Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a major risk factor for early recurrence and poor survival after curative surgical therapies. However, MVI can only be diagnosed by pathological examination following resection. The aim of this study is to identify serologic biomarkers for predicting MVI preoperatively to help facilitate treatment decisions. We used the sero-proteomic approach to identify antigens that induce corresponding antibody responses either specifically in the serum from MVI (+) patients or from MVI (−) patients. Six antigens were subsequently identified as HSP 70, HSP 90, alpha-enolase (Eno-1), Annexin A2, glutathione synthetase and beta-actin by mass spectrometry. The antibodies titers in sera corresponding to four of these six antigens were measured by ELISA and compared between 35 MVI (+) patients and 26 MVI (−) patients. The titers of anti-HSP 70 antibodies were significantly higher in MVI (−) patients than those in MVI (+) patients; and the titers of anti-Eno-1 antibodies were significantly lower in MVI (−) patients than those in MVI (+) patients. The results were subjected to multivariate analysis together with other clinicopathologic factors, suggesting that antibodies against HSP 70 and Eno-1 in sera are potential biomarkers for predicting MVI in HCC prior to surgical resection. These biomarkers should be further investigated as potential therapeutic targets. Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third most common cause of cancer deaths worldwide . The recIt is reported that the rate of tumor recurrence within 1 year following curative resection in MVI (+) HCC patients was significantly higher than that in MVI (−) patients . MVI is MVI was defined as the microscopic evidence of clusters of cancer cells observed in vessels located in the tumor capsule and/or in surrounding liver parenchyma . MVI wouThe clinicopathologic features of all 61 HCC patients with or without MVI are summarized in Table We extracted cell lysates from tumor tissue and paratumoral normal liver tissue of an MVI (+) HCC patient and an MVI (−) HCC patient, respectively, and subjected it to the Western blot by the serum from an MVI (+) patient. Protein bands on the blot of MVI (+) tumor tissue were similar to, but not completely, the same as those on the blot of MVI (−) tumor tissue. As anticipated, protein bands on the blot of MVI (+) paratumoral normal liver tissue are also similar to those on the blot of MVI (−) paratumoral normal tissue. By contrast, bands are more obviously different between the blots of tumor tissue and paratumoral normal liver tissues Figure . These rWe extracted cell lysates from tumor tissue of an MVI (+) HCC patient and subjected it to the Western blot by serum from 10 patients selected from the cohort randomly, including 7 MVI (+) and 3 MVI (−) patients, respectively. Specific autoantibodies appear to be present in sera of MVI (+) or in those of MVI (−) patients Figure .We extracted cell lysates from HCC cell line QGY-7703 and subjected it to the Western blot by serum from the same 10 patients selected from the cohort randomly, including 7 MVI (+) and 3 MVI (−) patients, respectively. Using the proteome of HCC cell line as antigens, we also found that specific autoantibodies appear to be present in sera of MVI (+) or in those of MVI (−) patients Figure .We thus attempted to identify the autoantibodies and their antigens associated with the status of MVI by following the sero-proteomic study schema described in Figure p value of 0.0419  and 35 MVI (+) HCC patients, respectively. The median relative titers of anti-HSP 70 antibody were 6.517 and 3.537 in the sera of MVI (−) and MVI (+) patients' serum, respectively. The relative titer of anti-HSP 70 antibody in the MVI (−) patients was significantly higher than that in the MVI (+) patients with a 9 Figure . The med0 Figure .We did not find a statistically significant difference of anti-HSP 90 antibody titer and anti-Annexin A2 antibody titer between MVI (+) and MVI (−) sera in the analysis of the initial 42 patients although a trend of difference toward anti-HSP 90 antibody titer was observed. We then measured the titers of anti-HSP 90 antibody in the sera of the entire cohort and found that anti-HSP 90 antibody titers are also significantly different between MVI (+) and MVI (−) sera . NeverthWe subsequently examined the expression of HSP 70 and Eno-1 in resected HCC specimens by immunohistochemistry and found that both HSP 70 and Eno-1 are expressed by HCC tumor cells, but minimally expressed by paratumoral normal hepatocytes . This reWe then determined whether anti-HSP 70 and anti-Eno-1 antibodies titers in patients' sera can predict the MVI status. We performed both the univariate  and multThe receiver operating characteristic (ROC) curve analysis showed that the cut-off value of titer of anti-Eno-1 antibody was 4.301 with 88.57% sensitivity and 50% specificity. The area under the ROC curve of Eno-1 was 0.7176 Figure . Thus, tOur study has demonstrated that HCC tumor tissues express antigens that can be recognized by presumably autoantibodies in sera from HCC patients. Both MVI (+) tumor tissues and MVI (−) tumor tissues express unique antigens that are recognized by autoantibodies from HCC patients. Autoantibodies appear to be different in MVI (+) patients comparing to MVI (−) patients. These results suggest that one would potentially be able to identify specific autoantibodies that are associated with MVI (+) HCC or MVI (−) HCC.Large vessel invasion can be recognized by the diagnostic imaging studies and often suggests that the patients would not benefit from upfront surgical treatments. These patients would often be treated by chemoembolization or radiation first. By contrast, MVI cannot be diagnosed preoperatively; however, it is known to be a poor prognostic factor for HCC following the current surgical treatments. Although these patients may still benefit from the surgery, neoadjuvant chemoembolization and/or radiation may be warranted to improve the outcome of the surgery. Therefore, a diagnostic biomarker to predict MVI preoperatively is highly demanded.In this study, we identified autoantibodies against HSP 70 as a potential biomarker associated with MVI (−) HCC. Whether anti-HSP 70 antibodies can be used to predict MVI will need to be examined prospectively in a larger patient population. Members of HSP 70 family play a pivotal role in assisting protein folding, preventing protein aggregating and transporting proteins across membranes under physiological conditions. HCC overexpress HSP 70 in the cytosol, present it on their plasma membrane and can be actively secreted via lipid vesicles such as exosomes into the extracellular milieu . It is rIn this study, we identified autoantibodies against Eno-1 as a potential biomarker associated with MVI (+) HCC. Whether anti-Eno-1 antibodies can be used to predict MVI will need to be examined prospectively in a larger patient population. Eno-1 is an isoenzyme of enolase, a key enzyme in the glycolytic pathway that catalyzes the conversion of 2-phosphoglycerate to phosphoenolpyruvate in the glycolytic pathway. Three enolase isoenzymes have been identified. The alpha form is present in most tissues and embryonically, the beta form is expressed in muscle tissues, and the gamma form, also known as neuron-specific enolase, is found only in neuronal tissues. Eno-1 has been detected not only in the cytoplasm but also at the membrane surface . It was In summary, this study identified two autoantibodies as potential biomarkers for predicting MVI preoperatively. Further studies are warranted to establish the usage of the titers of anti-HSP 70 and anti-Eno-1 antibodies in sera to predict MVI. HSP 70 and Eno-1 are also potential markers of MVI. Anti-HSP 70 and anti-Eno-1 antibodies may have the potential of being developed into anti-HCC therapeutics.Between 2010 and 2015, HCC patients who underwent curative resection as the first line of therapy at The Second Affiliated Hospital of Zhejiang University School of Medicine were consented and enrolled in this study. Patients who received preoperative anti-cancer therapies had been excluded. Following the surgical resection, patients whose pathology examination presented macrovascular invasion were excluded. A total of 61 HCC patients were eligible. An experienced pathologist, who was blind to the patients' clinical histories, evaluated resected HCC specimens to identify MVI on H & E slides. All the patients were divided into MVI (+) and MVI (−) groups according to pathologic evaluation. This study was approved by the Ethics Committee of The Second Affiliated Hospital of Zhejiang University School of Medicine. Written informed consent was obtained from each patient. Clinicopathologic information of the 61 patients was collected. Serum samples were obtained from patients before surgery. HCC tissues and paraneoplastic liver tissues were obtained after resection immediately. Additional serums from 10 healthy individuals were collected at the time of routine physical examination, which were used in ELISA tests.2. The cell line QGY-7703 was developed from a 35-year-old Chinese female patient of primary hepatocellular carcinoma with a history of viral hepatitis [QGY-7703 cell line was purchased from the cell bank of the Chinese Academy of Sciences , which was maintained in RPMI-1640 media supplemented with 10% fetal bovine serum in a humidified incubator at 37°C and 5% COepatitis .The total proteins of HCC tissues, normal liver tissues and QGY-7703 cells were extracted using a tissue protein extraction kit  supplemented with a complete protease inhibitor cocktail  respectively. The samples were homogenized and incubated on ice for 20 min followed by centrifugation at 10000 g at 4°C for 15 min. The supernatant was collected. Protein concentration was determined using a BCA protein assay kit . The protein sample was denatured in SDS-PAGE loading buffer in boiling water bath for 10 min. A total of 100 μg/lane proteins were separated by electrophoresis on a 10% sodium dodecylsulfate polyacrylamide gel. Then proteins were transferred onto PVDF membranes . The membranes were blocked in TBST containing defatted milk powder  for 2 hours at room temperature (RT) and then incubated at 4°C with serum from MVI (−) or MVI (+) patient dilute in TBST containing bovine serum albumin (BSA)  overnight. After rinsing in TBST, the membranes were incubated with HRP-conjugated goat-anti-human secondary antibody  for 2 hours at RT. After rinsing again, membranes were detected by a chemiluminescence detection kit . Signals were captured by ChemiDoc XRX system and analyzed by Quantity One software 4.6.2 .® IPGRunner™ System  according to the manufacturer›s protocol. In brief, the QGY-7703 HCC cells were ultrasonically lysed in lysis buffer on ice. Supernatant was centrifuged at 16000 g for 30 min at 4°C. Protein concentration was measured by a protein assay kit. The concentration range is set as 8–10 mg/ml. The first-dimensional IEF was performed on a 7 cm pH 3–7 linear gradient strip. Each strip was rehydrated with 140 μl of the lysis buffer and 155 μl of the rehydration buffer overnight. IEF was run in four steps: 200 V for 20 min, 450 V for 15 min, 750 V for 15 min, and 2000 V for 30 min. The second-dimensional SDS-PAGE run was performed on a 4–12% Bis-Tris gel with 200 V for 40–50 min. After SDS-PAGE, one gel was stained with coomassie brilliant blue overnight. The proteins in another gel were transferred by electrophoresis to a PVDF membrane, and incubated with 150 μl sera from MVI (−) or MVI (+) patient diluted in 15 ml BSA solution  overnight. Then, the PVDF membrane was incubated with HRP-conjugated goat-anti-human secondary antibody  for 1 h at RT. The reaction was detected by a chemiluminescence detection kit . Signals were captured by ChemiDoc XRX system and analyzed by Quantity One software 4.6.2 .Sample preparation and 2DE was performed using ZOOMThe sample treatment had followed the commonly used protocol . In brie2SO4 for 5 min. Absorbance was measured at 450 nm in a microplate reader. Optical density was calculated by subtracting the optical density measured in uncoated wells from that measured in coated wells.A previously published ELISA assay was used to detect antibodies in serum of HCC patients . The calP values of < 0.05 were considered as statistically significant.All statistical analyses were performed using statistical software package SPSS version 19.0 for windows .


Based on experimental models of Parkinson’s disease (PD), it has been proposed that at early stages of the disease, stress-induced RTP801 upregulation contributes to mTOR repression, in an attempt to maintain cell function and viability. However, if RTP801 elevation is sustained, it leads to neuron cell death by a sequential inhibition of mTOR and Akt. Here, we will review RTP801 deregulation of mTOR in a context of PD and other neurodegenerative disorders.Mechanistic target of Rapamycin (mTOR) pathway regulates essential processes directed to preserve cellular homeostasis, such as cell growth, proliferation, survival, protein synthesis and autophagy. Importantly, mTOR pathway deregulation has been related to many diseases. Indeed, it has become a hallmark in neurodegenerative disorders, since a fine-tuned regulation of mTOR activities is crucial for neuron function and survival. RTP801/REDD1/Dig2 has become one of the most puzzling regulators of mTOR. Although the mechanism is not completely understood, RTP801 inactivates mTOR and Akt via the tuberous sclerosis complex (TSC1/TSC2) in many cellular contexts. Intriguingly, RTP801 protects dividing cells from hypoxia or H DDIT4). It was initially identified and cloned in 2002 by two different groups simultaneously.RTP801  is a protein encoded by the stress responsive gene DNA-damage-inducible transcript 4 , the mouse homolog of DDIT4 as a rapidly upregulated gene under multiple cellular stresses, such as heat shock  or REDD2 that displays ~50% sequence identity to RTP801 and has similar functions  that regulate its translation and the post-translational events in charge of regulating RTP801 protein levels.Previous studies suggest that RTP801 toxicity in neurons is proportional to its protein levels  in the promoter , needs co-activation of HIF-1 and Sp1 to induce RTP801  damaging agents, including ionizing radiation and the DNA alkylating agent methyl methane sulfonate (MMS) also boosted RTP801 expression  stress caused by tunicamycin or thapsigargin upregulated RTP801 via activating transcriptional factor 4  is a heterodimer formed by two proteins, the tuberous sclerosis tumor suppressors TSC1 and TSC2. TSC2 has a catalytic function as a GTPase activating protein (GAP), and it acts toward the small GTPase Rheb, an upstream positive mTOR regulator  is not conserved within species and the crystallized RTP801 structure analysis does not reveal any established mode for 14-3-3 binding. Thus, the inhibitory mechanisms of RTP801 towards mTOR need to be further investigated.However, this RTP801 direct binding to 14-3-3 has been questioned by others  and Mancozeb (MZ) induced DNA damage and elevated RTP801 mRNA and protein expression  responsive gene. Amyloid-beta is a neurotoxic molecule and the main component of senile plaques in Alzheimer’s disease  degenerating neurons of PD patients. Furthermore, RTP801 knockdown with short hairpin RNAs (shRNAs) protected the cultures from PD mimetic toxins . This RTP801 upregulation was coincident with a reduced phosphorylation of mTORC1/2 signaling targets S6K, 4EBP1, and Akt. In agreement, RTP801 was elevated in postmortem PFC of patients with major depressive disorder compared to psychiatrically healthy controls. RTP801 knockout mice were resilient to the synaptic and behavior deficits caused by stress, while RTP801 overexpression in rat PFC was sufficient to promote neuronal atrophy and depressive behavior pyridopyrimidin-7-one (FLZ), a synthetic squamoside derivative from a Chinese herb that protected dopaminergic neurons from apoptosis triggered by MPPAnother promising compound is rapamycin, an allosteric inhibitor of some but not all mTOR activities. Rapamycin conferred neuroprotection in both cellular and animal models of PD  signaling pathway (Wu et al., The most advanced example of RTP801 as a therapeutic target is found in retinopathies. RTP801 has an important role in the pathogenesis of retinopathies, since the absence of RTP801 expression in a mouse model attenuated the development of the disease (Brafman et al., In summary, RTP801 is one clear example of a protein that is upregulated to cope with cellular stress although its sustained progressive elevation leads to neuron degeneration and death. RTP801 progressive elevation and its inhibitory function towards pro-survival kinases mTOR and Akt could explain its role in several neurodegenerative diseases.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


DNA-binding proteins (DBPs) rapidly search and specifically bind to their target sites on genomic DNA in order to trigger many cellular regulatory processes. It has been suggested that the facilitation of search dynamics is achieved by combining 3D diffusion with one-dimensional sliding and hopping dynamics of interacting proteins. Although, recent studies have advanced the knowledge of molecular determinants that affect one-dimensional search efficiency, the role of DNA molecule is poorly understood. In this study, by using coarse-grained simulations, we propose that dynamics of DNA molecule and its degree of confinement due to cellular crowding concertedly regulate its groove geometry and modulate the inter-communication with DBPs. Under weak confinement, DNA dynamics promotes many short, rotation-decoupled sliding events interspersed by hopping dynamics. While this results in faster 1D diffusion, associated probability of missing targets by jumping over them increases. In contrast, strong confinement favours rotation-coupled sliding to locate targets but lacks structural flexibility to achieve desired specificity. By testing under physiological crowding, our study provides a plausible mechanism on how DNA molecule may help in maintaining an optimal balance between fast hopping and rotation-coupled sliding dynamics, to locate target sites rapidly and form specific complexes precisely. A huge effort has previously been employed . The conformational energy was estimated by using a native topology based model  is Heaviside function, given by H (x) = 1 for x > 0 and H (x) = 0 otherwise. cageK is set as 100.0 and C = 4.0 Å. A clear advantage of this kind of confinement over the use of many spherical inert crowders is to lower computation cost significantly yet effectively mimic crowded cellular environment.The DNA confinement was modeled by assuming a spherical cylinder ,74 aroungiven by ,\documeCs) was varied from 20–200 mM to investigate salt dependence of target search dynamics. Production runs were typically 108 MD steps long during which multiple associations between protein and DNA molecules were observed. The relative interaction strengths were such that the Sap-1 remained completely folded at simulation temperature, while the DNA model was kept unchanged to maintain its physical properties. It should be noted that during our simulations only DNA was under confinement and not the protein. This helped to investigate the interplay of degree of confinement with DNA dynamics in isolation. Crowding effect on protein is however, only to force it to be in close proximity of DNA molecule, which can also be achieved by lowering salt condition and thereby increasing the effective electrostatic attractions between protein and DNA.The initial B-DNA structure of a random 200 base pair (bp) sequence was generated in w3DNA (3D DNA structure) web server . The folα level) and DNA molecules were then identified and modeled through a soft attractive Lennard–Jones potential. In addition, Sap-1 was allowed to interact non-specifically with DNA bases outside the target region. We modelled this by another short-ranged Lennard–Jones potential, where the bases could randomly interact with protein residues, belonging to its recognition region. Associated interaction strength (ϵij) was chosen from a Gaussian distribution (F (ϵ) = (1 / (2πσ2)1/2) exp [− ((ϵ − ϵnonspecific)2 / 2σ2)]) with mean (ϵij) = 0.6 and standard deviation (σ) of 0.1  of 0.1 . To achiThe structural discrimination between various search mechanisms, namely sliding, hopping and 3D diffusion were done by following the methods prescribed in previous works ,43,44 anIn order to investigate how DNA dynamics under various degree of confinement modulates target search dynamics and kinetics of DBPs, we performed coarse-grained molecular dynamics simulations of Sap-1 with a randomly selected 200 bp DNA molecule that was encapsulated in a cylinder Figure . The radevE) and (ii) degree of correlation between dynamics of protein and DNA that determine their inter-communications. The evE term excludes repulsive interaction between DNA molecule and wall of the cage. The degree of correlation between dynamics of protein and DNA was measured by monitoring positions of center of recognition region of protein and closest DNA bp from it with time. Our result suggests  but low correlation in their dynamics as their movements are in opposite direction. With increase in R, available space for DNA movement increases and protein follows it via non-specific electrostatic interaction, resulting decrease in number of steric collisions  but rise in correlation between their dynamics (up to 80%). The initial increment in evE is due to intra-DNA excluded volume interaction in dynamic DNA, which is constant in rigid B-DNA.Clearly, dynamic DNA has more degrees of freedom compared to rigid B-DNA. The structural fluctuation caused by its inherent dynamics often results into bent DNA axis and non-uniform groove geometry Figure  unlike ts Figure  that witevE and correlation in dynamics), we intended to examine how they together with the deformations in DNA groove geometry affect target search dynamics of DBPs. One recent study has suggested that steric collisions may facilitate 1D diffusion of DBPs  and poor correlation . By analysing the structural details of each snapshot evolved during simulations, we estimated propensities of various search techniques as functions of salt conditions  that forces DNA to frequently encounter Sap-1 even though the correlation between their dynamics is extremely poor. For R > 20Å, number of steric clashes decreases, which along with poor correlation in dynamics between protein and DNA ensures no inter-communication between these molecules and consequently 3D diffusion propensity rises.Having identified suitable salt conditions (20 mM and 100 mM), we turned to probe the influence of DNA confinement on dynamics of DBPs by performing extensive simulations of Sap-1 with DNA molecule being placed inside cylindrical cage of various radii. The sampled conformations of protein–DNA pairs during simulations were examined to measure propensities of sliding, hopping and 3D diffusion with respect to R and are presented in Figure The degree of DNA confinement also plays crucial role in altering mechanistic details of different search mechanisms. To analyse such effects, we investigated structural details of all snapshots in which Sap-1 slides along DNA contour and monitored how rotational angle (θ) and transversal displacement  of the protein vary.In Figure Furthermore, the number and length of sliding events also vary with degree of DNA confinement. By sliding event we mean a time stretch during which Sap-1 continuously probed DNA through sliding only. Between two sliding events, protein can perform hopping and 3D diffusion. The length of each sliding event is measured from Z-axis displacement during an event. In Figure et al.  and monitored how the different degrees of DNA confinement affect the time to locate DNA target sites by Sap-1 and thereby forming the specific protein–DNA complex. The progress of association was monitored by <et al. have shown that heterogeneous energy landscape is common to biological systems such as protein–DNA, where variability to the energy of interactions correlated over the protein footprint size greatly diminishes efficiency of sliding  in rigid B-DNA , a dynamic bond formation and breaking process can be realized from fluctuating <spQ> values in dynamic DNA with confinement. This stems from the interplay between structural fluctuations in dynamic DNA and affinity to form specific protein–DNA complexes. With rise in binding affinity , such behaviour fades out. One interesting trait observed in our kinetic experiment is that even though the target search kinetics on rigid B-DNA and on DNA with confinement are comparable, the former lacks conformational flexibility to achieve desired specificity. For example, in absence of structural flexibility in DNA, Sap-1 could not approach the target DNA site precisely and was forced to remain at a larger distance from DNA  compared to when DNA is dynamic. This is because of smooth rotation coupled sliding ability of Sap-1 on rigid DNA that helps it to probe DNA sites continuously and recognize target site precisely. However, it is argued that during one-dimensional search along DNA, sequence-specific proteins may come in close contacts and therefore may interact non-specifically with DNA sites that shares at least partial sequence similarity with the target one. Typically, such non-specific interaction slows down sliding dynamics . In deed sliding . When cos Figure , which is Figure , too mucin vivo concentration of cytoplasm is estimated to be 50–400 mg ml−1, which is ∼0.9–7 mM if the average molecular weight is assumed to be ∼55 kDa. This indicates role of physiological crowder concentration in target search dynamics of DBPs.To this end, it is desirable to translate the related degree of DNA confinement into concentration of crowders. If we assume that radius R = 15 Å means a crowder molecule to be present at least 30 Å (diameter of cylinder) away from DNA molecule and if this is the average distance between DNA and crowder molecules, the concentration of crowders would be ∼0.37 mM according to the prescription given in reference . In compSearching DNA target sites by DBPs is immensely complex process to understand fully. While many insights have been gained on protein part to underpin the effects of its flexibility and composition on the target search efficiency, little has been explored on DNA part. Furthermore, DNA is more commonly treated as linear rigid molecule with negatively charged surface with which protein interacts non-specifically through electrostatic interactions.In this study, we considered DNA dynamics, which is inherent to the molecule and investigated the impact of its interplay with DNA confinement on target search dynamics of DBPs. Imposing confinement on DNA was inspired from the crowded environment of cell, which along with DNA self-crowding may impact shape and function of the molecule significantly . By perfWe further highlight the role of DNA confinement and its dynamics by investigating the details of sliding mechanism and find that decreasing degree of DNA confinement promotes many but short rotation-decoupled sliding events interspersed by hopping dynamics. While such modulation in sliding dynamics and associated high hopping propensity help DBPs to scan maximum number of DNA sites by allowing them to move faster (high 1D coefficient) over the weakly frustrated protein–DNA landscape in presence of non-specific contacts, they often lack the precision. The poor correlation between rotational and translational motion during short sliding events prevents the DBPs to scan DNA base sites thoroughly and in addition to that, high hopping propensity makes DBPs to often jump over the target DNA sites to miss it. Our simulations therefore suggest that it is not just naive speed rather mixture of speed and accuracy that causes DBPs to successfully form the specific protein–DNA complex upon recognizing the target DNA sites precisely. By providing a weighted balance between hopping and rotation-coupled sliding, DNA dynamics under physiological crowding helps DBPs to achieve this goal.Supplementary Data are available at NAR Online.


SORL1) is involved in cellular trafficking of amyloid precursor protein and plays an essential role in amyloid-beta peptide generation in Alzheimer disease (AD). The major A allele in a SORL1 single nucleotide polymorphism (SNP), rs3824968, is associated with an increased AD risk. However, the role of SORL1 rs3824968 in the normal ageing process has rarely been examined in relation to brain structural morphology. This study investigated the association between SORL1 rs3824968 and grey matter (GM) volume in a nondemented Chinese population of 318 adults within a wide age range (21–92 years). Through voxel-based morphometry, we found that participants carrying SORL1 allele A exhibited significantly smaller GM volumes in the right posterior cingulate, left middle occipital, medial frontal, and superior temporal gyri. Considerable interaction between age and SORL1 suggested a detrimental and accelerated ageing effect of allele A on putamen. These findings provide evidence that SORL1 rs3824968 modulates regional GM volume and is associated with brain trajectory during the adult lifespan.Sortilin receptor 1 ( SORL1) encodes a mosaic protein belonging to at least two families: the vacuolar protein sorting 10 domain-containing receptor family and the low density lipoprotein receptor family. The encoded protein acts as an apolipoprotein E (APOE) binder and sorting receptor that regulates the intracellular transport and processing of the amyloid precursor protein (APP) in neuronsSORL1 is genetically associated with late-onset and sporadic ADSORL1 regulates and traps APP in the Golgi apparatus and reduces the production of the amyloid-beta peptide (Aβ), the main component of senile plaques in ADSORL1 underexperssion leads to overproduction of AβSORL1 overexpression considerably reduces cellular APP and extracellular AβSORL1 in AD and neurodegenerative processes remain unclear, a recent comprehensive review on multiple functional domains of SORL1 suggested that SORL1 can assume various characters in AD pathogenesisThe sortilin-related receptor gene SORL encodes SORL1, Rogaeva et al.SORL1 in the processing of APP holoprotein. The association between SORL1 rs3824968, neurodegenerative disease, and ageing process has recently been reported6789101-42 were observed in carriers of the AD risk allele A in SORL1 rs3824968 compared with noncarriers9SORL1 rs3824968 A allele and increased AD risk. However, the association between SORL1 rs3824968 and AD was not consistent across different populations. This discrepancy may be owing to ethnic difference, suggesting the existence of population-specific locus. It should be noted the reference allele for the SORL1 rs3824968 is the T over all populations. In the Chinese population, however, the reference allele is the A. The association between SORL1 variants and accelerating cognitive ageing in relation to spatial and episodic memory performance were observed in a follow-up study of a nondemented populationSORL1 rs3824968 A allele and reduced spatial-span tasking in cognitively healthy participants was notedet al.SORL1 variants and hippocampal volume, as an intermediate phenotype, in a gene-wide association analysis of healthy young adults.Among single nucleotide polymorphisms (SNPs) in SORL1 can vary according to the age grades during the lifespan. A previous study reported that healthy elderly individuals exhibit two-fold higher SORL1 expressions than do younger adultsSORL1 risk variants may predict decrease in white matter (WM) integrity and SORL1 mRNA expression, most prominently during the ageing process, and they may be associated with high Aβ deposition in the postmortem brain, thus conferring neuropathological risk for SORL1 variants through the amyloidogenic pathwaySORL1 effect, and regional grey matter (GM) volume changes in a healthy population. Thus, deducing the risk factors for accelerated brain ageing by evaluating the interaction of SORL1 variants with age and their association with the regional brain volume changes over the adult lifespan would benefit current ageing-related genetic imaging studies.In addition to the differences in the effects of the SNP alleles within the brain, the expression of SORL1 in the normal ageing process, this study examined regional GM volume changes as an intermediate phenotype in nondemented adults during their lifespan to infer the relationship between the genetic effect of SORL1 rs3824968 and brain ageing. By using a relatively large sample size with a broad age range in combination with an unbiased and whole-brain voxel-based morphometry (VBM) approach, we assessed two hypotheses based on previous studies: (1) independent of the age effect, the variants of SORL1 rs3824968 are associated with the difference of regional GM volume in nondemented adults and (2) SORL1 rs3824968 interacts with age and is associated with regional GM volume alterations across lifespan.Considering previous findings and the role of SORL1 genotyping or extensive motion problems during MRI scanning. Of the 318 participants that completed SORL1 genotyping, MRI acquisitions, and cognitive assessments, 42, 146, and 130 carried the SORL1 TT, AT, and AA genotypes, respectively. APOE genotypes were also determined to reveal five genotypes: ε2/ε2 , ε2/ε3 , ε3/ε3 , ε2/ε4 , and ε3/ε4 . The frequencies of the genotypes followed the Hardy–Weinberg equilibrium. The demographics and clinical characteristics of the study group are shown in SORL1 TT, AT, and AA genotypes showed no significant differences in age (P = 0.230), sex (P = 0.695), educational level (P = 0.380), APOE allele distribution (P = 0.291), total GM volume (P = 0.304), total WM volume (P = 0.243), total CSF volume (P = 0.849), total intracranial volume (P = 0.425), or neuropsychological characteristics.Twelve participants were excluded because of failure in SORL1 TT, AT, and AA genotypes, the post hoc analyses revealed positive dosage effect of the T allele on GM volume of the left middle occipital gyrus .Regarding the voxelwise covariate interaction analysis, we observed considerable interaction effect between age and the three n region , and theSORL1 showed correlation with MMSE, digit span forward (DSF), or digit span backward (DSB) scores. For the correlation between the cluster of voxelwise interaction and cognitive tests, although the results failed to survive from the criteria of Bonferroni correction, a positive trend between DSB score and putaminal volume was observed in the participants carrying AA .In the partial correlation analysis between regional GM volume and cognitive performance levels in all participants, none of the regional GM volume changes for the effect of SORL1 rs3824968 on regional GM volume and age-related changes in the brain GM volume of nondemented participants within a wide age range. The findings of this study showed that the SORL1 rs3824968 A allele carriers had lower GM volume in the left superior temporal, left medial frontal, right posterior cingulate, and left middle occipital gyri than did those carrying TT. A considerable interaction between age and the SORL1 genotypes was observed, showing an accelerated (downward) slope of GM volume differences in the right putamen of the participants carrying the AD-risk (A) allele compared with those carrying TT. Thus, our findings support the hypothesis that SORL1 rs3824968 is associated with regional GM volume differences in early adulthood and can influence ageing-related GM morphological changes during the nondemented adult lifespan.We performed VBM analysis to examine the effect of SORL1 rs3824968 A carriers showed significantly decreased GM volume in the left superior temporal, left medial frontal, right posterior cingulate, and left middle occipital gyrus, suggesting detrimental effects of the A allele on brain GM volume. A trend towards gene dosage effect of the A allele was also observed in the left middle occipital, medial frontal, and right posterior cingulate gyri. SORL1 SNPs have been found to be associated with an increased risk of neurodegenerative disease in Swedish and African American population through meta-analysis2142 levels in CSF and higher AD risk in Asian population1423SORL1 risk variants may alter the Aβ expression levels in either healthy adults or patients with neurodegenerative disease1213SORL1 rs3824968 variants may influence amyloid pathology during ageing. Greater amyloid burden has been shown to be positively associated with regional GM atrophy262729et al.SORL1 increases dementia risk through effects on cerebrovascular abnormalities; SORL1 rs3824968 and its haplotypes were found to be associated with brain volume and WM hyperintensities in their study. This biological and neuroimaging evidence may suggest that SORL1 rs3824968 is associated with brain morphology in terms of A allele-related volumetric reduction caused by high Aβ load, cardiovascular disease, or both. However, additional studies are necessary to explore the potential mechanisms of SORL1 rs3824968 on the cellular and molecular determinants of brain structural differences in both demented and nondemented individuals.The SORL1 on age-related changes in the brain cortical morphology of nondemented participants over a wide age range. The right putamen of the participants carrying AA and AT showed a considerable effect of interaction between age and the SORL1 genotype with a steeper downward slope of age-related GM volume reduction compared with that of those carrying TT. This finding supports the hypothesis that SORL1 polymorphism plays a role in the ageing process of the human brain with a partially detrimental effect on age-related differences of regional GM volume, particularly in the putamen of the participants with the A allelic variant. A previous study reported that participants with presenilin-1, presenilin-2, or APP mutations showed higher amyloid load and greater GM atrophy in the putamenSORL1 variants contribute to the risk of AD neuropathogenesis through the amyloidogenic pathway, putaminal volume could be a vulnerable subcortical area in the brain ageing process with regard to the SORL1 effect. In addition, such an accelerated regional GM volume reduction effect in the putamen further links to cognitive decline in participants carrying the A allele. A trend of positive correlation between putamen volume and DSB scores was observed in the participants carrying AA. Studies have shown that the basal ganglia play a vital role in working memory and executive function, particularly in the putaminal region33SORL1 rs3824968 and putaminal volume and a trend of neural correlation of working memory (DSB), which may be a marker of accelerated cognitive impairment at preclinical stage3637Here, we revealed the first evidence of the influence of SORL1 variants could mediated neural risk of AD beginning from teenage yearsSORL1 rs3824968 effect on GM volume during the ageing process: (1) differences of regional cortical volume between the three genotypes occur from early adult life and preferentially stay stable during adulthood and (2) in contrast to the first pathway, variants of SORL1 rs3824968 do not modulate putaminal volume directly but interact with age during the adult lifespan. Further research is necessary to explore underlying mechanisms for various SORL1 genetic effects along brain ageing trajectory.We observed no age-by-SNP interaction effect in the significant clusters that detected the main effect of genotype: the left superior temporal gyrus, left middle occipital gyrus, left medial frontal gyrus, right cerebellar tonsil, and right posterior cingulate gyrus. In addition to a previous study, which proposed a pathway by which SORL1 SNP on GM volume might be affected through Aβ deposition or other mechanisms such as inflammatory signalling modulationSORL1 variants, biochemical data, and brain morphometry should be investigated simultaneously in future studies to explain the effect of SORL1 on GM volume reduction. In addition, rather than having a direct effect on regional GM volume, SORL1 rs3824968 may be in linkage disequilibrium with truly associated variants; in addition, it may be an intermediate phenotype. Since such a linkage could vary among different populations and the association between rs3824968 and AD was not replicated in studies of other populations, these may confound the generalisability of our findings, which were based on a homogenous Chinese cohort. In addition, no significant correlation was found between general cognitive function  and regional GM volume differences of the SORL1 effect. Although MMSE is a widely used tool for detecting dementia, it may lack sensitivity to and specificity for early signs of subtle cognitive changes. Despite the current samples being from nondemented participants, the ceiling effect of using MMSE, DSF, and DSB for cognition assessment may also have yielded false negative resultsThe necessity for statistically sufficient sample sizes in imaging studies of genetic variation is increasingly being recognised. The relatively large and  homogenous cohort of participants with a wide age range recruited in this study increased the credibility of our findings, based on previously proposed recommendations regarding cohort sizesSORL1 rs3824968 A allele was associated with decreased GM volume and exhibited a trend towards gene dosage effect in several brain regions during the adult lifespan. Considerable interaction between age and SORL1 suggested a detrimental and accelerated ageing effect of the AD-risk allele on putaminal volume. Although the underlying molecular mechanisms remain unclear, our findings support the hypothesis that SORL1-related genetic factors play a vital role in the process of normal ageing. The regional GM volume alterations associated with the effect of SORL1 rs3824968 might be a potential neuroimaging biomarker for accelerated brain ageing during the adult lifespan.The AD-risk This study initially recruited 330 nondemented ethnic Chinese participants from Northern Taiwan  through an advertisement in local communities and universities. Each participant was administered a diagnostic structured Mini-International Neuropsychiatric Interview (MINI)The cognitive functioning of the participants was evaluated using the MMSE for general cognitive status and the Wechsler Digit Span subtest for verbal working memory abilities. All participants exhibited sufficient visual and auditory acuity to undergo cognitive testing. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Taipei Veterans General Hospital. Written informed consent was obtained from all participants after they were adequately apprised of the study.SORL1 rs3824968 genotyping was performed using high-throughput matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. PCR and single-base extension primers were designed using DNA mass arrays . The genotyping analysis was performed using an iPLEX Gold Reagent Kit according to the manufacturer’s instructions. The purified extension products were spotted onto a 384 SpectroCHIP II array by using a MassArray Nanodispenser RS1000, followed by an analysis on a MassARRAY Compact Analyzer. The resulting spectra were processed, and the alleles were called using a SpectroTYPER. The APOE genotype was determined using the PCR-restriction fragment length polymorphism method, as described by Hong et al.Genomic DNA was extracted from peripheral blood with a commercial kit . All MRI scans were performed at National Yang-Ming University, Taipei, Taiwan, on a 3.0-T Siemens MRI scanner with a 12-channel head coil . High-resolution structural MR images were acquired through sagittal 3D magnetisation-prepared rapid gradient-echo sequencing . All images were acquired parallel to the anterior commissure–posterior commissure line. To minimise motion artefacts generated during image acquisition, each participant’s head was immobilised using cushions inside the coil. An experienced radiologist carefully checked each image to ensure the absence of scanner artefacts, motion problems, or gross anatomical abnormalities.http://dbm.neuro.uni-jena.de) within Statistical Parametric Mapping . VBM processing was performed using the following procedures: (1) The anterior commissure was set as the origin of each T1-weighted image. (2) A segmentation approach in the VBM8 toolbox was applied in the initial native space. (3) To achieve a higher registration accuracy and account for different brain size among subjects, the native space segments of the GM, WM, and cerebrospinal fluid (CSF) were initially affine registered to the tissue probability maps in the Montreal Neurological Institute (MNI) standard space. (4) All affine registered GM and WM tissue segments were used to generate group-specific templates through nonlinear warping by using the Diffeomorphic Anatomical Registration Through Exponentiated Lie (DARTEL) algebra toolboxIndividual T1-weighted volumetric images were processed using Gaser’s VBM8 toolbox  were eliminated. In this study, to further investigate the main effects of age-by-SNP interaction on regional GM volumes, voxelwise covariate interaction analysis was used with the SORL1 genotype as a condition and age as a covariate, controlling for sex and education level as nuisance variables. In each model, the main effect and interaction were explored using F tests. Post hoc t tests were subsequentially performed to examine the relationships between genotypic groups for any significant main effects and interaction clusters using Bonferroni corrections for multiple comparisons. The problem of multiple comparisons was corrected using a Monte Carlo simulation created in AlphaSim and implemented in the Analysis of Functional NeuroImages software (http://afni.nimh.nih.gov/afni/). Based on the simulation, the statistical threshold of F tests and post hoc tests were set at corrected Palpha of <0.05, with a minimum cluster size of 298 voxels  for multiple comparisons. The coordinates of each significant cluster were transformed from MNI coordinates into Talairach coordinates by using the GingerALE toolbox . The anatomical structures of the coordinates representing significant clusters were identified using the Talairach and Tournoux atlasStatistical analysis was performed using the Statistical Package for Social Sciences (SPSS) software package . Analysis of variance and chi-squared test were respectively applied to compare the continuous and categorical variables of the demographic data among the three groups . Smoothed and modulated GM segments were analysed with SPM8 by using the framework of a general linear model. Analysis of covariance was used by covarying age, sex, and educational levels to reveal the random effect of How to cite this article: Huang, C.-C. et al. Effect of Alzheimer’s Disease Risk Variant rs3824968 at SORL1 on Regional Gray Matter Volume and Age-Related Interaction in Adult Lifespan. Sci. Rep.6, 23362; doi: 10.1038/srep23362 (2016).


MicroRNA (miRNA) expression has rapidly grown into one of the largest fields for disease characterization and development of clinical biomarkers. Consensus is lacking in regards to the optimal sample source or if different circulating sources are concordant. Here, using miRNA measurements from contemporaneously obtained whole blood- and plasma-derived RNA from 2391 individuals, we demonstrate that plasma and blood miRNA levels are divergent and may reflect different biological processes and disease associations. MiRNAs are small RNAs that play an important role in the regulation of gene transcriptional networks. miRNAs are diverse in sequence and expression patterns and are evolutionarily conserved, suggesting they may participate in a wide range of genetic regulatory pathways and pathTo understand the comparability of cellular and extracellular sources of miRNA expression, we measured the levels of 159 microRNAs by high-throughput reverse transcription-quantitative polymerase chain reaction (RT-qPCR) isolated from both whole blood and plasma using samples obtained contemporaneously in 2,391 participants in the Framingham Heart Study (FHS). We hypothesized that expression of miRNAs in blood versus plasma would be discordant to support the notion that future studies in this field should be consistent in biofluids studied for translational biomarker development.th Offspring Exam of the Framingham Heart Study (March 2005 –January 2008). Venipuncture was performed on study participants in a supine position, using standard venipuncture techniques. Blood was collected into blood collection tubes with a liquid buffered sodium citrate additive (0.105M). Blood collection tubes were centrifuged at 2,500g for 22 minutes at 4°C. Plasma was separated from the cells and frozen at -80°C within 90 minutes of draw. An aliquot of 170 μl of plasma samples was transferred to our laboratory in March 2014 and stored at -80°C. Plasma samples were centrifuged at 8,000g for 5 minutes immediately before RNA isolation.The Framingham Heart Study (FHS) Offspring Cohort is a community-based, prospective study of cardiovascular disease (CVD), with serial examinations every 4–8 years. Blood samples were collected during the 8Whole blood was collected in PAXgene  tubes from each study participant after an overnight fast and stored at -80°C. The same laboratory, personnel and technical platform were used for measurement of plasma and whole blood miRNA. All participants gave written informed consent. The Boston University Medical Center Institutional Review Board approved FHS examination protocols and University of Massachusetts Medical School Review Board approved the study.The high throughput Gene Expression and Biomarker Core Laboratory at the University of Massachusetts Medical School profiled 346 miRNAs isolated from whole blood in 2,445 FHS Offspring cohort participants using TaqMan miRNA assays. RNA isolation was performed by Asuragen, Inc . Total RNA was isolated from the frozen PAXgene Blood RNA tubes. The initial miRNA list encompassed all TaqMan miRNA assays available at the start of the study. Isolated RNA samples were converted to complementary DNA using TaqMan miRNA Reverse Transcription Kit and MegaPlex Human RT Primer Pool Av2.1 and Pool Bv3.0.  in a 384-well Thermal Cycler. The complementary DNA samples were preamplified using TaqMan PreAmp Master Mix and PreAmp Primers, Human Pool A v2.1 and Pool B v3.0 (Life Technologies). Among 70 replicate samples, >95% of the data points had coefficients of variation <10% (mean ~4%). miRNA expression was quantified using quantification cycle (Cq), where higher Cq values reflect lower miRNA expression. We set miRNAs with Cq value ≤6 or ≥27 as missing for the subsequent whole blood miRNA analyses, as these values would indicate PCR results outside the linear range for the TaqMan chemistry used. By using Dynamic Arrays single copy can be detected in 26–27 cycles in TaqMan based qPCR analysis compare to 35–36 cycles in conventional plate-based qPCR analysis.Of the 2,822 eligible subjects from FHS Offspring Exam 8, 59 (2%) subjects were excluded due to laboratory error  resulting in 2,763 subjects as a final study cohort for plasma measurement. In total, expression of 331 human miRNAs was assayed from plasma. RNAs were reverse transcribed by using miScript II RT Kit . miScript Microfluidics PreAMP Kit  was used for preamplification reactions. Dynamic Array 96.96 GE  and Microfluidics qPCR Master Mix  with EvaGreen dye used for high-throughput qPCR reactions. miRNA assays were purchased from Qiagen in dried down format. PCR reactions were stopped at 23 cycles (Cq</ = 23). Similar to TaqMan chemistry, Dynamic Arrays used for high-throughput qPCR reactions provide single copy detection around 23 cycles of amplification in EvaGreen based qPCR analysis. Those plasma miRNAs with Cq value ≤6 or ≥23 cycles were considered outside the linear range for the Qiagen chemistry used, and were regarded as missing for the subsequent analyses.260/A280 ratio, and concentration). A Bonferroni-adjusted P value (for multiple comparisons) was used to assess significance. We constructed a volcano plot  on the y-axis and magnitude of correlation on the x-axis) to display the distribution and significance of each bivariate association , and constructed linear models for the association between blood and plasma miRNA expression  for those miRNAs in Given the general concordance between Pearson and Spearman correlations, we constructed linear models for the association between whole blood and plasma miRNA expression  to assay expression of a wide array of miRNAs from concomitantly obtained blood samples. One important limitation is the use of different RNA isolation methods and PCR reagents. While different RNA isolation methods and PCR reagents  could potentially lead to differences in yield (even based on miRNA species), we attempted to account for characteristics of whole blood-derived RNA quality in regression models and had a high sample size relative to existing studies of miRNAs in blood and plasma. Nevertheless, direct comparisons across RNA isolation kits, PCR methodology, and biofluids are a subject of ongoing investigation in multiple groups and will further inform heterogeneity on the basis of these variables. miRNA profiling was performed from plasma or whole blood samples and as such, does not provide information about compartments for RNA, including exosomes, protein- or lipid-bound RNA, or free circulating fraction. While RNA sequencing methods would be more sensitive across a wider array of miRNAs and for other small and long non-coding RNAs, RT-qPCR is a well-validated method with facile clinical translation for the most widely studied extracellular RNAs to date (miRNAs). Finally, though hemolysis may affect miRNA abundance in plasma, we observed that differences in Cq value between miR-23a (in variant with hemolysis) and miR-451 (a red blood cell marker) was not elevated (>7) in the majority of samples (99%), suggesting an absence of hemolysis contributing to results.In summary, we compared expression of miRNAs measured by high-throughput RT-qPCR isolated from both whole blood and plasma using contemporaneously samples in 2,391 individuals in the community. There was only modest association between cellular and extracellular miRNA expression in this large study sample. These results demonstrate for the first time in a large human cohort that miRNA expression from cellular versus acellular sources may be divergent, and point to care in designing clinical studies to validate known and novel extracellular RNA markers in human disease. miRNA expression from different human sources  should not be used interchangeably as biomarkers of disease in translational investigation.S1 TableThe numbers of subjects in this table (N) correspond to the number of subjects with measured values for each miRNA in blood or plasma (but do not indicate the number of subjects in common).(DOCX)Click here for additional data file.


Avian influenza viruses show high genetic diversity, but give rise to pandemic human influenza strains which are much less diverse; we show that these contrasting degrees of viral diversity can be explained by both host (demography) and viral  factors. Avian influenza viruses (AIVs) have been pivotal to the origination of human pandemic strains. Despite their scientific and public health significance, however, there remains much to be understood about the ecology and evolution of AIVs in wild birds, where major pools of genetic diversity are generated and maintained. Here, we present comparative phylodynamic analyses of human and AIVs in North America, demonstrating (i) significantly higher standing genetic diversity and (ii) phylogenetic trees with a weaker signature of immune escape in AIVs than in human viruses. To explain these differences, we performed statistical analyses to quantify the relative contribution of several potential explanations. We found that HA genetic diversity in avian viruses is determined by a combination of factors, predominantly subtype-specific differences in host immune selective pressure and the ecology of transmission . Extending this analysis using a computational model demonstrated that virus durability may lead to long-term, indirect chains of transmission that, when coupled with a short host lifespan, can generate and maintain the observed high levels of genetic diversity. Further evidence in support of this novel finding was found by demonstrating an association between subtype-specific environmental durability and predicted phylogenetic signatures: genetic diversity, variation in phylogenetic tree branch lengths, and tree height. The conclusion that environmental transmission plays an important role in the evolutionary biology of avian influenza viruses—a manifestation of the “storage effect”—highlights the potentially unpredictable impact of wildlife reservoirs for future human pandemics and the need for improved understanding of the natural ecology of these viruses. Human populations have experienced several pandemics involving new subtypes of influenza virus over the past century. All of these pandemic strains contained gene segments that originated in wild birds, a host pool that supports a very large and genetically diverse array of influenza viruses. However, once an avian strain establishes itself within the human population, the genetic diversity of the resulting human subtypes is typically quite low compared to that of their avian counterparts. Here we compare the evolutionary dynamics of human and avian influenza viruses in North America and test different hypotheses that might explain these two contrasting evolutionary patterns. Our analysis shows that a combination of characteristics of the host  and virus (such as durability in water and mutability) explains the diversity observed. Using a theoretical model, we show that the combination of the short lifespan of wild birds, and greater durability of viruses in aquatic environments, is key to maintaining the high levels of influenza diversity observed in wild birds. Seasonal epidemics of influenza viruses are responsible for significant human morbidity and mortality The epidemiological and evolutionary histories of human and AIVs in North America from 1976–2001 are summarized in These patterns in human influenza are consistent with “immune escape,” a phenomenon that has been suggested to be common in directly transmitted, immunizing pathogens with a short infectious period, in which antigenic evolution results in partial cross-immunity between strains in birds .immunological hypothesis holds that more rapid loss of immunity and/or weaker heterologous cross-protection in birds than humans reduces competition among strains, leading to higher diversity; (2) the ecological hypothesis suggests that associations between virus lineages and avian host species diversity allow contemporaneous evolution within multiple bird species, sustaining an enriched gene pool; (3) the geographic hypothesis supposes that greater geographic isolation in birds than in humans leads to allopatric evolution; (4) the genetic hypothesis posits that mutation rate differences between avian and human viruses explains the disparity in viral diversity; (5) the demographic hypothesis focuses on the higher fecundity and shorter lifespan of birds compared to humans, which may mitigate the selective pressure of herd immunity via substantial recruitment of immunologically naïve individuals propagating the pathogen; and finally, (6) the epidemiological hypothesis predicts that there exists a long-lived environmental reservoir for avian strains, but not for human strains, facilitating coexistence of a broad spectrum of genetically, immunologically, and ecologically similar viruses.The mechanistic origins of these differences remain unclear. Here, we propose the following nonmutually exclusive hypotheses as possible explanations: (1) The To date, there have been no attempts to synthesize the available evidence for or against these different explanations. In this study, we address these hypotheses through a combination of statistical analysis of empirical covariates and epidemiological modeling to identify the most parsimonious explanation for the observed differences of HA genetic diversity between human and AIVs.We analyzed available AIV sequence data from GenBank for 11 HA subtypes from 1976–2013 (see STF that measures population differentiation through space), and host diversity .This regression analysis yielded the following conclusions  the proximity between susceptible and infected birds needed for infection and (ii) the scaling of transmission with the duration of infectivity. Furthermore, recent research points to direct bird-to-bird transmission via the respiratory route To quantify the influence of Hypothesis 5 (host demography) on influenza virus diversity, we first parameterized our model to mimic the within-subtype dynamics of human influenza, assuming only direct transmission. Seeding simulations with only a few antigenic variants, we observed the continual replacement of a dominant strain by new antigenic variants , driven We then addressed demographic explanations by exploring the impact of host biology alone, reparameterizing the model to take into account the reduced lifespan, increased fecundity, and seasonal breeding of birds compared with humans. Model output remained qualitatively unaffected, demonstrating continuous antigenic evolution , with loNext, we carried out sensitivity analyses spanning the parameter space of the most common influenza systems, including swine and equine influenza . We founFour testable predictions arise from our model , three oeN represents the size of an idealized population corresponding to observed levels of genetic diversity, and τ represents the generation time of the virus. Because long-term transmission chains resulting from environmental durability should directly increase AIV generation time (the average time between infections), a correlation between We tested for the presence of the first association using phylogenies inferred for 10 different avian influenza HA subtypes isolated from North American wild birds , we furtperature  or averarability . Third, rability , as predr = 0.15, p = 0.65).One final model prediction, that co-infection frequency should increase with environmental durability, is of great evolutionary relevance, as co-infection is necessary for reassortment, which may be a prerequisite to the evolution of pandemic strains Our study identified the mechanisms that act to determine hemagglutinin genetic diversity in avian influenza viruses. In particular, the analyses reveal that our hypotheses act in concert to shape the phylodynamics of AIVs. These results are consistent with prior studies that have examined each mechanism in isolation. For instance, it has been shown that strong spatial structuring is an important factor in the phylogeography of these viruses Our most surprising empirical finding is that HA genetic diversity increases with virus durability, as measured in experimental assays, across AIV subtypes . The corTwo limitations of this study warrant further investigation. First, a detailed understanding of cross-immunity remains an important empirical limitation to any study of avian influenza evolution. Establishing the duration and extent of protective immunity against heterotypic viruses, particularly, remains a priority. At present, comparative data to test this idea directly are lacking, although work showing the absence of immunity conferred by DNA subtype-specific vaccines to challenge strains from other subtypes Elsewhere, it has been shown that the environmental reservoir can be a crucial source for sparking off annual outbreaks Finally, our findings have practical implications for the management of influenza in wild birds. Particularly, our results indicate that—in addition to movement restrictions The human epidemiological time series presented in http://dx.doi.org/10.5061/dryad.dryad.8ct18For the phylogenetic analyses presented in We measured nucleotide substitution rate at third position sites as a proxy for nucleotide mutation, as evolution at third position sites should primarily represent synonymous change −1 to 10−8) were then made in 1× MEM supplemented with antibiotics. These titers were used to estimate Rt as the time required to reduce infectivity by 90%, assuming a linear association.Data on environmental durability for each subtype come from experimental data STF  of each subtype (geographic hypothesis), (ii) number of species where strains have been sampled (host diversity hypothesis), (iii) subtype-specific nucleotide mutation rate (genetic hypothesis), (iv) amino acid substitution rates characterizing an immune selective pressure , and (v) environmental durability Rt under natural physical conditions . Due to correlations among these variables, univariate analysis was not considered to provide reliable estimates of covariate effects. But ordinary least squares multiple regression would be equally ill-advised, resulting in weakly identifiable parameters and variance inflation. A generic solution to this problem is provided by penalized least squares models, such as ridge regression and elastic-net regression. These methods introduce a new estimator, which differs from the maximum likelihood estimator by an additional penalty. In effect, the penalized estimator trades a small amount of bias for a large reduction in the variance of the estimated coefficients. We chose to use elastic-net regression, which takes the maximum likelihood and ridge regression estimators as limit cases and therefore can be fine-tuned to balance the bias-variance tradeoff. Fitting of an elastic-net regression model requires the estimation of an additional tuning parameter , which was numerically selected using cross-validation following The goal of regression analysis was to estimate the size of statistical effect on AIV diversity of covariates corresponding to alternative causal hypotheses. As described above, a covariate corresponding to each hypothesis was developed and assessed for each antigenic subtype. These covariates were (i) SIR model in the limiting cases where analogous mathematical models can be still formulated The individual-based model developed here has been shown to generate evolutionary dynamics that are not statistically distinguishable from the classic 6 individuals, with a mean lifespan of 80 y and a transmission rate of β(t) = 7.8.105(1+0.035cos(t)) 4 individuals (host lifespan is 4 years) b(t) = b(1+0.8·sin(t)) t) has been chosen to ensure L = 6.73 t) = 20×(1+0.9·sin(t))  and avian (bottom panels) influenza viruses. When host lifespan is long and transmission is only via direct contact (as is the case with human influenza viruses), herd immunity to a given antigenic variant produces strong selection pressure for immune evasion, as indicated by strain replacement events in the top panel. With AIVs, however, the long-term environmental reservoir leads to the episodic introduction of older lineages and facilitates viral coexistence.(TIF)Click here for additional data file.Figure S2Temporal distribution of all sampled sequences.(TIF)Click here for additional data file.Figure S3Spatial distribution of avian influenza isolates.The areas shaded in red indicate that this state/province has been sampled.(TIF)Click here for additional data file.Figure S4Summary of environmental durability dataset from .(TIF)Click here for additional data file.Figure S5Maximal likelihood trees for AIVs ranked from H1 to H6. These trees have been calculated with PhyML.(TIF)Click here for additional data file.Figure S6Phylogenetic trees for AIVs ranked from H7 to H12 (note: H8 has been excluded due to paucity of sequences). These trees have been calculated with PhyML.(TIF)Click here for additional data file.Figure S7Illustration of neutral mutation reconstruction for inference of digital phylogenies.(TIF)Click here for additional data file.Figure S8Test of neutral mutation reconstruction algorithm. Panels on the left represent model parameters leading to a perfect immune escape pattern (each strain is replaced at the next time step). Panels on the right depict viral coexistence. The phylogenies reconstructed (Bottom) through the algorithm detailed in (TIF)Click here for additional data file.Figure S9Antigenic dynamics of the three configurations studied and represented in (TIF)Click here for additional data file.Figure S10Influence of shrinkage on coefficient values. (Left) Mean-squarred error according the log(λ). Optimal value is for log(λ) = −6.88. (Right) Coefficient values for different levels of log(λ); shaded area represents coefficients for the optimal value of log(λ).(TIF)Click here for additional data file.Figure S11−5 per base per day on 12,000 bases (assuming the whole genome is coding for antigenic variation) instead of 2,000 (assuming that only mutation on HA is coding). (D) Less efficient cross-protection Click here for additional data file.Figure S12d Click here for additional data file.Figure S13Comparable simulation to (TIF)Click here for additional data file.Figure S14Same simulation as for (TIF)Click here for additional data file.Figure S15Phylodynamics pattern generated for avian configuration (A) without and (B) with environmental transmission. (C) Considering direct transmission with an increased transmission rate ((TIF)Click here for additional data file.Figure S16Environmental durabilities estimated when temperature fluctuates through a cosinus function (with an average of 20 degrees) with an amplitude of 0 (black lines), 0.2 (blue lines), and 0.5 (red lines).(TIF)Click here for additional data file.Table S1Number of sequences for each avian subtype.(XLSX)Click here for additional data file.Table S2Parameters of the model. Values displayed here are used throughout the manuscript, except when sensitivity of parameter is explored. NA, not applicable.(XLSX)Click here for additional data file.Table S3HA subtype and corresponding statistics.(XLSX)Click here for additional data file.Table S44 centileters per day.Medium, minimal, and maximal values of strain diversity for the different extreme configurations across 10 replicates. “Human demography” assumes a lifespan of 80 y, and “Avian demography” assumes a lifespan of 4 y. Strong environmental transmission assumes a drking rate of 10(XLSX)Click here for additional data file.Table S5Results of the multiple regression analysis with a different amplitude of temperature seasonality.(XLSX)Click here for additional data file.Text S1Model description and validation, and supplementary statistical and theoretical analyses.(PDF)Click here for additional data file.


RNA editing is a molecular event that alters specific nucleotides in RNA post-transcriptionally. RNA editing has the potential to impact a variety of cellular processes and is implicated in diseases such as cancer. Yet, the precise mechanisms by which RNA editing controls cellular processes are poorly understood. Here, we characterize sequences altered by RNA editing in patient samples from lymphoma, neuroblastoma and head and neck cancers. We show that A-to-I RNA editing sites are highly conserved across samples of the same tissue type and that most editing sites identified in tumors are also detectable in normal tissues. Next, we identify the significant changes in editing levels of known sites between tumor and paired “normal” tissues across 14 cancer types (627 pairs) from The Cancer Genome Atlas project and show that the complexity of RNA editing regulation cannot be captured by the activity of ADAR family genes alone. Our pan-cancer analysis confirms previous results on individual tumor types and suggests that changes of RNA editing levels in coding and 3′UTR regions could be a general mechanism to promote tumor growth. We also propose a model explaining how altered RNA editing levels affect microRNA-mediated post-transcriptional regulation of oncogenes and tumor-suppressors. RNA editing is a post-transcriptional process that alters nucleotide sequences, and thereby potential function, of virtually all RNA types in eukaryotic cells. Several independently evolved editing mechanisms have been classified, including co- and post-transcriptional base insertions, deletions, substitutions or modifications. RNA editing sites are normally detected by identifying mRNA sequences that do not match the corresponding encoding DNA sequences. Current approaches to RNA editing site detection rely on the comparison of paired RNA-Seq and DNA-Seq data and on the identification of variant sites supported by the former but not the latter. In human, there are two predominant nucleotide substitution types (A to I and C to U) catalyzed by specific enzymes families. The adenosine deaminases acting on RNA (ADARs) catalyze Adenosine (A) deamination in double-stranded RNA to generate inosine (I). As inosine has similar base-paring properties as Guanosine (G), it is read as guanosine by translating ribosomes and reverse transcriptases used to generate next-generation sequencing librariesAZIN1 mRNA has been shown to stabilize the corresponding protein and to promote cancer proliferationRNA editing can alter protein coding sequences, splicing, transcript stability and expression levels45Despite this growing evidence, few transcriptome-wide studies have explored RNA editing events in cancer. In this work, we first evaluated whether a significant number of novel  RNA editing sites appeared in cancer samples. We leveraged publicly available parallel sequencing datasets (with both DNA-Seq and RNA-Seq data) to characterize RNA editing in cancer samples, and selected neuroblastoma (NB), Diffuse large cell lymphoma (DLBCL) and Head and Neck Squamous cell Carcinoma (HNSC) to cover tumors with various mutation rates and hematological and solid tumors9Given the lack of evidence for the presence of cancer-specific (somatic) RNA editing sites, we next focused on those sites that are significantly altered between tumors and paired normal samples. For this analysis, we focused on known RNA editing sites annotated in the Rigorously Annotated Database of A-to-I RNA Editing (RADAR), which is the most up to date RNA editing sites database1214RNA-Seq based Alignment Artifact Removal (RAAR) pipeline (https://github.com/bioliyezhang/RAAR) to eliminate these potential sequencing artifacts , and multiple alignment-related artifacts 16Improved Accuracy. We optimized our algorithms to select the best alignment out of Blat realignment output. We compared our method with the GATK realignment methodGATK realigned output (56.1%)  No reliance on genome annotation. Our pipeline works without genome annotation, while most existing tools depend on exon-intron annotations16Easy to use. We adopted the most commonly used VCF and BAM formats as the input/output of the pipeline; we also aimed to simplify installation by making the tool dependent only on commonly available tools such as samtools, bedtools and Blat.The main advantages of our pipeline are: 1)  (56.1%) . We alsoTo characterize RNA editing sites in 13 DLBCL samplessite if there is at least one sample with a minimum of 5 RNA editing supporting reads covering the site, and if at least 5% of all reads manifests the substitution  in all samples. For each of these sites, we counted the number of samples showing presence (non-zero level) of editing. The resulting distributions are displayed in We wanted to ascertain whether RNA editing could function as an additional mechanism contributing to malignant transformation by generating novel  A-I RNA editing sites that are unique to cancer samples. As the majority of A-I RNA editing sites could be seen in all samples, this seemed unlikely.Since RNA-Seq data from paired normal samples for DLBCL and NB were unavailable, we could not address this question directly in these tissues. However, based on the assumption that cancer specific RNA editing should not be detected in normal cells, we checked for the presence of the newly discovered RNA editing sites in the Illumina Human Body Map (HBM) data 2.0Thanks to the availability of paired normal samples in the HNSC dataset, we next examined whether the novel RNA editing sites identified in the tumors could also be observed in the paired normal tissues. As shown in Given the lack of evidence for the presence of somatic A-I RNA editing sites, we focused on the significant editing level changes between normal and tumor samples of known RNA editing sites included in the RADAR databaseOur analysis identified a total of 166 differentially edited genes  showed increased editing with nominal P-value ≤0.05   .RNA editing sites in the protein coding regions only account for a small proportion of total RNA editing sites in the genome. The majority of RNA editing sites resides at intronic region, and several of these intronic sites can affect RNA splicing to regulate the functions of genes2829MDM4 and CASP8 . We first focused our attention on cancer census genes listed in the COSMIC databasend CASP8 . Similarnd CASP8 . Taken tGiven the abundance of RNA editing sites in 3′ UTR and the important role microRNAs play in tumor suppressionand whose expression is known to be repressed by microRNAs. We further restricted our analysis to those genes that showed consistent editing level increase/decrease across the entire 3′ UTR. The resulting top ranking gene was MDM2, a key oncogene in the p53 pathway with elevated expression in multiple tumor typesMDM2 expression levels. To evaluate whether RNA editing may regulate MDM2, we first identified the microRNAs predicted to bind at RNA editing sites with significant editing level changes in at least one tumor type. We found three microRNAs matching these criteria  sites showed a significantly higher positive correlation in the former  (http://bioinfo.mc.vanderbilt.edu/TSGene/), we found 5 additional RNA editing sites whose levels are positively correlated with corresponding mRNA levels in at least one cancer type  . When wecer type . While mI and IV . Taken tIn summary, our RNA editing profiling of three tumor types found no evidence of cancer specific or novel RNA editing events. On the other hand, our pan-cancer analysis of TCGA datasets corresponding to 14 cancer types identified RNA editing sites recurrently altered in coding and 3′ UTR that may facilitate tumor progression, and predicted the potential regulation mechanisms for many of such alterations. These results can serve as a starting point to further understand the mechanisms of RNA editing regulation and the functional roles of RNA editing sites that significantly change in tumors vs. normals.https://cghub.ucsc.edu/) using the GeneTorrent application The dbGaP accession numbers of the accessed datasets are: phs000235.v2.p1 for the lymphoma datasetTrim-galore . The RNA-Seq reads were aligned to hg19 reference genome using Tophat v2.0.4samtools v0.1.18 rmdup functionmpileup file was generated by samtools for each sample and used to call variants using Varscan 2.2.11The adaptor trimming was performed on the FASTQ files from the illumina human body map by using When retrieving the lowly edited and low coverage RNA editing events based on a relaxed threshold for the previously identified highly confident sites, we discarded any event with a total coverage lower than 10 reads.To validate the novel RNA editing sites we identified in DLBCL, we calculated the read depth and editing levels for each of these sites in the HBM dataset (n = 16), and for each sample we excluded those sites with read depth below 10 reads. When the read depth was ≥10, the number of RNA editing supporting reads was ≥1 and the allele frequency was ≥1%, we considered the sample as supporting the RNA editing event. When an RNA editing event was detected in more than two samples, we considered the corresponding site a real RNA editing site based on the HBM dataset. When the RNA editing event was detected in only one sample, we considered this site likely to be a real RNA editing site. When no sample met the minimum read coverage threshold, we concluded we did not have enough information to make a call on this site. When at least one sample had sufficient coverage but no sample supported the RNA editing event, we considered this site not to be an RNA editing sites based on HBM. To validate the potential cancer-specific RNA editing sites in HNSC samples in their paired normal tissue samples, we adopted the same criteria.http://mircode.org/info.phpmiR-EdiTar to visualize the binding between a microRNA and its target 3′ UTR sequencesWe downloaded the predicted microRNA binding data of highly conserved microRNA families from For all the tumor-normal pairs in 14 TCGA datasets, we calculated the RNA editing level  for each coding and 3′ UTR RNA editing sites in RADAR database. Then we used the paired student’s t test to evaluate whether tumor samples showed a significantly increased or decreased editing level compared with normal samples for each site within individual tumor type. The FDR (B-H) cutoff of 0.05 was adopted to control for multiple hypothesis testing.MDM2 are frequently observed in tumors and can confound our correlation analysis, we first removed the tumor samples where a copy number alteration is detected by GISTIC2. We then measured the association between changed log2MDM2 expression level and changed MDM2 RNA editing levels by Pearson correlation for each site in each tumor type. We performed the Student’s t-test to compare of the correlation distributions within the three hsa-miR-200b sites and in the remaining (non hsa-miR-200b) sites.As copy number gains in Human breast cancer lines MDA-MB-231 and BT474 cells were purchased from ATCC, and grown at 37 °C in DMEM and RPMI 1640 media. Media were supplemented with 10% FBS and 1X pennicillin/streptomycin. Cells were transfected with 50 nM control microRNA or miR200 by Lipofectamine RNAiMAX reagents .Lysate was collected 72 h after transaction, and analyzed by immunoblotting using anti-MEM2  and anti-GAPDH  as previously describedHow to cite this article: Zhang, L. et al. Altered RNA editing in 3′ UTR perturbs microRNA-mediated regulation of oncogenes and tumor-suppressors. Sci. Rep.6, 23226; doi: 10.1038/srep23226 (2016).