Arboviruses have been emerging and reemerging worldwide, predominantly in tropical and subtropical areas. As many arbovirus infections, including dengue (DENV), Zika (ZIKV), and chikungunya (CHIKV), have similar signs and symptoms, clinical diagnosis of arbovirus infections is challenging. Therefore, reliable laboratory tests are necessary to improve the clinical management of patients with suspected arbovirus infections. Real-time reverse-transcription PCR (RT-qPCR) is among the more effective methods to distinguish these viruses. The aim of this study was to construct a unique positive external control derived from a unique plasmid using genetic engineering for specific use in RT-qPCR assays to detect Zika, dengue (1–4), and chikungunya. An external control derived from the MS2 bacteriophage was constructed using sequences from arbovirus and human genomes. Laboratories were asked to test the control in the ZDC Biomol kit, a RT-qPCR kit which is able to detect Zika, dengue serotypes 1–4, chikungunya, and an internal human control. RNA extracted from the external control was able to be amplified and detected in RT-qPCR assays for each virus detected by using the ZDC Biomol kit. The external control, samples from viral culture, and infected patient samples display similar amplification using this assay. The pET47b(+)MS2-ZDC vector is a viable expression system for the production of external control viral-like particles (MS2-ZDC). The RNA from the recombinant particles can be easily extracted and can function as a tool to validate all steps of process from the extraction to the amplification of all targets in specific reaction. Thus, the MS2-ZDC particles are laboratory-safe in order to avoid risk for operators, and the phages are effective as positive control for use in the ZDC Biomol kit amplifying all kit targets making them effective for commercial profile. Emerging and reemerging viruses transmitted by arthropod vectors, such as yellow fever virus, Zika, dengue, chikungunya, Rift Valley fever virus, Japanese encephalitis virus, West Nile virus, Saint Louis encephalitis virus, Murray Valley encephalitis virus, Usutu virus, Spondweni virus, and O'nyong-nyong virus have transmission cycles in urban environments. These viruses cause overlapping clinical symptoms, and many patients develop serious physiological manifestations that can include death in severe cases , 2.The anthropogenic environmental factors, such as disorganized urbanization, population displacement, and precariousness of basic sanitation, have favored the transmission and spread of these viruses. The last decade was chaotic in Brazil with a large number of infected people and many complications caused by these viruses, especially microcephaly (Zika), hemorrhagic syndromes (dengue), and severe arthralgias (chikungunya). This high prevalence brought loss for public health and country economy –5.Since the clinical symptoms of arbovirus infections overlap, laboratory diagnostics are necessary to distinguish between them. Viral infections can be diagnosed through different methods including viral culture, serology, and molecular methods, and these same techniques are used to test for arbovirus infections , 7.The advances in molecular methods are recognized in virology field. The qualitative molecular assays allow the early virus detection prior to the development of a detectable immune response or when it may be more difficult or impossible to grow in culture media or detect it by antigen tests. An early diagnosis can have a prompt significant impact on patient care .Flavivirus), Zika , and chikungunya  worldwide in different samples such as serum, urine, cerebrospinal fluid, and saliva. RT-qPCR is easier to conduct than the other methods used to detect pathogens, and its quality depends specifically on the samples, the human operator, the nucleic acid test (NAT) kit, and PCR equipment [Real-time reverse-transcription polymerase chain reaction (RT-qPCR) has been used to detect dengue  for diagnosis of Zika, dengue serotypes 1, 2, 3, and 4, and chikungunya with an internal human control. This kit was used as a tool for Zika detection in saliva and urine from symptomatic patients. The results were validated with the extraction of RNA from viral cultures, which is a laborious method . FurtherThe synthetic nucleotide sequence was designed based on targets of the ZDC Biomol kit . The synthetic sequence covers all specific targets that were analyzed using tools from the National Center for Biotechnology Information (NCBI). After the synthetic sequence was synthesized, it was cloned into the pET47b(+)-MS2 vector . The derived vector  is referred to as pET47b(+)-MS2-ZDC.Escherichia coli in accordance with the manufacturer's instructions . The protocol for expression and purification was described previously by Zambenedetti et al. [β-D-thiogalactoside (IPTG), and after centrifugation, the supernatant was collected and processed with filtration for viral-like particles purification. After purification, viral-like particles were stored at –20°C.The pET47b(+)-MS2-ZDC vector was transformed into NiCo21(DE3)-competent i et al. , with soμL using the QIAamp viral RNA mini kit  in accordance with manufacturer's instructions. RNA was eluted at a final volume of 60 μL.RNA was extracted from viral stocks obtained from infected cells  and from MS2-ZDC controls with all targets for the 4 different reactions of ZDC Biomol kit. The extractions were done with aliquots of 140 The RNA was extracted from MS2-ZDC particles stored at –20°C for 0, 6, and 12 months.MS2-ZDC particles were submitted to stress conditions before extraction. The particles were thawed and submitted to special temperature conditions: 37°C and 70°C twice for 1 or 2 hours. The RNA from these particles was then compared to RNA from particles which were not submitted to stress conditions.μL of extracted ZDC-MS2 RNA and RNA from arbovirus cultures as a template in a 20 μL final volume RT-qPCR to detect ZIKV, CHIKV, and DENV (serotypes 1–4) and an internal control using a 7500 Real-Time PCR Instrument (Applied Biosystems®) following the manufacturer's instructions  and  followed 40 cycles ; the fluorescence was collected at 60°C).Amplification was performed using 9.5 The Biomol ZDC (IBMP) kit uses two duplex reactions and two triplex reactions in the same plate. One patient can be tested in four different assays: (1) Zika (FAM) and internal control (HEX); (2) Chikungunya (FAM) and internal control (HEX); (3) Dengue 1 (HEX), Dengue 4 (FAM), and internal control (Quasar 670); (4) Dengue 3 (HEX), Dengue 2 (FAM), and internal control (Quasar 670).We developed a positive external control based on a MS2 bacteriophage vector to be used in assays to detect the nucleic acids from Zika, chikungunya, and dengue virus serotypes 1–4. The pET47b(+)-MS2-ZDC vector was constructed after the insertion of a synthetic sequence into a unique BamHI site of pET47b(+)-MS2, which falls within the replicase gene of the MS2 genome. All of the constructs were confirmed by sequencing. pET47b(+)-MS2-ZDC could successfully generate MS2-ZDC particles whose genome harbored the synthetic sequence in the replicase gene. Consequently, a nonfunctional enzyme was produced, blocking replication of the genome.The RT-qPCR design for our experiments was composed of 4 different reactions which were able to detect (1) Zika, (2) dengue serotypes 1 and 4, (3) dengue serotypes 2 and 3, and (4) chikungunya, as well as a human transcript in each reaction as an internal control. RNA was extracted from MS2-ZDC particles and subsequently evaluated using the ZDC Biomol kit. The amplification results were equivalent to those obtained using samples from patients positive for each virus tested or RNA from virus culture (data not shown). So, this profile show that the particle construction, expression, purification, and RNA extraction were efficient presenting amplification for all targets in ZDC Biomol kit; our construction can be compared with a coinfected patient with all targets. The MS2-ZDC RNA control showed specific amplification for all targets in each reaction (four different reactions) of the ZDC Biomol kit and was expected in the same well in which the internal control was also amplified. The MS2-ZDC control amplifications are shown in To determine if the MS2-ZDC particles could be stored long-term and maintain stability, RNA extracted from MS2-ZDC particles stored at –20°C were assayed for Zika, chikungunya, and dengue 1–4 using the ZDC Biomol Kit. Aliquots stored for 6 or 12 months were compared with RNA from fresh particles which had not been stored , and theTo further test the stability of the MS2-ZDC particles, we subjected them to temperature stress conditions as follows: 37°C and 70°C for 1 or 2 hours. After stress of the particles from which we extract RNA for assay by RT-qPCR, no significant difference in Ct values was seen when compared to particles stored –20°C. Therefore, the particles are quite stable, even under changing temperature conditions (data not shown).Our study was focused on creating a positive control for a RT-qPCR-based diagnostic assay that detects Zika, dengue serotypes 1–4, and chikungunya for ZDC Biomol kit. We proposed an external positive control based on the MS2 bacteriophage modified. Generally, this strategy has been chosen when only one target is being assayed, but we developed our control to cover all seven targets in 4 multiplex RT-qPCR assays, based on the first control of our group that was used in HCV RT-qPCR assay . The staIn conclusion, our study demonstrated successful construction, expression, and purification of a novel external control which can be integrated into the ZDC Biomol kit. Controls such as the one described here are especially important to ensure diagnostic RT-qPCR quality in the face of emerging outbreaks of Zika, dengue, and chikungunya. Furthermore, the development of the control was cost effective and less time consuming than the use of viral cultures.We demonstrated the importance of the positive control in new discovery of our partners as presence of Zika virus RNA in urine and saliva from patients of Rio de Janeiro, Brazil, and the absence of RNA from Zika, dengue (serotypes 1–4) , and chiThe control was a fundamental point for development of a commercial research kit ZDC Biomol with all preparation steps performed in Instituto de Biologia Molecular do Paraná, Brazil. Currently, all laboratories operate under strict quality standards.


RNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution ‘reactivities’ for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation.Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms.Detection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the https://github.com/BagheriLab/DUETT.Bioinformatics online. RNA molecules play diverse functional roles ranging from catalysis of splicing and peptide bond formation, regulation of mRNA processing and gene expression, and molecular scaffolding and localization . These fRecently developed experimental techniques can characterize cotranscriptional RNA folding at nucleotide resolution  Strobel. Both diHowever, analysis of the cotranscriptional reactivity matrices has been mostly qualitative, relying on manual identification of reactivity trends to identify key regions that have biological significance. As the number and complexity of these datasets grow, quantitative and automated techniques are needed to robustly identify patterns. This automated quantitative approach is challenging, as cotranscriptional SHAPE-Seq datasets with complete annotations and validated structures are not bountiful. This scarcity of ‘ground truth’ examples presents difficulties when defining statistical models . SimilarTo overcome this challenge, we sought to develop a quantitative and automated approach to identify trends in cotranscriptional SHAPE-Seq datasets. We designed a systematic detection method that remains user-tunable with an interpretable set of parameters to easily match qualitative user expectations with quantitatively identified folding events. Other computational approaches have successfully emulated subjective human-driven analyses, such as extracting RNA design rules from a crowd-sourced game  RNA. We extend the methodology to analyze the folding pathways of the Bacillus cereus crcB fluoride riboswitch and corroborate previous manually identified transitions. In both datasets, our analysis reveals unexpected behavior, such as subtle reactivity increases that consistently occur roughly 12 nt lengths before a reactivity decrease, suggesting a highly-reactive transient state. Finally, we conduct parameter sensitivity analysis to explore the relationship between DUETT’s parameter values and detected events. The flexibility and interpretability of our approach enables the broad application of DUETT to many high-throughput experimental systems that require event detection.We present a framework for detecting events in cotranscriptional SHAPE-Seq datasets termed Detection of Unknown Events with Tunable Thresholds (DUETT). DUETT detects swing events using a strategy inspired by proportional-integral feedback control  and deteStructural events are characterized by significant reactivity changes at specific nucleotide positions, across sequential transcript lengths. We consider two common, yet distinct, phenomena in cotranscriptional SHAPE-Seq datasets: swing and ramp events . These tin silico RNA by first using NUPACK  with the BioProject accession code PRJNA342175. The data were processed with Spats v1.01 (https://github.com/LucksLab/spats/releases/) and the reactivity calculation scripts are located at https://github.com/LucksLab/Cotrans_SHAPE-Seq_Tools/releases/.We applied DUETT to simulated data and two RNA sequences characterized by previous cotranscriptional SHAPE-Seq experiments  that rearranges into a long helical structure with a hairpin loop and multiple inner loops  undergoes local structural dynamics when no fluoride is bound and is stabilized when fluoride is bound  yielded fewer overall events  for a total of 84 combinations. ρ reactivities increase at unpaired positions as RNAs elongate due to the normalization used in their calculation  begin near the 3' end of the nascent RNA shortly after the nucleotide’s transcription by RNAP . This clculation . In someρ reactivities for the ability to compare reactivities between different length transcripts and across different experiments. We note that correlations within a ρ reactivity vector make interpretation of detected events harder, especially when concurrent up and down events occur, as it could be reflective of either structural changes or reactivity calculation. We hope this method is readily adopted when studying new RNA systems, or interrogating publicly available datasets in the RNA Mapping DataBase ; the Center of Cancer Nano-technology Excellence initiative of the NIH’s National Cancer Institute [U54 CA199091 to N.B.]; and Northwestern University’s Data Science Initiative Award (to N.B.). Support was also provided by the Northwestern University Graduate School Cluster in Biotechnology, Systems, and Synthetic Biology (to A.Y.X.); and Tri-Institutional Training Program in Computational Biology and Medicine [NIH training grant T32GM083937 to A.M.Y.].Conflict of Interest: none declared.btz449_Supplementary_DataClick here for additional data file.


Introduction of reporter groups at designed RNA sites is a widely accepted approach to gain information about the molecular environment of RNAs in their complexes with other biopolymers formed during various cellular processes. A general approach to obtain RNAs bearing diverse reporter groups at designed locations is based on site-specific insertion of groups containing primary aliphatic amine functions (amino linkers) with their subsequent selective derivatization by appropriate chemicals. This article is a brief review on methods for site-specific introduction of amino linkers in different RNAs. These methods comprise: (i) incorporation of a nucleoside carrying an amino-linker or a function that can be substituted with it into oligoribonucleotides in the course of their chemical synthesis; (ii) assembly of amino linker-containing RNAs from short synthetic fragments via their ligation; (iii) synthesis of amino linker-modified RNAs using T7 RNA polymerase; (iv) insertion of amino linkers into unmodified RNAs at functional groups of a certain type such as the 5'-phosphates and N7 of guanosine residues and (v) introduction of an amino linker into long highly structured RNAs exploiting an approach based on sequence-specific modification of nucleic acids. Particular reporter groups used for derivatization of amino linker-containing RNAs together with types of RNA derivatives obtained and fields of their application are presented. RNAs are key biopolymers involved in life and reproduction of all organisms, and information concerning interactions of RNAs with their molecular partners (isolated biopolymers or complex supramolecular ensembles) occurring in the course of a number of essential biological processes is crucially important. Such type information is gained by application of special tools, RNA derivatives that bear chemical labels at selected locations. These labels could be cross-linkers enabling covalent attachment of the RNA to its binding partner, fluorescent probes and spin labels making possible monitoring RNA environment and distance measurements in complexes of the RNA derivatives, and other types of reporter chemical groups.N-oxysuccinimide ester) and a reporter group . Thus, the main task at the introduction of a chemical probe at definite RNA location is to insert an amino linker at the desired RNA location, which can be achieved by using various approaches. This can be performed in the course of chemical synthesis of the RNA utilizing the respective synthon, a modified monomer containing either the chemically protected amino linker that can be deprotected after completion of the RNA synthesis, or a precursor, which can be substituted with an amino linker in the synthesized oligomer [A general approach enabling insertion of various chemical probes at selected RNA locations is based on introduction of a group containing a primary aliphatic amine function (amino linker) at the desired location. Chemical properties of aliphatic amines drastically differ from those of amine groups naturally occurring in RNA, in particular, they are much stronger nucleophiles and can be easily acylated under mild conditions when the native RNA amino groups remain completely unreactive. Therefore, chemical probe can be selectively introduced at the amino linker by using bifunctional reagents containing both acylating function  modification of nucleic acids with derivatives of oligodeoxyribonucleotides complementary to a sequence adjacent to the target site. The method of complementary addressed alkylation was originally proposed by N.I. Grineva as early as in 1967 [An alternative strategy is direct introduction of amino linkers into the target RNA. This can be carried out by coupling of the linkers to functional RNA groups of a certain type, e.g., terminal phosphates or N7 of guanosine residues ,9,10. Fo in 1967 . The met in 1967 , and the in 1967 ,16,17,18In this article, we present a brief description of methods for introduction of amino linkers at desired RNA nucleotides and their subsequent derivatization with chemicals that can specifically react with the inserted amino linkers not affecting unmodified RNA nucleotides; methods for chemical synthesis of amino linker-containing RNAs are mentioned only fractionally, and the approach based on complementary addressed modification of RNA is discussed in more detail. Advantages and shortcomings of the methods presented in this review are discussed through the text, and examples of RNA derivatives bearing chemically reactive and fluorescent labels obtained on the basis of amino linker-modified RNAs are mentioned together with fields of their application.Currently it is possible to obtain oligoribonucleotides containing amino linkers at the following positions of nucleotides: C8 of adenosine, C5 of uridine, exocyclic amino groups of cytidine (N4), adenosine (N6) and guanosine (N2), phosphate at the 3'- or the 5'-terminus, and 2'- or 2'-O of ribose ,28,29,30Some kinds of modified oligoribonucleotides are now commercially available. Derivatives modified at the C5 of pyrimidines or C8 of purines are of special interest for studying RNA-RNA and RNA-protein interactions since these nucleotide positions are not implicated in Watson-Crick base pairing; therefore, introduction of chemical probes at these positions does not significantly impair double helical structures formed with the modified RNA and does not impede its interactions with other nucleic acids and proteins. As examples of particular approaches fruitfully used for chemical synthesis of oligoribonucleotides bearing an amino linker, we can mention those described in a series of works from Ven’yaminova’s group devoted to introduction of aliphatic diamine residues at the positions C5 of uridine, N4 of cytosine and C8 of adenosine via pre-synthetic and post-synthetic variants in the course of H-phosphonate synthesis of oligoribonucleotides. In the pre-synthetic variant, amino linker-containing synthon is incorporated into RNA chain in the course of its synthesis, in the post-synthetic variant an oligomer containing a Br- or chlorophenyl-derivatized nucleotide is synthesized with the use of the respective modified synthon, and Br- or clorophenyl group is then substituted with an aliphatic diamine ,21,22,23An approach to obtain RNAs containing an amino linker in a desired internal location utilizes an appropriate synthetic oligoribonucleotides bearing amino linker-modified nucleotides for assembly of the complete RNA sequences from two or more RNA fragments. This approach is based on splint-aided joining of RNA fragments by a ligase ; DNA oligomer splint serves to bring together 3'- and 5'-termini of RNA fragments to be ligated . The metIt should be noted that nonenzymatic approaches for joining of RNA fragments can be used too. An example of such approaches is Click nucleic acid ligation based on the Cu(I)-catalyzed azide-alkyne (CuAAC) reaction between RNA fragments carrying the respective modifications ,5,6.Uridine-5'-triphosphates (rUTP) derivatized with an amino linker at the C5 or bearing 2'-amino group instead of 2'-OH retain properties of T7 RNA polymerase substrate , therefoN-methylimidazole, N,N'-dimethylaminopyridine or other in organic media . RNA in this case is converted into the cetyltrimethylammonium salt prior to the synthesis to make it soluble in these solvents. This reaction was used for preparation of amino linker-containing short oligoribonucleotides utilized for preparation of photoactivatable mRNA analogs [N-2-chloroethyl-N-methylamino)benzyl-5'-phosphoramide derivatives of oligonucleotides used as mRNA analogs [Amino linker can be easily introduced into relatively short RNA at the 5'-terminal phosphate by its condensation with aliphatic diamines . The rea analogs . The sam analogs ,38,39,40 analogs ,17,18.N-2-chloroethyl-N-methylamino)benzylamine benzyl-5'-phosphoramide derivative of a DNA oligomer complementary to RNA sequence adjacent to the target site. Any RNA nucleotide (except for U) near the alkylating group of the deoxy-oligomer derivative bound in the heteroduplex can be modified at its nucleophilic center [2NH2 attached to the specific RNA nucleotide  modification of nucleic acids. The first step of the approach is sequence-specific modification of RNA with a 4- ,18, two In general, the method based on the complementary addressed modification of nucleic acids has no formal limitations, but technical difficulties are expected while working with RNAs > 400 nt long that can relate to low yields of RNA recovery from polyacrylamide gels upon its purification and low solubility of long RNAs in acidic medium that is necessary for hydrolysis of phosphoramide bond to occur.N-oxysuccinimide esters of carboxylic acid derivatives containing the required probes. Typical derivatives for the introduction of photoactivatable cross-linker are the esters of p-azidotetrafluorobenzoic acid [p-azidotetrafluorobenzoyl cross-linker at the desirable locations were applied as mRNA analogs to identify structural elements of the human ribosome implicated in formation of the mRNA binding center and to find peptides of translation termination factor eRF1 involved in stop codon recognition [p-azidotetrafluorobenzoylated nucleotide in its SECIS element was applied to study interactions of this element with the ribosome [N-oxysuccinimide ester of dyes Cy3 and/or Cy5. The reaction allowed selective introduction of labels into RNA required for RNA microarray method [N-oxysuccinimide esters, isothiocyanate derivatives can be used for coupling of reporter groups to amino-linkers attached to RNA [The most frequently used reaction for coupling of a reporter group to an amino linker is its condensation with oic acid ,47 and toic acid ; the estoic acid ,52,53,54ognition ,47,55. Aognition ,18. Finaribosome . Diaziriribosome . As an ey method , single y method ,52,53,54d to RNA .As for other reactions used for coupling of reporter groups to amino linkers, the reaction with 2,4-dinitro-5-fluorophenyl azide is worth mentioning. This reaction rapidly proceeds at room temperature at pH10 without serious RNA damage. It was applied in early studies for introduction of aryl azide photo cross-linkers into tRNAs bearing amino linkers on guanosine residues ,15,41 anCurrently relatively short RNA fragments with amino linker-modified nucleotides at desired locations became easily available due to the progress in elaboration of various approaches applicable for their chemical synthesis. Such amino linker-containing RNAs were used mainly for the subsequent derivatization with photo cross-linkers, and the resulting photoactivatable RNA derivatives turned out to be very fruitful tools for studying translational machineries of bacteria and higher eukaryotes ,47,55,56N-ethyl-N-methylamino)benzylamine, which is rather long and flexible. Although information gained with RNA derivatives obtained on the bases of the mentioned linker turned out to be in a good agreement with the related data obtained by means of other approaches [Summarizing, we can conclude that an approach based on the complementary addressed modification of nucleic acids is the most appropriate one for selective introduction of chemical reporter groups into natural RNAs containing minor nucleotides and possessing complex specific spatial structure. Since amino linker-containing RNAs obtained by this approach were used only for coupling with photo cross-linkers so far, it seems attractive to apply such RNAs for derivatization with other type reporter groups, which could be a task for further works. However, the variant of complementary addressed modification described here enables insertion of only one type of amino linker, namely, 4-(proaches ,18, it s


RNA modifications are chemical alterations that diversify the functionality of the canonical RNA building blocks. New mapping methods have focused considerable attention on the modification content of eukaryotic mRNA and its potential for the regulation of gene expression.1A, and how they impact interpretation of the resulting modification maps1. Particularly striking is the revelation that a previously used antibody binds cap structures in addition to m1A, and that previously reported m1A mapping results likely contain an abundance of false positives at the 5′-end of mRNA.A recent study reports on the differential specificity of antibodies directed against the RNA modification m2, which present a significant diversity of functional groups to mediate affinity and specificity. This situation is different for nucleic acids, as they possess comparatively limited structural diversity, and correspondingly less well-defined primary epitopes.While variabilities in antibody specificity are commonplace, the study by Jaffrey and colleagues will perhaps drive home the importance of specificity validation for antibody-dependent RNA modification mapping. What makes this case so relevant? Antibody biotechnology—and the many important tools it has produced—often rely on a combinatorial approach to identifying molecules with high affinity to specific epitopes. In the context of peptide binding, the variable region of typical antibodies recognizes an epitope of 5–12 amino acids3, albeit not under that acronym. Only the combination with RNA-Seq transformed it into a breakthrough technology for the RNA modification field in 2012, when two teams independently reported maps of m6A modifications in mammalian mRNA5. Since then, several antibodies have been used for mapping various RNA modifications6 with substantial impact for the community. Problems with antibody specificity have been discussed7, but have remained largely under-recognized.Antibodies directed against nucleic acid modifications have played an important role in the fields of epigenetics and epitranscriptomics. Enrichment of DNA containing 5mC by methylated DNA immunoprecipiatation (MeDIP) has been a widely used technique in epigenetics for decades, before its RNA version, MeRIP became popular. Indeed, MeRIP experiments have been published as early as the 1980s8. Of note, the existence of dm6A and dm4C in DNA of higher eukaryotes10 has been questioned by an antibody-independent analysis11. Another recent study has shown that several antibodies directed toward DNA modification cross-react with short tandem repeats in a modification-independent manner, which can in turn generate experimental noise as high as 99%12.More recently, studies in the DNA modification field have begun to identify sources of artifacts. For example, issues of inherent cross-reactivity can be amplified by low abundance of the primary epitope. This is exemplified by the cross-reactivity of antibodies to contaminating bacterial nucleic acids that can confound the modification analysis of eukaryotic DNA13, modification-specific antibodies could be expected to recognize the modified nucleobase irrespective of whether they are found in DNA or RNA. Thus, the demonstration of specificity problems in MeRIP experiments in the current publication by Grozhik et al. should not come as a surprise; rather it is a long awaited, experimentally thorough and convincing demonstration of antibody-dependent artifacts in the RNA modification field1. In addition to providing experimental guidelines for the field as a whole, the study also uncovers the unexpected binding of a commercially available anti-m1A antibody to cap structures. Furthermore, the study provides important clarifications in the controversial discussion regarding the number of m1A residues present in mammalian mRNA, which have been reported in several publications17. More specifically, the results reported by Grozhik et al. suggest that m1A is infrequent in mRNA, and that the prevalence of this modification was substantially overestimated in previous studies. A comparative assessment of two m1A antibodies led to vastly different results in MeRIP-type experiments, likely pointing to a general problem in the field. For one, specifications and specificity claims for a given antibody should be taken with caution and preferably confirmed for each application using the relevant controls. Secondly, it should now be clear that confirming antibody specificity by simple methods such as dot blot experiments should be considered insufficient18. Of the many validation techniques that the field has developed6, Grozhik et al. judiciously applied mass spectrometry and thin layer chromatography to characterize the physicochemical properties of material isolated by MeRIP1. A systematic characterization of the various antibodies commonly used might be highly beneficial, as was shown in the not-so-distant field of histone modifications. There, a systematic evaluation of antibody specificity was conducted using peptide-arrays, and revealed substantial specificity problems already several years ago19.Given that the development of such antibodies includes a conjugation step to a protein via the oxidized sugar moiety of a modified nucleoside20. For relatively abundant modifications, such as m6A, this may still be sufficient to produce credible mapping results. However, this may not be the case for less abundant RNA modifications.On a more fundamental level, one might question if a single methyl group in a nucleic acid fragment can really provide a sufficient level of selectivity for MeRIP or other similar techniques. Although our understanding of binding modes is limited, it is clear that the primary epitope can not only be the modification itself (i.e. a methyl group), but can extend to the modified nucleobase (i.e. adenine) and beyond. It follows that all adenines present in the RNA also compete for binding, albeit with lower affinity than the methylated adenine. In such a situation, the enrichment will be governed by the relative affinities toward modified and unmodified residues, and by their relative abundances. This, in turn, means, that any adenine in unmodified RNA (including polyA-tails) may give rise to non-specific binding, especially if the modification is of low abundance. It is thus perhaps not surprising that enrichment factors reported in MeRIP experiments are as low as 4–10-fold6, leading to artificial amplification of noise by PCR21. Potential artifacts resulting from RNA-Seq adapter design have also been discussed22. Moreover, problems in the computational analysis of RNA-Seq data, such as ambiguity in read mapping, are among the known error sources23. Finally, the field needs standards for stringent statistical data analysis24. Taking into account all these limitations, the massive datasets obtained by newly reported mapping techniques for RNA modification analysis should be considered collections of candidate modification sites, rather than experimentally confirmed modification landscapes. Therefore, further efforts should aim to develop and apply multiple orthogonal methods for the validation of modification sites and genome-wide patterns6.With respect to MeRIP in general, several additional problems exist that extend beyond antibody specificity, and which could skew the results of modification mapping experiments. A number of these problems are related to the experimental design of Illumina sequencing and library preparation protocols used. For example, early modification calling reports have neglected the use of unique molecular identifiers (UMI)


Several different lying positions, such as lying on the left side, supine, lying on the right side and prone position, existed when healthy people fell asleep. This article explored the influence of lying positions on the shape of ECG (electrocardiograph) waveform during sleep, and then lying position classification based on ECG waveform features and random forest was achieved.By means of de-noising the overnight sleep ECG data from ISRUC website dataset, as well as extracting the waveform features, we calculated a total of 30 ECG waveform features, including 2 newly proposed features, S/R and ∠QSR. The means and significant difference level of these features within different lying positions were calculated, respectively. Then 12 features were selected for three kinds of classification schemes.The lying positions had comparatively less effect on time-limit features. QT interval and RR interval were significantly lower than that in supine (When subjects were lying on the left side during sleep, due to the effect of gravity on heart, the position of heart changed, for example, turned and rotated, causing changes in the vectorcardiogram of frontal plane and horizontal plane, which lead to a change in ECG. When lying on the right side, the heart was upheld by the mediastinum, so that the degree of freedom was poor, and the ECG waveform was almost unchanged. The proposed method could be used as a technique for convenient lying position classification. Sleep is an essential process in human life, which plays a necessary role in self-repair, self-recovery of body condition, as well as integration and consolidation of memory. It is an indispensable part of human health. About one-third of a person’s lifetime is spent during sleep. Good sleep can eliminate fatigue, restore one’s strength and energy, and ensure body functioning well. For healthy subjects during the overnight sleep, different lying positions appear such as lying on the left side, supine (lying on the back), lying on the right side, and prone (lying on the stomach). This may cause the skin to squeeze or stretch, and the distance between the electrodes to shorten or prolong. On the other hand, the heart is squeezed slightly, and chest is pressed so that breath is influenced. All these body changes will result in ECG (electrocardiograph) waveform changes.As early as in 1997, in the course of clinical myocardial ischemia monitoring, Adams et al. had found that the side lying position frequently caused obvious ECG changes . Shinar Existing studies have shown that body positions and chest changes could cause changes in ECG waveforms, but there’s no study exploring the consistent principle of such changes in ECG waveforms, systematically. It is of great importance for researchers to consider these impact in mind from lying position changing when studying the ECG waveform changes in different sleep stages. And furthermore, these changes in waveforms can be applied to non-artificial and low-intrusion lying position supervision. Consequently, in this article, we present a method of exploring the influence of lying positions on the shape of ECG waveforms during the overnight sleep in healthy subjects, and then lying position classification based on such principle and random forest is applied.The study presented in this article can be divided into 3 parts. Data process mainly includes ECG signal preprocessing, character points detection, data epoch segmentation, features extraction with three kinds of waveform features. Then the significant differences between lying positions of waveform features are calculated. Finally lying position classification based on ECG waveform and random forest during sleep is achieved. The workflow is shown in Fig. The data used in this article was from the ISRUC web sleep database, which provided a variety of physiological data from 10 healthy subjects . The oveThe ECG signal in the ISRUC database mainly contained two kinds of noises, myoelectric interference caused by muscle electrical activity with a frequency of 2 Hz–2 kHz, and baseline drift caused by human respiratory coupling. In this study, first of all, the mean filter was applied to remove the interference from AC  in the ECG signal. Secondly, the three-layer lifting wavelet decomposition method was used to remove the high frequency myoelectric interference. Finally, the effect of baseline drift was eliminated by the function fitting method. Since this article was to explore the changes of ECG waveform features, it was necessary to acquire high accuracy point locations of P-wave, QRS-wave, and T-wave. In this study, the multi-character points detection algorithm of ECG signals based on wavelet transform, proposed by Yang et al. was used to decompose and de-noise the original signal, and the position of the QRS complex were obtained . Then thThe ISRUC database divided the subject’s overnight sleep data into 30 s epochs. Then the sleep stage of each epoch was determined and the lying position was recorded. In this study, we excluded the time segments whose lying position duration was no longer than 1 min (two epochs), and those the ECG signal waveform disturbed during the body position changing so that the character points detection could not be performed.The characteristics of ECG waveform morphology features and their meanings are shown in Table The calculation methods for several special waveform features are described as follows.The height of the waveform reflects the amplitude of the electrical signal. In actual ECG signal, the amplitude of the reference equipotential is not zero, and it fluctuates within a certain range. Therefore, the heights of P wave, R wave, S wave, and T wave cannot be directly represented by the vertical coordinates of waveform points. It is necessary to calculate the reference equipotential amplitude and the amplitude of each waveform with respect to the reference equipotential line. In TP segment all myocardial cells are at rest, so that there is no potential difference between them, and almost no electrical activity appears. TP segment is longer and more stable than PR segment, so TP segment was selected in this study to calculate the baseline equipotential line.Firstly, the mean filter was selected with width 5 to smooth the TP segment. Then we selected 5 points Slope features can reflect both time and amplitude change at the same time. The absolute value of slope features will increase with the amplitude of waveform increasing, and will decrease with the time interval increasing. Taking RT slope as an example, the formula for calculating the absolute value of the slope of the connection line between the R wave peak point and T wave peak point is as follows:In order to reduce the influence of lying position changes on the depth of Q-wave and S-wave, in this study we used the method of calculating the triangular-like area when calculating the QRS complex area. The origin of T wave might be affected by the double effect of the baseline drift and the ST segment change, resulting in different heights between the T wave start point and end point. Therefore, this method was also used when calculating the area under T wave. As shown in Fig. Among them, Q represents the Q-wave peak horizontal ordinate, S represents the S-wave peak horizontal ordinate, Ts represents the beginning of the T-wave horizontal ordinate, Te represents the end of the T-wave horizontal ordinate. The meaning of other segments in the formula is shown in Fig. QTc  is heart-rate-corrected QT interval, that reflects the entire process of cardiac depolarization and repolarization. The calculation formula is Bazetts’s algorithm as follows:Among the formula As shown in Fig. Angle ∠QSR is the angle value of the inner angle ∠QSR of the triangular QRS. Firstly, the lengths of QR, RS and QS are calculated. Then according to the cosine theorem, ∠QSR can be obtained. In this article, the unit of ∠QSR is degree, and the formula is as follows:RF (Random forest) is a novel classification method proposed by Breiman in 2001 . It is aBecause of the advantages of fast calculation, high precision, strong anti-noise ability and avoiding over-fitting when compared with other good classification method, random forest was chosen in this study. The number of trees was set as 500. After significance analysis, 12 features, including QT, RR, TP, ∠QSR, S/R, QR, P peak, R peak, T peak, T area, QRS area, T area/QRS area, were selected for classification.When establishing each decision tree, there are two random processes to avoid over-fitting. The input data for random forest is sampled by bootstrapping procedure randomly, that is, there may be duplicate samples in the input data. Assuming N dataset, the number of input data is also N. This makes the input data of each tree not a full dataset during training, making it relatively easy to avoid over-fitting.Then from M features, m features (m ≪ M) are randomly selected. After that, the decision tree is created by completely splitting way, so that either one leaf node of the decision tree cannot continue to split, or all the samples inside belong to the same class. Since the two random processes applied, over-fitting does not occur even without pruning. Every tree obtained by this algorithm is very weak, but they are very powerful when combined as random forest.Each decision tree is like an expert proficient in a narrow field (because we choose m from M features to let each decision tree learn), so that there is a random forest including many experts who are proficient in different fields. When solving a new problem (new input data), they can view this from different perspective. And in the end, various experts vote to get the results. In this study, we separated the data as training data and testing data, building the RF as classifier by TreeBagger through MATLAB and the classification was achieved. We randomly selected 1–99% of the data in the database as training data, and the rest as testing data. Then the learning curves including accuracy and Cohen’ k were plotted to verifying the absence of overfitting. When the proportion of training data was more than 30%, the accuracy and Cohen’ k didn’t increase any more. But when the proportion of training data was more than 50%, the accuracy was stable and the Cohen’ k started to decrease, which meant that the overfitting existed. As we can saw in Fig. The performance of classifier was evaluated by accuracy, Cohen’s kappa statistic κ, ROC–AUC (receiver operating characteristic curve–area under curve), Sensitivity, Specificity and F1-scores. Accuracy stands for the percentage of correctly classified epochs in the whole dataset. Statistic κ is a more effective evaluator because it takes the prior probability into account. It can be calculated asROC curve is a graphical plot that presents the ability of a binary classifier system. It is created by plotting the FPR  and TPR (true positive rate) at various threshold. Because that the classifiers in this study are ternary classifiers, after classification results are obtained, in order to draw ROC curve and calculate the AUC, Sensitivity, Specificity and F1-scores of one lying position, the other two lying positions are combined. E.g. before drawing ROC curve and calculating such several indexes of lying on the left, epochs of supine and lying on the right are combined as not-left, then the 2 × 2 confusion matrix is built.Generally speaking, a good classifier should be associated with high values of accuracy, statistic κ and AUC.In this study, we developed three kinds of classification scheme for different cases, including subject specific scheme, subject independent scheme without feature normalization and subject independent scheme with feature normalization. The result of ECG waveform features significance analysis between different lying positions will be presented in “A total of 5114 epochs of the overnight sleep data from 9 subjects were included in this study. Due to the fact that most subjects did not have prone position, or only had several prone epochs in overnight sleep, the prone epochs were manually removed. Consequently, there are only three classes in classification including lying on the left, supine, and lying on the right. The details and workflow are shown in Fig. For each subject, 20% epochs of three kinds of lying positions were randomly selected for training the classifier, and the rest 80% were used as testing data. The reasons 20% for training and 80% for testing are that on the one hand, the waveforms were obviously different in 3 lying positions. Strong significant difference of waveform features appeared in “For each specific subject to be analyzed, all the records from other 8 subjects were pooled together to form the training dataset. This process repeated for 9 times. Finally, the same as the specific scheme, the average value and standard deviation of accuracy and κ statistic were calculated.However, because of the individual differences, all features need normalization before classifier training. One of the most widely used normalization method is to transform all the features scales to a new range, such as . But when the outliers of data appear, the transformed data scale will be unsymmetrical. To solve this problem, we developed a normalization method based on quantile. The 5% and 95% quantiles of data were selected firstly and the scale of these two samples was linearly transformed to , which covers 90% of the whole samples. The rest samples were transformed with the same linear coefficients.A total of 5114 epochs of the overnight sleep data from 9 subjects were included in this study. Table This study calculated the 30 waveform features of the overnight ECG sleep data from 9 healthy subjects in the database, and calculated the means and standard deviations according to the four lying positions. The calculation results and significant differences between the different lying positions are shown in Tables After significance analysis, 12 features, which showed strong significant difference between lying positions including QT, RR, TP, ∠QSR, S/R, QR, P peak, R peak, T peak, T area, QRS area, T area/QRS area, were selected for classification. Table Table Because the results of subject specific scheme presented in Table The reason why we developed three kinds of schemes is that firstly we tried to establish a database which could be used for many subjects. However, because of the individual difference, the results were not acceptable. Consequently, we applied the normalization method to transform all the features scales to a new range. The results of subject independent scheme with feature normalization were much better but the accuracy was still not enough for clinical application. Finally, we developed the subject specific scheme, which was similar to building a database with the ECG data from a specific subject and then classifying the lying positions for this subject based on the database. That’s why the results were acceptable and this method could be applied in clinical monitoring.As can be seen from Table It can be seen that the influence of lying position on ECG waveforms is mainly reflected in the amplitude features and double-direction features. The amplitude features include the heights of P wave, R wave, and T wave. The relative height features include QR potential difference, RS potential difference, R peak T peak potential difference, and RT slope. Area features includes QRS complex area and T wave area. These three types of amplitude features were significantly smaller when lying on the left side than those in supine or right, or less than those in other two lying positions simultaneously. Only a few features show significant differences between supine and lying on the right side.However, the S-wave-related waveform features are different. When lying on the left side, the depth of S wave is significantly greater than that in supine, and S/R is significantly greater than that both in supine and right. This feature reflects the decrease of R wave and the deepening of S wave in left-side lying. ∠QSR is significantly smaller in left than that in supine and right. This feature reflects the difference between the relative depth of the Q wave and S wave.The influence of lying positions on ECG waveforms is mainly reflected in the amplitude features. Since the ECG waveform directly reflects the potential difference of the leads, and the signal is extracted from the electrodes on body surface, the body position changes will cause a change of relative position between the electrodes and heart. Thus ECG waveform morphology changed. This change can be embodied in two aspects. On the one hand, when the chest is under pressure, the distribution of body fluids changes, so that the impedance of the chest changes. Also the heart is squeezed and deformed. On the other hand, the heart is affected by gravity when lying on the side. Different parts of heart have different degree of freedom, which results in heart rotation and swing.The significant differences of ECG waveform features in 3 lying positions could be utilized for automatic lying position classification during sleep. For three kinds of schemes, the overall classification accuracy of subject specific scheme reached 97.17%, κ statistic 0.91 and AUC > 0.97, which was almost perfect. This can be used for clinical lying position monitoring after setting up a subject specific dataset. Further study in Table The accuracy of classification results may be influenced by the ECG quality. Firstly, in order to distinguish the horizontal features , we chose the dataset with sampling rate 200 Hz. This could make sure that the time resolution was 0.005 s. Secondly, when the subjects were turning over during sleep, the signal was disturbed severely and we had to discard this epoch. But when the subjects were not changing their lying position, the signal was stable. Thirdly, we applied signal preprocessing based on wavelet transform, and it worked well. At last, the ECG signal acquisition technology is mature in recent years. As mentioned above, the ECG signal quality was good enough for this study, which could be reflected in the accuracy of character points detection.The bottom of heart in anatomical mainly consists of left atrium and a small part of right atrium, where the aorta and pulmonary artery cross . BecauseIn a complete cardiac cycle, action potential begins from the sinoatrial node firstly, and then passes through the anterior, middle and posterior inter-nodal tract to the atrioventricular node. During this process the electrocardial vector is always from the upper right to the lower left. The process of forming the P loop is shown in Fig. VCG intuitively reflects the direction and magnitude of the action potential vector in heart, and the ECG is actually the projection of the vector in different leads. The relationship between frontal VCG and limb lead, transverse VCG and chest lead are shown in Fig. The accessible volume of heart in chest is larger when the subject is lying on the left side, because the left lung of human body is smaller than the right part and the heart is at the left side inside human chest. Therefore, the swing amplitude of heart is relatively larger. When subject is lying on the right side, the apex of the heart moves towards the mediastinum, and the heart rotates clockwise along the long axis. There shows a right-leaning tendency on the electric axis . However, because the heart is upheld by the mediastinum, the range of motion is limited, so there is no obvious swing and rotation as lying on the left side. This may explain the results that waveform features rarely show significant differences between supine and lying on the right side.The changes of position and shape of heart in chest have drawn the researchers’ attention. Mincholé et al. modeled the changes in the Karhunen–Loeve transform coefficients of the QRS complex and the ST–T waveform. It was found that the changes in body position can be reflected in the gradual changes of the two coefficients series. Then based on ECG, they determined the lying position changes of healthy people. The resulting probability of detection reached 94%, and the probability of false alarm was 0%, respectively. However, the false alarm rate in ischemia database was once per hour . Since mKutbay et al. study showed that the AHI (activity apnea-hypopnea index) and average minimum oxygen saturation (SOP) were significantly lower in supine than those in other lying positions, and the heart rate as well as average awakening index were higher . George Researchers have tried to classify lying positions form ECG, but most of them can only detect body position changes without lying position classification. Shinar et al. used the R wave duration (RWD) as indicator of body position changes for healthy subjects, who were asked to rotate between four body positions . They could identify over 90% of the changes in body position. However, they couldn’t identify the exact body positions . In theiThe researchers also tried to classify lying position by other signals and sensors. In 2011, Zachary et al. presented a method for lying position classification using load cells placed under bed, which resulted in generalized accuracies of 0.68, 0.57, 0.69, and 0.33 for the back, right, left, and stomach positions respectively, and 0.92, 0.75, and 0.86 for the back/stomach, right, and left positions respectively . The resStudies about the influence of human lying position on ECG waveform during sleep can be widely applied in different field. First of all, changes of S wave and T wave can be used to correct the shape of ST–T segment, which can improve the determination accuracy of myocardial ischemia, and warn the sudden death early and effectively. Secondly, when studying the changes of ECG waveform and the related features in different sleep stages, the influence from lying position should be taken into consideration. Furthermore, in the process of collecting body signals and studying changes in physical conditions during sleep, if we can achieve lying positions determination based on ECG, the number of signal acquisition channels and the workload of researchers in monitoring process can be reduced. Also, patients will feel more comfortable. On the other hand, lying position monitoring can also prompt the patients to adjust their lying position during sleep, consciously. So that the frequency of respiratory disorders and sleep apnea events can be reduced. The occurrence of disease symptoms can probably be avoided and finally, sleep quality can be improved.In conclusion, this study explored the influence of lying positions on the shape of ECG waveform during sleep, and then lying position classification based on ECG waveform features and random forest was achieved. When subjects were lying on the left side during sleep, due to the effect of gravity on heart, the position of heart changed, for example, turned and rotated, causing changes in the VCG of frontal plane and horizontal plane, which lead to a change in ECG. When lying on the right side, the heart was upheld by the mediastinum, so that the degree of freedom is poor, and the ECG waveform is almost unchanged. The overall classification accuracy of subject specific scheme reached 97.17%, κ statistic 0.91 and AUC > 0.97, while the results of subject independent scheme with feature normalization were accuracy 63.87%, κ statistic 0.32 and AUC > 0.66, respectively. The proposed method could be used as a technique for convenient lying position classification.


Geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is characterized by irreversible loss of macular retinal tissue and retinal pigment epithelium (RPE) cells. Several studies have revealed that accumulation of Alu RNA in RPE cell causes RPE cell degeneration in AMD. In the present study, systemic Alu RNA expression levels were determined in 33 subjects with GA and 40 control subjects using a proprietary Alu RNA quantification method. It was observed that the expression level of Alu RNA was not significantly different between GA and Control groups . In addition, the systemic level of Alu RNA was not associated with subject characteristics, such as GA lesion size and SNP profiles of complement factors associated with increased risk of AMD. In conclusion, the usability of systemic Alu RNA expression level as a biomarker of GA secondary to AMD could not be established in this study. Age-related macular degeneration (AMD) is one of leading causes of blindness in elderly people in the United States . In earlcomplement factor H (CFH) are associated with an increased risk of developing AMD [complement pathway, such as in complement component C3 (C3), complement component C2 (C2)/complement factor B (CFB), and complement factor I (CFI), are also associated with an increased risk of AMD [AMD is a multi-factorial and complex disease. Several genetic factors, including single-nucleotide polymorphisms (SNPs) and the affected genes, have been reported to be associated with AMD . The intping AMD –12. In ak of AMD . The relk of AMD –15. ThesAlu elements in retrotransposons [Alu sequence. Thus, in the present study, we investigated the relationship between systemic expression of Alu RNA and the pathology of GA associated with AMD using a novel proprietary method to quantify the expression levels of Alu RNA in the blood that enables us to avoid the contamination of genomic Alu sequence.Alu RNAs are classified as short interspersed nuclear elements and are transcribed from nsposons . These ensposons . Recent nsposons –18. ThusThis study was a non-interventional, cross-sectional study, assessing the feasibility of using systemic Alu RNA expression level as a biomarker of GA in patients with AMD. The duration of this clinical study was up to 7 days. Blood samples were collected from eligible subjects on Day 1. Other assessments were made within 7 days prior to Day 1.The study was conducted as per the guidelines of Declaration of Helsinki and was approved by the institutional review boards at Research Ethic Committee of Santen Pharmaceutical  and Chesapeake IRB . All the subjects were enrolled at three clinical sites in the United States upon obtaining the approval from Chesapeake IRB. Written informed consent was obtained from all the study participants. Also, the sample analysis and statistical analysis were performed at Santen Pharmaceutical Co. Ltd. after obtaining the approval from Research Ethic Committee of Santen Pharmaceutical.2) to 7 DA (17.5 mm2) in either eye. The subjects without AMD, more severe than level 1 , a history of other retina diseases, and/or a history of other severe ophthalmic diseases were enrolled in Control group.The subjects (≥ 50 years old) were enrolled in GA and Control groups. The subjects with a medical history of exposure to factors that alter the expression of Alu RNA, such as those using glibenclamide, drug treatment for depression, or those who were diagnosed to have Alzheimer’s disease, were excluded from both the groups. The patients with GA secondary to AMD in either eye, and without a history of other retina diseases, were enrolled in GA group. The inclusion criterion of total GA lesion size was from 1 disc area , which included best-corrected visual acuity , slit-lamp biomicroscopy, indirect ophthalmoscopy, and fundus autofluorescence (FAF) imaging (for GA group only).FAF imaging was performed for the GA group only. The GA lesion area was defined as the area of decreased FAF signal detected by the fluorescein angiography feature of Confocal Scanning Laser Ophthalmoscopy (cSLO) system . The size of the GA lesion was semi-automatically measured using Heidelberg Eye Explorer software or its equivalent. The eye with the larger GA area  was chosen as the study eye.This analysis was performed only for the subjects in the GA group. Whole blood sample (8.5 mL) was collected from the subjects in the GA group in PAXgene Blood DNA tubes  at Day 1. DNA was extracted from whole blood samples using PAXgene Blood DNA Kit . Genotyping of SNPs in the genes of the complement pathway was performed by TaqMan SNP genotyping assays . Six SNPs were confirmed in the complement pathway .Alu genomic DNA. In brief, total RNA was extracted from 0.2 mL of plasma sample using miRNeasy Serum/Plasma Kit  and miRNeasy Serum/Plasma Spike-In Control . The extracted RNA samples were reverse transcribed to cDNA using miScript II RT kit . The cDNA was used as a template for quantitative real-time PCR that was performed on ABI Prism 7500 Fast Sequence Detection System. Quantification of Alu RNA was performed using miScript SYBR Green PCR kit  and Alu RNA specific forward primer (5'-CAA CAT RGT GAA ACC CCG TCT CT-3′). The synthesized RNA was used to normalize the data for the standard sample. Data set was normalized by the synthesized RNA, that had Alu RNA sequence, and miRNeasy Serum/Plasma Spike-In Control and then the copy number was calculated.In a previous study, it was indicated that systemic expression levels of Alu RNA have diurnal variations . TherefoSAS software  was used for statistical analyses. All measurements were summarized by subject group (GA and Control groups), descriptively. The expression levels of Alu RNA were determined for each group using log transformation. The analysis methods are summarized in A total of 73 subjects were enrolled for the study; 33 subjects were enrolled in GA group and 40 were enrolled in Control group. The demographics and characteristics of the subjects are summarized in The mean expression level (log transformation) (SD) of Alu RNA was 21.4 (0.6) in GA group and 21.2 (0.8) in Control group whereas the median level was 21.3 in both the groups. There was no significant between-the-groups difference (P = 0.251) .There were between-the-groups differences in subjects’ age and sex; adjusted between-the-groups comparison was conducted using ANCOVA. However, the results of ANCOVA showed that there was no between-the-groups difference in the expression levels (log transformation) of Alu RNA, adjusted for age or sex .Furthermore, there was no between-the-groups difference in the expression levels (log transformation) of Alu RNA in any of the sub groups .In GA group, BCVA in the right eye was very weakly correlated with the expression level of Alu RNA (correlation coefficient = -0.307). In the left eye in GA Group and in each of the eyes in Control Group, BCVA was not associated with the expression level of Alu RNA .In GA group, the size of GA lesion in the study eye was measured using the FAF images. The mean GA lesion size (SD) was 9.69 (4.98). The correlation between the expression level of Alu RNA and the total GA lesion size was also analyzed; we did not find any correlation between the two (correlation coefficient = 0.126).C2/CFB risk-allele, except for one subject for whom it could not be determined. All the subjects had the C3 risk-allele. The expression levels of Alu RNA in the presence or absence of the risk-allele were compared for CFI and CFH. No significant differences in the expression levels of Alu RNA were observed among the subgroups (The genotyping of genes associated with the complement pathway was performed in GA group. The heterozygotes and homozygotes for the risk alleles were defined as “Risk-allele Yes” because of the small sample size in this study. All the subjects had the ubgroups .This is the first study to analyze the relationship between the systemic expression levels of Alu RNA and GA secondary to AMD. Previous studies indicated that the expression of Alu RNA in the ocular tissue is associated with degeneration of RPE and development of AMD –18. HoweThe relationships between the systemic expression levels of Alu RNA and ocular conditions were also investigated. In GA group, BCVA in the right eye was very weakly correlated with the systemic expression levels of Alu RNA; however, this was not the case for the left eye in this group. Overall, the expression level of Alu RNA was not associated with BCVA in both the eyes. If the systemic Alu RNA levels were associated with BCVA in one eye, they should also be correlated with BCVA in the other eye. Therefore, the observed weak correlation in only the right eye cannot be considered to have any meaning.CFI and CFH but did not find any difference in the expression levels. These results indicate that risk alleles for CFI and CFH are not associated with the systemic expression of Alu RNA. The Alu RNA pathway may independently associate with the degeneration of RPE through the alternative complement pathway, especially CFI and CFH. The relationship between the expression level of Alu RNA and C2/CFB and C3 was not analyzed because all eligible subjects had these risk alleles.SNPs in the risk alleles associated with the complement pathway are associated with an increased risk of developing AMD ,9–12. ThIn conclusion, the usability of systemic Alu RNA expression level as a biomarker of GA secondary to AMD could not be established in this study. For further investigation of the utility of Alu RNA as a biomarker, the relationship between the expression levels of Alu RNA in ocular tissues and GA lesion size and/or rate of progression should be investigated. Chorioretinal tissues are ideal for the investigation; however non-/less-invasive collection of such a sample is difficult and not suitable for biomarker studies, and is therefore not possible in clinical settings. Although their feasibility is unknown, methods for measuring the expression levels of Alu RNA or related cytokines, such as IL-18, in the anterior aqueous humor and tear fluid, or novel bioimaging technologies for detecting biomarkers in chorioretinal tissues are warranted.