A long scientific journey has led to prominent technological advances in the RNA field, and several new types of molecules have been discovered, from non-coding RNAs (ncRNAs) to riboswitches, small interfering RNAs (siRNAs) and CRISPR systems. Such findings, together with the recognition of the advantages of RNA in terms of its functional performance, have attracted the attention of synthetic biologists to create potent RNA-based tools for biotechnological and medical applications. In this review, we have gathered the knowledge on the connection between RNA metabolism and pathogenesis in Gram-positive and Gram-negative bacteria. We further discuss how RNA techniques have contributed to the building of this knowledge and the development of new tools in synthetic biology for the diagnosis and treatment of diseases caused by pathogenic microorganisms. Infectious diseases are still a world-leading cause of death and morbidity, and RNA-based therapeutics have arisen as an alternative way to achieve success. There are still obstacles to overcome in its application, but much progress has been made in a fast and effective manner, paving the way for the solid establishment of RNA-based therapies in the future. A crucial characteristic of the prokaryotic world is its rapid ability to adjust to a changing environment. In the case of pathogenic organisms, it is also essential that they overcome the host immune system. This implies an extensive and prompt re-adjustment of the gene expression by complex regulatory networks, in which RNA metabolism has a crucial role. In fact, RNA is much more than a messenger as it is able to dynamically coordinate and instruct cellular functions, and it has also emerged as an important feature to be considered for the pathogenesis of microorganisms.Bacterial infections are associated with a high rate of human morbidity and mortality, and bacterial resistance to antibiotics is an escalating problem worldwide. The widespread use of conventional antibiotics has favored the apperance of drug-resistant pathogens, and there is a growing need for the development of novel antibacterial strategies. The idea of directly targeting RNA is emerging as a new frontier in drug discovery studies, with the ultimate goal of expanding the antibiotic arsenal. The differences between the molecular machinery that governs bacterial and eukaryotic RNA metabolism are fundamental to identify in order to take advantage of this attractive drug target.The stability of messenger RNA relies on several features and involves numerous players, with ribonucleases (RNases) being among the most important ones. These enzymes are ubiquitous and can perform the RNA degradation alone or in multiprotein complexes. The diversity of RNA molecules with regulatory roles is better understood now, including a wide range of small non-coding RNAs (ncRNAs) and the natural RNA interference (RNAi), CRISPR/Cas  and ERASE  systems. The ERASE system  is a DNAAltogether, recent advances in RNA studies at a global scale have given us a vast amount of information about the role of the RNA regulation of pathogens. In this context, synthetic biology (SynBio) is emerging as a field that is focused on engineering biomolecular systems for a variety of applications. SynBio devices contribute not only to improve our understanding of the disease mechanisms, but also provide novel diagnostic tools. The developments in this field have created strategies for pathogen characterization, cancer treatment, vaccine development, microbiome engineering, cell therapy, regenerative medicine and the production of new and more affordable drugs. Additionally, the targeting of regulatory RNA-based interactions has broadened the SynBio applications in antimicrobial therapeutics. The inherent modularity and compatibility of RNA-based control components enables them to be independently optimized or exchanged, thus expanding their applications.In this review, we discuss the connections between RNA metabolism and pathogenesis, uncovering how several techniques have helped to increase the knowledge in the field. Moreover, we cover some of the innovative SynBio systems in the area of the diagnosis and treatment of infectious diseases and the latest research on the usage of antisense antimicrobial therapeutics and CRISPR–Cas as a tool. Lastly, the present applications and the future prospects of mRNA vaccines are also examined. Overall, our main aim is to explore the emerging technologies in the RNA field and their application to current health problems.Ribonucleases (RNases) are the enzymes that determine the levels of functional RNAs in the cell, validate the quality control of all of the transcripts and allow the recycling of cellular ribonucleotides, which makes them key members of the RNA metabolism machinery . Their dvacB was required for the expression of several invasion factors in Shigella flexneri, and its deletion affected the bacterial capacity to adhere and spread inside host cells [vacB gene was demonstrated to code for the exoribonuclease RNase R, and it was renamed rnr [Tobe and colleagues showed that the product that was encoded by the gene st cells  . These amed rnr .Nidovirales viruses, which plays fundamental roles in coronavirus pathogenesis and in the evasion of the host’s innate immune system [RNases are found in all domains of life, and also in viruses. Viruses do not have their own metabolism, so many of them use host proteins during their life cycle, and they rarely code for RNases. When they are present, viral RNases are usually involved in specific steps of viral gene expression, genome replication, shutoff of host cell protein synthesis and host immune evasion, among others ,21,22,23e system ,31,32.Staphylococcus aureus, Escherichia coli, Mycobacterium tuberculosis, and SARS-CoV-2. In 2011, Olson et al. discovered a small molecule inhibitor of the protein component of the S. aureus ribonuclease P, and this inhibitor exhibited antimicrobial activity even against predominant antibiotic-resistant lineages [E. coli endoribonuclease. These inhibitors were also demonstrated to be effective against the endoribonuclease RNase G in vitro . Additionally, they were shown to bind and inhibit the catalysis of an M. tuberculosis homologue, thus demonstrating a wider application of these inhibitors [Taken together, these ribonucleases, which play so many critical roles in both bacterial and viral processes, are very attractive targets for drug designs. The use of small molecules to inhibit the enzymatic activity of these proteins was already reported in lineages . A few yhibitors .Recently, several in silico studies have proposed drug candidates that could inhibit SARS-CoV-2 ribonucleases A. From tSmall non-coding RNAs are ubiquitous in bacterial species, and they are essential for their adaptation and survival under stress . These tE. coli and Salmonella enterica [The first ncRNA to be characterized was MicF  as a regenterica .cis-acting ncRNAs that are transcribed from the opposing strand of their target mRNAs, with whom they present a perfect complementarity; (ii) trans-acting ncRNAs, which are encoded in a distinct location from their targets, thus they are only partially complementary with them, allowing the recognition of multiple targets by a single ncRNA [trans-acting ncRNAs often require the aid of a RNA chaperone, such as Hfq and/or ProQ, which act as matchmakers to promote attachment to the targets and to stabilize these RNAs (see section RNA chaperones). In addition, with the discovery of the CRISPR/Cas systems, a new class of ncRNAs has been considered .Broadly, ncRNAs can be divided into two major classes: (i) le ncRNA . In GramS. aureus RNAIII (agr quorum-sensing (QS) system, a key regulatory system that is engaged not only in virulence control [S. aureus pathogenicity [Many bacterial ncRNA have been shown to have roles in virulence, and this has been observed both in Gram-negative and -positive bacteria (extensively reviewed in ). It wass RNAIII . This nc control , but als control ,57,58,59genicity .Salmonella has been extensively used as model organism to study ncRNAs. Several ncRNA genes are encoded by the pathogenicity islands of a Salmonella virulent strain, with IsrJ playing a crucial role during the infection process [cis-acting ncRNA, AmgR, which has been shown to affect Salmonella virulence [ompD mRNA was demonstrated to be regulated by the ncRNAs InvR, MicC, RybB and SdsR [Salmonella, in combination with a mutation on a transcription regulator, allowed the rationale design of an attenuated vaccine for pigs [Salmonella could improve animal health and reduce antibiotic usage, ultimately increasing food safety. process . Anotherirulence . In thisirulence . ompD mRand SdsR ,68,69,70for pigs . VaccinaListeria monocytogenes, several ncRNAs have been identified. Some of them are exclusively expressed inside macrophages or in human blood, suggesting an important role of these transcripts for intracellular growth [RNA riboswitches) could also act as conventional ncRNAs by interacting with the 5′ UTR of the prfA mRNA which encodes a master regulator for Listeria virulence [In the foodborne pathogen r growth ,73,74,75irulence .Shigella, it was demonstrated that RyhB and RnaG ncRNAs influence pathogenesis [Shigella under hyperosmotic pressure [ompC mRNA that encode an OMP that is related with Shigella virulence.In ogenesis ,78,79. Tpressure , and Ryfpressure , impactsStreptococcus pneumoniae, a significant percentage from the identified ncRNAs has important roles in virulence traits [cis-dependent small RNAs) together with the ncRNA srn206 act to modulate competence [pilus regulation [In e traits . srn157 e traits ,84, fivempetence , and srngulation .trans-regulatory ncRNAs fall into two general approaches: (a) altering the expression of well-characterized natural ncRNAs to induce enhanced regulatory effects on protein levels, and (b) designing synthetic ncRNAs to knockdown the expression of individual proteins. The use of these synthetic ncRNAs as a gene-silencing tool mimics the RNA interference system (from eukaryotes) in bacteria. ncRNAs show high modularity, which enables synthetic biologists to decompose and recombine the ncRNA parts to engineer artificial riboregulators with different functions. The basic modular design includes a promoter, an antisense binding domain to target mRNA, a scaffold for stability and/or Hfq binding and a terminator. For instance, a MicC-based synthetic ncRNA was successfully used to fine-tune the gene expression in E. coli [P. putida where the binding of native Hfq to a MicC scaffold was also demonstrated. It was shown that when these synthetic sRNAs were induced, they could actively control the gene expression [Native RNA-based interactions of ncRNAs with proteins, RNA transcripts and/or DNA are essential for coordinating gene expression. These interactions, due to their advantages when compared to conventional gene knockouts, are being increasingly targeted in synthetic biology, and in the antimicrobial and therapeutic fields. Engineering strategies involving  E. coli . The plapression . The verncRNAs offer an additional suit of tools for engineering metabolic pathways in bacteria with an interest in industrial production, with several examples of successful applications. Despite being a less explored area, recent works also support promising applications in medicine. As mentioned above, the OMPs of Gram-negative bacteria play a main role in mediating bacterial antibiotic resistance and in the virulence of innumerous bacteria. Multiple natural ncRNAs have been found to control OMP expression . TherefoThere are also ncRNAs that bind to proteins to alter their activity, but fewer examples are known in comparison to the antisense ncRNAs. The Csr (carbon storage regulator) system is very important for central metabolism (reviewed in ). It is Salmonella, the deletion of csrA caused serious growth deficiencies and defects on invasion [Salmonella Pathogenicity Island 1 (SPI1) gene expression, and, as a consequence, epithelial cell invasion [S. flexneri, it was determined that CsrA activity is linked to virulence and to the cell membrane structure [Legionella pneumophila, it was demonstrated that the CsrA protein is crucial for replication inside the macrophages [Legionella, RsmY and RsmZ, were shown to be expressed depending on the growth phase, and the absence of both ncRNAs impaired the infection and interfered with the replication inside the host [Vibrio cholerae and in Pseudomonas aeruginosa, the Csr system is important for quorum-sensing regulation [Erwinia carotovora ssp. carotovora, it controls the production of extracellular enzymes and secondary metabolites [In invasion . The delinvasion ,99. Addiinvasion . In S. ftructure . In Legirophages , affectsrophages ,103 and rophages . The oththe host . In Vibrgulation ,107, andabolites .RNA chaperones facilitate the proper RNA folding, remodel the RNA structures to expose important regulatory elements, and, in several cases, they can protect the RNA molecules from degradation by ribonucleases. The two RNA chaperones that have been well described until now are Hfq and ProQ. For more information about their mechanism of action and their structural aspects, please see .Hfq is a highly conserved protein from the Sm family, and it has homologues in approximately 50% of all of the sequenced bacteria . This plSalmonella, it was demonstrated that Hfq is important for virulence, considering its role in the motility, membrane composition, invasion, and expression of genes from the SPI1 [L. pneumophila, Hfq plays a role in the iron uptake and storage system, and mutants lacking this chaperone showed defects during growth and in pigmentation, being slightly less efficient in infecting amoeba and macrophages [L. monocytogenes, Hfq was proven to be important for the tolerance to osmotic and ethanol stresses, for the long-term survival during amino acid starvation and in the pathogenicity in mice [V. cholera [P. aeruginosa [Neisseria gonorrhoeae [Francisella tularensis [In the SPI1 . In L. prophages . In the  in mice . Similar cholera , P. aeruruginosa , Neisserorrhoeae  and Franlarensis , therebycis-acting ncRNAs, which means that they regulate a different series of genes [Salmonella, the absence of ProQ causes a decrease in the expression of genes that are involved in the motility and chemotaxis pathways, leading to an impaired ability to infect HeLa cells [L. pneumophilia, two ProQ homologues were described (Lpp1663 and Lpp0148/RocC), and RocC was shown to be involved in natural competence [Dickeya dadantii [Erwinia amylovora [ProQ is a FinO-like protein that is specific to Gram-negative microorganisms. This protein was initially described as a factor that affects the activation of the osmoregulatory transporter ProP, and it has only recently been shown to be an important RNA chaperone that is involved in the regulation of ncRNAs . Contrarof genes . The invLa cells . In L. pmpetence . Furtherdadantii  and Erwimylovora , the losTwo classes of regulatory elements located at the 5′ UTR of mRNAs have been shown to play important roles in gene expression: RNA thermometers and RNA riboswitches. These RNA elements allow the bacteria to rapidly and efficiently react to environmental stimuli. Taking into consideration the mode of action and simplicity of these regulators, these RNA elements are very appealing for the development of new tools to regulate gene expression, and they can be used for many applications.RNA thermometers or thermosensors are molecules that sense temperature shifts, inducing a conformational change of the RNA molecule that will affect the expression of the downstream gene. Regulation by temperature using RNA molecules allows for a more rapid and cost-effective response of the cell. RNA thermometers act by regulating translational initiation: for instance, at lower temperatures, the Shine-Dalgarno (SD) region and/or the initiation codon are masked by a stable secondary structure; when the temperature increases, this region is melted, thus allowing translation to occur. For more information about the mechanism of action of these molecules, please read .Due to their nature, RNA thermometers are not conserved, and this is a challenge for bioinformatic prediction . The firagsA gene in Salmonella, the prfA gene in Listeria, the lcrF and ompA genes in Yersinia, the cssA gene in Neisseria, the toxT gene in Vibrio, and the ompA and shuA genes in Shigella [A temperature shift is one of the challenges that a pathogen faces during the infection process. As such, natural RNA thermometers are crucial regulatory elements that are involved in bacterial pathogenesis by controlling the expression of virulence genes. This is the case of the Shigella ,134,135.In recent years, a number of synthetic RNA thermometers was developed with success in diverse applications. Contrary to the natural molecules, they were designed to be simpler and more predictable to facilitate their usage . DespiteE. coli, further studies may be required to adjust this process to other pathogens.An interesting example of the use of these regulators in medicine is the development of RNA thermometers for microbial therapeutics in vivo. Two synthetic thermometers were designed to act between 32 °C and 46 °C, which could be used in three different in vivo scenarios to combat microbial infections: (i) the capacity to detect and respond to the host’s fever, (ii) the selective activation of the microbial function at a specific location using focused ultrasound , and (iii) the restriction of the survival of the administered microbes to the host’s body temperature and self-destruction at room temperatures, thus preventing possible environmental contaminations . Considecis-acting RNA elements that recognize metabolites, thus modulating gene expression in response to specific small molecules. In bacteria, most of the riboswitches are located at the 5’ UTR of a particular transcript, and are composed by two functional domains: the ligand-sensing domain (or the aptamer domain) and the regulatory domain (or the expression platform). In certain conditions, a small molecule binds the aptamer domain, inducing a conformational change that stimulates the expression platform. The expression platform will act over the coding sequence, thus regulating its expression. For more information about the mechanism of action of the riboswitches, please read [Riboswitches are ase read .L. monocytogenes, where the major regulator of virulence, PrfA, was shown to be controlled by two riboswitches, which also function as ncRNAs [Clostridium difficile, where it was shown that riboswitches are important for growth and infectivity [Riboswitches were first discovered in 2002 ,140,141,s ncRNAs , and in ectivity .S. aureus and Enterococcus faecalis [The existence of riboswitches in pathogenic bacteria presents novel targets for drug development. This has led researchers to start to manipulate how riboswitches bind to their ligands in order to design new molecules that could be used as antimicrobials ,145,146.faecalis ,154,155.Natural riboswitches combine both the sensory and regulatory functions. This principle of direct RNA-ligand interaction was exploited to synthetically design the aptamer-based conditional gene expression systems. Aptamers are single-stranded RNA or DNA molecules that can self-fold in a unique 3D-spatial conformation to specifically interact with their targets. The selection of aptamers with the capability to bind a plethora of different ligands can be performed in vitro through the so-called Systematic Evolution of Ligands by Exponential Enrichment (SELEX) Technology ,157,158.loci (approximately 20–50 base pairs) known as the CRISPR, in a stage called adaptation. These exogenous DNA fragments are inserted by the Cas proteins, Cas 1 and Cas 2, which are the only Cas proteins that are conserved amongst all the CRISPR–Cas systems. The repetitive loci are subsequently transcribed and processed into a library of short CRISPR-derived RNAs (crRNAs) that are complementary to the previous invading nucleic acids. This is the stage of crRNA expression and biogenesis. Then, comes the interference stage, in which each crRNA can guide the effector nucleases to destroy the foreign genetic material through specific cleavage [Upon a viral or plasmid invasion, bacteria (and archaea) integrate short fragments of foreign DNA into the host chromosome, namely, at a (variable) number of short repetitive cleavage ,163. Thuloci in bacteria and archaea, whereas the class 2 systems only represent 10% of the CRISPR loci that are found in bacteria. The specific types within each class are defined by the effector endonuclease— the Cas protein—which is responsible for cleavage [The CRISPR systems can be divided into two main classes, class 1 and class 2. The class 1 system is found in 90% of the CRISPR cleavage ,165.cas9 gene has been reported to play an important role in controlling virulence in various pathogens [The Cas effector proteins are, thus, non-specific nucleases that can be programmed by small guide RNAs, the crRNAs, to be directed to target DNAs or RNAs. Great emphasis has been given to these systems due to these RNA-guided programmable enzymes which exhibit remarkable flexibility in targeting. These have encouraged an ever-expanding array of applications. The most explored and used toolbox in genomic engineering is the class 2 (type II) system, which is better known as CRISPR–Cas9. Cas9 is the characteristic effector protein, and it is essential for immune mechanisms in bacteria . Furtherathogens ,167,168.Streptococcus sp., Cas9 was reported to influence key regulators of virulence traits, such as adhesion and infection [N. meningitidis [cas9 deletion in Campylobacter jejuni highly affects its sensitivity to antibiotics, regulating several genes that promote antimicrobial resistance [L. pneumophila, Cas2 and not Cas9 is the CRISPR enzyme that is involved in the infection process of macrophages [In nfection ,170. Thengitidis . Curioussistance . This prrophages . Both thStreptococcus mutans contains a class 1-type I CRISPR, whose effector protein is Cas3. In the absence of the cas3 gene, the strain formed less biofilm, became more sensitive to fluoride, and the expression of the virulence genes was significantly downregulated [S. enterica isolate 211 [P. aeruginosa UCBPP-PA14, the cas3 gene has been shown to be involved in the achievement of lower pro-inflammatory host responses in cell and mouse models [The interest in the relationship between CRISPR and virulence has grown, and it was later discovered that the CRISPR–Cas type I systems also have an important role in the evasion of bacteria from the host. egulated . Similarlate 211 . Additioe models .Enterococcus faecalis and P. aeruginosa strains [Acinetobacter baumannii, specific genes that are involved in biofilm formation appear almost exclusively in strains that are enriched with CRISPR–Cas systems [P. aeruginosa UCBPP-PA14 by the bacteriophage DMS3 inhibits biofilm formation and swarm motility in a manner that is dependent on the CRISPR regions and cas genes [Biofilm development and antibiotic resistance are intimately connected since the biofilm matrix can delay the penetration of antimicrobial agents. Biofilm formation is a highly regulated process, and CRISPR has proven to be one of these regulators. Most pathogens involved in nosocomial infections have biofilm-forming abilities. Interestingly, an increased ability to form biofilms has been reported in CRISPR–Cas positive  strains . Additio systems . It alsoas genes . This stSerratia marcescens, it appears that CRISPR–Cas immunity is integrated into the QS circuit, enabling greater defense at higher cell densities [P. aeruginosa UCBPP-PA14 also uses the QS process to activate cas gene expression [The existence of group behaviors among the bacteria is indeed extremely important. During biofilm formation, bacteria have the ability to communicate with each other through the process of QS. In ensities . Similarpression . Thus, btrans-encoded small RNA (tracrRNA) that was transcribed upstream and in the opposite strand of the CRISPR locus, with 24 nucleotides that were complementary to the repeat regions of the crRNA precursor transcripts (pre-crRNA). This tracrRNA is responsible for pre-crRNA maturation by promoting the cleavage of the tracrRNA-pre-crRNA duplex by the very well-known and widely conserved endoribonuclease RNase III [Francisella novicida, Cas9 uses a small CRISPR/Cas-associated RNA (scaRNA) to repress an endogenous mRNA transcript encoding a bacterial lipoprotein, which elicits a proinflammatory innate immune response in the host [L. monocytogenes pathogenesis [In 2011, Charpentier and co-workers  reportedNase III . Soon afNase III . This diNase III  , which is also referred to as deep-sequencing, or high-throughput sequencing, has provided a set of diverse modern technologies with applicability to the study of DNA, RNA and proteins . In partNowadays, there is a panoply of distinct RNA-seq-based approaches that aim to uncover and characterize the RNA species being expressed at each moment in a cell culture or a single cell. Many fields of study have benefited from such methodologies ,201,202.Streptococcus suis type 2 [In a recent study, messenger RNA sequencing (mRNA-seq) was used to elucidate the function of a specific gene which was postulated to be involved in the virulence of the zoonotic bacterial pathogen s type 2 . In anots type 2 , served s type 2 .As already addressed, ncRNAs are crucial regulators. NGS, and particularly small RNA sequencing (sRNA-seq), has largely contributed for the identification of new ncRNAs species in several pathogenic microorganisms ,82,210.6-methyladenosine, m6A), and they may also play a prominent role both in the bacterial stress response and pathophysiology, and in host adaptation [6A-seq which identifies the methylated residues in the transcripts [Moreover, RNA modifications can influence the structure, stability, decoding, and recognition of RNA molecules. They often occur during transcription  or post-transcriptionally  was designed to identify the RNA–RNA interactions, and this has been particularly useful to elucidate pairs of ncRNAs and their respective mRNA targets. In pathogenic nnotated . In turnnnotated , and it nnotated . In factnnotated .One of the major breakthroughs in this field was the establishment of single-cell RNA sequencing (scRNA-seq), which allows for the discrimination between RNA species being expressed in different cells belonging to the same population or different populations in the same sample (reviewed in ). This tIn the last decade, differential RNA-seq emerged with the advantage of distinguishing between the primary and processed transcripts. This way, it has provided an opportunity to map the transcriptional start sites (TSS), and exposed the existence of pervasive transcription and a generally high abundance of ncRNAs in the bacterial genomes ,226,227.Aspergillus fumigatus (fungus) and Cytomegalovirus (CMV) [Differential RNA-seq served as an inspiration for dual RNA-seq which has the capability of sequencing RNA molecules of two or more species simultaneously  Figure A. The maus (CMV) .While NGS technologies usually produce short reads, the Third-Generation Sequencing (TGS) has emerged, enabling the sequencing of longer fragments (long reads). As the raw reads can be disclosed in real time, TGS permits data interpretation to occur prior to the samples being fully sequenced . There aOverall, when they are compared with first-generation sequencing (Sanger sequencing), the NGS and TGS methods are faster, more sensitive and produce a greater amount of data encompassing a wide repertoire of RNA molecules . The empFinally, the above-mentioned RNA-seq strategies might help in the identification of diagnostic biomarkers, the choice of the appropriate treatment for different severity stages of a certain disease, of drug target candidates and potential drugs which can also be repurposed and used for the efficient treatment of specific infectious diseases ,245.In fact, independently of the specific RNA-seq method that is employed, it will always require bioinformatic pipelines to process the enormous volume of data. In the past, programming skills were a prerequisite, but many tools with graphical user-friendly interfaces have been progressively developed and made accessible for everyone, as it is the case of the Galaxy platform . Many onAn alternative strategy to fight the growing antibiotic resistance phenomena is to design gene-specific oligomers that can specifically target any single pathogen. Antisense antimicrobial therapeutics are a biotechnological form of antibiotic therapy using short, single-stranded oligomers that mimic the structure of DNA or RNA and bind to specific, complementary RNA in a target organism ,250. In A key advantage of this antisense approach is that ASOs can be rationally designed to target any microbe through sequence complementation, thus, significantly enlarging the available selection of potential therapeutic targets . A main The use of antisense therapeutics has been progressively advancing towards clinical use, but in recent years the field has been accelerating. The identification of essential genes and the number of sequenced genomes has largely contributed to this. However, despite the fast advances in the eukaryotic fields , the proASOs are typically 10–30 nucleotides in length. The cellular nucleases rapidly attack the unmodified ASOs. Therefore, numerous chemical modifications have been described  to confer resistance against nucleases, to improve the stability of the ASO/mRNA hybrid formation and/or to preserve the target specificity.In the sense of overcoming the challenge of bacterial cellular uptake, the most common strategy for facilitating antisense oligonucleotide delivery is the conjugation of a cell-penetrating peptide (CPP) to the antisense oligonucleotide. The attachment of a compound that can penetrate the bacterial cell wall facilitates the delivery of synthetic antisense oligomers into the bacterial cytoplasm. CPPs are short cationic or amphipathic peptides, which are usually composed of less than 30 amino acids. CPPs have been used with success to deliver modified ASOs in different bacteria ( for a reAcinetobacter (A. lwoffii and A. baumannii) and Klebsiella pneumoniae [E. coli gyrA, a highly conserved gene that is found across multiple bacterial species [gyrA CPP-PMO reduced the viability of both the Gram-positive and Gram-negative bacterial strains .Phosphorodiamidate morpholino oligomers (PMOs) are synthetic single-stranded oligomers with a modified backbone which makes them resistant to nucleases . The useeumoniae . Wesolow species . The autS. pyogenes, but it used a CPP-PNA. Peptide nucleic acids (PNAs) are constructed by attaching bases to a modified polyamide backbone. The PNAs are uncharged, which in part accounts for their high affinity for RNA [C. jejuni, the cmeABC operon encodes a multidrug efflux pump that confers resistance to a broad range of antibiotics [cmeABC operon restored the antibiotic susceptibility [GyrA mRNA was also targeted in  for RNA . Success for RNA ,257. In ibiotics . The usetibility .S. aureus to target the ftsZ mRNA, a gene that is required for cell division [Locked nucleic acids (LNAs) are oxyphosphorothioate analogues with a 2′-O,4′-C-methylene bridge that locks the ribose ring in the C3′-endo conformation . Both thdivision ,262.rne gene is a good target for antisense antibiotic development. Using E. coli as a model, Goddard and colleagues have used LNA gapmers, oligonucleotides consisting of a central region of DNA that is flanked by regions of chemically modified LNA nucleotides, to target RNase E [rne mRNA in vitro, introducing the way to the use of this novel anti-bacterial target in different pathogens  or mimic the mRNA sequence to sequester the ncRNA (anti-ncRNA ASO). In both cases, the ncRNA–mRNA interaction is impaired  prevented the Qrr-hapR mRNA interaction. This impaired the HapR downregulation, locking it in the HapR expression state (high cell density profile), with antibacterial implications.Henderson and co-workers  designedS. aureus, L. monocytogenes and E. coli, which widen the lists of possible targets of this antimicrobial alternative system [The specific inhibition of a riboswitch by an ASO lead to the inhibition of the growth of e system . The potRNA interference (RNAi) is a biological process in which small ncRNAs recognize a specific mRNA, thereby promoting their degradation by Argonaute proteins, thus leading to gene silencing. This eukaryotic mechanism works as an innate defense mechanism against invading viruses . The RNAM. tuberculosis and Plasmodium spp. [Vaccination continues to be the most successful and cost-effective public health intervention to control and prevent infectious disease outbreaks. In fact, the conventional application of inactivated, live-attenuated or subunit vaccines had enormous success in the eradication of several infectious diseases, with a classic example being the complete eradication of the smallpox virus; however, many others were not as efficient in treating human immunodeficiency virus (HIV), ium spp.  and otheium spp. .1-methylpseudouridine could provide even better results [Despite the promising results in the mRNA therapeutics field ,277, mRN results . In addi results .In face of a sudden new coronavirus pandemic, previous advances in mRNA technology have enabled the rapid release of two highly efficacious mRNA vaccines in the market, BNT162B2 by Pfizer-BioNTech  and mRNAStreptococcus sp. infections, preliminary mRNA vaccine studies in animal models are already being developed [For HIV, since the virus was reported in 1981, many unsuccessful attempts to produce a vaccine were announced ,288,289,eveloped ,295.With increased scientific interest in this area, the next-generation mRNA technology will continue to mature both for vaccine development and therapeutics. The field of nucleotide-based vaccines came to the spotlight as a novel, faster and cheaper way to achieve vaccine development when compared with the conventional technologies . NeverthRNA is back in the spotlight. The diverse role of RNA in all biological processes, together with the recognition of its important functional properties, have led to its exploitation in a wide range of biotechnological and medical applications. A great contributor to this change of perspective was the validation of the mRNA vaccines at an unpredictable scale and speed at which they fought against the COVID-19 pandemic. As they are natural molecules, RNAs present, in general, low toxicity and immunogenicity. The use of RNA elements presents advantages such as its independent control, tunability, composability and portability which empower their use as genetic tools. However, the advances in the application of these RNA tools have been limited by the rhythm of the progression of the technological advances which have enabled the characterization of new molecules and biological mechanisms. Built upon decades of scientific research, robust and prompt RNA technologies have now emerged, highlighting the importance of fundamental and applied research. For instance, we have testified in recent years, a fast discovery for the new classes of RNA molecules and molecular mechanisms that have transformed our comprehension of RNA metabolism. This review puts together the major discoveries regarding the connection between RNA metabolism and pathogenesis, and how this knowledge has been used to create new strategies to fight microbial pathogenicity. Antibiotic resistance is a serious problem that requires the creation of alternative therapeutics. As such, several RNA tools have surfaced as alternatives to control the virulence of pathogenic bacteria, namely, using synthetic non-coding RNAs, antisense antimicrobial therapeutics with antisense oligonucleotides (ASOs) or CRISPR–Cas antimicrobials. The application of these tools in prokaryotic organisms has been limited by different obstacles. In the case of ASOs, their use has been mostly limited by the development of delivery systems to improve their uptake by the bacterial cells. The same has happened with the CRISPR–Cas tools, and despite the new delivery systems which have been used with success, more research is needed to assure their safety and effectiveness. In the case of the mRNA vaccines, their implementation was possible thanks to the curiosity-driven studies of lipids and experiments with synthetic mRNA. The establishment of mRNA vaccines seems promising because of the speed with which they can be developed and produced, and their flexibility and adaptability to variants.


Diabetes mellitus (DM) is a fast-growing chronic metabolic disorder that leads to significant health, social, and economic problems worldwide. Chronic hyperglycemia caused by DM leads to multiple devastating complications, including macrovascular complications and microvascular complications, such as diabetic cardiovascular disease, diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy. Numerous studies provide growing evidence that aberrant expression of and mutations in RNA-binding proteins (RBPs) genes are linked to the pathogenesis of diabetes and associated complications. RBPs are involved in RNA processing and metabolism by directing a variety of post-transcriptional events, such as alternative splicing, stability, localization, and translation, all of which have a significant impact on RNA fate, altering their function. Here, we purposed to summarize the current progression and underlying regulatory mechanisms of RBPs in the progression of diabetes and its complications. We expected that this review will open the door for RBPs and their RNA networks as novel therapeutic targets for diabetes and its related complications. Diabetes mellitus is one of the fastest-growing metabolic disorders characterized by chronic hyperglycemia. In recent decades, the global prevalence of diabetes in adults has been growing at an astonishing rate. It is estimated that in 2045 there will be 693 million adults who suffered from diabetes worldwide . DependiRBPs are typically considered as proteins that are responsible for modulating post-transcriptional gene expression in the eukaryotic cells . ThousanIn this review, we will provide a brief overview of the mechanisms by which RBPs exercise their functions. Then, we will explain how these RBPs are dysregulated and their contribution to the pathological process of diabetes mellitus. In addition, we would like to focus on the relationships between RBPs and diabetic complications. We believe that a comprehensive understanding of the RBPs’ role in diabetes and its associated complications may aid in the development of innovative treatments in clinic.The mRNA life cycle is a complex system that includes the process of transforming the newly transcribed mRNA molecules to fully functional mature mRNA transcripts. RBPs play an essential role in this process. Recent studies have revealed some RBPs do not have typical RNA binding domains (RBDs) but are replaced by at least one intrinsically disordered region (IDR) through which they can not only be involved in aggregation of RNPs, but also directly engage in RNA binding . Howevervia pre-mRNA alternative splicing, polyadenylation, RNA editing, and the addition of the 5′ cap in the target RNAs. RBPs can regulate pre-mRNA alternative splicing (AS) through binding to the pre-mRNA and interacting with the spliceosome components, which generate variant protein isoforms from a single gene, resulting in transcriptome and proteome diversity  mRNA  mRNA . In lines2) mRNA . Accordis2) mRNA . Furthers2) mRNA . RBP hnRs2) mRNA . In addiby let-7 . DDX1, aby let-7 . RNA-binby let-7 .RNA-binding proteins can not only directly bind to mRNA to regulate β-cell function and insulin secretion, but there have also been instances of RBPs and circular RNA interacting to exercise regulatory activities . For exavia interacting with AU-rich elements (AREs) in the 3′UTR of target mRNAs(in vitro and in vivo (via FAK and integrin-linked kinase (ILK) activation  is an RBP that depresses post-transcriptional gene expression get mRNAs. TTP is  in vivo . The lev in vivo . RBP hnR in vivo . In addi in vivo . Insulin in vivo . Furthertivation .in vitro growth defects in injured dorsal root ganglion (DRGs) from diabetic rats , while the fourth is HuR or HuA (encoded by Elav-like 1 gene) is omnipresent  is one of the most common chronic complications of both type 1 and 2 diabetes and is considered as a main cause of end-stage renal disease (ESRD). Glomerular basement membrane thickening, mesangial growth and hypertrophy, and the accumulation of extracellular matrix (ECM) proteins are all hallmarks of DN . In a tyvia β1-integrin/FAK signaling in human type 2 diabetic glomeruli  are pre-mRNA binding proteins that can regulate the processing of mRNA. In renal proximal tubular cells of Akita hnRNP F-Tg mice, selective overexpression of RBP hnRNP F lowers expressions of angiotensinogen (Agt) and TGF-β1 and reduces kidney hypertrophy and glomerulotubular fibrosis . HnRNP Flomeruli .2+ signaling pathway and lead to cardiomyopathy and heart failure . And it occurs in the absence of basic cardiac diseases such as hypertension, coronary artery disease, and heart valve disease . CELF1,  failure . Lin28,  failure . Lin28a  failure . Moreove failure . Another failure .Vascular endothelial cell (EC) dysfunction is largely acknowledged as a major contribution to the pathophysiology of cardiovascular disease in people with diabetes. RBP QKI is a member of the signal transduction and activation of RNA (STAR) family, and it is linked to diabetic cardiomyopathy and atherosclerosis . Quakingvia stabilizing VEGF-A mRNA and modifying endothelial cell angiogenic activity , which in turn develops into coronary microvascular disease (CMD) . Previouactivity . In additic mice .via intravitreal injection of small interfering RNA (siRNA) nanoparticles, which protect rat retinal tissue from damage caused by DM  is a common complication of DM, which remains a leading cause of vision damage or loss among working-age adults worldwide. Neovascularization plays an indispensable role in DR . DR invoed by DM . Furthered by DM . And VP1ed by DM . HuR may pathway . The expactivity . The staactivity . It has activity .7GTP cap of mRNA and assemble it into eIF4F complexes, which can recruit ribosomes onto mRNA to perform translational functions. The recruitment of ribosomes, which can occur via a cap-dependent or cap-independent mechanism, limits the rate of mRNA translation. The interaction between 4E-BP1 and eIF4E promotes the dissociation of eIF4E from eIF4F complexes, which inhibits cap-dependent and promotes cap-independent translation , Which is also an important mechanism that determines the occurrence and development of diseases . PTMs geIn summary, we expect to fully understand the dynamic RBPs-mediated regulatory network in diabetes. Correcting gene expression abnormalities in diabetic patients by targeting the interaction between RBPs and their target RNAs could be an effective approach. RNA-based therapies are primarily designed drugs to imitate or antagonize specific RNA processes by mimicking the action of protective RBPs or inhibiting the action of pathogenic RBPs. Many candidate strategies are being applied to target RBPs for therapeutics in pre-clinical or clinical trials, such as small-molecule inhibitors, therapeutic small peptides, anti-sense oligonucleotides (ASOs), and siRNA . Besides


Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules  with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales. Predicting RNA degradation is a fundamental task in designing RNA-based therapeutics. Two crowdsourcing platforms, Kaggle and Eterna, united to develop accurate deep learning models for RNA degradation on a timescale of 6 months. However, the chemical instability of RNA sets a fundamental limit on the stability of RNA-based therapeutics8, with RNA hydrolysis specifically setting a limiting factor on stability in lipid nanoparticle (LNP)-based formulations10. Hydrolysis in LNP formulations degrades the amount of mRNA remaining during shipping and storage, and hydrolysis in vivo after vaccine injection limits the amount of resulting protein produced over time9. Better methods to develop thermostable RNA therapeutics would allow for increasing the equitability of their distribution, reducing their cost and possibly increasing their potency11.Therapeutics based on messenger RNA (mRNA) have shown immense promise as a modular therapeutic platform, allowing potentially any protein to be delivered and translated633 mRNA sequences that all code for the SARS-CoV-2 spike protein antigen. With an astronomical number of mRNA sequences available for a given therapeutic target, it is likely that some of these sequences may harbour structural features that make them more resistant to hydrolysis than first-generation mRNA vaccine formulations. Indeed, initial results have demonstrated that more stable mRNAs for model protein systems can be designed by optimizing candidate RNA sequences, scored with a model for RNA hydrolysis13. These initial studies indicate that stabilized mRNAs can produce equivalent, and for some designs, more protein compared with non-optimized mRNAs13. These design strategies are predicted to be able to produce mRNAs that do not activate double-stranded RNA immune sensors12 such as RIG-I14. These strategies have also demonstrated compatibility with mRNAs synthesized from modified nucleotides including pseudouridine13, which are used in mRNA vaccine formulations15.An underexplored path to more shelf-stable mRNA therapeutics lies in the prospect of synonymous sequence design. A simple calculation reveals that there exist 1012. Computational studies with this model suggested that at least a twofold increase in stability could be achieved through sequence design, while maintaining a wide diversity of sequences and features related to translatability, immunogenicity and global structure13. However, it is unlikely that degradation depends only on the probability of a nucleotide being unpaired: local sequence- and structure-specific contexts may vary widely, as evidenced by ribozyme RNAs found in nature, whose sequences adopt specific structures that undergo self-scission16.However, the potential of any such mRNA design algorithm is limited by the accuracy of the underlying model in predicting RNA degradation. Previous models for RNA degradation have assumed that the probability of any RNA nucleotide linkage being cleaved is proportional to the probability of the 5′ nucleotide being unpaired18, a target function such as sensor activity19, or, in this case, high chemical stability13. We used degradation data from short RNA fragments designed on the Eterna platform, which comprised a wide diversity of sequences and structures, and hypothesized that crowdsourcing the problem of obtaining a machine learning architecture would result in a model capable of expressing the resulting complexity of sequence- and structure-dependent degradation patterns , which opened on 26 March 2020 and closed on 19 June 2020 21, a technique to characterize RNA secondary structure. SHAPE experiments were performed analogously to the In-line-seq experiments described above, but instead of degradation conditions, the RNA was subjected to a chemical modifier  which acylates the 2′-OH group. When the RNA is reverse transcribed, such an acylation causes the reverse transcriptase enzyme to terminate. The resulting cDNA fragments were used to create a ‘reactivity profile’ for each molecule.We then obtained nucleotide-level degradation profiles for the first 68 nucleotides of these RNAs using In-line-seqThe Kaggle competition was designed to create models that would have predictive power for three of these data types, given RNA sequence and secondary structure as input Fig. . In addiN required predicting 3 × N values for the three data types. In addition to these experimental data, Kaggle participants were also provided with features related to RNA secondary structure computed from available biophysical models to use if they wished. These features included N × N base-pairing probability matrices from EternaFold22, a recently developed package with state-of-the-art performance on RNA structural ensembles; dot-parenthesis notated minimum free energy (MFE) RNA secondary structure from the ViennaRNA package23; and a six-character featurization of the MFE structure calculated using bpRNA24.In total, each independent construct of length We developed training and ‘public test’ datasets from the RYOS-I dataset Fig. . The pub2+, pH 10, 1 day, 24 °C; and 10 mM Mg2+, pH 7.2, 1 day, 50 °C. Models were scored using the mean column RMSE (MCRMSE) across three data types, defined asNt is the number of scored data types, n is the number of nucleotides in the dataset, yij is the measured data value, and i in sequence j. Two additional data types were included in the training data corresponding to RNAs degraded for 7 days without Mg2+ rather than 1 day with Mg2+: . However, these data were not collected for the second round to accelerate competition turnaround.To ensure that the majority of the data used for the private test set were fully blind, we initiated a second ‘Roll Your Own Structure’ challenge that was launched for Eterna design collection on 18 August 2020. Given that useful models for degradation should be agnostic to RNA length, we designed the constructs in RYOS-II to be 34 nucleotides longer (102 versus 68 nt) than the constructs in RYOS-I to discourage modelling methods that would overfit to constructs of length 68. Design collection was closed on 7 September, 3 days before the launch of the Kaggle challenge on 10 September. The RYOS-II wet-lab experiments were conducted concurrently with the Kaggle challenge, enabling a completely blind test for the models developed on Kaggle. The Kaggle competition was closed on 6 October. The RYOS-II was similarly clustered and filtered to ensure that the test set used for scoring consisted primarily of singly and doubly clustered constructs. Three data types were used to score models: SHAPE; 10 mM Mg13 by 37% in MCRMSE for the public test set and 25% for the private test set . Kaggle participants developed feature encodings beyond what was provided. One of the most widely used community-developed featurizations was a graph-based distance embedding depicted in Fig. https://www.kaggle.com/code/mrkmakr/covid-ae-pretrain-gnn-attn-cnn/), which alone achieved a MCRMSE of 0.24860 on the public and 0.36106 on the private test set  and used these RNAs in training as well (see Supplementary Information for more detailed descriptions of solutions from Kaggle teams).An additional benchmark model that used the DegScore windowed featurization (see Methods) with improved XGBoostset Fig. . This noset Fig.  depicts set Fig.  demonstrWe explored whether increased accuracy in modelling could be achieved by ensembling models, that is, combining predictions from multiple models; a common feature of Kaggle competitions is that winning solutions are dissimilar enough that ensembled models frequently improve predictive ability. We found that ensembling resulted in only modest improvements (Methods), suggesting the majority of signal had been captured by the top two models.We analysed predictions from the first-placed model (‘Nullrecurrent’) in greater depth to better understand its performance. Across all nucleotides in the private test set, 41% of nucleotide-level predictions for SHAPE reactivity agreed with experimental measurements with an error margin that was lower than experimental uncertainty; for comparison, if experimental errors are distributed as normal distributions, a perfect predictor would agree with experimental values over 68% of data points. For Deg_Mg_pH10 and Deg_Mg_50C, 28% and 42% of predictions were within error, respectively.13. The most reactive RNA structure motifs were triloops, a previously unknown biological finding. Another unexpected finding from these data was that symmetric internal loops were more stable against degradation than internal loops with asymmetric lengths. The fact that the Nullrecurrent model was able to capture this trend indicates that using such models within a design algorithm would allow for an automated way to model such biochemical attributes within a designed mRNA. Constructs with the highest RMSE highlight instances in which the provided structure features were incorrect. Figure 17, and RMSE summed per construct, suggesting that, in general, quality of the input structure features was not a limitation in model training  at varying timepoints after degradation was initiated. The lengths of these mRNAs ranged from 504 to 1,588 with a median length of 928 , these mRNAs could not be probed at a per-nucleotide level akin to the datasets used in the Kaggle experiments. However, their overall degradation rates (related to the per-nucleotide degradation rate via equation ), were c928 Fig. , nearly p = 3.3 × 10–12) and 0.43 (p = 9.5 × 10–10), respectively. Both Kaggle models outperformed unpaired probability values from ViennaRNA RNAfold v. 2.4.1423 , the DegScore linear regression model  and the DegScore-XGBoost model . An ensemble of the Nullrecurrent and Kazuki2 models did not outperform the Kazuki2 model , again suggesting that the models themselves had reached their predictive potential. To estimate an upper limit for correlation considering experimental error, we resampled the measured degradation rates from within experimental error and calculated the correlation to the mean degradation rate. This resulted in a Spearman correlation of 0.88  and coding sequence (CDS) Fig. . CarryinThe OpenVaccine competition uniquely leveraged resources from two complementary crowdsourcing platforms: Kaggle and Eterna. The participants in the Kaggle competition were tasked with predicting stability measurements of individual RNA nucleotides. The urgency of timely development of a stable COVID-19 mRNA vaccine necessitated that the competition be run on a relatively short timeframe of three weeks, as opposed to three months, which is more common with Kaggle competitions.12 to minimize the predicted degradation. There is likely further opportunity to leverage advancements in natural language processing to use datasets such as the ones presented here to generate mRNA designs using text-generation approaches31. The degradation data used in this competition were from RNA synthesized with unmodified nucleotides, but mRNA vaccines are being formulated with modified nucleotides including pseudouridine or N-1-methyl-pseudouridine15. Modified nucleotides in general will have differing underlying thermodynamics32, and there is a need to develop datasets and predictive models to predict structures and resulting stabilization of mRNAs formulated with modified nucleotides. The In-line-seq method can be performed using RNA with modified nucleotides, and the resulting data could be used to re-train models with architectures such as the ones presented here. Short of developing complete new thermodynamic parameters for modified nucleotides, it may be possible to develop principled heuristics to adapt models to mRNAs synthesized with modified nucleotides. For instance, Leppek et al. modified the DegScore model for pseudouridine by setting all uridine degradation measurements to zero to mimic the stabilization effect of pseudouridine, and saw moderate improvement in correlation13.The models presented here are immediately useful for mRNA design in that they could be called within a stochastic mRNA design algorithmKaggle competitions with relatively small datasets can be subject to serious overfitting to the public leaderboard, which often leads to a ‘shake-up’ of the leaderboard when the results on the unseen test set are announced. In this competition the shake-up was minimal—most of the top teams were ranked close to the same position on the private leaderboard as they were on the public leaderboard. As the private leaderboard was determined on data that had not been collected at the time of the competition launch, this result suggests that the models are robust and generalizable. We demonstrated the top two models generalized to the task of predicting degradation for full-length mRNA molecules that were tenfold longer than the constructs used for training. We speculate that the use of a separate, independently collected dataset for the private leaderboard tests—a true blind prediction challenge—was important for ensuring generalizability. The winning solutions all combined neural network architectures that are commonly used in modelling 1D sequential data, including multihead attention, recurrent NNs (LSTMs and GRUs) and 1D CNNs. The effectiveness of pseudo-labelling has two implications: more data will likely benefit any future modelling efforts, and the simple architectures that were used have enough capacity to benefit from more data.22 and integrating into inference frameworks for RNA structure prediction33.An under-investigated aspect of the models presented here is the effect of training on multiple data types. We speculate that because SHAPE reactivity has higher signal-to-noise than the degradation data types  and the base-pair probability matrix according to the EternaFold22 energy model. These features were generated using Arnie (https://github.com/DasLab/arnie).As a starting point for Kaggle teams, we supplied a collection of features for each RNA sequence, including the minimum free energy (MFE) structure according to the ViennaRNA 2 energy model13The first experimental dataset used in this work, for the public training and test set, resulted from the ‘Roll-Your-Own-Structure’ Round I lab on Eterna, and had been generated previously by Leppek et al.13 In brief, DNA templates were ordered via custom oligonucleotide pool from Custom Array/Genscript, prepended by the T7 RNA polymerase promoter. Templates were amplified via PCR, transcribed to RNA via the TranscriptAid T7 High Yield Transcription Kit , and the purified RNA was subjected to degradation conditions: (1) 50 mM Na-CHES buffer (pH 10.0) at room temperature without added MgCl2; (2) 50 mM Na-CHES buffer (pH 10.0) at room temperature with 10 mM MgCl2; (3) phosphate-buffered saline  at 50 °C without added MgCl2; and (4) PBS (pH 7.2) at 50 °C with 10 mM MgCl2. In parallel, purified RNA was subjected to SHAPE structure-probing conditions, and one sample was subjected to the SHAPE protocol absent addition of the 1-methyl-7-nitroisatoic anhydride reagent.The second experimental dataset used in this work, for the private test set, was generated for this work specifically. To produce these data, and for precise consistency with the public training and test set, In-line-seq was carried out as described by Leppek et al.https://eternagame.org/software) following the recommended steps for sequence assignment, background subtraction of the no-modification control, correction for signal attenuation and reactivity profile normalization as described previously20.cDNA was prepared from the six RNA samples . We pooled 1.5 μl of each cDNA sample together, ligated with an Illumina adapter, washed and resuspended the ligated product, which was quantified by qPCR, sequenced using an Illumina Miseq. Resulting reads were analysed using MAPseeker  within either dataset  and its secondary structure type assignment s . The secondary structure type assignment ‘X’ for external loop was replaced with ‘E’ in the DegScore model in ref. 13, to reflect the biophysical similarity between the two categories. w is the maximum window distance, set to be 12 by Leppek et al.13. For a window size of w = 12, there are 251 parameters .We compared Kaggle models to the ‘DegScore’ linear modelWe explored whether increased accuracy in modelling could be achieved by combining models. We used a genetic algorithm to ensemble maximally 10 of the top 100 models. The score on the public dataset was used to optimize, with the final ensembled model evaluated on the private dataset. With this method, ensembling achieved a public MCRMSE of 0.2237 (compared to the best public MCRMSE of 0.2276) and a private MCRMSE of 0.3397 (compared to the best private test set MCRMSE of 0.3420). By comparison, averaging the outputs of the top two models gave a result of 0.2244 public, 0.33788 private. Blending the top two solutions with the third solution did not improve the result. An estimated bound of ensembling can be found by optimizing directly to the private ensemble score. With this method, it was possible to achieve a private ensemble score of 0.3382 . The improvement of 0.0038 over the leaderboard for this last approach is about the distance between the first-placed and tenth-placed teams, and the ‘correct’ way gives an improvement that is the distance between the first- and fifth-placed teams. All these experiments suggest that most of the signal has been captured by the top two models, and that the use of further ensembling provides, at best, modest improvements. The seemingly puzzling result that the simple ensemble of the top two models outperforms the genetic algorithm blend of the top 10 (on the private test set) suggests that the genetic algorithm did not find a global minimum for model weights.Further information on research design is available in the Supplementary InformationDescriptions of model architectures of top 13 models, supplied by Kaggle teams.Reporting SummarySupplementary TablesSupplementary Table 1: Eterna participants in both ‘Roll-Your-Own-Structure’ labs. Supplementary Table2: Construct attributes and predictions corresponding to PERSIST-seq independent mRNA degradation test. Supplementary Table3: List of OpenVaccine Donors.


Based on a comprehensive multi-filtering strategy and a machine-learning classification model trained with comprehensively curated features, RNA-SSNV achieved the best precision–recall rate (0.880–0.884) in a testing dataset and robustly retained 0.94 AUC for the precision–recall curve in three validation adult-based TCGA (The Cancer Genome Atlas) datasets. We further showed that the somatic mutations called by RNA-SSNV tended to have a higher functional impact and therapeutic power in known driver genes. Furthermore, VAF  analysis revealed that subclonal harboring expressed mutations had evolutional selection advantage and RNA had higher detection power to rescue DNA-omitted mutations. In sum, RNA-SSNV will be a useful approach to accurately call expressed somatic mutations for a more insightful analysis of cancer drive genes and carcinogenic mechanisms.The usage of expressed somatic mutations may have a unique advantage in identifying active cancer driver mutations. However, accurately calling mutations from RNA-seq data is difficult due to confounding factors such as RNA-editing, reverse transcription, and gap alignment. In the present study, we proposed a framework (named RNA-SSNV,  Cancer is the leading cause of death and an important barrier to increasing life expectancy . AccordiMutations within exons are supposed to be transcribed into RNA, and be reflected in the translated protein. However, many DNA mutations within exons were not found in RNA because they were located in the non-transcribed allele or had no or low expression . Yizhak https://github.com/pmglab/RNA-SSNV). It is a unified framework containing a universal pipeline to call RNA somatic single nucleotide variants from the combination of tumor RNA-seq and normal WES data, a multi-filtering strategy to remove doubtful calls with little loss of sensitivity and a supervised machine learning model to identify somatic mutations and artifacts. Our framework achieved the best overall performance for precision and recall, requiring only public reference resources. To validate the generalization performance of our framework, we utilized RNA-SSNV within TCGA lung squamous cell carcinoma (LUSC), bladder urothelial carcinoma (BLCA), and glioblastoma multiforme (GBM) independent datasets. RNA-SSNV achieved similar performance in the area under curve (AUC) for the precision–recall curve with 0.94 for all three datasets. Given its high precision–recall performance, RNA-SSNV will help exploit expressed somatic variants, further extend the range of RNA-seq applications and make full use of abundant RNA-seq data resources.Here, we introduce a framework named RNA-SSNV (Our RNA somatic single nucleotide variant identification framework (RNA-SSNV) consists of three major components, including a RNA somatic mutation calling step, a multi-step filtering process and a machine-learning based prediction . The undhttps://docs.gdc.cancer.gov/Data/Introduction/) to generate RNA-seq and DNA-seq data. All RNA-seq datasets were aligned to GRCh38 build using a two-pass method with STAR, which required preprocessing before mutation calling. All DNA-seq datasets were aligned to the GRCh38 reference using bwa  cohort as the training dataset that contained the largest patient scale (511) compared with other available cancer cohorts. Our training dataset comprised paired tumor RNA-seq and tumor/normal WES data derived from 511 LUAD patients, which simultaneously generated DNA and RNA somatic mutations. Our independent validation datasets comprised paired tumor RNA-seq and normal WES data derived from 498 LUSC, 441 BLCA, and 198 GBM patients, for which we called RNA somatic mutations to get validating records.annotated exon regions) within mature mRNA can be sequenced . Thus, we chose to utilize Mutect2 to perform somatic variant calling and only retain mutations within targeted coding regions (overlap of exons and WES targets).Theoretically, calling somatic mutations within RNA-seq data can be easily conducted using callers designed for DNA. Haplotype-based callers , our aligned RNA-seq bam was passed to the MarkDuplicates tool to identify duplicate reads and help remove PCR-related artifacts. Next, SplitNCigarReads hard-clipped and reformat some alignments which span introns causing large-scale mistaken indels. Finally, it shall undergo base quality recalibration conducted by BaseRecalibrator and ApplyBQSR to detect and correct patterns of systematic errors in the base quality scores.Normally, our STAR-2-pass aligned RNA-seq data required a co-cleaning process to conduct mutation calling. Following GATK recommended procedures  already provided open-access DNA somatic mutations detected by four different callers MuSE, MuTect2, SomaticSniper, and VarScan (https://github.com/wwysoc2/gdc-maf-tool), we combined the curated mutations from four callers, and constructed a union set of all available DNA somatic mutations for each cancer type  to maximize the sensitivity. In addition, given that GDC somatic variant calling pipeline had strict criteria leading to the loss of some true positive somatic mutations, we called our own DNA somatic mutations using raw sequencing data and retrieved GDC-omitted DNA somatic mutations.For all TCGA projects involved in our study, the GDC Data Portal , our main purpose was to identify true positive RNA somatic mutations correctly. We chose precision, recall, F1 scores, and areas under the precision–recall curve (PR-AUC) as major performance metrics in our study because they are insensitive to class imbalance. Other metrics derived from the confusion matrix  were alsRecords within the training dataset were split into training and testing subsets (9:1). We utilized the training subset for model parameter tuning, feature selection, and model training. For the testing subset, we utilized them for testing the model’s generalization performance.To handle the imbalanced distribution for TP and TN classes, we chose a weighted random forest classifier  to reduce the bias by assigning inversely proportioned weights to different classes . First, Following the procedures mentioned earlier, we conducted somatic single nucleotide variants calling in LUSC, BLCA, and GBM cohorts, utilized a multi-filtering strategy and built validation datasets based on extracted features. We applied our discriminant model in these validation datasets and retrieved assessing metrics to further demonstrate the generalization performance.Also, we validated the necessity of introducing a new training dataset from another cancer type. We added the GBM dataset into the initial training dataset and constructed a new random-forest classifier. After retrieving and assessing metrics for the new random-forest classifier within LUSC and BLCA independent validation datasets, we compared them with our initial model’s performance.https://github.com/slundberg/shap)  is computed as the total reduction of the criterion brought by that feature and retrieved through our model’s attribute feature_importances_. Because traditional feature importance mainly focused on overall model interpretation, we also introduced the SHAP   python prg/shap)  and provrg/shap) . We provhttps://github.com/snakemake/snakemake) was applied to manage standard bioinformatic workflows involved in this study  and conduct task auto-management without complicating shell scripts. Function-oriented python scripts contained feature extraction, model training and testing, and model utilizing function. Both Snakemake-based workflows and python scripts were available within our project repository (https://github.com/pmglab/RNA-SSNV), which helped create reproducible analysis.We built our whole framework using Snakemake  and clasWe integrated predicted RNA somatic mutations with known DNA mutations to analyze the relevance between RNA and DNA. We examined their intersectionality and split them into three parts  and two sub-categories  to explore their enrichment patterns  between expressed (RNA–DNA overlap part) and un-expressed (DNA-only part) somatic mutation panels. For pathogenicity prediction, Combined Annotation–Dependent Depletion (CADD)  of enriched distribution and odds ratio (OR). A two-sided independent t-test was used to determine the significance  of the difference between the means of two prediction groups.Cancer driver genes were under positive selection during tumorigenesis . Here, wn (CADD) , Eigen Pn (CADD) , Polymorn (CADD) , Proteinn (CADD) , UMD-Pren (CADD) , Rare Exn (CADD) , and Sorn (CADD)  were topn (CADD) . Thus, wn (CADD)  databasehttps://xena.ucsc.edu/). Then, we chose only heterozygous SNVs in both tumor RNA-seq and WES data (RNA–DNA overlap part), and implemented chi-squared tests on the RNA and DNA allelic depths with a significance cutoff of p-value 0.01 to identify somatic SNV ASEs  to identify ASE events in somatic mutations and their impacts on gene expression which affected carcinogenesis. We chose cases containing both tumor and paired-normal RNA-seq data from LUSC and BLCA cohorts , and curated their gene expression profiles from the UCSC Xena database . Thus, critical mutations within cancer driver genes can be reliably identified in RNA-seq data, which also guarantees our framework’s clinical value.To inspect the generalization performance of our framework, we applied our RNA somatic mutation discriminant model to three independent validation datasets. As a result, RNA-SSNV successfully discriminated GDC high confidence somatic variants from WES-targeted coding RNA mutations with significantly higher precision, recall, and PR-AUC  for each case of three cancer types , median p = 1.14 e-68, BLCA: OR = 2.57, p = 9.89 e-119, GBM: OR = 2.70, p = 1.73 e-16. p-value < 1 e-5, To investigate whether the RNA-expressed somatic mutations tended to have larger functional impacts than those that only existed in DNA, probably resulting from the positive selection of cancer subclonal, we compared the impact scores of mutations within cancer-specific driver genes  between https://www.oncokb.org/, p = 8.35e-19, BLCA: OR = 3.27, p = 4.26e-16, GBM: OR = 4.26, p = 3.66e-4, Furthermore, we want to explore whether actionable mutations tend to get expressed in RNA and exhibit clinical effects. Thus, we assessed the therapeutic power for mutations in cancer driver genes between RNA–DNA overlap and DNA-only variants using the OncoKB database  (https:/FDR < 0.1), the RNA somatic mutations led to a higher proportion of known cancer driver genes from the Intogen database  also had a similar proportion. This result suggested that the RNA-level may lead to fewer false-positive estimations for driver genes than DNA-level.Given that RNAs were enriched with mutations of higher functional impact and therapeutic value, we assessed the performance of RNA-level somatic mutations for discovering cancer driver genes by other statistical methods. Here, WITER  was adopdatabase  in two oq-value of 0.081 in BLCA. CTNNB1’s mutations have been found to cause aberrant WNT/CTNNB1 signaling and are associated with the susceptibility and prognosis of breast, endometrial, and gastric cancers  played an important role in the repair of DNA damage, and its heterozygous mutations had been found to be causing genetic susceptibility to lung cancer , indicating the trend of cancer evolution for subclonal harboring RNA somatic mutations. Interestingly, RNA VAF was significantly higher than DNA VAF within expressed mutations of RNA–DNA overlap part (p < 1 e-5), suggesting an expression tendency for the mutant allele. The common cancer WES study has a mutation limit of detection (LoD) at 5% VAF, and reporting these subclonal mutations incurs the risk of sequencing error–induced false positives  was the fraction of sequencing reads harboring the mutation when performing NGS , measuriositives . For thehttp://docm.info). We found that 17 out of 102, 14 out of 120, and 2 out of 8 DNA-overlooked “driver” mutations in LUSC, BLCA, and GBM had literature support from one or more publications  to measure gene differential expression status. After excluding infinite FC values, we found that the median gene FC for RNA-expressed mutations was significantly higher than unexpressed mutations . Thus, gWe detected somatic SNV-level ASEs, and found that 24.8% of 3876 and 23.2% of 1700 somatic mutations exhibited ASE events in LUSC and BLCA RNA–DNA overlap parts. As expected, most (∼90%) ASE somatic mutations had over-expressed mutant alleles. The results showed that certain expressed somatic mutations had higher expression superiority in the mutant allele than the wild allele, which further enhanced the mutation detection power in RNA. Furthermore, we curated gene lists for 10 signaling pathways in cancer  and explAlthough common somatic mutation detection practices come with WES, important and actionable mutations are often conserved in RNA-seq. Therefore, we developed RNA-SSNV, an integrative framework to identify RNA somatic single nucleotide variants called within tumor RNA-seq and paired-normal WES data. To maximize performance, we combined multi-filtering strategies and a machine-learning model. For the multi-filtering strategy, we found that it removed massive artifacts (∼70%) while omitting few true positive calls (∼0.1%). Before constructing the classification model, we also evaluated the performance of the GATK-recommended filtering tool  for the LUAD training dataset and LUSC, BLCA, and GBM validating datasets using precision–recall metrics. The result showed that FilterMutectCalls achieved a satisfying recall but a low precision rate , which may lead to large false-positive calls. Because FilterMutectCalls was originally designed based on DNA somatic mutation filtering strategy, which may not be fully compatible with RNA, we adopted a machine learning model with comprehensive features to conduct classification. For model training, we adopted various techniques to ensure its reliability. To construct a high-quality training dataset, we used GDC DNA mutations as the golden standard and self-called DNA mutations as important supplementary information to separate pure true positive and true negative sets from multi-filtered RNA mutations. In a comparison of using two data sources (RNA mutations and golden-standard DNA mutations) to construct the training dataset, the introduction of self-called DNA mutations significantly improved our machine model’s performance . We also conducted feature selection and fine-tuning to improve the model’s performance. Eventually, our trained model achieved superior performance of 88.0% precision and 88.4% recall rate in the testing dataset compared with other state-of-art RNA somatic mutation detection tools such as RNA-Mutect  precisi (precisip = 0.003) and BLCA (p = 5.63 e-6), possibly due to innate upregulation of DNA repair mechanisms , RNA-SSNV achieved similar performance as in the testing dataset, which had 0.871–0.895, 0.876–0.871, and 0.902–0.830 precision–recall rate, respectively. Not only can our framework reliably detect RNA somatic mutations, but it also can conduct pairwise analysis with provided DNA mutations. Although our framework achieved satisfying performance within somatic RNA single-nucleotide variants’ identification, limited scenarios in which only RNA somatic mutations can be retrieved such as the GTEx project  contain. Thus, wchanisms . We retrchanisms . Intereschanisms , and subchanisms . Althougchanisms , improvechanisms , and enhWe also examined the potential of improving our model’s performance by introducing additional training data from different cancer types. After adding GBM cancer–type data into the training dataset, we only observed a slight improvement within the testing dataset  and the AUC for P–R curves for TCGA LUSC, BLCA–independent validation datasets remained stable at 0.94 . The uncFor cancer research involving both WES and RNA-seq data, the conventional analysis strategy uses WES data to call somatic mutations and then validates whether somatic mutations exist in RNA-seq data. However, the conventional strategy may still omit some somatic mutations in RNA-seq data. Our study significantly improved the capability to call RNA somatic mutations and further revealed the association between somatic mutations derived from RNA and DNA, providing valuable supplementary information for conventional cancer somatic mutation analysis.


Prospective studies exploring the effects of psychosocial work factors on self-reported health (SRH) are lacking, especially those studying effect modifications. The objectives were to examine the prospective associations of these factors, and multiple exposures to these factors, with SRH in a national representative sample, and effect modifications by gender, age, and occupation.The prospective study relied on the three data collection waves  of the national French Working Conditions survey and was based on a sample of 15,971 employees, in good SRH at the beginning of the follow-up period. The occupational exposures were time-varying variables measured in 2013 and 2016, and included: 20 psychosocial work factors grouped into 5 broad domains, 4 exposures related to working time/hours and 4 physical-biomechanical-chemical exposures. The incidence of poor SRH three years later was the outcome. Discrete time Poisson regression models were performed using weighted data and with adjustment for gender, age, marital status, life events, and occupation.Almost all the studied psychosocial work factors were predictive of poor SRH. Some physical-biomechanical-chemical exposures were found to predict poor SRH. Only rare effect modifications were observed according to gender, age, and occupation. Dose-response associations between multiple exposures and the incidence of poor SRH were observed for 4 among 5 domains of psychosocial work factors.Our study underlined the effects of psychosocial work factors, as well as multiple exposure effects, on the incidence of poor SRH. However, most of these effects were the same across population groups related to gender, age, and occupation.The online version contains supplementary material available at 10.1186/s12889-022-13773-x. Psychosocial work factors defined by psychological and social exposures derived from the work organization and environment have been found as risk factors for various health outcomes, in particular mental disorders and cardiovascular diseases . HoweverSelf-reported health (SRH) is recognized as a marker of general health and has been recommended for use by both WHO and EU commission . SRH hasThe objectives of the present study were therefore to explore the prospective associations between occupational exposures, including a large set of psychosocial work factors and multiple exposures, and SRH in a nationally representative sample of the working population and potential effect modifications by gender, age, and social position.The study relied on the prospective data from the national French Working Conditions survey collected in 2013, 2016, and 2019 and set up by the French ministry of labour (DARES). The survey design is an open prospective cohort, i.e. people may have moved in and out the cohort during the follow-up. At each wave , the data were collected using a questionnaire administered by interviewer and a self-administered questionnaire. Three of our previous publications explored the cross-sectional associations between psychosocial work factors and various mental health outcomes  using the 2016 wave of the survey –28 and aSRH was chosen as the health outcome and was collected in the questionnaire in 2013, 2016, and 2019, using one single item  with 5 categories  that was dichotomized into two groups: good  and poor  SRH. This dichotomization was retained because the prevalence and incidence of poor/very poor SRH were very low (< 5 and < 3% respectively). Such a dichotomization was used previously in other studies among the French working population . We studPsychosocial work factors were collected in the questionnaire and the self-administered questionnaire in 2013 and 2016 and included twenty factors constructed using a total of 61 items. As no validated or recommended questionnaire was used in the survey, these items were selected because they were proxies of the items from the COPSOQ . Some itAll statistical analyses were done using weighted data to correct for potential non-response and attrition bias and provide results that could be extrapolated to the target population. The prospective associations between occupational exposures, as time-varying variables (i.e. in 2013 or 2016), and the incidence of poor SRH three years later (i.e. in 2016 or 2019) were explored using discrete time Poisson regression models, with weighted data (option Stata pweight) and robust variance estimation (option Stata vce (robust)), and with adjustment for gender and the following time-varying covariates: age (4 10-year age groups), marital status (cohabiting or not), life events within the 3-year period , and occupation  as a marker of social position. To explore effect modifications by gender, age, and occupation, multiplicative interaction terms were tested. Statistical trend tests were performed using orthogonal polynomial contrasts to study the dose-response associations between multiple exposures (i.e. the number of exposures) and the incidence of poor SRH. Multiple testing was corrected using False Discovery Rate (FDR), which is a method to control “the expected proportion of errors among the rejected hypotheses” . All staSensitivity analyses included: (i) performing additional adjustment for chronic disease, (ii) performing additional adjustment for full/part time work, (iii) restricting the study sample to employees who stayed in the same job during follow-up, and (iv) performing additional adjustment for working time/hours and physical-biomechanical-chemical exposures in the study of multiple exposures to psychosocial work factors and SRH.Among the sample of 19,431 employees, we retained for analysis only those with good SRH at the beginning of each follow-up period (in 2013 or 2016), i.e. 15,971 employees. Among them, 12,669 employees entered into the cohort in 2013 and were followed up from 2013 and 2016, including 6938 employees who continued to be followed up from 2016 to 2019. A total of 3302 employees entered into the cohort in 2016 and were followed up from 2016 to 2019. The incidence of poor SRH was 18.2% for the first period of follow-up  and 14.4% for the second period of follow-up (i.e. between 2016 and 2019). Descriptive statistics can be found in Supplementary Tables Almost all psychosocial work factors were predictive of poor SRH, except cognitive demands and temporary employment Table . After mA total of 6 effect modifications were observed: one by gender, one by age, and four by occupation Table . NeverthStatistical trend tests were significant for all measures of multiple exposure to psychosocial work factors for all domains (except violence) and for all psychosocial work factors together Fig. , suggestSensitivity analyses showed that the results were unchanged after additional adjustment for chronic disease or full/part time work. Restricting the study sample to the employees who remained in the same job during follow-up provided the same results, although two psychosocial work factors were no longer predictive of poor SRH: low influence and work-family conflict. Additional adjustment for working time/hours and physical-biomechanical-chemical exposures in the study of multiple exposures to psychosocial work factors and SRH did not change the results.This study showed that almost all the 20 studied psychosocial work factors were predictive of poor SRH. There was however only rare effect modifications by gender, age, and occupation, suggesting that most of the exposure-outcome associations were the same according to these variables. Dose-response associations were found between multiple exposures to psychosocial work factors and the incidence of poor SRH.Previous prospective studies reported associations of job insecurity , 21, 24,Strengths of our study deserve to be presented. Our study had a prospective design with clear chronological order between exposure and outcome. Response and follow-up rates were satisfactory and did not lead to major response and attribution bias. Indeed, the comparison between respondents and people lost to follow-up did not show major differences  in the studied covariates, occupational exposures and SRH  and covariates  may be missing. We tested multiplicative interaction terms to study effect modifications, and this approach is considered conservative, compared to other approaches (additive interaction for example). Nevertheless, the test of additive gender-related interactions in our study provided two significant interactions only for job insecurity –that was also found with the test of multiplicative interactions- and for noise. Consequently, there was no major differences between multiplicative and additive interactions. This might be explained by a high statistical power related to large sample size.To conclude, psychosocial work factors were found to predict the incidence of poor SRH. Almost all the associations of these factors with SRH were the same across gender, age, and occupation groups. Multiple exposure to these factors displayed dose-response associations with poor SRH. Comprehensive prevention oriented towards the psychosocial work environment is likely to improve SRH in the whole working population. More attention should be given to multiple exposures to psychosocial work factors.Additional file 1.Additional file 2.