RNA therapeutics comprise a diverse group of oligonucleotide-based drugs such as antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and short hairpin RNAs (shRNAs) that can be designed to selectively interact with drug targets currently undruggable with small molecule-based drugs or monoclonal antibodies. Furthermore, RNA-based therapeutics have the potential to modulate entire disease pathways, and thereby represent a new modality with unprecedented potential for generating disease-modifying drugs for a wide variety of human diseases, including central nervous system (CNS) disorders. Here, we describe different strategies for delivering RNA drugs to the CNS and review recent advances in clinical development of ASO drugs and siRNA-based therapeutics for the treatment of neurological diseases and neuromuscular disorders.Abbreviations 2’-MOE: 2’-O-(2-methoxyethyl); 2’-O-Me: 2’-O-methyl; 2’-F: 2’-fluoro; AD: Alzheimer's disease; ALS: Amyotrophic lateral sclerosis; ALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Rating Scale; ARC: Antibody siRNA Conjugate; AS: Angelman Syndrome; ASGRP: Asialoglycoprotein receptor; ASO: Antisense oligonucleotide; AxD: Alexander Disease; BBB: Blood brain barrier; Bp: Basepair; CNM: Centronuclear myopathies; CNS: Central Nervous System; CPP: Cell-penetrating Peptide; CSF: Cerebrospinal fluid; DMD: Duchenne muscular dystrophy; DNA: Deoxyribonucleic acid; FAP: Familial amyloid polyneuropathy; FALS: Familial amyotrophic lateral sclerosis; FDA: The United States Food and Drug Administration; GalNAc: N-acetylgalactosamine; GoF: Gain of function; hATTR: Hereditary transthyretin amyloidosis; HD: Huntington's disease; HRQOL: health-related quality of life; ICV: Intracerebroventricular; IT: Intrathecal; LNA: Locked nucleic acid; LoF: Loss of function; mRNA: Messenger RNA; MS: Multiple Sclerosis; MSA: Multiple System Atrophy; NBE: New Biological Entity; NCE: New Chemical Entity; NHP: Nonhuman primate; nt: Nucleotide; PD: Parkinson's disease; PNP: Polyneuropathy; PNS: Peripheral nervous system; PS: Phosphorothioate; RISC: RNA-Induced Silencing Complex; RNA: Ribonucleic acid; RNAi: RNA interference; s.c.: Subcutaneous; siRNA: Small interfering RNA; SMA: Spinal muscular atrophy; SMN: Survival motor neuron; TTR: Transthyretin This ha channels or α-syn channels.Despite the progress in drug discovery efforts deploying NCE and NBE approaches, patients with neurological diseases still face major unmet needs due to the limited efficacy of symptomatic treatments, drug resistance, and absence of preventive and disease-modifying treatments. Gene therapy represents an interesting modality for a restricted number of patients within neurological diseases by providing a maintained supply of therapeutic proteins in patients with genetic disorders and certain rare neurological diseases. HoweverRNA therapeutics comprise a highly diverse group of oligonucleotide-based drugs, including antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), short hairpin RNAs (shRNAs), aptamers, microRNA inhibitors (antimiRs), and microRNA mimics. Therapein vivo  in length, designed to bind and inhibit the functions of complementary RNA transcripts,11. Mostin vivo . NucleasImproved stability and increased binding affinity is achieved using nucleotides with sugar modifications, including different 2’-modified sugars such as the 2’-O-methyl (2’-O-Me), 2’-O-methoxyethyl (2’-MOE), or 2ʹ-fluoro (2’-F) modifications, or conformationally restricted nucleotides such as the bicyclic locked nucleic acid (LNA) modification, in which the ribose sugar is locked in a C3’-endo conformation by introduction of a 2’-O,4’-C methylene bridge. NucleasTwo different design paradigms are used for ASOs: (i) gapmers for gene knockdown, in which a central deoxynucleotide region is flanked at both ends by modified ribonucleotides and, (ii) mixmers that serve as steric blockers for microRNA (miRNA) inhibition or splice modulation. Gene knockdown is mediated by RNase H, which binds and cleaves RNA/DNA heteroduplexes primarily in the nucleus , whereasThe endogenous RNA interference mechanism was discovered by Fire and Mello in 1998 and can in vivo[Therapeutic approaches that deploy the RNAi pathway require chemical modifications of the siRNA molecules for target mRNA specificity, reduced immune activation, and enhanced stability in vivo. Therapein vivo, while sin vivo,26. Furtin vivo in the ain vivo, whereasin vivo,30.Adequate drug delivery to the brain is one of the biggest hurdles in the development of therapeutics for CNS disorders. Many drug targets have been deemed undruggable due to difficulty encountered with CNS delivery, and thus, new, and improved delivery approaches have become an important aspect of development of RNA-based therapeutics for treatment of neurological and neuromuscular diseases.To elicit its function in the CNS, a drug needs to enter the brain parenchyma. The blood-brain barrier (BBB) protects the brain from the periphery and is composed of capillary endothelial cells with tight junctions, which together with astrocytes, pericytes, and neurons comprises the neurovascular unit tightly regulating drug access to the brain. The tigsubstantia nigra and ventral tegmental area in Parkinson's disease[Once the drug has penetrated the parenchyma, the level of drug distribution within the brain is crucial for its therapeutic effects . Many nes disease. These ns disease highlighThe final step for the drug to elicit its therapeutic effect is cellular uptake . ASOs haSMN2 facilitated delivery of this compound into the brain and spinal cord upon systemic delivery and resulted in increased tissue levels of the full-length survival motor neuron protein. Furthermore, treatment with the Pip6a-conjugated PMO rescued the SMA phenotype and extended the life span in a mouse model of SMA[Despite the ability to bypass the BBB by IT or ICV injections, cellular uptake, and adequate distribution of RNA drugs in the CNS remain challenging. Thus, development of different carrier systems to enhance BBB transport, increase CNS distribution, and cellular drug uptake has become an active field of research aimed at improving CNS delivery of RNA drugs. Bioconjugation of RNA molecules has shown promising results upon the development of N-acetylgalactosamine -conjugated RNA therapeutics for delivering the RNA cargo to hepatocytes in the liver,56. RNA el of SMA.in vitro. Moreover intravenously administered RVG-9 R-siRNA showed enhanced delivery of the compound across the BBB in mice and silencing of superoxide dismutase 1 (SOD1) in the mouse brain [CPPs can also be conjugated with other peptides to enable cell specific delivery. For example, a nona (D-arginine) peptide was fused to the rabies virus glycoprotein, which binds the acetylcholine receptor present in neurons. The resulting RVG-9 R CPP was shown to bind and deliver an siRNA cargo to Neuro 2a cells se brain ,64.Additional delivery methods include the use of nanotechnology to package RNA drugs into protective structures such as exosomes, spherical nucleic acids (SNAs), DNA nanostructures, and lipid nanoparticles (LNPs). Exosomes are naturally occurring extracellularly secreted lipid bilayer vesicles with a diameter of 40 to 150 nm and carry out important functions in intercellular communication. They coLipid nanoparticles (LNPs) consist of lipid bilayers that encapsulate the RNA drug cargo, thereby protecting it from nucleases and endosomal breakdown. The lipids can either be solid or liquid-crystalline or a combination hereof and are manufactured in well-defined combinations with surfactants and other components, including targeting ligands to increase their pharmacokinetic properties. Due to SNAs are formed by specifically oriented siRNA or ASOs attached in a high-density to a hydrophobic core composed of e.g. gold or liposomes by a linker and therefore the RNA molecules are exposed to their surroundings. SNAs arDNA nanostructures such as DNA cages are self-assembled into precise structures due to base-pairing and can be designed for optimal pharmacokinetic properties and loading potential including loading of RNA drugs. The chaDespite the technological advances in enhanced RNA drug delivery to the CNS, this area of research needs further maturation and scientific advances before the search for a ‘brain GalNac’ is completed. Once the challenge of CNS delivery of systemically administered RNA therapeutics is solved, many patients suffering from intractable neurological disorders will see new therapies becoming available.The RNA drug, Spinraza, was approved in 2016 by the FDA for treatment of the neuromuscular disease SMA. SubsequALS is a rare neurological disorder classified as a motor neuron disease that involves the loss of nerve cells located in the spinal cord responsible for controlling voluntary muscle movement. ALS oftn = 72, 100 mg tofersen) or placebo (n = 36) failed to meet the primary endpoint of change in the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R), which is a validated rating instrument for monitoring progression of disability in patients with ALS[Tofersen is an investigational ASO targeting the gene encoding SOD1 and is currently in a phase III trial. Results with ALS. However with ALS.ATTR gene leading to a misfolded, liver produced aggregation-prone transthyretin (TTR) protein that is deposited in amyloid aggregates. Amyloidosis causes cardiomyopathy and/or polyneuropathy (PNP) and under these circumstances the disease is called FAP[ATTR amyloidosis. Eplontersen is developed by Ionis /Akcea Therapeutics and is administered s.c. once every four weeks. The GalNAc-conjugated ASO utilizes the tissue ligation system, named LICA, to specifically target the liver. Vutrisiran is a GalNAc-conjugated siRNA-drug administered s.c. once every three months and was developed by Alnylam Pharmaceuticals[Familial amyloidosis is caused by GoF mutations in the alled FAP. Before ceuticals. Conjugaceuticals.ATTR mRNA through RNAse H (eplontersen) and RISC (vutrisiran), respectively. Eplontersen has recently entered a clinical phase III study and completed the enrollment of 160 patients with hereditary transthyretin-mediated amyloid polyneuropathy[The aforementioned RNA drugs promote degradation of the mutant and the wildtype europathy. Resultseuropathy. Vutrisieuropathy. Currenteuropathy.HD is a rare, inherited, genetic progressive disease that is caused by the expansion of CAG trinucleotide repeats in the huntingtin (HTT) gene. The mutGFAP mRNA for the treatment of AxD and is currently in phase III trials. This study is carried out as a double-blinded placebo-controlled study with 58 patients with AxD that will receive an IT injection once every 12 weeks through week 109. Results from this clinical study are expected in 2024[Alexander disease (AxD) is a rare, fatal, and progressive neurological condition characterized as a leukodystrophy . Four mad in 2024.de novo deletion of the maternally inherited 15q11-q13 region encompassing the UBE3A gene[UBE3A antisense transcript (UBE3A-AS). Thus, a functional maternal copy is essential for proper development. AS affects 1 in 12,000 to 20,000 people with equal frequency between males and females. The first symptoms become noticeable by the age 6–12 months, and include intellectual disability, impaired motor coordination, seizures, abnormal sleep, lack of speech, and high comorbidity with autism spectrum disorders[UBE3A-antisense transcript to reactivate the paternal UBE3A allele in neurons are currently in clinical studies by Roche/Genetech and Gene Biotherapeutics/Ultragenyx Pharmaceutical, respectively[Angelman syndrome (AS) is a rare genetic neurological disorder caused by loss of the UBE3A protein due to a BE3A gene. The patdisorders. Two ASOpectively,98.MTM1 gene, while three different genes, DMN2, BIN1, and RYR1, have been identified to cause an autosomal dominant CNM (AD-CNM) form[rx) in a phase I/II trial for the treatment of DNM2 AD-CNM, which accounts for 50% of all CNM patients. IONIS-DMN2-2.5rx is an ASO targeting the gene encoding dynamin 2 (DMN2), which leads to abnormalities primarily in the distal lower leg muscle[Centronuclear myopathies (CNM) comprise a group of rare genetic congenital muscle disorders characterized by abnormal localization of the nucleus in the center of skeletal muscles cells. Patients with CNM experience muscle weakness slowly worsening over time, breathing problems, cardiomyopathy, neuropathy, and intellectual disability. Lung failure or infection can lead to early infancy death. Mutations in several genes have been identified for CNM. The most severe form is the X-linked CNM (XL-CNM), which is caused by LoF mutations in the myotubularin CNM) form. Ionis heg muscle.Alzheimer's disease (AD) is a progressive neurological disorder that leads to neuronal death and shrinking of the brain parenchyma. AD is tsubstantia nigra of the motor system. PD is the second most common neurodegenerative disease affecting about 1% of the population over age 60. Patients present with tremors, loss of balance and coordination, rigid muscles, slowing of movement, changes in speech and sense of smell, sleep disorder, and in some cases cognitive decline. PD can be idiopathic and familial, where hereditary mutations include a GoF mutation in the leucine-rich repeat kinase 2 (LRRK2) gene and dominant mutations in the SNCA gene. Mutation in the LRRK2 is the most common genetic mutation in PD, and induces neuronal cell death in tissue culture, but the mechanism in humans remains uncertain. However, there are no differences in clinical features between hereditary PD and idiopathic PD, suggesting that LRRK2 is implicated in all forms of PD[Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by loss of dopaminergic neurons in rms of PD. ION859 rms of PD.Multiple system atrophy  is a rare, rapidly progressing, fatal neurodegenerative disease affecting the autonomic functions such as blood pressure, breath, bladder function, and motor function. Symptoms of MSA often appears between age 50–60 but can begin as early as age 30. Two types of MSA are classified: the parkinsonian and the cerebellar type. The former shares many symptoms with PD, whereas the latter presents with ataxia, autonomic changes, dysarthria, and vision disturbances. Accumulation of the α-synuclein protein in MSA is unique in that it is prominent not only in neurons, but also in glia cells. Ionis hMultiple sclerosis (MS) is a devastating neurological disease that progressively destroys the insulating myelin sheaths surrounding neurons causing symptoms such as blurred vision, difficulty walking, numbness, and ultimately paralysis and death. The risk factors include female gender, sun exposure and vitamin D, Caucasian ethnicity, smoking, and obesity. The onset of MS is usually between age 20–30 but can occur at any age. Since progression of MS involves infiltration of T-cells and B-cells from the periphery into the CNS, most MS therapies focus on reducing systemic inflammation. The VLAde novo mutations in the SCN1A gene, a sodium channel required for proper function of neurons. The severity of the disease is correlated with the location and type of the mutation. Most children experience intellectual disability, ataxia, autistic behavior, and sleep disorders, and do not respond well to conventional anti-epileptic treatment such as clobazam and valproic acid[Dravet syndrome  is a rare, genetic epileptic encephalopathy characterized by episodes of prolonged seizures beginning in infancy or early childhood. The prevalence of Dravet syndrome is 1 in 20,000 to 40,000 and in 80–90% of Dravet patients, the disease is caused by roic acid. Stoke Troic acid. Stoke iroic acid.There are currently seven FDA-approved RNA-based drugs on the market for the treatment of neurological and neuromuscular diseases. Nusinersen (Spinraza) was approved by the FDA for treatment of SMA in 2016, whereas four drugs have been approved for DMD: eteplirsen (Exondys51), golodirsen (Vyondys53), casimersen (Amondys53), and viltolarsen (Viltepso), and two for the treatment of FAP: patisiran (Onpattro) and inotersen (Tegsedi) . In thisSMA is a severe autosomal recessive and progressive disease affecting both the CNS and peripheral nervous system (PNS), and thereby also affecting voluntary muscle movement. SMA is classified as a motor neuron disease that involves the loss of the neurons in the spinal cord responsible for the innervation of muscles. The lack of innervation causes muscular atrophy, which can result in death due to failure of respiratory muscles and pneumonia. SMA aff(DMD). Lack of dystrophin protein in muscle cells leads to breakdown of muscles. Depending on the mutation, it can lead to either a more severe phenotype of DMD (complete LoF) or a milder form of BMD [Muscular dystrophies (MD) are a group of more than 30 different inherited genetic diseases that gradually cause progressive weakness and loss of muscle mass, leading to progression of physical disability. There atial LoF). The pretial LoF),115. Thetial LoF). All druDMD mutations) have a deletion at the end of exon 50 and the beginning of exon 52 and will thus benefit from eteplirsen treatment. Eteplirsen is the first FDA-approved RNA drug for treatment of DMD[Eteplirsen (Exondys51) is a single-stranded 30-nucleotide neutrally charged phosphorodiamidate morpholino oligomer (PMO). Eteplirsen targets exon 51 of DMD causing an exclusion of this exon during pre-mRNA processing. By skipping exon 51, the translational reading frame is corrected resulting in the production of a functional, but truncated dystrophin protein. Approximately 20% of DMD patients (with nt of DMD. Howevernt of DMD. The lacnt of DMD. Howevernt of DMD. In clinnt of DMD.Inotersen (Tegsedi) is an ASO targeting the gene encoding TTR for the treatment of FAP. As the in vivo with enhanced potency and efficacy. Delivery of ASOs and siRNAs to the brain is one of the biggest hurdles in development of RNA therapeutics for CNS disorders and requires bypassing of BBB by IT or ICV injections. Thus, further advances in drug delivery methods to the CNS are required to increase patient comfort and clinical ease of drug administration. On the other hand, increased stability of chemically modified RNA drugs with prolonged duration of action may create potential adverse effects, if the treatment is not tolerated by the patient. Reversal of siRNA and ASO activity has already been demonstrated experimentally[RNA-based therapeutic approaches hold great promise in the treatment of a wide variety of human diseases, including CNS disorders. Therapeutic ASOs and siRNAs represent the most clinically advanced RNA drug platforms and exhibit several advantages compared to NCE- and NBE-based drugs such as efficient targeting of previously undruggable targets, the potential to modulate entire disease pathways, rapid screening of lead candidates and readily scalable drug manufacturing process. Furthermore, advances in chemical modifications have enhanced the drug-like properties of ASOs and siRNA molecules making them safer and more stable imentally,124, but


B细胞淋巴瘤中涉及免疫球蛋白重链（IGH）基因座的染色体易位常具有重要意义，如伯基特淋巴瘤中的t（8;14）（q24;q32）（IGH/MYC）、套细胞淋巴瘤中的t（11;14）（q13;q32）（IGH/CCND1）和滤泡淋巴瘤中的t（14;18）（q32;q21）（IGH/BCL2）。t（14;19）（q32;q13）（IGH/BCL3）于1983年首次被描述，但其临床和病理意义至今仍未明确1. 病例：回顾性分析2013年4月至2020年12月就诊于南京医科大学第一附属医院血液科，经CpG-寡脱氧核苷酸（CpG-ODN）培养后染色体核型具有t（14;19）（q32;q13）异常的病例。共发现20例患者，均为小B淋巴细胞增殖性疾病，其中10例参照中国慢性淋巴细胞白血病工作组（cwCLL）2018版CLL诊疗指南归类为CLL2. 方法：收集并分析20例患者的临床资料，包括性别、年龄、Rai和Binet分期、症状及体征、实验室检查、免疫表型、细胞遗传学及分子学特征。3. 随访：通过查阅门诊及住院病历或电话进行随访，随访截至2021年11月25日，中位随访时间36（4～163）个月。至开始治疗时间（TTT）定义为自诊断到开始接受治疗的时间间隔，总生存（OS）期定义为自诊断至任何原因导致死亡或随访截止的时间间隔。4. 统计学处理：计量资料采用中位数（范围）进行描述，计数资料采用例数（百分比）描述。9/L，19例出现淋巴结肿大，10例脾肿大。在18例行EB病毒DNA检测的患者中，11例（61％）阳性。10例（50％）诊断为CLL，其临床及免疫表型特征见9/L，中位年龄53（35～70）岁。2例患者年龄<40岁，分别为35岁和37岁，在CLL中不常见。CLL分期中5例为Rai Ⅰ/Binet B期，5例为Rai Ⅲ/Binet C期。有2例患者发生了Richter转化，分别为例12和例19。β2-微球蛋白（β2-MG）平均水平为3.85 mg/L。中位随访36个月，2例失访，18例患者中3例死亡，2例病情进展，11例患者TTT≤12个月。1. 临床特征：男10例，女10例，诊断时中位年龄53.5（35～88）岁。患者的诊断及临床特征见+CD20+CD10−，其中16例（80％）为CD5阳性。所有CLL病例均表达B系抗原且CD5阳性、CD22或CD79b弱阳性（+CD23−。7例（70％）CLL显示κ或λ表面免疫球蛋白（SIg）中或强表达。10例CLL中仅1例患者FMC7阳性。CLL评分使用改良的Matutes评分系统，3例（30％）CLL的评分为3分，主要与CD23阴性和SIg强表达有关。CLL积分虽未达到常规4分诊断标准（该标准可区分96.6％的CLL和非CLL），但结合CD79b弱表达、IGH/CCND1排除性检查阴性、CLL辅助免疫标志物检测（如CD43+、CD200+）、流式细胞术免疫表型特点及形态学特征，此3例患者最终诊断为CLL。2. 免疫表型结果：所有t（14;19）病例免疫表型均为CD193. 细胞遗传学结果：20例染色体核型检出t（14;19）的病例均通过IGH和BCL3两种分离重排荧光原位杂交（FISH）探针进行了验证。多色FISH（M-FISH）探针还用于1例复杂核型t（14;19）病例（例3）的检测。所有20例患者均采用FISH检测IGH、BCL3、CEP12、IGH/CCND1，除3例患者细胞不足无法检测外，17例患者FISH探针加测TP53、ATM、MYB、D13S319，细胞遗传学结果详见4. 分子遗传学结果：除例3因免疫球蛋白重链可变区（IGHV）重排检测阴性，无法进一步检测外，其余19例患者IGHV体细胞突变状态及基因使用情况见2-微球蛋白水平（平均值3.85 mg/L）。Richter综合征可能由病毒（如EB病毒）感染引发，且往往提示预后不良将本组病例与此前国外报道的t（14;19）病例进行综合分析既往研究显示，t（14;19）患者中男性占优势，尤其是在+12病例中Haferlach等很多研究显示NOTCH1突变与未突变IGHV及+12显著关联，近40％的NOTCH1突变患者携带+12t（14;19）（q32;q13）IGH/BCL3及其变体t（2;19）（p12;q13）IGK/BCL3和t（19;22）（q13;q11）IGL/BCL3均涉及BCL3基因（IκB蛋白家族成员），因BCL3被鉴定为CLL中的原癌基因并在调节NF-κB信号传导中发挥核心作用，近年来关于BCL3作用的研究逐渐增多，有研究提出抑制NF-κB通路可能在诱导肿瘤细胞凋亡或生长抑制中发挥作用


RNA methylation is a widely known post-transcriptional regulation which exists in many cancer and immune system diseases. However, the potential role and crosstalk of five types RNA methylation regulators in diabetic nephropathy (DN) and immune microenvironment remain unclear.The mRNA expression of 37 RNA modification regulators and RNA modification regulators related genes were identified in 112 samples from 5 Gene Expression Omnibus datasets. Nonnegative Matrix Factorization clustering method was performed to determine RNA modification patterns. The ssGSEA algorithms and the expression of human leukocyte antigen were employed to assess the immune microenvironment characteristics. Risk model based on differentially expression genes responsible for the modification regulators was constructed to evaluate its predictive capability in DN patients. Furthermore, the results were validated by using immunofluorescence co-localizations and protein experiments in vitro.We found 24 RNA methylation regulators were significant differently expressed in glomeruli in DN group compared with control group. Four methylation-related genes and six RNA regulators were introduced into riskScore model using univariate Logistic regression and integrated LASSO regression, which could precisely distinguish the DN and healthy individuals. Group with high-risk score was associated with high immune infiltration. Three distinct RNA modification patterns were identified, which has significant differences in immune microenvironment, biological pathway and eGFR. Validation analyses showed the METTL3, ADAR1, DNMT1 were upregulated whereas YTHDC1 was downregulated in DN podocyte cell lines comparing with cells cultured by the normal glucose.Our study reveals that RNA methylation regulators and immune infiltration regulation play critical roles in the pathogenesis of DN. The bioinformatic analyses combine with verification in vitro could provide robust evidence for identification of predictive RNA methylation regulators in DN.The online version contains supplementary material available at 10.1186/s40246-023-00457-9. Diabetic nephropathy (DN) is one of the most common microvascular complications of diabetes mellitus and the leading cause of end-stage renal disease (ESRD) . The latLike DNA methylation, RNA methylation modifications regulate gene expressions without alter the sequence of RNA and can influence the interaction between the genes and microenvironment. Increasing evidences demonstrate that genes associated with the progression of DN are regulated not only by traditional signaling pathways but also by epigenetic mechanisms. As a post-transcriptional modification, N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), Alternative polyadenylation (APA), and adenosine-to-inosine (A-I) editing were the main RNA methylation modifications involving in RNA splicing , transla+ T cells from systemic lupus erythematosus patients [RNA methylation has been proven to be related to human physiologies and its mis-regulation are linked with a variety of human diseases . Previoupatients . Externapatients . A-I edipatients . Depletipatients . APA genSince these five RNA methylation patterns were crucial in the gene expression programing, immune cells regulatory and cell metastasis, identifying the epigenetic signatures in DN and complicated interrelations occurring between m6A and other well-known epigenetic modifications are urgently needed. In DN, RNA methylation not only affect the microenvironment but, importantly, mediate the persistent expression of DN-related genes and phenotypes induced by long-term hyper-glycemic exposure. However, previous studies on DN mainly focus on the specific amino acid residues of histone modification or DNA methylation . The abeThe flowchart of the present study is shown in Fig. Single-sample gene set enrichment analysis (ssGSEA) algorithm was used and 28 gene sets labeling different immune-infiltrating cells were contained for further analysis . EnrichmP value were less than 0.05. The R pheatmap package (Version 1.0.12) was used to visualize the results of HALLMARK and REACTOME pathways.In order to investigate the variation in biological signaling pathways among different RNA methylation regulators, gene set variation analysis (GSVA) enrichment analysis with R ‘GSVA’ package were employed. Gene sets “h.all.v7.5.1.symbols.gmt,” “kegg.v7.5.1.symbols.gmt,” “biocarta.v7.5.1.symbols.gmt,” “reactome.v7.5.1.symbols.gmt,” and “wikipathways.v7.5.1.symbols.gmt” were downloaded from MSigDB database (v7.5.1). The gene set was considered significantly enriched if adjusted k that before the highest variation in clustering was selected. The R package "NMF" (Version 0.24.0) with brunet algorithm and 100 nruns was performed for the consensus clustering. The RNA methylation regulators among three cluster subtypes and immune-infiltrating cells, immune reaction pathways and HLA gene expression were compared by ANOVA test.Based on 37 RNA methylation regulators, Nonnegative Matrix Factorization (NMF) clustering was utilized to determine the optimal number and stability among all classifications. The i means the RNA regulatory genes and the coefficients were calculated form the LASSO regression algorithm. Then, High- and Low-risk groups were divided according to the median risk score. To characterize the immune-infiltrating cells and immune reaction pathways affected by risk score, we performed differential expression and correlation analysis to identify the immune characteristics which were significantly correlated with risk score.The overlapping of differentially expressed genes (DEGs) among three distinct subtypes were identified as RNA methylation-related genes by using empirical Bayesian approach. The R package "limma" were used to evaluated DEGs between three different modification clusters. Twenty-four RNA methylation regulators were also introduced for the model construction. In total, 52 RNA regulatory genes for univariate logistic regression and the significant differently expressed genes were extracted. The R package "caret" (Version 6.0-93) was used to split data into training and test cohort. Identified genes related to prognosis using Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct a risk model in the training cohort. The riskScore of each sample was separately quantified in the training and validation cohorts as follows:To evaluate the correlation between estimated Glomerular Filtration Rate (eGFR) and RNA methylation regulatory genes, we performed a web-based analysis with Nephroseq V5 tool and visualized data by using "ggplot2" R package (Version 3.4.0).Conditionally immortalized human podocyte cell line (HPC) and mouse podocyte cell line (MPC) were obtained from Jinling Clinical Medical College of Nanjing Medical University. The cells were cultured in RPMI-1640 , 10% Fetal Bovine Serum (gibco), and penicillin/streptomycin medium at 33 °C in growth permissive conditions. IFN-γ was specially supplemented for MPC and Insulin-Transferrin-Selenium for HPC. HPC and MPC were treated with high glucose medium (HG) in a concentration of 30 mM glucose for 48 h when cells were cultured at 37 °C 7 days for differentiate. The normal concentration of glucose (5.5 mM) serves as control.Total cellular RNA was extracted using RNeasy Mini Kit  in accordance with the manufacturer’s protocol. Complementary DNA (cDNA) was synthesized with RevertAid First Strand cDNA Synthesis Kit . Quantitative Real-time PCR (qRT-PCR) was performed using the Fast Start Essential DNA Green Master . The primer for target mRNA was designed and synthesized by Sangon Biotech   lysis buffer supplemented with protease inhibitor  and phosphatase inhibitors . Equal amounts of proteins were resolved by 10% or 6% SDS-PAGE and were subjected to immunoblot analysis using standard methods with the following antibodies: NHPS2 , Synaptopodin (21064-1-AP), METTL3 , YTHDC1 , ADAR1, DNMT1 , WTAP   and β-actin (used at 1:5000). The Secondary antibodies were purchased from Dingguo Biotechnology . Semiquantitative analysis of the protein density by western blotting was performed using ImageJ (Version 1.5.3).HPC and MPC grown on coverslips were fixed with 4% paraformaldehyde for 10 min at room temperature, followed by permeabilization with 0.5% Triton X-100 for 10 min and blocked with 3% bovine serum albumin and immunostained with anti-METTL3 , ADAR1, WTAP  or anti-DNMT1  overnight at 4 °C. After washing with PBS, HPC or MPC were incubated with Alexa Fluor 488 goat anti-rabbit IgG  and DAPI  for nuclear staining. The specimens were visualized and analyzed by using Zeiss LSM880 confocal microscope .P < 0.05 was considered has a statistically significant.All statistical analyses were performed using R version 4.1.2 and SPSS 24.0. The expression levels of the RNA methylation regulators were compared in DN samples versus controls using Wilcoxon rank-sum test. The normal distribution data was statistically analyzed by Student's-t test between two groups and more than two groups were performed by one-way ANOVA. Univariate logistic regression analyses were performed to determine the independent prognostic factors. A total of 37 RNA modification regulators  algorithm was conducted and the results are shown in Additional file n = 56) and testing (n = 55) groups at a ratio of 1:1. LASSO regression was performed and 10 RNA regulatory genes were remained according to the minimum partial likelihood deviance  which represented critical identified of three RNA modification patterns were illustrated by Venn diagram Fig. B. These To further demonstrate the characteristics of the risk model, we compared the expression of RNA methylation regulators between high- and low-risk group. Significant differences in RNA gene profiles were observed and this result was in accordance with the difference between DN and control group, which suggested our risk model had the robust capability to distinguish DN and control samples Fig. F. As shoTo elucidate the roles of our risk model genes, we investigated the correlation between ten genes and eGFR in the Nephroseq Database. Unfortunately, five of them were removed in further analyses since they were not currently involved in the database. Among the remaining five genes, TRMT61B and CPSF4 was positively correlated with eGFR, which means higher expression indicates better renal function in DN patients Fig. A. The BLThe pathophysiology of diabetic nephropathy is complicated and involves interactions between genetic and epigenetic factors. Currently transcription factors have not been fully efficacious in the progression of DN, suggesting that further understanding of the epigenetic mechanisms is necessary for the improved management of this disease. With the rapid growing RNA research in the burgeoning field of RNA methylation, it becomes clear that these modifications play vital roles on modulating gene expression and controlling cell death, thereby igniting the new insights in RNA-based therapeutic strategies. As such, it is crucial to investigate the renal expression of these differentially methylated genes in diabetic individuals. In our current study, we firstly identified the expression profiles of RNA methylation modifications with special emphasis on m6A, m5C, m1A, APA, A-I editing, and their effects on immune microenvironment in DN patients. By explaining the regulation pattern of gene expression through post-transcriptional modification, our results provide novel scientific evidences for understanding the pathogenesis of DN.To well understand the relationship between RNA modification and DN, the catalytic mediators of its “writer”, “eraser,” and “reader” proteins need to be firstly interpret. There were 37 RNA methylation regulators, in which, 24 of them were significant differently expressed in DN patients, suggesting the involvement in DN pathogenesis. Most regulators showed close interactions in PPI network revealing the cooperative regulation of RNA methylation. Among these 24 regulators, METTL3, DNMT1, ADAR1, RRP8, and CSTF2 from m6A, m5C, A to I, m1A, and APA exhibited the highest fold change values in DN patients, respectively. METTL3, acting as a “writer,” was found to be a methylation regulator inhibiting the expression of HHLA2 in renal cell carcinoma . In our +FoxP3+cells into mice could alleviate it, which was accordance with our results [Next, to gain further insight into the immune characteristics of glomeruli in DN patients, we identified the expression of immune-infiltrating cells and HLA gene sets. The results shown that CD4 memory T cells, follicular helper T cells, T regulatory cells (Treg), gamma delta T cells, and neutrophils play pathogenic roles in immune defense of DN. Eller et al. found that depleting Treg cells with anti-CD25 antibody in db/db mice could exacerbate diabetic renal injury including elevated albuminuria and glomerular hyperfiltration, transplanting CD4 results . Emergin results . In our  results .The correlations between RNA regulators and immune characteristics were close, implying that RNA modification also contributed to the immune microenvironment regulation. We found that DNMT1 and FTO were highly correlated with activated CD8 + T cells and T follicular helper cells, respectively. DNMT1 potentiated T cell recruitment and deletion of DNMT1 in double-positive thymocytes leading to the activated T cells proliferation impaired , 34. Up-Although there was limited study reported the association between HLA-gene sets and RNA methylations in DN. The aberrant expressions of HLA were contributed to different inflammation consequences in various immune-related diseases. In diabetic patients, the HLA-DQB1*0501 allele showed protective effects on the progression of diabetic kidney disease in a Chinese Han population . On the Then, we evaluate the biological pathways of RNA modification types. DN samples were differently enriched in HALLMARKS pathway, especially glycolysis, epithelial-mesenchymal transition and oxidative phosphorylation. Zeng et al. reported that glucose dysregulation can initiate macrophage glycolysis and the release of inflammatory factors, causing renal injury in mice . ReducedWe identified three distinct RNA methylation modification patterns by nonnegative matrix factorization clustering of the DN samples. Each subtype characterized by different expression of RNA methylation regulators and immune characteristics. Sub-2 and Sub-3 patterns were associated with elevated activated infiltrating immunocytes and innate immune cells such as neutrophils and macrophages. Macrophages were demonstrated to be the most prevalent infiltrating leucocytes in diabetic kidneys and associated with declining renal function in DN patients . Then, wBesides elucidated the results of riskScore model, we also explored the ten RNA methylation regulatory genes in Nephroseq Database. TRMT61B was responsible for 1-methyladenosine of mitochondrial tRNA and participated in altered gene expression which involved in mitochondrial processes in Alzheimer’s disease , 44. CPSTo validate the results of our bioinformatic analyses, we proved that METTL3, ADAR1 and DNMT1 were all expression in DN cell lines and higher expression when compared with normal glucose condition. YTHDC1 expression was downregulated in HPC and MPC. The expression of DNMT1 and WTAP were not significantly different in HPC, whereas DNMT1 expression was increased and WTAP were downregulated in MPC. These all localizes with speckles in interphase nuclei and associated with nuclear pre-mRNA splicing components .Nevertheless, our study had some limitations. Although our study primarily reveals the expression of serval RNA regulators in vitro, its functions and metabolic network are required further intensive laboratory work. The series of new identified RNA regulators were not obtained from the databases and high throughput sequencing methylation profiling are needed for further verification. Besides, it was difficult to obtain datasets that simultaneously included clinical data, the small sample size of the GEO row data and the shortcoming of the constructed riskScore model might lack of prognostic value. A larger number of clinical data and modification of the risk model are still required to evaluate the clinical correlations.In conclusion, our study is the first comprehensive approach by integrating epigenetic and genetic signals to identify novel disease-driving pathways and therapeutic targets. We reveal that the aberrant expression of RNA methylation regulators and immune infiltration regulation play critical roles in the pathogenesis of DN, which could promote predictive-based DN management. Greater understanding of the relationship between RNA methylation regulators and kidney function has the potential not only to further the understanding of immune renal disease at a fundamental level but also to lead to the development and application of more effective, specific and less toxic therapies for DN treatment.Additional file 1. Figure S1. Batch normalization of four datasets to remove batch effects. Figure S2. Immune microenvironment characteristics in diabetic nephropathy. Figure S3. Unsupervised clustering of 52 RNA methylation regulatory genes in the meta-GEO cohort. Figure S4. Immune characteristics between high- and low-risk groups.Additional file 2. Table S1. The primer sequences for target mRNA. Table S2. The baseline information of included individuals in four GEO cohorts. Table S3. Correlation analysis of the 24 RNA methylation regulators. Table S4. Immune cell infiltration characteristics of diabetic nephropathy samples in eight GEO cohorts. Table S5. Enrichment score of immune reaction pathways in four GEO cohorts. Table S6. Enrichment score of HLA gene set in four GEO cohorts. Table S7. Differentially expressed genes between Subtype_1, Subtype_2, and Subtype_3. Table S8. The univariate logistic regression analysis to identified the risk genes of diabetic nephropathy. Table S9. Samples clustering in four GEO cohorts.


Combined diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS) secondary to immune checkpoint inhibitors (ICIs) is extremely rarely reported among ICIs- diabetes mellitus (DM) cases and is always ignored by physicians. This study aimed to conduct a systematic review to recognize better the rare adverse event of combined DKA-HHS associated with immune checkpoints.A electronic search in Pubmed/Cochrane/Web of Science, complemented by manual searches in article references, was conducted to identify clinical features of ICIs-combined DKA-HHS.we identified 106 patients with ICIs- type 1 diabetes mellitus (T1DM) from 82 publications: 9 patients presented a coexistence of metabolic acidosis, severe hyperglycemia, and/or DKA; All patients were not diagnosed as combined DKA-HHS. Compared with ICIs-DKA patients, combined DKA-HHS cases were prone to higher hyperglycemia (1020 ± 102.5 vs 686.7 ± 252.6mg/dL). Moreover, acute kidney injury (87.5% vs 28.6%) and prior chemotherapy (66.7% vs 31.6%) showed higher occurrences with the onset of ICIs-HHS or combined DKA-HHS.BCombined DKA-HHS portends a poor diagnosis in patients with coexistence features of DKA and HHS, which healthcare professionals and patients should be aware of due to differences in treatment. Our observational retrospective case series shows that patients with more risk factors were more likely to develop combined DKA-HHS. We are the first to report this group of patients’ clinical characteristics and outcomes. Although various treatment methods, such as surgery, chemotherapy, and radiation therapy, have been used to treat various tumors, immune checkpoint inhibitors significantly promote tumor cell death as a new class of drugs. Immune checkpoint inhibitors, such as programmed cell death 1 (PD-1)/program death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen 4 (CTLA-4) destroy tumor cells through T-cell-mediated mechanisms by reversing immune escape or evasion. An unfortunate side effect of ICIs treatment is the development of immune-related adverse events (irAEs) due to increased T-cell activation  and potentially life-threatening –4. Most The hyperosmolar hyperglycemic state is characterized by severe hyperglycemia and hyperosmolality without significant ketosis and acidosis. Interestingly, ICIs-induced combined DKA-HHS have combined features of hyperosmolality and metabolic acidosis, and/or DKA . In addiHere we performed a systematic review and compared ICIs- induced combined DKA-HHS with ICIs-DKA cases to describe the clinical characteristics and predisposing factors, as well as the optimal treatment of ICIs- induced combined DKA-HHS. The findings of this study will provide a valuable reference for the recognition of ICIs- induced combined DKA-HHS.We conducted a systematic review on PubMed, Web of Science, and Cochrane databases for case reports and series that reported new-onset diabetes after ICIs therapy published before March 2022. The comprehensive search used the terms “immune checkpoint inhibitor” and “diabetes” (details of the search strategy were shown in The inclusion criteria included the following items: 1) case report included ICIs related medication information, blood glucose at admission, and other related laboratory values; 2) the reported case was diagnosed HHS or DKA in published papers or met the DKA/HHS and combined DKA-HHS criterion , 9; 3) tHyperosmolality was defined as an effective plasma osmolality ≥320 mOsm/kg + glucose (mg/dl)/18 referenced from ref , hyperketonaemia and metabolic acidosis(pH ≤7.3 or bicarbonate ≤15 mEq/L) , 9Combined DKA-HHS: plasma osmolarity ≥ 320 mOsm/kg, initial blood sugar ≥ 600 mg/dL, pH ≤ 7.3, and ketonuria and/or ketonemia Qualitative and quantitative variables were respectively expressed as frequency (percentage), median with interquartile range (IQR) for abnormal distribution data, and mean with standard deviation for normal distribution data. Differences in qualitative variables between the HHS and DKA cases were assessed with χtwo tests or Fisher exact test. Student’s t-test or Mann-Whitney U test was used for quantitative ones as appropriate. Multivariate analysis for risk factors was unable to perform because of the small cases of HHS. A p-value <0.05 was considered statistically significant. All statistical analyses and data management were performed with SPSS .A total of 106 ICIs-DM cases were identified from 82 papers. The whole screen process was shown in the flow diagram in 2O, 3 of which showed even higher than 340 mOsmol/kg H2O, 4  in the DKA group, which is the most common cooccurrence with type 1 diabetes related to ICIs , 22. We Although the relationship between diabetes and immune checkpoint inhibitors has been widely studied, the exact pathophysiological mechanism of ICIs -DM is still not wholly unknown. There is growing evidence supporting such findings , 27, andWe implied that ICIs- induced combined DKA-HHS could be caused by multiple physiological and pathological mechanisms based on the following reasoning: First, type 1 diabetes results from a deficiency of circulating insulin and increased levels of the counter-regulatory hormones catecholamines, glucagon, cortisol, and growth hormone. Immune checkpoint inhibitors destroy the pancreatic β-cells, which leads to loss of insulin secretion and absolute insulin deficiency. Second, infection, T1DM-related autoantibodies, and genetic factors have been identified as important increased risks in the pathogenesis of ICIs-DM , 32–39, There are limited data to guide the management of ICIs- induced combined DKA-HHS, so management strategies are also not clear for individuals with ICIs- induced combined DKA-HHS. Treatment of this case and other cases with ICIs- induced combined DKA-HHS in our literature were mainly based on ICIs-DM , includiIn conclusion, we highlight combined DKA-HHS as an unusual presentation and complication of ICIs-T1DM.A mixed presentation with features of DKA and HHS is a frequent yet underrecognized and underreported emergency in ICIs-DM. Identifying hyperosmolarity is essential, as its treatment requires more-aggressive fluid administration than treatment of ICI-DKA, and improper management may increase the risk of complications. Our data show that increased exposure to more risk factors accounts for the increased incidence of ICIs-induced combined DKA-HHS. It is the first retrospective, observational case series summarizing the clinical characteristics of combined DKA-HHS patients associated with ICIs. We hope that clinicians gain more excellent experience in the side effects of still relatively immune checkpoint inhibitors and that new cases of ICIs-induced combined DKA-HHS will be detected earlier.WZ and JC took part in all data analysis, writing and editing of the manuscript. JB and ND involved the screening and identification of all case reports. XC reviewed and edited the manuscript especially in language spelling. YJ was responsible for the management of our patient case. ZW mainly contributed to the critical revision of the manuscript and supervision of all edition processes. All authors contributed to the article and approved the submitted version.


The loading of the exosomes is regulated by RNA-protein complexes, and recent studies show that cytosolic RNA granules and exosomes share common content. Intracellular RNA granules and exosome loading may therefore be related. Exosomes can also transfer pathogenic molecules of CXG diseases from cell to cell, thereby driving disease progression. Both intracellular RNA granules and extracellular RNA vesicles may serve as a source for diagnostic and treatment strategies. In therapeutic approaches, pharmaceutical agents may be loaded into exosomes which then transport them to the desired cells/tissues. This is a promising target specific treatment strategy with few side effects. With respect to diagnostics, disease-specific content of exosomes, e.g., RNA-signatures, can serve as attractive biomarker of central nervous system diseases detecting early physiological disturbances, even before symptoms of neurodegeneration appear and irreparable damage to the nervous system occurs. In this review, we summarize the known function of cytoplasmic RNA granules and extracellular vesicles, as well as their role and dysfunction in CXG repeat expansion disorders. We also provide a summary of established protocols for the isolation and characterization of both cytoplasmic and extracellular RNA organelles.RNA is a vital biomolecule, the function of which is tightly spatiotemporally regulated. RNA organelles are biological structures that either membrane-less or surrounded by membrane. They are produced by the all the cells and indulge in vital cellular mechanisms. They include the intracellular RNA granules and the extracellular exosomes. RNA granules play an essential role in intracellular regulation of RNA localization, stability and translation. Aberrant regulation of RNA is connected to disease development. For example, in microsatellite diseases such as CXG repeat expansion disorders, the mutant CXG repeat RNA’s localization and function are affected. RNA is not only transported intracellularly but can also be transported between cells  RNA-protein interactions are of prime importance in many cellular processes, both in healthy as well as disease conditions. Proteins recognize specific structural elements of RNA with RNA-hairpin structures being an important example. Abnormal hairpin structures aberrantly recruit cellular proteins, for example in monogenic diseases caused by expanded CXG repeat hairpin-motifs. CXG repeat diseases are a group of neurodegenerative diseases caused by a pathogenic expansion of a CXG repeat in the disease-causing genes (reviewed in ) Table . They invice versa . Often,iewed in ). TherefIntracellular RNA granules differ with respect to their localization, composition, and size. Based on their localization, they are mainly divided into two groups: Nuclear and cytoplasmic . NuclearNuclear granules are integral for regulating cell growth, proliferation, gene expression, splicing, and survival. The nucleoli help in ribosomal assembly, contribute to DNA repair, modulate stress-response, apoptosis, and senescence (reviewed in ). Cajal Besides the intracellular RNA transport that is crucial to fulfil tightly spatiotemporally regulated RNA-function, RNA can also be transported intercellularly, thus contributing to cell-cell communication. For this intercellular transport, extracellular vesicles (EVs) play an essential role.In this review, our focus will be on function and dysfunction of cytoplasmic and extracellular RNA organelles in CXG repeat expansion diseases.via mTOR. As a result, 4E-BP1 unleashes the RNA and loses binding to elF4E (eukaryotic translation initiation factor 4E). ElF4E can then bind to elF4G and eIF4A, and the resulting complex binds to the preinitiation complex and the 5′UTR of the RNA. On the other hand, phospho-activated S6K phosphorylates and activates the ribosomal subunit S6. The preinitiation complex includes the small ribosomal subunit, the methionine-loaded start tRNA, the initiation factor eIF2, and GTP (guanosine triphosphate). GTP hydrolysis then releases eIF2 so that the large and small ribosomal subunits can complete forming the 80S ribosome.SGs are one type of RNA granules that can assemble in the cytosol. SGs are approximately 100–200 nm in size (reviewed in ). EnviroDifferent types of environmental stresses activate different kinases that phosphorylate eIF2. Phosphorylation of eIF2a reduces the formation of the ternary complex and thus prevents the assembly of the preinitiation complex. This inhibits translation induction, which in turn leads to polysome degradation. Transcripts that are no longer polysome-bound, are then actively organised into SGs through RNA-binding proteins), which oligomerize RNA . Examplevia G3BP cleavage (via interferons (IFN). In addition, viral RNA and antiviral proteins such as retinoic acid-inducible gene I-like receptors (RLRs) can localize into SGs, thereby changing the normal SG composition. RLRs detect cytoplasmic viral RNA and trigger the production of IFNs , inhibit the formation of SGs. Encephalomyocarditis virus (EMCV) proteins, as well as poliovirus proteins also inhibit SG formation cleavage . One rea of IFNs . These dAnother way in which SGs can influence cellular processes is by sequestering and thereby limiting the access to cytosolic proteins. SGs modulate signaling cascades, for example, by recruiting components of the mTOR, RACK1 (Receptor For Activated C Kinase 1) or TRAF2 (TNF Receptor Associated Factor 2) signaling pathways . Since SAnother group of cytoplasmic RNA granule is the PBs which are approximately ≥100 nm in size (reviewed in (24)). PBs consist mainly of mRNAs in conjunction with proteins responsible for translational repression and 5′-to-3′-mRNA decay . Neurons regulate synaptic plasticity by spatiotemporal regulation of protein synthesis. In this way, the presence of a particular protein at a particular location in the right time is ensured. The neuronal RNA transport granules control this phenomenon by transporting mRNAs together with the translation machinery to its target site. During transport, the translation is inhibited, and at the final destination the translational block is released to allow the mRNA to be translated . Neuronaiewed in ). Neuroniewed in ). Severaiewed in . One keyiewed in ). By timiewed in .de novo paraspeckles that are generally absent but assemble during embryonic stem cell patterning .The biogenesis of RNA granules occurs by liquid-liquid phase separation (LLPS). LLPS is the process of accumulating biomolecules into a dense phase by separating themselves from their surrounding light phase. Weak RNA-protein and RNA-RNA interactions aid LLPS. The droplets formed by LLPS are ball-shaped and can assemble or dissociate within seconds, thereby responding to signals quickly. This behaviour gives RNA granules the ability to assemble from smaller droplets that fuse, to condense, to dissolve, and to exchange content with their surroundings. Despite this flexibility, they are internally structured with a stable core structure and a more dynamic shell (reviewed in ) Figure.Granule formation and core-shell transitions are modulated by numerous RNA-protein interactions. These interactions require the hydrolysis of ATP to ADP by ATPases. In this process, different ATPase complexes are responsible for different steps of granule assembly, dynamics, and stability . For exaFor LLPS and the subsequent maintenance of RNA granules, both intrinsically disordered proteins (IDPs) and multivalent protein are required. While each type of RNA granule contains a certain signature of RNA-protein-content that distinguishes it from other granule types, some components like IDPs and multivalent proteins, are shared by different types of granules. Since there is a limited intracellular stock of these proteins critical for LLPS, the different types of granules compete for them. Thus, when there is an assemblage and expansion of one type of RNA granule, the other granules often shrink .Important proteins that reside in RNA granules, e.g., IDPs, often have a susceptibility to misfold and undergo aberrant interactions. Therefore, proteins in RNA granules are often under close surveillance by the cellular protein quality control system. The protein quality control system involves chaperones that assist the conformational folding of proteins and cellular protein degradation pathways. Deregulation of the protein quality control system, for example in neurodegeneration, is thus often linked to aberrant RNA granules composition and dynamics. Those aberrant RNA granules can no longer execute their physiological functions and thus contribute to disease development (reviewed in ).The appearance of aberrant RNA granules in CXG repeat expansion diseases was first reported more than 20 years ago, when intranuclear RNA granules, referred to as nuclear foci, where discovered in DM1. Here, the mutant CUG-expanded RNA encoding DMPK (Myotonic Dystrophy Protein Kinase) induces the assembly of nuclear foci in patient fibroblasts and muscle biopsies . In lineFMR1 gene. This mutation is associated with FXS, which is characterized by intellectual and developmental disabilities. The FMR1 gene encodes the RNA-binding FMRP protein, which regulates mRNA translation, localization, and stability (reviewed in (One example of a CXG repeat that causes the formation of aberrant cytoplasmic RNA granules is the mutant CGG repeat in the iewed in ). Normaliewed in . Fragileiewed in . During iewed in . This suiewed in . This deFMR1 deregulate formation of cytoplasmic RNA granules. Expansion of the CGG repeat in the FMR1 gene is associated with decreased expression and loss of function of FMRP and thus FXS. In the absence of FMRP or its mutation under disease conditions, SG formation process is inhibited. In addition, also the formation of fragile X granules is affected, with an increase in fragile X granules in the FRM1-knockout mice (APP) RNA, which FMRP normally recruits into PBs thereby inhibiting its translation . In FXSinto PBs . It is tinto PBs ), may ocinto PBs ).Drosophila, while simultaneously increasing their dynamics  which is a nuclear RNA/DNA-binding protein, is mislocalized to SGs in cytoplasm of G3BP1 positive granules  stress. Furthermore, SG-associated proteins are overexpressed in cortices of HD mouse models and HD patients. These include G3BP1, which initiates SG assembly granules .β-actin (ACTB) mRNA and reduction in the levels of HTT reduces the mRNA transport  in PBs. AGO2 belongs to the AGO family and is a component of the small RNA-mediated gene silencing pathway (reviewed in), and HTT through association with AGO2 contributes to RNA-mediated gene silencing in PBs. Mouse striatal cells expressing mutant HTT exhibit fewer PBs, indicating that PB formation is affected in HD . AGO2 anransport . This suAtaxin-2 (ATXN2) gene in SCA2 and the Ataxin-7 (ATXN7) gene in SCA7. As in HD, a correlation between disease and SG formation was discovered. The disease-causing protein in SCA2, ATXN2, has a paralog termed ataxin-2-like (ATXN2L), which is a component of SGs. Overexpression of ATXN2L leads to the induction of SGs, whereas low ATXN2L levels reduce the number and size of SGs. Therefore, ATXN2L can be considered as a SG regulator, probably via the interaction with G3BP1. Furthermore, overexpression or reduction of ATXNL2 impacts the presence of microscopically visible PBs. Thus, ATXNL2L is involved in the regulation of at least two types of RNA granules: SGs and PBs . Here, a reduced expression of BDNF (brain-derived neurotrophic factor) in SCA6 cerebellum and mislocalization of BNDF into aberrant dendritic granules, which are absent in controls, was observed (ATXN8 gene (ATXN8OS on the opposite strand), aberrant cytoplasmic inclusions have been described. Within these aberrant cytosolic granules that were found in an SCA8 autopsy case, small granular particles, identical to ribosomes were found. However, future work is required to show in closer detail how the reverse or forward transcripts of the expanded CAG/CUG repeat affect aberrant aggregation of ribosomes and neurodegeneration  and CUG-binding protein 1 (CUGBP1). Both MBNL1 and CUGBP1 are SG and PB components. In cell culture models of DM1, the structure and regulation of SGs and PBs is altered , and apoptotic bodies . While the apoptotic bodies could be regarded as the biggest of all EVs with a size ranging from 1–5 μM, microvesicles are ~100–1,000 nm large, making the 30–150 nm sized exosomes the smallest subpopulation (reviewed in ) Figure.Exosomes are spherical, cell-specific, nanosized EVs, which are secreted by almost all mammalian cells (reviewed in ). WithinExosomal biogenesis is a multistep process. Their biogenesis involves endosome formation that starts with inward budding of the cell membrane and internalisation of cargo into early endosomes. The early endosomes mature into multivesicular bodies (MVBs). By invagination of the MVB membrane, intraluminal vesicles (ILVs) are formed within the MVBs. Through this process, ILVs recruit cytosolic components like proteins, RNAs, lipids, and other exosome cargoes. The ILVs are then released from the MVBs into the extracellular space by fusion with the plasma membrane. These released ILVs are termed exosomes . The detIn summary, there are three different kinds of EVs: Exosomes, microvesicles and apoptotic bodies. While, apoptotic bodies are cleared by phagocytosis, exosomes and microvesicles play a vital role in intercellular communication and also help removing excess biomolecules from the cell. Besides supporting normal cell function and homeostasis, EVs are important in the development of neurodegenerative diseases.While upon disease conditions, the aberrant formation of the cytoplasmic RNA granules could push neurons towards degeneration, exosomes can act both as a friend and a foe. On one hand they are involved in the progression of the disease, on the other hand their potential as a therapeutic system cannot be ignored. Exosomes are involved in the pathogenesis, propagation, and progression of several neurodegenerative diseases (reviewed in ). Howevein-vitro and in-vivo. Upon this internalisation of the polyglutamine peptide, cytoplasmic aggregation is induced in the receiving cells  for 4 days resulted in the detection of mutant HTT aggregates in neurons. Additionally, upon intraventricular injection of exosomes from HD fibroblast in mice, mutant HTT was detectable in the striatum, and the mice developed an HD-like phenotype  occurs both in ng cells . In lineng cells . Furtherng cells . While ahenotype .2+ channels and affects transcription. They together lead to functional deficits of mitochondria . HD celiewed in . Manifesiewed in . Autophaiewed in  impairmeiewed in .Besides transferring mutant HTT protein, EVs also deliver the mutant CAG repeat RNA from one cell to another  reviewe). EVs thWhile all these studies have been conducted on HD, EVs also play an important role in other CXG repeat expansion disorders. For example, there is evidence from studies on DM1 suggesting that a specific pattern of mis-splicing signatures can be detected in body fluids like blood or urine. These include 10 splicing variants in exosomal RNA that may serve as biomarker for the disease . FurtherFMR1 that encodes FMRP, is connected to exosome loading. Specifically, FMRP controls exosomal cargo loading of miR-155 during inflammation . EV secIn context to CXG repeat disorders, several studies have confirmed the compelling nature of exosomes both as diagnostic biomarkers and therapeutic systems. With regard to diagnostic biomarkers, HTT is detectable in EVs isolated from pig HD model as well as in HD patient plasma, with elevated HTT levels in EVs from plasma of HD groups when compared to controls suggesting that EV cargo may serve as biomarker for the disease .BDNF. It is also known that in HD, the expression of miR-124 is decreased, which leads to increased levels of the miR-124 target gene REST. To compensate for the lack of miR-124 and thereby restore normal REST function, exosome-based delivery system called EXO-124 was developed. Therefore, exosomes were isolated from HEK293T cells overexpressing miR-124. Then EXO-124 was injected into the striatum of HD mice. While the REST expression level was successfully reduced, this treatment did not improve in the behavioural phenotype of the mice  is one. While normally HTT represses REST, mutant HTT does not silence the activity of REST any longer. This results in transcriptional deregulation, e.g., the repression of REST target genes like the mice . Neverthvia exosomes, hydrophobically modified siRNAs (hsiRNA) targeting HTT mRNA can be loaded into exosomes via co-incubation without altering their shape, size, or integrity. These loaded vesicles can then be internalized by the primary cortical neurons and lead to dose-dependent HTT mRNA silencing  of SCA3 patients and controls were isolated. They found one miRNA; mir-7014, that was upregulated in CSF exosomes and downregulated in plasma exosomes. Since exosomes can pass the blood–brain and blood–CSF barriers, these opposite changes in plasma- and CSF-derived exosomal mir-7014 levels are to date not clear and deserves further analysis. Further, mir-7014 level correlated with the age of onset, while no correlation with other clinical indexes, e.g., SARA scores, was found. Together these findings make mir-7014 an interesting candidate as exosomal biomarker for SCA3  and both originate from the cytoplasm. Interestingly, many RNA-binding proteins that are components of SGs or PBs  are also associated with EV loading, suggesting a possible link between intracellular RNA granules and loading of EV-RNA  reviewe). For exexosomes .It is tempting to speculate that neither single RNAs nor proteins are taken up from the cytoplasm into exosomes, but RNA-protein complexes segregate from RNA granules into exosomes. On average, cytoplasmic granules are larger than exosomes, meaning that RNA-protein complexes may separate from the large RNA granules, get internalised in the MVBs and finally get secreted as exosomes.via a synapse or via nanotube structures, or by endocytic pathways including micropinocytosis, phagocytosis, clathrin-mediated, caveolin-mediated, or receptor-mediated endocytosis  plays a role in regulating these proteins’ function in granule formation. Thus, a key aspect of future studies should be to investigate the impact of phosphorylation or ubiquitination events on RNA-protein interactions as key steps for phase separation and granule assembly/disassembly.via EVs. There is recent evidence that intracellular granules that are built via LLPS are precursors for EVs. Interestingly, like RNA granules assembly, also EV loading of miRNA cargo is regulated by ubiquitination and phosphorylation events: Ubiquitination of RNA-binding proteins, e.g., YBX1 helps selecting miRNAs for EV loading. Stress induced phosphorylation of Caveolin-1 leads to altered EV-miRNA-content . The uptake of cellular cargo via EVs depends on receptor-ligand interactions. Interestingly, recent studies show that the presence of viral proteins  increases uptake of EV-cargo that is important for neurodegenerative diseases   . These dvia exosomes as well as RNA toxicity in general also occur in C9orf72 repeat expansion mutations (reviewed in (The above-mentioned mechanism of transmission of pathogenic particles iewed in ) (Westeriewed in . Unlike iewed in . A similiewed in ). Comingiewed in . Changesiewed in  (revieweiewed in ). Taken iewed in  (revieweiewed in ). Furtheiewed in . In summiewed in .in-vitro and in-vivo  Yenuga. The incAn overview of different methods of isolation of EVs is given in


The ribozyme worldis thought to have evolved the burdensome complexityof peptide and protein synthesis because the 20 amino acid side chainsare catalytically superior. Instead, I propose that the Achilles heelof the RNA world that led to the extinction of riboorganisms was RNA’spolyanionic charges that could not be covalently neutralized stablyby phosphotriester formation. These charges prevented developmentof hydrophobic cores essential for integration into membranes andmany enzymatic reactions. In contrast, the phosphotriester modificationof DNA is stable. So, the fact that the charge was never removed inDNA evolution gives further credence to proteins coming before DNA. But isthis side-chain-diversity explanation unassailable?Let us take question (i) first because there is general agreementon the answer.ydroxyls 1, right https://genesilico.pl/modomics/) contains many hypermodifications that include most of the catalyticgroups of proteins. Admittedly, only 18 mostly simple modificationsare found in all three domains of life: eubacteria, archaebacteria,and eukaryotes.4 Of these, the carboxylicacid of t6A is the only significant catalytic-group-likemodification, but it should not have been hard to evolve others. Forexample, additional nucleotide modifications with catalytic functionalitywere argued to be part of the RNA world based on the universal coenzymeswith nonfunctional short RNA handles: NAD+, S-adenosylmethionine, CoA, FAD, and ATP.5 This implies that the RNA world contained ribozymes catalyzing redox,transmethylation, C–C bond formation, and phosphorylation reactions.5 So, perhaps the Achilles heel of ribozymes wasnot side-chain diversity after all. Instead, might it have been thepolyanionic backbone?Considerthat the list of more than 110 RNA modifications  and synthesizing enzymes with hydrophobic activesites. Charged groups prevent integration into membranes. Hydrophobiccores are crucial for protein enzymes catalyzing reactions that aresusceptible to hydrolysis or involve highly reactive intermediates, although nucleic-acid-basedhydrophobic pockets have been demonstrated10 and hydrophobic base modifications could assist their formation.11 Life’s ultimate solution of encodingpolymers with an uncharged backbone by evolving the complexity ofpeptide13 and then protein synthesis14 seems much more convoluted . An alternative is synthesis by post-transcriptionalmodification of RNA by alkylation or acylation. Although the phosphodiesterO– is relatively inert , the O– is more reactive than most other groups of nucleic acids that becomealkylated.19 The coexistence of two typesof RNA polymers could potentially create specificity problems in thecell, but today’s modified RNAs do not interfere with the templatefunctions of unmodified RNAs and vice versa. So, why did RNA not takethis short evolutionary path  RNAs might have been synthesizeddirectly from nucleic acid templates using appropriate nucleotideanalogue substrates, although RNA’s charge is very importantfor aqueous solubility and specific double-helical structures longerthan several base pairshttps://genesilico.pl/modomics/). Interestingly, the phosphodiester is the only chemical group ofRNA frozen in evolution: metabolism does not modify even a singleone.  The reason, presumably, is that the delocalizednegative charge on the phosphodiester group stabilizes it againstnucleophilic attack.3 Removal of this chargein RNA makes the phosphorus more susceptible to nucleophilic attackby the vicinal 2′ hydroxyl group  has been interpreted as favoringlate DNA.9 Proposed alternative chemical routes todeoxyribonucleotides24 that are much simpler than evolving a ribosome14 favor early DNA. Now consider the fact that phosphotriestergroups are much more stable in DNA than RNA courtesy of the 2′deoxy modification.22 Thus, if DNA came before protein in life,23 it would seem reasonable that evolutionwould have crossed a shorter evolutionary distance (versus inventingpeptide and protein synthesis) to invent DNAzymes25 that included phosphodiester charge removal to enable largehydrophobic cores. This could be tested by generating such modifiedDNAzymes in the lab, and they might be useful as therapeutics. Butgiven that this pathway did not evolve, this gives further credenceto proteins coming before DNA to give rise to a ribonucleoproteinworld8  above. This questionwas not resolved by comparative genomic and structural analyses.nworld8 1, left.3 an explanationof ultimately why nature did notchoose phosphates is their unsuitability for creating hydrophobiccores. It is very plausible that the inability of modified RNAs toshed negative charge stably was the evolutionary baggage that ledto the extinction of the RNA world, the mother of all extinctions.With regard to enzyme evolution,to paraphrase Frank Westheimer,


RNA secondary structure has broad impact on the fate of RNA metabolism. The reduced stability of secondary structures near the translation initiation site/start codon of the coding region promotes the efficiency of translation in both prokaryotic and eukaryotic species. However, the inaccuracy of in silico folding and the focus on the coding region limit our understanding of the global relationship between the whole mRNA structure and translation efficiency. Leveraging high-throughput RNA structure probing data in the transcriptome, we aim to systematically investigate the role of RNA structure in regulating translation efficiency.Here, we analyze the influences of hundreds of sequence and structural features on translation efficiency in the mouse embryonic stem cells (mESCs) and zebrafish developmental stages. Our findings reveal that overall in vivo RNA structure has a higher relative importance in predicting translation efficiency than in vitro RNA structure in both mESCs and zebrafish. Also, RNA structures in 3’ untranslated region (UTR) have much stronger influence on translation efficiency compared to those in coding regions or 5' UTR. Furthermore, strong alternation between in vitro and in vivo structures in 3' UTR are detected in highly translated mRNAs in mESCs but not zebrafish. Instead, moderate alteration between in vitro and in vivo RNA structures in the 5’ UTR and proximal coding regions are detected in highly translated mRNAs in zebrafish.Our results suggest the openness of the 3’ UTR promotes the translation efficiency in both mice and zebrafish, with the in vivo structure in 3’ UTR more important in mice than in zebrafish. This reveals a novel role of RNA secondary structure on translational regulation.The online version contains supplementary material available at 10.1186/s12859-022-05037-7. The secondary structure of RNA plays an essential role in post-transcriptional regulatory processes, including splicing, localization, stabilization and translation –5. RNA sCompared to the extensive research on the relationship between synonymous codon usage and the translation efficiency of RNA, the impact of RNA structure on translation has not been well-established. In the existing study of RNA structure and translation, one of the most widely used measurement to describe the RNA structure is the RNA folding energy, which is calculated in silico by various RNA folding algorithms , 10–12. Since the influence of RNA secondary structure on translation can directly result from the interaction between RNA and ribosomes, most existing research focus on the coding sequence (CDS) of the RNA, especially the translation initiation site. For example, it is reported that the high folding energy around the CDS start site promotes the efficiency of RNA translation , 7, 10. There are mainly three steps in our machine learning framework Fig. . First, We performed random forests  and elas/vitro structure are important in predicting the translational efficiency , which is known to be highly expressed at the protein level in mESCs [Prpf8 was also confirmed in our mESC data as it was among the top 25% TE transcripts in the positive set. By examining the in vivo reactivity along the transcript, we confirmed that Prpf8 has the highest accessibility at the 5’ end of its 3’UTR  is among the bottom 25% TE transcripts. It has been reported that Golga4-knockout mice do not show any discernable phenotype [Golga4 and Prpf8 have distinct in vivo reactivity patterns. While the in vivo reactivity is enriched at the 5’ end of the 3’ UTR in Prpf8, it is enriched at the 3’ end of the 3’ UTR in Golga4  may interact with 3’ UTRs, which forms specific RNA structures. Under in vitro conditions, those RBPs are removed, resulting in different 3’ UTR RNA structure patterns compared to in vivo conditions. It is reported that some RBPs target 3’ UTRs to modify the translatability of mRNAs , 31, 33.In silico RNA structures predicted from the RNA folding algorithms have been a major source of the structure information in the previous studies. In this paper, we systematically analyzed the influence of both sequence and structural features on the translational efficiency of mRNA using high-throughput RNA structure-probing data in mESCs and the zebrafish. We found that the in vivo reactivities in the 3’ UTR are the most important structural features in predicting translational efficiency in both mESCs and the zebrafish, with unfolded 3’ UTR structures possibly promoting translational efficiency. This finding may be used to guide future investigations of regulatory elements for controlling RNA translation.We collected the Ribo-seq and RNA-seq data that were generated from the mESCs by Ingolia et.al. . We calcWe also collected the Ribo-seq and RNA-seq data from the zebrafish by Beaudoin et al.  and perfWe then ranked the transcripts by their TE values. We selected the top 25% and bottom 25% transcripts as the positive and negative dataset, respectively. The aim was to build a classification model to differentiate transcripts with high TE and those with low TE. The transcripts with intermediate TE were removed from the following analysis.in-silico structural features was generated from the minimum free energy (MFE) structure predicted by the RNAfold [We created the feature space in two parts, sequence features and structural features. The sequence features of RNA include nucleotide frequency, codon frequency, amino acid frequency, GC content, codon repetitive rate, amino acid repetitive rate, and the lengths of CDS and UTRs. In total, we collected 219 sequence features. The structural features include three parts, the in vivo structure, the in vitro structure and the in silico structure. The in vivo and in vitro structural features were generated from the icSHAPE data (for mouse) and DMS-seq data (for zebrafish), and the  RNAfold . To note/vitro structural features for transcripts with various lengths, we normalized the lengths of all the transcripts to the same scale. We first normalized the length of each transcript to 60 bins, in which 20 bins are assigned to each of 5’ UTR, CDS, and 3’ UTR. The number of nucleotides in each bin is determined by the total length of the corresponding region. Then the average reactivity across all the nucleotide within each bin is calculated. The meta-gene analysis was performed by our newly developed Python package named Meta-Feature Analysis System (MFAS). We implemented more than 20 types of meta-feature analysis in both genome and transcriptome levels. The details of MFAS can be accessed at (https://github.com/ouyang-lab/MFAS).Reactivity of a nucleotide is defined as the accessibility of a nucleotide in the chemical probing with high-throughput sequencing data. A higher reactivity of a nucleotide compared to a lower reactivity indicates that the nucleotide is more likely to be single-stranded in the structure. The single nucleotide-level reactivities were from Spitale et. al.  for icSHWe selected random forests  and penaAdditional file 1: Supplementary document containing information about feature generation and an additional figure.


Escherichia coli mRNA. The purification success was monitored by qRT‐PCR and RNA sequencing. Neither method allowed complete removal of rRNA. Nevertheless, we were able to quantitatively analyze several modified nucleosides in the different RNA types. We found evidence for stress dependent RNA modification reprofiling in rRNA, but also several modified nucleosides in the mRNA enriched fractions showed significant changes.mRNA methylation is an important regulator of many physiological processes in eukaryotes but has not been studied in depth in prokaryotes. Working with bacterial mRNA is challenging because it lacks a poly(A)‐tail. However, methods for detecting RNA modifications, both sequencing and mass spectrometry, rely on efficient preparation of mRNA. Here, we compared size‐dependent separation by electrophoresis and rRNA depletion for enrichment of  mRNA methylation is an important regulator in eukaryotes but has not been studied in depth in prokaryotes. In this study, we compared size‐dependent separation by electrophoresis and rRNA depletion for enrichment of Escherichia coli mRNA and quantitatively analyzed modified nucleosides in the different RNA types. Chemical modification of DNA is a well‐known epigenetic regulatory mechanism. The recent development of highly sensitive analytical techniques has discovered increasing numbers of RNA modifications leading to new aspects of RNA regulation in the emerging field of epitranscriptomics. To date, more than 160 RNA modifications have been described in all domains of life.1, 2 EN6‐methyladenosine (m6A) is the most abundant mRNA modification in eukaryotes and is the best studied example of how mRNA methylation regulates both mRNA stability and gene expression.In eukaryotes, tRNA and rRNA modifications can affect the whole protein synthesis in the cell, whereas several mRNA modifications have been shown to be posttranscriptional regulators of specific gene expression programs involved in cell development and homeostasis.5, 6 N6A profiling for the two bacterial model organisms Escherichia coli and Pseudomonas aeruginosa revealed both conserved and distinct distribution patterns of this modification.6A/A ratios observed (>0.2 %) were comparable to those of mammals (0.1–0.4 %) and meiotic yeast (0.25 %). Most m6A‐modified transcripts were associated with respiration, amino acid metabolism, and stress response, suggesting a potential regulatory role for mRNA methylation in bacteria. Therefore, it was suggested that RNA methylation might be involved in the adaptation of bacteria to different conditions and developmental stages, as has been demonstrated for eukaryotic cells.E. coli and Pseudomonas spp., two other Gram‐negative cyanobacteria (Anabaena sp. PCC 7120 and Synechocystis sp. PCC 6803) and representative species of Gram‐positive bacteria (Staphylococcus aureus and Bacillus subtilis) showed low m6A/A ratios .5C) has been mapped in the mRNA of the archaeon Sulfolobus solfataricus.While bacterial tRNA and rRNA can be easily purified and the modification profile assessed through MS,11, 126A is the only described mRNA modification in bacteria, we were interested whether other modifications found in eukaryotes are also present in bacterial mRNA. To address this question, we isolated total RNA along the growth of E. coli, used two mRNA enrichment methods, electrophoretic separation by size and rRNA depletion, followed by stringent validation of RNA identity and analyzed the individual fractions by isotope dilution nucleoside mass spectrometry (LC‐MS/MS). Similar to a recent study in mice,Because mE. coli MG1655. Since RNA sequencing is only possible for a limited number of modifications,. After the addition of 1/10 volume RNA‐marker , the samples were denatured at 65 °C for 5 min, followed by immediate cooling on ice. The gels were run in running chambers (Bio‐Rad) using 20 mM MOPS as a running buffer at 5 V/cm for 3 h. As a size reference, the RiboRuler high range RNA ladder (Thermo Scientific) was used. RNA fragments with sizes from 3,200 to 2,800 nt (23S rRNA), 2,700 to 1,700 nt (long mRNAs), 1,600 to 1,400 nt (16S rRNA), and 1,100 to 400 nt (short mRNAs) were cut from the gel, leaving physical space between each of them. Cutting was documented before and after the excision of the fragments. RNA was isolated from the gel fragments with a commercially available kit . Elution from the column was performed in two consecutive steps using 25 μL water. The quality of each fraction was analyzed using chip gel electrophoresis  and another denaturing agarose gel electrophoresis. The efficiency of RNA separation was tested using Bioanalyzer chip electrophoresis and RT‐qPCR.E. coli, siTOOLS, Martinsried, Germany). The efficiency of rRNA depletion was tested by Bioanalyzer chip electrophoresis and RT‐qPCR.After removal of tRNA and small noncoding RNAs by SEC, the remaining mRNA+rRNA mixture was subjected to two consecutive rounds of rRNA depletion according to the manufacturer's protocol for oligonucleotide‐based depletion of rRNA  according to the manufacturer's protocol. The samples were mixed with SsoAdvanced Univ SYBR Green Supermix (Bio‐Rad), dispensed in triplicate into a 96‐well PCR plate (Bio‐Rad), and subjected to qPCR in a Bio‐Rad CFX real‐time cycler (see Table S1 for primers). For internal calibration, the data were evaluated using the ▵▵CT methodalaS  was used as internal reference for the long mRNA fractions and recA  for the short mRNA fractions and the mRNA after oligo‐based rRNA depletion with RiboPOOL.To evaluate the level of RNA separation and mRNA enrichment in the mRNA fractions extracted after denaturing gel electrophoresis (fractions 2 and 4) and after oligonucleotide‐based rRNA depletion, the ratios of mRNA : 16S rRNA and mRNA : 23S rRNA were determined by RT‐qPCR. To calculate the level of rRNA removal, the mRNA : rRNA ratios before  and after gel electrophoresis or oligonucleotide‐based rRNA depletion (mRNA fractions) were compared. The ▵▵recA : 16S rRNA and alaS : 23S rRNA ratios between the total RNA samples and the mRNA‐enriched fractions were used to calculate the final mRNA : rRNA ratios. For example, at a fold‐change of 90 between alaS : 23S rRNA, the mRNA : 23S rRNA ratio would be 2 : 1, and similarly, for a fold‐change of 90 between recA : 16S rRNA, the mRNA : 16S rRNA ratio would be 2 : 1. Taken together, the final ratio of mRNA : 23S rRNA : 16S rRNA would be 2 : 1 : 1 or 50 % mRNA and 50 % rRNA.To calculate the mRNA : rRNA ratios as percentage, a ratio of 90 % rRNA  and 1 % mRNA in the total RNA27, 28To evaluate the level of contaminating rRNA in the mRNA‐enriched fractions 2 and 4, the RNA was converted into cDNA libraries using the NEBNext Ultra RNA Library Prep Kit for Illumina . As a control, libraries were prepared from the corresponding total RNA. The libraries were analyzed in an HiSeq Illumina sequencer with an average sequencing depth of ∼8 million single‐end 50 nt‐long reads.Data analysis was performed with the CLC Genomic workbench software  with or without duplicate removal, and both data sets were analyzed independently. To evaluate the molar ratios of RNA types in the samples, the quotient of (reads for a specific gene)/ was divided by the length of the specific gene.2 13C/15N stable isotope labeled internal standard (SILIS) from S. cerevisiae tRNA was added2 gas temperature of 230 °C and N2 gas flow of 6 L/min, sheath gas (N2) temperature of 400 °C with a flow of 12 L/min, capillary voltage of 2500 V, nozzle voltage of 0 V, and nebulizer at 40 psi.Qualitative , semi‐quantitative  and absolute quantitative analyses  of the prepared RNA fractions were performed by LC‐MS/MS as previously described.The authors declare no conflicts of interest.1As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Panicum miliaceum L.), their expression under NaCl  and Na2SeO3 (sodium selenite)treatments. This study identified eight transporters  that have a potential role in Se uptake in proso millet. We analyzed physicochemical properties, conserved structures, sub-cellular locations, chromosome location, molecular phylogenetic analysis, promoter regions prediction, protein-protein interactions, three-dimensional structure modeling and evaluation of these transporters. The analysis revealed the chromosome location and the number of amino acids present in these transporters as RLM65282.1 (16/646); RLN42222.1 (1/543); RLN18407.1 (2/483); RLM74477.1 (15/474); RLN41904.1 (1/521); RLN17428.1 (2/522); RLN17268.1(2/537);RLM65753.1 (16/539). The sub-cellular locations revealed that all the selenite transporters are located in plasma membrane whereas among selenate transporters RLM65282.1 and RLM74477.1 are located in mitochondria and RLN42222.1 and RLN18407.1 in chloroplast. The transcriptomic studies revealed that NaCl stress decreased the expression of both selenate and selenite transporters in proso millet and the applications of exogenous 1µM Se (Na2SeO3) increased the expression of these Se transporter genes. It was also revealed that selenate shows similar behavior as sulfate, while selenite transport resembles phosphate. Thus, it can be concluded that phosphate and sulphate transporters in millets are responsible for Se uptake.Crops are susceptible to a variety of stresses and amongst them salinity of soil is a global agronomic challenge that has a detrimental influence on crop yields, thus posing a severe danger to our food security. Therefore, it becomes imperative to examine how plants respond to salt stress, develop a tolerance that allows them to live through higher salt concentrations and choose species that can endure salt stress. From the perspective of food, security millets can be substituted to avoid hardships because of their efficiency in dealing with salt stress. Besides, this problem can also be tackled by using beneficial exogenous elements. Selenium (Se) which exists as selenate or selenite is one such cardinal element that has been reported to alleviate salt stress. The present study aimed for identification of selenate and selenite transporters in proso millet ( Panicum miliaceum L.) has been valued as a healthy food for centuries. About ten thousand years ago, it originated in Northern China as a prime domesticated plant of the Poaceae family   application.Most plant species utilize only a little amount of Se from soil and they differ in their ability to accumulate Se in their tissues . Se absoosum L.) . We specosum L.)  and thusosum L.) . In Arabmilation . In ricemilation . Similarmilation . The mecArabidopsis thaliana L. Q9LIK9 (APS1_ARATH) and selenite transporter sequence of Oryza sativa L. Q8GSD9  were retrieved from UniProtKB database (https://www.uniprot.org/). In Arabidopsis thaliana Q9LIK9, a sulfur transporter controls the reduction of selenate and assists selenate and sulfur absorption and assimilation (Panicum miliaceum L. (Proso millet) database. The NCBI protein blast tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi) was employed to search for homologous genes in Proso millet.For this study, the experimentally reviewed selenate transporter amino acid sequence of milation . Q8GSD9,milation . These phttps://web.expasy.org/protparam). The trans-membrane domains were identified by the TMHMM server v2 (http://pfam.xfam.org).The physicochemical properties of resultant transporters were calculated with the help of ProtParam   .The molecular phylogenetic analysis of selenite and selenate transporter proteins was performed using MEGAX v 11 . The evohttps://blast.ncbi.nlm.nih.gov/Blast.cgi). These respective sequences were searched with similar proteins in their genome . Among the results 32 amino acid sequences for selenate and 52 for selenite were used with selected Se (both selenate and selenite) transporters in proso millet to construct a tree. Clustal W was used for aligning the sequences. Gap opening penalty of 10 and gap extension penalty of 0.1 in pair wise alignment and opening penalty of 10 and extension penalty of 0.2 in multiple alignments was kept with delay divergent cut-off as 30%. Tajima’s relative rate test with complete deletion method was carried to check P-values . These nucleotides were used to predict location of Pol II promoter in regions .The gene sequences of RLM65282.1, RLN42222.1, RLN18407.1, RLM74477.1, RLN41904.1, RLN17428.1, RLN17268.1, RLM65753.1 were screened for promoter regions. The gene sequences were converted into nucleotide sequences by Sequence manipulation suite reverse translate ( regions  (https:/The protein-protein interactions (PPI) of the selected transporter proteins were compared and examined by STRING V 11.5 and the multiple sequence searches of these transporter proteins were performed with proso millet.https://swissmodel.expasy.org). For the target sequence of interest, a Swiss repository was searched (https://swissmodel.expasy.org/repository). The target sequence and sequences with known structures comparable to the query sequence were aligned using homology modeling. A structural model of the target is created from the sequence alignment and template structure. Based on the obtained BLAST results, the sequence with the highest sequence identity score was chosen. To evaluate Swiss models, the in-build structure assessment tool was employed (https://swissmodel.expasy.org/assess).The three-dimensional structures of the transporter proteins from proso millet were determined using Swiss modeling software  solution (w/v), followed by washing in sterilized distilled water. The seeds were sown in pots that measured 20 cm in diameter that contained autoclaved sand. A controlled environment with a 26 ± 1°C temperature and a 16-h photoperiod was maintained (2SeO3) were applied to the pots (The seeds were sterilized in 70% (v/v) ethanol for 1 minute before being rinsed with sterile distilled water. Seeds were surface sterilized for 3 minutes in 0.1 percent HgClintained . Three sthe pots . A HoaglThe samples were prepared using liquid nitrogen and the trizol reagent (Invitrogen) was used to isolate total RNA from plant material . GenomicXLSTAT 2021 and graph pad prism v6 was used for data analysis.Panicum miliacieum L. produced significant alignments. Four sequences produced alignments for selenate transporters in Arabidopsis thaliana L. and four for selenite transporters in Oryza sativa L From the NCBI results, a total of 8 sequences from The conserved structures of the screened selenite and selenate transporters are shown in Arabidopsis thaliana were used to construct the tree  which showed an identity of 76.87%, 79.85%, 80.10% and 76.87% with RLM65282.1 (UniProt ID A0A3L6PUA6), RLN42222.1 (UniProt ID A0A3L6TPY9), RLN18407.1 (UniProt ID A0A3L6SBK6) and RLM74477.1 (UniProt ID A0A3L6QB84) respectively. The BLAST results for selenite transporters selected the template to be 7sp5.1 (eukaryotic phosphate transporter) which showed an identity of 34.9%, 34.61%, 40.09% and 34.54% with RLN41904.1 (UniProt ID A0A3L6TSS2), RLN17428.1 (UniProt ID A0A3L6S8X1), RLN17268.1 (UniProt ID A0A3L6S8F9) and RLM65753.1 (UniProt ID A0A3L6PXC9) respectively. The three-dimensional models selected for these transporters are shown in To investigate the expression of selenate and selenite transporters under salt stress only and salt stress followed selenium. It was revealed that the expression of RLM65282.1 was 5.42 folds in control, 4.64 in salt-treated plants and 5.81 folds in plants treated with salt and selenium together. Similarly, the expression of RLN42222.1 and RLN18407.1 was 2.80, 2.76, 2.81 in control, salt and salt with selenium treated plants respectively. Among selenite transporter genes, the expression of inorganic phosphate transporter was 3.24, 2.46 and 3.95 folds in control, salt and salt with selenium treated plants respectively. The expression of putative transporters was found to be 1.7, 1.3 and 1.98 whereas for putative inorganic phosphate transporter it was 2.5, 2.5 and 2.6 respectively. The expression studies reveal that both selenate and selenite transporters are down-regulated with salt and up-regulated upon selenium applications.Oryza sativa L. and Arabidopsis thaliana L. have revealed that phosphate and sulphate transporters play a vital role in Se uptake  which was used as the source of BLAST has an amino acid length of 463 and the selenate transporter amino acids sequences of proso millet have more length than Arabidopsis thaliana L. Similarly the number of amino acid in selenite transporter sequence of Oryza sativa L. (Q8GSD9) is 528, indicating a similar number of amino acids with proso millet selenite transporters. RLM65282.1 has a higher molecular weight of 72171.69 Da among selenate transporters whereas among selenite transporters the highest molecular weight was 59138.29 Da which corresponds to RLM65753.1. The theoretical isoelectric point of selenate and selenite transporters ranged from 7.4 to 8.38 and 8.1 to 8.32 respectively. From these, it is clear that all these transporters are basic in nature . Studies on lettuce plants revealed the role of selenite and selenate in the alleviation of salt stress . Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.