text
string | predicted_class
string | confidence
float16 |
|---|---|---|
The value of using survival analysis and MSM techniques, more often applied to analyze patterns in health data, to understand animal behavior has been demonstrated utilizing data from guide dogs. Survival curves showed that if guide dogs were going to be withdrawn for behaviour reasons this occured primarily before they had qualified. After qualification, there was a steady rate in dogs withdrawn for behavior reasons, which fits with previous findings from our group using alternative methods (13). The exact numbers of dogs transitioning between stages is not shown by our data due to exclusion of dogs, which were withdrawn for guide dog owner or health reasons before reaching retirement (~20% of all dogs). Rather, these examples of use of epidemiological methods illustrate patterns of timings and effects of key explanatory variables. Survival analysis allowed modeling of the entire training and working life of a guide dog, which previously has been modeled as two outcome variables: whether a dog completed guide dog training to work with a client (or not) (15); and the lifespan of a working guide dog (13). Modeling these two outcomes together provides a better overview and reduces the number of analyses that need to be undertaken, thereby potentially reducing type 1 errors. The MSM perhaps provided the most novel information compared to previous analysis on these data. The multistate models revealed how many years of dog training (including the time spent in the home of a puppy walker and more formal guide dog training) was required for each year of dog working (approximately 1:2). The timings of movements between stages of guide dog training were different for dogs scored as more anxious or excitable. Finding that some types of dogs move more quickly through training than others could be used to help identify the characteristics of dogs that suited to shorter training times. From an applied perspective, length of training has an associated cost and there have been differences found at breed level for the effective length of training in dogs (19). The added value of using the MSM compared to previous findings shown here serves to highlight the potential it could have for other areas of studying animal behavior. Since behavior is frequently recorded in terms of behavioral states, there could be benefit in the use of MSM to model the timing and probability of movement between states. | study | 99.94 |
Basic dog factors such as the sex, breed, and whether dogs were Guide Dog bred affected either the probability of withdrawal for a behavior reason [in keeping with previous findings (19–21)] or how long it was before dogs were withdrawn. The new findings on the length of time until withdrawal are important since they could inform training practices within Guide Dogs. When these data were collected, it was typical for most dogs that were withdrawn from training to be withdrawn between 1 and 1.5 years of age. Data here from a controlled behavior test and a questionnaire of dog behavior suggest that at least some dogs, which were later withdrawn for behavior could be identified at 6–8 weeks or 5 months of age, respectively. It is expensive to train a guide dog and these costs increase with progression in training. Thus, earlier identification of dogs, which are later withdrawn could result in large savings to Guide Dogs. | study | 100.0 |
One of the eleven scores assigned to dogs during a controlled puppy test, the PPA, was associated with survival and one with the time in training in MSM. Previously and in a separate dataset, these scores of response to a Squirrel-like moving object and retrieval of a toy were associated with later success in training using a logistic regression approach (17). Retrieval of objects in young dogs seems indicative of their ability to work cooperatively with people (22, 23). Scores on the retrieval element of the PPA have previously been found to be heritable above chance levels (18), suggesting this behavior could be selected for. In this study, Dam was an important effect in the survival models, which is further suggestion that behavior measured in this test could be heritable, or at least responsive to Dam environment. | study | 100.0 |
From behavioral questionnaires of 5-month-old dogs, those with high scores on a trait named “Excitability” were more likely to be withdrawn for behavioral reasons. More excitable dogs were also moved more quickly between puppy walking (socialization and basic obedience stage of training) and the more structured guide dog training. The former finding supports previous research from our group (15), but the latter is novel. It is possible that dogs that are excitable may be moved to training faster than less excitable dogs in an attempt to provide additional support and structure. Two other scores on the behavioral questionnaire were associated with survival in training rather than withdrawal for behavioral reasons or transitions between states of puppy walking, training, and withdrawal: a trait named General Anxiety and higher scores of Neutral body posture. While anxiety behavior has previously been associated with withdrawals from guide dog training (15, 24–26), questions about neutral body posture have not previously been used to understand dog behavior. Scoring neutral posture using the method applied in this study also combines information on the initial arousal response, the time taken to recover from the arousal, and generalization of the response across contexts. Dogs showing neutral posture more often are also showing an absence of signs of high arousal of either positive (e.g., excitement, distraction) or negative (stress, anxiety, fear, aggression) valence, more often. There is evidence that people find states such as aggression (27) difficult to recognize in dogs, so, a focus on neutral posture could offer a useful alternative approach. | study | 100.0 |
Survival analysis is a flexible statistical tool, which has been applied to some types of behavior data, perhaps, most notably, data on the length and outcome of animal contests (7). However, the approach could be usefully applied to many more types of behavioral data. For example, in cognitive studies, the probability of, and time to learn an association could be modeled. Such approaches could contribute to understand of learning through statistical modeling of the age at which a behavior is first performed or develops, or the number of trials taken for a response to be learned or extinguished. Similarly, MSM could be usefully applied to a wide range of behavioral data on short- and long-term changes in state. Much behavioral research has focused on the choices animals make and particularly how the current choice might depend on previous choice(s). Such choices and the state dependency of choices (28) could be modeled using multistate modeling. | review | 99.75 |
An important consideration when undertaking any statistical analysis is the independence of each data point in a dataset; this is an underlying assumption of many statistical tests. A lack of independence between points can lead to pseudo-replication of results and falsely low p-values for explanatory variables. Frequently, in animal behavior data, there may be reasons for similarity between data points; because data are collected from the same animal, from animals from the same location (e.g., pen or farm), or between animals with genetic similarities (e.g., siblings or half siblings). This leads to correlations between data points in a dataset that need to be accounted for (e.g., using random effects terms), to understand the true influence of explanatory variables (4, 29, 30). In this study, we considered the effect of Dam and Assessor as frailty terms, which are in essence random effects. The “Dam” effect accounts for the genetic influence of each dam (i.e., accounts for genetic similarities between litter mates) and also elements of each dam’s specific environment; these aspects were identified as important potential sources of correlation in our dataset. Assessor was included to account for variability in interpretation in behavior between different individuals but was not required in models, presumably due to the questionnaire’s high inter-rater reliability (15). However, dam was indeed an importance source of variance in survival models; the influence of this variable in the model altered the significance of explanatory variables in some models. This further illustrates the need to consider random effects and the structure of shared sources of variance in behavioral data analysis. | study | 100.0 |
The two epidemiological statistical approaches used here have some limitations, which it is worth briefly highlighting. The methods typically require more data to reach an adequate power than simpler statistical methods. As with any multivariate analysis, modeling more than one outcome can make interpretation more difficult. There are many different approaches to survival analysis and MSM and such choice can make it difficult to select the most appropriate method. Furthermore, each approach has assumptions and these assumptions can be hard to meet with real data. As models become progressively more complex, model selection and model fit become increasingly important considerations. Finally, it is worth highlighting that these approaches reveal associations and do not reveal causation. For example, in Guide Dogs’ data, the finding that some breeds and dogs not bred by Guide Dogs were withdrawn faster could have resulted from stereotypes in expectations of these dogs based upon breed or source, rather than characteristics of the individual dogs. | review | 99.9 |
Survival analysis and MSM permit data analysis of a time variable and discrete outcome or outcome(s) together, which are common data types in animal behavior. These methods can be used to provide additional insight to more traditionally used statistics in this area, providing an overview of temporal patterns and reducing multiple testing. Using data from guide dogs on length of time in states of training and working, multistate modeling particularly was useful in understanding the overall system-level patterns and individual differences. | other | 93.6 |
LA conceived of the manuscript, supervised design of data collection tools and data collection, conducted statistical analysis, and wrote the original draft manuscript; NH designed original data collection tools, collected data, and helped draft the manuscript; MG helped secure funding, participated in data analysis, and edited the manuscript; GE secured funding, supervised the study, and edited the manuscript. | other | 99.94 |
Authors of this publication frequently consult with Guide Dogs. Guide Dogs have approved the paper for publication, but have not altered the results or presentation of the results in any way. The terms of this arrangement have been reviewed and approved by the University of Nottingham in accordance with its policies on research. | other | 99.94 |
Diabetes is a chronic and metabolic disease characterized by hyperglycemia resulting from defective insulin secretion and/or insulin resistance . Diabetics are at a significantly elevated risk for nephropathy, peripheral neuropathy, and retinopathy. In the central nervous system (CNS), chronic hyperglycemia leads to the enhanced formation of advanced glycation end products (AGEs), which have potentially toxic effects on neurons, causing dementia . Hyperglycemia also causes a significant increase in generation of reactive oxygen species (ROS), resulting in cerebral angiopathy and abnormalities of neurons and glia in the brain, and subsequent dementia . The degree to which the neuronal abnormality is caused directly by hyperglycemia remains unclear . Moreover, the development of diabetes-induced dementia is not only closely associated with hyperglycemia but also with the action of insulin . | review | 99.8 |
Insulin and insulin receptors show abundant expression throughout the brain, especially in the hippocampus, which is involved in dementia . Peripheral insulin crosses the blood-brain barrier (BBB) via an active transport mechanism to exert its effects within the CNS . Insulin receptors in the brain are found at synapses on both neurons and astrocytes . Insulin signaling acts as a neuromodulator that regulates the release and reuptake of neurotransmitters, such as acetylcholine and norepinephrine in rat locus coeruleus , and regulates neuronal and glial functions such as synaptogenesis and synaptic plasticity via energy homeostasis, gene expression, and cognition . Interestingly, insulin administered by intranasal improved cognitive dysfunction and insulin signaling, reduced amyloid-β (Aβ) production and amyloid plaque burden, and increased neurogenesis in 4-month-old APP/PS1 mice showing early Alzheimer's disease (AD) pathologies . Furthermore, intranasal insulin application improved cognitive performance in healthy subjects, aged subjects, AD patients, and experimental models of insulin resistance . In the brain, insulin-degrading enzyme (IDE) is synthesized and secreted by neurons, oligodendrocytes, and microglia . IDE degrades extracellular Aβ in microglial and neuronal cultures and insulin can prevent this degradation, thereby impairing the clearance of extracellular Aβ . IDE mutant rats, which show reduced activity of the enzyme, lead to type 2 diabetes, resulting in the enhanced cerebral deposition of Aβ . However, to clarify the precise mechanisms involved in the development of diabetes-induced dementia, further research will be required. | review | 99.44 |
Streptozotocin (STZ), a glucosamine-nitrosourea compound, is a genotoxic methylating agent and preferentially destroys insulin-producing β cells of the pancreas through the generation of ROS and alkylation of DNA . STZ is a chemical used for the generation of diabetes phenotypes in most strains of rodents . A mouse model utilizing five doses of STZ at low dose (50–60 mg/kg/day) has been extensively used in studies of type 1 diabetes (T1D) due to the progressive destruction of pancreatic β cells induced . Importantly, the STZ-induced diabetes of mouse resulted in spatial learning deficits and impaired hippocampal long-term potentiation (LTP), which is thought to affect the cellular mechanisms of learning and memory . Accordingly, STZ-treated mice have been used extensively to examine the physiological and pathophysiological consequences of diabetes-induced dementia. | review | 99.8 |
In the present study, changes in the expression of hippocampal and cortical proteins and phosphoproteins in STZ-treated mice were examined using two-dimensional gel electrophoresis followed by staining with SYPRO Ruby and Pro-Q Diamond, respectively, and subsequent mass spectrometry to elucidate the molecular mechanisms involved in diabetes-induced dementia. | study | 100.0 |
Streptozotocin (STZ), urea, thiourea, sodium dodecyl sulfate (SDS), 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS), 2-mercaptoethanol (2-ME), dithiothreitol (DTT), bromophenol blue, iodoacetamide, RNase A, and DNase I were purchased from Wako Pure Chemical Industries, Ltd. (Osaka, Japan). Source information for all other assay reagents and materials is stated in the Materials and Methods section described below. | other | 99.9 |
C57BL/6 mice (Japan SLC, Inc., Shizuoka, Japan) were maintained in a standard 12 h light/dark environment (lights on at 7:00 A.M.). Food and water were available to mice ad libitum. All experimental procedures were performed in accordance with the National Institutes of Health Guidelines on the Care and Use of Animals and confirmed by the Himeji Dokkyo University Animal Experiment Committee. All efforts were made to minimize animal use and suffering. | other | 99.94 |
The diabetic model was set up by intraperitoneal injection of STZ 50 mg/kg once a day for 5 consecutive days . Ten weeks after injection, the mice were tested for sufficient levels of hyperglycemia. Blood glucose level was assessed using blood glucometer (Terumo Co. Tokyo, Japan) by tail vein puncture blood sampling. A serum glucose level higher than 400 mg/dl was considered diabetic . | study | 99.94 |
Protein extraction was performed as previously described . Both the STZ- and vehicle-treated mice were killed under anesthesia with pentobarbital sodium. The hippocampi and cortices were isolated from three mice in each group, and mixed separately for the two groups. Two-DE analysis was repeated in triplicate. In brief, mouse hippocampal samples were homogenized in lysis buffer [7 M urea, 2 M thiourea, 5% CHAPS, 2% immobilized pH gradient (IPG) buffer (GE Healthcare UK Ltd., Buckinghamshire, UK), 50 mM 2-ME, 2.5 μg/ml DNase I, and 2.5 μg/ml RNase A]. Solubilized extracts were centrifuged at 15,000 ×g for 30 min, and the supernatant was used for further analysis. | study | 100.0 |
Two-DE analysis was carried out as previously described [21, 22]. In brief, one-dimensional isoelectric focusing (IEF) gel electrophoresis was performed using IPG gel strips (pH 4–7; 7 cm; GE Healthcare, WI). Approximately 1000 μg of protein from each group was incubated with the IPG strips and run at 50 V for 6 h, at 100 V for 6 h, and finally at 2000 V for 6 h. After IEF gel electrophoresis, the IPG strips were equilibrated for 15 min in equilibration buffer [50 mM Tris-HCl, pH 8.8, 6 M urea, 1% SDS, 30% (v/v) glycerol, and 1% (w/v) DTT] and then for 15 min in equilibration buffer containing 2.5% (w/v) iodoacetamide instead of DTT. For the second dimension, the equilibrated IPG strips were transferred onto 15% SDS-PAGE gels at 5 mA/gel for 7 h. | study | 100.0 |
Protein or phosphoprotein gel staining and image acquisition were carried out as previously described [21, 22]. Briefly, the gels were fixed three times in 200 ml immobilization solution [10% acetic acid and 50% methanol] for 30 min and washed five times with 200 ml of water for 15 min. Under the dark, the gels were stained with Pro-Q Diamond phosphoprotein gel stain (Life Technologies, Carlsbad, CA) for 120 min at room temperature with gentle agitation and then washed three times with destaining solution [50 mM sodium acetate, pH 4.0 and 20% (v/v) acetonitrile] for 30 min. Image acquisition was performed on Fluorophorestar 3000 image capture system (Anatech, Tokyo, Japan) with a 520 nm excitation and 575 nm emission filter for Pro-Q Diamond detection. | study | 99.94 |
Next, gels were washed with washing solution [10% methanol and 7% acetic acid] for 30 min. The gels were incubated in SYPRO Ruby stain (Life Technologies) for 90 min in the dark. The gels were washed with destaining solution [10% methanol and 7% acetic acid] for 30 min and rinsed with MilliQ H2O. Image acquisition was carried out using a Fluorophorestar 3000 image capture system with a 470 nm excitation and 618 nm emission filter for SYPRO Ruby detection. | other | 99.9 |
Image analysis was performed as previously described [21, 22]. Image analysis and the quantification of gel spots were performed with Prodigy SameSpots software (Nonlinear Dynamics, Newcastle upon Tyne, UK). From the menu of SameSpots normalization options, we chose to normalize the intensity of each spot to the total intensity of all matched spots within each gel and to identify differentially expressed spots by comparing spot intensity differences between samples from STZ-treated mice and control mice using ANOVA. | study | 100.0 |
In-gel digestion was performed using a method described . Protein spots were cut from the gels, and the gel pieces were washed three times for 15 min each with 200 μl of 50 mM ammonium bicarbonate with 50% (v/v) acetonitrile and then dried under vacuum. The gel piece was rehydrated in 5 μl of sequencing-grade modified trypsin (10 ng/μl, Promega, Madison, WI) in 10 mM ammonium bicarbonate for 30 min at 4°C, and digestion was carried out for 18 h at 37°C. Peptides were extracted with 5 μl of extracting solution [50% (v/v) acetonitrile and 0.3% (v/v) trifluoroacetic acid] for 10 min by sonication. | study | 99.94 |
Mass spectrometry analysis was performed in accordance with the procedure described in our previous report . In brief, mass spectra were obtained using a MALDI-TOF MS/MS analyzer (ABI PLUS 4800, Applied Biosystems, Foster City, CA). One μl of each sample was mixed with 1 μl matrix solution [1 μg/μl α-cyano-4-hydroxycinnamic acid (CHCA, Wako Pure Chemical Industries Ltd.) in 50% (v/v) acetonitrile and 0.3% (v/v) trifluoroacetic acid]. Analyte and matrix were spotted onto a stainless steel MALDI target plate and dried under ambient conditions. The peptides were analyzed using a MALDI-TOF MS/MS analyzer, and the authors searched the database with the Mascot search engine (http://www.matrixscience.com; Matrix Science, Boston, MA) using a Mascot MS/MS ion search through NCBInr databases. Proteins were considered as identified by MALDI-TOF MS if they had Mascot scores of 60 or higher (P < 0.05). | study | 100.0 |
The isolated hippocampus and cortex samples were homogenized in buffer containing 20 mM Tris-HCl, pH 7.0, 6 M urea, 150 mM NaCl, 2 mM EDTA, and 1% Triton X-100. The homogenates were subjected to 8% SDS-polyacrylamide gel electrophoresis and analyzed by Western blot using rabbit anti-TCTP (diluted 1 : 1000, Abcam, Cambridge, MA), rabbit anti-SUCLA2 (diluted 1 : 1000, Abcam), rabbit anti-NSE (diluted 1 : 1000, Abcam), and rabbit anti-GAPDH (diluted 1 : 10,000, AbFrontier, Seoul, Korea) antibodies at 4°C overnight. The membranes were incubated with the indicated secondary antibody (diluted 1 : 5000, GE Healthcare, Madison, WI). All values were corrected with reference to the value for GAPDH, used as an internal standard. Immunoreactivity was detected by using an Amersham ECL Prime Western blotting detection kit (GE Healthcare). Western blot images were quantified using the Multi Gauge version 2.2 software (Fuji Photofilm, Tokyo, Japan). | study | 99.94 |
The expression changes of proteins and phosphoproteins in the hippocampus and cortex of STZ-treated and untreated control mice were quantified and identified on 2-DE gels using Prodigy SameSpot software and MALDI-TOF MS/MS. Image analysis showed approximately 400 protein spots and 200 phosphoprotein spots on each SYPRO Ruby-stained 2-DE gel (Figure 1) and each Pro-Q Diamond-stained 2-DE gel (Figure 2), respectively. We detected 16 (5 up- and 11 downregulated) hippocampal proteins (Table 1), 16 (7 up- and 9 downregulated) cortical proteins (Table 2), 3 (1 up- and 2 downregulated) hippocampal phosphoproteins (Table 3), and 4 (2 up- and 2 downregulated) cortical phosphoproteins (Table 3). These proteins and phosphoproteins were categorized into functional groups as shown in Tables 1–3 using the PANTHER (http://www.pantherdb.org/) database. | study | 100.0 |
The 5 proteins with increased expression levels were identified as type II peroxiredoxin 1, ATP-specific succinyl-CoA synthetase beta subunit, rho GDP-dissociation inhibitor 1, heme-binding protein, and phosphatidylethanolamine-binding protein 1, and the 11 proteins with decreased expression levels were identified as profilin-2, tubulin beta-5 chain, alpha-internexin, ketimine reductase mu-crystallin, L-lactate dehydrogenase B chain isoform 1, ubiquitin carboxy-terminal hydrolase L1, isoform CRA_a, proteasome subunit alpha type-3, N(G),N(G)-dimethylarginine dimethylaminohydrolase 1, ferritin heavy chain, translationally controlled tumor protein, and prohibitin (Table 1). | study | 100.0 |
The phosphoprotein with increased expression level was identified as dihydropyrimidinase-related protein 2, and 2 phosphoproteins with decreased expression level were identified as proteasome subunit alpha type-3 and beta-soluble NSF attachment protein (Table 2). | study | 100.0 |
The 7 proteins with increased expression levels were identified as alpha-tubulin, partial, tubulin alpha-1C chain, put. Beta-actin (aa 27–375), gamma-actin, alpha-internexin, beta-synuclein, and unnamed protein product, and the 9 proteins with decreased expression levels were identified as tubulin beta-5 chain, NADH dehydrogenase (ubiquinone) Fe-S protein 1, atp5b protein, partial, gamma-enolase isoform 1, calretinin, heme-binding protein, phosphatidylethanolamine-binding protein 1, COP9 signalosome complex subunit 4, and ubiquitin C-terminal hydrolase L3 (Table 3). | study | 100.0 |
The 2 phosphoproteins with increased expression levels were identified as dihydropyrimidinase-related protein 2 and enolase 1B, retrotransposed, and the 2 phosphoproteins with decreased expression levels were identified as gamma-actin and proteasome subunit alpha type-3 (Table 2). | study | 100.0 |
Western blot analysis was performed to validate the identity of translationally controlled tumor protein and ATP-specific succinyl-CoA synthetase beta subunit as differentially expressed hippocampal proteins and the identity of gamma-enolase isoform 1 as differentially expressed cortical protein. The protein level of translationally controlled tumor protein was significantly decreased about 0.8-fold in the hippocampus of STZ-treated mice compared with untreated control (P = 0.049) (Figure 3(a)). The protein level of hippocampal ATP-specific succinyl-CoA synthetase beta subunit tended to increase (P = 0.29) (Figure 3(b)) and that of cortical gamma-enolase isoform 1 tended to decrease (P = 0.057) (Figure 3(c)). | study | 100.0 |
In this study, we used 2-DE coupled with MS to investigate changes in the expression of proteins and phosphoproteins in the hippocampus and cortex of STZ-treated mice, that is, diabetic model mice showing dementia, and revealed that the expression of 32 proteins and 7 phosphoproteins changed significantly. | study | 100.0 |
Micorotubules are composed of α- and β-tubulin heterodimers and are present throughout neuronal dendrites and axons. Microtubule dynamics regulate axonal outgrowth, dendritic spine morphology, and synaptic plasticity . Treatment with paclitaxel, a microtubule dynamics inhibitor, leads to LTP deficits in the cortico-amygdala pathway in mouse brain slices . Actin exists in both monomeric (G-actin) and polymerized (F-actin) forms and presents in dendritic spines. Actin dynamics are essential in synaptic function and memory formation . Indeed, cytochalasin D, an inhibitor of F-actin polymerization, blocks the late phase of LTP but not the early phase . Thus, changes in the expression of tubulin and actin might affect synaptic plasticity, being involved with diabetes-induced dementia. | study | 100.0 |
Peroxiredoxins (Prxs) are antioxidant enzymes that contain one or two cysteine (Cys) residues in their active site . There are six isoforms divided into three groups: the 2-Cys Prxs (Prx 1, 2, 3, and 4), the atypical 2-Cys Prx (Prx 5), and the 1-Cys Prx (Prx 6) . In neurons, intracellular Prxs, which are induced by various oxidative stimuli, protect against oxidative radical damage by ROS . Type II Prx 1, also known as Prx 2, is predominantly expressed in the brain . The protein level of Prx 2 is increased in aging and AD brains, suggesting that Prx 2 is involved in the elevated neuronal antioxidant response under oxidative stress . The proteomic analysis of the hippocampus in AD patients shows that the expression of Prx 2 increases compared with age-matched controls . Prx 2-deficient mice show remarkably increased susceptibility to oxidative stress-induced tissue damage . Therefore, the increased expression of Prx 2 might decrease oxidative damage, improving abnormalities of neurons and glia in the brain and subsequent dementia. | study | 99.94 |
Translationally controlled tumor protein (TCTP) is a multifunctional protein that is involved in immune responses, cell proliferation, cancer progression, and apoptosis . TCTP, also known as histamine-releasing factor (HRF), induces the secretion of histamine that is widely distributed in the brain . Histamine-expressing neurons project to wide areas of the brain, including regions especially important for cognitive functions such as the frontal cortex and hippocampus . Histamine and TCTP are significantly reduced in AD brain compared to age-matched control, suggesting that decreased histamine levels impair cognitive function in AD . Thus, the decreased expression of TCTP might decrease the histamine release and be one of the main causes of dementia. | study | 100.0 |
In humans, four ubiquitin carboxy-terminal hydrolase (UCH) proteins (UCH-L1, UCH-L3, UCH37/UCH-L5, and BAP1) have been identified, but only UCH-L1 and L3 have been studied in detail . UCH-L1 is a neuronal deubiquitinase that cleaves peptide adducts from the C-terminus of ubiquitin . UCH-L1 is predominantly expressed in the brain . The proteomic analysis of the hippocampus in AβPPswe/PS1dE9 mice shows that the expression of UCH-L1 decreases compared with age-matched wild-type mice . UCH-L1 is downregulated in the brain of patients with Parkinson's disease and AD . The administration of UCH-L1 protein fused to the transduction domain of HIV transactivator (TAT) protein into APP/PS1 model mice of AD provided a protective effect against amyloid-induced neurodegeneration in synaptic function and contextual memory . UCH-L3 is universally expressed in all tissues . UCH-L3-deficient mice exhibit significant impairment in learning and memory using Morris water maze and 8-arm radial maze task . Indeed, our findings showed that UCH-L1 and L3 were downregulated in diabetes-induced dementia. Thus, the decreased expressions of UCH-L1 and L3 could contribute to neurodegeneration, resulting in dementia. | study | 100.0 |
The human synuclein family has 3 members, α-synuclein, β-synuclein, and γ-synuclein . α-Synuclein and β-synuclein are predominantly localized at presynaptic nerve terminals in the CNS . In contrast, γ-synuclein is abundant in the peripheral nervous system . β-Synuclein protects neurons against apoptosis induced by staurosporine and 6-hydroxydopamine, which is linked to the suppression of p53 transcriptional activity . Thus, the increased expression of β-synuclein could protect against neuronal apoptosis in diabetes-induced dementia. | study | 99.94 |
Calretinin is an EF-hand calcium-binding protein involved in calcium signaling . Calretinin is expressed in hilar mossy cells and in widely distributed subsets of GABAergic interneurons in the normal mouse hippocampus . Calretinin maintains appropriate calcium ion concentration in cells and participates in the modulation of neuronal activity and synaptic plasticity . Knockout mice lacking calretinin show no alteration in basal synaptic transmission but impaired LTP in the dentate gyrus . These findings indicate that the decreased expression of calretinin might be involved in diabetes-induced dementia. | study | 100.0 |
Dihydropyrimidinase-related protein 2/collapsin response mediator protein 2 (DPYL2/CRMP2) binds to tubulin heterodimers to promote microtubule formation and stability, resulting in axonal growth and neuronal polarity . Cyclin-dependent kinase 5 (Cdk5) and glycogen syntheses kinase 3β (GSK-3β) regulate DPYL2 activity . The phosphorylation of DPYL2 by GSK-3β can inactivate DPYL2 function . DPYL2 is phosphorylated at Ser522 by Cdk5 and subsequently at Ser518, Thr514, and Thr509 by GSK-3β in brain tissue from human AD patients and in some mouse models of AD . Aβ25–35-induced impairment of cognitive function and LTP was not observed in DPYL2 phosphorylation-deficient knock-in mice, in which Ser522 of DPYL2 was replaced with alanine , suggesting that the phosphorylation of DPYL2 at Ser522 was associated with Aβ25–35-induced cognitive memory deficit and impairment of LTP. Indeed, our findings showed that phosphorylated DPYL2 was increased in the hippocampus and cortex of STZ-treated mice. Taken together, it is suggested that phosphorylated DPYL2 plays an important role in diabetes-induced dementia. Interestingly, DPYL2 was identified from two phosphoprotein spots on 2-DE gels stained with Pro-Q Diamond staining in the hippocampus. The shift in the position of these phosphoprotein spots may imply translational modifications such as phosphorylation, acetylation, and degradation. | study | 100.0 |
In conclusion, we found 32 proteins and 7 phosphoproteins with significantly altered levels in the hippocampus and cortex of STZ-treated mice. We propose that the identified proteins and phosphoproteins might play important roles in the molecular mechanisms involved in diabetes-induced dementia. | study | 100.0 |
Glaucoma is a group of eye diseases that result in damage to the optic nerve and potentially leads to irreversible blindness . The nerve damage involves loss of retinal ganglion cells in a characteristic pattern [2–5] with intraocular pressure (IOP) the only modifiable risk factor and IOP reduction the only treatment. Previous studies have reported that vascular factors may play a critical role in the development of glaucoma in addition to elevated IOP [3–7]. | review | 99.5 |
Pre-perimetric glaucoma patients represent a group of individuals with risk factors such as elevated IOP or ocular findings such as optic disc cup enlargement that are suspicious for glaucoma, but often do not have the classic optic nerve neuroretinal rim loss or characteristic visual field defects to meet the formal definition of definite glaucoma. In terms of functional assessments, visual field (VF) testing using standard automated perimetry remains the standard criterion for glaucoma diagnosis and assessment, but it has substantial variability, often with poor reproducibility . Optical coherence tomography (OCT) gives an objective measurement of retinal nerve fiber layer (RNFL) thickness and/or ganglion cell complex (GCC) which is important for glaucoma assessment but is limited utility in advanced disease and does not relate to cause of disease as opposed to the final presentation . | review | 99.9 |
Previously, microvascular changes of optic nerve head and peripapillary area have been demonstrated in patients with glaucoma . Recent studies suggested that another possible imaging modality OCT angiography (OCTA), may be used for early diagnosis and monitoring of glaucoma . Although no current technology including Laser Doppler Flowmetry can provide flow rate in smaller retinal vessels, OCTA has been developed as a non-invasive imaging technique that generates three-dimensional, depth encoded images of small and large caliber retinal vasculature within the eye by using motion contrast. It is based on comparison of repeat scans acquired at the same position in the retina to look for changes in the scan of blood flow. It does mapping by A scan to A scan comparison of two or more OCT volumetric cubes which provides detailed vasculature of the retina and optic nerve head, in a noninvasive manner, using OCT scanning alone and without the use of any kind of exogenous dye needed in other vascular imaging techniques like fluorescein angiography . To the best of our knowledge, there are no previous published reports that have evaluated the optic nerve head, papillary and peripapillary retinal vasculature using OCT angiography in eyes with mild POAG and compare the results with pre-perimetric glaucoma and healthy eyes. Understanding the relationship between retinal vessel density and early glaucoma may increase our understanding of the role of retinal blood flow in the glaucoma cascade and in the pathophysiology of glaucoma. The purpose of this study was to measure the retinal vessel density using OCT angiography in mild POAG eyes vs pre-perimetric glaucoma eyes vs normal eyes, and to investigate correlations between retinal vessel density measurements to other structural parameters like RNFL thickness and functional VF parameters. | study | 99.94 |
Our prospective, observational study was performed between February 2016 and May 2016 at the UCLA Doheny Eye Center glaucoma clinic. The research protocols were approved by the Institutional Review Board of UCLA, performed in accord with the tenets of the Declaration of Helsinki. Written informed consent was obtained from each participant. | study | 99.94 |
Initially, a total of 24 eyes with mild POAG, and 21 pre-perimetric glaucoma eyes were age matched with 16 eyes from normal controls. Four eyes from the POAG group and 1 eye from the pre-perimetric glaucoma group were not analyzed because of poor OCT angiography quality, leaving 56 eyes for statistical analysis. | study | 100.0 |
The diagnostic criteria for glaucoma included all of the following: 1) the presence of characteristic glaucomatous optic disc damage and abnormal thinning of the circumpapillary RNFL; 2) visual field defects consistent with glaucoma, confirmed on at least two visual field examinations; 3) normal open angles on gonioscopy; and 4) no history of any other ocular or systemic diseases causing non-glaucomatous optic nerve damage. Only mild stage POAG eyes based on Hoddap-Anderson-Parrish scale were included in our study as measured by visual field mean deviation scores (MD > −6.0 dB). | study | 100.0 |
The inclusion criteria for the normal subjects were defined as IOP of ≤21 mmHg, normal appearing optic nerve head, intact neuroretinal rim and normal RNFL thickness, and normal standard automated perimetry (defined as a glaucoma hemifield test within normal limits and a pattern standard deviation within 95% confidence-interval limits). | study | 99.94 |
The exclusion criteria for all eyes were the following: (1) best-corrected visual acuity less than 20/40, (2) age younger than 30 years or older than 80 years, (3) refractive error greater than +3.00 diopter (D) or less than −6.00 D, (4) previous intraocular surgery except for uncomplicated cataract extraction with posterior chamber intraocular lens implantation, (5) any non-glaucomatous conditions that may cause VF loss or optic disc abnormalities, or (6) inability to perform reliably on automated VF testing. One eye from each participant was imaged and analyzed in a random manner. | study | 99.94 |
All subjects were interviewed regarding their medical history. Thorough ophthalmic examinations included refractive status, slit-lamp biomicroscopy, fundus examination, IOP, central corneal thickness (CCT), and gonioscopy, performed by glaucoma specialists. The RNFL thickness, from a 3.4-mm diameter circle scan centered on the disc, was assessed with SD-OCT (The Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA). IOP was measured using Goldmann applanation tonometry and CCT was measured with a handheld ultrasound pachymeter (Ipac Pachymeter, Reichert Ophthalmics, NY, USA). Visual field tests were performed with the Humphrey Field Analyzer II (Carl Zeiss Meditec, Inc). The system was set for the 24–2 threshold test, size III white stimulus, SITA-standard algorithm. | study | 100.0 |
The optic disc region was imaged using a 3 × 3 mm scan by a 1050-nm-wavelength spectral OCT system (DRI OCT Triton, TOPCON). We used custom grading software (OCTORA) of the device to generate the maps. En-face images of the vasculature were generated from the optic nerve and retinal layers and collapsed into a single two-dimensional image set between the internal limiting membrane and retinal pigment epithelium (Fig 1). Quantitative analysis of the vessel density was performed using the publically available GNU Image Manipulation Program GIMP 2.8.14 (http://gimp.org). Average pixel density was determined from the vessels after background subtraction (Photoshop) as previously reported . This was done for the entire image (Fig 1A) as well as 3 regions of interest: 1) papillary region (3 mm circular region centered on the ONH) (Fig 1B), 2) peripapillary region (700 micron wide elliptical annulus centered on the disc) (Fig 1C), and 3) the optic nerve head (Fig 1D). Vessel intensity ratios for each region of interest was calculated by dividing their average pixel density by that of the entire image. Separately, since mild glaucoma often presents with focal, rather than global defects, we divided the vessel intensity ratio for the papillary region into superior and inferior domains. | study | 100.0 |
1A. A full-thickness retinal slab was selected by selecting the ILM and RPE as boundaries. The boundaries were not changed for the measurements in the disc area, and the instrument automatically connected the two ends of the RPE as outer boundary. Vessel intensity ratios from 3 regions of interest were calculated, 1B. Papillary region (3 mm circular region centered on the ONH), 1C. Peripapillary region (700 micron wide elliptical annulus centered on the disc), 1D. The optic nerve head | study | 99.94 |
Intra-visit repeatability of the vessel densities was calculated with 2 sets of images obtained sequentially from a single visit. Variability was assessed by the coefficient of variation (CV), calculated as the root-mean-square measurement variation divided by the mean of the measured values. Inter-visit, intra-visit and inter-operator reproducibility of 2 operators were calculated from 16 normal eyes using the mean value averaged from 2 sets of images per visit. | study | 100.0 |
Data are shown as a mean value with the standard deviation. The Kruskal-Wallis test was used to analyze the significance of differences among the 3 groups and if there was a difference T test was used to check where the difference came from. Multiple linear regression analysis was used to determine the relationships between the vessel density and traditional glaucoma measurements of structure (OCT-derived RNFL thickness) and function (VF mean deviation, VF pattern standard deviation [PSD]) in glaucomatous eyes. The receiver operating characteristic (ROC) curve for the disc parameters was plotted to determine the optimum cutoff point, and area under the ROC curve (AUC) was used to determine the discrimination power between the normal and POAG. Repeatability and reproducibility were analyzed by using the CV and the ICC between measurements. The CV is the standard deviation of the measurements divided by their mean, expressed as a percentage. The ICC measures the proportion of total variability in measurements contributed by variability in measurements between different subjects, and was determined using the random-effects mixed model. The significance level was set at p < 0.05. All analyses were performed with statistical software (SPSS for Windows, version 19.0; SPSS, Inc., Chicago, IL, USA). | study | 100.0 |
A total of 40 eyes from 40 patients were age-matched with 16 eyes from 16 normal controls. Kruskal Wallis testing was first performed to evaluate for overall differences among the groups. Table 1 summarized the baseline clinical characteristics of each group. No significant differences were found among these (p-values ranged from 0.421 to 0.7) except for IOP and number of glaucoma medications. As expected, the POAG patients were treated with medications, whereas the pre-perimetric glaucoma and normal eyes were not on glaucoma medications. For treatment in the POAG group, prostaglandin analogue once daily at bedtime was used in 10 eyes, while dorzolamide-timolol combination twice daily was used in 5 eyes, and brimonidine tartrate twice daily was used in 5 eyes. The duration of medication use was 3.5±2 years. Pairwise t-tests comparing POAG to suspects (p = 0.01) and POAG to normals (p = 0.09) confirmed that POAG patient had lower intraocular pressures which was consistent with the fact that they were under treatment. Use of an ocular antihypertensive eye drop or any subclass was not correlated with the vessel density measurements as determined by the Mann-Whitney U test. | study | 100.0 |
Pairwise t-tests showing a statistically significant difference between POAG and pre-perimetric glaucoma patients (p = 0.08) but not between controls and suspects (p = 0.6–1.0) for visual field performance suggested that the overall difference found came from worse visual field scores in POAG patients (Table 3). However, pairwise t-tests with statistically significant differences between both POAG vs suspects (p = 0.02) and controls vs suspects (p = 0.012) for mean RNFL thickness, help confirm that structural loss often precedes perimetric loss (since pre-perimetric glaucoma eyes had lower RNFL thickness compared to normal) (Table 3). There was no otherwise statistically significant difference found for VF MD, VF PSD, and C/D area ratio between the normal and pre-perimetric glaucoma patients (Table 3). | study | 100.0 |
In OCTA-measured vessel density, overall differences were found between the groups for optic nerve head, papillary, and peripapillary vessel density (all p<0.001, Table 2). Like for OCT RNFL, pairwise t-tests showed that for all vessel density measures (except papillary area vessel density comparing POAG to suspects) that POAG to suspects (p = 0.003–0.023) and controls to suspects (p = 0.001–0.007) were statistically significantly different with the suspects having values in between those of POAG and normal controls (Table 3). For intra-visit, inter-visit and inter-observer repeatability, the ICC values were based on measurements from 16 normal subjects (Table 4). | study | 100.0 |
In the POAG group, the univariate regression analysis using the Pearson correlation coefficient showed that optic nerve head vessel density was significantly correlated with VF MD (r = 0.829, p<0.001), VF PSD (r = 0.47, p = 0.04), and RNFL thickness (r = 0.512, p = 0.04). Also superior papillary area vessel density was significantly correlated with superior RNFL thickness (r = 0.436, p = 0.02), inferior papillary area vessel density was significantly correlated with inferior RNFL thickness (r = 0.523, p = 0.04). | study | 100.0 |
The areas under the receiver operating characteristic curve (ROC) for differentiating normal from POAG was 0.956 for peripapillary vessel density, 0.931 for optic nerve head vessel density and 0.956 for papillary area vessel density respectively (Table 5). | study | 100.0 |
The area under the ROC for differentiating normal and pre-perimetric glaucoma eyes was 0.956 for papillary area vessel density. The ROC curves showed that the cutoff point was 91.3% for peripapillary vessel density (94% sensitivity), 86.5% for optic nerve head vessel density (94% sensitivity) and 92.15% for papillary area vessel density (100% sensitivity) between the controls and POAG eyes at 95% specificity values (Table 6). | study | 100.0 |
We also performed the analysis of the area under the ROC for glaucoma and suspect eyes and it was 0.778 (p = 0.06) for peripapillary vessel density, 0.789 (p = 0.05) for optic nerve head vessel density. Since the p-values are almost statistically significant, it is possible that expanding the study cohort to include more eyes might reveal that optic nerve head vessel density can differentiate pre-perimetric glaucoma from perimetric glaucoma. | study | 100.0 |
Area under the ROC for mean RNFL in POAG and controls, POAG and suspects, suspects and controls were 0.916 (p< 0.0001), 0.711 (p = 0.042) and 0.772 (p = 0.022) respectively. This result reveals that RNFL thickness is a good parameter with high sensitivity and specificity values for differentiating glaucoma from pre-perimetric glaucoma and normal eyes. | study | 100.0 |
In the POAG group, multiple linear regression analysis in which the vessel density was considered as the dependent variable, was performed. RNFL thickness was found as a predictor of optic nerve head vessel density. Age, IOP, VF MD, VF PSD and C/D area ratio were not significant explanatory variables when grouped with RNFL thickness in the multivariate models. This showed that optic nerve head vessel density in POAG group were more strongly linked to RNFL thickness than to any other variables (p = 0.65 for peripapillary vessel density, p = 0.04 for optic nerve head vessel density and p = 0.112 for papillary area vessel density). | study | 100.0 |
In our current study using OCT angiography, we were able to demonstrate lower retinal vessel densities for eyes with mild glaucoma compared with normal eyes, as well as, compared with pre-perimetric glaucoma eyes. In addition, one of the most revealing findings in our study is that we were able to demonstrate lower retinal vessel densities in our pre-perimetric glaucoma group (which includes patients with essentially normal perimetry testing but mild reduction in OCT-derived RNFL thickness measurements) compared with normal eyes. Thus, we were able to distinguish patients with pre-perimetric glaucoma from age-matched normal eyes using OCTA-derived vessel density measurements, and found correlation with mild reduction in OCT-derived RNFL thickness measurements. This may have important implications in increasing our understanding of the pathophysiology of glaucoma and its relationship with retinal vasculature, as previously suggested and in agreement with previous studies [10–13]. Furthermore, having novel structural parameters such as OCTA-derived retinal vascular measurements, in addition to the currently used RNFL and neuro-retinal rim thickness measurements, may enhance the clinician’s ability to detect early/mild glaucoma–which can often be challenging in the clinical setting, especially in cases with normal perimetry testing. | study | 99.94 |
We defined functional loss in our study as seen in standard automated perimetry which is widely used in the clinical setting and it has been shown previously that structural parameters such as loss of RNFL thickness can be typically identified before perimetric loss is detected since structural loss can precede functional loss by years . A lower vessel density found in our cases of early POAG and even pre-perimetric glaucoma eyes suggests that the retinal vasculature attenuation may start early in the course of glaucomatous disease cascade. Previously, Kerr et al reported that patients with untreated POAG had a reduction in lamina cribrosa and temporal neuroretinal rim blood flow compared to patients with ocular hypertension. They also indicated that reduced ONH blood flow may be an early event in glaucoma which is consistent with many studies . Additionally, Pareira et al evaluated retinal vessel density in a 3.46 mm circle with scanning laser ophthalmoscope from a Fourier domain OCT and concluded that vessel density may have a clinically relevant influence on the RNFL distribution. Thus, it is possible that retinal vessel attenuation may become a parameter that is particularly useful for detection of early glaucomatous disease. Future studies that include more advanced levels of glaucoma and longitudinal data analyses are needed to determine whether vessel density continues to be correlated to functional loss. | study | 100.0 |
There is evidence that altered optic nerve head blood flow may play a role in the development and progression of glaucoma. A large number of clinical studies have been performed to evaluate this issue by using different techniques for the assessment of ocular blood flow [19–22]. | review | 99.25 |
Previously, fluorescein angiography (FA) has been demonstrated to evaluate perfusion defects of the optic disc of patients with glaucoma and were correlated with visual field alterations . FA, however, is not commonly used for detection and monitoring of glaucoma because of its invasive nature and the difficulty in quantification. Scanning laser doppler flowmetry was used in a study to investigate the ocular hemodynamic effects of patients with glaucoma or ocular hypertension and there was no significant difference in ocular blood flow parameters between patients with POAG and those with ocular hypertension. Laser doppler flowmetry and laser speckle flowgraphy, which are noninvasive techniques, have also been reported as measures of disc perfusion. In a previous study, laser doppler flowmetry showed no significant difference in blood flow parameters of the optic nerve head between POAG patients and glaucoma suspects. However, in the same study, blood flow in the optic cup, superior-temporal rim and the inferior-temporal rim was found to be significantly lower in glaucoma suspects than in healthy control subjects . Laser speckle flowgraphy is a noninvasive instrument that has been used to assess intraocular circulation and was also able to show a reduction in the microcirculation of the optic nerve head of glaucoma patients . However at current scan speeds, OCT angiography is not able to measure actual blood flow, but it can measure the caliber of vessels, suggesting that the angiography-based measurements mainly gives information about the disc and retinal microvasculature. Using OCTA, our study did find statistically significant differences in vessel density between glaucoma suspect and normal control groups. Previously, OCT angiography on a swept-source system reported the ability to differentiate glaucomatous eyes from normal eyes based on quantitative analysis of the findings . Some OCT angiography studies found a decrease in the disc / peripapillary flow index and vessel density in the glaucomatous eyes, which was correlated with the severity of glaucoma damage. It was concluded that OCT angiography disc blood flow calculations showed excellent correlation with the severity of glaucoma . Wang et al reported that altered flow index and vessel density values may be good indicators of eyes with severe open angle glaucoma . Our study adds to these findings since our results showed that vessel density changes may be seen even in very early phases of glaucomatous disease. | study | 99.94 |
The current study’s results of lower vessel density values in glaucoma vs age-matched normal eyes are in agreement with previous swept-source OCT studies [6, 12, 13] and with previous studies . Additionally, a statistically significant difference was found between POAG and pre-perimetric glaucoma groups for peripapillary and optic nerve head vessel density. Therefore, structural vascular changes may play an important role in POAG pathophysiology. Further, vessel density was found to be closely correlated to the structural loss and visual field loss in POAG eyes. The multiple linear regression analysis was performed in POAG group and it was found that optic nerve head vessel density in POAG group was more strongly linked to RNFL thickness than to any other variables. The close correlation between the optic nerve head vessel density and visual field MD and PSD, and OCT mean RNFL thickness suggests that OCT angiography needs to be investigated for correlation with possible progression of glaucoma. Additionally, the peripapillary, optic nerve head and papillary area vessel density was lower in pre-perimetric glaucoma group than in control group, showing that vessel density measurements may have the potential to be used for differentiating suspect eyes from healthy eyes. | study | 99.94 |
Previous OCT angiography reports showed that flow index and vessel density decreased in glaucomatous eyes with different OCT technologies than that employed in our study. In fact, similar results between different OCT angiography devices gives more support to our results examining retinal vascular changes in the cascade of glaucoma. | study | 100.0 |
In our study, we compared sensitivity at 95% specificity value to find out if the variables deviate significantly from a normal distribution. The specificity for papillary area vessel density and mean RNFL thickness of POAG eyes was fixed at the same level (95%), while the sensitivity for papillary area vessel density was high (100%) and same as the mean RNFL thickness (100%). So the papillary area vessel density may be used as a potentially reliable diagnostic parameter to differentiate normal eyes from POAG eyes. Area under the ROC curve for differentiating normal and POAG eyes for papillary area vessel density (0.956 (p<0.001)) showed that this parameter may have potential to be used in challenging conditions for glaucoma diagnosis. In agreement with other OCT angiography studies , our current study also had high intra-visit repeatability and high inter-visit reproducibility. | study | 100.0 |
Even though we found a strong correlation between reduced RNFL thickness and attenuated retinal vascular density, there were significant limitations to our study. One important limitation was that we were not able to measure retinal blood flow index; this was due to a software limitation of our OCT device. Additionally, this software had a projection artifact from superficial blood vessels to deeper tissue levels which prevented us from separately measuring superficial and deep ONH vessel density. As OCT angiography technology evolves with automated calculation of retinal blood flow, it would be useful to correlate retinal vessel density to retinal blood flow. Optic nerve head area does not have a basement membrane, and cupping varies significantly between normal individuals and glaucoma patients. To evaluate this area specifically, it would be useful to measure prelaminar vessel density in future studies with more advanced software. Another important limitation of our study is that we cannot rule out the effect of glaucoma and blood pressure medications on our measurements. Most patients in our glaucoma group were receiving multiple ocular antihypertensive eye drops. In our statistical analyses of our sample, use of an ocular antihypertensive eye drop or any subclass was not correlated with the vessel density measurements but our study may not have been powered to detect such a difference. Therefore, it is not possible to determine their individual effects on vessel density with our small sample size, and we cannot entirely rule out the possibility that the glaucoma drops could somehow be responsible for the vascular changes. We consider this less likely because glaucoma medications were previously reported to have been no significant effect . We are planning to perform a study in the future to remove the confounding effects of the drugs. In addition, although our results noted statistically significant differences between groups, our sample size was not very large and additional patient recruitment will likely address this shortcoming. All these measurements were taken on initial visits with the patient before treatment was initiated, if any. The current study is cross-sectional and needs longitudinal follow-up to further assess associations over time. Furthermore, we included both large and small vessel measurements in our data analysis, and thus cannot comment on the relative contributions of micro- vs. macro-vessels towards their individual contributions to glaucoma. | study | 100.0 |
To the best of our knowledge, our study is the first to report that retinal blood vessel density measures derived using non-invasive OCT angiography showed a stepwise decrease from normal eyes to pre-perimetric glaucoma eyes to mild POAG eyes. Furthermore, this difference in vessel density was seen in all three of the anatomic sites measured: optic nerve head, papillary, and peripapillary regions. Importantly, this difference was seen even though the POAG group in our study consisted only of mild disease, and lower retinal vessel density was also noted in our pre-perimetric glaucoma group. This shows that OCT angiography may provide new structural parameters that could potentially be used by clinicians to diagnose glaucoma at earlier stages. Our data suggest that retinal vascular changes may develop early in the glaucomatous process and may not develop solely as a result of advanced glaucoma damage. Even though the importance of retinal blood flow in glaucoma has been reported in prior research studies [10–24], the inability to get easily acquired measurement values has limited the wide-spread utility and applicability of these parameters in a clinical setting until now. Reduced retinal vessel density and/or retinal blood flow measurements using the evolving OCT angiography techniques may provide additional parameters that can be utilized in a clinical setting for glaucoma diagnosis and management. | study | 99.94 |
High-density genetic linkage maps are essential for genetic and genomic research in crops1–4. Molecular breeding is more effective if the molecular map is dense to provide more choices in the quality and type of markers and to increase the probability of detecting polymorphic markers in important chromosomal intervals. In wheat, the large genome size (17 gigabases), hexaploid nature (AABBDD), high percentage of repetitive regions and low level of polymorphism have complicated the acquisition of high-resolution genetic maps by molecular markers1–4. To date, several kinds of molecular markers, including restriction fragment length polymorphism (RFLP)5, 6, amplified fragment length polymorphism (AFLP)7, simple sequence repeats (SSRs)8, 9, and diversity array technology (DArT)3, 4, 10, 11 have been used to construct genetic linkage maps in wheat. Information regarding wheat molecular markers and genetic maps is available in some datasets such as GrainGenes 2.0 (https://wheat.pw.usda.gov/GG3/), URGI (https://urgi.versailles.inra.fr/), etc. Most of these markers are mapped on the telomeric regions, and there is very limited map resolution in proximal part of the chromosomes3. Therefore, the density and coverage of the current genetic maps are less than satisfactory. | review | 99.6 |
Single-nucleotide polymorphisms (SNPs) are the most abundant type of molecular marker. Accurate and reliable methods have been developed to perform high-throughput genotyping based on SNPs12. With the development of new sequencing technologies, increasing numbers of SNPs have been discovered in wheat1, 2, 13–15. Cavanagh et al.12 released a hexaploid wheat consensus genetic map with 7504 SNP markers from the Wheat9K SNP array using a combination of seven mapping populations. Wang et al.16 mapped 46977 SNPs from the Wheat90K array to the hexaploid wheat genetic map using a combination of eight mapping populations. Using both Wheat9K and Wheat90K arrays, Maccaferri et al.17 released a high-density tetraploid wheat consensus genetic map with 30144 markers (including 26626 SNPs and 791 SSRs) by integrating 13 data sets from independent biparental mapping populations. Recently, Winfield et al.18 documented a hexaploid wheat consensus map with 56 505 SNP markers from the Wheat820K array, spanning 3739 cM in length, using three independent biparental populations. However, although a high-density hexaploid wheat genetic map (>100 000 markers) based on an individual biparental mapping population would be valuable for further genetic research, such as high-resolution mapping and map-based cloning of a targeted major quantitative trait locus (QTL), no such map has been released. | study | 74.7 |
A new Whole Genome Shotgun (WGS) assembly of the Chinese Spring (CS) reference wheat genome is now available (http://plants.ensembl.org/index.html; https://urgi.versailles.inra.fr/download/iwgsc/IWGSC-WGA_Sequences/). However, genetic and genomic studies in wheat continue to lag behind the research in other members of the grass family (Gramineae), such as rice and maize. The gradual enrichment of SNP markers and the sequences released for CS (https://urgi.versailles.inra.fr/download/iwgsc/IWGSC-WGA_Sequences/), Triticum urartu 2 and Aegilops tauschii 1 have facilitated comparative genomic analysis in wheat. Comparative genomic analysis with species whose genomes have been well characterized has been used as an effective method for the construction of high-resolution genetic linkage maps of target wheat genes and for the prediction of candidate genes in regions of interest. For instance, the construction of high-density genetic maps has facilitated the mapping of the gene grain protein content-B1 (Gpc-B1)19, the yellow rust resistance gene Yr36 20 and the powdery mildew resistance gene Pm41 21. Comparative genomics studies have also furthered the understanding of the basic processes of genome evolution and the transfer of information from model species to related organisms and facilitated the cross-referencing of various types of information, such as QTLs, mutants, and gene expression22, 23. These correlations and integrations will take full advantage of the collective intellectual contributions from scientists across many disciplines22. | review | 99.9 |
Wheat660K, the Affymetrix® Axiom® Wheat660, was designed by the Chinese Academy of Agricultural Sciences and synthesized by Affymetrix. This Wheat660K SNP array is genome-specific with high density and is highly efficient with a wide range of potential applications (http://wheat.pw.usda.gov/ggpages/topics/Wheat660_SNP_array_developed_by_CAAS.pdf). However, genetic position in relation to Wheat660K SNPs has not yet been documented. In this work, for the first time, we report a high-density map for wheat constructed from this Wheat660K SNP Array. Based on SNP flanking sequences, we assigned SNPs to the genome assembly of T. aestivum cv. Chinese Spring (CS) (https://urgi.versailles.inra.fr/download/iwgsc/IWGSC-WGA_Sequences/). We also compared our high-density genetic map with the consensus genetic maps of Wheat90K and Wheat820K based on the common contigs assembled in the chromosome survey sequencing (CSS) project. Comparative genomic analyses based on the mapped SNP flanking sequences and the corresponding contig sequences were also performed with the genomes of Brachypodium distachyon, Oryza sativa, Zea mays, and Sorghum bicolor. Using this mapping resource along with the phenotypic data, we identified important QTLs for yield-related trait. A major stable QTL for kernel number was identified and then characterized in detail based on the high-density genetic map and comparative genomic analysis. | study | 100.0 |
An F8:9 recombinant inbred line (RIL) population including 188 lines derived from a cross between Kenong 9204 (KN9204) and Jing 411 (J411) (denoted as KJ-RILs), 65 KN9204-derived advanced lines/authorized varieties, three parental lines of KN9204, three control varieties from the Winter Wheat Performance Trial of the Northern Huang-Huai Regional Nursery of China, and Chinese Spring (CS) were genotyped using the 630517 SNPs on the Wheat660K SNP array as probes. The sample call rates ranged from 18.6% to 100.0%, with an average of 98.9% for the 265 accessions (data not shown). The scores for the probes were classified into one of the following six categories according to the cluster patterns produced by the Affymetrix software (Table S1): (i) Poly High Resolution (PHR) (188040; 29.8%); (ii) No Minor Homozygote (NMH) (133246; 21.1%); (iii) Off-Target Variant (OTV) (18471; 3.0%); (iv) Mono High Resolution (MHR) (163308; 25.9%); (v) Call Rate Below Threshold (CRBT) (22635; 3.6%); and (vi) Other (91425, 14.5%). Only the first three groups (PHR, NMH and OTV) were considered useful, and they accounted for 53.9% of the Wheat660K SNP array probes. | study | 100.0 |
Of the 339757 functional SNPs, 136973 (40.3%) were polymorphic between KN9204 and J411. Of these, 8407 had more than 10% missing data in the 188 KJ-RILs, and were removed from the linkage analysis. Among the remaining 128566 SNPs, 90567 (70.4%) were transitions, and 37999 (29.6%) were transversions. The 128566 functional SNPs and the previously reported 591 loci3 were used for linkage analysis and map construction. The 129157 markers fell into 5175 bins, and to create a chromosome frame, only one marker was selected as a representative from each bin. | study | 100.0 |
The 5175 bin markers were used for linkage analysis based on their scores in the 188 KJ-RILs. In total, 4959 bin markers were mapped to the wheat genetic map. The co-segregated markers (redundant markers) were then added to the high-density genetic map based on information of bin serial number and the genetic information of the corresponding bin markers. A high-density genetic map with 119566 loci spanning 4424.4 cM was constructed (Table S2). Of these loci, 119001 were SNP markers derived from the Wheat660K SNP array, and the remaining 565 markers were reported previously by Cui et al.3. Of the 119001 SNPs, 83953 (70.5%) were transitions, and 35048 (29.5%) were transversions (data not shown). Most markers were mapped to the B (44.6%) and A genomes (43.7%), and only 11.7% markers were mapped to the D genome. For the map lengths, the A, B, and D genomes covered 36.4%, 27.7%, and 35.8% of the total map length, respectively. The chromosome sizes ranged from 84.4 cM (chromosome 1BL) to 289.1 cM (chromosome 5D), averaging 210.7 cM per chromosome. The number of markers on each chromosome ranged from 78 (chromosome 1BL) to 13898 (chromosome 3B), with a mean of 5693.6 loci per chromosome. Due to the 1BL/1RS translocation of KN9204, the 1RS- or 1BS-specific markers not only showed co-segregation but also exhibited distorted segregation in the KJ-RILs as shown by Cui et al.3. These markers were excluded from the linkage analysis and map construction, which reduced the genetic maps for analysis to 1BL only. In addition, no markers on chromosome 5BS were polymorphic between KN9204 and J411, resulting in the release of the 5BL genetic map only. Of the 119566 loci, 33598 (28.1%) were distributed on chromosomal regions near the centromeres. Marker density per genetic distance unit peaked at the centromeric regions, possibly due to a combination of low recombination rate in the centromeric regions and even gene distribution along the chromosomes (Fig. 1). The 4 959 bin markers are shown in the genetic map (Fig. 2). The following mapping-bin sets were observed: approximately 3.7% and 3.2% of the markers were unique for genomes A and B, respectively, and approximately 9.6% of the markers for the D genome showed unique segregation patterns.Figure 1Distribution of the 119 566 loci on the 21 wheat chromosomes. The 21 circles indicate the 21 wheat chromosomes, with 7D to 1D, 7B to 1B, and 7A to 1A from inside to outside, respectively. Each chromosome was evenly divided into 100 segments based on the map length, which are shown with different colours. The arc lengths indicate the percentage of markers on each segment (The total number of markers on the corresponding chromosome divided by the number of markers on the segments). The arc in red colour indicates the chromosomal bins near the centromere. The upper left histogram in the circle indicates the distribution of markers on each chromosome; the upper right histogram in the circle indicates the distribution of markers near the centromere on each chromosome; the bottom right histogram in the circle indicates the distribution of map length on each chromosome; the bottom left histogram in the circle indicates the distribution of markers in/near the coding sequences. Figure 2The high-density genetic map of wheat developed using an RIL population derived from the cross of cultivars KN9204 and J411. For the redundant loci that showed co-segregation in the 188 KJ-RILs, only one unique informative marker is shown in this figure. The approximate positions of the centromeres are indicated by pink. Short arms are at the top. The positions of the marker loci are indicated using a ruler on the left side of this figure. The names of the marker loci are listed to the right of the corresponding chromosomes. Loci in red were best hits to Chinese Spring (CS) contigs of the short arm of the corresponding chromosomes. Loci in green were best hits to CS contigs of the long arm of the corresponding chromosomes. Loci in black were unknown. Contigs from chromosome 3B were not separated into short/long arm bins, as individual arm datasets were not generated for this chromosome in the Chromosome Survey Sequence (CSS) project. | study | 100.0 |
Distribution of the 119 566 loci on the 21 wheat chromosomes. The 21 circles indicate the 21 wheat chromosomes, with 7D to 1D, 7B to 1B, and 7A to 1A from inside to outside, respectively. Each chromosome was evenly divided into 100 segments based on the map length, which are shown with different colours. The arc lengths indicate the percentage of markers on each segment (The total number of markers on the corresponding chromosome divided by the number of markers on the segments). The arc in red colour indicates the chromosomal bins near the centromere. The upper left histogram in the circle indicates the distribution of markers on each chromosome; the upper right histogram in the circle indicates the distribution of markers near the centromere on each chromosome; the bottom right histogram in the circle indicates the distribution of map length on each chromosome; the bottom left histogram in the circle indicates the distribution of markers in/near the coding sequences. | study | 99.94 |
The high-density genetic map of wheat developed using an RIL population derived from the cross of cultivars KN9204 and J411. For the redundant loci that showed co-segregation in the 188 KJ-RILs, only one unique informative marker is shown in this figure. The approximate positions of the centromeres are indicated by pink. Short arms are at the top. The positions of the marker loci are indicated using a ruler on the left side of this figure. The names of the marker loci are listed to the right of the corresponding chromosomes. Loci in red were best hits to Chinese Spring (CS) contigs of the short arm of the corresponding chromosomes. Loci in green were best hits to CS contigs of the long arm of the corresponding chromosomes. Loci in black were unknown. Contigs from chromosome 3B were not separated into short/long arm bins, as individual arm datasets were not generated for this chromosome in the Chromosome Survey Sequence (CSS) project. | study | 100.0 |
Considering the unique markers (the 4 959 bin markers), the highest marker saturation was found in genome A (39.3%), followed by genomes B (33.6%) and D (27.1%). The average distance between adjacent bin markers ranged from 0.6 cM for 6B to 1.5 cM for 4D, with an average of 0.9 cM per marker pair. Gaps greater than 20.0 cM but less than 30.0 cM were present in chromosomes 3 A (24.9 cM), 3B (21.9 cM) and 4D (20.4 cM); gaps greater than 10 cM but less than 20.0 cM were present in chromosomes 1 A (13.9 cM, 11.7 cM), 2 A (14.6 cM), 2B (10.6 cM), 2D (10.5 cM), 3 A (19.9 cM, 19.2 cM), 3D (12.1 cM), 4D (11.5 cM, 11.2 cM, 11.0 cM, 11.0 cM), 5 A (12.1 cM), 5D (13.2 cM), 6 A (19.2 cM), 6B (13.8 cM, 13.2 cM, 12.7 cM), 6D (14.6 cM, 13.6 cM, 10.9 cM, 10.7 cM), 7B (16.3 cM, 10.1 cM), and 7D (18.8 cM) (Fig. 2). | study | 100.0 |
Of the 119566 loci, 118998 (99.5%) were best hits to 57036 CSS contigs, with 2.1 polymorphic markers per contig. In total, 93.0% contigs had coincident physical and genetic positions, 4.6% were mapped to homoeologous chromosomes such as 1 A in physical position and 1B in the KJ-RIL genetic map, and 2.4% were disordered (Fig. 2; Figure S1; Table S3). Based on the SNP flanking sequences, we assigned 116 261 SNPs to the recently released wheat genome assembly. SNP order in the present genetic map was in good agreement with that in the wheat genome assembly, with the exception of chromosome 7DL, in which a segment inversion was identified (Fig. 3; Figure S2; Table S3).Figure 3Schematic representation of the syntenic relationships between any one marker in wheat genetic and physical maps. Gen-1A to Gen-7D represent the 21 wheat chromosomal genetic maps released in this paper; Phy-1A to Phy-7D represent the 21 wheat chromosomal physical maps, which were constructed by assigning 116 261 SNPs to the wheat genome assembly using SNP flanking sequences as the query. For the redundant loci that showed co-segregation in the 188 KJ-RILs, only one unique informative marker is shown in this figure. | study | 100.0 |
Schematic representation of the syntenic relationships between any one marker in wheat genetic and physical maps. Gen-1A to Gen-7D represent the 21 wheat chromosomal genetic maps released in this paper; Phy-1A to Phy-7D represent the 21 wheat chromosomal physical maps, which were constructed by assigning 116 261 SNPs to the wheat genome assembly using SNP flanking sequences as the query. For the redundant loci that showed co-segregation in the 188 KJ-RILs, only one unique informative marker is shown in this figure. | study | 99.9 |
An overview (from the wheat genetic map perspective) of the relationships between the five grass family genomes at the resolution of the genetic map in centimorgans is provided in Figures S3 and S4. There were 113288 markers that corresponded to the CDS of Brachypodium (65357 markers) (Table S4; Figure S3a, S4a), rice (58825 markers) (Table S4; Figures S3b, S4b), maize (53745 markers) (Table S4; Figures S3c, S4c), and sorghum (59994 markers) (Table S4; Figures S3d, S4d). In general, chromosomes belonging to the same homoeologous groups showed correspondence with the same grass chromosomes, but some differences were observed. Large and especially small synteny blocks across wheat and grass family chromosomes were observed, indicating the complexity of the wheat genome among the grass family genomes. The structural relationships among the genomes indicate that for some individual wheat chromosomes, there is a preponderance of corresponding grass genes from one or two certain linkage groups. For example, wheat chromosomes 2 A/2B/2D corresponded to Bd5, wheat chromosomes 3 A/3D corresponded to Bd2 and Bd3, wheat chromosomes 4 A/4B/4D corresponded to Os3, and wheat chromosomes 7 A/7B/7D corresponded to Sb10. However, for most synteny blocks, the chromosomes were more fragmented and scattered, with a high frequency of breakdown. | review | 99.56 |
To specify the coding-region SNPs (cSNPs), perigenic SNPs (pSNPs), and intergenic SNPs (iSNPs) among the SNP markers mapped in the KJ-RIL genetic map, a BLASTX search was performed against the CDS of T. aestivum, using SNP flanking sequences and the corresponding contig sequences as queries. Using SNP flanking sequences as the query, 8.9% (10104 SNPs) were best hits to the CDSs of the T. aestivum, and these were considered to be the cSNPs. When using the contig sequences where the markers were best hits to as the query, approximately 36.8% (41689 SNPs) were best hits for the CDSs of the T. aestivum, indicating that 27.9% (36.8–8.9%) of SNPs were pSNPs. The remaining 63.2% are likely iSNPs (Table S5). | study | 100.0 |
Using the mapping resource along with the phenotypic data we identified important QTLs for yield-related trait. A major stable QTL for kernel number per spike (KNPS; qKnps-4A) was verified in 10 environments by using MapQTL 6.0, IciMapping 4.1, and QTLNetwork 2.0 (Table 1; Fig. 4; Figures S5, S6). qKnps-4A contributed to 8.0–21.2% of the KNPS phenotypic variation in the 188 KJ-RILs, with alleles from J411 increasing kernels per spike by 1.2–2.4. The QTL peaks of qKnps-4A were in 149.8–155.0 cM, 149.7–154.5 cM, and 149.8 cM, as detected by MapQTL 6.0, IciMapping 4.1, and QTLNetwork 2.0, respectively, with Ax-109844107–Ax-110540586 in 149.6–150.3 cM in the overlapping confidence intervals. Thus, we predicted that the candidate genes for qKnps-4A might be within the overlapping confidence intervals of 0.7 cM. Based on the genome assembly of T. aestivum cv. CS (https://urgi.versailles.inra.fr/download/iwgsc/IWGSC-WGA_Sequences/), the overlapping confidence intervals of Ax-109844107–Ax-110540586 spanned 3.23 Mb (4 A:680398739–4 A:683638403) in physical position (Figure S7). This region harbours 65 predicted genes in wheat (Figure S8), which might include the candidate gene for qKnps-4A.Table 1 qKnps-4A as detected by MapQTL 6.0, IciMapping 4.1, and QTLNetwork 2.0.SoftwareLOD valuePosition (cM)Additive effectPVE %MapQTL 6.03.4 to 9.7149.8 to 155.0−1.5 to −2.48.0 to 21.2IciMapping 4.12.8 to 16.8149.7 to 154.5−1.2 to −2.49.5 to 20.3QTLNetwork 2.0 P value: 0.000000149.8−1.910.9 Figure 4Overlapping confidence intervals of QTLs for kernel number per spike (KNPS) based on MapQTL 6.0, IciMapping 4.1 and QTLNetwork 2.0. The segments in red are the overlapping confidence intervals of qKnps-4A, and the overlapping flanking markers of qKnps-4A are indicated by triangles in red, which were detected by QTLNetwork 2.0 based on the combined 10 environmental phenotypic values. | study | 100.0 |
Overlapping confidence intervals of QTLs for kernel number per spike (KNPS) based on MapQTL 6.0, IciMapping 4.1 and QTLNetwork 2.0. The segments in red are the overlapping confidence intervals of qKnps-4A, and the overlapping flanking markers of qKnps-4A are indicated by triangles in red, which were detected by QTLNetwork 2.0 based on the combined 10 environmental phenotypic values. | study | 99.94 |
The high-density SNP map developed in the present study first documented the genetic positions of 119 001 SNPs from the Wheat660K SNP array. Based on the SNP flanking sequences, we assigned 118 785 SNPs to 56 904 CSS-assembled contigs (Table S3). The physical positions of the corresponding CSS-assembled contigs could be used to validate genetic position (chromosome and chromosomal arms assignment). As shown in Fig. 2 and Table S3, the physical and genetic positions of these mapped markers were generally in agreement. | study | 100.0 |
In previous studies, 7504 SNPs from the Wheat9K SNP array12, 46977 SNPs from the Wheat90K array16, and 56505 SNPs from the Wheat820K SNP array18 were genetically mapped to the hexaploid wheat genome. These SNPs have also been physically assigned to the corresponding CSS-assembled contigs. Based on the common CSS contigs, we analysed the synteny of the mapped SNPs (Wheat660K SNP array vs. Wheat90K array and Wheat660K SNP array vs. Wheat820K SNP array) across different mapping populations (Figs 5 and 6). These common contigs were generally aligned with the chromosomes in a consistent order across different mapping populations, verifying the accuracy and credibility of our high-density genetic map. Regarding the total map length, the Wheat90K/820 SNP consensus integrative genetic map is 2.4/1.9 times longer (data not shown) than that of the present map. The increased genetic map length is proportionate to the increased mapping population size16, 18. A relatively small mapping population size resulted in limited identification of recombination events and lower resolution of the genetic map, contributing to the short map length in this study24, 25. In addition, comparative analysis among these three SNP maps revealed that genetic maps of chromosomes 3 A, 4B, 5D, 6D, and 7B derived from the wheat 820 K SNP array were inverted, with long arms at the top and short arms at the bottom (data not shown)18. Chromosome 4 A, derived from the Wheat820K SNP array, might be involved in chromosomal rearrangement compared with the genetic maps derived from the Wheat90K SNP array and our Wheat660K SNP array. The genetic information from the common contigs and their genetic collinearity analysis across different mapping populations will lay the foundation for obtaining a consensus integrative genetic linkage map.Figure 5Synteny of the mapped SNPs from the Wheat660K SNP array (in blue) with that from the Wheat90K array (in red) based on their common CSS-assembled contigs. Figure 6Synteny of the mapped SNPs from the Wheat660K SNP array (in blue) with that from the Wheat820K array (in green) based on their common CSS-assembled contigs. Notes: The genetic maps of chromosomes 3A, 4B, 5D, 6D and 7B derived from the Wheat820K SNP array are shown in inverted form, with long arms at the top and short arms at the bottom18. In this figure, these inversions were corrected. | study | 100.0 |
Synteny of the mapped SNPs from the Wheat660K SNP array (in blue) with that from the Wheat820K array (in green) based on their common CSS-assembled contigs. Notes: The genetic maps of chromosomes 3A, 4B, 5D, 6D and 7B derived from the Wheat820K SNP array are shown in inverted form, with long arms at the top and short arms at the bottom18. In this figure, these inversions were corrected. | study | 99.94 |
The SNP order in the present KJ-RIL genetic map was also in good agreement with that in the physical position, with the exception of chromosome 7DL, in which a segment inversion was identified (Fig. 3; Figure S2; Table S3). A previous report showed that wheat chromosome 7 was likely to involve a complex interchange26. These findings prompted us to search for candidate genes of targeted major QTLs. In wheat, a majority of recombination events occurred on the most distal portions of the chromosomal arms, whereas the recombination events tend to be suppressed around the centromere3, 27, 28. These characteristics result in a low resolution of the genetic map in the centromeric region, which was evident in the small genetic distance in the KJ-RIL genetic map corresponding to a large physical region around the centromere compared with the most distal portions of the chromosomal arms (Fig. 3; Figure S2; Table S3). These findings also indicate the difficulty of high-resolution mapping and map-based cloning of a QTL around the centromere because of the low coverage of genetic markers and the suppression of recombination events. | study | 100.0 |
The genetic and genomic research of wheat has lagged behind similar research regarding other important crops, such as rice and maize29. Conservation of gene identity and collinearity among gramineous plants will depend on the rates of genome/gene evolution and rearrangement in the investigated species22. There is a high level of genome-synteny among gramineous plants, especially wheat, Brachypodium and rice, with wheat being more closely related to Brachypodium than to rice22, 23, 30–32. Wheat improvement programs can benefit from the use of comparative genetics to transfer information about genes from model species to wheat, to help identify genes controlling traits of interest, and to assess within-species allelic diversity so that the best alleles can be identified and assembled in superior varieties. | study | 99.75 |
In this study, synteny analyses among common wheat, Brachypodium, rice, sorghum and maize genomes were performed based on the collinearity of the corresponding orthologous genes (best hits of CDSs) (Table S4; Figures S3 and S4). Features of the wheat-grass genome relationships revealed in this study included a high frequency of breakdown in microcollinearity throughout the genomes compared to the previous RFLP-based maps22, 31, 33–36. Both large and especially small synteny blocks across wheat chromosomes and grass family chromosomes were observed in this study. These features might be attributed to the higher number of markers used in this study. More recently, Russo et al.32 conducted collinearity analysis across durum wheat, Brachypodium, and rice based on a high-density durum wheat genetic map derived from the Wheat90K SNP assay. Both large and especially small synteny blocks across wheat chromosomes and grass family chromosomes were also observed in that study. Based on a high-density genetic map, we documented a wheat genome perspective of homologous sorghum and maize genome locations based on comparative sequence analysis. A wheat genome view of homologous gramineous plant genome locations based on comparative sequence analysis would considerably improve the predictability and efficiency of information transfer, and would be benefit evolutionary studies. | study | 99.94 |
Wheat yield is determined by three yield components: productive spikes per unit area, KNPS, and kernel weight, determine wheat yield. Among them, the KNPS value has steadily increased, indicating a substantial contribution of increased KNPS to increased wheat yield37, 38. Over the past several decades, numerous QTLs (or genes) for wheat kernel number have been documented based on both linkage-mapping and association-mapping analyses4, 38–54. Some of these studies have also reported QTLs for KNPS on chromosome 4 A4, 40, 44, 50, 51, 53, 54. Based on genetic marker sequence flanking for these QTLs and the recently released WGS assembly, we compared physical positions with qKnps-4A to determine whether they were common interacting QTLs or regions across genetic backgrounds. The results are shown in Supplementary Table S6. qKnps-4A from our previous research shared a confidence interval (4 A:664209916–4 A:736771450) with qKnps-4A in this study (4 A:680398739–4 A:683638403). Interestingly, qKnps-4A has been detected in both the WY and WJ populations (two related RIL populations sharing one common parental line of Weimai 8)4. QKNS.caas-4AL detected by Gao et al.54, also shared a confidence interval (4 A:632 864 778–4 A:688 093 018) with qKnps-4A in our study. These coincidences confirmed the authenticity of qKnps-4A, which should be subjected to fine mapping and map-based cloning in the future. In fact, this work is being conducted based on the secondary mapping population of qKnps-4A’s (data not shown). | study | 100.0 |
QTL mapping based on the primary mapping population could precisely characterize and locate genes underlying specific agronomic traits, which was also true for both major and moderate/minor QTLs55. Previous studies have confirmed that tagged or cloned genes were near their original QTL positions (logarithm of the odds (LOD) peak)56–67. In this study, qKnps-4A was repeatedly identified using MapQTL 6.0, IciMapping 4.1, and QTLNetwork 2.0, based on mathematical models of composite interval mapping (CIM), inclusive composite interval mapping (ICIM) and the mixed linear model approach (MLMA), respectively. In addition, the KNPS values of the 188 KJ-RILs and their parental lines were evaluated in 10 different environments. Based on the 10 environmental phenotypic values along with our high-density genetic map, we confirmed QTL peak position with the aforementioned QTL mapping software and found the Ax-109844107–Ax-110540586 overlapping confidence interval, which spanned 3.23 Mb (4 A:680 398 739–4 A:683 638 403) in physical position (Table 1; Fig. 4; Figures S5, S6, S7). In addition, qKnps-4A has been mapped to 4 A:664 209 916–4 A:736 771 450 in both the WY and WJ populations4 and to 4 A:632 864 778–4 A:688 093 018 in the Zhou 8425B/Chinese Spring population54 (Supplementary Table 7). These coincidences across different genetic backgrounds, multiple environments and diverse QTL models strongly supported the hypothesis that the genes underlying qKnps-4A are likely located within 4 A:680398739–4 A:683638403. One of the 65 predicated genes within this interval might be the candidate gene for qKnps-4A (Figure S8). This information is very valuable for future high-resolution mapping and map-based cloning of qKnps-4A. | study | 100.0 |
Using Ax-110540586 as a probe, the 188 KJ-RILs were divided into two groups, one group with alleles from KN9204 and the other with alleles from J411, to perform mean comparisons regarding KNPS. The positive alleles from qKnps-4A’s increased the KNPS value from 3.0 to 4.6, indicating a tremendous potential for their application in wheat molecular breeding programmes designed to increase yield (Figure S9). To characterize the use of qKnps-4A’s positive alleles in wheat breeding programmes, we dissected the genotypes of 65 KN9204-derived advanced lines/authorized varieties, three parental lines of KN9204, three control varieties from the Winter Wheat Performance Trial of the Northern Huang-Huai Regional Nursery of China, and CS near Ax-109844107–Ax-110540586 (Figure S10). None of KN9204′s three parental lines carried favourable alleles for increasing KNPS, which accounted for the negative alleles of KN9204 at this QTL. Only 3 (4.6%) of the 65 authorized/advanced lines derived from KN9204 lines carried favourable alleles for increasing KNPS. Interestingly, two advanced lines (KN1002-13-10 and O-97) carried heterozygous alleles, which were used to develop the secondary mapping population of Knps-4A via self-cross (data not shown). Of the three control varieties from the Winter Wheat Performance Trial of the Northern Huang-Huai Regional Nursery of China, S4185 carried negative alleles, decreasing KNPS; chromosomal regions of Knps-4A in LX99 and JM22 were recombinant regions with alleles that cannot be categorized as favourable or negative alleles that increase or decrease KNPS; and CS carried favourable alleles, increasing KNPS. These findings indicated that favourable qKnps-4A alleles have not been fully utilized in wheat breeding programmes in the Huang-Huai winter wheat region in China. Wheat breeders should strengthen the selection of qKnps-4A favourable alleles in molecular breeding programmes aimed at the development of high-yield varieties. | study | 99.94 |
In summary, this paper reports a high-density wheat genetic map based on an individual mapping population. In total, 119 001 SNP markers derived from the Wheat660K SNP array were mapped onto the KJ-RIL genetic map. We observed good good collinearity of our high-density genetic map with the Wheat90K and Wheat820K consensus genetic maps, increasing the possibility of obtaining a consensus integrative higher-density wheat genetic map in the future. This high-density genetic map is also in good accordance with the recently released wheat genome assembly. Our high-density wheat genetic map provides a major resource for future wheat genetic and genomic research. Moreover, this paper provides an overview of the structural relationships between wheat and other gramineous plant genomes based on comparative genomics analysis. Finally, a major stable QTL for kernel number was thoroughly characterized based on this high-density genetic map and comparative genomics analysis. | review | 80.1 |
In this study, F8:9 KJ-RILs derived from a cross between KN9204 and J411 were used for map construction and QTL analysis. The original KJ-RIL population contained 427 RILs. In total, 188 randomly sampled lines from the 427 KJ-RILs were used for genetic linkage analysis. In addition, 65 KN9204-derived advanced lines/authorized varieties, three parental lines of KN9204, three control varieties from the Winter Wheat Performance Trial of the Northern Huang-Huai Regional Nursery of China, and CS (Table S7) were genotyped to trace the key chromosomal segment harbouring the major stable QTL for kernel number per spike (KNPS). | study | 100.0 |
KNPS values of the 188 KJ-RILs and their parental lines were evaluated in ten different environments (five trials that included both low- and high-nitrogen treatments). The nitrogen treatments, field arrangements and experimental designs of the ten environments were performed as described previously3, 68, 69. | study | 100.0 |
For all subjects, leaf tissues were sampled. Genomic DNA was extracted and hybridized on the Wheat660K SNP genotyping array by Compass Biotechnology Company (Beijing, China). The DNA samples were prepared, and the chip genotyping was performed on the Wheat660K SNP array according to the Affymetrix Axiom 2.0 Assay Manual Workflow protocol. DNA integrity was confirmed on agarose gels, and DNA quantity was measured spectrophotometrically. The Wheat660K chip contains 630517 markers (http://wheat.pw.usda.gov/GG2/index.shtml). Variant quality from the Wheat660K chip genotyping was initially assessed according to Affymetrix best practices. The 188 RILs and their parents were aslo assayed using the ‘Wheat PstI (TaqI) 2.3D’ DArT array (the medium density array) (http://www.triticarte.com.au/). The PCR-based markers were genotyped as described in our previous study3. | study | 100.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.