Recently, due to the coronavirus pandemic, the need for early diagnosis of infectious diseases, including viruses, is emerging. Though early diagnosis is essential to prevent infection and progression to severe illness, there are few technologies that accurately measure low concentrations of biomarkers. Plasmonic nanomaterials are attracting materials that can effectively amplify various signals, including fluorescence, Raman, and other optical and electromagnetic output. In this review, we introduce recently developed plasmonic nanobiosensors for measuring viral DNA/RNA as potential biomarkers of viral diseases. In addition, we discuss the future perspective of plasmonic nanobiosensors for DNA/RNA detection. This review is expected to help the early diagnosis and pathological interpretation of viruses and other diseases. In addi studied . In orde studied , fluores studied , SERS bi studied ,10, colo studied , etc.).  studied , superio studied , excelle studied , and ant studied . Among t studied . Irradia studied ,18. The  studied . For exa studied . The str studied . In thisThe unique properties of plasmonic nanomaterials have been frequently applied to the development of superior biosensors for the improvement of biosensor performance, such as the sensitive detection of biomarkers ,26,27,28In biosensing applications, the interaction between biomolecules and plasmonic materials changes various optical properties of plasmonic materials . By measuring the changed optical properties, it is possible to detect a biological material, and this change occurs sensitively. For example, the intrinsic emission intensity of a fluorescent material decreases (quenching effect) at a constant distance from the plasmonic nanoparticles, changes in wavelength (FRET), and is amplified (MEF) ,33,34. IMost DNA and RNA viruses show similar structures, such as the capsid proteins and nucleic acids, including double- or single-stranded DNA or RNA ,40. As mFRET is a distance-dependent energy-transfer phenomenon between the donor (fluorophore) and acceptor (another fluorophore or quencher) ,46,47. FAs described above, plasmonic nanomaterials have great optical properties, originating from the surface plasmon resonance. In the case of FRET, Au nanoparticle is one of the good acceptors and can absorb emitting light from the fluorescent materials (donor) and hinder the emission. Lu et al. developed the Au nanorod-based FRET biosensor for the detection of the hepatitis B (HBV) virus with a simple strategy  Figure a. FAM-la−14 to 10−9 g/mL. Choi et al. demonstrated the CRISPR-Cas12a-based DNA biosensors by integrating the MEF effect for signal amplification and sensitive detection [Besides the FRET effect, the combination of plasmonic nanomaterials and fluorescence molecules can induce a change in fluorescence property, which is an enhanced emission intensity . The suretection   and Re-CO (2025 cm−1) by the intercalation of DNR, complementary binding of cfDNA could induce an increase in the intensity of 2113/2025 cm−1 ratio. The detection range was from 600 to 60,000 copies/mL using an isolated DNA sample from patients and healthy donors. Kim et al. developed the novel hierarchic interfacial Au nanocube for an effective SERS generation [Among the several optical properties, Raman scattering is one of the specific properties of every molecule. Raman is a light scattering phenomenon by incident light. Most of the scattered light from molecules can divide into two scatters, Rayleigh Scatter and Raman scatter, a small amount of light at different wavelengths . This waneration  , the individual localized plasmon fields are coupling, and the absorbance peak moves from blue to red, which shows a change of color ,65,66 T. Li et aBesides the optical detection strategies for viral DNA sensing, electrochemical methods have been developed using plasmonic nanomaterials due to their great electrical property. Shariati et al. exhibited the impedimetric electrochemical biosensor for the ultrasensitive detection of the human papillomavirus (HPV) DNA  Figure d. Using As described in For the diagnosis of the viral infection, RNA is also a potential biomarker, similar to the viral DNA. Representatively, the common cold, influenza, and SARS-CoV-2 have RNA as a genetic material to generate a viral disease. Similar to viral DNA assays, RNA is mainly measured using nucleic acid amplification methods such as PCR or using complementary binding phenomenon. Several kinds of research on improving the performance of sensors using plasmonic nanomaterials have been actively conducted . In part3O4@Au@Ag@SiO2 nanoparticles were functionalized by TAR RNA, which was to specifically bind the tetraphenylethylene (TPE)-labeled TAT peptide. Once the target ligand was bound to the TAR RNA, the TPE was far away from the Fe3O4@Au@Ag@SiO2 nanoparticles, and the MEF effect was decreased. The LOD for each ligand could be obtained as 63 nM for neomycin B, 502 nM for chlorpromazine, and 36 nM for mitoxantrone, respectively. The authors claimed that this sensing system could provide a potential drug screening platform related to viral RNA-associated diseases. Shen et al. exhibited an ultrasensitive norovirus RNA quantification biosensor by integrating DNA-Ag nanocluster for the MEF effect [For the sensitive detection of RNA strands, MEF-based fluorescence biosensors have been developed by using plasmonic phenomena of diverse nanomaterials. Lee et al. fabricated the magneto-plasmonic multifunctional nanorod for the ultrasensitive measurement of exosomal microRNA (miRNA)  Figure c. The maF effect . The DNA2 to 106 copies/mL with 51.38 copies/mL of LOD. Dardir et al. developed the intracellular RNA biosensor for the monitoring of influenza RNA mutation using thiolated DNA hairpin and Raman dye-functionalized Au nanostar [As with the measurement of viral DNA, the Raman-based measurement has been consistently used for the diagnosis of viral RNA. In particular, the use of plasmonic nanomaterials in the process of converting the amount of RNA into a measurement signal can induce the amplifying of the existing Raman signal (SERS), so it can be effectively used for RNA detection, which is less stable than DNA and requires more sensitive measurement . Nasir enanostar   and machine learning (ML). Among the analysis methods described above, signals obtained through SERS are complex for humans to determine the infection or not. Because the interpretation of Raman peaks is important for the precise analysis of viral nucleic acids, if ML techniques are combined, more accurate and precise diagnostic results can be obtained. Third, it can be effectively integrated with the measurement method using CRISPR gene scissors, a nucleic acid diagnostic method that has been widely applied recently. Although research on diagnostic methods using CRISPR gene scissors is being actively conducted, research using plasmonic nanomaterials still needs more research for effective viral detection. If the advantages of CRISPR gene scissors and plasmonic nanomaterials are combined, better results can be obtained for measuring existing viral DNA and RNA.In this review, we briefly discuss recent plasmonic biosensors that detect viral DNA and RNA. Plasmonic nanomaterials can serve to convert and amplify external light energy into various other optical readouts due to their unique surface plasmon properties. Using this, it was possible to rapidly and sensitively measure various viral DNA and RNA. In this summary, representative measurement methods include FRET and MEF using fluorescence, SERS amplifying Raman signals, and LSPR-based color-changing biosensors that can detect with the naked eye. In plasmonic biosensors for the detection of virus-derived nucleic acids, the advantages of each analytical method are different, but in common, the measurement sensitivity is improved, the required time is reduced, and a simpler measurement is possible. The development of such advanced plasmonic biosensors is expected to increase the survival rate of patients through accurate diagnosis of various nucleic acid-based fatal diseases as well as viral diseases in the future and enable early diagnosis and personalized treatment through gene analysis.


Ribonucleic acid (RNA) therapeutics are being actively researched as a therapeutic modality in preclinical and clinical studies. They have become one of the most ubiquitously known and discussed therapeutics in recent years in part due to the ongoing coronavirus pandemic. Since the first approval in 1998, research on RNA therapeutics has progressed to discovering new therapeutic targets and delivery strategies to enhance their safety and efficacy. Here, we provide an overview of the current clinically relevant RNA therapeutics, mechanistic basis of their function, and strategies to improve their clinical use. We discuss the 17 approved RNA therapeutics and perform an in‐depth analysis of the 222 ongoing clinical trials, with an emphasis on their respective mechanisms and disease areas. We also provide perspectives on the challenges for clinical translation of RNA therapeutics and suggest potential strategies to address these challenges. RNA therapeutics take on various forms and this review will largely focus on messenger RNA (mRNA), antisense oligonucleotides (ASOs), small‐interfering RNA (siRNA), and aptamers. These therapeutics have been used to treat diverse diseases by regulating protein production and function, and in doing so, various biological functions. The first clinical approval of an RNA therapeutic came in 1998 when the Food and Drug Administration (FDA) approved Vitravene (fomivirsen) for the treatment of retinitis caused by cytomegalovirus. There have since been 16 additional approvals with the most recent approval in early 2022 when Spikevax  was upgraded from emergency use authorization. While RNA therapeutics have been studied in research laboratories for over 30 years,2The central dogma of RNA therapeutics is the modulation of protein function and/or production, either by directly targeting proteins, interfering with RNAs encoding the relevant proteins, or providing the genetic code for protein production. There are three major ways in which this central goal is accomplished: (i) binding and blocking of proteins using aptamers; (ii) targeting and binding to native RNAs using ASOs, siRNA, and miRNA mimics; and (iii) expressing target proteins using mRNA., and a variety of other nanoparticles.mRNAs are currently the most investigated RNA therapeutic type in active clinical studies.The most successful, in terms of number of approved products, of the RNA therapeutics are ASOs. ASOs are single‐stranded oligonucleotides that target endogenous RNA, including noncoding RNA (ncRNA) and mRNA. ASOs are usually between 13 and 30 base pairs long and rely on complementarity for their function.siRNA therapeutics share similarities to ASOs. siRNAs are double‐stranded RNAs (dsRNAs) that are usually around 20 base pairs long. They are used for gene downregulation or complete silencing. siRNAs comprise of a “passenger” strand and an “antisense” strand that is complementary to the mRNA sequence of interest, similar to ASO‐mediated RNA knockdown or steric translation inhibition Figure . After eThe final RNA therapeutics discussed here are aptamers. Aptamers are ssRNAs of 25–80 base pairs that incorporate hairpin folding to form highly specific binding surfaces Figure , analogoNotably, RNA therapeutics hold several advantages over other therapeutic modalities such as small molecules, antibodies, and DNA therapeutics. One major advantage is their capability to target undruggable targets that conventional therapeutics cannot.3Seventeen RNA therapeutic products using mRNA, ASOs, siRNA, or aptamers have been approved worldwide Table . These t3.1Two mRNA therapeutics (Comirnaty developed by Pfizer‐BioNTech and Spikevax developed by Moderna) have been approved; both are mRNA‐based COVID‐19 vaccines. These vaccines utilize mRNA sequences that code for the SARS‐CoV‐2 spike protein, which is responsible for virus binding to host cells. Upon expression of spike protein analogs by muscle cells and antigen presenting cells local to the intramuscular injection site, patients develop antigen‐specific cellular and humoral immune responses.3.2Ten ASO products have been approved worldwide for treating genetic and infectious diseases Table . Nine ofWhile Vitravene was unsuccessful in the clinic, its landmark approval introduced ASO therapeutics to the clinic. Nine ASO products have since received approvals, all for treating genetic diseases Table . Four ofSpinraza (nusinersen) is an ASO approved for treating another muscular genetic disorder, spinal muscular atrophy (SMA).Milasen is a personalized medicine approved by the FDA in 2018 for treating Batten's disease in a then 8‐year‐old patient. The patient had an insertion mutation in the gene encoding major facilitator superfamily domain containing 8 (MFSD8) protein, which likely plays a key role in cellular ion transport, that leads to significant central nervous system malfunction.Tegsedi (inotersen) is an ASO that was approved for treating transthyretin (TTR)‐mediated familial amyloidotic polyneuropathy (FAP). TTR is an important thyroid hormone transport proteinKynamro (mipomersen) is an ASO that was approved for treating homozygous familial hypercholesterolemia (HoFH). FH results from a mutation in the gene encoding low‐density lipoprotein receptor (LDLR), which decreases its expression. LDLR facilitates LDL cholesterol (LDLC) endocytosis and hepatic clearance. Patients with an under‐expressed LDLR have elevated LDLC levels, which can lead to atherosclerotic cardiovascular disease (ASCVD), from buildup of plaque on blood vessel walls.Of the 10 approved ASO products, only one had received approval by the EMA but not the FDA. Waylivra (volanesorsen) is a 2' MOE ribose substituted ASO that was approved for treating familial chylomicronemia syndrome which occurs from loss‐of‐function mutations in the gene encoding lipoprotein lipase, a key enzyme for degradation of triglycerides into free fatty acids. Abnormally high circulating triglyceride can cause pancreatitis.3.3O‐methyl (2'‐OMe) ribose substitutions to enhance stability and complementary base pair binding, is formulated in lipid nanoparticles (LNPs), and is injected intravenously.Our search identified four approved siRNA products which all received approvals in or after 2018. Onpattro (patisiran) is the first approved siRNA product that was indicated for treating TTR‐FAP. It cleaves mRNA at the complementarity site using the RISC, resulting in decreased TTR expression. Onpattro utilizes 2'‐GalNAc conjugate mediated transfection provides the ability to target diseases in hepatic cells. To date, two other siRNA‐GalNAc therapeutics have received approval for modulating hepatic enzyme expression. Givlaari (givosiran) received approval for treating acute hepatic porphyria. Mutation in the delta‐aminolevulinic acid synthase (ALAS) gene causes ALAS overexpression. ALAS is an important enzyme in the heme synthesis pathway and its overexpression can lead to an increase in porphyrins (heme precursors) causing a negative feedback loop.3.4Macugen (pegaptanib) is the only aptamer therapeutic approved for treating neovascular macular degeneration (wet AMD). Wet AMD arises from angiogenesis toward the outer retina causing fluid accumulation in and around the retina. This can lead to blurred vision and eventually blindness if left untreated.4clinicaltrials.gov to identify active clinical trials on RNA therapeutics. We performed searches in both the “Other Terms” and “Intervention/Treatment” categories, using the terms “RNA therapeutics,” “mRNA,” “siRNA,” “ASO,” “aptamers,” “miRNA,” and alternative versions . Under the “Recruitment Status” section we checked “Not yet recruiting,” “recruiting,” “enrolling by invitation,” and “Active, not recruiting.” Also, in the “Study Type” category, we selected “Interventional Studies .” We excluded studies that were listed as “Not Applicable” in the “Phase” category. A total of 415 trials were initially identified. We then excluded 43 trials that were regarding cell therapies that incorporated one of the listed RNA therapeutics. Additionally, 189 trials were excluded because they mentioned one of the RNA types but were not regarding treatments, for example, trials that mention measuring endogenous mRNA transcripts. This resulted in 183 trials of interest. We also searched the websites of relevant biotech and pharma companies and found an additional 39 trials, bringing the total to 222 trials. The data were collected in November 2021 and the RNA therapeutic type is summarized in Figure We performed a search on 4.1Our analysis showed that the majority of trials 53.2%) are mRNA‐focused with fewer siRNA (24.3%) and ASO (19.8%) trials. Six ongoing trials are outside of these three major RNA therapeutics and related to aptamer, miRNA mimic, self‐replicating RNA (srRNA), and self‐amplifying RNA (saRNA) Figure , Table 53.2% are 4.2The largest segment of ongoing RNA therapeutic clinical trials is the 118 mRNA‐based trials. A majority of these trials are in early‐phase with 72.5% in Phase 1 or 2. However, the mRNA category has a higher percentage of late‐phase trials than the collective RNA therapeutics Figure . Of noteAfter removing the COVID‐19 mRNA trials, 38 novel therapeutic trials were left. Most of these trials (95.8%) are in early‐stage with a high percentage (60.4%) of Phase 1 trials Figure . As expeThe genetic disorders studied in the identified mRNA trials are mostly endocrine disorders, with one metabolic disorder, ornithine transcarbamylase deficiency Table . One of The non‐COVID‐19 infectious disease trials aspire to vaccinate against viruses including cytomegalovirus, rabies, respiratory syncytial virus, zika, influenza and HIV Table . They alThe most common administration route within the non‐COVID‐19 mRNA trials is IV (44.7%), all for treating diseases of genetic origin and cancer Figure . Both di4.3A total of 44 active ASO clinical trials were identified. A substantial number (25.0%) of these trials are in late‐phase but are mostly investigating previously approved products for assessing their longer‐term safety/efficacy and their application in different patient populations Figure . Only thFurthermore, cancer (15.9%) and physiological diseases 11.3%) make up most of the rest of the ASO trials Figure . The can% make upThe administration method for these ASO trials is mostly IV 36.4%), intrathecal (31.8%), IVT (13.6%), or SC (15.9%) Figure . All of 6.4%, int4.4Accounting for ~25% of the ongoing RNA therapeutic trials are 54 siRNA trials. These trials are mostly comprised of Phase 1 (25.5%), Phase 2 (32.7%), and Phase 3 (40.0%) Figure  with onlGenetic diseases (33.3%) and physiological diseases (38.9%) are the clear leaders in disease categories Figure . All of Additionally, 11.1% of the identified siRNA trials focus on cancer Figure . All of 4.5Six of the identified RNA therapeutic trials involve RNA types beyond mRNA, ASOs, and siRNA Table . All but5While the clinical trials and approvals demonstrate the promise of RNA therapeutics, their development is still relatively in the early‐stage when compared to small molecule drugs or protein biologics, which each had 34 and 14 new approvals, respectively, in 2021 alone.Additionally, formulation for enhanced stability and delivery is certainly a challenge for RNA therapeutics to advance in clinical trials and to treat broader diseases. While GalNAc conjugates are widely used for siRNA, these only provide targeted delivery to the liver. Many of the ongoing trials use an administration route for improving delivery to the desired tissue, for example, all ophthalmological RNA therapeutics are administered IVT or intraocularly. In addition, vehicles may need to be designed based on the target cell of interest. For example, LNPs designed to transfect antigen‐presenting cells for vaccine may not work well when the goal is to produce cytokines like interleukin 2, mostly expressed by T cells.Additional challenges that RNA therapeutics face is the storage and stability issue. Generally, cold chain conditions are used to mitigate degradation and aggregation of RNA therapeutic molecules and their carriers.6Despite all the challenges, RNA therapeutics are emerging as a major therapeutic modality. The ability to control protein expression has broad reaching impacts in diverse diseases, as highlighted by the wide range of diseases discussed in this review. With treatment modalities now approved in each of the major RNA therapeutic categories and significant academic and industrial research into improving their clinical use, RNA therapeutics are poised to revolutionize the way diseases can be treated. While the 1990s and 2000s introduced RNA therapeutics to the clinic, the 2010s provided only a few approvals, mostly in orphan genetic diseases, the early progress since 2020 and clinical trials highlighted here show that the next decade should prove monumental in the evolution of RNA therapeutics. While this process will be met with challenges in manufacturing, delivery, and safety, it will be an exciting process to see unfold in preclinical and clinical trials.Alexander Curreri: Data curation (lead); formal analysis (lead); methodology ; validation ; writing – original draft (lead); writing – review and editing . Disha Sankholkar: Data curation (supporting); formal analysis ; methodology ; writing – original draft (supporting). Samir Mitragotri: Conceptualization ; supervision ; writing – review and editing . Zongmin Zhao: Conceptualization ; supervision ; validation ; writing – original draft (supporting); writing – review and editing .https://publons.com/publon/10.1002/btm2.10374.The peer review history for this article is available at Figure. S1 Landscape of disease indications for 141 ongoing clinical trials for RNA therapeutics that are not COVID‐19 vaccines.Figure S2. Landscape of mRNA therapeutics with 118 ongoing clinical trials. The trials were analyzed based on (a) Phase and (b) Prior Approval Status.Figure S3. Landscape of ASO therapeutics with 44 ongoing clinical trials. The trials were analyzed based on (a) Phase and (b) Prior Approval Status.Figure S4. Breakdown of the Modification and Substitution types utilized in ongoing ASO clinical trials. (a) Backbone modifications. (b) Ribose substitutions. (c) Bridge substitutions.Figure S5. Landscape of siRNA therapeutics with 54 ongoing clinical trials. The trials were analyzed based on (a) Phase and (b) Prior Approval Status.Figure. S6. Breakdown of the conjugate types utilized in ongoing siRNA clinical trials.Click here for additional data file.


Coral reef restoration activity is accelerating worldwide in efforts to offset the rate of reef health declines. Many advances have already been made in restoration practices centred on coral biology , and particularly those that look to employ the high adaptive state and capacity of corals in order to ensure that efforts rebuilding coral biomass also equip reefs with enhanced resilience to future stress. We horizon scan the state-of-play for the many coral restoration innovations already underway across the complex life cycle for corals that spans both asexual and sexual reproduction — assisted evolution , biobanking, as well as scalable coral propagation and planting — and how these innovations are in different stages of maturity to support new 21st century reef management frameworks. Realising the potential for coral restoration tools as management aids undoubtedly rests on validating different approaches as their application continues to scale. Whilst the ecosystem service responses to increased scaling still largely remain to be seen, coral restoration has already delivered immense new understanding of coral and coral-associated microbial biology that has long lagged behind advances in other reef sciences. Coral reef management is at a historical turning point. Management strategies until now have centred on conservation through marine protected areas alongside mitigation of local and regional stressors, such as declining water quality from coastal urbanisation, and intensive agriculture and industry in reef catchments. Whilst these strategies undoubtedly slow the decline of reef health , reef dein situ or land-based nurseries. To develop more standardised practice as well as catalyse innovative tools, activities have become increasingly networked via regional or global bodies  with goals to more systematically and collectively build knowledge [sensu [The last decade has witnessed acceleration of reef restoration activities around the world , often tnowledge , whilst nowledge  Box 1)..in situ nowledge ,15. Of pe [sensu ) and intBox 1.Protect existing reefs first: Minimise impacts that increases the extent of restoration required.Work together: Collective action across practitioners as well as across disciplines is required to build scalable solutions.Maximise biodiversity to reach recovery goals: Biodiversity — including genetic diversity of individual species — is a key factor regulating reef resilience to stress.Select appropriate reef areas: Not all areas can (or should) be restored, and requires consideration of ecological, economic and cultural values.Use natural regeneration where possible: Adopt-nature-based solutions — as well as identifying barriers slowing natural recovery — is essential where possible.Select species to maximise biodiversity: Adopt a species priority framework relative to ecosystem service recovery goals.Use resilient material: Rebuilding ‘reefs of tomorrow' with coral that is more tolerant to anthropogenic stressors .Plan ahead for infrastructure, capacity and  supply: Ensuring access to sufficient and appropriate material, and longevity of monitoring out-planting needed to gauge ecological success over time.Learn by doing: Outcomes are often context specific and so practices require ‘fast-fail' exercises to tailor, as well as fully reporting ‘failures’.Make it pay: Ensure financing is in place to deliver goals — in particular where coral is not a CO2 sink and therefore does not directly fall under blue carbon market economies.Several disciplines are now captured within the new field of coral reef restoration, including geoengineering  [Most coral restoration efforts have historically used coral genotypes from the sites in need of restoration or from nearby — less affected and healthier — sites. However, adaptation to the local prevailing environmental conditions means that such genotypes inevitably carry low survivorship to further disturbance events of similar nature to those that caused severe degradation initially. This is of particular concern for climate change-driven summer heatwaves, which are predicted to become more frequent, protracted and severe over the coming decades ,4, causiion (AE) . AE is aion (AE) ,19.Assisted gene flow and assisted colonisation (or assisted migration) refer to the translocation of individuals within and beyond their distribution range, respectively. The rationale is that local adaptation has resulted in different alleles and/or allele frequencies at loci underpinning thermal tolerance, and that adaptation to rising temperatures can be enhanced by translocating individuals harbouring these adaptive alleles. Following translocation, it is anticipated that transplants interbreed with the native population and that heat tolerance alleles will be incorporated into the native genetic background. Translocation can be achieved with both early life stages or adults, within a reef [n a reef , to a nen a reef  or over n a reef  or betwen a reef . Findingex situ with individuals of lower tolerance inhabiting cooler reefs, followed by deployment of intraspecific hybrid offspring onto depauperate (cooler) reefs for restoration in anticipation of future heatwaves [selective breeding for production of more thermally tolerant offspring for restoration [interspecific hybrid offspring ; thus, Preconditioning is an AE technique whereby an environmental stress memory is induced by exposing corals to sublethal stress to drive enhanced stress tolerance [in situ nurseries (Propagation and planting).olerance . Stress olerance . Whilst olerance  and acidolerance  stress colerance . On balaex hospite (in culture) via experimental evolution [Host-associated microbes play critical roles in holobiont physiology, stress tolerance and adaptation to environmental change . Corals volution  , promptn corals . Inoculan corals . In recen corals ,70 or a n corals  lowered n corals  — as oppcoral and coral-associated microbial biobanks are necessary to safeguard diversity against uncertain future environmental conditions. Developing and scaling infrastructure to ensure a living repository is therefore a core requirement for coral restoration ..coral anon (e.g. ), cryopr-Pacific , and in -Pacific . Major e-Pacific , given t-Pacific  and phen-Pacific ). Bio-ba-Pacific  and willthe) fundamental operational bottleneck to ultimately rebuild biomass back to reefs. Adult coral is commonly propagated asexually using in situ nurseries as well as land-based aquaria [In situ nursery practices have advanced by tailoring species choice, location to optimise growth, and/or design to improve cost-effectiveness [nutraceuticals (and prebiotics) — remains largely unexplored within coral restoration despite decades of research identifying how specific resources enhance coral tolerance to suboptimal environmental conditions (e.g. [Capacity to effectively propagate and (re)plant coral is a . New totiveness . Nurseritiveness . Land-bativeness  but willtiveness . As suchns (e.g. ).en mass once or twice per year [ex situ [larval settlement devices that support early growth of corals  on the ng (e.g. ). In allPlanting (‘out-planting' or ‘deployment’) adult coral material  or newly settled larvae is a resource- and time-intensive restoration step. Heterogeneous topographies and habitats of reef systems make random deployment highly ineffective. Fixing material to the reef has increasingly evolved towards rapid and high-throughput chemical [chemical  or physichemical   to overchemical ). Practichemical ) needed Together, these various steps have already increased the scale — and hence cost-effectiveness — of planting over the past few years ,100,101,Addressing the root causes of human disturbances such as climate warming and deteriorating water quality, and targeted conservation measures will be essential for healthy coral reefs to persist into the next century; however, it is inevitable that coral restoration-centred activities become an increasingly dominant aid to regional reef management portfolios in the short- to mid-term, particularly where local stakeholder motivation intensifies to actively steward improved reef health ,7. Conse(1)Advancing the scale of practice to achieve ecological outcomes. Whilst scalable and cost-effective coral propagation and planting activity is increasingly evident ,107(1)Ant Building social support to motivate political and industry buy in. Reef livelihoods and cultures rely on healthy sites, and so industries such as tourism — but also traditional owners — already see the value of coral restoration to enhance existing stewardship activities .(3)Overcoming economic inertia to value the environment. Mitigation strategies almost certainly rest on prioritising economic capital into a low carbon economy and safeguarding reef systems. However, investing into more sustainable use of reef systems is required as the human footprint continues to grow. Diverse funding streams — spanning philanthropy, corporate investment and Government grants — have provided the capital to initiate coral restoration programs (e.g. [ms (e.g. ). Howevems e.g. ,112) — t — t(3)Oms (e.g. ) — is crUltimately cost is no doubt a fundamental constraint for local uptake of many activities outlined here and a major barrier to equitable access and/or deployment at relevant scales. Investment therefore needs to prioritise mobilisation of restoration tools for adoption by the global stakeholders (locations) most in need. In turn, local governance will need to be pre-armed with capacity to rapidly activate adoption within risk-assessed 21st century management frameworks to capitalise on the rapidly advancing financing opportunities for coral and reef restoration.Within a 21st century reef management framework, acceleration of coral reef restoration practices will undoubtedly be fuelled via the UN Decade of Ecosystem Restoration ,106, whiCoral restoration is a rapidly emerging discipline within reef sciences, and approaches that exploit the natural adaptive capacity of corals across sexual and asexual life cycle phases are already in various stages of maturity towards more diverse reef management frameworks. Feasibility and scalability by which approaches are applied are advancing continuously. Whilst significant steps and time-frames are still required to understand how increasing activities in reef systems are impacting key ecosystem service values, global coral restoration efforts have already catalysed new knowledge of coral and coral-associated microbial biology that has progressed more slowly compared with advances in other reef sciences (e.g. ecology).Restoration practices centred on coral biology  have rapidly accelerated via a global drive for practical local action whilst critically tackling climate change.Many methods are in various stages of maturity, all of which encompass the high adaptive capacity of corals across their complex life cycle where proliferation occurs through sexual reproduction and asexual  propagation.Innovations in assisted evolution-based tools, biobanking and propagation practices are transforming understanding of coral biology that has long lagged behind advances in other reef sciences (e.g. ecology).in situ, but how these deliver ecosystem service outcomes remains to be seen, requiring longevity in economic inertia and social gains, and ensuring restored coral carries increase resistance to on-going environmental stress.Efforts continue to deliver increases to the scale of feasible coral restoration


To gain a molecular understanding of the deFADding reaction, an RNase AM crystal structure with three manganese ions coordinated by a sulfate molecule and the active site amino acids was generated that provided details underlying hydrolysis of the FAD cap. Our findings reveal a general propensity for 5′-3′ exoribonucleases to hydrolyse and degrade RNAs with 5′ end noncanonical caps in addition to their well characterized 5′ monophosphate RNA substrates indicating an intrinsic property of 5′-3′ exoribonucleases.Identification of metabolite caps including FAD on the 5′ end of RNA has uncovered a previously unforeseen intersection between cellular metabolism and gene expression. To understand the function of FAD caps in cellular physiology, we characterised the proteins interacting with FAD caps in budding yeast. Here we demonstrate that highly conserved 5′-3′ exoribonucleases, Xrn1 and Rat1, physically interact with the RNA 5′ FAD cap and both possess FAD cap decapping (deFADding) activity and subsequently degrade the resulting RNA. Xrn1 deFADding activity was also evident in human cells indicating its evolutionary conservation. Furthermore, we report that the recently identified bacterial 5′-3′ exoribonuclease RNase AM also possesses deFADding activity that can degrade FAD-capped RNAs  S-adenosyl methionine (SAM) , flavin adenine dinucleotide (FAD), coenzyme A (CoA) and ne (SAM) . These mne (SAM) .Escherichia coli, the NAD cap protects RNA from 5′ end degradation . Nevertheless, the function of these metabolite caps in RNA metabolism remained elusive. It is important to note that identification of the enzymatic machinery involved in addition and removal of these metabolite caps is imperative to understanding their biological function. Our initial screening of putative metabolite cap decapping enzymes for FAD cap (deFADding) led to the identification of Rai1 along with its mammalian homolog DXO  and identified novel FAD cap associated proteins in budding yeast. Interestingly, in addition to their role in hydrolyzing NAD-capped RNA , Xrn1 anin vitro transcribed FAD and m7G capped RNAs (∼400 pmol) were immobilized onto Dynabeads™ MyOne™ Streptavidin T1 (Invitrogen™). Yeast cells (7G-capped RNA linked Dynabeads in 2 ml microcentrifuge tubes. These tubes were next incubated at 4°C on an end-to-end rocker for 90 min. After extensive washing (at least 5 times with 1× lysis buffer) the associated proteins were eluted with 2× Laemmli buffer (Bio-Rad Laboratories). The samples were denatured at 85°C for 5 min and were next run on Novex™ WedgeWell™ 4–20 %, Tris–glycine protein gel (Thermo Fisher Scientific). The protein gel was stained with SYPRO Ruby (Bio-Rad Laboratories) or with silver staining as described before  was mixed with a fixed concentration (5 pmol) of 32P uniformly labeled NAD- or FAD- or pA- in vitro transcribed RNA in 1× RBB buffer  and incubated on ice for 30 min. The samples were resolved on to a 6% acrylamide gel using 0.5× TBE at 120 V for 2–3 h. The dried gel was exposed to phosphor screen overnight and visualized with an Amersham Typhoon RGB Biomolecular Imager .Recombinant in vitro assays were purified as described previously (® Snap Assembly (TaKaRa Bio). For the construction of different point mutants, site directed mutagenesis were performed using SPRINP  and cells grown at 18°C on a shaker for ∼18 h. Cells were collected by centrifugation at 5000 g for 15 min, washed with PBS. For protein purification, cell pellets were resuspended in ∼60 ml of buffer (25 mM HEPES (pH 7.5), 250 mM KCl, 10% glycerol, and 100 μM MnCl2) containing 0.4 mg/ml protease inhibitor phenylmethanesulfonylfluoride fluoride (PMSF). Cells were lysed by sonication, and the insoluble cell debris was separated by centrifugation. The nickel-NTA affinity purification was performed as described previously  was included in the transcription reaction. In vitro transcription was carried out at 37°C overnight, using HiScribe™ T7 High yield RNA Synthesis kit (New England Biolabs). To generate 32P uniformly labeled RNA, the transcription reactions were performed in the presence of [α-32P] GTP.NAD, FAD and mart site . For m7G32P] GTP uniformly labeled FAD-capped RNAs were incubated with the specified amounts of recombinant protein in NEB buffer 3 . For RNase AM, 4 mM MnCl2 was used instead of MgCl2. Reactions were stopped by heating at 85°C for 2 min. In vitro 5′end RNA decay was carried out as described (xrn1Δ strains were incubated with [α-32P] GTP uniformly labeled FAD and m7G-capped RNA immobilized onto Dynabeads™ MyOne™ Streptavidin T1 (Invitrogen™) in NEB buffer 3.The [α-escribed , cell exBacterial and yeast total RNA were extracted using hot phenol , whereasin vitro transcribed FAD capped RNA was used. For the Xrn1 hydrolysis reactions, 100 mM of the WT or E178Q catalytic-inactive mutant Xrn1 was used. For RNase AM, 150 nM of WT or catalyically-inative D20A mutant was used.To demonstrate the release of intact FAD upon Xrn1 or RNAse AM mediated deFADding of FAD capped RNAs, FAD-capQ assay using different amounts of E. coli RNase AM was used to transform E. coli BL21 (DE3) Rosetta cells. His-tagged RNase AM was produced and purified like previously reported , 20 mM, imidazole, 5 mM β-mercaptoethanol (BME), 10% (v/v) glycerol, 0.1 mM MnSO4 and 1 mM phenylmethanesulfonylfluoride (PMSF)) was incubated with 2 ml of Ni-NTA superflow resin (Qiagen) with nutation for 1 h. RNase AM was eluted with 5 ml of buffer containing 250 mM NaCl, 20 mM Tris (pH 7.5), 250 mM imidazole, 5 mM BME, 10% (v/v) glycerol and 0.1 mM MnSO4 and further purified with gel filtration  chromatography  and 2 mM DTT).An expression construct for full-length reported . Prior tP21.Attempts to crystallize RNase AM alone or supplemented with FAD did not yield diffraction quality crystals. Good-quality RNase AM crystals were obtained in the presence of dsRNA  using tChromobacterium violaceum (PDB: 2YB1) (Molecular replacement (MR) using full-length amidohydrolase from B: 2YB1)  did not B: 2YB1) . A correB: 2YB1)  and manuB: 2YB1) . The finin vitro transcribed FAD-capped RNA containing a 3′-biotin was used as bait to capture and identify proteins associated with the FAD cap from S. cerevisiae lysate as illustrated in Figure 7G or FAD cap) were used to account for common RNA-binding proteins. Affinity purified proteins associated with the different RNAs were identified by mass spectrometry and the m7G-capped RNA was used as the control. Mass spectrometry identification of proteins selectively bound to the FAD-cap (xrn1Δ) strain which still retains the association of Rat1 and Rai1 but as expected is devoid of Xrn1 association  where Rat1 is still detected with the FAD-capped RNA (We recently employed NAD cap RNA affinity purification (NcRAP) to characterize NAD cap binding proteins to study their biological roles . Similar FAD-cap  revealed FAD-cap ,28 . Equimolar concentration of Xrn1 and uniformly labeled s Figure  that is s Figure . Use of s Figure  that wers Figure . These f32P-labeled FAD-capped RNA was degraded by wild-type Kluyveromyces lactis Xrn1 and Schizosaccharomyces pombe Rat1, but not their corresponding catalytically inactive mutant protein xrn1-E178Q and rat1-E207Q respectively  RNA degradation under the same assay conditions, we carried out time-course Xrn1 decay assays with 5′P- or FAD-capped RNAs. Intriguingly, the activity of Xrn1 on 5′P RNA was only ∼1.3-fold more efficient than FAD-capped RNA at equivalent enzyme concentrations Figure . A compaTo further delineate the differential preference of FAD- over NAD-capped RNA by Xrn1, its degradation activity was deciphered in the presence of excess NAD/FAD or their respective RNA mimics. Consistent with the decay assays, free NAD or NAD3′P which would mimic the 5′ end of an NAD-capped RNA, were inefficient in altering the decay of either 5′P- or FAD-capped RNA . Minor iWe next compared the activity of Rat1 on the hydrolysis of 5′P to that of FAD-capped RNA. As shown in Figure en masse . We conin vitro   ,15, whicM (yciV) . IntriguM (yciV) . Strikino Figure . Additioo Figure .rnase AM knockout E. coli strains were subjected to FAD-capQ and levels of released FAD detected. As shown in Figure rnase AM knock out strain relative to the WT strain. Our findings demonstrate that E. coli RNase AM can function as a deFADding enzyme in cells.The effect of RNase AM on endogenous FAD-capped RNA was next determined. Total RNA from WT or E. coli RNase AM in complex with FAD in an effort to elucidate the mechanism for RNase AM monophosphate hydrolysis and deFADding activities. RNase AM expressed in the presence of the iron chelator 2,2′-bipyridyl and MnSO4, to prevent Fe from being the cation in the active site  as the search model s Figure  and D. Ms Figure . Mnγ is Figure i  shows thFigure i  and D. DFigure i . TogetheAF)  is buried in a positively charged cavity between the PHP and insertion domains comprised primarily of residues Arg107, Arg110, and Arg141  . RNase AÅ Figure  and C. C Figures  and F. S7G) on the 5′ end of mRNAs in eukaryotes is the most common and well-studied 5′ RNA cap (S. cerevisiae that can interact with the FAD cap. Surprisingly, the predominant FAD-cap interacting proteins were the 5′P dependent exoribonucleases that also possessed deFADding activity. Moreover, we demonstrated that the deFADding activity of 5′ end exoribonucleases was apparent in bacteria, yeast and humans is indicative of convergent evolution of this intrinsic property. RNase AM is so far the only 5′-3′ exoribonuclease known in E. coli, the enzymatic activity of which has been shown to be essential for the processing of bacterial ribosomal RNA (The 5′ cap of RNA is a critical determinant of the RNA fate. The N7-methyl-Guanosine (m RNA cap . Recent  RNA cap . These comal RNA .Klebsiella pneumoniae, Salmonella enterica, Vibrio cholerae, Yersinia pestis (We recently reported that both Xrn1 and Rat1 possess deNADding activity  in addita pestis  and the E. coli cells assessed by FAD-capQ analysis validated that these 5′-3′ exoribonucleases can impact levels of total FAD-capped RNA in cells. These findings establish Xrn1 and RNase AM as novel deFADding enzymes adding to the existing list of enzymes that function in FAD cap decapping in cells including Rai1 (Our analysis of the levels of FAD capped RNA isolated from both yeast and human Xrn1 knock out cells and RNase AM knock out ing Rai1 , its maming Rai1 . We alsoing Rai1 , FAD caping Rai1 ,36.bona fide high affinity cap binding proteins. The association of FAD caps with nucleases may suggest the cap functions as a mark for targeted decay analogous to the NAD cap in mammals. Further analysis to delineate a possible function for FAD caps will require novel methodologies that can selectively isolate and identify cellular RNAs that harbor this noncanonical cap.At present it is difficult to discern any potential biological function of FAD caps. Unlike a role for NAD caps in mitochondrial NAD homeostasis which was deciphered by uncoupling 5′P directed and NAD-cap directed decay , we wereAtomic coordinates and structure factors for the reported crystal structures have been deposited with the Protein Data bank under accession number 7UG9. Mass spectrometry data has been deposited at MassIVE repository with the Mass Spectrometry Accession Number: MSV000089147.gkac617_Supplemental_FilesClick here for additional data file.


Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform.Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30–75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software,  Only approximately 1% of the human genome and less than 3% of RNA transcripts encode proteins . lncRNAsAdvances in high-throughput sequencing techniques have produced a large amount of publicly available RNA sequencing data from various tissues, cell lines, and disease models. To date, there are more than 12,700 study series in the NCBI Gene Expression Omnibus (GEO) database with available RNA-seq datasets. This large amount of data offers great opportunities to identify novel lncRNAs expressed in specific biological samples. In addition, the rapid development of computational methods for analyzing sequencing data, including methods for transcript assembly . Additionally, true lncRNAs cannot be distinguished from false lncRNAs based purely on expression levels, because some known functional lncRNAs have low expression levels, such as VELUCT .MALAT1 is a single-exon lncRNA with critical functions in various diseases, ranging from diabetes to cancer [Several approaches have been developed which improve the accuracy of true lncRNAs identification, including examining mammalian conservation, selecting multiple-exon transcripts, or integrating additional transcriptional initiation data to determine TSSs. Each of these suffers from drawbacks. Approaches that identify lncRNA using mammalian conservation ,31 can fo cancer . The trio cancer , includiFlnc, a software program that can directly identify true lncRNAs, including novel and annotated lncRNAs, from stand-alone RNA-seq data. We assessed Flnc by comparing the results produced using only RNA-seq data to our previous method [Flnc pipeline significantly reduced the rate of false positives and achieved over 85% prediction accuracy, significantly outperforming the prediction accuracy of the conventional RNA-seq only method (50%). The Flnc pipeline integrates pre-processing, mapping, transcript assembly, evaluation of protein-coding ability, and evaluation of transcripts using our machine-learning algorithm. The Flnc pipeline, which is implemented on the user-friendly Singularity platform, has minimal pre-requisites and is easily portable. The Flnc can also run with parallelize tasks to optimizes computing resources. The Flnc is accessible from https://github.com/CZhouLab/Flnc (accessed on 10 September 2022).We developed a machine learning (ML)-based method to distinguish between true and false lncRNAs. Incorporating this new method, we developed s method , which uTo benchmark our new tool, we first updated our previous computational pipeline to use the latest available tools ,35 Figu. This piAmong all putative lncRNAs in the benchmark dataset, 39% are true lncRNAs, which are supported by both RNA-seq and H3K4me3 ChIP-seq data; these transcripts, with clearly defined transcription start sites C,D, we cFor the remaining 61% of the putative lncRNAs, we could not locate a transcription start site . These tLOC100287042 and PURPL, have multiple isoforms. True lncRNAs identified in this study are named with the prefix “lncRNA” followed by the locus number assigned during assembly. False lncRNAs are named with the prefix “false-lnc” followed by the locus number assigned during assembly.For each lncRNA track, H3K4me3 peaks mark the site of transcription initiation . RNA-seq reads supporting true lncRNA transcripts are shown in red (RNA-seq sense). RNA-seq reads supporting annotated genes in the sense strand are shown in green (RNA-seq sense). Antisense transcripts are shown in blue. RNA-seq reads supporting false lncRNAs are shown in gray or in green when these reads continue the RNA-seq reads of supporting protein-coding transcripts. The genomic structure for each gene is shown below the RNA-seq tracks. True lncRNAs are shown in red, false lncRNA are shown in grey, annotated genes in the antisense strand are shown in blue, and annotated genes in the sense strand are shown in green. Boxes represent exons, lines represent introns, and arrows represent the start and direction of transcription. Two annotated genes, To build a new pipeline B which cMost false lncRNAs are transcript fragments or transcriptional noise. Therefore, we hypothesized that true lncRNAs would tend to be longer than false lncRNAs. To test this hypothesis, we examined the transcript length of all putative lncRNAs in the benchmark dataset. Read length and sequencing depth differ across various RNA-seq datasets and these factors affect transcript assembly ,39,40. TWhereas RNA fragments and transcripts resulting from noisy transcription lack promoters and TSSs, true lncRNAs should have upstream promoters. Therefore, we used TSSG software  to identp-value < 3.27 × 10−17; Because the vast majority of single-exon transcripts result from transcriptional and alignment noise ,43, we hOver 60% of lncRNAs have been shown to be divergently transcribed from the promoter regions of protein-coding genes ,35. ThisIn conclusion, true and false lncRNAs show significant differences in transcript length, promoter signature, multiple exons, and genomic location. Therefore, we integrated these features into ML models to distinguish between true lncRNAs and other noncoding transcripts.Flnc  Curve (AUROC) score . The ranFlnc’s ability to predict true lncRNAs from stand-alone RNA-seq data by testing Flnc on a test set composed of five independent datasets that were released to the GEO database after 2019 -depleted RNA and some are sequenced without strand information. To evaluate the performance of epletion A and on epletion B.On both stranded rRNA-depleted RNA-seq and unstranded polyA-selected RNA-seq data, four models—naïve Bayes, linear SVM, RBF SVM, and logistic regression—had similar F1 scores and based on the F1 scores, these four models performed better than other three models . On thesWe evaluated the significance of the selected genomic features for each ML model. The feature importance score indicates the relative importance of each genomic feature  for Flnc in detecting true lncRNAs from RNA-seq data. Because the genomic location feature includes three categories , we evaluated the feature importance for six categories: transcript length, promoter signature, multiple exons, divergent transcript, antisense transcript, and intergenic transcript. We found that divergent transcript feature is the most important category across all ML models . For thrFlnc has been trained and tested at the transcript level for each RNA-seq dataset. To examine the performance of Flnc at the locus level, we further tested Flnc on the lncRNA gene loci which encode only lncRNA transcripts. This test showed that Flnc can achieve similar performance at the lncRNA gene locus level as at the lncRNA transcript level  of the true lncRNAs in the benchmark dataset have multiple exons. Among them, 43% of multi-exon lncRNA genes have undergone alternative splicing events, generating multiple lncRNA isoforms from the same lncRNA gene locus. pt level , althougFlnc, which uses ML models to accurately identify true lncRNAs from RNA-seq data. Flnc does not require matched transcription initiation profiles which are usually marked by H3K4me3 histone modifications. We trained the ML models on four types of features, transcript length, promoter signature, multiple exons, and genomic location, which show a high degree of discrepancy between true lncRNAs and false lncRNAs in our benchmark datasets. We benchmarked Flnc on both the transcript level, using all 46 datasets, and the dataset level, using the 46 RNA-seq datasets individually. Flnc can achieve up to 85% prediction accuracy using only RNA-seq data. This software improves of prediction of true lncRNAs from samples, such as clinical samples, where ChIP-seq data are unavailable. Flnc can save time and resources by making the generation of H3K4me3 unnecessary and allowing the identification of lncRNAs from publicly available RNA-seq data.We have developed a comprehensive pipeline and software Flnc relies on the quality of the benchmark datasets and the identification of true and false lncRNA within them. Therefore, the prediction power of Flnc may be limited by factors that affect the reliability of the benchmark datasets. These factors include RNA-seq and ChIP-seq data quality, accuracy of transcript assembly and ChIP-seq peak calling, and sequencing depth. To minimize the effects of these factors, we examined the data quality and sequencing depths of both RNA-seq and ChIP-seq data in the benchmark datasets and only keep the data with good data quality and adequate sequencing depths in this study. To improve the accuracy of transcript assembly, we integrated multiple transcript-assembly methods. It has been shown that the accuracy of ChIP-seq peak calling can be improved by using different approaches for long (≥70 bp) and short (<70 bp) reads studies [The predictive power of  studies ,45, and Flnc (with random forest) are more likely to be divergent transcripts of nearby protein-coding genes and to have promoter signatures .Compared to the true lncRNAs identified by H3K4me3 profiles, true lncRNAs predicted by gnatures . This reBecause different methods of generating and sequencing RNA-seq libraries result in slightly different collections of transcripts, feature importance may determine model performance for different types of RNA-seq data. We found that the random forest, decision tree, and KNN models outperform than other models for stranded RNA-seq data generated from polyA-selected RNA . In contFlnc includes seven embedded ML models  and an ensemble approach, which makes Flnc suitable for identifying lncRNAs from different types of RNA-seq data. Model performance depends somewhat on the type of RNA-seq data that is used as input. The best three models  for the stranded polyA-selected RNA-seq data achieve very high consistency (93–96%) in true lncRNA prediction . This process resulted in selection of 46 datasets. The RNA-seq and ChIP-seq data within each of the selected 46 high-quality datasets were generated from the same type of sample. The datasets are heterogenous, including both single-end (50%) and paired-end (50%) RNA-seq data and each dataset has one to four RNA-seq replicates. We indexed these datasets 1 to 46 in chronological order by submission date  and usedAfter identifying true and false lncRNAs in each dataset (see below), we split the benchmark dataset into training and testing sets based on the submission dates of the RNA-seq data into the NCBI GEO database . The traFirst, we mapped the RNA-seq data to the human reference genome (hg38) using HISAT2 v 2.0.5  and asseWe aligned H3K4me3 ChIP-seq reads to the human reference genome (hg38/GRCh38) using the BWA v0.7.5a toolkit . For reaWe used our previously established approach  to identWe calculated the length of putative lncRNAs by summing the length of all the transcript’s exons. Next, we log-transformed the lengths. We calculated the upper and lower limits as mean plus and minus 3 × standard deviation, respectively. Then, we set the outlier data points of the log-transformed lengths as the value of upper or lower limits. Finally, we scaled the log-transformed values of transcript lengths for each putative lncRNA into the range between 0 and 1 using the min-max normalization technique.We used TSSG software  to identWe used the genomic locations of protein-coding genes annotated by the GENCODE Project release 29 , and comThe number of exons of putative lncRNAs were counted based on the assembled transcripts in GTF format, which include the information of exon numbers and coordinates of each exon.To determine the feature importance for all machine learning models, we used the permutation approach . We scalFlnc’s performance on other types of RNA-seq data, we collected four additional datasets, each corresponding to one sample from the GEO database (To evaluate database . These f


The human mitochondrial genome is transcribed as long strands of RNA containing multiple genes, which require post-transcriptional cleavage and processing to release functional gene products that play vital roles in cellular energy production. Despite knowledge implicating mitochondrial post-transcriptional processes in pathologies such as cancer, cardiovascular disease and diabetes, very little is known about the way their function varies on a human population level and what drives changes in these processes to ultimately influence disease risk. Here, we develop a method to detect and quantify mitochondrial RNA cleavage events from standard RNA sequencing data and apply this approach to human whole blood data from > 1000 samples across independent cohorts.MRPP3, TBRG4 and FASTKD5), including a potentially novel role for RPS19 in influencing cleavage rates at a site near to the MTATP6-COX3 junction that we validate using shRNA knock down data.We detect 54 putative mitochondrial RNA cleavage sites that not only map to known gene boundaries, short RNA ends and RNA modification sites, but also occur at internal gene positions, suggesting novel mitochondrial RNA cleavage junctions. Inferred RNA cleavage rates correlate with mitochondrial-encoded gene expression across individuals, suggesting an impact on downstream processes. Furthermore, by comparing inferred cleavage rates to nuclear genetic variation and gene expression, we implicate multiple genes in modulating mitochondrial RNA cleavage (e.g. We identify novel cleavage junctions associated with mitochondrial RNA processing, as well as genes newly implicated in these processes, and detect the potential impact of variation in cleavage rates on downstream phenotypes and disease processes. These results highlight the complexity of the mitochondrial transcriptome and point to novel mechanisms through which nuclear-encoded genes can potentially influence key mitochondrial processes.The online version contains supplementary material available at 10.1186/s12915-022-01373-5. In humans, mitochondria play important roles in many fundamental and interconnected cellular processes, such as thermogenesis, cellular energy production and cell death , and mitMitochondria are unique organelles that contain their own independent genome, a remnant of their ancestral bacterial origin . The humThe human mitochondrial genome itself is transcribed as polycistronic RNA containing multiple genes, which is then processed under the ‘punctuation model’ whereby tRNAs that intersperse coding and ribosomal sequences are targeted and cleaved by nuclear-encoded proteins to release gene products . CanonicMTATP6 and MTCO3), and thus, other proteins and mechanisms are needed to cleave RNA. For example, FASTKD family proteins have been associated with RNA processing at some gene boundaries   using a stringency parameter value of 3  annotated boundary for ribosomal and messenger RNA mitochondrial genes. Within each region, we then identified the position with the highest cleavage ratio as representative of the gene boundary and then obtained the distribution of cleavage ratios for this site across all individuals. For the background rate, we randomly selected 100 sites from locations at least 50bp away from a gene boundary region and followed the procedure as above for each site. We used one-sided https://github.com/nanopore-wgs-consortium/NA12878). Within this study, data was aligned with minimap2 to the GRCh38 human genome reference, which contains the exact same mitochondrial sequence as the reference used here (1000G GRCh37 reference) and data was merged across all sequencing runs to create a single alignment file for each of the native RNA and cDNA data. For each alignment file, we extracted sequencing reads that mapped to the mitochondrial genome, were labelled as the primary alignment and had mapping quality greater than 30, and then removed reads that had segments that aligned elsewhere in the mitochondrial genome and those that aligned elsewhere in the nuclear genome with equal or greater mapping quality score. For the remaining data, we calculated the start and end positions of each read using the CIGAR string (which contains information on sequence matches and insertions/deletions for each read versus the reference sequence). To test for an overlap with putative RNA cleavage sites identified in short read data, we identified start and end positions in long read data that had at least 200 supporting reads and were in the top 1/50 of sites across the mitochondrial genome in terms of the number of reads that started or ended at that position. For validation, we required these positions to be within 3bp of the putative RNA cleavage site.To validate mitochondrial RNA cleavage sites using data generated with other library preparation and/or sequencing techniques, we first obtained an additional 16 whole blood RNA sequencing datasets from healthy controls, generated after ribosomal RNA depletion (rather than polyA enrichment) and sequenced on the Illumina Hiseq 4000 platform  (GEO acchttps://qtltools.github.io/qtltools/) was used to check for consistent sample labelling between genotype and RNAseq data. Quality control was carried out using PLINK [v1.90b3.44]  [QTLtools [v1.0]  with a minimum calling threshold of 0.9 and then filtered for having an IMPUTE2 info score <0.8, genotype missingness > 5%, MAF < 5%, HWE P < 0.001 or for being multi-allelic. GTEx data, which was genotyped on two different arrays, were imputed separately and then merged.Phased chromosomes were imputed using IMPUTE2 [v2.3.2] , 63 usinQuantitative trait loci mapping was carried out for the 54 reproducible cleavage sites identified in both CARTaGENE and GTEx datasets. Analyses were carried out separately for each position (therefore comparing samples that were generated using the same library preparation and sequencing protocols), using linear models in PLINK [v1.9]. Covariates used in the linear model included 5 study-specific genetic PCs and 10 PEER factors calculated from RNAseq data using PEER [v1.0] . PEER faP-value was adjusted for Bonferroni correction using the p.adjust function and the two lists of significant associations we intersected , identifying 52 significant associations . Since the discovery dataset identified a significantly larger number of associations  when compared to the replication dataset (465), we checked the influence of sample size on these results by randomly resampling the discovery dataset without replacement down to the same size as the replication set (n = 344) and repeating the analysis as above. In doing so, we find 2259 significant associations (after Bonferroni correction), which more closely matches the number of associations found in the replication dataset. The remaining differences may be driven by random variation between the samples or systematic differences that may include RNA degradation levels, read length or sequencing depth.Mitochondrial and nuclear gene expression were generated as in . The lm P-value as a representative cis-eQTL and only used associations that were significant after correction for multiple tests (FDR 5%). For nuclear genes/genetic variants that pass these criteria, we tested whether the expression of the nuclear gene significantly mediated the relationship between the peak nuclear variant and associated inferred mitochondrial RNA cleavage ratios using 1000 bootstrapping simulations with the ‘Mediation’ package in R, correcting the P-value for the number of genes tested using Bonferroni correction. To test whether nuclear genes might be influencing mitochondrial-encoded gene expression levels through mitochondrial RNA cleavage, we obtained all mitochondrial RNA cleavage sites that were significantly associated with the expression levels of both a nuclear- and mitochondrial-encoded gene  and then performed mediation analysis by first testing whether the nuclear- and mitochondrial-encoded genes were correlated in discovery data (P < 0.05) and then second whether this relationship is significantly mediated by the inferred cleavage rate of the associated site in discovery data .To identify the direction of effect between associated nuclear gene expression values and mitochondrial RNA cleavage rates, we carried out mediation analysis. First, we used PLINK to identify cis-eQTLs within 1MB of the start and end for each of the genes identified as significant in the comparison of nuclear gene expression and mitochondrial RNA cleavage rates using the CARTaGENE dataset, including 5 study-specific genetic PCs and 10 PEER factors as covariates. We then selected the SNP with the lowest TBRG4 and RPS19) had RNA sequencing data available after shRNA knock down (KD) as part of the ENCODE project. For each gene, there were 8 RNA sequencing datasets available in total (4 from KD and 4 from controls) in 2 different cell lines (HepG2 and K562). We obtained raw RNA sequencing datasets for each sample via the ENCODE portal  data for any gene that has been implicated in mitochondrial RNA processing in our analyses, including those linked through quantitative trait loci mapping (four unique genes) and expression correlation analyses  down experiments in the ENCODE portal.


The full-length double-strand cDNA sequencing, one of the RNA-Seq methods, is a powerful method used to investigate the transcriptome status of a gene of interest, such as its transcription level and alternative splicing variants. Furthermore, full-length double-strand cDNA sequencing has the advantage that it can create a library from a small amount of sample and the library can be applied to long-read sequencers in addition to short-read sequencers. Nevertheless, one of our previous studies indicated that the full-length double-strand cDNA sequencing yields non-specific genomic DNA amplification, affecting transcriptome analysis, such as transcript quantification and alternative splicing analysis. In this study, it was confirmed that it is possible to produce the RNA-Seq library from only genomic DNA and that the full-length double-strand cDNA sequencing of genomic DNA yielded non-specific genomic DNA amplification. To avoid non-specific genomic DNA amplification, two methods were examined, which are the DNase I-treated full-length double-strand cDNA sequencing and poly(A) capture full-length double-strand cDNA sequencing. Contrary to expectations, the non-specific genomic DNA amplification was increased and the number of the detected expressing genes was reduced in DNase I-treated full-length double-strand cDNA sequencing. On the other hand, in the poly(A) capture full-length double-strand cDNA sequencing, the non-specific genomic DNA amplification was significantly reduced, accordingly the accuracy and the number of detected expressing genes and splicing events were increased. The expression pattern and percentage spliced in index of splicing events were highly correlated. Our results indicate that the poly(A) capture full-length double-strand cDNA sequencing improves transcript quantification accuracy and the detection ability of alternative splicing events. It is also expected to contribute to the determination of the significance of DNA variants to splicing events. Alternative splicing plays an important role in normal cellular and pathogenic processes caused by diverse diseases4. It has been reported that several alternative splicing events, including alternative 5’ or 3’ splicing site usage, exon skipping, intron retention, and mutually exclusive exons, occur in abnormal cells in various diseases and normal cells7. These alternative splicing events produce assorted mRNA that translates to different protein isoforms with different coding sequences. In normal cells, these alternative splicing events are controlled in an appropriate expression pattern10. Alternatively, an inappropriate expression pattern occurs in some human diseases, including cancers13. Therefore, it is important to accurately analyze the state of the repertoire of mRNA splicing variants and its changes associated with the pathological condition.The large majority of human genes are processed at several levels, including transcriptional and post-transcriptional regulation. Alternative splicing of pre-mRNAs that include exons and introns is one of the essential regulatory mechanisms at post-transcriptional regulation16. The use of NGS technologies in the clinical field has led to an unprecedented increase in variants identified in different patients harboring genetic disorders. The 48% of all variants listed on ClinVar are asserted to the variant of uncertain significance (VUS) variants17. The current genetic counseling practice almost considers variants that directly affect protein structure. However, the VUS can affect RNA splicing, which causes protein damage. RNA splicing is expected to be disrupted by approximately 62% of all pathogenic variants18. It is also reported that aberrant RNA splicing affects the transcription level19. Thus, it is also needed to accurately measure RNA splicing and transcription level in precise genetic diagnosis.Next-generation sequencing (NGS) is a powerful technology used in the clinical field for genetic diagnosis20. However, so far, RNA-Seq is primarily used to measure the expression level of transcripts but rarely used to detect alternative splicing variants22. Presently, RNA-Seq is almost performed using short-read sequencers, such as the Illumina NGS. Although RNA-Seq by short-read sequencer can detect alternative splicing events between two exons, short-read RNA-Seq cannot detect the full-length transcript information, including all alternative splicing events. Recently, third-generation sequencers, such as Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), can be facilitated for alternative splicing analysis of the full-length transcript due to the production of long sequencing reads (> 10 kb)23. When creating a library in traditional RNA-Seq, because the captured mRNAs using oligo dT magnet beads are sheared randomly into fragments, then reverse transcribed into cDNAs, it is impossible to apply the library for analysing the full-length transcripts. Alternatively, due to no fragmentation, full-length double-stranded cDNA library can adapt to long-read sequencer for analysing the full-length transcript24. In addition, since the full-length cDNA is yielded by PCR amplification, a library can be prepared from a small amount of sample, even as a single cell  is a powerful technology that can be used to measure not only transcriptional levels but also alternative splicing repertoire reads > 0 kb23. W26  is that genome DNA in Total RNA solution is digested enzymatically using DNase I enzyme26 Fig. B. Other  27 Fig. C. It was2) in StemFit medium (REPROCELL) at 37 °C in 5% CO228. The cells were passaged as clump with TrypLE Select (Life Technologies) at a ratio of 1:6 every 4–5 days.The human induced pluripotent stem cell-line (hiPSC), strain 1383D6, which was provided by RIKEN BioResource Research Center were cultured on iMatrix 511 (Takara)-coated plates  were measured using Nanodrop .The genomic DNA sample used in this study was extracted from the whole peripheral blood using a rapid extraction method31. RNA concentration and purity were measured spectrophotometrically . The RNA integrity number was determined using a TapeStation 4200 with High Sensitivity RNA ScreenTape .The total RNA was extracted from hiPSCs with TRIzol reagent (Thermo Fisher Scientific) following the manufacturer’s instructions, as described previouslyTotal RNA Library was synthesized from total RNA using a SMART-Seq HT kit  and the Nextera XT DNA Library Kit , following the manufacturer’s standard protocol. The library quality was further evaluated using the TapeStation 4200 with High Sensitivity D1000 ScreenTape . The library was quantified using the HS Qubit dsDNA assay .Genomic DNA Library was synthesized from genomic DNA using a SMART-Seq HT kit  and the Nextera XT DNA Library Kit , following the manufacturer’s standard protocol. The library quality was further evaluated using the TapeStation 4200 with High Sensitivity D1000 ScreenTape . The library was quantified using the HS Qubit dsDNA assay .According to the manufacturer’s instruction, the genomic DNA contained in total RNA was digested using TURBO DNA-free Kit (Thermo Fisher Scientific). The RNA integrity number of DNase I-treated total RNA was determined using a TapeStation 4200 with High Sensitivity RNA ScreenTape . According to the manufacturer’s standard protocol, full-length double-stranded cDNA was synthesized from DNase I-treated total RNA using a SMART-Seq HT kit .25 Beads and washed. The captured mRNA was resuspended with RNA binding buffer and was denatured at 65 °C for 5 min. Then, captured mRNA was eluted at 80 °C for 2 min. According to the manufacturer’s standard protocol, full-length double-stranded cDNA was synthesized from eluted mRNA using a SMART-Seq HT kit .According to the manufacturer’s instruction, the mRNA was captured from total RNA with NEBNext Poly(A) mRNA Magnetic Isolation Module (New England BioLabs). Briefly, the mRNA was captured using NEBNext Magnetic Oligo d(T)32. The library quality was further analyzed using the TapeStation 4200 with High Sensitivity D1000 ScreenTape . All libraries were quantified using the HS Qubit dsDNA assay . All libraries were sequenced (2 × 75 bp) using Illumina NextSeq 500 . The FASTQ files were generated using the bcl2fastq software . The FASTQ data (GSE192928) are deposited in the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/).The DNase I-treated or Poly(A) capture full-length double-stranded sequencing libraries were prepared using the Nextera XT DNA Library Kit  for Illumina sequencing following the manufacturer’s instructions, as described previously33. The sampled FASTQ files were aligned to the reference human genome (hg38) using HISAT2 (version 2.1.0) and SAMtools (v.1.9)35. The StringTie algorithm (v.1.3.4d) was then used to assemble RNA-Seq alignment into annotated transcripts to quantify their expression36. The transcript expression was normalized using the transcript per million (TPM) algorithm. For differential expression analysis, the R package (edgeR) was used37. The mapping rate was measured using HISAT2. For alternative splicing analysis, we used SplAdder software (v.2.4.2) and RSeQC (v.3.0.1)39.The FASTQ files were sampled with the same number of reads using Seqkit (version 0.13.2)The study was conducted according to the guidelines of the Declaration of Helsinki, and the Institutional Review Board of Kanazawa Medical University  approved this study. Written informed consent was obtained by Y.N., and the ethics review board of Kanazawa Medical University approved the study design (G111).First, the full-length double-stranded cDNA library was generated from only the genomic DNA to verify that non-specific amplifications are yielded from genomic DNA. The library was generated from only the genomic DNA and to be sequenced the same as the library was generated from total RNA. Comparing to Total RNA sequencing , the reads of genomic DNA sequencing were randomly mapped to the genomic areas in Fig. . AlthougTo evaluate the performance of Total RNA sequencing, DNase I-treated and Poly(A) capture full-length double-stranded cDNA Sequencing, their accuracy of splicing event detection was compared. The quality of DNase I-treated total RNA was the same as DNase I-untreated total RNA Fig. A. The peTo evaluate the performance of Total RNA, DNase I-treated, and Poly(A) capture sequencing, their accuracies of gene detection and expression patterns were compared. The number of detected expressing genes in Poly(A) capture sequencing was slightly higher than that in Total RNA sequencing Fig. A. Altern38. To evaluate the performance of Total RNA, DNase I-treated, and Poly(A) capture sequencing, we compared the number and accuracy of the alternative splicing events between these three sequencings. The number of alternative 5’ splicing sites, alternative 3’ splicing sites, and Exon skipping in Poly(A) capture sequencing was slightly higher than that in Total RNA sequencing  capture sequencing  is an analytical technology that can make this possible.17, and its pathological significance should be determined. Presently, most NGS -based applications in clinical diagnosis target only coding regions, such as whole-exome and gene targeting panel sequencing. Nevertheless, the variants in the intron region, sometimes even in coding region, affect RNA splicing which causes protein damage. Although variants that may change RNA splicing can be computationally predicted, it is difficult to apply to clinical diagnosis because of an incomplete understanding of alternative splicing and normal transcriptome across tissues4. For this reason, in addition to the information of the traditional coding regions, it is necessary to determine whether an intron variant alters RNA splicing directly to confirm its pathogenicity in clinical situations, such as an analysis for undiagnosed inherited disease or cancer gene panel.NGS-based applications for clinical laboratories also have been adopted as a gold standard for diagnostics of Mendelian-inherited diseases and cancers because of its analytic accuracy, high throughput, and cost-effectiveness. The VUS variants are half of all variants on Clinvar43. Although the standard RNA-Seq method for short-read sequencers has been well-established, the standard RNA-Seq method is unavailable for long-read sequencers due to fragmentation at the library preparation step. On the other hand, the full-length double-strand cDNA sequencing method is available for long-read sequencing. Given these points, in order to clarify how DNA variants in deep intron or intragenic regions affect mRNA splicing, it seems useful to perform a DNA sequencing including non-coding regions and a full-length cDNA sequencing simultaneously with the same sample. Nevertheless, a previous study indicated that nonspecific genomic amplifications are yielded at the library preparation step and affect transcriptome analysis, such as transcript quantification and alternative splicing analysis25. This study also showed that the same results and non-specific genome amplification were yielded from only the genomic DNA. It is needed to eliminate contamination of genomic DNA for precise transcriptome analysis.Current short-read sequencer-based transcriptome analysis cannot characterize the full-length transcripts because of the limitations of read length. Recently, long-read sequencers, such as Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), have advanced and are gradually used because of their ability to overcome the limitations of read lengthIn this study, two methods were employed, which are the DNase I-treated full-length double-strand cDNA sequencing and poly(A) capture full-length double-strand cDNA sequencing to avoid non-specific genomic DNA amplification. Unexpectedly, non-specific genomic DNA amplification increased in the DNase I-treated sequencing than original Total RNA sequencing despite DNase I treatment. These non-specific genomic DNA amplifications affected quantification analysis and alternative splicing analysis. These non-specific genomic DNA amplifications might probably yield from the genome DNA that could not be completely digested in the DNase I-treated sequencing. It is possible that the efficiency of non-specific PCR amplification during double-strand cDNA synthesis in the SMARTer method was increased by digesting genomic DNA by DNase I and decreasing its molecular size. Alternatively, non-specific genomic DNA amplification was significantly reduced in poly(A) capture sequencing. The genes that are not actually detected, such as non-coding RNA, which does not have poly(A) tail, were detected in total RNA and DNase I-treated sequencings due to non-specific amplification noise. On the other hand, non-coding RNAs were undetected correctly in poly(A) capture sequencing. Complete novel splicing junctions were dramatically reduced in Poly(A) capture sequencing Fig. E compariOur poly(A) capture full-length double-strand cDNA sequencing improves the accuracy and detection ability of transcript quantification and alternative splicing events owing to the elimination of non-specific amplification noise from genomic DNA. Poly (A) capture sequences could be able to more accurately detect minor splicing events that occur in certain genes associated with cell differentiation or pathogenicity. Also, Poly(A) capture sequencing will provide information on all splice events in full-length transcripts because a highly accurate library created by this method can be applied to long-read sequencings, such as ONT and PacBio. Moreover, by combining with whole genome sequencing, this method will contribute to determine the VUS variants’ pathological significance that affects splicing events in clinical diagnosis.Supplementary Information 1.Supplementary Information 2.


Background RNA extraction is a step that precedes several molecular techniques. The fibrous tissue, more specifically the dura mater, has several limitations in routine protocols, and lacks optimization protocols to overcome these problems.Objective To test stock reagents and purification kits, optimizing commercial kit protocols for RNA extraction from the dura mater.Methods Dura mater samples were obtained from eight Wistar rats and maintained in two different stabilizers. The samples were purified using four different protocols, and the RNA was evaluated for the yield and purity in NanoDrop 2000 . Beta-actin gene was used for analyzing gene expression, since is one of the most used reference genes.Results The RNA preservation was similar in both stabilizers. The addition of an incubation step prior the purification protocols allowed better tissue digestion and RNA recovery. The RNA purified using the protocols membrane-based showed higher quality than liquid-liquid purification. This impact was observed in the 3-week evaluation using RT-qPCR.Conclusion Stabilizers are efficient for RNA preservation and membrane-based purification protocols are more suitable for RNA recovery from dura mater tissue, allowing the evaluation of gene expression in this type of tissue. Adaptations in the dura mater RNA extraction protocol differ from the pre-established protocols because it takes into account the peculiarity of fibrous tissue and low cellularity. In addition to providing a low-cost mechanism, based on techniques that are part of the laboratory routine, it is possible to improve the quality of the extracted material, ensuring greater efficiency in the use of subsequent techniques. The dura mater, the meninge that lines the central nervous system externally, is classified as fibrous, due to a large number of collagen fibers, making it a thick and resistant membrane.However, in order to understand the pathophysiological mechanisms of some diseases, molecular analyzes are applied. To perform molecular techniques, it is necessary to extract from the tissue molecules, such as nucleic acids and proteins, which are employed in subsequent techniques. High-quality RNA is required for various techniques, including sequencing, reverse transcription quantitative polymerase chain reaction (RT-qPCR), microarray, etc.There are many commercial products available; however, most of the time RNA extraction kits do not bring specifications/adaptations to the different existing tissue types, often leading to a low-yielding RNA without precision and reliability of results. Not only in the case of fibrous tissues but some adaptations have already been made in other types of tissue, such as adipose tissue, since many protocols routinely used to isolate RNA from adipose tissue result in partially degraded RNA and/or low RNA yield or the small RNAs are lost in the process.Therefore, the aim of the present article was to evaluate different combinations of commercially available kits for dura mater maintenance and RNA purification in order to establish a suitable combination between stock and purification reagent and evaluate the viability of RNA through the repeatability of gene expression.ad libitum.All experiments were developed according to the guidelines of the Animal Ethics Commission of the Universidade Federal de Pernambuco. Eight adult Wistar rats (between 8 and 10 weeks old) were purchased at the animal testing facility at the Academic Center of Vitória at the Universidade Federal de Pernambuco. Before surgeries to remove the dura mater, the animals were individually conditioned in polypropylene cages, stored in rooms under standardized laboratory conditions of temperature (22 ± 2°C), relative humidity (45 ± 5%), exhaust system with air renewal, luminance of 60 lux, and circadian cycle of 12 hours in light period and 12 hours in dark period. During the period of biological maintenance, the animals were fed with commercial feed and waterlater. Then, samples were frozen at - 80°C for subsequent RNA extraction and purification.The animals were anesthetized with ketamine (115 mg/kg) and xylazine (10 mg/kg), followed by tissue perfusion with 0.9% saline solution. To perform the dura mater collection procedure, the calvaria was removed through an incision in the occipitofrontal direction, the dura mater underlying the overlying bone disk was removed with the help of an 18-cm Molt detacher . Two dura mater samples were collected from each animal, weighing between 5.0 and 8.6 ηg. The samples removed were quickly preserved in the following stabilization solutions: TRIzol  or RNAlater, according to the compatibility of each of them with solvents. The samples were quantified on the NanoDrop 2000 spectrophotometer (Thermo Scientific). The manufacturer protocol was followed, with minor modifications. A pretreatment was adopted for all kits, in which the samples were placed outside the freezer 24 hours before RNA extraction, being maintained at room temperature (19°C to 21°C) to assure complete thawing of all samples, and to allow the TRIzol reagent to act for sample digesting. Additionally, samples preserved in RNAlaterwere incubated for 10 minutes at 60°C in a water bath before extraction to dilute the crystals in the solution. In the first step of separation in the purification protocol, all samples were submitted to a centrifugation time of 5 minutes at 12,000 × g, at 4°C.RNA isolation was performed in duplicate using the following kits: TRIzol Reagent ; RNeasy Mini Kit ; Direct-zol , and ReliaPrep . The kits were tested for stored samples TRIzol and RNAFor cDNA synthesis, the QuantiTect Reverse Transcription Kit  was used following its standard protocol, which is divided into two main steps: the removal of genomic DNA (gDNA) and the synthesis of cDNA. In the first step, 0.5 µl of the gDNA Wipeout Buffer was added to 3.0 µl of the total RNA. The first cycle was performed on the Thermocycler , at a temperature of 42°C for 2 minutes, then the tubes were immediately placed on ice. For cDNA synthesis, 1.5 µl of a mix containing 0.25 µl of reverse transcriptase, 1 µl RT Buffer, and 0.25 RT µl primer mix was added to the sample. The samples went through the second cycle of 15 minutes at 42°C, 3 minutes at 95°C and 4°C ∞. Quantification was performed on the NanoDrop 2000 spectrophotometer .The qPCR reaction was performed by the StepOne Real-Time PCR-System  with the GoTaq qPCR Master Mix kit (Promega). Each PCR was performed in duplicates, in 96-well plates. They were used with fixed volumes, 5 μL SYBR  and 1 μL of Carboxy-X-Rhodamine (CXR). A total of 0.2 μL of the beta-actin reference gene (Actb), Rn_Actb_1_SG assay  was added. To complete the volume of 9 μL of reagents, 2.8 μL of water was added. Finally, 1μL of the cDNA sample was added, totaling the final reaction volume with 10 μL. For cycling, the fast protocol was used with 1 initial cycle of 95°C for 2 minutes for activation of Taq DNA polymerase, followed by 40 cycles of denaturation (95°C for 3 seconds) and annealing (60°C for 30 seconds). To assess the quality of the cDNA over the days, three qPCR plates were performed for 3 weeks. During the 3 weeks in which the plates were being performed, the cDNA samples were kept at −80°C.The Ct values were expressed as mean and standard deviation (SD), calculated based on the values obtained over the 3 weeks. In the analysis of intrarepeatability, the mean and SD between duplicates were calculated over the 3 weeks.The present study aimed to optimize and evaluate yield and purity levels of dura mater total RNA isolation protocols based on commercially established methods. An additional step of thawing the sample 24 hours before the procedure facilitated the RNA purification once the phase separation after the centrifugation at the first steps of the extractions became more evident.The RNA yield could not be related to the sample weight, since high RNA yield could be obtained even in samples with lower mass input, varying from 5.5 to 35.5 ηg/μL. Regarding the RNA purity against DNA contamination, 50% of samples fit in the acceptable range (Abs 260/280 = 1.80 to 2.00). Only 1 sample was above the limit, and the others reaching values as low as 1.42. However, the deviation was significant for protein contamination, with most samples showing very low or even negative records in some samples. Only one sample fit in the range (Abs 260/230 = 2.0–2.2). The TRIzol/TRIzol combination was tested twice (in duplicate), but only the second experiment was included in the table, as only these two showed Ct values.To understand the impact of these numbers in the gene expression, samples were submitted to cDNA synthesis and evaluated in RT-qPCR test for β-actin expression.stweek of RT-qPCR, the best Ct average among duplicates, with a value of 22.4, was obtained using the RNAlater/ReliaPrep method, followed by RNAlater/RNeasy, with an average of 24.4. The TRIzol/ReliaPrep and TRIzol/RNeasy methods showed a small range of variation, with an average of 24.2 and 25.1, respectively. Regarding the 3-week evaluation, TRIzol/ReliaPrep showed the best values compared wth the RNAlater/ReliaPrep method. The RNAlater/RNeasy method, on the other hand, showed better results compared with the TRIzol/RNeasy method  as responsible for the overestimated concentration of nucleic acids. It also influences downstream analysis, leading to an increase in Ct and SD values , as observed in the present study for the dura mater. Thus, despite the low cost of the TRIzol method, it is important to note some inconveniences regarding harmful odors, laborious handling, in addition to the unsatisfactory purity that the method can present.laterprovides nucleic acids of high quality and conserved morphology for diverse tissues, besides not needing a quick-freezing system after immersing the sample. The method based on RNAlaterstabilization has a higher cost but does not emit toxic odors, as in the case of TRIzol, in addition to presenting yields as good as those of TRIzol, with a low rate of RNA contamination.laterhad the best results.In contrast, the preservation in RNAlater/ReliaPrep, showed the best Ct values for β-actin expression, which indicates that the evaluation of RNA yield and concentration could have been misled in the spectrophotometer evaluation.It is interesting to note that the samples with low levels of purity related to contamination by proteins (260/230), like TRIzol/Direct-zol and RNAThe suggested adaptations may contribute to RNA extraction in human dura mater, since there are no structural differences between tissues. Furthermore, the study of gene expression of molecules present in the dura mater may contribute to elucidate mechanisms of diseases that are related to this structure, such as migraine. In addition, the physiopathogenic understanding of the behavior of structures and molecular units related to the dura mater aims to contribute to the development of tools and answers that clarify points in clinical research, and that these can contribute to application in possible therapeutic targets.Although the adaptations suggested in the protocols are inexpensive, molecular biology techniques are generally very sensitive, requiring well-equipped laboratories and high-cost materials. Most of the data reported here are derived from dura mater samples; analyzing other similar tissues can expand the effectiveness of the method. Overcoming the challenges and limitations of RNA extraction protocols from specific tissues, such as the dura mater, will increase the use of molecular biology techniques and, thus, may contribute to the quality of analysis, providing better diagnostic tests.