular docking investigations led to consistent results with the currently available structural data for the interactions of IMI and THI with 3D nAChR models. Therefore, it provides a starting point for analyzing the relative behavior of neonicotinoid insecticides to cockroach Pameα7 homomeric receptor at the atomic level. The trends that have been highlighted by the electrophysiological results, which suggest that IMI and THI are considered to be positive and negative modulators of the cockroach Pameα7 receptor, respectively. Further electrophysiological, molecular modeling and binding studies on these compounds and other representatives of neonicotinoid insecticides  are ongoing in our groups. An exciting prospect for Pameα7 nAChR is to confirm the importance of amino acid residues in the ACh binding domain, particularly in the loop B-C interval in the neonicotinoid selectivity. Finally, the present data reinforce our findings that neonicotinoid insecticides, including nitro- and cyano-substituted neonicotinoids act as agonists and/or modulators of insect homomeric nAChR subtypes. Moreover, insect α-7-like receptors could be used to analyze and compare the binding properties of neonicotinoid insecticides, similar to studies conducted with hybrid receptors.ACh, nicotine, IMI, THI and α-Bgt were purchased from Sigma Chemical Co, .Xenopus laevis oocytes were purchased from the University of Rennes, France  and kept in standard oocyte saline (SOS) of the following composition: in mM, 100 NaCl, 2 KCl, 1 MgCl2, 1.8 CaCl2 and 5 HEPES, pH 7.5. Stages V and VI oocytes were harvested and defolliculated after treatment with 2 mg·mL−1 collagenase IA  in Ca2+-free SOS solution, supplemented with 0.8 mg·mL−1 trypsin inhibitor. Defolliculated oocytes were injected into nuclei with 2 ng of α7 cDNA cloned in pSGEM [−1), streptomycin (100 mg·mL−1), gentamycin (50 mg·mL−1) and sodium pyruvate (2.5 mM).in pSGEM ,44,45. ICurrents were recorded 4 days after injection, using two microelectrodes filled with 3 M KCl. The oocyte membrane potential was held at −80 mV , and perPeriplaneta americana Pameα7 subunit were extracted from the Uniprot server  [Aplysia californica (Ac) (PDB ID: 3C79) [www.pdb.org) [The amino acid sequence of the cockroach ly 2021) . The nealy 2021) . The cryD: 3C79)  was seleD: 3C79) . The crypdb.org) . The paipdb.org) . Furtherpdb.org) .The chemical structures of the neonicotinoids IMI and THI are shown in 50 is the concentration giving half the maximum response and X is the logarithm of the compound concentration.Data were shown as mean ± SEM and analyzed using Prism 7 . Responses to experimental neonicotinoid applications were determined relative to the preceding nicotine control responses in the same experimental conditions. The dose response curves were derived from the fitted curve following the equation: Y = Imin + (Imax − Imin)/(1 + 10(log(EC50 X)H)), where Y is the normalized response, Imax and Imin are the maximum and minimum responses, H is the Hill coefficient, EC


The voltage-gated sodium channel is a type of protein normally expressed in the ‘excitable’ tissues (nerves and muscles) of the body. Epithelial tissues , which are normally devoid of such a channel, express it at high levels upon becoming cancerous. This occurs also in colorectal cancer cells where the channel subtype is the embryonic  variant, nNav1.5. In colorectal cancer cells, as in other solid cancer cells, channel activity promotes invasiveness. However, there is little information on the status of nNav1.5 in human colorectal tissues and how this might relate to patient outcome. Here, we show (i) that nNav1.5 expression is much higher in cancer tissues compared with normal; (ii) that nNav1.5 co-occurs with several other biomarkers of pathological importance; and (iii) that disease-free survival of colorectal patients is inversely correlated with channel expression. In conclusion, nNav1.5 has combined diagnostic and therapeutic potential in clinical management of colorectal cancer.+ channels (VGSCs) are expressed widely in human carcinomas and play a significant role in promoting cellular invasiveness and metastasis. However, human tissue-based studies and clinical characterization are lacking. In several carcinomas, including colorectal cancer (CRCa), the predominant VGSC is the neonatal splice variant of Nav1.5 (nNav1.5). The present study was designed to determine the expression patterns and clinical relevance of nNav1.5 protein in human CRCa tissues from patients with available clinicopathological history. The immunohistochemistry was made possible by the use of a polyclonal antibody (NESOpAb) specific for nNav1.5. The analysis showed that, compared with normal mucosa, nNav1.5 expression occurred in CRCa samples (i) at levels that were significantly higher and (ii) with a pattern that was more delineated . A surprisingly high level of nNav1.5 protein expression also occurred in adenomas, but this was mainly intracellular and diffuse. nNav1.5 showed a statistically significant association with TNM stage, highest expression being associated with TNM IV and metastatic status. Interestingly, nNav1.5 expression co-occurred with other biomarkers associated with metastasis, including hERG1, KCa3.1, VEGF-A, Glut1, and EGFR. Finally, univariate analysis showed that nNav1.5 expression had an impact on progression-free survival. We conclude (i) that nNav1.5 could represent a novel clinical biomarker (‘companion diagnostic’) useful to better stratify CRCa patients and (ii) that since nNav1.5 expression is functional, it could form the basis of anti-metastatic therapies including in combination with standard treatments.Voltage-gated Na RAS, EGFR, and BRAF mutations; microsatellite instability, CpG island methylation status; expression of SCNA, and TS, P21, and PTEN proteins  × 100, where ND is the number of fields of view (amongst the 20) showing overt ‘discrete’ staining. Accordingly, samples with diffuse, ‘all-over’ cytoplasmic staining would have a DF value near zero whilst those with discernible apical and/or basal membrane staining would have a higher DF value, maximum 100%.Delineation Factor (DF), defined as  × 100, where ND is the number of fields of view showing discrete staining. Samples with TS = 0 had to be excluded from this analysis.The immunohistochemical staining of the tissues was quantified by two parameters: (i) Intensity (I) was scored as 0, 1, or 2  and expressed in arbitrary units (a.u.). (ii) Extent (Ext) of epithelial staining was estimated visually as a percentage of the area stained in randomly chosen fields of view. These assessments were performed by EL and checked independently by either of two other scientists (MG and SPF). There was agreement in more than 90% of cases. Averages were taken in the minority disagreements. We also used a composite parameter for ‘total staining’ (TS) defined as I x Ext (a.u.). The cases of “no staining” were scored as TS = 0. For streamlining the analyses, the TS values were grouped into four classes of “Score” as follows: Score 0 (TS = 0); Score 1 (TS = 1−100); Score 2 (TS = 101−200) and Score 3 (TS = 201−300). In addition, we introduced a parameter, “delineation factor” (DF) to represent the pattern of immunostaining. This was determined as follows: (i) twenty fields of view were chosen randomly for each tissue section; (ii) the staining of each field of view was scored as “discrete” or “diffuse”; and (iii) DF (%) was calculated as [NData were analysed using the statistical software GraphPad Prism 8.0.2  and Intercooled Stata 9.1 . Data were analysed by either Mann–Whitney test to compare continuous variables and Chi-square test or Fisher exact test, when appropriate, to compare categorical variables.Progression free survival (PFS) was defined as the time elapsed between intervention and disease progression or last tumour evaluation and overall survival (OS) was defined as the time elapsed between intervention and death from any cause or last follow-up.p-value < 0.05 was considered statistically significant.Kaplan–Meier estimated survival curves of PFS and OS were plotted and compared using log-rank test. The impact of clinical and biomolecular parameters on PFS and OS was testes using univariate and multivariate Cox hazard models reporting hazard ratios (HR) and their 95% confidence intervals (95% CI). All statistical tests are two-tailed and a The data presented here taken together with other available evidence are consistent with nNav1.5 being a viable  biomarker of CRCa aggressiveness. Accordingly, nNav1.5 expression can be used to better stratify CRCa patients enabling, in turn, more focussed therapies. Furthermore, nNav1.5 expression being ‘cancer specific’ could be targeted by selective small-molecule drugs  and/or an antibody. Thus, nNav1.5 has significant diagnostic and therapeutic potential in the clinical management of CRCa.


Compromise of the vascular system has important consequences on cognitive abilities and neurodegeneration. The identification of the main molecular signatures present in the blood vessels of human hippocampus could provide the basis to understand and tackle these pathologies. As direct vascular experimentation in hippocampus is problematic, we achieved this information by computationally disaggregating publicly available whole microarrays data of human hippocampal homogenates. Three conditions were analyzed: ‘Young Adults’, ‘Aged’, and ‘aged with Mild Cognitive Impairment’ (MCI). The genes identified were contrasted against two independent data-sets. Here we show that the endothelial cells from the Younger Group appeared in an ‘activated stage’. In turn, in the Aged Group, the endothelial cells showed a significant loss of response to shear stress, changes in cell adhesion molecules, increased inflammation, brain-insulin resistance, lipidic alterations, and changes in the extracellular matrix. Some specific changes in the MCI group were also detected. Noticeably, in this study the features arisen from the Aged Group , pose the need for further experimental verification to discern between the occurrence of arteriogenesis and/or vascular remodeling by capillary arterialization. This article has an associated First Person interview with the first author of the paper. Summary: An integrative picture about the mechanisms operating in the hippocampal vasculature under normal and pathological scenarios is achieved by the computational dissection of microarray data corresponding to whole tissue samples and focusing on gene splice forms. Multiple pieces of evidence point to the incidence of brain vascular disease on cognitive deficits and neurodegeneration . MoreoveDiagnosis of brain vascular diseases can be reached by several imaging techniques . Some of these techniques not only provide structural information and detect the injuries in the parenchyma, but allow the quantification of cerebral blood flow (CBF) . HoweverMicroarray analysis is a relatively simple way to interrogate cells about their transcriptional state . But, toWe have previously analyzed a set of published microarray data concerning aging in human hippocampus. By using an optimized algorithm named Q-GDEMAR, we detected many features not evident in their primary analysis. The quantification of the false discovery rate (FDR) is the current way to establish the significance of these findings. Within Q-GDEMAR we have also developed an alternative algorithm to compute the FDR associated to differential genes. A plethora of significant genes and functional dimensions arose from these analyses.in vitro labelling for FACS sorting is still necessary . To overcome this problem, fluorescence-activated cell sorting (FACS) followed by single-cell RNA-sequencing (scRNA-seq) has been implemented. In some cases, transgenic animals were used to produce labelled cells for FACS analysis, but aggressive conditions were required to separate endothelial cells from mural cells . scRNA-secessary . The RNAecessary . Althougecessary , only 5.Given the enumerated problems, we pose that we can exploit the information of ‘global’ microarrays as a proxy, to progress in the comprehension of the hippocampal endothelium compartment. This is accomplished by making a ‘computational deconstruction’ of these microarrays, and establishing which of the analyzed genes could belong to the endothelial cells, based on specific databases. Moreover, to gain depth into their physiological relevance, we will restrict our list to genes belonging to the endothelium class, focusing only on those subsets of genes that being differentials, have splice forms .In fact, an important fraction of the genome has evolved developing isoforms. Although sometimes the isoforms are redundant, very often distinct isoforms of a given gene deal with different biological functions . In the Table S1). Some of these functionalities, mainly those related with aspects of angiogenesis, are shown in Our computational strategy allowed us to detect almost a hundred differential functionalities among the three groups assessed . This differs markedly from both the evolution of ‘ECs migration’ and ‘ECs chemotaxis’  and the evolution of ‘ECs differentiation’ and ‘ECs adhesion’ . The loss of ‘negative regulation of BBB establishment’  did not avoid the fall of the ‘positive regulation of the BBB establishment’ . In turn, ‘vascular branching’ showed a completely independent evolution .In Fig. 2D, ‘artery development’, ‘VSMC proliferation’, ‘IL-8’ and ‘inflammation’ appeared maximized in the Aged Group, and run together as was expected . They weThe fact that ‘vasculogenesis’ and ‘endothelial cell development’ in the Younger Group  appeared as significant as ‘angiogenesis’ , suggests that neovascularization occurs through endothelial progenitor cells EPCs, . The invYounger=4.9×10-21 versus FDRAged=1.5×10-8), but also in the ‘endothelins signaling’ (FDRYounger=1.9×10−4 versus FDRAged=3.8×10−2), and in the ‘Glypican 1 circuit’ (FDRYounger=2×10−3 versus FDRAged=7.1×10−7). There are minor differences in the ‘VEGF signaling’ (FDRYounger=1.1×10−8 versus FDRAged=6.5×10−7), but the interacting partners are distinct. Thus, Neuropilin 1 (NRP1) and Angiopoietin 1 (ANGPT1) appear enriched in the Younger Group, whereas Neuropilin 2 (NRP2), Angiopoietin 2 (ANGPT2), Angiopoietin Like 4 (ANGPTL4), and Paxilin (PXN) are enriched in the Aged Group.The Younger Group differs from the Aged Group not only in ‘angiogenesis’  them, such as the so-called cellular adhesion molecules (CAMs) also provided direct and indirect valuable information see A and B.Younger=1.3×10−11 versus FDRAged=4.5×10−4). In the Younger Group, this response involved the following genes: GPC1, RHOA, JUN, BMPR2, MAPK14, ICAM1, PRKAA1, AKT1, TNFRSF1A, TXN2, NOS3, MEF2C, VCAM1, VEGFA, whereas in the Aged Group it comprised of PECAM1, RELA, BCL2, ITGAV, BMPR1B, NFE2L2, PLAT, CTNNB1, TNFRSF1A, VEGFA. In contrast, the ‘extracellular matrix organization’ and ‘proteoglycans synthesis’ prevailed in the Aged Group: LAMA4, ITGA6, ITGAV, DCN, EFEMP1, ADAM17, ADAMTS9, FN1, PECAM1, VCAN, VWF, CTF1, PLAT, PDGFC, VEGFA, TNFSP13B, TGM2, PGF, PLXND1, SPOCK2, CLEC7A, LTBP1, LTBP3, PLEC, WNT5A, IGFBP3 (FDRAged=4.5×10−4).A differential feature is given by the ‘response to shear stress’  versus HLA-A, PECAM1, ITGAV, ITGA6, CD40, PTPRC, VCAN (FDRAged=8.5×10−3). Importantly, endothelial cells in the Younger Group showed not only ‘homotypic cell adhesion’, but also ‘heterophilic cell adhesion’ . In fact, the Younger Group showed ‘leukocyte adhesion to the blood vessel wall’ , mainly macrophages, monocytes, and lymphocytes (FDRYounger=10−7 to 10−9).Both the Younger Group and the Aged Group showed a dysregulated transcription of genes corresponding to CAMs. However, the genes involved in each group are substantially different: The dysregulation of sphyngosine-1P receptor signaling, IL-4, and HSPAA8 in the Younger Group could reflect an anti-inflammatory response to balance the inflammatory environment due to Thromboxane A2 and other cytokines in this group . The dysregulation of VCAM-1 and ICAM-1 in the Younger Group is consistent with our results about RAGE activation, NOD-like signaling, HMGB1 occurrence, TLRs activation (‘RAGE’ section), and the effects of Reelin on LRP8 , as well as with other reported data .JAM-3 and Selectin-P also appeared associated to the increments of VCAM-1 and ICAM-1 in the Younger Group. Noticeably, PECAM-1, which is required for trans-endothelial migration of leukocytes, appeared dysregulated in the Aged Group, but not in the Younger Group. Hence, the heterophilic cell adhesion detected in the Younger Group could reflect the normal mechanism of immune surveillance, without leukocyte transmigration, but probably after having completed the previous steps of tethering, rolling down, and crawling . In factYounger=2×10−3 versus FDRAged=2.7×10−8), whereas ‘Venous morphogenesis’ appeared dysregulated in both groups .‘Artery morphogenesis’ prevailed in the Aged Group . However, while proliferation of VSMCs was associated in the Younger Group to significant ‘VSMC chemotaxis’ and ‘VSMC migration’, this did not occur in the Aged Group. These differences open the question about the functionality of the artery morphogenesis detected. Data seem to pinpoint that artery morphogenesis in the Aged Group could involve mainly a hypertrophic process of the muscular layer surrounding the arteries and arterioles. Moreover, the artery morphogenesis in the Aged Group is supported also by the detection of a dysregulated constellation of characteristic genes . Tortuosity affects not only arteries and arterioles, but also veins and capillaries. The excess of tortuosity, through its effects on the local variation of the shear stress   see B. As ath−10) see B.Younger=1.2×10−10, FDRAged=2.7×10−10). However, these responses involved distinct genes in each case . Also, distinct target genes were transcribed by the factors HIF1α and HIF2α in each case .Noticeably, local variations in the shear stress can explain the occurrence of local hypoxic micro-regions along the brain vasculature. This could be the case of the ‘cellular response to hypoxia’ observed in both ‘healthy’ groups studied  as well as in the reactive nitrogen species (RNS) . However, there was an asymmetry in the ‘response to oxidative stress’ (FDRYounger=1.7×10−6 versus FDRAged=7.5×10−10). The response to oxidative stress in the Younger Group comprised the genes FOXO3, MET, JUN, PSEN1, PDGFD, PRKAA1, ETS1, AKT1, TXNRD2, TXN2, NOS3, EPAS1, SOD2, whereas in the Aged Group it was given by FOXO3, EPOR, SP1, HIF1A, PDGFRB, AXL, MCL1, RELA, BCL2, PRDX2, KLF4, ITGAV, CFLAR, EGFR, NFE2L2, TLR4, AKR1C3, PXN, CTNNB1, FYN, HSPA1B, NOS3, SOD2.Consistently with the occurrence of hypoxia, in both groups we have also detected a dysregulated generation of reactive oxygen species (ROS)  is the central regulator for the transcription of genes related with ROS detoxifying enzymes . In addiAlthough the item ‘inflammation’ peaks in the Aged Group, it is evident that the Younger Group exhibits a sub-inflammatory response, which can be characterized as a phenotype of ‘endothelial cell activation’ . In the Younger=7,11×10−3, FDRAged=2.4×10−2), but ‘thromboxane A signaling’ prevailed in the Younger Group (FDRYounger=3.3×10−7 versus FDRAged=4.46×10−2). Conversely, the ‘production of prostanoids and prostaglandins’, particularly the prostaglandin E2 (PGE2), prevailed in the Aged Group (FDRAged=3.4×10−3), whereas it was not significant in the Younger Group (FDRYounger=0.17). Note that thromboxane A is a pro-inflammatory, procoagulant, and vasoconstrictor factor, whereas PGE2 is anti-inflammatory.Both the Younger Group and the Aged Group exhibited significant ‘arachidonic acid signaling’ , whereas Interferon-γ (IFNG) was practically constant up to the Aged Group . Instead, other interferons  increased from the Younger Group to the Aged Group, where the high plateau persisted in the MCI group . This last pattern can be understood as a reactive response to the occurring inflammation mediated by IL-1, since IFN-β is a potent anti-inflammatory molecule .Table S1).The ‘TNF-α signaling’ in the Younger Group showed a slight diminution in the Aged Group, but increased further in the MCI stage . The transitory diminution of the TNF-α could be explained in the Aged Group by the exacerbated ‘response to TGF-β’   as well as by the loss of negative regulation of TGF-β. The activation of LTGFB implies the mobilization of TGF-β stored in the extracellular matrix (ECM), which is remodeling because of the occurring peak of inflammation .Aged =4.5×10−4), ‘ensemble of ECM genes’ (FDRAged=8.4×10−3), and ‘proteoglycans’ (FDRAged=2.2×10−2). The ‘ensemble of ECM genes’ was associated to VWF, FN1, LAMA4, SPOCK2, DCN, EFEMP1, IGFBP3, VCAN, LTBP1, LTBP3, WNT5A, CLEC7A, PLXND1, PGF, TGM2, ADAMTS9, TNFSF13B, PLAT, CTF1, ADAM17, PDGFC, VEGFA, while the ‘ECM organization’ associated with the genes FN1, PECAM1, LAMA4, ITGA6, DCN, ITGAV, EFEMP1, ADAMTS9, VCAN, ADAM17, LTBP1, LTBP3, PLEC. Proteoglycans comprised VWF, FN1, LAMA4, EFEMP1, IGFBP3, LTBP1, LTBP3, ITGA6, ITGAV, DCN, VCAN. Also peaking in the Aged Group were the transcripts of the interleukins IL-8, IL-12, IL-13. Instead, ‘IL-6 signaling’ appeared as non-significant in the Younger Group but it raised and persisted at high levels of significance in the Aged and MCI Groups .In fact, differential categories in the Aged Group were: the ‘ECM organization’ , but differ about the ‘cellular response to insulin’ (FDRYounger=2.5×10−4 versus FDRAged=4.4×10−2) and in the ‘sensitivity to insulin’ (FDRYounger=3.7×10−6 versus FDRAged=8.1×10−3). The ‘insulin receptor signaling’ in the Younger Group involved the genes MET, KITLG, PRKAA1, INSR, AKT1, MAPK1, whereas in the Aged Group it involved the genes VWF, FN1, PDGFRB, CAMK2B, CSF2RB, FGFR1, IL17RD, FYN. The ‘insulin sensitivity’ in the Younger Group was given by the dysregulation of the genes PPARGC1B, PPARA, AKT1, NOS3, INSR, PRKAA1, PRCKQ, TNFRSF1A, whereas in the Aged Group it appeared linked to the genes FOSL2, THRA, STAT3, PPARA, PTEN, CTF1, CREBBP, and NFE2L2.The Younger Group and the Aged Group showed minor differences in the ‘insulin receptor signaling’  triggers a positive crosstalk with INSR (insulin substrate receptor). As result, MET activation exerts an anti-inflammatory effect by diminishing the secretion of TNF-α, IL-6, and MCP-1 . HoweverIn brief, the sensitivity to insulin reaches a minimum in the Aged Group and is further maintained at low level in the MCI stage . This kind of response can be understood as a phenomenon of brain resistance to insulin . The braPeripheral insulin resistance is frequent in advanced cases of type II diabetes, and has been associated to impaired cognition and/or mood disorders. However, similar symptoms can also appear in brain insulin resistance, even without any evidence of diabetes. Insulin resistance in the brain is considered as a risk factor for Alzheimer’s dementia, but cognitive limitations in type II diabetes are not accompanied by senile plaques (Aβ peptide), nor neurofibrillary tangles (Tau protein). Interestingly, cerebral endothelial cells coming from an experimental model of diabetic mice showed very few common dysregulated genes respect to the observed in the ‘aged’ mice. However, one of the main shared features was the diminution of the immune response .Younger=2.6×10−7 versus FDRAged=7.1×10−3). In the Younger Group, this feature is supported by the differential transcription of ICAM1, VCAM1, VEGFA, CDC42, JUN, MAPK14, MAPK, which is compatible with the indicators of the ‘activation state’ of these endothelial cells . Again, the transcription of ICAM-1 and VCAM-1 indicates that NFKB is operating in the Younger Group, and that endothelial cells are thus ‘activated’ (−3).In fact, we detected a dysregulated transcription of the DAMP-protein HMGB1 (high mobility group box 1 protein) in the Younger Group. The protein HMGB1 is capable to translocating to distinct localizations , to perform different functions. HMGB1 can act as ligand and inducer of RAGE, but depending on the context, it can develop angiogenic effects and/or inflammatory effects, which include exacerbated induction of ICAM-1, VCAM-1, SEL-P . When RAtivated’ . In agretivated’ , we detehttps://clinicaltrials.gov/ct2/show/NCT03980730).However, RAGE has 19 isoforms. Most of them localize at the plasma membrane, but two are extra-cellular . While (s)RAGE is generated by shedding from the membrane-bound RAGE, (es)RAGE is synthesized by alternative splicing . The difHerein, we cannot discern whether the enhanced signaling through the RAGE receptor in the Younger Group is associated to a plasma membrane form, to a soluble form, or to a mixture of both. This last situation seems compatible with the low-grade inflammatory context detected in the Younger Group . This feature peaks in the Aged Group  and persists in the MCI Group . Moreover, ‘atherosclerosis’ evolves in close parallelism to other related items, such as ‘vascular inflammation’ and ‘response to mechanical stimulus’ , which drive the cellular infiltration of the blood vessel wall, following the chemo-attraction provided by several chemical gradients . This infiltration is the consequence of an inflammatory environment generated by cytokines, as well as by the activation and/or transactivation of integrins  and TLRs .Atherogenesis is a very complex, multi-step disorder . In thisThe balance of lipids in the brain has upmost relevance to maintain the neurophysiological activities, and the protein ApoE has a crucial role connecting the different players within the neurovascular unity . HoweverWe have detected that the Younger Group is featured by the receptor LRP8, whereas LRP1 predominates in the Aged Group. Although both receptors recognize ApoE-containing lipoproteins, they are not equivalent. Note that the clearance of the peptide Aβ will depend on the type of ApoE variant present. Hence, while LRP8 has the same affinity for all the ApoE isoforms , LRP1 has different affinities (ϵ3>ϵ2>>ϵ4) . In factLRP8 in the Younger Group could offer some advantages. It improves the anti-coagulant balance by contributing to neutralize the side-effects of prothrombin on the barrier properties of the endothelial cells . LikewisICD) to the cytoplasm . The Aged Group showed a dysregulated transcription of the enzyme HMGCR (which speeds up the synthesis of cholesterol), the ABCB1 ATPase-pump , and the receptor SCARB1 (which clears cholesterol from the lipoproteins). These effects are also potentiated by the enhanced the transcription of the genes SREBF and CREBBP . While functionalities grouped in Fig. 2H are upregulated , those grouped in Fig. 2I are downregulated .Concerning the interleukins up-regulated in the MCI Group, IL-15 is produced by endothelial cells after their stimulation by TNF-α and IFN-γ, leading to their chronic activation . The stiMCI=7.3×10−3). Note that there is no conflict between the scarcity of adherens junctions in MCI (see In full agreement with  MCI see C and the MCI see .−3). VEGFA and FGF2, both lead to the synthesis of lactosylceramide, which enhances the angiogenic effect of its inducers . Hippocampal vessels express the receptors CRHR1 and CRHR2  in the MCI Group was evidenced by dysregulation of the nuclear receptor genes nd CRHR2 , so thatnd CRHR2 . Once acnd CRHR2 . Moreovend CRHR2 .MCI=7.6×10−6) could be explained by the convergent effects of NR4A3 and lactosylceramide on the VSMCs, together with the effects of NR4A1 on the endothelial cells. Moreover, arteriogenesis appeared associated to an intense proliferation of VSMCs (FDRMCI=1.3×10−8). These features seem to result in the progression of the phenomena observed in the Aged Group (see The arteriogenesis that was observed in the MCI Group (FDRroup see A. The ex1 ∪ Reference2)]. In the Younger Group (n=65 genes), the accuracy was 98.5%, in the case of the Aged Group (n=147 genes), accuracy was 99.3%, while in the MCI Group (n=37 genes), accuracy resulted 97.3%. Hence, the inaccuracy in our approach resulted less than or equal to 2.7%. Even this maximum level of error does not seem to compromise the main biological inferences achieved, mainly because conclusions were not drawn on individual genes but from consistent networks.The performance of our computational pipeline was assessed by determining the intersection between the list of identified genes in each studied group with respect to the occurrence of these genes in some of the two data-sets taken as reference . Importantly, these data sets comprised balanced groups of men and women, matched by age. Hence, Group 1 comprised of nine men  and nine women , Group 2 included 15 men  and 14 women , while Group 3 was made up by four men  and four women .Human hippocampal microarray data by Affymetrix HG133 plus 2 was gathered by Cotman's group , 2014b. By a multi-step computational pipeline, the micro-array data corresponding to a total homogenate of human hippocampus was transformed into a set of data corresponding to the endothelium fraction of this tissue. This computational dissection, allowed us the re-analysis of the recovered data to establish some profiles of genes that might be operating in the endothelial compartment of the human hippocampus see .Fig. 5.As is depicted in http://jiaqianwulab.org/braincell/RNASeq.html).To assess the accuracy of our bioinformatic pipeline, we have tested whether the differential genes recovered as output of the computational procedure belong to the endothelial compartment. This was achieved by comparing the list of identified genes against two independent reported sets of data. One is the data-set generated by