This cohort study examines the clinical efficacy of immune checkpoint inhibitors (ICIs) in patients with advanced malignant peritoneal mesothelioma. Immune checkpoint inhibitors (ICIs) are likely to be effective in treating MPeM as a result of its proinflammatory microenvironment and abundant programmed cell death–ligand 1 expression.2 However, exclusion of patients with MPeM from mesothelioma clinical trials with ICIs has created a knowledge gap regarding their efficacy in MPeM. Because of promising results seen in malignant pleural mesothelioma (MPM), ICIs have been used off-label  at MD Anderson Cancer Center.4 Given the lack of prospective data, we aimed to define the clinical efficacy of ICIs in patients with MPeM.Malignant peritoneal mesothelioma (MPeM) is a rare malignant entity with an annual incidence of 0.10 cases per 100 000 population in the US.P < .05. Statistical analyses were performed using SPSS statistical software version 25.0.0.1 (IBM) and Prism software version 8.00 (GraphPad). See eMethods in the We performed a cohort study of all consecutive patients with advanced MPeM treated with ICIs between January 2016 and December 2020 under an MD Anderson Cancer Center institutional review board–approved protocol. Informed consent was waived because the data were deidentified and the study was considered to pose minimal risk to participants, in accordance with 45 CFR §46  with MPeM treated with ICIs  are shown in the 4 However, caution is needed when extrapolating data from MPM to MPeM because these diseases appear to be molecularly and immunologically different .5 Results from a phase 2 study of atezolizumab and bevacizumab in MPeM showed very promising confirmed ORR of 40% with durable responses and notable 1-year PFS of 61% and OS of 85%.6To our knowledge, in this cohort study, we report the first real-world evidence regarding clinical outcomes for a cohort of patients with advanced MPeM receiving ICIs. We observed encouraging clinical activity, with an ORR of 19.2% and a median PFS of 5.5 months. Disease response was seen regardless of response to prior platinum-pemetrexed chemotherapy. No significant difference in ORR was seen between key clinical subgroups, including dual-agent and single-agent ICI. Therapy also appeared to be well tolerated; only 1 patient had treatment discontinuation associated with toxic effects. This efficacy seems comparable to activity of ICIs in MPM.Although our study has some inherent limitations of a retrospective single-institution series, pending prospective trials, our results provide much-needed data supporting the role of ICIs in patients with this rare disease, who cannot participate in clinical trials and otherwise have no or limited treatment options. The study also demonstrates that the clinical benefit associated with conventional ICIs is restricted to a small subset of patients and argues that there is a critical need for dedicated trials and larger cohorts to define biomarkers of response or resistance, early referral to clinical trials, and novel combinatorial strategies to enhance responses and outcomes for patients with MPeM.


It is well documented that the structure, and thus function, of nucleic acids depends on the chemical environment surrounding them, which often includes potential proteinaceous binding partners. The nonpolar amino acid side chains of these proteins will invariably alter the polarity of the local chemical environment around the nucleic acid. However, we are only beginning to understand how environmental polarity generally influences the structural and energetic properties of RNA folding. Here, we use a series of aqueous-organic cosolvent mixtures to systematically modulate the solvent polarity around two different RNA folding constructs that can form either secondary or tertiary structural elements. Using single-molecule Förster resonance energy transfer spectroscopy to simultaneously monitor the structural and energetic properties of these RNAs, we show that the unfolded conformations of both model RNAs become more compact in apolar environments characterized by dielectric constants less than that of pure water. In the case of tertiary structure formation, this compaction also gives rise to more energetically favorable folding. We propose that these physical changes arise from an enhanced accumulation of counterions in the low dielectric environment surrounding the unfolded RNA. The biological functions of noncoding RNAs are often closely related to their ability to form intricate three-dimensional structures. These structures are stabilized by several weak interactions within the RNA and between the RNA, the solvent, and any other solutes in solution. Here, we demonstrate that chemical environments that are less polar than water  generally compact unfolded RNA conformations and, in some cases, can shift conformational equilibria to favor the formation of folded structures. These finding shed light on the physical rules that govern how noncoding RNAs adopt intricate structures essential to all living entities.+ . Unfortunately, polarity can be a difficult property to quantify, particularly for systems containing multiple cosolvents (εr), is a bulk property that is closely related to polarity and has been extensively measured for nearly a century  with a 5 basepair stem and a large poly-rA loop  derived from the Tetrahymena ribozyme (The two fluorescently labeled model RNA-folding systems used in this study are 1) a hairpin RNA (HP-RNA) construct B, left -rA loop  and 2) a-rA loop B, rightribozyme . Previouribozyme ,38. To u2HPO4, NaH2PO4, HEPES, EDTA, MeOH, and IPA were purchased from Sigma-Aldrich . EtOH (190 proof) was purchased from Decon Labs . DMK and ACN were purchased from EMD Millipore  and Thermo Fisher Scientific , respectively. All the chemicals, solvents, and reagents used in this research were of the highest purity grade available at the time.Doubly distilled water was used to prepare all solutions. KCl, NaOH, NaA commercially synthesized  custom-designed bireactive RNA construct was purchased with the following nucleotide sequence: 5′-/5ThioMC6-D/ GUC UUC CAA AAA AAA AAA AAA AAA AAC CAA AAA AAA AAA AAA AAA AA/iAmMC6T/ GAA GAG AAA AAA-3′ (HP-RNA).N-hydroxysuccinimide (NHS)-functionalized Cy3B (donor) fluorophore was coupled to the RNA via a 6-carbon amino-modified deoxythymidine (/iAmMC6T/) at nucleotide position 48 (B). The doubly labeled and high-performance liquid chromatography (HPLC)-purified HP-RNA was flash frozen and stored at −70°C until use. A more detailed description of labeling reactions and purification process can be found elsewhere (The length of the poly-rA loop and the number of complementary basepairs in the hairpin RNA (HP-RNA) construct were specifically designed to balance the rate constants describing formation and disruption of the 5 basepair duplex such that the folding of this secondary structural element is nearly isoenergetic in solution conditions with near-physiological salt concentrations while also maintaining a conformational change that could be easily resolved with FRET. This bireactive oligonucleotide was dissolved in a buffered aqueous solution to covalently and site-specifically couple the FRET donor and acceptor fluorophores to the RNA. Specifically, a maleimide-functionalized CF660R (acceptor) fluorophore was coupled to the RNA via a 6-carbon thiol-modified phosphate (/5ThioMC6-D/) at the 5′ end of the oligonucleotide and an ition 48 B. The dlsewhere .The three-piece construct was assembled following a previously established protocol . The twoTetrahymena ribozyme and linked together with a short single-stranded RNA linker, which controls the effective local concentration of the two species. The length of the linker was chosen such that the resulting folding reaction is nearly isoenergetic in solution conditions with near-physiological salt concentrations while also maintaining a conformational change that could be resolved with FRET. The 6-carbon amino-modified phosphate (/Am6/) at the 5′ ends of both RNA oligos was covalently labeled with either CF660R (TL-RNA1) or Cy3B (TL-RNA2) using NHS-coupling chemistry (B). Briefly, the RNA samples were each dissolved in 0.1 M sodium phosphate buffer at pH 8.0. Approximately, 10 nmol of the RNA was repeatedly diluted to ∼500 μL using additional sodium phosphate buffer and concentrated to ∼25 μL using 3 kDa MWCO centrifuge filter tube (Millipore) to filter out any small reactive primary amino-groups present in the starting material. The concentrated RNA oligo solutions were each mixed with solutions containing ∼10-fold molar excess of either the NHS-functionalized donor or acceptor dyes  and then allowed to incubate for 30 min in dark. After incubation, the reaction mixtures were run through pre-equilibrated (0.1 M sodium phosphate (pH 7.0) with 1 mM EDTA) Zeba spin desalting columns  three consecutive times according to the manufacturer’s guidelines to remove excess free dye. The degree of labeling was determined by measuring the absorbance ratio of the purified sample at 260 nm as well as at either 560 nm (Cy3B) or 660 nm (CF660R) using a DS-11 FX + spectrophotometer . The labeled TL-RNA1 and TL-RNA2 oligos were further purified using a reversed-phase C18 column  and an HPLC system equipped with a multiwavelength light detector . After labeling and purification, the TL-RNA construct was assembled by annealing the three oligos . The annealing was achieved by incubating 6 nmol aliquots (100 μL each) of the three oligos in 0.1 M sodium phosphate buffer at pH 7.0 with 1 mM EDTA for 2 h at room temperature. Annealed TL-RNA construct was flash frozen in liquid nitrogen and stored at −70°C until use. A biotin linker (/5Biosg/) was included in the design of the TL-RNA to enable future single-molecule FRET experiments in which the RNA will be immobilized to the surface of a glass coverslip.The nucleotide sequences for the two interacting secondary structural elements of the GAAA TL-RNA construct were taken from the hemistry B. BriefμL of the RNA sample solution, which was prepared using an appropriate volume of concentrated RNA stock solution diluted into the measurement buffer to achieve a final RNA concentration of ∼100 pM. The resulting RNA sample solution also contains the following components: 25 mM HEPES, 12.5 mM NaOH, 250 mM KCl, 0.01% v/v Tween 20, and a specified amount of organic solvent. HEPES and NaOH were used to buffer the solution pH. KCl was added to ensure that the folded and unfolded states of the two model RNA constructs were approximately equally populated in the absence of organic solvents and thus that the folding reactions were nearly isoenergetic. Tween 20 was added to prevent adsorption of the RNA to the hydrophobic surfaces of the 15-well microscope slides. Organic solvents were used to lower the dielectric constant of the cosolvent system. For each measurement, the final value of εr was interpolated from published data on binary aqueous-organic solvent mixtures  containing 40 mixtures , 31, 32.A). Two dual-mode diode lasers  operating in the continuous-wave mode are used to produce ∼100 μW of vertically polarized light at wavelengths of 515 and 642 nm to excite the donor and acceptor fluorophores, respectively. Alternating excitation , resulting in two streams of linearly polarized fluorescence: one parallel to the polarization of the laser light, the other perpendicular. The two streams are then each split again using a long-pass dichroic mirror  to spectrally resolve donor and acceptor emission, resulting in a total of four separate detection streams. Band-pass filters in the appropriate detection streams reject any fluorescence not associated with the donor  and acceptor  fluorophores. Each stream of fluorescence terminates at a single-photon avalanche photodiode-based detector , where the arrival times of individual photons are recorded by a time-correlated single-photon counting module  with the temporal resolution set to 16,384 (214) ps. The duration of each measurement was 900 s.All single-molecule measurements were performed using a slightly modified MicroTime200 system  consisting of an inverted confocal fluorescence microscope equipped with two excitation sources and four detectors A. Two dcitation  of the tB). The detection records of the photons emitted from these freely diffusing fluorescently labeled RNA molecules were analyzed using Mathematica 12.0 (Wolfram Alpha) in conjunction with Fretica, a C++ based MATHLINK module for the analysis of time-correlated single-photon-counting single-molecule FRET data . Time bins with a total photon count rate (Totn = 1n515 + 1n642 + 2n515 + 2n642 + 3n515 + 3n642 + 4n515 + 4n642) of less than 20 photons per bin were used to calculate the average background photon count rate for each of the four streams during either 515 or 642 nm excitation. Then, corrected photon count rates (N) were determined for all time bins by accounting for background, spectral cross talk, direct excitation of the acceptor, and nonidentical excitation and detection efficiencies of the donor and acceptor fluorophores. Those time bins with a corrected total photon count rate (TotN = 1N515 + 1N642 + 2N515 + 2N642 + 3N515 + 3N642 + 4N515 + 4N642) of more than 20 photons per bin were considered bursts of fluorescence arising from single molecules diffusing through the confocal volume  during 515 and 642 nm excitation were used to calculate values for both the FRET efficiency (E) and fluorescence stoichiometry (S) using D).Under our experimental conditions, the confocal volume is most often devoid of fluorescent molecules, with an average occupancy of less than 0.01 molecules. Nevertheless, individual fluorescently labeled molecules will inevitably diffuse through the confocal volume, where they will be excited by the two alternating lasers, resulting in a transient burst of fluorescent photons B. The dRET data . The datval time C. Time l volume . CorrectS) of a burst arising from a molecule containing only donor or acceptor fluorophores will yield values near S = 1 and S = 0, respectively. Therefore, values of fluorescence stoichiometry are used to restrict our analysis and interpretation of FRET efficiencies to only those bursts arising from molecules containing active donor and acceptor fluorophores . This allows us to effectively filter out unwanted contributions from any potential donor-only or acceptor-only molecules that, for example, may not have been removed during the HPLC purification of the RNA constructs.The fluorescence stoichiometry (E) values from bursts with TotN > 50 were then compiled into histograms (D), in which the widths of the resulting distributions were largely limited by shot noise  and thus the standard state Gibbs free energy difference  between the two subpopulations at T = 294.2 K. For the ease of data interpretation, all ΔG°fold values in apolar solvent conditions were referenced to aqueous conditions, i.e., ΔΔG°fold = ΔG°fold (mixture) − ΔG°fold (H2O).The FRET efficiency . In this study, the structural and energetic aspects of RNA folding were studied in the presence of different binary mixtures of water and various amounts of MeOH, EtOH, IPA, ACN, and DMK. Because the dielectric constants of these pure organic solvents are lower than that of pure water, increasing amounts of organic solvent produce mixtures with decreasing values of εr (A).Several different scales have been introduced to characterize the polarity of pure solvents. Empirical scales for solvent polarity are often based on a variety of spectroscopic, equilibrium, or kinetic measurements of the solvent or solutes dissolved within it . Multipa systems A. In thes of εr A.B). The HP-RNA molecules that adopt more expanded and unfolded conformations will have lower transfer efficiency values than the more compact folded conformations. Similarly, the fluorophores of the TL-RNA construct are located near the GAAA tetraloop and its cognate receptor. When the tetraloop docks into its receptor, the resulting tertiary structure is temporarily held in place by multiple noncanonical hydrogen-bonding and base-stacking interactions . The mean transfer efficiency and fractional abundance of each subpopulation were quantified by fitting the distributions to a sum of two normal distributions , the high-transfer-efficiency folded subpopulation has a mean value of HP⟨E⟩f ≈ 0.95 and the low-transfer-efficiency unfolded subpopulation has a mean value of HP⟨E⟩u ≈ 0.25. Under these conditions, the fractional abundance of the folded conformation is HPΘf = 47.5%. The folded and unfolded conformations of the TL-RNA construct  are located at slightly more intermediate transfer efficiencies with TL⟨E⟩f ≈ 0.8 and TL⟨E⟩u ≈ 0.4, respectively. Here, the fractional abundance of the folded conformation of the TL-RNA is TLΘf = 29.2%.In single-molecule FRET measurements under standard aqueous conditions, both RNA constructs adopt two distinct conformations, as indicated by the bimodal distributions observed in transfer efficiency histograms , where εr systematically decreases as the amount of organic solvent increases. The same general trend is also apparent when using any of the other four organic cosolvents, i.e., more apolar environments, characterized by lower values of εr, consistently yield higher values of HP⟨E⟩u and TL⟨E⟩u.Upon inspection of the MeOH data sets , one can⟩ vs. εr , A and BA) and TL-RNA (B) constructs become more compact in apolar environments.The overall magnitude of the increase is largely independent of cosolvent identity, suggesting that other, nondielectric properties of these aqueous-organic mixtures  are not responsible for the observed trends . Additiod TL-RNA B constrB). Although the change in transfer efficiency is quite small, it persists across all cosolvents, and therefore this finding may be indicative of a slight molecular compaction and/or conformational rearrangement that occurs when RNA tertiary structures are exposed to apolar conditions.Interestingly, the mean transfer efficiency of the folded conformation of the HP-RNA was largely insensitive to the polarity of the local chemical environment. This finding likely suggests that the geometry and orientation of the canonical basepairing interactions responsible for the formation of RNA secondary structure are not heavily influenced by the polarity of the local chemical environment. Presumably, this is because the bases are sequestered within the interior of the resulting duplex, where the polarity of the local chemical environment is dominated by the dielectric properties of the nucleic acid rather than the solvent. However, our quantitative analysis does reveal a subtle increase in the mean transfer efficiency associated with the folded conformation of the TL-RNA construct in aqueous-organic mixtures B. AlthoΘ, of the folded and unfolded species in each of the measurements, which reflects the relative concentrations of the two subpopulations in solution.One of the many advantages of single-molecule FRET over conventional ensemble methods is its ability to study individual subpopulations  within a heterogeneous sample. The transfer efficiency histograms for both the HP-RNA and TL-RNA clearly exhibit at least two easily distinguishable subpopulations across a wide range of solution conditions . Using oKfold = Θf/Θu) and the Gibbs free energy change associated with folding (ΔG°fold = −RT ln Kfold) allows us to use our single-molecule FRET data to determine how the polarity of the local chemical environment alters the energetics of RNA folding. To accomplish this, we calculate the difference between ΔG°fold in the binary aqueous-organic solvent mixtures and pure water, where ΔΔG°fold = ΔG°fold (mixture) − ΔG°fold (H2O). As summarized in the plots of ΔΔG°fold vs. εr (C and D), folding free energies associated with the two RNA constructs responded quite differently to increasing amounts of the various organic cosolvents. Indeed, previous investigations of nucleic acid folding in aqueous-organic mixtures have also reported differential effects on the energetics of secondary and tertiary structure formation , suggesting that the energetic stability of this tertiary interaction is primarily influenced by the changing polarity. In contrast, the values of ΔΔG°fold for the HP-RNA vary nonmonotonically with increasing organic composition and are highly dependent on the identity of the cosolvent (C), clearly indicating that cosolvent polarity is not the only physicochemical properties of these cosolvents that influences the folding energetics of this RNA.The direct relationship between the folding equilibrium constant . First, at low to intermediate concentrations, the protic cosolvents tend to be stabilizing, whereas the aprotic cosolvents tend to be destabilizing. Second, the changes in folding energetics for the HP-RNA correlate quite well with the cosolvents ability to act as a hydrogen-bond acceptor (HBA), as quantified via the β1 scale , whereas folding becomes slightly more unfavorable in solutions where the cosolvents are less effective HBAs . Together, these observations seem to indicate that these aqueous-organic mixtures not only alter the polarity of the solution, but they also alter hydrogen-bonding interactions within or between the solvent and the solutes  and that these alterations noticeably influence the energetics associated with HP-RNA folding.Upon further inspection, our data provide a potential explanation for the highly variable folding energetics associated with the HP-RNA C. Firstβ1 scale . SpecifiG°fold for the HP-RNA (C) mirrors reported values (H°solv) associated with transferring KCl from pure water to a binary aqueous-organic solvent mixture  associated with distinct subpopulations  within heterogeneous molecular ensembles. We chose to systematically alter solvent polarity, quantified via its dielectric constant, by mixing increasing weight percentages of five different organic solvents with water (A). Our results show a clear compaction of unfolded RNAs in increasingly apolar environments with dielectric constants less than that of pure water, regardless of which solvents are used. The overall extent of compaction, reflected by an increase in the ⟨E⟩, scales with the decreasing dielectric constant of the binary aqueous-organic solvent mixtures (A and B). Importantly, a similar degree of compaction can also be achieved by simply increasing the concentration of monovalent cations in a purely aqueous solution  the electrostatic potential generated by negatively charged phosphates and 2) nearby solvated cations will increase the total positive charge density in the ion atmosphere (Thus, the attractive force between an RNA molecule and any neighboring counterions in solution will increase in apolar environments where the value of mosphere  surroundFurthermore, the compaction of these unfolded RNAs increases the effective local concentration of the nucleotides involved in the formation of higher-order structures. Compaction may also bias the unfolded ensemble to mimic the compact transition states commonly observed in RNA folding experiments , 50, 51.In this study, we altered the polarity of the local chemical environment surrounding two model RNA using binary mixtures of water and various organic cosolvents. Our results indicate that apolar environments, characterized by dielectric constants less than that of water, generally compact unfolded RNA molecules. We propose that these apolar environments give rise to enhanced condensation of counterions around the negatively charged phosphate groups of the RNA, which minimizes interphosphate repulsion and allows the unfolded RNA to sample more compact conformations. Indeed, our conclusion is bolstered by a recent study that showed nanoscale hydrophobic environments increase the strength of salt bridges formed in an otherwise aqueous media , a phenoεr is thought to be three to four times lower than that of pure water (Furthermore, we observed that this molecular compaction is also accompanied by an energetic stabilization of folded RNA tertiary structures, presumably reflecting a reduction of the conformation search associated with the folding process. Here, it is of course important to note that environments near the surfaces of proteins are quite apolar, where the value of re water . From thre water .E.D.H. conceived project, designed experiments, provided samples, analyzed data, and wrote the manuscript. S.M.G. designed experiments, performed experiments, analyzed data, and wrote the manuscript.The authors declare no competing interests.


The ability of the ribonucleic acid (RNA) to self-replicate, combined with a unique cocktail of chemical properties, suggested the existence of an RNA world at the origin of life. Nowadays, this hypothesis is supported by innovative high-throughput and biochemical approaches, which definitively revealed the essential contribution of RNA-mediated mechanisms to the regulation of fundamental processes of life. With the recent development of SARS-CoV-2 mRNA-based vaccines, the potential of RNA as a therapeutic tool has received public attention. Due to its intrinsic single-stranded nature and the ease with which it is synthesized in vitro, RNA indeed represents the most suitable tool for the development of drugs encompassing every type of human pathology. The maximum effectiveness and biochemical versatility is achieved in the guise of non-coding RNAs (ncRNAs), which are emerging as multifaceted regulators of tissue specification and homeostasis. Here, we report examples of coding and ncRNAs involved in muscle regeneration and discuss their potential as therapeutic tools. Small ncRNAs, such as miRNA and siRNA, have been successfully applied in the treatment of several diseases. The use of longer molecules, such as lncRNA and circRNA, is less advanced. However, based on the peculiar properties discussed below, they represent an innovative pool of RNA biomarkers and possible targets of clinical value. Freewheeling, audacious, and non-conformist: These terms perfectly define the unique nature of RNA and underline the differences with DNA, straightlaced and uniform by virtue of its genetic responsibilities [bilities . In 2020bilities . Here, tbilities . A furthbilities  and RNA-bilities  designatbilities , and gavbilities . As rareThe existence of distinct roles for RNA was initially suggested by the discovery of messenger (mRNA) ,10, riboIn the last years, research on RNA was fostered by the emergence of the Next-Generation Sequencing (NGS) technologies, which offered the chance to deepen the analysis of multiple (cell and tissue) transcriptomic landscapes ,31,32,33Herein, we will highlight the most recent examples of RNAs implicated in the pathophysiology of muscle, with a glance at their use in clinical trials. While the literature on RNA and myogenesis is vast, we will specifically focus on the importance of coding and non-coding RNAs to the process of regeneration for both, skeletal and cardiac muscles. Current analyses in this field still pose unanswered questions as to whether a stem cell compartment in the adult muscle exists, how long it maintains its regenerative potential, or how it can be regulated . In skelFor a very long time, researchers have documented only one of the RNA-specific tasks, which was related to its ability to code. Later studies have revealed that, instead, RNA can play additional functions and act as a non-protein-coding protagonist . As suchHistorically, the potential of being translated upon exogenous delivery has made mRNA the most suitable vehicle for the expression of almost any kind of protein, including antibodies ,50, antiThe biochemical versatility of RNA is maximized by certain lncRNA features, such as their dual (nuclear and/or cytoplasmic) distribution and the extraordinary cell-type and timely regulated expression ,59. NotaA different set of features applies to small ncRNAs (of length shorter than 200 nt). MiRNAs D are traThe therapeutic use of different RNA types is not only feasible but also extremely promising. However, despite the overall benefits discussed above, prior publications mostly referred to the small species, such as miRNAs. When it comes to longer RNA molecules, examples of their applications in muscle regeneration are very limited and are mainly represented by the use of mRNA. In fact, contrarily to miRNAs and mRNAs, many lncRNAs are still poorly characterized. The lack of gene annotations and their low abundance required the set-up of orthogonal “omics” to describe the structure, the interactions, and, importantly, to provide evidence on the biological significance of these transcripts ,84. NeveFor therapeutic purposes, researchers can use small ncRNA-based drugs functioning as miRNA “mimics” A or “inhIn cardiac muscle, one of the challenges being tackled by researchers using these drugs was to re-establish heart functionality upon myocardial infarction (MI). In particular, different strategies have attempted to revert the necrotic death caused by MI-dependent hypoxia, either by increasing the proliferation of cardiomyocytes or by developing new blood vessels . An examIn skeletal muscle, regeneration follows a very different path in respect to the heart due to the presence of satellite cells, the most representative muscle stem cells. Historically, satellite cells’ specification and self-renewal were ascribed to the activity of the paired-box Pax7 transcription factor ,111,112.ASO provided good performances in the treatment of several muscle pathologies . They caDMPK (dystrophia myotonica protein kinase) transcripts [While no clinical trial is currently ongoing, promising ASO-based approaches are being applied in mice that model different muscle pathologies, such as centronuclear myopathies ,129,130 nscripts  that indnscripts  and sequnscripts . Subcutanscripts ,136 and nscripts , thus faIn the future, several efforts will be made to improve the stability and uptake of ASO in vivo . In thisOther strategies that employ small RNAs are based on the use of small interfering RNAs (siRNAs), which exploit RISC to base-pair and degrade target mRNAs, thus impeding the production of the corresponding protein . Along thttps://clinicaltrials.gov accessed on 10 September 2021) to prevent virus infections, such as Coronavirus (NCT04470427), Zika virus , and Cytomegalovirus , or in the treatment of solid tumors [mRNA is the ideal instrument for treatments that require the expression of specific proteins. Over the years, this opportunity has inspired researchers to find new strategies for increasing its stability and minimizing immunogenicity through the modification of specific nucleosides. This culminated with the production of modRNAs, synthetic and chemically modified mRNAs originally applied in phase I and II clinical trials  attracted the scientific community and currently represents the most exploited way to dose the relative abundance of miRNAs and their targets in vivo A 156,15,158. StaOver the years, other lncRNAs were shown to act as ceRNA for miRNAs ,174. A rModulation of CircHipk3 expression in MI mice impacts on cardiac regeneration through different targets. In fact, while in cardiomyocytes CircHipk3 influences the acetylation and the half-life of N1ICD (Notch1 intracellular domain) , in endoThe ability to act as sponges is mostly executed by cytoplasmic RNAs. Nuclear and chromatin enriched lncRNAs act as epigenetic rheostats of myogenesis through a variety of mechanisms ,65. AmonTogether with clinical treatment, the possibility to identify a pathological condition quickly and precociously is extremely important to prevent the worst outcomes. For this reason, studies aimed at the identification of specific RNA biomarkers for different diseases have been steadily growing in the latest years. Both coding and ncRNAs have been found in nearly all peripheral bodily fluids ,193 and In muscular diseases, the measurement of circulating biomarkers can lead to extremely rapid, non-invasive, and easy-to-perform diagnostic paths, which overcome the need for surgical biopsies. An increasing number of studies have demonstrated the validity of using circulating miRNAs as biomarkers for muscular disorders, such as DMD and DM1. For instance, the expression of miR-1, miR-206, and miR-133 myo-miRs was found to be high in the serum of DMD patients and strongly correlated to disease severity ,195. HowIdentifying new biomarkers is especially important for cardiac diseases, in consideration of the often-abrupt way they occur. In 2016, Vausort and colleagues purified circMICRA from the plasma of more than 600 acute MI patients and demonstrated its possible use as a biomarker for left ventricular dysfunction . CircYODRNA shows incredible potential for both diagnosis and treatment of a vast number of diseases, including muscular and cardiovascular pathologies. The use of RNA-based drugs has several advantages, mainly (i) the quick and easy method of design, (ii) the high specificity in target recognition, mostly achieved by base-pairing, (iii) the possibility to target specific cell types or tissues, and (iv) their functional versatility. The usefulness and reliability of small ncRNAs-based drugs  have already been recognized by the competent FDA and EMA institutions, which have given approval for their use in SMA and DMD. Long RNAs also represent appealing candidates for the development of innovative approaches. Chemical modifications to improve mRNA stability and prevent its immunogenicity have also allowed researchers to pursue their use for the treatment of several conditions. As of now, clinical trials that are being conducted to test the effect of modRNAs expression are still in Phase I or II; however, they already show promising results for future use in human patients. Among the non-coding species, lncRNA-based drugs could be exploited to directly target the nucleus, thus influencing the early stages of gene expression, such as gene transcription, epigenetic regulation, and RNA processing. Despite several studies demonstrating the feasibility of using these molecules for therapeutic purposes in animal models, their application in human patients is still far from being tested. Nevertheless, it is undeniable their potential to revolutionize, in the future, the approaches to therapeutic treatments.


One of the most important goals of modern medicine is prevention against pandemic and civilization diseases. For such tasks, advanced IT infrastructures and intelligent AI systems are used, which allow supporting patients’ diagnosis and treatment. In our research, we also try to define efficient tools for coronavirus classification, especially using mathematical linguistic methods. This paper presents the ways of application of linguistics techniques in supporting effective management of medical data obtained during coronavirus treatments, and possibilities of application of such methods in classification of different variants of the coronaviruses detected for particular patients. Currently, several types of coronavirus are distinguished, which are characterized by differences in their RNA structure, which in turn causes an increase in the rate of mutation and infection with these viruses. One of the main challenges of modern medicine is the fight against pandemic and civilization diseases. In such activities, computer methods and systems play a huge role in supporting all diagnostic processes, and even managing hospital infrastructure or staff . Many arThis paper will present new possibilities of using mathematical linguistics techniques in supporting such medical processes and will indicate new areas of healthcare, in which such techniques will play an important role . In partLinguistic methods focus on analyzing various types of formal grammars and refer directly to cognitive informatics. Mathematical linguistic formalisms were first developed by N. Chomsky for modeling natural languages . He propNow we can observe the constantly growing opportunities for application of formal grammars especially in creation of cognitive information systems, image understanding methods and linguistic cryptographic procedures. Such formalisms are also applicable in creations of modern algorithms for information sharing , 8.Below is presented basic definition of formal grammars, which can be used in the creation of linguistic threshold schemes .N—set of non-terminal symbols; T—set of terminal symbols; PS—set of rewriting rules (productions) in the form: STS—a start symbol of the grammar, and A formal grammar (a grammar) is a quadruple: This definition is a general definition of all sequential grammars, which after introducing additional constraints for rewriting rules can define particular grammar classes introduced by N. Chomsky.The purposes of this section are to define a new extension of secret sharing techniques, called linguistic threshold schemes, where security will be based on the application of mathematical linguistic formalisms.Computer techniques used for secret information division are a new field of IT application. They allow to split strategic or medical data in such manner that it can be distributed among certain group of authorized persons or users, who after collecting them can reveal the original message Fig. .Fig. 1AnIn Fig. There are two main groups of secret division algorithms, i.e., secret splitting and secret sharing. Both types allow to generate any number of secret parts of the information, but for its reconstruction secret splitting procedures require the whole number of parts, but in secret sharing it is necessary to collect the smaller number of parts for secret reconstruction .More universal are secret sharing methods, which allow to divide data for any number of parts depending only on threshold values.Application of linguistic formalisms in information division procedures extends the functionality of traditional information sharing schemes by generating an additional secret part having linguistic form Fig. . Such adBelow is presented a generalized grammar formula for linguistic threshold procedure, which converts secret parts into independent bit blocks, which have the form of sequences of grammar production numbers. Such grammar representation can be defined in the following manner.{N, T, PS, STS}, where N = —grammar non-terminal symbols; T = —grammar terminal symbols; ε—an empty grammar symbol; STS—grammar start symbol; PS—grammar rules defined in the following manner:DataInfo_prart Info_prartInfo_prartBit_sequence Bit_sequence | εBit_sequence  | 2bit | 3bit …| nbit | εThis grammar is a context-free sequential grammar for which efficient parsing algorithms exist, with polynomial computational complexity. Grammar defined in presented way enables the data to be divided in several different ways depending on the number of trusted users or requested security levels . ExamplePresented linguistic solutions for data division extend classical security protocols and have important characteristic features. It allows to generate secret parts, available only for members of the authorized group. It also enhances traditional threshold procedures by adding a linguistic stage, at which binary representations of secret are coded into sequences representing the grammar rules. This causes that security of these techniques is independent of the length of encoded blocks, and complexity of the whole schemes remains polynomial.Mathematical linguistics methods can also be used in the medical applications against the coronavirus pandemic states. Currently, many artificial intelligence methods are used in the detection and treatment of this viral disease , 12. HowThe first coronaviruses were identified in the 1960s. There are four types of viruses, and they can be transmitted by humans, mammals and birds. Coronaviruses are viruses that contain only one strand of RNA nucleic acids in their structure. Despite the long-known structure of the coronavirus, 2019 saw a global epidemic caused by mutations of this virus. These mutations are still being discovered and differ from each other in their structural features, i.e., characteristic nucleic acid sequences in RNA strands composed of nearly 30,000 nucleotides. Their diagnosis is possible thanks to tests that recognize such structures.Currently, several types of coronaviruses are defined that affect the course of the disease in a patient and the speed at which the infection spreads.British variant , which was detected in September 2020 in southern England. This variant is an accumulation of 17 previous mutations and is characterized by the fact that the infection lasts longer, i.e., about 13.3 days than in the case of the earlier variant, i.e., 8.2 days.South African variant  that appeared in South Africa in January 2021 and is now detected in many other countries.Brazilian  variant that was identified in mid-2020.Three of them are currently being given special attention:Due to the differences in RNA strands, their classification is also possible using linguistic methods that allow to introduce the description of unique RNA structures using formal grammar. For this purpose, the following grammar can be defined to distinguish mentioned types of coronavirus based on the analysis of their RNA strands or its parts.{N, T, PS, STS}, where N = —grammar non-terminal symbols; T = —grammar terminal symbols; ε—an empty grammar symbol; STS—grammar start symbol; PS—grammar rules defined in the following manner:RNA| African | BrazilianCOV19-Type British, African, Brazilian | A | C | G | U | εN-Bases The proper analysis toward COVID-19 type identification can be done with application of syntax analyzer, which can parse the linguistic representation of the whole nucleic acid sequence or only its parts responsible for type of viruses. Example of such analysis is presented in Fig. During the analysis, the syntax analyzer tries to find special RNA sequences characteristic for particular coronaviruses variants. Special lexical analyzer called tokenizer allows to extract such unique RNA sequences and forward as input values for syntactic parser. Performing syntactic analysis parser can perform recognition of coronavirus variants, and place recognized case in one of predefined variants classes, i.e., British, South African, and Brazilian. In conducted experiments, it was possible to consider several cases representing different variants, which allows to define previously mentioned formal grammars and implement parser for syntactic analysis.Because proposed grammar belongs to context-free class of formal grammars, we can implement for syntactic analysis parsers, which can perform top-down or bottom-up syntactic classification. The most common solutions in real application of syntax analyzers are implementation of reduction analysis  in which input pattern should be reduced by parser to the grammar start symbol.Presented grammar can be easily extended toward description and recognition of others sequences defining future COVID-19 variants, by extension of grammar rules, and adding non-terminal symbols, which can be introduced especially for newly defined viruses.This paper describes the possibilities of using linguistic pattern recognition methods in medical applications. Linguistic methods are based on formal grammars and allow to describe complex medical structures and can also can be applied for security purposes and guarantee the security of medical data. Therefore, the paper describes two basic areas of using such techniques. The first one is application as intelligent methods for division of medical data, i.e., linguistic threshold schemes. The second important area of application is the analysis of disease states related to the detection of various mutations of the COVID-19 coronavirus. The paper defines a special context-free grammar that allows the classification of different variants of the coronaviruses, based on the analysis of selected fragments of the RNA strands. Such an analysis can support diagnostic processes related to the detection of disease states caused by the coronavirus, as well as support therapies thanks to information about the type of the detected disease variant .Linguistic methods ca be also applied in many other areas connected with medicine and healthcare. One of the most important is classification of morphological changes of internal organs caused by disease processes. In such cases, linguistic methods allow to support diagnostic procedures by classification visible pathologies. In future, such techniques can be also implemented in intelligent cognitive systems, which imitate the natural processes of human thinking.


Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences in the absence of observational data. A discrete choice experiment (DCE) is a commonly used stated preference method—a quantitative methodology that argues that individuals make trade-offs when engaging in a decision by choosing an alternative of a product or a service that offers the greatest utility, or benefit. This methodology is widely used in health economics in situations in which revealed preferences are difficult to collect but is much less used in the field of digital health. This paper outlines the stages involved in developing a DCE. As a case study, it uses the application of a DCE to reveal preferences in targeting the uptake of smoking cessation apps. It describes the establishment of attributes, the construction of choice tasks of 2 or more alternatives, and the development of the experimental design. This tutorial offers a guide for researchers with no prior knowledge of this research technique. Understanding how the public values different aspects of digital health tools, such as smoking cessation or physical activity apps, can help providers of the tools to identify functionality that is important to users, which may improve uptake  . This isStated preference methods are survey-based methods aiming to elicit individuals’ preferences toward a specific behavior, particularly those that are not well understood. The most widely used type of stated preference method is the discrete choice experiment (DCE) . Accordichoice tasks. Attempting to mimic real-world decision making, in each choice task, participants then have to choose a product or a service from two or more options, known as alternatives [attributes, with at least two types, known as attribute levels [Discrete choice differentiates from other stated preference methods in the way that responses are elicited . The DCErnatives . Each ale levels . Particie levels ,14.The outputs from statistical models developed using DCE data can be beneficial for estimating uptake of new products or services, including digital health tools, where observational data are not available or are difficult to obtain otherwise ,16. LackIn our research, we wanted to understand how to present health apps on curated health app portals to increase their uptake. This paper describes the development of a DCE in digital health that aims to elicit potential user preferences on smoking cessation app uptake. It explains how the attributes and their levels are selected and describes the construction of choice tasks and the experimental design. The study protocol of the research this paper is based on is registered on the Open Science Framework .The development of a DCE should follow published recommendations, including the checklist for good research practices , guides An important step in designing a DCE is the identification of the relevant attributes for the subject matter. Attributes in a DCE can be quantitative, such as cost, or qualitative, such as the design of a product . The ideOur DCE was based on a comprehensive systematic review investigating factors influencing the uptake and engagement with health and well-being smartphone apps  and a quAn important step in designing a DCE is in ensuring the content validity of the instrument: the identification of relevant attributes for the subject matter. Following administration of the survey, methods are available for the measurement and assessment of the content validity of the instrument, although their use is not widely reported .The next step is to establish attribute levels. The level of an attribute must also be of a range that ensures a trade-off between attributes. A trade-off is defined as an exchange in which a participant gives up some amount of one attribute to gain more of another. It has been suggested that increasing the number of levels for an attribute increases the relative importance of that attribute  and thatIf a range is not suitable, participants might consider the differences between levels unimportant . For exaOnce the attributes and their levels are identified, the decision to develop full- or partial-profile tasks with or without an opt-out option needs to be made. A full profile refers to the display of all five attributes in both alternatives in each choice set. A partial profile DCE will not present certain attributes for certain alternatives. For example, if a DCE is used to investigate the trade-off between a higher number of attributes , it could be beneficial to limit the number of attributes shown at one time  to limit participant cognitive load. Five attributes are generally considered low enough to complete a full-profile choice task, which consequently maximizes the information about trade-offs . Hence, forced-choice scenario can be included, where participants who chose the opt-out option are prompted to make a choice regardless. An example of a scenario with an opt-out option is shown in A neutral option (“Neither of these 2”), known as an opt-out alternative, was included, in addition to selecting alternative apps. The opt-out option has the potential to make the choices more realistic  by simulAn experimental design is a systematic method of generating choice sets that are presented to respondents. This enables the specification of the choice sets that respondents see, with the objective of obtaining a high-quality data set . When crThe first step in the generation of an experimental design is to specify the analytical model to estimate the parameters of the DCE. This step is an important component of choosing the type of choice matrix design, described later in this paper. The approach selected here needs to be accounted for when generating the structure of the experimental design.choice variable), which indicates the choice made by participants [A discrete choice model describes the probability that an individual will choose a specific alternative. This probability is expressed as a function of measured attribute levels specific to the alternative and of characteristics of the individual making the choice. This probability is represented by the dependent variable  model has been previously described as the “workhorse” of DCE estimation ,39, and j [Based on the model specified in our DCE, the underlying utility function for alternative j  is shownj = (βcost × Xjcost) + (βdeveloper × Xjdeveloper) + (βratings × Xj ratings) + (βdescription × Xjdescription) + (βimages × Xnjimages) + εUNote:1) U is the overall utility derived from alternative j.j estimated in the analysis and represents the part-worth utility attached to each attribute level.2) β is the coefficient attached to X3) ε is the random error of the model—in other words, the unmeasured factors influencing the variation of preferences.The next step in model specification is deciding whether main effects or interaction effects will be investigated. The main effects, the most commonly used, investigate the effect of each attribute level on the choice variable. The effect on the choice variable gained by combining two or more attribute levels  refers to an interaction effect . In our In a labeled experiment, the alternatives are specific and different  and alternative specific attributes could be used . This is in contrast to an unlabeled experimental design, where the alternatives are unspecified  and also must have the same attributes. Given that a DCE model estimates parameters for each of the alternatives being considered, these alternative specific parameters must be included in the structure of the experimental design (described in the next section) in a labeled experiment; in an unlabeled experiment, because alternative specific parameters are arbitrary, they are excluded ,42,43. IOnce the model is specified, the structure of the experimental design can be generated. For this stage, hypothetical alternatives are generated and combined to form choice tasks, based on the chosen attributes and their levels. Several different software packages may be used to generate the experimental design of a DCE, such as Ngene, SAS, SPEED, SPSS, and Sawtooth. For our DCE, Ngene software was used .The next step in the generation of an experimental design is to decide on the choice task and blocking. To minimize respondent and cognitive burden, and the risk of participants losing interest during the DCE task, consideration must be paid to the target population, the number of tasks, and their complexity . The higWe developed 48 choice tasks and blocked them into 4 survey versions (12 choice tasks for each). Each block represented a separate survey, and participants were randomly assigned to one of the four survey versions. Blocking is a technique widely used in DCEs to reduce cognitive burden by partitioning large experimental designs into subsets of equal size, thereby reducing the number of choice tasks that any one respondent is required to complete . Blocks It is noteworthy that to undertake the sample size calculation, it is crucial to know the number of alternatives per choice set, the largest number of levels of any attribute (for DCEs looking at main effects only) or the largest level of any two attributes (for a DCE looking at interaction effects), and the number of blocks . TherefoAL, where L represents the number of levels and A the number of attributes [AL = 42 × 33) to a fractional sample of 96 alternatives, arranged in 48 choice pairs.Depending on the number of attributes and their levels, a full- or fractional-factorial design can be applied. A full-factorial design would include all possible combinations of the attributes’ levels and allow the estimation of all main effects and interaction effects independent of one another . Howevertributes . If the tributes , represeSystematic approaches for generation of fractional-factorial designs may be further categorized into orthogonal design and efficient design. An orthogonal design is a column-based design based on orthogonal arrays that present properties of orthogonality  and level balance  and does not introduce correlation between the attributes . An orth. Experimental designs generated using this approach are known as D-efficient designs. A D-efficient experimental design is also recommended to maximize statistical efficiency and minimize the variability of parameter estimates [For studies with five or more attributes with two or more levels, an orthogonal design may not be practical. There has therefore been a recent change in thinking toward a nonorthogonal and statistically more efficient design . When pestimates .An efficient design requires that known prior information about the parameters (known as priors) be made available to the algorithm and also requires the analyst to specify the analytical model specification, as described previously. Depending on what information is available, one of three types of D-efficient design can be generated :z-efficientD design (z stands for zero priors): If no prior information about the magnitude or directions of the parameters is available. Dz-efficient design is an orthogonal design. This design assumes the parameters are zero.p-efficientD design (p stands for priors): This assumes a fixed, certain value and direction for the parameters.b-efficientD design (b stands for Bayesian): A Bayesian approach is whereby the parameter is not known with certainty but may be described by its probability distribution.z-efficient or Dp-efficient design is sensible. In our DCE, we chose to apply a Dp-efficient design, as the direction of priors of the app was known from the previously conducted survey, to narrow down the attribute levels and to provide prior estimates of the parameters for the attribute levels. For example, we knew that a trusted organization will likely positively influence uptake and cost estimated negatively so. The direction of priors was assumed to be a small near-zero negative or a positive value for the design.The best practice is to pilot the DCE. For the pilot phase, there is limited information available and using the Db-efficient design for the final DCE. It is noteworthy that when the parameter priors are different from zero, the efficient design generated produces smaller prediction errors than orthogonal designs [p-efficient design will outperform a Dz-efficient design [b-efficient design will outperform a Dp-efficient design. Therefore, it may be advisable to start piloting with a Dp-efficient design and to generate a Db-efficient design for the final DCE. The DCE literature provides a detailed and more comprehensive description of orthogonal and efficient designs [The pilot phase provided the estimation that we used to generate a D designs ,49,50. Ht design . Further designs  and the  designs .The attribute-level balance aims to ensure all attribute levels ideally appear an equal number of times in the experimental design. The allocation of the attribute levels within the experimental design can affect statistical power; if a certain level is underrepresented in the choice sets generated, then the coefficient for that level cannot be easily estimated. How attributes levels are distributed is therefore an important consideration when designing the choice sets. Dominant alternatives, where all attribute levels of one alternative are more desirable than all attribute levels in the others, do not provide information about how trade-offs are made, as individuals usually would select the dominant alternatives. Therefore, avoiding dominant alternatives in the experimental design is important and can be achieved by consulting the software manual to ensure the correct algorithm is used. The syntax used in Ngene to generate choice sets of the pilot phase and more information about the algorithm used can be accessed on the Open Science Framework .In addition to providing estimations for the choice matrix design described above, piloting offers an opportunity to ensure that the information is presented clearly and that the choices are realistic and meaningful. It also provides insight into how cognitively demanding it is for respondents to complete. This can be achieved by gathering feedback on the survey completion process. The findings of the pilot may suggest that the DCE needs to be amended, such as reducing the number of choice sets or the number of attributes, so that the responses are a better reflection of the participants’ preferences and improve the precision in the parameter estimates .monthly price of the app, (2) the ratings of the app, (3) who developed the app, (4) the description, and (5) images shown to the one listed in There is no formal guidance on how large the pilot sample should be, and this is largely guided by the budget and complexity of the experimental design. Accuracy of the priors will improve with increasing sample size, but as few as 30 responses may be sufficient to generate useable data . In our the app,  the ratiIn our research, the data from the pilot phase were analyzed using the freely available Apollo package in R software . The coestability validity test, a choice task would be repeated later in the sequence to investigate the consistency of the participants’ decision, whether they would choose the same alternative [within-set dominated pairs type of internal validity, in which one alternative is a dominant alternative in which all attributes are the most desirable ones. The choice sets designed to measure internal validity are excluded from the analysis. There are several internal validity tests that are built into software packages such as MATLAB [Assessing the internal validity of a DCE can help with understanding the consistency and trade-off assumptions made by participants . There aernative . Anothers MATLAB , althougAlthough internal validity checks provide some measure of data quality, it should be noted that answering a repeat choice inconsistently is not a violation of random utility theory . FurtherAll additional study materials used in our example, including the full data set and the results of the DCE, can be accessed on Open Science Framework .This paper describes the development of a DCE, following the stages required to establish attributes and their levels, construct choice tasks, define the utility model, decide on labeled and unlabeled choices to apply, decide on the number of choice tasks that need to be generated, and make decisions on the structure of the experimental design, how to achieve attribute-level balance, how to assess the internal model validity, and how to pilot-test. In doing so, the intention is to advance methodological awareness of the application of stated preference methods in the field of digital health, as well as to provide researchers with an overview of their application using a case study of a DCE of smoking cessation app uptake.Although DCEs are widely used to understand patient and provider choices in health care ,10,15,55DCEs bring several benefits to help overcome the issue of measuring uptake in digital health or in other areas where the measurement of the predictors of uptake in a good or service is required. For example, as illustrated by the case study here, they enable the researcher to gain measurable insights into situations in which quantitative measures are hard to otherwise obtain, such as the factors impacting the uptake of health apps on curated health app portals. A DCE also helps to quantify preferences to support more complex decisions . An examDespite their benefits, the application of DCEs presents several challenges. As with all expressed preference methodologies, the hypothetical nature of the DCE choice set raises concerns about external validity and the degree to which real-world decisions might equate to those made by study participants under experimental conditions, a phenomenon known as the intention-behavior gap . As suchA final significant concern associated with the use of a DCE is that any single choice set is unlikely to be able to present the user with all relevant attributes, regardless of how well it has been developed . ChoosinIn summary, DCEs have significant potential in digital health research and can serve as an important decision-making tool in a field where observational data are lacking. We hope that the content of this paper provides a useful introduction and guide to those interested in developing such experiments in digital health.


Senataxin (SETX) is a DNA/RNA helicase critical for neuron survival. SETX mutations underlie two inherited neurodegenerative diseases: Ataxia with Oculomotor Apraxia type 2 (AOA2) and Amyotrophic Lateral Sclerosis type 4 (ALS4).This review examines SETX key cellular processes and we hypothesize that SETX requires SUMO posttranslational modification to function properly.SETX is localized to distinct foci during S‐phase of the cell cycle, and these foci represent sites of DNA polymerase/RNA polymerase II (RNAP) collision, as they co‐localize with DNA damage markers 53BP1 and H2AX. At such sites, SETX directs incomplete RNA transcripts to the nuclear exosome for degradation via interaction with exosome component 9 (Exosc9), a key component of the nuclear exosome. These processes require SETX SUMOylation. SETX was also recently localized within stress granules (SGs), and found to regulate SG disassembly, a process that similarly requires SUMOylation.SETX undergoes SUMO modification to function at S‐phase foci in cycling cells to facilitate RNA degradation. SETX may regulate similar processes in non‐dividing neurons at sites of RNAP II bidirectional self‐collision. Finally, SUMOylation of SETX appears to be required for SG disassembly. This SETX function may be crucial for neuron survival, as altered SG dynamics are linked to ALS disease pathogenesis. In addition, AOA2 point mutations have been shown to block SETX SUMOylation. Such mutations induce an ataxia phenotype indistinguishable from those with SETX null mutation, underscoring the importance of this modification. This mini‐review summarizes the most pertinent aspects of SETX biology that have recently been found to coalesce around its SUMO posttranslational modification. Senataxin (SETX) is a helicase that functions to maintain genomic stability, and SETX class‐specific mutations cause either a severe recessive ataxia (AOA2) or a dominant motor neuron disease (ALS4). RNA processing and binding proteins such as SETX remain of major focus in neurologic disease. Yet, the precise mechanism leading to pathology is elusive. At this point, SETX may utilize its conserved helicase function to unwind R‐loops, which form behind stalled RNAP II. Thereafter, SETX would direct incomplete RNA transcripts to the exosome for degradation. The exosome complex is the major eukaryotic 3′ → 5′ exonuclease, consisting of nine conserved core subunits that function in the accurate degradation of RNAs in the nucleus and cytoplasm  are found in the cytoplasm and consist of proteins and non‐translating mRNAs at high concentration. SGs may form during cell stress to temporarily pause translation initiation until conditions are more favorable. An unappreciated point is that SGs components may be cell type and stress‐specific (Marmor‐Kollet et al., The SG disassembly process was previously thought to rely on autophagy and ubiquitin pathways; however, new studies points to heat shock proteins and RNA helicases as key regulators. The DEPs were defined using FMR1‐APEX2 as bait after sodium arsenite washout for 30‐, 60‐, and 120‐min recovery. SETX was included as part of 22 DEPs tested for SG dynamics modification by siRNA knockdown and live‐imaging during disassembly. After 1 hr of recovery, concomitant with siRNA knockdown, 9 of the 22 DEPs resulted in a significant change in disassembly dynamics. SETX depletion resulted in SGs being ~40% larger versus siRNA control treatment (Marmor‐Kollet et al., 4.1SETX is now recognized as a major contributor to genome stability and plays an important role during S‐phase of cycling cells. Its SUMOylation is necessary for Exosc9 interaction, a core component of the nuclear exosome. These two events appear necessary for SETX’s function at S‐phase foci where it facilitates exosome‐mediated degradation of stalled RNA transcription at DNA polymerase/RNAP II collision sites. Also, SETX co‐localizes with the DNA damage markers 53BP1 and H2AX at these foci. Because SETX is maintained at very low cellular levels, transient over‐expression may trigger cell cycle check points. But neurons do not replicate, and both AOA2 and ALS4 result primarily in neuron degeneration. AOA2 loss‐of‐function mutations appear to block both SUMOylation and Exosc9 interaction. Hence, unless RNAPII self‐collision is of major concern for neuronal pathogenesis, other functions of SUMO‐SETX must contribute to neurodegeneration. The obvious candidate for this dysfunction would be a role for SETX in the regulation of SG dynamics, a core pathological hallmark in various forms of neurodegeneration. Based upon recent work, we now know that SETX, when found in SGs in close proximity to FMR1 and FXR1, plays an important role in SG disassembly, likely by its helicase domain‐dependent function of unwinding certain SG RNAs. Identifying and characterizing the SG RNA clients of SETX will yield crucial insights into the pathobiology of neurodegenerative diseases characterized by SG dysregulation.The authors declare no conflict of interest.All authors contributed equally to this work.No human subjects or animals were used in the production of this review.


RNA sequencing holds great promise to improve the diagnostic of hematological malignancies, because this technique enables to detect fusion transcripts, to look for somatic mutations in oncogenes, and to capture transcriptomic signatures of nosological entities. However, the analytical performances of targeted RNA sequencing have not been extensively described in diagnostic samples. Using a targeted panel of 1385 cancer-related genes in a series of 100 diagnosis samples and 8 controls, we detected all the already known fusion transcripts and also discovered unknown and/or unsuspected fusion transcripts in 12 samples. Regarding the analysis of transcriptomic profiles, we show that targeted RNA sequencing is performant to discriminate acute lymphoblastic leukemia entities driven by different oncogenic translocations. Additionally, we show that 86% of the mutations identified at the DNA level are also detectable at the messenger RNA (mRNA) level, except for nonsense mutations that are subjected to mRNA decay. We conclude that targeted RNA sequencing might improve the diagnosis of hematological malignancies. Standardization of the preanalytical steps and further refinements of the panel design and of the bioinformatical pipelines will be an important step towards its use in standard diagnostic procedures. A part of this information can be captured by morphology and immunophenotyping, but it relies more and more on the analysis of the genomic alterations of the neoplastic cells.2 Nowadays, conventional cytogenetics and targeted sequencing of relevant genes are still the standard procedures. However, technological outbreaks such as whole genome sequencing, assay for transposase-accessible chromatin-sequencing, or RNA sequencing (RNA-seq) might refine the diagnosis by unraveling genomic alterations outside coding regions,3 epigenetic signatures,5 and gene expression profiles, respectively.6In hematological malignancies as in cancer in general, the goal of the diagnosis procedures is not only to accurately classify the patient’s disease according to the consensual World Health Organization guidelines,7In this study, we have chosen to assess the diagnostic value of RNA-seq, because this technique allows to explore 3 levels of genetic information: gene sequence, gene fusions, and gene expression. Interestingly, each of these different levels of analysis brings independent information about the neoplastic cell, and accordingly, their integration should refine the precision of the diagnosis. For example, acute myeloid leukemia (AML) patients prognosis is evaluated by cytogenetics , further refined by the analysis of the mutational status of a few genes, and could maybe be improved by transcriptomic signatures such as the 17-gene leukemia stem cell score (LSC17) which is a proxy of the number of leukemic stem cells.8 Based on these considerations, we have decided to evaluate the performances of a targeted RNA-seq panel of 1385 genes involved in cancer biology. We present here the analytical performances of targeted RNA-seq to detect fusion transcripts, to identify transcriptional profiles associated with clinically relevant entities, and to detect the recurrent mutations with clinical significance in hematological malignancies.Different techniques of library preparation for RNA-seq have been described, enabling the analysis of all the RNA molecules of a sample, or using enrichment step to target genes of interest such as messenger RNA, or small RNA species. Of note, the choice of the library preparation should optimize the balance between the number of targets of interest and the required depth of sequencing, in order to remain economically affordable in a routine setting. To date, most of the genes involved in cancer have been already identified by large programs of whole exome sequencing.http://links.lww.com/HS/A125). The procedures followed were in accordance with the Helsinki Declaration, as revised in 2008.One hundred diagnosis samples from patients with the following hematological malignancies were included as follows: acute leukemia ; myeloproliferative neoplasms ; hypereosinophilic syndromes ; chronic myelomonocytic leukemia ; and myelodysplastic syndrome with multilineage dysplasia (n = 1). These samples were chosen to enrich the cohort in fusion transcript due to chromosomal translocations, based on the results of conventional cytogenetics, in order to test the performances of targeted RNA-seq to detect fusion transcripts. Moreover, we used 4 controls (C1 to C4) prepared by pooling blood samples from 5 healthy donors for each, and 4 bone marrow samples from healthy donors. The characteristics of the samples are provided in Supplemental Table 1 , the analysis of a panel of 105 genes was already performed for routine diagnostic procedures, as already described.http://links.lww.com/HS/A125). For ALL samples and 3 bone marrow samples from healthy donors, RNA was extracted with Trizol reagent . For AML samples, RNA was extracted with NucleoSpin RNA kit . For CML, HES, CMML, and myelodysplastic syndrome samples, RNA was extracted with MN or the Maxwell 16 LEV simplyRNA Blood Kit . For control samples C1 to C4, RNA was extracted after Ficoll enrichment with either Trizol or MN methods, in order to assess the effect of extraction protocol on transcriptomic analysis performances. RNA quality was assesses by reverse transcription quantitative polymerase chain reaction (RT-qPCR) of the ABL1 messenger RNA (mRNA), which was always above 32 000 copies.Three different protocols of RNA extraction were used . Libraries from 16 samples were multiplexed and sequenced on a Nextseq 500 device (Illumina) with a 2 × 81 paired-end run on a mid-output flowcell according to the manufacturer’s instructions .Library preparation was performed from 20 ng of RNA using TruSight RNA Pan-Cancer Panel  targeting 1385 genes involved in cancer biology  pipelines. Putative fusions were validated by reverse transcription and polymerase chain reaction . Gene expression analysis , principal component analysis, k-means clustering, 2-tailed t-test, and Heat Map generation followed by hierarchical clustering were performed using Omics Explorer software . For gene mutation analysis on RNA-seq data, we looked at all the mutations found at the DNA level by combining the same homemade workflow as for DNA and visual inspection of the binary alignment map files in case of unfound mutation. In brief, we first gather the variant alleles called with Freebayes and Varscan2.13 Among this raw set, we kept alleles whose read frequency was either above 20% or for those below, if their frequency was more than 5-fold the median of the frequencies of all the samples from the same run. A second filtering step was applied to get rid of variants whose occurrence was above 1% in Genome Aggregation Database mixed populations.14After demultiplexing, adapter sequences were trimmed with Cutadapt and reads were mapped to the human genome (Genome Reference Consortium Human Build 37). The percent of reads aligned to ribosomal RNA determined with the RSeqC software was around 0.25% of the total reads before filtering on the bed. The detection of gene fusions was performed first with the commonly used STAR-Fusion pipeline  and STAR-2pass,BCR-ABL1 canonical and rare transcripts , as well as all the PML-RARA transcripts  and MLL (KMT2A) fusions (n = 19). Of note, 2 samples with FIP1L1-PDGFRA fusion transcripts and 1 with KMT2A duplication were missed when analyzed with the STAR-fusion pipeline but recovered with nf-core and Arriba bioinformatics pipelines.Fusion transcript positivity threshold was determined by the detection of at least 1 junction read and 1 spanning read between 2 different genes. All putative new fusion transcripts have been validated by PCR. All of the 57 rearrangements identified by cytogenetics or molecular biology were identified by targeted RNA-seq  . These fusion transcripts were in frame, probably leading to the expression of an abnormal fusion protein . We hypothesize that this fusion might represent a new mechanism of IKZF1 gene inactivation recurrently identified in Phi+-ALL.17Finally, we detected a fusion transcript in 5 samples without detectable translocation on conventional cytogenetics: 19 several fusions with open reading frame were also detected in control and patients’ samples. Some of them, such as TFG-GPR128, POLE-FUS, or OAZ1-DOT1, were expressed at high level and have been also validated by RT-PCR and sequencing.As it was previously described in noncancer tissues and cells,PML-RARA and BCR-ABL fusion transcripts, respectively. The detection threshold was below 6% for both fusion transcripts.Finally, in order to assess the sensitivity threshold of RNA-seq to detect fusion transcripts, we analyzed serial dilutions of 2 patients with 20 or Nanostring technology.21 Here, we assessed the feasibility of transcriptome analysis based on RNA-seq of a panel of 1385 genes.The analysis of transcriptome in the routine diagnosis procedure is technically challenging, because of interferences linked to the source of the samples analyzed , the preparation of the samples (isolation of the mononucleated cells with Ficoll or not), the RNA extraction method, and the batch effect of library preparation and sequencing. Instead were developed signatures based on a limited number of transcripts analyzed with technical platforms such as reverse transcription multiplex ligation-dependent probe amplificationFirst, we evaluated the magnitude of systematic biases in transcriptomic analysis introduced by the protocol of RNA extraction and the sequencing process. The same blood samples from healthy donors were extracted after Ficoll enrichment either with Trizol (n = 4) or with Macherey Nagel kits (n = 4). A supervised analysis based on extraction method identified 20 differentially expressed genes  Figure A. On theKMT2A-AFF1 (n = 7), ALL with TCF3-PBX1 (n = 4), and normal bone marrow controls (n = 3). Of note, these RNA were extracted at the time of diagnosis, over a period of 19 years, introducing a potential bias due to differences in RNA conservation. Clustering of these samples in 3 categories (by the k-means method) distinguishes the 3 groups of samples according to the diagnosis, with no misclassification , which suggests that targeted RNA-seq might also be able to evaluate prognostic signatures based on gene expression.Then, we analyzed bone marrow samples extracted with the same method (Trizol) from 3 groups with at least 3 patients: ALL with 9 Among the 95 genes captured in both panels, 122 mutations were detected at the DNA level in 39 different genes . As shown in Figure P < 0.0001). Two other missed mutations  were in low coverage areas (<30×). Of note, when analyzing only the genes contained in both panels (DNA and RNA), we did not find any additional mutation on RNA-seq.Forty-five patients analyzed with targeted RNA-seq were also analyzed at the DNA level for a panel of 105 genes recurrently mutated in hematological malignancies.This work reports the analytical performances of RNA-seq of a panel of 1385 genes to improve the diagnosis of hematological malignancies, based on a series of 100 diagnosis samples and 8 controls.FIP1L1-PDGFRA fusions, which often require nested PCR to be identified because of low levels of expression.24 Of note, 2 fusion transcripts were found only by alternative bioinformatics pipelines, which highlights the major impact of the bioinformatics analysis on the performances of targeted RNA-seq. This might explain suboptimal detection of KMT2A and PDGFRA fusions in previous studies.25 Interestingly, RNA-seq allowed the identification of 12 fusion transcripts which were not suspected with usual analysis recommended in the diagnosis of hematological malignancies.26 As more and more case reports describe successful opportunistic use of targeted therapies in patients with fusion transcripts,29 the identification of unexpected fusion transcripts might offer interesting targets in relapsed/refractory patients, as was the case for the patient treated with imatinib for the EEA1-PDGFRB fusion-driven HES. Moreover, as translocations are most of the time drivers events which are stable during disease evolution,30 they can be used to track minimal residual disease with high-sensitivity RT-qPCR and adapt therapeutic intensity accordingly. However, it remains to be determined if the prognostic impact of minimal residual disease described for core binding factor AML,31 CML, or APL is also true for the less recurrent fusion transcripts. In 11 patients with a chromosomal translocation, we did not detect a fusion transcript. We can hypothesize that these translocations contribute to oncogenesis without a fusion transcript, as is the case for the translocations involving the immunoglobulin locus in B-cell lymphomas, for example. Alternatively, these translocations might result in fusion transcripts with low expression in the bulk of the disease, being under the threshold of detection with targeted RNA-seq, or might involve 2 genes that are not included in the panel used in this study.Overall, this technique detect 100% of fusion transcripts of these samples, including 7 or the more recently described six-gene leukemia stem cell score of prognostic value in pediatric AML32 signatures in AML, but it will need an optimization of the design of the panel to capture all relevant mRNAs.Regarding the analysis of the transcriptomic profile, we show that targeted transcriptome analysis can be used for nosological purposes if the preanalytical workflow is the same for the samples analyzed. Larger series are needed to precise the performances of targeted RNA-seq to resolve this task. Another interesting question would be to assess the performances of targeted RNA-seq to measure clinically relevant signatures such as the LSC1733 and this will remain a biological limitation of RNA-seq for mutation assessment. Finally, given the growing importance of clonal architecture analysis based on variant allele frequency (VAF) deconvolution,34 we should keep in mind that the VAF measured at the mRNA level might not be good surrogate markers of clonal architecture, because it takes into account allelic expression bias.The third clinical interest of targeted RNA-seq assessed here is the detection of acquired somatic mutations. Even if most of the mutations identified at the DNA level were found in RNA-seq data, the nonsense mutations were rarely detected. This is probably at least in part due to the phenomenon of mRNA decay, which degrades preferentially truncated mRNA,Altogether, RNA-seq of a targeted panel of genes might improve the diagnosis of hematological malignancies and highlight potential therapeutic targets. Some of the limitations of this technique might be resolved with the optimization of the panel design and the bioinformatics pipelines for hematological malignancies. However, because some limitations have a biological explanation, such as poor performances to detect nonsense mutations, RNA-seq should not replace the analysis of genomic DNA but could be rather a good orthogonal method for verifying genomic mutations and a powerful complement to increase the molecular characterization of hematologic malignancies at diagnosis.The authors have no conflicts of interest to declare.