System-wide approaches have unveiled an unexpected breadth of the RNA-bound proteomes of cultured cells. Corresponding information regarding RNA-binding proteins (RBPs) of mammalian organs is still missing, largely due to technical challenges. Here, we describe ex vivo enhanced RNA interactome capture (eRIC) to characterize the RNA-bound proteomes of three different mouse organs. The resulting organ atlases encompass more than 1300 RBPs active in brain, kidney or liver. Nearly a quarter (291) of these had formerly not been identified in cultured cells, with more than 100 being metabolic enzymes. Remarkably, RBP activity differs between organs independent of RBP abundance, suggesting organ-specific levels of control. Similarly, we identify systematic differences in RNA binding between animal organs and cultured cells. The pervasive RNA binding of enzymes of intermediary metabolism in organs points to tightly knit connections between gene expression and metabolism, and displays a particular enrichment for enzymes that use nucleotide cofactors. We describe a generically applicable refinement of the eRIC technology and provide an instructive resource of RBPs active in intact mammalian organs, including the brain. Characterization of RNA-binding proteins (RBPs) in tissues has been hampered by technical constraints. Here, the authors describe ex vivo eRIC, a method for global profiling of RBPs active in mammalian organs, and report comprehensive RBP atlases from mouse brain, kidney and liver. Illustrating the importance of RBPs for cellular homeostasis, numerous diseases, including neurological disorders and cancer, have been linked to RBP malfunction2.RNA-binding proteins (RBPs) constitute a versatile ensemble of proteins that play key roles in fundamental biological processes. They orchestrate the life cycle of messenger RNAs, from their synthesis in the nucleus to their translation and decay in the cytoplasm, and are thus essential for shaping cellular proteomes. They are also essential for the processing, function, and decay of all other classes of RNA. There is growing evidence that protein activity can conversely be regulated by RNA8. These methods start with the crosslinking of RBPs to RNA in cellulo to stabilize RNA–protein interactions that occur within the native cellular environment. Irradiation of cells with ultraviolet (UV) light has been widely used to crosslink single-stranded nucleic acids and proteins with high specificity, because UV light is inefficient in protein–protein crosslinking; hence it selects for direct RNA–protein interactions. The crosslinked RNA–protein complexes are subsequently isolated under highly stringent, denaturing conditions. In RNA interactome capture (RIC), oligo(dT)-coated magnetic beads are used to select polyadenylated transcripts together with their crosslinked RBP partners4. In an enhanced version called eRIC, the capture probe is modified with locked nucleic acids (LNA), improving specificity and the signal-to-noise ratio9. Methods to isolate the whole RNA-bound proteome regardless of RNA biotype have also been developed11. After the different capture strategies, the RNA-bound polypeptides are retrieved and analyzed by mass spectrometry.Unbiased, system-wide approaches have paved the way for the determination of the composition, subcellular distribution, and dynamics of RNA-bound proteomesDrosophila embryos13, Caenorhabditis elegans14, zebrafish15, and plants18, RBP profiling methods have principally been used to study the RNA-bound proteomes of unicellular organisms or cultured cells. The lack of knowledge regarding mammalian organs and tissues is owed to technical limitations. The low penetration depth of UV light into biological specimen19 limits the applicability of UV crosslinking in large multi-cellular organisms. A first approach to characterize the RNA-bound proteome of a mammalian organ was recently reported, which identified a relatively limited set of 119 RBPs active in mouse liver20. This study used formaldehyde crosslinking and hence required measures to reduce contamination from protein–protein crosslinking. A method for the sensitive and specific detection of the RNA-bound proteomes of mammalian organs is thus still missing and the subject of this report.With only a few exceptions from non-mammalian model organisms such as Mus musculus. We also adapted the eRIC methodology to isolate and characterize the non-poly(A) RNA-bound proteomes of these organs. Our work represents an in-depth profiling of RBPs from mammalian organs, refining the scope of RBPs, and revealing remarkable differences in RBP activity between organs as well as between organs and cultured cells.We adapted the stringent eRIC protocol by using cryosectioning of ex vivo specimens to comprehensively characterize the poly(A) RNA-bound proteomes of the brain, liver, and kidneys from the house mouse 19 restricts UV crosslinking of RNA–protein contacts to the surface of intact tissues. To overcome this limitation and to determine the RNA-bound proteomes of murine liver, brain, and kidneys, we flash froze intact dissected organs in liquid nitrogen. The frozen organs were sectioned in a cryostat and the sections were transferred onto slides placed on a metal surface in contact with dry ice  analyses performed with equal amounts of RNA from eRIC eluates and inputs, showing a 50- to 100-fold enrichment of two housekeeping mRNAs  over 18S rRNA  RNAs that otherwise prevail, such as rRNA and tRNA -labeled and analyzed in a single liquid chromatography/tandem mass spectrometry (LC-MS/MS) run Fig. . Total pHigh-confidence eRIC hits were defined as proteins significantly enriched in eRIC eluates from UV-irradiated compared to no-UV samples (fold-change (FC) >2, false discovery rate (FDR) <0.05). 622, 1345, and 1238 hits were identified, respectively, from the brain, kidney, and liver Fig. . Of note22  Fig. , as expe22 Fig. . These eTaken together, ex vivo eRIC captures poly(A) RNA and crosslinked RBPs with high specificity and reproducibility from intact mouse brain, liver, and kidneys, enabling the robust, comprehensive determination of whole organ poly(A) RNA-bound proteomes.We next compared the poly(A) RNA-bound proteomes of the brain, kidney, and liver to each other. In total, 589 active RBPs are shared between the three organs, and an additional 648 active RBPs were identified both in the kidney and liver Fig. . Seventy23. To identify interacting poly(A) transcripts irrespective of their sequence, we generated biotinylated oligo(dT) probes anchored to the 3′UTR/poly(A) boundaries via two randomized nucleotides at its 3′end  RNA-binding activity of RBPs in situ. We used a proximity ligation assay (PLA) adapted to detect protein–RNA interactions using a specific antibody against the RBP of interest and a biotinylated DNA probe complementary to the interacting RNAWe strategically selected four RBPs that displayed higher RNA-binding activity in kidneys, although their expression levels, judged by both proteomics and immunofluorescence, are similar or even lower in kidneys than in the brain and liver  RNA–protein interactions are globally controlled in an organ-specific manner.25,), we tested whether the proportion of RBPs bound to poly(A) transcripts might be affected by the stoichiometry of the two RNA biotypes. RBPs bound to non-poly(A) RNA were extracted from eRIC supernatants (depleted of poly(A) RNA) with guanidinium thiocyanate-phenol-chloroform, purified over a silica matrix, and identified by MS, Fig. We wondered whether the overall changes in RBP activity across organs could be explained by differences in poly(A) RNA content. Poly(A) RNA levels in the brain are indeed lower than in the kidney and liver Fig.  panel. NTwo irradiated and one non-irradiated sample were employed per organ. Non-poly(A) RBPs were defined as the proteins significantly enriched in UV-treated over non-crosslinked controls . 355 non-poly(A) RBPs were identified Fig. . These pWe then combined the non-poly(A)RIC with our previous eRIC data to determine the relative binding of proteins to poly(A) and non-poly(A) RNA Fig. . 222 RBPTogether, our results suggest that the overall poly(A) RNA content, as well as the relative levels of poly(A) versus non-poly(A) RNA may impact the composition of the poly(A) RNA interactome.We also generated a comprehensive non-poly(A) RBP atlas of mouse liver from one non-irradiated and four irradiated samples. About 1000 proteins were significantly enriched over the non-crosslinked control   and as exclusive non-poly(A) binders if they displayed the opposite behavior (n = 404). Dual binders scored as hits/candidates in both eRIC and non-poly(A)RIC (n = 588)  RBPs  binders if they scored as eRIC hits/candidates but were not detected in non-poly(A)RIC  RNA across brain, kidney, and liver tissues. We next asked whether and how the binding of individual RBPs to poly(A) RNA differs between organs. For this, we analyzed the ex vivo eRIC data assuming equal mean signal intensity across samples . In mouse organs, less than half of eRIC hits have been formerly annotated as RBPs . Because we expected that the large integrated RBP atlas (6518 RBPs) includes nearly all of the 1349 organ RBPs, we were surprised to find that 291 ex vivo eRIC hits had not been found as RBPs  RBP dataset of 1349 proteins with the large integrated atlas of published RNA-binding proteomes derived from studies conducted in mouse or human cell lines - or total RNA-binding proteinsBPs Fig. . These nBPs Fig. . RemarkaBPs Fig. . This isBPs Fig. . InteresAs ex vivo eRIC uses a higher UV dose than previous RIC studies, we addressed whether protein–protein crosslinking contributes “piggy-back riders” as false positives to our datasets . Differences between the observed and predicted masses could be UV-/eRIC-independent. For example, while the reported mass of Ncl is 76.7 KDa, it runs above the 100 KDa marker in both eRIC eluates and non-irradiated inputs from the liver , but absent from the organ poly(A) RBP datasets , they did not exceed 7% of the total  RNA in mouse organs Fig. , with atEnrichment analysis shows that the >250 enzymes which bind poly(A) RNA in organs are involved in over 20 pathways connected to energy, carbon, fatty acid, or amino acid metabolism Fig. , glycolyAgainst this trend, only two pathways, oxidative phosphorylation and glutathione metabolism, are highly represented in cell culture but not in the organ RBP datasets Fig. . PossiblInterestingly, the vast majority of enzyme-RBPs also bind non-poly(A) RNA  RBPs identified in the kidney and liver + and FAD) have been proposed as potential interfaces for RNA bindingver Fig. . Furtherver Fig. . Domainsver Fig. .In addition to these architectural features, some enzymes may correspondingly use cofactors via domains that are still ill-defined. Therefore, we considered cofactor usage Fig. , taking 15. The resulting atlases of RBPs have paved the way for unexpected insights into RNA biology, especially regarding the role of protein–RNA interactions in cell biological processes such as e.g., metabolism, DNA methylation, protein ubiquitination, or autophagy43. Nonetheless, corresponding data at the organismal level, including an assessment of organ RBPs had not been reported, likely for technical reasons.Since pioneering work about a decade ago, numerous analyses revealed that RNA-bound proteomes extend well beyond the previously known RBPs involved in core steps of RNA biology, at least in cultured cells and non-mammalian organisms20. Because the applicability of UV crosslinking is constrained by the limited penetration depth of UV light, the authors alternatively employed 4% formaldehyde to crosslink proteins to RNA. Although formaldehyde is advantageous over UV light for crosslinking proteins to double-stranded RNA, formaldehyde, unlike UV crosslinking, efficiently promotes protein–protein crosslinks and hence necessitates measures to distinguish protein–protein crosslinking from direct RNA–protein interactions. FAX-RIC identified a total of 119 RBPs in mouse liver, far fewer than anticipated, missing numerous “housekeeping RBPs” that are part of the core mRNA-bound proteome, and reflecting the non-trivial challenges associated with the adaptation of cell-based RBP discovery technologies to mammalian organs.Very recently, FAX-RIC was used to characterize the RBPs of mouse liverHere we combined organ cryosectioning and UV crosslinking with the strengths of eRIC. Our data show that ex vivo eRIC comprehensively interrogates the RNA-bound proteomes of intact organs, uncovering both shared and organ-specific features of RBPs from mouse brain, kidney, or liver. In comparison to FAX-RIC, eRIC uncovered more than 1200 high-confidence poly(A) RBPs from the same organ Fig. , resulti44. These methods show a prevalence for proteins that interact with highly abundant, non-poly(A) RNA species such as rRNAs or tRNAs. While being more inclusive regarding the RNA biotype, they miss a significant fraction of less abundant RBPs that may exclusively or predominantly bind to the polyadenylated transcripts captured by eRIC, since poly(A) RNAs represent only a minor fraction (~3–5%) of the cellular RNA. Here we adapted 2C44 to eRIC supernatants (depleted of polyadenylated transcripts) to purify and identify proteins associated with non-poly(A) RNA in organs. A total of 1225 non-poly(A) binders active in the brain, kidney, or liver were identified  RNA, other RBP profiling methods such as OOPS, XRNAX, 2 C, or PTex have been used to identify RBPs regardless of the RNA biotype that they bind toraction ~–5% of thraction ~–5% of th46 and the essential roles of RBPs in neuronal functions48, the determination of CNS-/brain-related proteome-wide RBP datasets has been overdue. Therefore, we expect the data reported here to be particularly valuable for neurobiologists. The adapted ex vivo eRIC pipeline can, in principle, be applied to many other animal models or human samples, opening novel opportunities for studying RBP biology in health and disease.Considering the enormous efforts to investigate the mammalian brainWe observed unexpected and marked differences between the RNA interactomes of organs versus cultured cells. On the one hand, ex vivo eRIC identified 291 RBPs in organs not detected in any of the 30 published RBP profiles of cell lines; this is remarkable, because the cumulative organ dataset is five times smaller than that of cell lines. On the other hand, dozens of previously identified RBPs were not detected by ex vivo eRIC or non-poly(A)RIC despite being present in the tissues analyzed. Whether this reflects differences in RBP activity due to e.g., the metabolic environment  or the activity of oncogenic pathways in cell lines versus normal cells of healthy tissue is currently not known.We noticed that the overall quantity of proteins crosslinked to a given quantity of poly(A) RNA differs substantially between organs, with protein binding to poly(A) RNA being globally superior in the kidney compared to the liver and brain Fig. . Total p25), we hypothesized that the proportion of RBPs bound to poly(A) RNA might be affected by the ratio of the two RNA biotypes. In support of this notion, comparing the liver and kidney, we found that: (1) non-poly(A) RNA levels are higher  RNA–protein interactions. We found that poly(A) RNA levels are lowest in the brain among the organs studied, which could explain the overall reduced association of proteins to poly(A) RNA observed in the brain. Nevertheless, poly(A) RNA levels are higher in the liver than kidney, and still, the liver displays reduced poly(A) RBP activity. As many RBPs seem to interact with both poly(A) and non-poly(A) RNA  duher Fig. . Thus, t39. Here we show that this is not a peculiarity of cultured cells. On the contrary, RNA-binding metabolic enzymes are even more prevalent in organs  and non-poly(A) RNA and that this binding is widespread across and within multiple metabolic pathways, and even more prominent overall in organs than in cultured cells Figs. , 8. Yet,52.A large fraction (60%) of the enzyme-RBPs identified in this study bind mono- or di-nucleotide cofactors, with “NAD(P) binding” being particularly frequent among the kidney and liver RBP repertoires Fig. . FurtherTaken together, we report comprehensive atlases of RNA-binding proteins in mammalian organs. Far from only confirming the expected, our data reveal numerous surprising differences that form the basis for further explorations of the scope of RNA biology in mammalian physiology and disease. The method(s) described here should be broadly applicable to other organs and organisms.9 for a detailed step-by-step protocol. The capture probe  contains a primary amine at the 5′ end, a flexible C6 linker, and 20 thymidine nucleotides in which every other base is an LNA: /5AmMC6/+TT + TT + TT + TT + TT + TT + TT + TT + TT + TT . Prior coupling, carboxylated magnetic beads  were washed three times with five volumes of 50 mM 2-(N-morpholino)ethanesulfonic acid  buffer, pH 6.0. The washed beads were then combined with a mix made of one volume of probe solution  and five volumes of freshly prepared N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride  solution (20 mg/mL in MES buffer).See ref. The coupling reaction was performed at 50 °C for 5 h with constant agitation (800 rpm in a Thermomixer). The beads were then washed twice in phosphate-buffered saline (PBS) and incubated for 1 h at 37 °C in 200 mM ethanolamine pH 8.5 (with constant agitation at 800 rpm) to inactivate residual carboxyl groups. The coupled beads were finally washed three times with 1 M NaCl and stored in 0.1% PBS–Tween at 4 °C until use.Male mice on a homogenous C57BL6/J genetic background were housed under specific pathogen-free and light- (12:12 h light:dark cycles), temperature- (21 °C), and humidity (50–60% relative humidity)-controlled conditions. Food  and water were available ad libitum. The mice were sacrificed at 11 to 13 weeks of age by cervical dislocation; they were fastened 2 h prior to sacrifice (with access to water only). Animal handling (license 22-008_HD_LAR) was in accordance with guidelines approved by the animal care and use committee of the European Molecular Biology Laboratory (EMBL). Organs were immediately harvested and flash-frozen in liquid nitrogen. They were stored at −80 °C until use.Organ sections (thickness 30 µm) were prepared in a Cryostat  set to −20 °C and deposited onto SuperFrost glass slides ; the glass slides were pre-cooled to −20 °C in the cryostat chamber to maintain the samples at the lowest temperature possible. 15–20, 20–30, and 10–15 sections from the brain, kidney, and liver, respectively, were placed on a glass slide, and a total of around 15 slides were prepared per organ per mouse . Each section contains the following approximate quantities of poly(A) RNA and total protein : brain: 41.6 ng of poly(A) RNA and 342 μg of protein; kidney: 35.5 ng of poly(A) RNA and 94.4 μg of protein; liver: 113.4 ng of poly(A) RNA and 304.2 μg of protein.2 in an XL 1500 UV Spectrolinker (Spectronics Corporation). After UV exposure, organ sections were recovered by scraping directly into 15 mL ice-cold lysis buffer (see composition below) supplemented with protease inhibitors  and RNase inhibitor . For the non-crosslinked control, every other section was lysed directly without exposure to UV; sections from four mice were pooled. The lysates were pipetted up and down several times to dislodge tissue sections. They were finally passed once through a 25-Gauge needle  and seven times through a 27-Gauge needle . The homogenates were snap frozen in liquid nitrogen and stored at −80 °C until use.Tissue sections on glass slides were then transferred onto metal plates placed in direct contact with dry ice to preserve sample integrity during UV irradiation. The tissue sections were exposed to UV light (λ = 254 nm) at a dose of 1 J/cm9 for a reference step-by-step protocol. Cell lysates were thawed at 37 °C, incubated for 15 min at 45 °C, cooled down on ice, and the debris was pelleted for 5 min at 16,000×g at 4 °C. The supernatant was transferred to 15 mL DNA LoBind tubes  and complemented with 5 mM extra of dithiothreitol . About 200 µL of each sample were taken as input, and the rest was mixed with 15 mg of capture probe-coupled beads previously equilibrated three times with three volumes of lysis buffer. The samples were incubated for 1 h at 37 °C with gentle rotation. The beads were then collected on a magnet, and the supernatant was transferred to a fresh 15 mL DNA LoBind tube for a second round of capture. After the capture, the beads were transferred to 5 mL DNA LoBind tubes  and washed first with the lysis buffer, and then twice with each of the buffers 1, 2, and 3 (see composition below). Each wash was performed with 5 mL of the corresponding buffer for 5 min at 37 °C with gentle rotation. A “pre-elution” step was performed by incubating the washed beads with 220 μL of nuclease-free water (Ambion) for 10 min at 40 °C and 800 rpm. The bead suspension was then divided into two aliquots: 200 μL were used for RNase-mediated elution of RNA-bound proteins, and the rest (20 µL) was heat-eluted to recover the nucleic acid. The beads were collected on a magnet and the supernatant was discarded. For RNase elution of proteins, the beads were resuspended in 150 μL of 1× RNase buffer (see composition below) containing 5 mM DTT, 0.01% NP40, ∼200 U RNase T1 , and ∼200 U RNase A . Following a 60 min incubation at 37 °C, 800 rpm, the beads were collected on a magnet, and the eluate was transferred to a fresh tube. The eluate was placed once more on the magnet to eliminate any remaining beads, and safely stored on ice. Heat elution of RNA was performed by incubating the aliquoted beads in 15 μL of water for 5 min at 95 °C, 800 rpm. The beads were immediately collected on the magnet, and the supernatant recovered as quickly as possible to avoid a drop in temperature (with the consequent re-capture of RNP complexes). Any trace of beads was removed by a second round of collection, as explained. Corresponding eluates from the two consecutive rounds of capture were combined. Eluates obtained by RNase treatment  were supplemented with 0.05% of SDS (2 μL of 10% SDS), concentrated at 45 °C to a volume of ~100 µL in a SpeedVac, snap frozen, and stored at −80 °C. Crosslinked eluates from two mice were combined for in-depth proteomic analyses.See ref. Lysis buffer: 20 mM Tris-HCl (pH 7.5), 500 mM LiCl, 1 mM EDTA, 5 mM DTT, 0.5% (w/v) LiDS.Buffer 1: 20 mM Tris-HCl (pH 7.5), 500 mM LiCl, 1 mM EDTA, 5 mM DTT, 0.1% (w/v) LiDS.Buffer 2: 20 mM Tris-HCl (pH 7.5), 500 mM LiCl, 1 mM EDTA, 5 mM DTT, 0.02% (v/v) NP40.Buffer 3: 20 mM Tris-HCl (pH 7.5), 200 mM LiCl, 1 mM EDTA, 5 mM DTT, 0.02% (v/v) NP40.10× RNase buffer: 100 mM Tris-HCl (pH 7.5), 1.5 M NaCl44 with the following modifications. Two purification rounds with a total of 14 mL were performed with supernatants of brain and kidney origin. One purification with 2.5 mL of liver supernatant was performed. One non-crosslinked control and four crosslinked samples per organ were processed. Employed reagents belong to the Quick-RNA Miniprep Kit  unless specified. Supernatants were combined with four volumes of RNA Lysis Buffer, mixed, deposited in a 20 mL syringe barrel, and passed through a Spin-Away Filter column applying pressure with a plunger. The flow-through was combined with 1 volume of 100% ethanol, mixed well, and loaded into a Zymo-Spin IIICG Column applying vacuum . Columns were centrifuged at 16,000×g for 30 s at room temperature (rt). Flow-though was discarded. Columns were washed with 400 µL of RNA Wash Buffer, centrifuged at 13,000×g for 30 s at rt, and then incubated for 15 min at rt with 80 µL of DNA digestion mix ). Columns were washed with 400 µL of RNA Prep Buffer, centrifuged at 13,000×g for 30 s at rt, washed with 700 µL of RNA Wash Buffer, centrifuged likewise, washed with 400 µL of RNA Wash Buffer, and centrifuged at 16,000×g for 60 s at rt. To elute RNA and RNP complexes, columns were incubated with 100 µL (liver samples) or 75 µL (brain and kidney samples) of water for 5 min at rt and then centrifuged at 13,000×g for 30 s at rt. Eluates from the two purification rounds (brain and kidney) were pooled. Eluates were brought to a volume of 146 µL with water, combined with 16.8 µL of 10x DNase buffer and 5 µL of TURBO DNase  and incubated 30 min at 37 °C. Samples were subjected to a second round of purification . RNA and RNP complexes were eluted with 100 µL of water. About 100 µL of brain and kidney eluates, and 50 µL of liver eluate diluted in one volume of water, were combined with 10 µL of 10x RNase buffer (100 mM Tris-HCl (pH 7.5), 1.5 M NaCl, 0.5% (vol/vol) IGEPAL CA-630), 5 mM DTT, 0.2 µL RNase A and 0.5 µL RNase T1, and incubated at 37 °C for 1 h. The non-poly(A) RBP landscapes of the brain, kidney, and liver were analyzed using one non-crosslinked and two crosslinked samples (each of the latter resulting from the combination of two independent RNase-treated eluates) and 2 µg of captured RNA per sample. For in-depth characterization of the liver non-poly(A) RBPs, we used 40 µg of captured RNA per sample, one non-crosslinked and four independent crosslinked samples.Poly(A) RNA-depleted supernatants of brain, kidney, and liver origin obtained after two consecutive rounds of eRIC were stored at −80 °C until use. Then non-poly(A) RNP complexes present in the supernatants were isolated using the 2 C silica-based solid-phase extraction methodThe concentration of the captured RNA (heat-eluted) was estimated using a NanoDrop spectrophotometer (Thermo Fisher Scientific). About 10 ng of eluted RNA was analyzed using an Agilent 2100 Bioanalyzer System using the RNA 6000 Pico Kit, following the manufacturer’s instructions. Total RNA in the input was extracted using Trizol LS (Thermo Fisher) and analyzed the same way.28S rRNA , Actinb , Gapdh  and 18S rRNA .For RT-qPCR analysis of RNA, ~100 ng of captured RNA or input RNA were treated with DNase I (Thermo Fisher), and reverse transcribed using SuperScript III (Life Technologies) together with random hexamers (Life Technologies), following the manufacturer’s instructions. For qPCR analysis of DNA, the same reaction was performed in parallel but without the DNase and reverse transcriptase enzymes. Real-time qPCR was performed in a QuantStudio 6 Flex system (Life Technologies) using the SYBR Green PCR Master Mix  and the following primers : Proteins isolated from the liver by ex vivo eRIC after applying the indicated UV dose  were combined, and 2 µL of the bead mix were added to each sample. Acetonitrile  was then added to a final concentration of 50%, and samples were incubated off a magnet for 8 min. Beads were captured on a magnetic rack for 2 min, and the supernatant was removed. Beads were washed twice with 70% ethanol  and then once with 100% acetonitrile. Beads were reconstituted in digestion buffer (50 mM HEPES pH 8.5) supplemented with trypsin  at an enzyme-to-protein ratio of 1:50 and incubated at 37 °C overnight. The digested peptides were recovered in HEPES buffer by applying two successive elution steps. The peptides were subsequently labeled with TMT10plex55 Isobaric Label Reagent (Thermo Fisher) according to the manufacturer’s instructions. The samples were combined and cleaned using an OASIS® HLB µElution Plate (Waters). Offline high pH reverse phase fractionation was carried out on an Agilent 1200 Infinity high-performance liquid chromatography system, equipped with a Gemini C18 column .eRIC and non-poly(A)RIC samples were concentrated to a volume of ~100 µL using a SpeedVac apparatus and treated with 10 mM DTT in HEPES buffer  for 30 min at 56 °C to reduce disulfide bridges in proteins. Reduced cysteines were then alkylated for 30 min at room temperature with 20 mM 2-chloroacetamide in HEPES buffer (protected from light). Samples were prepared using the SP3 protocolAn UltiMate 3000 RSLC nano-LC system (Dionex) fitted with a trapping cartridge  and an analytical column  was used. Trapping was carried out with a constant flow of trapping solution (0.05% trifluoroacetic acid in water) at 30 µL/min onto the trapping column for 6 min. Subsequently, peptides were eluted via the analytical column running solvent A (0.1% formic acid in water and 3% DMSO) with a constant flow of 0.3 µL/min, with an increasing percentage of solvent B (0.1% formic acid in acetonitrile and 3% DMSO) from 2 to 8% in 4 min, from 8 to 28% for a further 104 min, from 28 to 40% in another 4 min, and finally from 40 to 80% for 4 min, followed by re-equilibration back to 2% B in 4 min. The outlet of the analytical column was coupled directly to an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Scientific) using the Nanospray Flex ion source in positive ion mode.5 ions. Data-dependent acquisition (DDA) was performed with the resolution of the Orbitrap set to 30,000, with a fill time of 94 ms and a limitation of 1 × 105 ions. A normalized collision energy of 38 was applied. MS2 data were acquired in profile mode.The peptides were introduced into the Fusion Lumos using a Pico-Tip Emitter 360 µm OD × 20 µm ID; 10 µm tip (New Objective) and an applied spray voltage of 2.4 kV. The capillary temperature was set to 275 °C. A full mass scan was acquired with a mass range of 375–1500 m/z in profile mode in the orbitrap with a resolution of 120,000. The filling time was set to a maximum of 50 ms with a limitation of 4 × 1056 and Mascot (v2.2.07) were used to process the acquired data, which was searched against the Mus musculus (UP000000589) Uniprot proteome database containing common contaminants and reversed sequences. The following modifications were included in the search parameters: Carbamidomethyl (C) and TMT10 (K) (fixed modification), acetyl (Protein N-term), oxidation (M), and TMT10 (N-term) (variable modifications). A mass error tolerance of 10 ppm and 0.02 Da was set, respectively, for the full scan (MS1) and MS/MS (MS2) spectra. Further parameters were: Trypsin digestion with a maximum of two missed cleavages tolerated; a minimum peptide length of seven amino acids, at least two unique peptides required for protein identification. The false discovery rate was set to 0.01 on both the peptide and protein level.IsobarQuant57 and further normalized using the vsn  package58. Only proteins that were quantified with at least two unique peptides were considered for the analysis. Proteins were tested for differential expression using the limma package applying the normalization strategies indicated below. The replicate information was added as a factor in the design matrix given as an argument to the “lmFit” function of limma. A protein was annotated as a hit with a false discovery rate (FDR) smaller than 5% and a fold-change of at least 100% and as a candidate with an FDR below 20% and a fold-change of at least 50%.The protein output files of IsobarQuant were processed in R (ISBN 3-900051-07-0). Raw TMT reporter ion intensities  were first cleaned for batch effects using the “removeBatchEffects” function of the limma packageTwo different normalization strategies were used, as indicated. In the first analysis, we estimated different normalization coefficients for each tissue and condition  of the eRIC samples. In the second approach RIC samples were considered. eRIC and non-poly(A)RIC samples were treated individually but evaluated in the same analysis. Different normalization coefficients were estimated for +UV and −UV conditions. To determine the relative association of RBPs with poly(A) and non-poly(A) RNA across organs Fig. , proteinTwo different normalization strategies were used, as indicated. In the first analysis, proteins quantified in ex vivo eRIC or non-poly(A)RIC in the liver were considered. eRIC and non-poly(A)RIC samples were treated individually but evaluated in the same analysis. Only the +UV samples were normalized in order to maintain the abundance difference from the -UV control. In the second approach  glycerol, 10% (v/v) beta-mercaptoethanol, 8% (w/v) SDS, 1.54% (w/v) DTT, 0.04% (w/v) bromophenol blue), incubated at 95 °C for 5 min and loaded into 4–15% acrylamide gels . Using as a reference the bands of a ladder  loaded into the same gel, the lanes were cut using a scalpel at the following molecular weights (KDa): 20, 25, 37, 50, 75, 100, 150, and near the top of the gel. The isolated bands were subjected to in-gel digestion with trypsin. Peptides were extracted from the gel pieces by sonication for 15 min, followed by a quick spin and supernatant collection. A solution of 50:50 water: acetonitrile, 1% formic acid (2 x the volume of the gel pieces) was added for a second extraction, and the samples were again sonicated for 15 min, quickly spun down and the supernatant pooled with the first extract. The pooled supernatants were dried by vacuum centrifugation (SpeedVac). The samples were dissolved in 10 µL of reconstitution buffer  and analyzed by LC-MS/MS.An UltiMate 3000 RSLC nano-LC system (Dionex) fitted with a trapping cartridge  and an analytical column  was used. Trapping was carried out with a constant flow of trapping solvent (0.05% trifluoroacetic acid in water) at 30 µL/min onto the trapping column for 6 min. Subsequently, peptides were eluted and separated on the analytical column using a gradient composed of Solvent A (0.1% formic acid in water and 3% DMSO) and solvent B (0.1% formic acid in acetonitrile and 3% DMSO) with a constant flow of 0.3 µL/min. The outlet of the analytical column was coupled directly to an Orbitrap Fusion Lumos Mass Spectrometer (Thermo Scientific) using the nanoFlex source.5 ions. The instrument was operated in data-dependent acquisition (DDA) mode and MSMS scans were acquired in the Iontrap using a Rapid scan rate, with a fill time of up to 35 ms, and the AGC target was set to standard. A normalized collision energy of 30 was applied. MS2 data was acquired in centroid mode.The peptides were introduced into the Orbitrap Fusion Lumos via a Pico-Tip Emitter 360 µm OD × 20 µm ID; 10 µm tip (CoAnn Technologies) and an applied spray voltage of 2.4 kV, the instrument was operated in positive mode. The capillary temperature was set at 275 °C. Full mass scans were acquired for a mass range of 350–1500 m/z in profile mode in the orbitrap with a resolution of 120,000. The filling time was set to a maximum of 250 ms with a limitation of 4 × 1059 and searched against the Uniprot Mus musculus database (UP000000589). The data were searched with the following modifications: Carbamidomethyl (C) (fixed modification), Acetyl (N-term), and Oxidation (M) (variable modifications). The mass error tolerance for the full scan MS spectra was set to 20 ppm and for the MS/MS spectra to 0.5 Da. A maximum of two missed cleavages was allowed. For protein identification, a minimum of one unique peptide with a peptide length of at least seven amino acids was required. A false discovery rate below 0.01 was required on both the peptide and protein levels.The raw mass spectrometry data were processed with MaxQuant (v1.6.17.0)The raw output file of MaxQuant (proteinGroups.txt – file) was processed using the R programming language (ISBN 3-900051-07-0). Only proteins that were quantified with at least two unique peptides and in at least two out of three replicates in a specific gel fraction, were considered for the analysis. iBAQ values were cleaned for batch effects using the “removeBatchEffect” function of the limma package. In order to calculate the proportion of observation for each protein and sample (replicate and gel fraction), the corresponding iBAQ value was divided by the sum of all valid iBAQ values over all samples for the specific protein. The density distribution of observed molecular weights was weighted by this calculated proportion.To estimate the poly(A) RNA content, ex vivo eRIC heat eluates (see “eRIC: Capture of RNP complexes”) from two consecutive rounds of capture were combined and the concentration of RNA quantified in a Nanodrop spectrophotometer (Thermo Fisher Scientific). The protein concentration of the eRIC lysates was determined using the DC Protein Assay (Bio-Rad) following the manufacturer’s instructions. For the assessment of the total RNA content, a small piece of the organ of 20–45 mg was transferred to a 1.5 mL tube, and lysed and homogenized in 200 µL of RIPA lysis buffer  using a plastic pestle. Half of the volume (100 µL) was employed to purify total RNA using the Quick-RNA Miniprep Kit  following the manufacturer’s instructions, and the isolated RNA was quantified in a Nanodrop spectrophotometer (Thermo Fisher Scientific). The other half of the lysate was employed to determine the protein concentration using the DC Protein Assay (Bio-Rad) following the manufacturer’s instructions. Four independent measures per organ were performed.https://rbpbase.shiny.embl.de, v.0.2.0). The R package “ggupset” was used for the visualization of overlaps between datasets with UpSet plots61. Fisher’s exact with independent hypothesis weighting (IHW) for multiple hypothesis testing corrections was used for the overrepresentation analysis of protein domain information from MouseMine62.Mouse and human RNA interactome studies, along with functional annotations, were downloaded from the RBPbase , using the following parameters: analysis type: PANTHER overrepresentation test (Released 20200407); Annotation version and release date: GO ontology database Released 2020-02-21; reference list: Mus musculus ; test type: Fisher’s exact with Bonferroni correction for multiple testing. Graphical representations were made with the ggplot2 R package69. KEGG- and remaining GO-term enrichment analysis were performed in g:Profiler (https://biit.cs.ut.ee/gprofiler/gost)71, using the following parameters: organism: Mus musculus; statistical domain scope: only annotated genes; significance threshold: g:SCS threshold . In Fig. GO-term enrichment analysis in Fig. 22 (http://www.pantherdb.org) (v.16.0). The percentages shown were calculated against the total number of protein class hits.PANTHER protein class analysis was performed using the PANTHER classification systemThese analyses were performed on proteins listed in the PANTHER protein class: “metabolite interconversion enzyme” (enzyme). InterPro domain and cofactor annotations were retrieved from the UniProt mouse database (release-2021_03). Protein domains were classified as shown in Supplementary Data 23. An anchor probe was designed to target the 3′UTR/poly(A) boundaries of mRNAs. The probe is composed (5′ to 3′) of a 5′biotin tag [BtnTg], a poly(dT) 18mer followed by two random nucleotides (“NN”). The control probe lacks the biotin tag but is otherwise identical.The PLA was adapted from a previously described protocolMouse organs  were obtained as described above. 10-μm-thick sections were prepared in a cryostat , transferred onto SuperFrost Plus adhesion slides (Carl Roth), encircled by a hydrophobic border with a 2-mm-thick pap pen (Sigma-Aldrich), and fixed with 4% paraformaldehyde at room temperature for 20 min. The fixed tissue sections were washed with PBS and permeabilized with PBS containing 1% BSA and 0.1% Triton X-100 at room temperature for 30 min. Tissue sections were then washed once for 5 min with 0.1 M Triethanolamine containing acetic anhydride and twice with PBS-T (0.02% Tween-20). The sections were further washed twice with hybridization buffer  Tween-20, 0.1% (w/v) CHAPS, 5 mM EDTA, 1 mg/mL RNase free tRNA, 100 µg/mL heparin) and then incubated in hybridization buffer containing 100 nM of the probe in a wet chamber at 37 °C overnight. The probe was boiled for five minutes at 95 °C prior to addition. After hybridization, the sections were subjected to 5 min washes first in 50% (v/v) deionized formamide/5xSSC , then in 25% (v/v) deionized formamide/1xSSC, 12.5% (v/v) deionized formamide/2xSSC, 2xSSC/0.1% (v/v) Tween-20, and finally 0,2xSSC/0.1% (v/v) Tween-20. Subsequently, the Duolink PLA Fluorescence protocol (Sigma) was followed using an anti-biotin antibody  and either the anti-ENO1 , anti-SLC3A2 , anti-DDX6  or anti-PKM1 antibody  for detection of the protein–RNA signal. The primary antibodies were incubated at room temperature for 90 min. After the last wash of the Duolink PLA Fluorescence protocol, the slides were incubated at room temperature for 45 min with antibody diluent mixed with DAPI  and nanobodies targeting rabbit IgG coupled with Alexa Fluor 488 . The tissue sections were washed once with PBS-T and a glass cover was mounted using ProLong Diamond Antifade Mountant . The prepared slides were stored at 4 °C until fluorescence microscopy.Microscopy was performed using an LSM 780 Laser Scanning Microscope (ZEISS) equipped with an AxioCamera and a 63x/1.4 objective with immersion oil . The microscope was operated using the ZEN 2012 software (ZEISS). The DAPI signal was recorded in one plane to act as a reference for counting the number of cells. The PLA (Alexa Fluor 594) and the protein (Alexa Fluor 488 nanobody) signals were recorded as a Z-stack (ten pictures for a 10 μm stack). Two images were taken per tissue section from the brain, kidney, and liver of three different mice. The images were acquired as .lsm files using the same settings  for the same protein and analyzed using the Fiji software. The .lsm files were split into individual channels, the Z-stacks for the PLA signal and the Alexa Fluor 488 signal were projected into a single plane and the brightness was set to the same level in all images of the same channel to enable the comparison of the results. The individual channels were ultimately saved as .tiff files.To count the PLA signals per cell for each of the different conditions, CellProfiler Software version 4.1.3 was used. The range of the signal spot size was set to 8 to 20 pixels and the range of the nuclear size  was set to 100 to 400 pixels. In both instances, the global threshold strategy minimum cross-entropy was used. The threshold smoothing factor was 1.3488 for the PLA signals and 20 for the nuclei. The Alexa Fluor 488 signal was used as an outline of the cells. Clumped objects were separated by the intensity and objects touching the border of the images were discarded. The PLA signal per cell was counted by combining the information of the cellular outline (Alexa Fluor 488) and the DAPI signal. The statistical analysis of these results was performed using GraphPad Prism version 9 (one-way ANOVA with Tukey post hoc test).Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


Arenaviridae is a family for ambisense RNA viruses with genomes of about 10.5 kb that infect mammals, snakes, and fish. The arenavirid genome consists of two or three single-stranded RNA segments and encodes a nucleoprotein (NP), a glycoprotein (GP) and a large (L) protein containing RNA-directed RNA polymerase (RdRP) domains; some arenavirids encode a zinc-binding protein (Z). This is a summary of the International Committee on Taxonomy of Viruses (ICTV) report on the family Arenaviridae, which is available at www.ictv.global/report/arenaviridae. Arenavirids produce virions that are spherical or pleomorphic in shape and 40–200 nm in diameter, with dense lipid envelopes . The virArenavirid genomes consist of two or three single-stranded, typically ambisense, RNA segments . Some of these RNAs encode two proteins in non-overlapping open reading frames of opposite polarities that are separated by non-coding intergenic regions (IGRs) . The S RArenavirions attach to cell-surface receptors or attachment factors and enter via the endosomal route. Some viruses engage intracellular receptors in endosomes. pH-dependent fusion with late endosomes releases the virion RNP complex into the cytoplasm. The virus RNP directs both RNA genome replication and gene transcription. During replication, L protein reads through the IGR transcription–termination signal and generates uncapped antigenomic and genomic RNAs. Transcription of mRNAs encoding GPC and Z occurs only after the first round of virus replication, during which S and L antigenomes are produced. Arenavirid mRNAs lack 3′-terminal poly(A) tracts but have several non-templated 5′ bases, consistent with the use of a cap-snatching mechanism to initiate transcription. Virion budding occurs from the cellular plasma membrane, thereby providing the virion envelope .ictv.global/taxonomy. The family Arenaviridae is included in the negarnaviricot order Bunyavirales. Arenavirids are most closely related to mypovirids, nairovirids, phenuivirids and wupedevirids. Arenavirids differ from most other bunyavirals by having segmented genomes with ambisense organization. The family includes several genera and >59 species. Some arenavirids can cause severe diseases in humans (e.g. Lassa fever) [Current taxonomy: a fever) . Other aa fever) .Arenaviridae: www.ictv.global/report/arenaviridae.Full ICTV Report on the family


The major source of ccfNAs are the cells of hematopoietic system under healthy conditions. These ccfNAs include fragmented circulating cell free DNA (ccfDNA), coding or messenger RNA (mRNA), long non-coding RNA (lncRNA), microRNA (miRNA), and mitochondrial DNA/RNA (mtDNA and mtRNA), that serve as prospective biomarkers in assessment of various clinical conditions. For, e.g., free fetal DNA and RNA migrate into the maternal plasma, whereas circulating tumor DNA (ctDNA) has clinical relevance in diagnostic, prognostic, therapeutic targeting, and disease progression monitoring to improve precision medicine in cancer. The epigenetic modifications of ccfDNA as well as circulating cell-free RNA (ccfRNA) such as miRNA and lncRNA show disease-related variations and hold potential as epigenetic biomarkers. The messenger RNA present in the circulation or the circulating cell free mRNA (ccf-mRNA) and long non-coding RNA (ccf-lncRNA) have gradually become substantial in liquid biopsy by acting as effective biomarkers to assess various aspects of disease diagnosis and prognosis. Conversely, the simultaneous characterization of coding and non-coding RNAs in human biofluids still poses a significant hurdle. Moreover, a comprehensive assessment of ccfRNA that may reflect the tumor microenvironment is being explored. In this review, we focus on the novel approaches for exploring ccfDNA and ccfRNAs, specifically ccf-mRNA as biomarkers in clinical diagnosis and prognosis of cancer. Integrating the detection of circulating tumor DNA (ctDNA) for cancer genotyping in conjunction with ccfRNA both quantitatively and qualitatively, may potentially hold immense promise towards precision medicine. The current challenges and future directions in deciphering the complexity of cancer networks based on the dynamic state of ccfNAs will be discussed.The circulating cell-free nucleic acids (ccfNAs) consist of a heterogenous cocktail of both single (ssNA) and double-stranded (dsNA) nucleic acids. These ccfNAs are secreted into the blood circulation by both healthy and malignant cells  The discovery of cell-free nucleic acids in the body fluids by Mandel and Metais  opened aThe heterogeneity of circulating cell free RNA (ccfRNA) further expands the battery of non-invasive biomarkers in reflecting the origin and state of the disease. Unlike the messenger or coding RNA, non-coding RNAs (micro RNA and long non-coding RNA) have been studied extensively in multiple diseases but the functionality of non-coding RNAs is still not clearly validated . In contEvidence based information indicates that the source of ccfDNA lies in the hematopoietic cells present in a healthy individual . LongituThe fraction of circulating cell-free DNA derived from malignant cells is termed as circulating tumor DNA (ctDNA) that constitutes less than 1% of the total ccfDNA . These cN-RAS mutation proposed that bone marrow aspiration does not consistently contain the diverse population of malignant clones that are implicated in the disease . The ccfRNAs isolated from body fluids (plasma and serum) have been used as biomarkers for diagnosis, early detection and provide insights on the recurrence, prediction of survivability and cancer monitoring and have been widely used in cancers of breast, prostate, pancreas, colon, thyroid and skin , small nuclear RNA (snRNA) and piwi-interacting RNA (piRNA). The small non-coding RNA have been studied extensively and are known to play diverse roles in gene regulation, RNA interference, modification or formation of spliceosome . MoreoveThe long non-coding RNA (lncRNA) has also gathered attention of cancer researchers in the last decades . The lncmiRNA are the short non-coding RNA that are extensively studied as biomarkers for different diseases. These are ∼20–25 nucleotide long oligonucleotides and are vital for regulating the post-transcriptional expression of genes in association with mRNAs. Studies conducted by As opposed to long non-coding RNA (RNA molecules that are not translated into proteins), mRNAs are the protein coding regions that encompass the information produced as a result of DNA transcription . These mThe quest for exploring ccfRNA [both coding (mRNA) and non-coding ] for their value as biomarkers in monitoring cancer, cardiovascular disorder, neurodegenerative disorder, and infection is ongoing. Various RNA species including mRNA and miRNA are released either in bound form with lipoproteins or inside exosomes as a means of cell-to-cell communication . ExosomeThe exosomes contain a large number of mRNA transcripts and other RNA species such as miRNA and long non-coding RNA that are protected against degradation by RNases in blood circulation  Figure . The exoThe mRNA inside exosomes can potentially act as biomarkers as they are prevented from degradation by RNases in the circulation and can also remain stable at 4°C . The staSimilar to exosomal mRNA, miRNA can also be potentially treated as diagnostic and predictive biomarkers for diseases to reflect the internal environment of the cells along with their physiological and pathological conditions . miRNA iThe extent to which ccfRNA may reflect human physiologic and disease state still remains to be elucidated. Approximately 19,000 unique transcripts of mRNA were detected in circulation among which, 65–75 percent of these coding transcripts represent the most significant proportion of circulating RNA that remains consistent with different clinical categories . Based oRecent developments in the field of RNA sequencing have allowed better and efficient transcriptome analysis in plasma to facilitate the discovery and application of novel diagnostic, prognostic, and therapeutic biomarkers to detect and monitor the disease. Next-generation sequencing revealed that ccf-mRNA is enriched in the transcripts from bone marrow when compared with the transcripts from circulating cells . To suppThe data from fewer studies conducted on mRNA transcripts indicated their presence as typically fragmented in the blood circulation. Studies have been conducted on the transcripts of human telomerase reverse transcriptase (hTR and hTERT), the two constituents of telomerase enzyme that are found in serum samples of breast cancer patients as potential biomarkers for disease prognosis . These tThe thyroid stimulating hormone receptor (TSHR) mRNA could detect the presence of thyroid nodules and was utilized for predicting the rate of recurrence in thyroid cancer . The expSimilarly, the overexpression of EGFR mRNA in circulation  was linked to non-small cell lung cancer, pancreatic cancer and colon cancer . FurtherIncreased levels of metastatin mRNA in the serum correlated with poor survival rate and metastasis to lymph nodes in breast cancer . The chap = 0.042; p = 0.008), at diagnosis predicting suboptimal debulking surgery and indicated shorter progression-free survival  and overall survival .The first study in ovarian cancer demonstrated circulating Metastasis-associated in colon cancer 1 (MACC1) and S100 calcium-binding protein A4 (S100A4) transcripts as potential liquid biopsy markers . Both MAAMACR, GOLM1, TRPM8 and NKX3-1 genes were overexpressed and were closely associated with cancer aggressiveness, extracapsular extension and vesicular seminal invasion  was able to identify high-risk prostate cancer cases (85% of sensitivity and 58% of specificity). This ccf-mRNA signature has emerged as a superior marker with a better overall performance compared with the classical biopsy Gleason score and prostate-specific antigen. Pre-transplant measurable residual disease status of WT1 ccf-mRNA expression has been assessed for prognostic value in peripheral blood obtained from patients with Acute Myeloid Leukemia and Myelodysplastic syndrome (MDS) (The ccf-mRNA has been employed for risk assessment of prostate cancer. Transcripts of invasion . A combime (MDS) .n = 46 + 30]) and non-cancer (n = 89) participants from the Circulating Cell-free Genome Atlas (NCT02889978) was reported by Larson et al.  patients, which were enriched in AD-associated biological processes . Albeit,n et al. . Of 57,8BCL2L1, CXCL9, IDO1, IL13, MIF, MYD88 and TLR4p ≤ 0.001, versus high disease burden). In patients with therapeutic response relative to baseline assessment, a statistically significant increase in BCL2L1, CCL4, CCL5, CXCL9, GZMB and TNFSF10 genes was noted. Moreover, in stage IV melanoma patients with brain metastases, CCL18, CCR1, CCR4, CD274, CSF2, EGF, and PTGS2 genes were significantly over-expressed .Notable differences were reported in the plasma transcriptomic profile between low disease burden compared to advanced melanoma patients . SeveralKPNA2, DTL, BACE2 and DTYMK genes between melanoma patients’ and healthy donors’ plasma . Furthermore, ccf-mRNA levels correlated proportionally with increasing TMB and radiological absence of disease. The cellular sources of ccfRNA were traced by comparison with transcriptomes obtained from single cells, that identified a pan-tumour origin beyond melanoma cells . The genes with low baseline ccfRNA levels were associated with a prominently longer progression-free survival (PFS) and overall survival (OS). An increase in the ccfRNA levels of KPNA2 and DTYMK at the time of therapy predicted shorter PFS. Supportively, the levels of ccfRNA substantially increased after therapy in non-responders when correlated against the responders, independent of therapy and mutational subtypes.In another study, ccf-mRNA facilitated the minimally invasive pan-tumor monitoring of melanoma therapy that was not dependent on the mutational genotype of the patient . High diPlasma ccfRNA expression levels were quantified by a tagmentation-based library sequencing in primary NSCLCs . The com+ and CD8+ T-cells at the center of tumor and also alongside its margin (Immunoscore)  , expressA pre-clinical mouse model was utilized to temporally demonstrate the potential of profiling ccf-mRNA in plasma through drug-induced molecular alterations that are associated with inflammatory and immune responses. On one hand, lipopolysaccharide induced systemic immune responses and dysregulated signaling pathways were captured by cell-free mRNA-Seq whole transcriptome profiling. On the other hand, JAK inhibitor was shown to diminish these inflammatory pathways, including interferon and STAT signaling that was reflected in the ccf-mRNA . InteresBCL2L1, GZMB, HLA-A, IRF1, MYD88, TLR2, and TP53 genes were significantly over-expressed in glioma patients . Immune-related genes GZMB, HLA-A and BCL2L1 were significantly over-expressed in high-grade glioma patients  and a concordance between differentially expressed genes in plasma- and glioma-derived RNA was reported.A noteworthy study demonstrated the differential expression of genes involved in cancer inflammation and immunity. Among patients with different glioma grades, a positive correlation between their transcriptomic profile in plasma and tumour samples was shown . The BCLA potential usability of plasma-derived exosomal RNAs was described in non-small cell lung cancer patients for the characterization of molecular phenotypes of osimertinib resistance . The traLMNB1, TGF-β, and MCM6 from plasma samples were proposed as potential biomarkers for hepatocellular carcinoma. The MYC mRNA from serum samples of B-cell lymphoma was linked to short overall PFS and partial treatment response. However, some restrictions that limit the use of mRNA in clinical practice can be attributed to the labile nature of mRNA in circulation  in the non-small-cell-lung cancer (NSCLC). The following study proposed the applicability of both ccfRNA and ccfDNA for detection of fusion genes. A standard protocol facilitated the detection of fusion genes by ccfRNA and formulated analytical validity and functionality in 30 samples from 20 patients with fusion positive NSCLC. The conclusions drawn from ccfRNA-based assays were compared against the ccfDNA based assay (Guardant360 plasma NGS assay). The cumulative sensitivity of the ccfRNA based assays was 26.7% as opposed to 16.7%, for ccfDNA based assay. The analysis that was restricted to the samples obtained at chemo-naïve or progressive disease status and available for both assays showed that sensitivity of ccfRNA based assays was comparatively higher about 77.8% than ccfDNA based assay (33.3%). The identification and characterization of fusion-genes in ccfRNA correlates with the treatment response and these results support the usefulness of ccfRNA assay in diagnosing patients with insufficient tissues to channelize proper first-line treatment. Subsequently, the assay can also be used to monitor the progression of NSCLC that can be further considered for second-line of treatment .The cell free nucleic acids in circulation hold great potential as minimally invasive biomarkers for diagnosis and prognosis of cancer. Both plasma and serum serve as a major source for isolation of cell free nucleic acids but plasma is the preferred choice for ccfDNA. Since the ctDNA is relatively low in concentration, especially in the early stages of cancer, therefore highly sensitive advanced detection methods for successful profiling of mutation, methylation, and fragment patterns are recommended. Various conventional and ultrasensitive technology platforms for genotyping ctDNA were described by our group and others earlier . The seqFurther technological advancements have been witnessed towards better characterization of ccfDNA . Cell-frTargeted error correction (TEC-Seq) based sequencing is a recent UMI-based technique for ccfDNA detection that has been employed for detection of mutations in 58 genes involved in cancer of ovaries, breast, and lung . HoweverThe lack of optimum reproducibility across different studies occurring from lack of knowledge of standard isolation methods for ccfRNA has restrained the progress in the applications and versatility of liquid biopsies. A comparative analysis of ten methods used for isolation of ccfRNA from different biofluids have provided insights on the complexity and reliability of consequent small RNA-seq profiles . An asseSimilarly, the gene expression characterization of ccfRNA involves the use of conventional methods such as quantitative real time-polymerase chain reaction , microarrays and Next-generation sequencing . Every mModifications have been introduced in small RNA sequencing strategy to facilitate the simultaneous detection of ccfRNA. Yao J et al. performed Thermostable group II intron reverse transcriptase sequencing (TGIRT-Seq) in combination with peak calling to concomitantly profile the different biotypes of RNA in human plasma that was pooled from different disease-free individuals. From this study, the researchers proposed that the plasma contains mRNA that has undergone substantial fragmentation derived from more than 19,000 protein-coding genes, high population of full length and mature tRNA and other structured small non-coding RNA with a minor population of mature and pre-miRNA . DiffereA novel detection technique that determines small fragments of RNA called “Phospho-RNA-Seq” reveals that other standard small RNA-seq techniques are not sensitive enough to detect circulating mRNA and lncRNA in human plasma that can potentially serve as biomarkers. Therefore, this new method used treatment of RNA with T4-polynucleotide kinase in assessing these small circulating RNA fragments . A paralFurther modification was described by Newer methods for biomolecule detection through graphene quantum dots (GQDs) was described that could estimate the circulating nucleic acids like DNA, mRNA, miRNA, mtDNA and lncRNA in plasma and serum samples . These GSILVER-Seq  is a newly proposed method for sequencing integral as well as fragmented form of ccfRNA in as small as a droplet of volume 5-7 µl from liquid biopsy . This teShort-read deep RNA-Seq has been reported to identify the mRNA, lncRNAs and circRNAs (circular RNAs) in plasma as novel biomarkers for diagnosing coronary artery disease (CAD). The circulating cell-free RNA was sequenced in patients with stable CAD and controls. The fragments from 160 of 3,986 mRNA, 10 of 164 lncRNAs and 2 of 405 novel lncRNAs were observed to be altered in patients with disease. The transcripts that were differentially elevated were enriched in mRNA that is coded by mitochondrial DNA that stipulates myocardial ischaemia and oxidative stress .FRET-based intramolecular kinetic fingerprinting for direct digital counting of single molecules of nucleic acid that is also known as intramolecular single molecule recognition through equilibrium Poisson sample (iSiMREPS) is employed for a quick determination and counting of miRNA and mutant ctDNA with a very high sensitivity and specificity to detect as small as single molecule  Figure . This isThe ccfDNA has been employed in the prediction of transplant rejection, disease monitoring, predicting prognosis, prenatal diagnosis, relapse of disease, and clinical trials . RegardlCirculating cell free RNA, on the other hand that has emerged alongside ccfDNA, holds the transcriptome which can be contemplated as transcript from different tissues . The ccfThe ccfRNA contains necessary signatures in the characterization of oncological patients that have a strong potential to be developed as markers alongside ctDNA for diagnosis and monitoring . This ccIn vitro studies carried out on serum had shown that RNA present inside the apoptotic vesicles does not undergo degradation (Although, the detection and study of ccfRNA is an incredible discovery but RNA is more highly prone to degradation due to the presence of several ribonucleases in the blood. It is shown that RNA is present inside apoptotic bodies, other vesicular structures, or in bound form within DNA nucleosomes to escape the denaturing action of RNases in the blood . In vitrradation . The levradation .The mRNA is released from the cells through apoptosis and necrosis however it is also shed from tumor tissues even at a low abundance of circulating tumor cells. As shown with circulating cell-free hTERT and MUC1 mRNA that can provide a screening tool for the early detection of gastric cancer and its relapse . The ccfThe major challenge that persists in the applicability of liquid biopsies as definitive screening, diagnostic and prognostic tools is the issue of biological and technical reproducibility and the absence of any gold standard for sample storage, processing, and data analysis . Apart fAn efficient RNA biomarker discovery is also hindered by biological challenges that include presence of high interpersonal variability, that implies that the expression of some genes is higher in certain individuals than others. This stipulates the importance of invariable normalization to address this biological variability.One of the challenges encountered with biomarker identification and discovery using RNA sequencing is the interpretation and analysis of the data obtained. The bioinformatics analysis depends on data quality control, quantification of the transcript, and other downstream processes. Furthermore, the input quality also serves as a critical parameter for the quality assessment of the data. RNA sequencing is an extensively used tool for estimating gene expression, however it does not provide absolute quantification of the data like RT-qPCR methods .This limitation maybe overcome by normalization based on spike-in controls that can be done during the step of library preparation that can provide absolute quantification of transcriptome and is reproducible when working with plasma samples . NormaliThe studies conducted in the field of liquid biopsies are limited. However, the application of liquid biopsies, especially ccf-mRNA biopsies is very diverse. The use of coding RNA molecules as promising biomarkers for diagnosis, prognosis, and therapeutic use holds potential in incorporating non-invasive diagnostic tests in routine clinical practice. The growing interest in RNAs as biomarkers has not been very recent, but the shift of focus from microRNA to long-coding RNAs has only been in last 5 years that has steered the discovery of several new disease-associated RNA. Even though there is much that needs to be explored and studied to successfully bring the use of mRNAs into clinical practice, the recent studies conducted on ccf-mRNA as biomarkers for various cancers and other diseases suggest that the use of ccf-RNA may complement ctDNA in bringing a new revolution for screening, diagnosis, disease monitoring and improve patient management.


Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, small RNA-seq, CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease. The most common symptoms include severe changes in social and personal behaviour as well as a general blunting of emotions. Clinically, genetically, and pathologically there is considerable overlap with other neurodegenerative diseases including Amyotrophic Lateral Sclerosis (ALS), Progressive Supranuclear Palsy (PSP) and Cortical Basal Degeneration (CBD)2. Research into FTD has made major advances over the past decades. Up to 40% of cases3 have a positive family history and up to 60% of familial cases can be explained by mutations in the genes Microtubule Associated Protein Tau (MAPT), Granulin (GRN) and C9orf724 which has been key to the progress in our understanding of its molecular basis. Several other disease-causing genes have been identified that account for a much smaller fraction of cases5. Mutations in MAPT lead to accumulation of the Tau protein in neurofibrillary tangles in the brain of patients while mutations in GRN and C9orf72 lead to the accumulation of TDP-436, as well as dipeptide repeat proteins (DPRs) and RNA foci in the case of C9orf727.Frontotemporal Dementia (FTD) is a devastating pre-senile dementia characterized by progressive deterioration of the frontal and anterior temporal lobesAs of today, no therapy exists that halts or slows the neurodegenerative process of FTD and to develop successful therapies there is an urgent need to determine whether a common target and therapy can be identified that can be exploited for all patients, or whether the distinct genetic, clinical, and pathological subgroups need tailored treatments. Therefore, the development of remedies relies heavily on a better understanding of the molecular and cellular pathways that drive FTD pathogenesis in all FTD subtypes.Although our knowledge of FTD pathogenesis using molecular and cellular biology approaches has significantly advanced during recent years, a deep mechanistic understanding of the pathological pathways requires simultaneous profiling of multiple regulatory mechanisms.Post-mortem human brain tissue is an important source for studying the disturbance of molecular processes in patients with FTD. However, systematic studies on human post-mortem brain tissue with genetic subtypes of FTD are currently lacking. Therefore, the Risk and modifying factors in Frontotemporal Dementia (RiMod-FTD) consortium has generated a multi-omics data resource with the focus on mutations in the three most common causal genes for FTD: MAPT, GRN and C9orf72.Here, we report four datasets from the frontal lobe of post-mortem human brain from RiMod-FTD samples. We present RNA-seq, CAGE-seq , small RNA-seq (smRNA-seq), and methylation datasets from matched samples, which enable precise profiling of transcriptional dysregulations in genetic FTD subtypes Fig. , Table 1www.rimod-ftd.org.We show that known differences and commonalities of the investigated FTD subtypes can be recapitulated with initial analyses of the datasets, such as for instance neuronal loss due to neurodegeneration. More importantly, interesting new aspects about these FTD subtypes can be easily obtained from this dataset. We therefore believe that a thorough analysis of these data from the RiMod-FTD project can reveal helpful new insights about FTD and believe that researchers will find these datasets helpful for their own studies. To make the data easily accessible, we provide a gene browser which allows to compare expression and methylation information between disease groups for specific genomic locations at www.brainbank.nl). All Material has been collected from donors for or from whom a written informed consent for a brain autopsy and the use of the material and clinical information for research purposes had been obtained by the NBB. Additional samples were provided by the Queen Square Brain Bank of Neurological Disorders and MRC, King College London. We complied to all relevant regulations of the mentioned brain banks for the work with these samples.The brain samples and/or bio samples were obtained from The Netherlands Brain Bank, Netherlands Institute for Neuroscience, Amsterdam  tissue from each subject was divided into three pieces for transcriptomic, proteomic, and epigenetic experiments in a dry-ice bath using precooled scalpels and plasticware.Genomic DNA was isolated from 50 mg of GFM frozen brain tissue by using the Qiamp DNA mini kit (Qiagen) following the manufacturer protocol. DNA concentration and purity were assessed by nanodrop measurement. DNA integrity was evaluated by loading 100 nanogram per sample on a 0,8% agarose gel and comparing size distribution to a size standard.Presence of C9orf72 hexanucleotide repeat expansion (HRF) in post-mortem brain tissues was confirmed by primed repeat PCR according to established protocols. Reported mutations for MAPT and GRN were verified by sanger sequencing.Total RNA for CAGE-seq and RNA-seq was isolated from ±100 mg of frozen brain tissue with TRIzol reagent (Thermo Fischer Scientific) according to the manufacturer recommendation, followed by purification with the RNeasy mini columns (Qiagen) after DNAse treatment.Total RNA for smRNA-seq was isolated from frozen tissue using the TRIzol reagent (ThermoFischer Scientific). After isopropanol precipitation and 80% ethanol rinsing RNA pellet was resuspended in RNAse free water and up to 10 micrograms of RNA was incubated with 2U of Ambion DNAse I (ThermoFischer) at 37 °C for 20 minutes. DNA-free RNA samples were then further purified by phenol-chloroform-isoamyl-alchol extraction followed by ethanol precipitation.For each RNA sample, RNA concentration (A260) and purity (A260/280 and A260/230) were determined by Nanodrop measurement and RNA integrity (RIN) was assessed on a Bioanalyser 2100 system and/or Tape station 41200 (Agilent Technologies Inc.)Total RNA-seq libraries were prepared from 1 microgram of total RNA from frozen brain tissue using the TruSeq Stranded Total RNA with Ribo-Zero Gold kit (Illumina) according to the protocol specifications. RNA-seq libraries were sequenced on a Hiseq2500 and HISeq4000 on a 2 × 100 bp paired end (PE) flow cell (Illumina) at an average of 100 M PE/sample.8. Libraries were sequenced on a HiSeq2000 and/or HiSeq2500 on a 1 × 50 bp single read (SR) flow cell (Illumina) at an average of 20 M reads/sample. On average, 21,212,923 reads were generated per sample.CAGE-seq libraries were prepared from 5 micrograms of RNA from frozen brain tissues according to a published protocolThe small RNA-seq libraries were prepared in two different batches. They were prepared from frozen tissue starting from 2 micrograms of total RNA using the Nextflex Small RNA-seq kit v3 (Bioo Scientific) and the NEBNext Small RNA library prep set for Illumina (New England Biolabs), respectively. Libraries were sequenced on a NextSeq550 on a 75 cycles flow cell.To assess the methylation status of over 850000 CpG sites in promoter, gene body and enhancer regions we have used the MethylationEPIC bead chip arrays (Illumina).Bisulfite conversion of genomic DNA, genome amplification, hybridization to the beadchips, washing, staining, and scanning procedure was performed by Atlas Biolabs . Cases and controls DNAs were distributed randomly across each array.9, with trimming enabled. Gene quantification was subsequently done using Salmon (v0.14.1)10 on the trimmed FastQ files. Alignment and mapping were performed against the human genome hg38. On average, 82,165,192 reads could be uniquely mapped, which relates to on average 88.6% uniquely mapped reads per sample 12. For training we used the human brain training dataset used in the Scaden publication. Each ensembl model was trained for 5000 steps. Cell type deconvolution was then performed with the trained Scaden model on the RNA-seq count data. Relative changes in cell type composition were quantified by first calculating the average fractions of a cell type for all groups and then calculating the percentual change of cell fractions compared to the average control fractions. This allows to detect relative changes in cell type compositions. To test for statistical differences between disease groups, we have performed an ANOVA test with a post-hoc Tukey HSD test using R.We performed cell type deconvolution on the RNA-seq data using Scaden13. Sequencing artefacts were removed using TagDust (v1.0)14. Processed reads were then aligned against the human genome hg38 using STAR (v.2.4.1)15. On average, 16,306,077 could be uniquely mapped per sample  for visualization on the website.Sequencing adapters and barcodes in CAGE-seq FastQ files were trimmed using Skewer v.0.1.126)613. Sequ17 for analysis. On average, 3,430,613  reads could be uniquely mapped per sample , leaving 810,290 loci for analysis. Data normalization was done using stratified quantile normalization. Sites with a standard deviation below 0.1 were considered uninformative and filtered out, leaving 170,595 sites that were used to perform a principal component analysis.The Infinium MethylationEPIC BeadChip, which consists of 866,091 CpG locations, data was analyzed using the minfi R package19. Subsequently, the CollectAlignmentSummaryMetrics and CollectGcBiasMetrics from the Picard toolkit (https://broadinstitute.github.io/picard/) were run on the aligned data. Sample swap analysis on sequencing assays was performed with reads aligned against the hg38 reference genome using SMaSH20. We verified sample identify between assays by checking the sample with the most significant p-Value between assays up to a p-value of 0.2. This was not possible for all combinations of samples and assays as insufficient variant overlap led to bad p-values, especially between CAGE-seq and smRNA-seq. However, no sample swaps could be detected and for the majority of sample comparisons the correct identity could be verified. We furthermore verified the sex of all samples by comparing the ratio of reads mapping to the X and Y chromoses.To gather additional sequencing quality control metrics, all sequencing assays  were aligned against the hg38 reference genome  or miRbase (smRNA-seq) using bowtie221 (RNA-seq), E-MTAB-1264622 (CAGE-seq), E-MTAB-1267423 (Methylation) and E-MTAB-1273124 (smRNA-seq). The sequencing datasets  contain raw FastQ files and metadata information. The methylation dataset contains raw intensity values and metadata information. Processed data in the form of count tables have been uploaded to FigShare for ease of access: 10.6084/m9.figshare.23825595.v125. The cell type fractions from the deconvolution analysis have been uploaded to FigShare as well.All datasets have been submitted to ArrayExpress with the following accessions: E-MTAB-12647We only included samples with an RNA integrity value above 5. The mean RIN value is 6.9 with a standard deviation of 1.0.20.We confirmed the sex of all samples by comparing the ratio of reads mapping to the X and Y chromosomes. All sequencing assays  were checked for potential sample swaps using the SMaSH softwareTo gain an overview of the dataset, general sample statistics are visualized in Fig. The detection P-value has been calculated for all positions and samples. This value is calculated by comparing the total DNA signal to the background level, which is estimated using negative control positions. All samples had very low detection P-values, with a maximum value of 0.0005 Fig. . We used27, the increase microglial inflammatory response in FTD-GRN28, or very recently, neurovascular dysfunctions in FTD-GRN29.To evaluate how the predicted cell type composition of the RiMod-FTD samples reflects the disease, we performed cell type deconvolution with the RNA-seq data using the Scaden algorithm (see Methods). As expected, neuronal cells make up the largest fractions for all samples, although neuronal fractions are smaller in samples with FTD Fig. . Comparihttps://www.rimod-ftd.org. The website allows to query the RNA-seq expression levels of single genes. It is easily visible, for instance, that GRN RNA levels are lower in brains from patients with GRN mutations, compared to the other groups (Fig. The data from the different assays of the RiMod-FTD project can be easily inspected using the RiMod-FTD Browser at ups Fig. . When seups Fig. . The broSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4


Interactions between RNA and proteins are the cornerstoneof manyimportant biological processes from transcription and translationto gene regulation, yet little is known about the ancient origin ofsaid interactions. We hypothesized that peptide amyloids played arole in the origin of life and that their repetitive structure lendsitself to building interfaces with other polymers through avidity.Here, we report that short RNA with a minimum length of three nucleotidesbinds in a sequence-dependent manner to peptide amyloids. The 3′–5′linked RNA backbone appears to be well-suited to support these interactions,with the phosphodiester backbone and nucleobases both contributingto the affinity. Sequence-specific RNA–peptide interactionsof the kind identified here may provide a path to understanding oneof the great mysteries rooted in the origin of life: the origin ofthe genetic code. Considering the importance of electrostatics in peptide–RNAinteractions, we first investigated the pH dependence of the bindingbetween the RNA heptanucleotide GUGUGUG ([GU]3G) and aseries of peptide amyloids of varying electrostatic character. Forthese assays we used a stoichiometric excess of the peptide, with100 μM peptide and 50 μM RNA. As shown in 3 shows the lowest dependence on pH because it retains anoverall positive charge character even at a neutral pH, while forpeptides 2 and 1, the C-terminus and theGlu side chain, unless protonated, can interfere with binding to the[GU]3G oligonucleotide. To further investigate the roleof electrostatics, [GU]3G binding to peptide amyloids thatcontain multiple ionizable groups , including peptide 6 whichhas no cationic group due to the acetylation of its N-terminus, wasmeasured at pH 3. The results show that more positive charges on thepeptide yield more RNA binding and that at least a single positivecharge, such as the N-terminus in peptide 5, is requiredfor binding . The role of the RNAnucleobases in the binding process was analyzed with a series of RNAheptanucleotides: [GA]3G, [GU]3G, [CA]3C, and [CU]3C. For this analysis, we chose the highlyionizable peptide 7 which, relative to peptide 4, has an additional N-terminal Val residue  (S4 of Table S1). The pH dependence of the binding of the RNA heptanucleotidesto peptide 7 was similar to that observed with the bindingto peptides 1 and 2 but also revealed asignificant RNA sequence dependence , is A > U > G > C. Based on their measured pKa values, the positive charge character of the bases atacidicpH values should be C > A > G > U.26 Oneobvious trend is that sequences with purine bases bind better thanthose with pyrimidines.Considering the known interactionsbetween RNA and amyloids andtheir ability to stabilize each other, we investigated the moleculardeterminants of RNA–amyloid interactions. For this, we tookadvantage of the insolubility of amyloids in order to detect the bindingof RNA, whereby mixtures of RNA and amyloids were centrifuged, andthe supernatant RNA was quantitated by reverse-phase HPLC. RNA-onlycontrols were used to quantitate the total RNA and to account forpossible nonspecific binding of the RNA to the plastic DNA LoBindtubes (Eppendorf). An amyloidogenic nature for the peptides used inthis study 1 was exp binding S4. The r binding S4. The r3G to peptide 4 . The data were fit to a single bindingsite, and the results presented in Figure S7 reveal a strong correlation between the affinity and the lengthof the oligonucleotide. The Kd valuesfor the series pGUG, pGUGU, and pGUGUG are 82 μM, 1.3 μM,and 75 nM, respectively, while that of hexanucleotide [GU]3 (0.85 μM) lies between that of the phosphorylated tetra- andpentanucleotides, decreasing to 49 nM for the heptanucleotide [GU]3G. For each additional nucleotide, the binding increases bymore than an order of magnitude, which confirms our assumption ofan avidity-enhanced RNA–amyloid interaction. Interestingly,phosphorylated oligonucleotides bind better than their longer, nonphosphorylatedcounterparts bearing the same number of phosphates (pGUG vs [GU]2 and pGUGUG vs [GU]3) (S7). Thiscould be due to the extra charge density on the 5′ phosphatescontributing more to the binding than the additional nucleotides.In the context of the relatively low specificity of the codon–anticodoninteraction,28 it is noted that the μMaffinity of this potentially prebiotic interaction between the peptideamyloid and the RNA trinucleotide is at least 1–2 orders ofmagnitude stronger than the expected affinity between a complementarypair of trinucleotides.30 Based on the fractionbound at saturation, the binding stoichiometry for the heptanucleotideis three peptides per RNA molecule, while for the trinucleotide itis one to one.Considering the nearly 100% binding of 50 μM[GU]eptide 4 2B, we reeptide 4 3. Intereeptide 4 3C.Consis [GU]3) 3 and S7.7 and 9. The results shownin Figure S8 indicate a strong dependenceon the RNA sequence for binding, even in the context of a codon-sizedtrinucleotide. Here, the lack of binding of pAAA and pCCC to peptides 7 and 9 could be explained by the expected positivecharge character on adenine (pKa ≈3.7) and cytosine (pKa ≈ 4.3) atpH 3. In line with this argument, we find that at pH 5, these sametwo trinucleotides exhibit sequence-selective binding to the V andA variants of peptide set S7 from Table S1 .Having observed significant sequence selectivityfor the RNA heptanucleotidesequences 2C, we al(1/3)) with an inverselyproportional decrease of the diffusion coefficient by a factor of0.63. However, the observed diffusion coefficients ((5.37 ± 0.23)× 10–10 m2/s for pGGG and (5.19± 0.52) × 10–10 m2/s for pGAG)have a ratio of 1.03 .Consideringthe significantly better binding of pGGG to all ofthe tested peptide amyloids, and despite sodium being the only metalcation added to the system, we questioned whether the poly-G sequencemay be supporting the formation of a quadruplex. However, we couldnot detect any temperature dependence on the UV absorption spectrumof pGGG that would indicate that an oligomerization occurs. Also,we measured the diffusion coefficients of pGGG and pGAG by diffusion-orderedNMR spectroscopy (DOSY) and found them to be too similar to be consistentwith a difference in oligomerization state; assuming roughly sphericalmolecules, tetramerization would lead to an increase in the radiusby a factor of ∼1.59  displayed very similar binding, as did the3′-phosporylated structural isomer. However, the 2′–5′linked structural isomer bound significantly less to the amyloids,in particular to the peptide 9 amyloid, for which thebinding was negligible (1H NMR). Next, we probed the spacing and flexibilitybetween the terminal nucleotides with a series of diol linkers of2–4 carbons in length . The three different pGLnG RNAs all had a similar percentage bound to the amyloids and werein a range similar to that of pGdG (single baseless RNA). The abovedata, summarized in 7 amyloidwas similar, with both displaying a weaker binding than the [GU]3G RNA  and their analogue (pGdG) to peptides 7 and 9 and found similar observations of better RNA than DNA binding , for which the DNA trinucleotide pGUGhad a Kd of 117 μM and the RNA trinucleotidepGUG had a Kd of 82 μM.To probe the role of the RNA structure in the binding interaction,we assayed a series of RNA and RNA analogues of the pGGG trinucleotidefor binding to peptides gligible 4. This mU]3G RNA 5A. While binding 5. This iFigure S10 reveal a striking enhancement of the [GU]3G RNA stabilizationagainst hydrolytic degradation when in the presence of the peptide 7 amyloid, suggesting that a prebiotic environment like heatedrock pores, in which convection and thermophoresis lead to thermalcycling, could select for RNAs that are bound to and protected byamyloids.31 On the other hand, we alsofound that the presence of [GU]3G enhanced the aggregationof peptide 7 at pH 2.6, which at this pH did not completelyaggregate on its own, likely due to the protonation of the glutamateresidues disrupting the electrostatic interactions with the lysineresidues  for sequential assignment and structurecalculation (following procedures described in detail in the Supporting Information). The spectra with intermolecularcross peaks between the RNA and the protein indicate an interactionof the RNA with both the N- and C-termini of the peptide. Both thephosphates as well as the bases of G1, U2, and C3 interact with theamyloid via residues Val1 at the positively charged N-terminus orIle8 on the C-terminus of the peptide. Sufficient distance and angularrestraints could be collected from the spectra (Table S2) for an RNA–peptide amyloid complex structuredetermination, yielding a well-defined peptide amyloid structure withan overall root-mean-square deviation (RMSD) of 0.8 Å and boundto a less well-defined RNA molecule having an overall RMSD of 1.5Å (Table S2). The 3D structure ofthe peptide amyloid is composed of two parallel β-sheets thatinteract with each other face-to-face ,burying a hydrophobic core comprised of residues Ala2, Ala4, Ile6,and Ile8 and forming a class 1 steric zipper32  aswell as the NMR data indicate that the interaction of the RNA withthe peptide amyloid spanning about 2–3 peptides on the edgeof the fibril is rather dynamic in nature. The broad phosphate NMRcross peaks in Figure S11 indicate thepresence of structural plasticity and a weak interaction. Consideringthe structural data for the pGUCAp/VAQAQINI-NH2 complex,with binding modes of the electrostatic interaction between the RNAphosphates and the peptide N-terminus and the mostly hydrophobic interactionsbetween the bases and the edge of the hydrophobic core of the peptideamyloid, a wide range of interactions could be expected, with thecalculated structure providing some snapshots.In order to gain a deeper insight into the determinantsof amyloid–RNAinteractions, we undertook the structural characterization of oneinteracting pair by solid-state NMR. In the study of the aggregatedstates of peptides and RNA, solid-state NMR has many advantages overother methods, but it requires that the peptide and the RNA sequenceshave low spectral overlap in order to be able to resolve the resonances.For this task we chose the peptide VAQAQINI-NHzipper32 7. ThisyTable S1). Considering the RNA–amyloid structure presented above,we mostly chose the variable amino acid positions to be located eitherat the N-terminus with up to three repeats or at the C-terminus, withsome at expected solvent-exposed positions.With the aim to establish codon (anticodon)–aminoacid specificity,we pursued a more detailed investigation of the sequence-specificnature of the RNA trinucleotide–amyloid interactions. For this,we analyzed the interactions between phosphorylated trinucleotideswith the sequence pGNG or pGNC  and 18 sets ofamyloidogenic peptides for which one or more positions were systematicallyvaried as Ala, Val, Gly, and Asp/Glu 4 peptide , with the physical association between themplaying a role in the primordial soup before the translation machinerydeveloped.35 There are several lines of evidencethat support this hypothesis, including early work that demonstratedcodon–amino acid interactions,36 the physical proximity between codons and their cognate amino acidresidues in the ribosome and other RNA binding proteins,38 and the extremely small probability that pure chance has led cognatecodons to appear at such a high frequency in the amino acid bindingregions of natural amino acid binding RNAs and artificial aptamers.40 It has been argued that the codon specificity for the four mostprominent prebiotic amino acids  could beencoded by the central nucleotide of a GNC code.41 In the context of our data, this suggests that nature’sselection of a codon length of three nucleotides was in order to generatesufficient avidity in the defining interactions of RNA with aminoacids  because a dinucleotidecodon would have been sufficient to code for the few amino acids inthe early phase of life. Finally, it is worth noting that amyloids,with their periodic structure and well-defined surface, now have theproven potential to increase the local concentration and order ofnucleotides in an otherwise dilute disordered system. The sequence-selectivenature of said interactions as well as the catalytic ability of theamyloid could have promoted the synthesis of distinct and longer ribonucleotides,possibly an important step for the evolution of catalytic RNAs.In summary, we have found that small RNAs can bindto peptide amyloidsin a sequence-dependent manner and that such interactions could bemutually beneficial for the RNA and the peptide by stabilizing theamyloid structure and reducing the extent of RNA hydrolysis. We haveidentified the important elements of the interaction, namely, a minimumof three ribonucleotides in which three phosphates and three nucleobasescontribute to binding. While there are many theories on the originof the genetic code, they all must be able to explain the self-evidentfact that at one point, specific interactions developed between RNAand amino acids. Thus, our finding that small, codon-sized RNA moleculesbind peptide amyloids through both nonspecific electrostatic interactionsbetween the phosphate backbone of the RNA and cationic groups on thepeptide amyloid (such as the N-terminus) as well as sequence-specificinteractions via the nucleobases of the trinucleotide suggests a mechanismby which RNA–amyloid interactions could support the originof a genetic code. Such a mechanism is interesting due to the simplicityof both the RNA and peptide species involved and suggests that theinitial specificity in the genetic code could have been establishedbefore RNA molecules that are large enough to bind to amino acidsexisted. Considering the plausible prebiotic nature of peptide amyloids,