However, current transcriptome-wide experimental methods to chart these interactions are limited by their poor sensitivity. Here we extend the hiCLIP atlas of duplexes bound by Staufen1 (STAU1) ∼10-fold, through careful consideration of experimental assumptions, and the development of bespoke computational methods which we apply to existing data. We present Tosca, a Nextflow computational pipeline for the processing, analysis and visualisation of proximity ligation sequencing data generally. We use our extended duplex atlas to discover insights into the RNA selectivity of STAU1, revealing the importance of structural symmetry and duplex-span-dependent nucleotide composition. Furthermore, we identify heterogeneity in the relationship between transcripts with STAU1-bound 3′ UTR duplexes and metabolism of the associated RNAs that we relate to RNA structure: transcripts with short-range proximal 3′ UTR duplexes have high degradation rates, but those with long-range duplexes have low rates. Overall, our work enables the integrative analysis of proximity ligation data delivering insights into specific features and effects of RBP-RNA structure interactions.The structure of mRNA molecules plays an important role in its interactions with  It is becoming increasingly evident that the structure of RNA molecules plays an important role in this interplay. In particular, there are RBPs that contain protein domains that specifically bind double-stranded RNA (dsRNA) duplexes. The Staufen family is one such group of proteins with important roles in mRNA localisation, stability and translation. In order to understand the relationships between RNA structure, Staufen binding and the functional consequences, a comprehensive transcriptome-wide atlas of the bound structures in vivo, following which cells are lysed and RNA partially fragmented by RNase I. RBP-bound duplexes are enriched by immunoprecipitation, and the two fragments of interacting RNA strands ligated together. After digesting the bound RBP, cDNA is prepared from these molecules and sequenced. Two successfully ligated RNA fragments will yield a hybrid read, which we define as a sequencing read that maps non-contiguously to the transcriptome and thus represents two proximity ligated fragments , an RNA proximity ligation method Figure  was devein vivo STAU1 duplexes have been recovered from the data.Although proximity ligation experiments are primarily aimed at detecting hybrid reads, recovered hybrids typically constitute only a minority of the sequencing data due to both experimental and computational challenges: the ligation efficiency is low and accurate hybrid read identification and delineation of the original interacting RNA fragments is non-trivial. A data analysis challenge here is to determine unambiguously where the two arms start and end within sequencing reads, so that they can be mapped to the correct transcript locations. To address these issues, the hiCLIP method improved on previous methods , most nMoreover, the paucity of robustly-detected duplexes limits the extent to which hiCLIP results can be broadly integrated with other transcriptome-wide RNA duplex datasets, such as PARIS , or funcTo improve the sensitivity of duplex detection from hiCLIP data, we refined and extended the computational approach. Specifically, we re-evaluated the experimental steps to understand where duplexes could potentially have been ‘lost’ in the original data analysis through misassignation of non-hybrid reads. We challenged three key assumptions of specific steps of the experimental protocol through which this could have occurred Figure .First, we sought to recover hybrids containing truncated linker adapter sequences. 20–30% of the reads were found to contain linker-sequencing adapter dimers rather than sequencing adapters alone  hybTosca  to make it computationally tractable; largely by removing unannotated regions of the genome. We started to create this reference sequence by combining rDNA and 5S rRNA sequences from NCBI (U13369.1 and NR_023363.1); mature tRNA sequences from gtRNAdb  annotated in Gencode V33 as: ‘rRNA’, ‘rRNA_pseudogene’, or ‘snRNA’; (ii) annotated in gtRNAdb as tRNA; and (iii) annotated by RepeatMasker (obtained from the UCSC table browser) as: ‘rRNA’, ‘tRNA’, ‘snRNA’, ‘srpRNA’, ‘scRNA’ or ‘RNA’ (the latter corresponding to 7SK). We used this as input to BEDtools  to mask https://github.com/ulelab/icount-mini) and as previously detailed for iCLIP  for direct proximity ligation reads without a linker adapter, in order to ensure consistency we also aligned each hybrid arm in one step to our reference sequences using STAR v 2.7.7a .in silico for these reads which we knew contained hybrids, we stitched together the two hybrid arm sequences after removing the intervening linker adapter sequence.To mimic direct proximity ligation (i.e. hybrid reads without a linker adapter) Tosca, in Nextflow . Furthermore, duplexes are converted into BED files for visualisation. Optionally, selected genes of interest can be supplied and arc plots generated for viewing in IGV and nucleotide-resolution or binned contact map matrices for static plotting.To identify STAU1 crosslink peaks from hiCLIP reads in which no hybrids had been detected, and HuR and TDP-43 crosslink peaks from iCLIP data . Subsequent processing was using Tosca as above.We trimmed sequencing adapters using Cutadapt and collapsed PCR duplicates in advance of alignment using the readCollapse.pl script used in the original publication (available at https://github.com/caochch/RIC-seq). Low-complexity fragments from each end of reads were then trimmed with Cutadapt as in the original publication. Paired reads were then merged with BBMerge. The merged FASTQ files were used as input for Tosca as above.We trimmed sequencing adapters using Cutadapt and collapsed PCR duplicates prior to alignment using the remove_duplicated_reads.pl script used in the original publication  by broadly replicating the original analysis. The dataset consists of two replicates (high and low RNase library preparation conditions) and contain 2 429 385 and 2 996 034 reads respectively. One of the original rationales for the linker adapter was to enable easy bioinformatic deconvolution of the two hybrid arms from a sequencing read  and 2 891 834 (96.5%) sequencing reads were classified as not containing linker adapters in the high and low RNase conditions respectively and formed the starting point for the subsequent analysis.Tosca computational pipeline, Next, we explored the occurrence of hybrids from direct proximity ligation. This challenged the validity of the assumption that a linker adapter was necessary for proximity ligation. However, identifying the two arms of a hybrid within a sequencing read without the benefit of the linker to demarcate the join is challenging. Using a partial alignment and hybrid reconstruction approach on a read-by-read basis  of the reads (P < 2.2 × 10−16). Furthermore, the length of the shorter arm fragment within a hybrid read was also significantly shorter (P < 2.2 × 10−16). It is likely that these shorter fragments align to multiple loci on the transcriptome and thus cannot be assigned uniquely; this prevents the reconstruction of a unique hybrid solution for these reads. There were no differences between high or low RNase digestion conditions  data shows that our computational approach can recover direct proximity ligation hybrids with high accuracy. It also demonstrates the importance of an adequately long cDNA to be able to do so. There is a balance between the level of RNase digestion, sequencing read length and maintaining the spatial resolution of the identified structure: we would recommend reads at least 75, if not 100 nt long and that RNase digestion is titrated so that each hybrid arm is at least 25–30 nt long.While the new methods described thus far have considerably improved the recovery of hybrids from the STAU1 hiCLIP data, for 2 383 462 and 2 946 736 of the reads in the high and low RNase conditions we were ultimately unable to resolve a hybrid after mapping and hybrid identification and were thus non-hybrid reads. Owing to the stringent purification steps, these non-hybrid reads still represent sites where STAU1 has crosslinked to specific transcripts and thus reflect STAU1 binding. We set out to explore whether they could additionally represent short duplex structures whose loop regions were protected from RNase digestion Figure , but thiTo explore the secondary structure around 3′ UTR STAU1 crosslink peaks, we first calculated the local base-pairing probability of each nucleotide and generated a meta-profile centred on the peak start position Figure . As expeNext, we focused on the individual peaks to characterise the presence of this structural motif further Figure . We usedP < 2.2 × 10−16). NMR studies have found that stems of at least 8 bp were needed for STAU1 to bind  and the derived duplexes, and applied our graph-based clustering approach to consolidate them into a complete atlas of STAU1-bound duplexes Figure . We asseTosca, to enable robust, reproducible and scalable implementation of our analysis approach (https://github.com/amchakra/tosca). Reference transcriptomes are provided for human, mouse and rat, but custom references can also be user-provided. Tosca performs transcriptomic alignment and hybrid read identification and PCR de-duplication. This is followed by conversion to genomic coordinates and hybrid annotation. Graph-based clustering of hybrids enables delineation of the duplexes they represent. For these, hybridisation energies are calculated to assess duplex stability  the stabilisation of RNA-RNA interactions and (ii) their purification. Both STAU1 hiCLIP and PARIS enrich for interactions mediated by base-pairing, by virtue of UV crosslinking and purification of STAU1, which specifically binds dsRNA (hiCLIP) and psoralen crosslinking dsRNA (PARIS). In contrast, RIC-seq employs formaldehyde (FA) crosslinking to stabilise interactions. FA crosslinks protein-RNA and protein-protein interactions, thus interactions obtained by RIC-seq include not only RBP-mediated duplexes, but also RNA–RNA contacts mediated by an RBP or protein-protein interactions, which may not involve base-pairing interactions or extended/stable duplexes. Therefore, PARIS and RIC-seq provide two different reference global datasets to compare our STAU1 data against: PARIS as representative of duplexes, and RIC-seq as representative of protein-mediated interactions.Tosca with the same gene annotations as for the hiCLIP analysis, and clustered the hybrid reads to generate an atlas of 116 929 intra-transcript duplexes for PARIS (excluding rRNA and tRNA) from 537 448 unique hybrids across three biological replicates) and 63 966 unique intra-transcript interactions for RIC-seq .To ensure a valid comparison, we re-processed the PARIS and the RIC-seq data using Duplexes recovered experimentally (i.e. through proximity ligation) likely reflected a separate group of STAU1-bound structures compared to those recovered through computational derivation (for which only 3′ UTR regions were considered) Figure , B. We kAs previously noted, STAU1 often binds long-range 3′ UTRs duplexes, which can span intervening regions over 100 nt in length, and in some cases even kilobases . The proWe next sought to describe the stability and the features of 3′UTR intra-transcript contacts for STAU1 hiCLIP compared to the other methods. Because some of the metrics we calculate for our comparisons are dependent on interaction length, which in turn is dependent on the original hybrid arm length, we first assessed the latter for STAU1 hiCLIP, PARIS and RIC-seq datasets . While SGiven the non-uniform distribution of duplex spans for STAU1-bound duplexes, we classified them into short-range , medium-range , long-range  and derived (6206 duplexes).P = < 2.2 × 10−16, Figure We found that despite similar ranges of arm lengths, STAU1 hiCLIP duplexes had more bases paired compared to PARIS  or RIC-seq interactions (P = 2.51 × 10−60) .Intriguingly, we noted that the hybridisation energies for the shuffled controls, where dinucleotide content had been preserved, were more negative for PARIS and RIC-seq than for STAU1 hiCLIP. This led us to question whether there were differences in GC content; STAU1 hiCLIP duplexes were indeed significantly more AU-rich when compared with PARIS duplexes or RIC-seq interactions Figure  and furtP = 4.2 × 10−238, vs RIC-seq median 68%, P < 2.2 × 10−16). This prompted us to explore the composition of the duplexes in terms of bulges (i.e. single-nucleotide unpaired residues within the stem) and internal loops. Because the following measurements are directly linked to hybrid lengths, which are only comparable for PARIS and hiCLIP, and because RIC-seq data also contains interactions that are not driven by duplex formation (as discussed above), we only compared the STAU1 and PARIS structural features. We found that while there were similar numbers of bulges or internal loops overall in the two , our findings are consistent with other biochemical and modelling studies. Intriguingly, the original NMR studies exploring the mechanisms of Staufen double-stranded RNA binding domain recognition of dsRNA found, through generation of synthetic stem loops that disruption of the helical structure through the introduction of bulges significantly reduced binding . Furtheral loops . The preSRSF1 3′ UTR  containing STAU1 hiCLIP (and/or PARIS) duplexes (STAU1 genes), (ii) containing only PARIS duplexes (PARIS genes) or (iii) not containing any detected duplexes. We then used the gene copy numbers calculated using spike-ins by  to assesP < 2.2 × 10−16 and P < 2.2 × 10−16 respectively) and significantly lower degradation rates . Given the known role of STAU1 in RNA degradation through Staufen-mediated decay  as expected given the known relationship between 3′ UTRs and RNA stability .It appears that these long-range 3′ UTR duplexes identified by STAU1 hiCLIP compensate for the degradation effect of longer 3′ UTRs. To quantify this we adapted the concept of circularisation score  to the 3To explore this effect in an alternative way, we assessed the positions of the proximal and distal duplex arms on the 3′ UTR Figure . We diviThe integrative computational analysis we present here examines the RNA metabolism features of genes with STAU1 hiCLIP identified 3′ UTR duplexes and identifies a correlative relationship between the two. The original RNA-seq data identified a very subtle overall abundance change for mRNAs containing 3′UTR hiCLIP duplexes when comparing STAU1 knockdown and rescue conditions , but thiTosca, which is broadly applicable to multiple kinds of proximity ligation methods and includes downstream duplex characterisation and visualisation. Finally, we have integrated our atlas (i) with global RNA duplexes to determine features of STAU1 RNA selectivity and (ii) with RNA metabolism rates to uncover putative relationships between RNA structure and degradation.In conclusion, first, we have extended the atlas of STAU1-bound duplexes ∼10-fold primarily through: demonstrating that i) direct proximity ligation occurs at sufficient levels in the absence of a linker adapter; and ii) short-range STAU1-bound duplexes exist, but cannot be recovered through proximity ligation and require computational derivation. Thus we propose that the linker adapter can be omitted from the hiCLIP protocol. Second, we have developed a robust, reproducible and scalable computational pipeline, https://github.com/luslab/comp-hiclip. Tosca is available at https://github.com/amchakra/tosca. The pipeline is also deposited on Zenodo: https://doi.org/10.5281/zenodo.7728671. Publicly available data was used for this project. Raw STAU1 hiCLIP data is available from ArrayExpress at E-MTAB-2937 with matched RNA-seq data at E-MTAB-2940. Raw PARIS data is available from GEO at GSE74353. Raw RIC-seq data is available at GEO under accession number GSE127188. Raw RNA metabolism data is available from GEO at GSE84722, with processed data obtained from the supplementary files. Raw RNA degradation data is available from GEO at GSE99517, with processed data obtained from the supplementary material. HuR and TDP-43 iCLIP data are available from ArrayExpress at E-MTAB-11854 and E-MTAB-4733 respectively.All analysis code and RMarkdown notebooks to re-generate the figures are available at gkad221_Supplemental_FileClick here for additional data file.


With advances in sequencing technology, metatranscriptome sequencing from a variety of environmental and biological sources has revealed the existence of various previously unknown RNA viruses. This review presents recent major RNA virome studies sampled from invertebrate and vertebrate species as well as aquatic environments. In particular, we focus on severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) and related RNA virus identification through metatranscriptome sequencing analyses. Recently developed bioinformatics software and databases for RNA virus identification are introduced. A relationship between newly identified RNA viruses and endogenous viral elements in host genomes is also discussed. This review introduces the progress of RNA virus discovery using large‐scale sequencing data. Particularly, severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) and related RNA virus identification through metatranscriptome sequencing analyses were summarized. We also discussed development of bioinformatics tools to search for RNA virus identification. Future RNA virus discovery studies are also discussed. Orthornavirae is a major kingdom of Riboviria that contains all the double‐strand RNA (dsRNA) and single‐strand RNA (ssRNA) viruses, excluding retroviruses (belonging to kingdom Pararnavirae). For their genome replications, RNA viral genomes of Orthornavirae code RNA‐dependent RNA polymerase (RdRp), while retroviruses code RNA‐dependent DNA polymerase . As for Ribozyviria, it consists of only a single family Kolmioviridae containing Deltavirus such as hepatitis delta virus [Ribozyviria do not code RdRp, they utilize RdRp of other RNA viruses or host replication enzymes for their replications [Eptesicus bats also code RdRp that was integrated into their genomes [Viruses are often classified as something between organisms and material. Although viruses own their genetic materials and successfully reproduce themselves in host cells, they cannot grow and replicate by themselves. Viruses that utilize RNA as a genetic material comprise realms named ta virus . As RNA  genomes . Recentl genomes , 8, 9, o genomes , 11, and genomes , 17, 18.RNA viruses have been comprehensively identified using metatranscriptome (RNA metagenome) data. The standard procedure of metatranscriptome analysis is summarized in Fig. Jingmenvirus was found to be associated with human febrile illness [Orthonairovirus was identified in patients showing acute febrile illness with thrombocytopenia and leukopenia after a tick bite [Since arthropod species transmit various RNA viruses that infect humans, various metatranscriptome analyses of arthropod species have been conducted and revealed a vast diversity of RNA viruses not previously isolated. Li et al.  conducte illness , while Yick bite .Rhinolophus affinis) [Manis javanica) were also found to be similar to SARS‐CoV‐2, particularly with respect to the receptor‐binding domain of the spike protein [One of the most prominent examples of metatranscriptome analyses for RNA virus detection was the discovery of SARS‐CoV‐2 and related coronaviruses. The genome sequence of SARS‐CoV‐2, a coronavirus causing severe respiratory illness named COVID‐19, was revealed by metatranscriptome analyses , 24, 25.affinis) . In addi protein , 29, 30. protein , 33, 34; protein , 9. Esse protein . Indeed, protein . FurtherPicornavirales, Tombusviridae, and Umbravirus were obtained. By using 454 sequencing technology which was the first commercially available next‐generation sequencer, Djikeng et al. obtained RNA aquatic samples at Lake Needwood, a freshwater lake in Maryland, USA, and identified various RNA virus‐derived assemblies [Metatranscriptome analyses have also revealed RNA virus communities in aquatic environments. In one of the oldest studies of the aquatic RNA virome, Culley et al.  sampled semblies . Subsequsemblies  obtainedsemblies  analyzedhttps://serratus.io. Neri et al. [https://riboviria.org). Those studies clearly suggest that there must be a huge number of RNA viruses that have not been identified to date. Such RNA viruses could also be revealed by metatranscriptome analyses in the future, which will be beneficial for understanding the nature of viruses and their impacts on human health. Meanwhile, no standards exist for the various current RNA virome studies; the quality and definition of RNA viruses reported for each study vary widely. In addition, the definition of RNA taxonomy is frequently revised to accommodate research on large‐scale RNA virus discoveries.Transcriptome data of various species and environments have been stored in public nucleotide sequencing databases, such as SRA , ERA 4040, and Di et al.  also anaMetatranscriptome analyses to detect RNA viruses largely depend on RNA viral genome sequences stored in databases, as the assembled contigs are usually compared with existing RNA viral genomes to determine whether they are derived from RNA viruses. Owing to the advancement of DNA sequencing technology, the number of RNA viral gene and genome sequences stored in public databases has been increasing. Using such RNA viral gene and genome information, various bioinformatics tools and databases have been developed.Orthornavirae). Palmscan with PALMdb also targets RdRp domains utilizing position‐specific scoring matrix approaches [Table proaches .blast [bowtie2 [bwa [kraken 2 [diamond [viromatch [virmine [virusseeker [virfind [virkraken (https://github.com/Strong‐Lab/VirKraken), viromescan [tar‐vir [k‐mer (length of k nucleotides) frequencies of viral genes/genomes. virfinder [Sequence similarity search programs, such as blast , 58, bow[bowtie2 , bwa [60kraken 2 , and dia[diamond , have beiromatch , virmine[virmine , virusseusseeker , virfind[virfind , virkrakromescan , and tar[tar‐vir . Recentlirfinder  is one oirfinder , CHEER [irfinder , CNN_Virirfinder ), recurrirfinder ), and muirfinder ), and atirfinder ) were reirfinder . Howeverk‐mer‐based support vector machine (SVM) classifier using EVE sequences as a training dataset and identified possible EVEs with low similarity to known RNA viruses in the human genome. Such EVEs could also be informative in the search for novel RNA viruses in nature. On the contrary, Kryukov et al. and Irwin et al. reported various viral contaminations in host genome data [Advances in genome research have also revealed many RNA virus‐derived sequences in eukaryotic genomes. For example, approximately 8–10% of the human genome corresponds to retroviral origins , 78, 79,ome data , 17, whiMetatranscriptome analyses have revealed a variety of RNA viruses in various hosts and environments. However, it has also been shown that the RNA viruses found to date represent only a small subset of all RNA viruses. One of the bottlenecks in RNA virus identification from metatranscriptome data is its reliance on similarity to known RNA virus sequences. As noted, machine‐learning‐based methods could be useful; however, it is also difficult to confirm whether those detected sequences are derived from RNA viruses or not. To overcome such problems, a sequencing method named ‘fragmented and primer ligated dsRNA sequencing’ (FLDS) has been developed , 83, 84.The authors declare no conflict of interest.SN, AO, KM, TE, YS, and TG designed the concept of the manuscript. SN wrote the manuscript. SN and SS made a table. SS made a figure. All authors edited and approved the manuscript.


Liquid-liquid phase separation (LLPS) has emerged as a crucial mechanism for cellular compartmentalization. One prominent example of this is the stress granule. Found in various types of cells, stress granule is a biomolecular condensate formed through phase separation. It comprises numerous RNA and RNA-binding proteins. Over the past decades, substantial knowledge has been gained about the composition and dynamics of stress granules. SGs can regulate various signaling pathways and have been associated with numerous human diseases, such as neurodegenerative diseases, cancer, and infectious diseases. The threat of viral infections continues to loom over society. Both DNA and RNA viruses depend on host cells for replication. Intriguingly, many stages of the viral life cycle are closely tied to RNA metabolism in human cells. The field of biomolecular condensates has rapidly advanced in recent times. In this context, we aim to summarize research on stress granules and their link to viral infections. Notably, stress granules triggered by viral infections behave differently from the canonical stress granules triggered by sodium arsenite (SA) and heat shock. Studying stress granules in the context of viral infections could offer a valuable platform to link viral replication processes and host anti-viral responses. A deeper understanding of these biological processes could pave the way for innovative interventions and treatments for viral infectious diseases. They could potentially bridge the gap between basic biological processes and interactions between viruses and their hosts. To our knowledge, the term “stress granule (SG)” was first used in a 1988 paper characterizing the heat shock protein granules formed in chicken embryo fibroblastsStress granules comprise ribonucleoprotein complexes formed under various internal and external environmental stimuli, mainly in the cytoplasm. The so-called nuclear stress granules contain the HSF1 transcription factor, primarily formed under heat shock stressAs its name indicates, SGs are not constitutive organelles in the cell under normal growth conditions. SGs are also heterogeneous in composition and dynamics with respect to the cell type and stress conditions. Although research on SGs has spanned over four decades, how SGs are assembled and the physiological and pathological roles they play have only started to be answered in the past few years. Recently, liquid-liquid phase separation (LLPS) has been suggested to be a mechanism of SG formation. It is recognized as a common mechanism for the assembly of many membrane-less organelles. With the identification of hundreds of SG protein components and thousands of RNA substrates, more insight has been revealed into the role of SG-dependent and SG-independent functions of SG-localized biomolecules. More will be learned from SG research in the future.Research on stress granules dates back to the 1970s when electron-dense structures formed under heat stress in plants and animal cells could be an early indication of stress granule assembly  . Later Small hsp aggregates or hsp granules are also observed in vertebrate cells, including hsp24 granules in chicken embryo fibroblast cellsSGs are not static structures, and early studies recognized the dynamic nature of SG components and suggested that SGs may function as a triage for mRNA fate for translation, decay, or recycling ,16–18 A genome-wide RNAi screening in 2008 for SG and p-body regulators in mammalian cell lines revealed an essential role of GlcNAC modification of ribosomal proteins in these RNP granule dynamicsin vivo contextWhat is the physiological relevance of SGs if they can only be induced by harsh stresses, such as 0.5 mM sodium arsenite or 30 min of 43°C heat shock? These early robust stimuli-induced SG models revealed some common biomolecule interactions and assembly mechanisms of SG and related RNP granules. Nevertheless, the formation and function of SGs will be better understood under specific physiological or pathological conditions. There are a couple of examples showing the relevance of SG to physiology and pathology. First, SGs have been observed in ischemic brain regions in a mouse model, indicating that SGs can be formed in anC.elegans early embryo development, proposing a dissolution-condensation process of P granule components as the underlying mechanism of the asymmetric P granule distribution. Since then, the field has expanded with numerous examples of LLPS features within and outside cells [Liquid-liquid phase separation (LLPS) is a form of biomolecular phase separation, as illustrated effectively in the landmark paper published by Clifford Brangwynne, Anthony Hyman, and others in 2009e cells ,47 . ThWhile the biomolecular condensate field is new and rapidly expanding, the concept has its roots in biological research dating back to 1899 when E. B WilsonC.elegans embryogenesisP granules, or polar granules, are germ-line-specific structures crucial for germ cell fate determination in various organisms. The partitioning of P granules from a homogenous state in the fertilized embryo to germ cell localization after the first cell division remained mysterious until the dissolution-condensation model was proposed in 2009Intrinsically disordered protein (IDP) or intrinsically disordered region (IDR)-containing proteins are widely identified as initiators of LLPS. The features encoded in the IDR sequence ultimately determine the LLPS capacity. Fused-in-sarcoma (FUS) is well studied and has been used to reveal that positively charged arginine and aromatic residues are critical for FUS homotypic LLPSRNA-binding proteins with IDR regions serve as nucleators for multiple condensates, and mutations in several such proteins cause amyotrophic lateral sclerosis (ALS). Investigations into the pathogenic mechanisms of these ALS mutations have spearheaded our understanding of the functions and molecular framework of biomolecular condensation. Well-studied examples include hnRNPA1, FUS, and TDP43 proteins ,68 . SiThe re-emergence of the LLPS concept in biology has been noted in various biological processes, including cell signaling, chromatin organization, gene transcription, splicing, nuclear pore transport, and membrane invagination and association. The field of biomolecular condensates is multidisciplinary and has benefitted from biophysical studies, polymer biochemistry, computational simulations, and other disciplines. For any identified condensate, several aspects need to be examined: what are the cellular conditions or biological context it formed? What are the key components in the condensate, and what is the biochemical/biophysical basis for condensate formation? Are there a few scaffold proteins or RNAs for condensation? How are condensation processes regulated, and could they be harnessed for therapeutic or bioengineering purposes? We refer readers to the many recent reviews published on LLPS and MLO studies ,71–74 Stress granules can form in response to various stimuli, and the general shutdown of protein translation is critical for SG formation. The translation initiation factor eIF2α occupies a key node of this signaling pathway, converging upstream signals with four kinase branches, including general control nonderepressible 2 (GCN2), protein kinase RNA-like ER kinase (PERK), heme-regulated inhibitor (HRI), and protein kinase R (PKR). Activation of any of these kinases leads to the phosphorylation of eIF2α and reduced cap-dependent protein translationSince the early indication in 1989 that SGs assembled from precursor structures, further identification of protein and RNA composition and the successful reconstitution of SG in test tubes have provided evidence for the current model of SG assembly. Generally, following the inhibition of translation initiation and ribosome run-off from mRNA, the pools of untranslated mRNA bound by various RNA-binding proteins increase. These proteins include G3BP1/2, Caprin1, UBAP2L, DDX3, small subunits of ribosomes, and many other abundant RNA-binding proteins in the cytoplasm. During stress, nuclear-cytoplasmic transport is perturbed, leading to the accumulation of many nuclear RNA-binding proteins in the cytoplasm, including TIA1/TIAR, FUS, and TDP43. The increased mRNA-RNA binding protein (RBP) interaction, RNA-RNA interaction, and protein-protein interaction lead to the phase separation of these RNP complexes, forming the nucleation cores of SG cores .FigureThe initial observation of SG formation under stress correlates with a general shutdown of translation. Translation inhibitors, including emetine and cycloheximide, either block SG formation or promote SG disassembly. Conversely, puromycin, which promotes ribosome disassembly, enhances SG assembly. Thus, the shutdown of translation and ribosome run-off are indispensable for SG formationPKR-KO cells, albeit at lower levels than in wild-type cells. These results indicate that an additional kinase downstream of RNase L is involvedSodium arsenite (SA) is the most commonly used stimulant for inducing SG formation in the field thus far. SA can cause various downstream effects, including ROS generation and eventually eIF2α phosphorylation. This effect is primarily mediated through the heme-regulated inhibitor (HRI) kinaseThe relationship between canonical SG proteins and viral infection remains unclear. PKR is activated in response to dsRNA recognition, and early evidence showed the localization of PKR in SGs. Two recent reports revealed that activated PKR forms novel cytoplasmic clusters distinct from SGs and proposed roles for these clusters in signaling activation ,89 . InIn the previous section, we examined the upstream signaling pathways leading to SG assembly established in the past few decades. This can form the basis for later perturbation of SG formation during viral infection and other human pathologies. In the following sections, we will discuss the critical roles of proteins and RNA in SG assembly and dynamics.in vitro SG reconstitution. Investigation into the features of these proteins could provide principles for SG assembly and could further guide the study of SG assembly and dynamics.Several proteins have been reported as essential for SG assembly over the past few years, including TIA1/TIAR, G3BP1/2, UBAP2L, PRRC2C, and CSDE1. We propose that these proteins function together in a protein-RNA network, with different contribution from each protein in the network. We aim to reconcile the contradictions in the literature using the network theory. A quantitative analysis of yeast p-bodies revealed seven abundant proteins inside the p-body, correlated with a high partition coefficient (PC)>30TIA1 (T cell restricted intracellular antigen) is an RNA-binding protein involved in mRNA splicing and translation and is typically localized in the cell nucleus. TIA1 contains three N-terminal RRM domains and a C-terminal disordered low complexity domain/prion-like domain. TIA1/TIAR can translocate to the cytoplasm to form SGs upon various stimulations and have been widely used as markers for SG labelingTIA1/TIAR has been shown to bind to West Nile and Dengue virus RNA, facilitate viral genome replication, and inhibit SG formation, preventing host protein translation shutoffin vitro and shows dynamic exchange with the surrounding environment. TIA1 mutations can lead to ALS, and point mutations within the PrLD can accelerate the liquid-to-solid transition of the condensateTIA1 contains a prion-like domain (PrLD), an intrinsically disordered domain. TIA1 protein can undergo LLPSG3BP1 andG3BP2 encode proteins of 466 and 488 amino acids, respectively, and are highly paralogous. HumanG3BP2 encodes a shorter isoform with 449 amino acids. G3BP1/2 is redundant in SG formation and has been used as a bait protein for SG core isolation and transcriptome analysis. G3BP1/2 has an N-terminal NTF2L domain, indicating potential involvement in nuclear-cytoplasmic transport. G3BP can interact with various NUP proteins, and NUP can be recruited into SGs under stress, which has implications for nuclear-cytoplasmic transportG3BP1 has been used as a bona fide marker for SG for decades. G3BP1 mainly localizes in the cytoplasm and is abundant in many cell types. G3BP1/2 is also broadly expressed in various tissues. The combined concentration of G3BP1/2 can reach over ~1.8 μM in U2OS cells, ~2.2 μM in Hela cellsG3BP1/2-dKO cells exhibited complete blockage of SG formation upon sodium arsenite treatment. This study indicated that G3BP1/2 might function as a scaffold for SG formation under SA treatment. Other SG proteins were tested to function as clients recruited to SGs, but they played a less critical role in SG assembly. This scaffold-client model has been examined in many cellular MLOsC.elegans centrosome assembly [Combining the genome-wide RNAi screening in cell line and the SG proteome identified by the Parker group, we identified a protein network underlying SA-induced SG formation in the U2OS cell linessembly ,109 .in vitro with a crowding reagent or with RNA. The features underlying crowding and RNA-triggered phase separation are different. We believe that the G3BP-RNA LLPS system can better recapitulate SG in cells. Multivalency encoded in the G3BP protein and RNA determines the phase separation threshold and SG formation in cells. The interaction valence is critical in many LLPS systems. The multivalency is encoded in the tandem RNA-binding motifs in G3BP1, as well as the length of RNA. Both RNA-binding motifs are required for LLPS, and the valence of arginine in the RGG domain is also important. Mutation of 5 out of 11 arginine residues in G3BP1 abolishes RNA-triggered LLPS, but crowding-induced LLPS can still occur. Longer RNA can better facilitate G3BP1 LLPS. Single-strandedness is a crucial feature, but G3BP shows less sequence specificity for the RNA sequence [G3BP1 protein can undergo LLPSequence ,39 .The valence, but not the binding affinity, matters. With the SWAP of RBD with the KH motif to create chimeric G3BP proteins, the rescue of binding affinity cannot rescue SG formation, but a valence of no less than two motifs is required for G3BP LLPS. Network theory can also help explain the contribution of other SG proteins to SG formation. Caprin1 and TIA1 are binding partners of G3BP, and both can promote G3BP1-RNA LLPS, indicating positive cooperativity. It has long been recognized that USP10 is a negative regulator of SG, with inhibition of SG by USP10 overexpression ,38 . ThMultiple pieces of evidence have shown the importance of post-translational modification (PTM) of G3BP in regulating SG dynamics. Upon SA treatment, G3BP undergoes arginine-demethylation within their RGG RNA-binding domains, likely increasing their RNA binding and promoting SG assemblyUBAP2L KO alone significantly reduced SG formation in U2OS cells. Proximity labeling with BioID and APEX2 fused to G3BP1 identified UABP2L/UBAP2 as SG components [UBAP2 and UBAP2L are SG proteins. They are homologous proteins in the human proteome containing ubiquitin-binding and RNA-binding domains. UBAP2L is considered one of the key proteins required for SG formation, andponents ,119 . Aponents ,120 . Qponents ,121 . UG3BP1/2 dKO cellsC.elegans have been shown to localize to SGs under heat shock stress in the germline and embryo stages. Genetic deletion mutants showed the dispensability of PQN-59 and GTBP1 in SG formation in the worm modelUBAP2L can still form granules inMany SG proteins are nuclear proteins. The nucleolus has a tight connection with SG formation; many nucleolus proteins shuttle to SGs in the presence of stress and are regulated by several cell signaling pathways ,125 . Nc9orf72 gene can phase separatein vitro, form RNA foci in patient neurons, and exert toxicity to cells. The exact sequence and repeat number are critical for repeat RNA phase separationin vitro RNA condensate can recapitulate the RNA transcriptome of SG in cellsRNP granules are prevalent under physiological and pathological conditions ,127 andA.gossypii form two types of condensates withBIN1 andCLN3 mRNA. The secondary structure of RNA determines the specificity of the condensate [Super-resolution microscope imaging reveals the heterogeneity of RNA clusters inside SG and germ granules ,135 . Tdensate ,139 . TWhat is the concentration of free RNA in cells? It is estimated that the concentration of cytosolic RNA in mammalian cells can reach 80-300 ng/μL under stressLLPS provides a solid framework for studying the dynamics of SGs, whose disassembly plays critical roles in various pathological conditions. Aberrant clearance of SGs can lead to the formation of fibril-like protein aggregates, resulting in loss of function and toxicity. SG disassembly occurs mainly through two pathways: degradation via the autophagy/UPS pathway and recycling of SG components to a diffused state. ALS mutations often result in the stabilization of SG and delayed clearance. Similarly, SG dynamics are regulated during viral infection, with active remodeling of SG composition by targeting the phosphorylation state of eIF2α or cleavage/hijacking key SG proteins.Cytoplasmic SG localization can regulate a protein’s function by sequestering the protein away from normal localization. For example, 14 out of 20 tested NUPs, which form nuclear pore complexes, have been shown to localize to SGs. Nuclear-cytoplasmic transport is defective in cells under stress when SG formation occursThe composition of SG varies under different stresses and in different cell lines. An important and closely related analog of SG is the neuronal granule in the nervous system. Neuronal granules are constitutively present in neurons, primarily in neurites, and contain many RNA-binding proteins commonly identified in arsenite or heat shock-induced SGs, including FMR1A.gossypii, suggesting that the membrane contact site can regulate the size and dynamics of MLOsSGs are also connected to the ER network, and the membrane structure plays a crucial role in SG divisionViral infection significantly remodels the host’s intracellular signaling network and structures. The formation of specialized structures enriched in viral/host protein/nucleic acids, also known as inclusion bodies (IBs), viral replication compartments (vRCs), viral factories, virosomes, or viroplasms, has been extensively investigated. With the rapid growth of understanding in LLPS, these structures are now increasingly recognized as membrane-less biomolecular condensates. In this section, we review recent advances in the role of liquid-liquid phase separation in viral replication and virion assembly, highlighting phase separation as an essential and common strategy supporting viral replication .FigureMononegavirales, established in 1991, includes viruses with non-segmented, linear, single-stranded, negative-sense RNA genomes. According to the International Committee on Taxonomy of Viruses (ICTV), 11 families are classifiedFiloviridae family, whose members include another deadly virus, Marburgvirus. EBOV replication occurs in the inclusion body, where viral genomic RNA is synthesized and encapsidated by the nucleoprotein NP. The viral inclusion body was initially suggested to be a nonfunctional protein aggregate. However, recent studies have indicated their essential roles in host factor recruitment, viral RNA replication, and protein expressionThe Ebola virus is classified into thein vitro and drive nucleocapsid assembly, indicating enhanced viral particle assembly by phase separationThe measles virus (MeV), a member of the Paramyxoviridae family, replicates its RNA in cytoplasmic inclusion bodies (IBs). Reports of MeV IBs date back to the 1950s, when these structures were noted to be globular, elongated, or irregular in the cytoplasm or the nucleus ,159 . VThe neurotropic virus RABV causes fatal encephalitis in humans and animals, posing a severe threat to human healthcare. The IBs of RABV, also known as Negri bodies (NBs), were first identified in 1903 by Adelchi Negriin vitroLLPS-induced IB formation has also been reported in respiratory syncytial virus (RSV). RSV IBs harbor viral proteins N, P, and L, as well as the viral transcription anti-terminator M2-1. The M2-1 protein can form a subcompartment within IBs, known as IB-associated granules (IBAGs)A similar example of IBs comes from the widely used model virus VSV. The replication compartments of VSV are formed by the co-expression of N, P, and L proteins, which exhibit dynamic fusion and fissionOverall, IBs of negative-stranded RNA viruses have been proven to be liquid-like structures formed by transcription or replication-associated factors in the cytoplasm of infected cells. RNA viruses replicating in the nucleus, such as influenza A virus (IAV), also utilize LLPS to facilitate their propagation.Influenza A viruses belong to the Orthomyxoviridae family and contain a segmented single-stranded, negative-sense RNA. An eight-partite RNA genome is characterized, and each RNA segment is encapsidated as an individual viral ribonucleoprotein (vRNP) complexAlthough the vRCs of positive-stranded RNA viruses are usually associated with cellular membrane systems, increasing evidence shows that proteins encoded by positive-stranded RNA viruses can also undergo phase separation and play a crucial role in the viral life cycle. For example, the SARS-CoV-2 nucleocapsid N protein has been intensively investigated, revealing its strong LLPS propensity and important function in viral genome packaging and innate immunity repression.in vitro without RNA [in vitro [During SARS-CoV-2 virion assembly, the structural nucleocapsid N protein associates with viral genomic RNA and packages into RNP complexes. Approximately 40% of the N protein sequence is predicted to be intrinsically disorderedout RNA –175 . Hn vitro ,176 . Mn vitro ,175 . Ain vivoApart from its critical role in assembling viral particles, N protein condensation is also crucial in repressing MAVS aggregation, thus inhibiting the MAVS-dependent interferon responsein vivode novo viral granules distinct from host SGs. In addition, the fluidity of G3BP1 is reduced in N protein-invaded SGs; other SG-associated proteins, such as FUS, TDP43, and hnRNPA2, are found to form enhanced amyloid aggregation, suggesting that the N protein alters SG dynamics and promotes SG phase transition from liquid-like to solid-likeStress granules are formed upon viral invasion and are considered one of the host’s antiviral strategies by shutting off viral mRNA translation. N protein is reported to be enriched in SGs –181 . HIn addition to SARS-CoV-2, another example of LLPS of positive-strand ssRNA virus comes from Rotavirus. The interaction between viral non-structural protein 2 (NSP2) and non-structural protein 5 (NSP5) results in spherical puncta formation in the cytoplasm, which also includes other viral factors, such as VP1, VP3, VP2, and VP6, along with its genomic RNA2+ chelator TPEN disrupts NC LLPS and relieves the HIV-1-induced blockade of SGAs a retrovirus, HIV-1 bears a positive-sense single-stranded RNA, which will be reverse-transcribed into a DNA molecule and integrated into the host genome. The HIV-1 nucleocapsid protein (NC) has recently been reported to phase separate in a zinc ion-dependent manner. The Znin vitroHerpes simplex virus 1 (HSV-1) is a DNA virus that primarily replicates in the nucleus. HSV-1 replication was reported to be associated with phase separation or assembly processes, but the evidence remains a matter of debateKSHV’s extrachromosomal circular DNA formation and virally encoded latency-associated nuclear antigen (LANA) can form LANA-associated nuclear bodies (LANA-NBs) during latent viral infection. Recently, LANA-NBs have been shown to form via the liquid-liquid phase separation processEBV is a strong oncogenic virus for human cells and is closely associated with various types of human cancer. As the master transcription factor and its co-factor, EBNA2 and EBNALP form nuclear condensates with liquid-like properties at the super-enhancer sites of MYC and Runx3 to activate these oncogenesThe brief examples provided above, which include positive-strand and negative-strand RNA viruses as well as DNA viruses, demonstrate the prevalence of viral inclusions formed during the viral life cycle. The characteristics of these structures align with the properties of biomolecular condensates such as P granules, SGs, and p-bodies. Two areas require further study, which will not only enhance our understanding of viral intervention but also contribute to our knowledge of basic biological processes.First, a comprehensive characterization of the compositions of each viral inclusion would provide insight into the assembly and regulatory mechanisms of these viral condensates. What host proteins are recruited into the condensate? Are biomolecules other than proteins involved, such as RNA and small metabolites? What is the relationship between viral and host condensates, such as SGs, p-bodies, and nucleolus?Second, how does the condensation of viral and host biomolecules facilitate viral genome replication and protein expression? How does the viral condensate evade the host immune system, and what roles do these condensates play in viral-host interactions? Given the broad suggestion in the literature of the anti-viral effect of SG, further studies focusing on the viral condensate and human SG could facilitate the discovery of the roles condensates play in this arms race interplay and may lead to the development of novel therapeutic interventions.In the following sections, we will discuss viral remodeling in eIF2α signaling and interactions with SG proteins. The assembly of SGs upon viral infection is multifaceted, with many examples in the literature reporting the inhibition of SGs during viral replication. However, caution is necessary, as many studies use only one or a few SG markers. Whether the canonical SG is formed or a specific SG protein is recruited to the structures is typically not examined. Collective studies suggest an antiviral function of SGs during certain stages of viral infection. Viral protein synthesis is strictly dependent on host translational machinery. Bulk translational shut-off alongside SG formation significantly hinders efficient viral propagation. Consistent with this assumption, there is substantial evidence that viruses have evolved various strategies to antagonize SG .FigureMet ternary complex binds with the 40S subunit in a GTP-dependent manner. During translation initiation, inactive eIF2-GDP is recycled back into the active form (eIF2-GTP) by eIF2BGiven the absolute dependence on host translational machinery for viral protein production, the host defense system is rapidly mobilized to arrest translation upon sensing viral pathogen-associated molecular patterns (PAMPs)Phosphorylation of eIF2α can be catalyzed by one of four eIF2α serine/threonine kinases: PKR, PERK, HRI, and GCN2. To antagonize host translation inhibition induced by eIF2α phosphorylation, viral proteins typically target either the four kinases or eIF2α itself. Newly translated viral proteins can bind to viral RNA, thus protecting it from PKR recognition and inhibiting PKR activation. Examples include the SARS-CoV -2 N proteinIn addition to targeting upstream kinases, the phosphorylation of eIF2α can also be directly affected by certain viruses. The nucleoprotein (N) and glycoprotein precursor (GPC) of Junín virus (JUNV) impair the phosphorylation of eIF2αSGs serve as crucial antiviral hubs for hosts. Therefore, direct targeting of core SG proteins such as G3BP is a common and straightforward way to manipulate the host antiviral response. SG assembly occurs at the early stage of poliovirus (PV) infection, but the viral 3C protein cleaves G3BP at the later stage, leading to SG disassemblyIn addition to G3BP, several other SG proteins are degraded to disrupt antiviral SGs. The 2A protease of picornaviruses  induces what is termed atypical SG by cleaving eIF4GI and selectively sequestering host mRNA, leaving viral mRNA intact and thereby facilitating viral translationin vitro reconstitution experiments. One unresolved question is whether the effect on viral replication occurs through the G3BP protein or the mesoscale SG formed by G3BP. Future interventions aimed at disrupting SGs without affecting the integrity of the G3BP protein could help answer this question.The cleavage sites of G3BP are located before the RRM and RGG motifs. The cleaved fragments are defective in RNA binding and in mediating LLPS with RNA, as indicated in cellular andin vitro reconstitution of SG also provides a platform to systematically investigate the effect of virus-encoded proteins on SG assembly.Apart from the cleavage of G3BP and related RBPs, viruses also employ strategies to disrupt SGs by sequestering components such as G3BP, Caprin1, and TIA-1. SARS-CoV-2 N protein inhibitspoly(I:C)-induced antiviral SGs by interacting with G3BP1 ,227 . TWhile G3BP1 has an antiviral effect in stress granules, it is now clear that direct degradation or inhibition of G3BP does not always benefit viral replication. Several viruses have evolved a sophisticated strategy to inhibit SGs and coopt G3BP1 for their own replication. A prime example is the alphavirus, a genus of approximately 30 members. Old-world alphaviruses, such as Chikungunya virus (CHIKV), Semliki Forest virus (SFV), and Sindbis virus, bind to G3BP via their nsP3 protein through an FGxF motif, mimicking the host protein Caprin-1/USP10 –245 . NG3BP1 impairs MNV replication, similar to what has been reported for alphaviruses [Similar observations can be found in murine norovirus (MNV) infection, where the virus rewires the G3BP1 interactome to activate metabolic reprogramming without inducing SG. Consequently, silencing ofviruses ,249 . HViral proteins can disrupt host cell condensates to facilitate replication and inhibit the host innate immune responses. SGs and p-bodies are prevalent targets due to their crucial role in RNA metabolism, including RNA translation and stability. SARS-CoV-2 can inhibit SG formation via its nuclear capsid protein N ,227 . SThe context of SG research can further provide insight into the physiological and pathological functions of proteins related to SGs, which have traditionally been studied in model systems. The interaction between SGs and viruses provides a frontier in the evolutionary selection process of viral infection. Research over the past decades has revealed many phase separation-related organelles formed by viral proteins, and many host MLOs are remodeled by viral infection. The intricate connection to innate immunity provides another important angle to study the role of SGs and related MLOs in viral-host interactions.


Although dpCoAhas been known to cap RNAs in various species, the identities of itscapped RNAs (dpCoA-RNAs) remained unknown. To fill this gap, we developeda method called dpCoA tagSeq, which utilized a thiol-reactive maleimidegroup to label dpCoA cap with a tag RNA serving as the 5′ barcode.The barcoded RNAs were isolated using a complementary DNA strand ofthe tag RNA prior to direct sequencing by nanopore technology. Ourvalidation experiments with model RNAs showed that dpCoA-RNA was efficientlytagged and captured using this protocol. To confirm that the taggedRNAs are capped by dpCoA and no other thiol-containing molecules,we used a pyrophosphatase NudC to degrade the dpCoA cap to adenosinemonophosphate (AMP) moiety before performing the tagSeq protocol.We identified 44 genes that transcribe dpCoA-RNAs in mouse liver,demonstrating the method’s effectiveness in identifying andcharacterizing the capped RNAs. This strategy provides a viable approachto identifying dpCoA-RNAs that allows for further functional investigationsof the cap.Recent discoveries of noncanonical RNA caps, such asnicotinamideadenine dinucleotide (NAD The reaction was performed at 24 °C for 1 h by mixingthe following reagents: 20 μM of tagRNA-azide (25 nt), 20 μMPM, 10 mM PBS buffer (pH 6.5), click mix  and incubated it with 5 μM TCEP at 24 °C for 5 minto reduce the dithiol bond. Then, a reaction between the tagRNA-PMand model dpCoA-RNA was performed by incubating 40 μM tagRNA-PMand 0.5 μM model dpCoA-RNA in 20 mM PBS buffer (pH 6.5) for1 h at 37 °C. We termed these replicates as PM+ group, whereasa negative control replacing the tagRNA-PM group with tagRNA-azidewas labeled as “PM–”. The PM+ and PM–groups were analyzed by 10% urea-PAGE gel electrophoresis before RNAstaining with RedSafe dye. Besides the model dpCoA-RNA (50 nt), modelm20 RNA samples were extractedfrom mouse liver by using TRIzol. The RNA samples were purified withsize-exclusion column NAP-5 to remove small molecules. Then, 50 μgRNA was digested by nuclease P1 in a reaction containing 2 mM ZnSO4 and 10 U nuclease P1. The experimental group was called theP1 group, while the noP1 group was used as a negative control thattreated 50 μg RNA in the absence of P1 nuclease. The digestionwas performed at 37 °C for 2 h. The RNA samples were then analyzedby using LC–MS/MS. Ten microliters of the P1 or noP1 samplewere injected for LC–MS/MS analysis. The dpCoA was quantifiedusing the parallel-reaction monitoring (PRM) mode by combining itsprecursor ion (m/z = 688.1562) and two product ions(m/z = 261.1266 and 348.0703).LC–MS analysis of the presence of dpCoA cap was performedaccording to previous reports.Total RNAs extracted from the mouse liver were purified to removesmall RNAs containing tRNA by using the RNA clean kit. The purifiedRNAs were eluted using RNA binding buffer mixed with 50% ethanol accordingto the manufacturer’s instructions. The synthesized model dpCoA-RNA was spiked into 100 μg of the above sampleas a positive control. The mixed RNA was incubated with 25 μMtagRNA-PM  or tagRNA-azide  in 10 mMPBS buffer (pH 6.5) at 24 °C for 1 h. The PM+ and PM–groups were both purified by the RNA clean kit.2, and 1 mM DTT. The NudC– and NudC+ sampleswere purified with the RNA clean kit before performing the above taggingreactions. All the samples underwent a polyadenylation reaction using 1× poly(A) polymerasereaction buffer (pH 7.5) containing 2 mM ATP and 100 U Escherichia coli poly(A) polymerase. The tagged RNAswere isolated using single-strand DNA immobilized on streptavidinbeads with a complementary sequence to the tag RNA. Afterward, theRNAs were purified with RNA Clean XP beads and underwent library preparationfollowing the manufacturer’s protocol (Supplementary Information).Finally, the RNA samples were directly sequenced using nanopore technologies.28A pyrophosphataseNudC was used to verify the identified dpCoA-RNAs. In NudC–group, the NudC was not used, while for NudC+ group, 0.5 μg/μLNudC was added to treat the RNA sample at 37 °C for 30 min ina NudC buffer containing 10 mM Tris–HCl, pH 8.0, 50 mM NaCl,5 mM MgClhttps://github.com/rocketjishao/tagSeq).29 First, Guppy in the MinKNOW software(Version 19.10.1) was used to call reads. RNA reads containing 12consecutive nucleotides from the tag RNA in the first 50 nucleotideswere categorized as tagged RNA. The tagged and untagged RNAs werethen mapped to a genome database (GRCm39/mm10 genome fasta file formouse) and counted for each gene (GRCm39 gene annotation file forcoding RNAs and NONCODEv5’s lncRNA annotation file for non-codingRNAs), with normalization to transcripts per million reads (TPM).We identified dpCoA-RNA transcription by three criteria: (i) taggedRNA in the PM+ group with an average TPM value >2, and (ii) a >5-foldincrease in tagged RNA reads in the PM+ group compared to the PM–group for a given gene, and (iii) significance p value<0.05 when performing t-test for PM– andPM+ groups.To analyze the sequencing data, we employed a pipeline similarto NAD tagSeq, with detailed data processing steps available on GitHub(S1a). We synthesized model dpCoA-RNAs(50 and 277 nt) using T7 RNA polymerase and purified the completeRNA product using the RNA gel extraction method . Successful synthesis of the model RNAs wasconfirmed by observing a main band of dpCoA-RNA monomers (50 and 277nt) in the gel analysis of the extracted RNA products  for dpCoAtagSeq, we used commercially available RNA-azide (25 nt) and PM toperform a click reaction copper-catalyzed azide-alkyne cycloaddition(CuAAC) . The model RNA experimentsprovided evidence of the feasibility of applying maleimide-thiol reactionto ligate dpCoA-RNA with tag RNA.The shorter dpCoA-RNA model (50 nt) was utilizedto test the maleimide-thiolreaction with tagRNA-PM. The reaction between the maleimide-containingtagRNA-PM and dpCoA-RNA occurred at pH 6.5. The results illustratedin 32 There is a need forspecific strategies to identify low-abundance RNAs capped with NCINs.The dpCoA molecule is an example of NCIN caps that has the potentialto regulate cellular activities, but few methods have been developedto identify its capped RNAs.33 To thisend, we developed a tagSeq protocol by applying the maleimide-thiolreaction to attach a tag RNA onto the dpCoA cap. The tagSeq procedurewas tested by identifying dpCoA-RNAs in mixed samples of real RNAextract and model dpCoA-RNA  tail to theRNAs before direct nanopore sequencing. This process labeled the dpCoA-RNAwith tag RNA at its 5′ end, attached a poly(A) tail to its3′ end, and enriched the tagged RNAs through beads containingsingle-strand DNA with complementary sequences to the tag RNA. Finally,we prepared the enriched tagged dpCoA-RNA for nanopore direct RNAsequencing.Noncanonical initiating nucleotides (NCINs) cap RNAs and play essentialroles in regulating RNA activities.pCoA-RNA 1a. The R23 In brief, an RNA read was regardedas tagged if its first 50 base-called nucleotides in the 5′end had 12 consecutive nucleotides from the sequence of tag RNA. Thetagged RNAs were then mapped to the mouse genome and the model RNAsequence to identify the gene they belonged to. Finally, we countedthe RNA reads for each gene and normalized the counts as TPM (transcriptsper million reads) for each sequencing run.To process the sequencing data, we first sortedout the tagged RNA reads in PM–, PM+, NudC–, and NudC+groups, respectively 2a. We usTable S1), indicating successful ligation of tagRNA-PM onto the model dpCoA-RNA.In contrast, the PM– group had an average TPM read of only69.1. Additionally, the untagged read of model dpCoA-RNA was lessthan half of its tagged read in the PM+ groups, indicating a successfulenrichment of the tagged model RNAs. It is worth mentioning that,due to nanopore’s inherent defects that can miscall both RNAends and cause RNA fractures during the nanopore channel passage,certain ″fake″ untagged reads may arise, potentiallycausing overcounting of the untagged reads.36The PM+ group was shown to have significantly moretagged modeldpCoA-RNAs than the PM– group 2b. The aWe interrogated the original electronic current obtained fromnanoporeRNA sequencing to confirm that the dpCoA-RNA model was ligated withthe tag RNA. By aligning the electronic current signal of the taggedRNA, Table S1). Thus, the incorporation of NudC treatment groups is aneffective strategy for verifying the tagged RNAs as dpCoA-capped.The pretreatment by NudC to cleave pyrophosphategroups of noncanonicalcaps including dpCoA cap is a meaningful method to differentiate dpCoA-RNAfrom other sulfur-containing RNAs. Notably, the tagged model dpCoA-RNAread was dramatically reduced from a TPM of 22,741.5 in the NudC–group to 1.7 in the NudC+ group, confirming that NudC can efficientlycleave the dpCoA cap in the NudC+ group . Although LC–MS and in-gel fluorescence assay suggestedthe presence of dpCoA cap in RNA samples from mouse liver, it remainselusive whether the thiol-containing RNAs arise from dpCoA cap, andfurther rigorous methods are needed to identify the capped RNAs.To prepare for the application of dpCoA tagSeq in real samples, weinitially investigated the presence of the cap in mouse liver. RNAmacromolecules were cleaved using nuclease P1, resulting in the releaseof the dpCoA molecule from the cap 3a. LC–MSt-test to performstatistical analysis between PM– and PM+ groups and used 3cutoffs to obtain authentic tagged RNAs: tagged RNA reads have averageTPM value >2 in the PM+ group, average TPM of tagged RNAs in thePM+group >5-fold that in the PM– group, and statistical significancevalue p < 0.05 between PM– and PM+ groups.Using these criteria, we identified 44 genes to possibly transcribedpCoA-RNAs (Table S1).We conducted tagSeq on RNA extracts from mouseliver and analyzedthe resulting sequencing data. In the PM+ samples, 2,240,099 readswere mapped to the mouse genomes, of which 15,541 contained the RNAtag. In the PM– samples, we found 335 tagged reads out of atotal of 3,442,580 reads, which was considered noise 4a. To idThrough analysis of the identified dpCoA-RNAs,both with and withoutNudC treatment, we discovered that NudC treatment decreased the RNAtagging ratios 4c. The N39 To investigate further, the tagSeq procedure could be performedon isolated mitochondria, as in vitro studies verified mitochondrialRNA polymerase uses NAD+ and dpCoA as 5′ cap tosynthesize RNA.11The study identified 44 genes, of which 18 were protein-codinggenes (mRNA), 24 were non-protein-coding genes (ncRNA), and 2 remainuncharacterized. Further gene ontology (GO) analysis of the mRNAsrevealed their association with the production of lipoproteins (Apoa2and Apoe), which could regulate CoA-transferase activities 4d. Inter40 To address this issue, hybridization methodsfor removing tRNAs can be explored in future studies.42 Moreover, in the case of RNA fragmentation occurring in ligatingtag RNA with maleimide group using copper-catalyzed reaction, copper-freeclick reactions may also find uses in future research.It is worth mentioningthat small RNAs were removed from the liversamples, originally aiming to eliminate thiol-containing tRNAs, whichmay have led to the omission of small dpCoA-RNAs. Nevertheless, smallRNAs to cap with dpCoA were previously found in bacteria.45 In our study, we analyzed the transcripts of all the 44 genes discoveredto carry a dpCoA cap and found no structural differences between cappedand noncapped transcripts. Our findings were supported by the tworepresentative genes shown in Thenanopore direct RNA sequencing technique enables long-read sequencing,making it possible to identify intact dpCoA-RNA structural characteristics.In this study, we developed a tagSeq strategyto investigate dpCoA-RNA.This strategy is the first of its kind and lays the foundation forfuture research into the cellular functions of dpCoA-RNA. First, byusing the model dpCoA-RNA to perform the tagSeq experiments, we directlyanalyzed the electronic signal pattern of the tagged dpCoA-RNA, whichverified the model RNA was successfully ligated with maleimide-containingtag RNA. The results confirmed the tagSeq procedure was practicalto identify the capped RNA in real samples. A pyrophosphatase NudCwas also incorporated to cleave the dpCoA cap prior to performingthe tagSeq, which also serves as a negative control to eliminate sequencingof other thiol-containing RNAs. We identified 44 genes from the mouseliver to possibly transcribe dpCoA-RNA and found they were ratherlow in abundance and shared an RNA structure similar to other cappedRNAs. Further analysis is also required to determine whether the dpCoAcap plays a role in recruiting proteins to regulate the biologicalfunctions of the capped RNAs.


Drosophila larval development and growth. Detection of these nutrients requires at least one of the 6 closely related taste receptors encoded by the Gr28 genes, one of the most conserved insect taste receptor subfamilies.Ribonucleosides and RNA are an underappreciated nutrient group essential during Drosophila about 65 and 260 million years ago, respectively, can taste RNA and ribose. We also tested whether the Gr28 homologous genes of the mosquitoes Aedes aegypti and Anopheles gambiae can sense these nutrients when expressed in transgenic Drosophila larvae.We investigated whether blow fly larvae and mosquito larvae, which shared the last ancestor with Drosophila larvae. For the mosquito Aedes aegypti, we developed a new 2-choice preference assay that accommodates the aquatic environment of these insect larvae. Finally, we identified Gr28 homologs in these species and expressed them in Drosophila melanogaster to determine their potential function as RNA receptors.Taste preference in blow flies was examined by adapting a 2-choice preference assay that has been well-established for Cochliomyia macellaria and Lucilia cuprina are strongly attracted to RNA (0.5 mg/mL) in the 2-choice feeding assays (P < 0.05). Similarly, the mosquito Aedes aegypti larvae showed a strong preference for RNA (2.5 mg/mL) in an aquatic 2-choice feeding assay. Moreover, when Gr28 homologs of Aedes or Anopheles mosquitoes are expressed in appetitive taste neurons of Drosophila melanogaster larvae lacking their Gr28 genes, preference for RNA (0.5 mg/mL) and ribose (0.1 M) is rescued (P < 0.05).Larvae of the blow fly The appetitive taste for RNA and ribonucleosides in insects emerged about 260 million years ago, the time mosquitoes and fruit flies diverged from their last common ancestor. Like sugar receptors, receptors for RNA have been highly conserved during insect evolution, suggesting that RNA is a critical nutrient for fast-growing insect larvae. Gustatory receptors (Grs) genes in insects, different proteins of the same family may recognize ligands from several different taste modalities, such as carbohydrates, which elicit sweet taste, or alkaloids and phenols, which elicit bitter taste sensation . H. HGr28 gsensing) ,24. It ie system . In factUntil very recently, virtually nothing was known about the taste or dietary requirement of RNA and ribonucleosides in mammals. This changed with the timely report by Toda et al. , who fou


Neurospora crassa, unpaired genes are subject to a silencing mechanism known as meiotic silencing by unpaired DNA (MSUD). MSUD targets the transcripts of an unpaired gene and utilizes typical RNA interference factors for its process. Using a reverse genetic screen, we have identified a meiotic silencing gene called sad-9, which encodes a DEAD-box RNA helicase. While not essential for vegetative growth, SAD-9 plays a crucial role in both sexual development and MSUD. Our results suggest that SAD-9, with the help of the SAD-2 scaffold protein, recruits the SMS-2 Argonaute to the perinuclear region, the center of MSUD activity.During the sexual phase of  Neurospora crassa is primarily haploid, with a transient diploid sexual stage  pairing process that may involve SAD-6 (chromatin remodeler) and REC8 (meiotic kleisin) . SAD-3, Drosophila melanogaster germ cells (DEAD-box RNA helicases contain the Asp-Glu-Ala-Asp (DEAD) motif and depend on ATP for their RNA-binding ability (rm cells  and haverm cells . In thishttps://www.fgsc.net//neurospora/neurosporaprotocolguide.htm) was followed during the course of this study. Genotypes of Neurospora strains used are listed in The Neurospora online protocol guide  can be found in FungiDB  and sexual (SRR957218) RNA-seq datasets from To compare the transcript levels of different MSUD genes, Neurospora transformation by electroporation of conidia  was adapted from Standard molecular techniques were used throughout this investigation . Green fin vivo protein–protein interaction assay, bimolecular fluorescence complementation (BiFC) relies on the reconstitution of the yellow fluorescent protein (YFP) when its nonfluorescing halves are brought together by two interacting proteins  were obtained from an M205 FA stereomicroscope and a DFC345 FX camera from Leica. For photography of perithecia (fruiting bodies), an Apple iPhone 5 with a Magnifi photoadapter  and a Vanguard 1231CM microscope were utilized. Images of asci (spore sacs) were captured using an Olympus DP74 camera attached (via a 0.63× C-mount) to an Olympus BX45 microscope. For fluorescence microscopy, preparation and visualization of asci were performed as previously described . To verify that ncu09093 actually plays a role in silencing, we subjected it to a quantitative MSUD suppression assay.To expedite research in the MSUD pathway, we have developed a high-throughput reverse genetic screen to identify silencing mutants from the Neurospora produces American football-shaped ascospores. However, in a cross where only one round spore gene is present (i.e. r+ × rΔ), it is unpaired and subject to silencing (. sad+ × sadΔ). This self-defeating mechanism is known as “silencing the silencer.” As shown in r-unpaired cross contains the ncu09093 deletion, 14.5% of the progeny are football-shaped. This suggests that ncu09093Δ acts as a semidominant suppressor of MSUD. In accordance with our naming convention, ncu09093 is hereafter referred to as sad-9 (suppressor of ascus dominance-9).Normally, ilencing , cross 1sad-9 is located on the left arm of linkage group I and encodes a 593-residue polypeptide. Based on a BLASTP search, SAD-9 has a region (residues 134–367) that can be classified as a “DEAD-box helicase domain of ATP-dependent RNA helicases DDX3 and DDX4” . The DDX3 (DEAD-box polypeptide 3) subfamily includes DDX3X (Homo sapiens), PL10 (Mus musculus), Belle (D. melanogaster), and Ded1 (Saccharomyces cerevisiae) (H. sapiens), Vasa (D. melanogaster), and GLH-1 (Caenorhabditis elegans) .evisiae) , whereaselegans) . Both Drfamilies , providisad-9, we examined its transcript levels using RNA-seq datasets . Like other MSUD genes listed in sad-9 has a robust expression level in the sexual cycle (163 FPKM), consistent with its role in meiotic silencing. While sad-9 has a relatively low vegetative expression , it is still easily detectable (1.87 FPKM), leaving open the possibility that sad-9 may have a function in the asexual cycle.To ascertain the expression pattern of S. cerevisiae, most DEAD-box RNA helicases are indispensable for cell growth  role in the silencing pathway.For an MSUD gene that is absolutely required for ascospore production, it is not easy to evaluate its true significance to silencing, as we can only reduce its expression through the “silencing the silencer” knockdown. However, since a cross without Many MSUD proteins localize in the perinuclear region, the center of silencing activity. Some of these proteins form the meiotic silencing complex, which seeks out and modifies certain RNA molecules .SAD-2 is a scaffold protein that recruits the SMS-2 Argonaute, among others, to the perinuclear region .Bombyx mori, Vasa acts as an assembly platform for Argonautes and others , it must employ a different mechanism to anchor itself to the perinuclear region. Indeed, our results are consistent with the notion that the SAD-2 scaffold tethers SAD-9 to the outer nuclear periphery.For its perinuclear localization, the worm GLH-1 helicase relies on its N-terminal FGG repeats (possibly through their interactions with the FG meshwork of nuclear pore proteins) and the MIP-1/2 scaffolds (Besides recruiting the SMS-2 Argonaute to the perinuclear region, SAD-9 may also assist in its silencing process. For example, as an RNA helicase, SAD-9 could unwind secondary structures of a transcript and allow its sequence to be scanned by an siRNA-bound Argonaute. Alternatively, said Argonaute could utilize SAD-9's helicase activity to release itself from a processed mRNA target and move on to the next one (jkad083_Supplementary_DataClick here for additional data file.


Characterization of microbial activity is essential to the understanding of the basic biology of microbial communities, as the function of a microbiome is defined by its biochemically active (“viable”) community members. Current sequence-based technologies can rarely differentiate microbial activity, due to their inability to distinguish live and dead sourced DNA. As a result, our understanding of microbial community structures and the potential mechanisms of transmission between humans and our surrounding environments remains incomplete. As a potential solution, 16S rRNA transcript-based amplicon sequencing (16S-RNA-seq) has been proposed as a reliable methodology to characterize the active components of a microbiome, but its efficacy has not been evaluated systematically. Here, we present our work to benchmark RNA-based amplicon sequencing for activity assessment in synthetic and environmentally sourced microbial communities.Escherichia coli and Streptococcus sanguinis, 16S-RNA-seq successfully reconstructed the active compositions of the communities. However, in the realistic environmental samples, no significant compositional differences were observed in RNA (“actively transcribed — active”) vs. DNA (“whole” communities) spiked with E. coli controls, suggesting that this methodology is not appropriate for activity assessment in complex communities. The results were slightly different when validated in environmental samples of similar origins , where samples were differentiated both by environment type as well as by library type, though compositional dissimilarities between DNA and RNA samples remained low (Bray–Curtis distance median: 0.34–0.49). To improve the interpretation of 16S-RNA-seq results, we compared our results with previous studies and found that 16S-RNA-seq suggests taxon-wise viability trends  in samples of similar origins.In synthetic mixtures of living and heat-killed This study provides a comprehensive evaluation of 16S-RNA-seq for viability assessment in synthetic and complex microbial communities. The results found that while 16S-RNA-seq was able to semi-quantify microbial viability in relatively simple communities, it only suggests a taxon-dependent “relative” viability in realistic communities. Video AbstractThe online version contains supplementary material available at 10.1186/s40168-022-01449-y. Cultivation-free metagenomic sequencing provides an unprecedented level of detail on the composition, diversity, structure, and encoded functions of microbial communities in distinct environments. However, the ability to differentiate viable  microbes from nonviable (inactive) community members remains a major challenge  — most hSeveral high-throughput sequencing-based methods have been developed for community-wise viability (activity) characterization, although their accuracy and the usage in complex microbial communities remain in debate , 7. ThesAs alternatives to the chemical-based methods, RNA-based methods can be utilized to characterize only the active component of microbial communities, as only biochemically active microbes should be vibrantly transcribing. Additionally, RNA molecules have a shorter half-life than DNA in the cellular and extracellular compartments, which thus reflects the viable or transcriptionally active microorganisms . These fHowever, there are atypical properties of the 16S rRNA transcript making it potentially problematic for viability assays, given that viability is a complex multifaceted term including not only cells that are actively growing but also those in dormant states that may or may not actively transcribe ribosomal RNA. The 16S rRNA gene produces large, nonprotein-coding RNA transcripts, which are structurally much more stable than most protein-coding transcripts . The perIn this study, we provide the first systematic evaluation of RNA-based amplicon sequencing for viability (activity) assessment of human-associated and BE microbial communities. We found that while 16S-RNA-seq was able to semi-quantify activity in simple mixtures of live/heat killed microbes, it only suggested a trend of overall activity of certain taxa within complex communities. This could potentially be due to the biology of the 16S rRNA genes as well as the limitations within the 16S-RNA-seq technology.E. coli strain ATCC 47,076 and S. sanguinis strain ATCC 10,556. The bacteria were subcultured on brain–heart infusion (BHI) agar and incubated overnight at 37 °C in room air (for E. coli) or 5% CO2 (for S. sanguinis). The bacteria were then inoculated into 5 mL fresh BHI broth and incubated at 37 °C while shaking at 250 rpm until early log-phase growth (OD600 = 0.1) was achieved. The cultures were adjusted to 105 CFU/ml by serial tenfold dilution in BHI broth. For each of the strains, half of the cultures were killed by heat at 75 °C for 10 min (for E. coli) or 65 °C for 30 min (for S. sanguinis). The heat-killed bacteria were then mixed with live culture proportionally as shown in Fig. We constructed ten synthetic microbial communities Fig.  comprisiE. coli cells into microbial samples collected from computer screens, computer mice, soil, and human saliva. As calibrated previously . SiR2 = 61.3%) by sample type (PERMANOVA FDR q = 0.0015), but not library type . This was also reflected in the principal coordinate analysis (PCoA) plot, where samples were grouped by types but not by libraries  , in addition to further unique behaviors such as variable copy numbers of the rRNA gene across different microbial genomes, as well as variable transcription levels in different growth phases within single microbes , 39. It q = 0.005, R2 = 0.117). However, sample type still remained as the major (R2 = 34.2%) driving effect of the compositional differences in the BE communities (FDR q = 0.002). This was also reflected in the ordination analysis, where samples were differentiated by sample source as well as by library type , the Solirubrobacter genus , and the Pilimelia genus . Intriguingly, several highly abundant taxa commonly found in human or built environments  were found depleted in RNA libraries across different sample types  appeared enriched in the same way. Alternatively, this would suggest that some microbes are simply more or less visible in 16S rRNA vs. DNA amplicons due to the rRNA production rate, differences in DNA vs. cDNA amplification efficiency, rRNA molecules stability, or growth phase of the underlying microbes, regardless of their actual activity.The compositional dissimilarity in DNA vs. RNA libraries resulted from differentially abundant taxa, several of which increased (or decreased) consistently between DNA and RNA libraries across sample types despite the overall stability Fig. d. Most oq = 0.001, R2 = 64.3%, Fig. Numerous factors are thought to account for compositional differences in RNA libraries in 16S-RNA-seq . To asseThis joint ordination analysis suggested that 16S-RNA-seq somewhat differentiated viable bacteria from whole communities in samples of similar origins. Particularly, samples from indoor air showed the highest compositional dissimilarities between library types Fig. b. This sq < 0.25)  was used as an index to infer the metabolic activity/viability potential in previous studies , 40. AltThe role of rRNA as an indicator of microbial activity has long been scrutinized, with the concern that the correlation between real-time microbial activity and rRNA quantities in environmental samples is inherently inconsistent due to differences across microbes biologically and across environments , 44, 45.The performance of 16S-RNA-seq in real microbial communities varied by environment, likely as a result of both biological and technical factors, including the biochemical characteristics of each sample, the biology of the microbes within communities , and community composition . Previous reports have indicated that many biochemical factors can influence 16S-RNA-seq readouts . EspeciaDespite these potential limitations, ribosomal RNA has been widely used to investigate active microorganisms in human and environmental samples on the basis that they are relatively stable and accessible both inter- and intracellularly . However16S-RNA-seq was qualitatively and semiquantitatively informative in simple synthetic communities, but it is not always effective in complex communities and only indicates relative activity trends in BE samples. It is thus important to set proper criteria for better interpretation of 16S-RNA-seq results. This may be achieved by filtering the taxa based on appropriate prevalence or absolute abundance (read count) so that some systematic interference, such as contamination or taxa ubiquitous in those environments, may be minimized. This, however, risks losing information on rare taxa, which were reported being more differentially abundant in RNA vs. DNA libraries in BE samples , 40. As Another potentially helpful consideration is to include a threshold 16S abundance ratio to determine active taxa . A ratiorpoB, gyrB, and cpn60 [A comprehensive evaluation of 16S-RNA-seq in microbial communities would require extensive effort, and there are limitations of the current study as a result. Our synthetically co-cultured and spiked “communities” use only a very small number of representative microbes, if anything leading us to underestimate the variability of 16S-RNA-seq between protocols and settings. While it may be impossible to directly interpret 16S-RNA-seq results for accurate viability quantifications, the thoughts here are promising in terms of qualitative assessment: determining which microbes are generally more active in a (or similar) environmental source. Other potential improvements might include the combination of other microbiological experiments, such as metabolic capacity measurements that directly capture the metabolism activities –57, biocnd cpn60 –61. ThesIn conclusion, our results found that while 16S-RNA-seq alone may never fully quantify viability in microbial communities, it can provide a qualitative profile of which community members are generally viable across similar environments and remain to be coupled with additional molecular approaches to understand the mechanisms of persistence, metabolism, and potential health consequences in the BE, environmental, and human microbiomes , 63.Additional file 1: Supplementary File 1. Metadata of this study.Additional file 2: Supplementary File 2. OTU table obtained from bioBakery workflow of this study.Additional file 3: Supplementary File 3. Statistical tests results.  Additional file 4: Supplementary File 4. Metadata of five studies.Additional file 5: Supplementary File 5. OTU table obtained from bioBakery workflow of five 16S-RNA-seq studies.Additional file 6: Supplementary File 6. Differentially significant taxa in the comparative study (part 4) from MaAsLin 2Additional file 7: Supplementary Figure 1. Approval from the MBTA. We received the letter of approval from MBTA, by way of the General Manager’s Office, to carry out the study and confirmed the detailed sampling plans with the MBTA prior to any public work. Their assistance and input were invaluable both for study design and for safe execution of sample collection, and the letter includes the initial information from Evan Rowe approving the work.Additional file 8: Supplementary Figure 2. Taxonomic composition of control samples. Relative abundance of eight control samples is presented here, prepared from swabs dipped (exposed) in 102E.coli culture, 106 E.coli culture, indoor air, outdoor air, blank culture broth, one blank DNA extraction buffer (EB buffer), one distilled water and one sterile 0.85% saline used in the lab space.Additional file 9: Supplementary Figure 3. Determining microbial biomass of realistic community samples with(out) spike-in (part 2). qPCR was performed targeting 16S rRNA gene V4 region in DNA and RNA (cDNA) extractions from samples collected from four computer screens, four computer mice, three human saliva and three soil. Each subject was sampled in parallel, with one set spiked with 102CFU (computer screens and computer mice) or 107 CFU  E. coli culture.Additional file 10: Supplementary Figure 4. Relative abundance of six differentially abundant taxa in DNA and RNA libraries in subway samples (part 3). Each column represents the average relative abundance of respected taxa in four DNA (labelled as sample(DNA)) or RNA (sample(RNA)) libraries. Dot represents each sample. FDR adjusted q values were calculated from mixed effects linear model (MaAsLin2), listed in Supplementary File Additional file 11: Supplementary Figure 5. Relative abundance of the most abundant taxa in different samples across different sample types. Relative abundance of the 15 most abundant taxa in samples of oil facilities, built environment , human saliva, soil, human stools.Additional file 12: Supplementary Figure 6. Heatmap presenting 16S RNA/DNA ratios of six differentially abundant families in different samples (part 4). Log10 RNA vs. DNA Relative abundance ratio was calculated for each genus within each family.


Advances in next-generation sequencing technology have identified many genes responsible for inborn errors of immunity (IEI). However, there is still room for improvement in the efficiency of genetic diagnosis. Recently, RNA sequencing and proteomics using peripheral blood mononuclear cells (PBMCs) have gained attention, but only some studies have integrated these analyses in IEI. Moreover, previous proteomic studies for PBMCs have achieved limited coverage (approximately 3000 proteins). More comprehensive data are needed to gain valuable insights into the molecular mechanisms underlying IEI. Here, we propose a state-of-the-art method for diagnosing IEI using PBMCs proteomics integrated with targeted RNA sequencing (T-RNA-seq), providing unique insights into the pathogenesis of IEI. This study analyzed 70 IEI patients whose genetic etiology had not been identified by genetic analysis. In-depth proteomics identified 6498 proteins, which covered 63% of 527 genes identified in T-RNA-seq, allowing us to examine the molecular cause of IEI and immune cell defects. This integrated analysis identified the disease-causing genes in four cases undiagnosed in previous genetic studies. Three of them could be diagnosed by T-RNA-seq, while the other could only be diagnosed by proteomics. Moreover, this integrated analysis showed high protein–mRNA correlations in B- and T-cell-specific genes, and their expression profiles identified patients with immune cell dysfunction. These results indicate that integrated analysis improves the efficiency of genetic diagnosis and provides a deep understanding of the immune cell dysfunction underlying the etiology of IEI. Our novel approach demonstrates the complementary role of proteogenomic analysis in the genetic diagnosis and characterization of IEI. Genetic diagnosis plays a central role in the clinical management of patients with inborn errors of immunity (IEI). However, the diagnostic yield for IEI based on the sequencing of germline DNA is still low and is estimated to be approximately 30%. This study shows the utility of integrated analysis with proteomics and targeted RNA sequencing (T-RNA-seq) of peripheral blood mononuclear cells. We identified the molecular cause and immune cell defects in patients with IEI, increasing the diagnostic yield by 6%. Notably, even in cases missed by T-RNA-seq, proteomics could identify the genetic etiology of the disease, suggesting the pivotal role of proteomic analysis in diagnosing IEI. Our novel approach improves the efficiency of the genetic diagnosis and elucidates the pathogenesis of IEI.Patients with inborn errors of immunity (IEI), previously known as primary immunodeficiency disorders, demonstrate increased susceptibility to infectious diseases, autoimmunity, autoinflammatory diseases, allergies, and malignancies . These cRNA sequencing (RNA-seq) has been well employed as one of the most valuable tools to study Mendelian disorders , 13, becMore recently, researchers have shown an increasing interest in proteomics due to technological advances in mass spectrometry (MS)-based protein identification , 28. To Here, we propose a state-of-the-art method for diagnosing IEI, providing notable insights into the pathogenesis of IEI. Our single-shot DIA-MS approach, which was high-throughput and cost-effective, enabled proteomic analysis of PBMCs at greater depth. Furthermore, this improved analytical depth achieved protein coverage nearly equivalent to the depth of transcriptome analysis by RNA-seq and allowed integrated analysis with T-RNA-seq. This study aims to highlight the complementary role of integrated analysis of proteomics and T-RNA-seq to canonical genomic analysis in determining the molecular pathogenesis of IEI.SI Appendix, Table SI Appendix, SI Appendix, Fig. The current study encompassed a cohort of 70 patients diagnosed with IEI but without a known genetic etiology. Of these, 48 patients underwent WES, and the remaining 22 underwent T-NGS of a 400 IEI gene panel. Prior genetic analysis was conducted based on criteria established by the American College of Medical Genetics and Genomics (ACMG), along with the patients’ phenotypes and the disease’s inheritance mode. However, no pathogenic variants were identified that satisfied these criteria.  based on the PCA for raw data  and mRNA   but a considerable reduction in protein expression   Fig. A and B, ly) Fig. .SI Appendix, Fig. XIAP region identified a large deletion containing a noncoding exon with promoter activity. These results were previously reported by Sbihi et al., and “patient 2” corresponded to this case  in a two-group comparison (B-cell-deficient clusters vs. others). The results showed that many genes involved in B-cell function were strongly downregulated in the B-cell-deficient group, even in the total protein profile, suggesting that the clustering results were valid . This finding is consistent with previous findings by Rivas et al., who demonstrated that variants generating premature stop codons and predicted to trigger NMD were prone to demonstrate ASE . NeverthAnother important finding was that target enrichment of RNA-seq allowed us to identify the genomic basis of an ADA2-deficient case. The expression levels of aberrant transcript was very low due to mRNA instability; Leafcutter results show that the number of aberrant splicing reads is only 0.008% of the cluster. However, target enrichment increased the read depth and revealed the aberrant splicing with intronic variant. These results reflect those of Gildea et al. who also found that target RNA-seq method increased the efficiency of identification of rare splice isoforms, which was difficult with standard RNA-seq . Given tAICDA. This rather contradictory result may be due to inadequate target enrichment of AICDA; in fact, some cases showed missing values. In contrast to B-cell analysis, T-cell analysis showed that the proteins involved in ribosome biogenesis and ribosomal RNA processing were downregulated to the same extent as those involved in T-cell function. However, paradoxically, these results coincide with those of well-regarded studies indicating that T-cell activation via T-cell receptor signaling enhances ribosome biosynthesis  were excluded due to the large number of samples containing MVs for that protein. Moreover, these proteins included 85 genes covered in the T-RNA-seq , which may have caused some bias in the results of correlation analysis. Additionally, analyzing only at a one-time point may underestimate the correlation as proteins and mRNAs have different temporal contexts , 48. In Seventy IEI patients were recruited from five institutions in three cohorts, with 34, 28, and 8 patients, respectively. In addition, six HCs participated in another period. Throughout this paper, we refer to the cohorts as Batch1 (B1), Batch2 (B2), Batch3 (B3), or Ctrl (C), and patients are identified by group and a unique ID, for example, B1_P1, B2_P35, or B3_P63. Clinical information, such as classification from IUIS, presumptive diagnosis, and candidate genes, was obtained from clinicians. The primary inclusion criterion for IEI patients was the lack of genetic diagnosis via a canonical diagnostic approach such as WES or T-NGS; that is, patients without pathogenic variants in genes consistent with their clinical features and mode of inheritance, and the interpretation of “pathogenic” was according to the ACMG criteria . TherefoThe local ethics boards approved this study of Hiroshima University, Tokyo Medical and Dental University, National Defense Medical College, Gifu University, and Kyoto University.Methods for sample preparation are described in “SI methods.”Methods for Mass spectrometry-based proteomics and T-RNA-seq are described in “SI methods.”To understand the etiology and pathogenesis of IEI, we carried out three different approaches using R v4.1 and Bioconductor v3.14 packages.z-scores. The absolute value of the z-score greater than two was defined as significant change. The absolute value of the z-score greater than or equal to 2 was defined as significant change. We also analyzed the quantitative differences between each case and the HCs to obtain further information about the biological significance. We calculated the log fold-change (LFC) and mean expression values using limma  on the comprehensive proteomic data to further validate the clustering results. DEA was compared in the cell-deficient cluster vs. others and was performed using DEP  enrichment analysis of significantly suppressed proteins using ClusterProfiler  proteins with higher specificity and (ii) proteins without MVs or with fewer MVs. In T-RNA-seq, on the other hand, the analysis was based on gene profiles selected based on the criteria described in (i), since T-RNA-seq data are already target-enriched and contain no MVs. We then normalized the data with the nefilter , and perHeatmaps . The k vsing DEP , which bsing DEP . In the Profiler . GO termpgad104_Supplementary_DataClick here for additional data file.