C-to-U RNA editing is prevalent in the mitochondrial and chloroplast genes in plants. The biological functions of a fraction of C-to-U editing sites are continuously discovered by case studies. However, at genome-wide level, the cis and trans determinants affecting the occurrence or editing levels of these C-to-U events are relatively less studied. What is known is that the PPR (pentatricopeptide repeat) proteins are the main trans-regulatory elements responsible for the C-to-U conversion, but other determinants especially the cis-regulatory elements remain largely uninvestigated.Arabidopsis thaliana roots and shoots, combined with RNA-seq data from hybrids of Arabidopsis thaliana and Arabidopsis lyrata, we perform genome-wide investigation on the cis elements and trans-regulatory elements that potentially affect C-to-U editing events. An upstream guanosine or double-stranded RNA (dsRNA) regions are unfavorable for editing events. Meanwhile, many genes including the transcription factors may indirectly play regulatory roles in trans.By analyzing the transcriptome and translatome data in The 5-prime thymidine facilitates editing and dsRNA structures prevent editing in cis. Many transcription factors affect editing in trans. Although the detailed molecular mechanisms underlying the cis and trans regulation remain to be experimentally verified, our findings provide novel aspects in studying the botanical C-to-U RNA editing events. Arabidopsis thaliana, the 5-prime nucleotide and the RNA secondary structures affect C-to-U RNA editing in cis while many transcription factors play regulatory roles in trans.In Zea mays) and rice (Oryza sativa) [Oenothera gene nad1 is required for proper splicing of pre-mRNA [Beta vulgaris) and tobacco (Nicotiana tabacum) found that mitochondrial C-to-U editing events were able to create translation start codons and produce functionally important proteins [In the plant kingdom, C-to-U RNA editing is one of the most prevalent RNA modifications and is enriched in the chloroplast and mitochondrial genes –6. The b sativa) –13. Mechpre-mRNA . Early sproteins –17.Apart from these case studies on editing function, the large-scale identification of C-to-U editing sites appeared in recent years with the development of next generation sequencing (NGS) technique. Bioinformatic tools are published to systematically identify bona fide C-to-U editing sites –21 and dDespite these fruitful findings in functional RNA editing and the convenience for accessing the editing sites, it remains uninvestigated that, at genome-wide level, what are the cis and trans determinants affecting the occurrence or editing levels of these C-to-U events? The established knowledge is that multiple factors are responsible for C-to-U RNA editing in plants, the best-studied of which is the PPR (pentatricopeptide repeat) protein . HoweverThis issue could be partially resolved with the aid of next generation sequencing technique. RNA-seq (or mRNA-seq) typically sequences the fragmented reads from cellular mRNAs and could provide information on the abundance of each transcript. Ribosome profiling followed by deep sequencing  capturesArabidopsis thaliana roots and shoots [Arabidopsis thaliana and Arabidopsis lyrata [In this study, by analyzing the transcriptome (RNA-seq) and translatome (ribosome profiling) data in d shoots , 27, coms lyrata , we perfArabidopsis thaliana, we identify 130 C-to-U RNA editing sites genome-wide, with 12 sites in chloroplast genome, 111 sites in mitochondrial genome, and 7 sites in nucleus genome . To demonstrate the reliability of the editing sites we find, we show that the C-to-U alterations compose more than 80% of the total variations detected in mRNA-seq . Expectedly, edited positions have significantly lower fractions in structured regions compared to the unedited positions Fig. a. TogethA. thaliana and A. lyrata [A. thaliana and A. lyrata could be covered (otherwise the read would be mapped at least twice).We seek for data to verify our hypothesis. Hybrid individuals are perfect systems to study the effect of cis elements on C-to-U editing events because the two different parental alleles are subjected to identical trans environment. We find mRNA-seq data conducted in the hybrids of . lyrata . With thA. thaliana (103 reads covered) but completely unedited in A. lyrata (31 reads covered). Interestingly, the two cytidines are located in single-stranded RNA regions in A. thaliana but in double-stranded regions in A. lyrata . Two cytidines, position 362 and position 376 on gene ATMG00580 (NADH dehydrogenase subunit 4), are fully edited in We have shown that the cis-elements like the flanking nucleotides and the RNA secondary structures could play a role in determine the occurrence or levels of C-to-U editing sites. Next, we wonder whether we could find any trans regulatory factors affecting the editing levels. Although it is known that PPR proteins are responsible for C-to-U editing events, it does not exclude the possibility that other regulatory factors may also affect the editing levels. A previous study on mammalian A-to-I RNA editing  used GTEA. thaliana. Some sites have positive correlations with many genes while some other sites tend to show negative correlations or no correlation with gene expression . Under these criteria, each editing site has less than 100 correlated genes  while the genes specific to type II and type III sites are mainly mitochondrial genes . This indicates the differential functions of the genes as well as the editing sites located on them.Enlightened by this study, it is conceivable that if we intend to estimate the protein level of each gene with NGS data, Ribo-seq (ribosome profiling followed by sequencing) should have stronger power than mRNA-seq. With the six mRNA-seq samples and the matched Ribo-seq data, we perform pairwise correlation test between the editing levels of each site in six samples and the expression level (Ribo-seq) of each gene in six samples Fig. a. We useion Fig. a. With mnes Fig. b. The 11nes Fig. b. Intuitnes Fig. c and alsnes Fig. d than clnes Fig. e. IndeedThe above paragraph focuses on the classification of editing sites according to their correlations with Ribo-seq gene expression levels. Here we wonder what kind of genes have the strongest correlation with levels of editing sites. We first define two sets of genes, n1 = genes with significant positive correlations with at least 10 editing sites, n2 = genes with significant negative correlations with at least 10 editing sites. Next, we define three classes of genes according to n1 and n2. The definition is as follows. “Positive” genes = set difference (n1 – n2) = n1 ⋂ ¬n2, “negative” genes = set difference (n2 – n1) = n2 ⋂ ¬n1, “both” genes = intersection (n1 & n2) = n1 ⋂ n2. With this definition, it is understandable that “positive” genes (331 genes) represent the genes with the greatest numbers of positively correlated editing sites, “negative” genes (479 genes) represent the genes with the greatest numbers of negatively correlated editing sites, and “both” genes (433 genes) have positive or negative correlations with different sets of editing sites.Interestingly, the functional annotation shows that all these three sets of genes are significantly enriched in transcription factors Fig. a. We illAs previous study proposed  or indicAt this moment, it is difficult to figure out how transcription factors could affect C-to-U RNA editing events, and there is no direct evidence to show that the trans determinants like transcription factors are related to the sequence motif around C-to-U editing sites. However, we stress the idea that the editing events might be indirectly affected by various factors apart from the PPR proteins. Moreover, the trans-regulatory elements and cis elements might be interconnected and it is not necessary to tear them apart in the functional studies as if they were independent.Mutations act as the source for natural selection. Missense mutations lead to amino acid alterations and are intuitively subjected to selection force , 35. SynArabidopsis thaliana roots and shoots [Arabidopsis thaliana and Arabidopsis lyrata [Apart from the PPR proteins as direct catalyzer of C-to-U conversion, other indirect trans or cis features may also contribute to the editing events. By analyzing the transcriptome and translatome data in d shoots , combines lyrata , we demos lyrata –45, whers lyrata . WhetherWe also reveal that the double-stranded RNA regions are unfavorable for editing events. The structural basis we found could be supported by the fact that editing factors PPR proteins are likely to bind single-stranded RNAs . More inMeanwhile, by performing genome-wide correlation tests, we also find that different transcription factors might contribute positively or negatively to different editing sites. As we have mentioned, the previous study on mammalian A-to-I RNA editing  using GTLast but not least, it is hypothesized that C-to-U RNA editing in plants could be designed for reversing the potentially unfavorable T-to-C DNA mutations  and therOur study reveals that the 5-prime nucleotide and the RNA secondary structures affect C-to-U RNA editing in cis. An upstream guanosine or double-stranded RNA regions are unfavorable for editing events. Meanwhile, many genes including the transcription factors play regulatory roles in trans. Different transcription factors might contribute positively or negatively to different editing sites.Arabidopsis thaliana were downloaded from TAIR database. The TAIR 9 version of annotation was used. The mRNA-seq and Ribo-seq data of Arabidopsis thaliana (three replicates for roots and shoots) were retrieved from a previous study [Arabidopsis Columbia-0 (abbr. Col-0) seeds (around 1500 seeds per vessel) were surface sterilized and imbibed at 4 °C for 2 days. They were grown hydroponically on fine nylon mesh supported by a customized rack in Magenta vessel GA-7-3  with filtered sterile liquid media and shaken at 85 rpm under 16-h light (110–115 μmol m− 2·s− 1 from cool white fluorescent bulbs) and 8-h dark at 22 °C [The reference genome and CDS sequences of us study . As descat 22 °C . The tisArabidopsis thaliana and Arabidopsis lyrata were generated by a previous study [A. thaliana was used as a mother  and A. lyrata was used as a father (Arabidopsis lyrata ssp. lyrata genotype MN47). Hybrid seeds were germinated and grown on germination medium containing Murashige and Skoog salts, 1% sucrose, and 0.8% agar. The plants were stratified for 5 days at 4 °C, and then grown for 4 weeks in a growth chamber at 22 °C /16 °C under 16 h light/8 h dark. Then, different treatments were done to the hybrid samples. For dehydration, plants were removed from the agar and dehydrated in plastic dishes for 1 h at 22 °C under dim light (0.7 ± 0.8 mmol s− 1 m− 1). For cold exposure, plants were grown under dim light (0.7 ± 0.8 mmol s− 1 m− 1) at 4 °C for 1 h. Leaf samples of plants growing in non-stressful conditions (standard treatment) were collected on 4 week-old plants grown at 22 °C. To control for circadian changes in gene expression, all samples were collected at 12:00 pm. In the mRNA-seq data analysis, to increase the power in detecting RNA editing sites, all the mRNA-seq libraries from different conditions including the control samples are pooled.The RNA-seq data from hybrids of us study  under acA. thaliana data, we map the mRNA-seq reads to the reference CDS sequences using Bowtie2 [A. thaliana and A. lyrata, we only focus on the C-to-U editing sites detected in chloroplast and mitochondrial genes.For the  Bowtie2 . For theA. lyrata genome (CDS sequences) is downloaded from the Ensembl website with the following code . We combine the chloroplast and mitochondrial genomes of these two species and map the hybrid mRNA-seq data to the combined genome with Bowtie2. We extract uniquely mapped reads so that only the regions that are non-identical between A. thaliana and A. lyrata could be covered (otherwise the read would be mapped at least twice). The uniquely mapped reads are extracted using SAMtools [The SAMtools .The variant calling process is accomplished by SAMtools “mpileup”. The minimum mapping quality of reads is set to be 20 (parameter –q 20) and minimum base quality is set to be 30 (parameter –Q 30) to increase the accuracy of variation sites. The output file of mpileup is “vcf” format, which contains one variation site per line. The information for each variation site includes total depth on each site, the reference allele count and each alternative allele count. Sites with more than one variation type are discarded. Variation level = alternative allele count / total depth. Take C-to-U RNA editing sites for instance, editing level = T/(C + T). To avoid the false positive variants caused by technical limitations, the variant sites with levels higher than 0.05 and with at least 10 covered reads are maintained –53. NoteA. thaliana (103 reads covered) and completely unedited in A. lyrata (31 reads covered).In the RNA-seq of hybrid, with the uniquely mapped reads, we have verified two species-specific sites with adequate sequencing depth. Two cytidines, position 362 and position 376 on gene ATMG00580 (NADH dehydrogenase subunit 4), are fully edited in We use RNALfold  to deterhttp://rna.urmc.rochester.edu/RNAstructureWeb/Servers/Predict1/Predict1.html). The only input message needed is the nucleotide sequence.The diagram of RNA structure is accomplished with online tools RNA structure (The expression level of a gene is defined as RPKM (reads per kilobase per million mapped reads). In the correlation test between editing levels and gene expression, the RPKM of Ribo-seq reads are used to represent the expression of a gene.The functional annotation of the genes IDs is performed using the online software DAVID .http://www.R-project.org/). All codes used in the analyses are available under request.All statistical analyses  and the graphic work were conducted in R environment (


A significant proportion of the world’s population does not have access to essential health services, incurs catastrophic health expenditure, and suffers impoverishment owing to out-of-pocket (OOP) expense. The World Health Organization (WHO) has identified “achieving Universal Health Coverage (UHC), with one billion more people benefitting from the coverage, as a strategic priority for achieving SDG 3: “Ensuring healthy lives and promoting well-being for all at all ages” . AchieviIn this editorial, we describe the evolution of SRH and highlight the significant gains of the global commitment to furthering the ideals of SRH. We identify existing gaps in the current SRH landscape and call attention to the urgent need to reprioritize SRH in the face of competing demands for scarce resources within the health sector. Finally, we provide pragmatic approaches to closing the identified gaps and upscaling SRH services.Reproductive and sexual health were first defined at the 1994 International Conference for Population and Development (ICPD) as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity, in all matters relating to the reproductive system and to its functions and processes” . SignifiThe SDG target 3.1 aims at reducing global maternal mortality rate (MMR) to less than 70 per 100,000 live births by 2030. Despite the tremendous gains of the commitment from the international community to adressing SRH issues, there are still significant challenges. For example, the global MMR was reduced by 38% between 2000 and 2017 with an average global decline of 2.9% per annum. However, the maternal deaths in 2017 was 295,000 [uncertainty interval 80%: 279 000–340 000] with approximately 86% of the global mortality occurring in sub-Saharan Africa and South Asia [Referring to SDG target 3.7 “by 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes”, some progress has been made. For example, the proportion of women who have been provided with modern family planning methods has risen from 74 to 76 percent (SDG indicator 3.7.1) between 2000 and 2019 . HoweverThe control of sexually transmitted infections (STIs) contributes towards the attainment of several SDGs namely SDG 3.2 ; SDG 3.3 ; SDG 3.7 ; SDG 3.8  . AlthougAbout 1.8 billion, a sixth of the world’s population, is comprised of 10 to 24 year olds who have vast SRH needs at one of life's most complex stages . Every dThe WHO considers women’s cancer a major issue. Both breast and cervical cancer are responsible for more deaths in women than any other cancer in low-income countries. Early cancer detection and screening tests are still unavailable in most low-income settings. In 2018, an estimated 2.09 million deaths occurred from breast cancer and there were an estimated 570,000 new cases of cervical cancer in the same year with a resultant 311,000 deaths worldwide (90% of these occurred in low-middle income countries) .Unmet SRH needs pose a unique and monumental burden on society at large. Access to SRH services is vital to achieving SDGs goals. Across many countries, SRH services, which encompass a range of preventive, promotive and curative services across the life-course, are critical components of Primary Health Care (PHC). SRH services address broad and cross-cutting health needs which touch all aspects of health, at all ages, for all genders. SRH services also serve as entry points to the health system. For example, women's visits for SRH needs provide opportunities for screening for malaria, HIV or cervical cancer, and seeking care for gender-based violence (GBV) and sexual assault . DespiteThe challenges of SRH services have been exacerbated by the recent COVID-19 pandemic that has triggered the reallocation of resources and attention away from SRH with untoward consequences. The disproportionate impact on the health, wellbeing, and economic stability of women, girls, and vulnerable populations has become more obvious with the COVID-19 pandemic. SRH services are being deemed non-essential and providers reassigned to frontline roles to control COVID-19 . For exaThe systemic racism, discrimination, and stigma that has characterized the COVID-19 pandemic is likely to further impact access to SRH care for women and vulnerable groups . In the Ensuring global actors meet current funding commitments for SRH services;Increasing domestic public funding for SRH services;Improving efficiency and equity of existing resources;Strengthening integration of SRH in HBPs; andEnsuring an effective delivery of SRH services through accountability measures and community participation in the provision of these services.Inadequate integration of comprehensive SRH services in HBPs results in the burden of payment falling on service users that are already poor. Funding for SRH is mostly through OOP payments, with only a small proportion funded by domestic public funding. There is an urgent need to reprioritize SRH and make the services more resilient to shocks that emanate from disease outbreaks and competing health conditions. This cannot be achieved without securing political commitment from national stakeholders . Promoti


Recent studies have demonstrated the significance of sperm RNA function as a transporter of important information directing the course of life. To determine the message contained in sperm RNA, it is necessary to optimize transcriptomic research tools. The current study was performed to optimize the processing of sperm RNA from sample storage to quantitative real-time PCR and assess the corresponding method with to evaluate male fertility and its representative markers, equatorin (EQTN) and peroxiredoxin (PRDX). Following successive steps of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines, several options were compared using boar spermatozoa. To evaluate the optimized procedures, the relationship between mRNA expression of EQTN and PRDX in spermatozoa and the fertility (litter size) of 20 individual boars was assessed. Unexpectedly, DNase treatment during RNA isolation had the deleterious effect by decreasing the RNA concentration by 56% and eliminating the correlation between EQTN and PRDX4 mRNA expression and male fertility. Moreover, when sperm RNA was processed using the corresponding method, the results showed the highest exon sequence expression, male fertility prediction power, and consistency. This optimized protocol for predicting male fertility can be used to study the transport of messages directing the life course from spermatozoon to offspring. Although the condensed nucleus of spermatozoa is thought to be in a transcriptionally inert state, sperm RNA was identified in many mammalian species, such as human2, bovine3, porcine4, equine5, and rodent6. Several plausible hypothesis and experimentation/observations on sperm RNA function, i.e. contribution during fertilization by delivering novel paternal RNA to the oocyte9 and signaling in mitochondrial translation10, are widely known. Moreover, the possibility of using sperm RNA as a marker for diagnosis of male fertility and infertility has recently gained attention13. Recent studies suggested that many phenotypes from parent generation are transferred to the progeny through sperm RNA15. Additionally, sperm RNAs were suggested to relay the message to one’s life course, providing the opportunity to study health at key stages of life from preconception to offspring16. However, despite abundant evidences on the functional significance of sperm RNAs, the contribution on sperm biology remains controversial18 and efforts are needed to optimize transcriptomic research tools. This may lead to improvements in the understanding of the underlying mechanisms of how spermatozoon affects the course of life.A spermatozoon is a haploid gamete that conveys paternal genetic information to offspring. During the differentiation to spermatozoon, the transcriptional activity of the cell shuts down20. To facilitate sperm RNA research, these protocols require some modifications because of the complexity of sperm biology, including the lower RNA levels compared to other cell types21 and disulfide bond-rich protamine-packed chromatin structure that resists lysis during RNA isolation22. Moreover, most RNAs except for a portion of the nucleus exists in a highly fragmented state in spermatozoa23. Thus, isolation of intact sperm RNA is important and has been widely studied24. As a result, various transcriptomic studies of spermatozoa related to sperm function, male fertility, idiopathic infertility, sperm-oocyte fusion, and inheritance of the paternal phenotype have been conducted26.The main transcriptomic research tools currently being used are RNA sequencing (RNA-seq) and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR)19. Compare to RNA-seq, RT-qPCR is useful for screening specific target genes for predicting prognosis and diagnosing disease, phenotypes, and other biological aspects in various samples27. According to Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, the use of RNA stabilizing solution and DNase is essential28. Many researchers perform DNase treatment to increase the RNA ratio in the sample. Additionally, an RNA-stabilizing solution (e.g. RNAlater) is widely used to stabilize and protect cellular RNA because of its immediate RNase inactivation characteristics29. However, it is not examined that whether these treatments could be used to improve RT-qPCR results in spermatozoa or not.Despite the advantages of RNA-seq, such as its high resolution on total aspects of biological sample, it has several limitations including low specificity, sensitivity, and reproducibility when used for the prognosis and diagnosis of specific phenotypes31, only a few studies have reported that sperm mRNA expression is correlated with male fertility34. One of the reasons is the protocols for accurate prognosis and diagnosis of male fertility have not been optimized. Therefore, we selected several steps that may affect sperm RNA and RT-qPCR data according to MIQE guidelines28. Sperm sample storage, RNA isolation, cDNA synthesis, and RT-qPCR conditions were compared to maximize the power of predicting male fertility using mRNA markers. We used a porcine model to test these conditions because of its large available amount of fertility data from artificial insemination (AI)35 and well-established genetic traits related to fertility, particularly in large white pigs36. The correlation between mRNA expression in spermatozoa and the phenotype related with the life course from spermatozoon through offspring was evaluated using RNA markers related to litter size, equatorin (EQTN)38 and peroxiredoxin (PRDX)40 after improving sperm RNA preparation and RT-qPCR. The EQTN and PRDX4 are related to sperm acrosome function40 and ample evidence suggests the levels of EQTN and PRDX4 protein in spermatozoa are correlated with male fertility39. Therefore, in this study, we try to establish whether transcript levels of an EQTN/PRDX4 can be used as proxies for the corresponding protein levels.Compare to proteomic markers in sperm2) or treated with RNAlater and stored in 4 °C or− 80 °C for 7 days. Subsequently, sperm RNAs were isolated with DNase treated or non-treated protocol. Neither RNAlater nor DNase treatment alone affected RNA quantity  gene. The cDNA synthesis efficiency for oligo dT and random hexamer showed no significant difference , intron primers for each marker were also checked with RT-qPCR. Total cDNAs of 20 individual boar spermatozoa were prepared and the mRNA expression of the EQTN and PRDX4 genes was evaluated. EQTN and PRDX4 expression levels were significantly correlated with litter size when RNA was isolated without DNase and when the Cq value from exon primer RT-qPCR was considered as mRNA expression  and reducing agents . To successfully isolate the large number of sperm RNAs in sperm nuclei58. In this study, DNase affected RNA concentration and quality in RNAlater-treated samples. Although the DNase alone had no significant effect on RNA quality during the optimization procedure, the exon Cq value of DNase-treated samples was similar with the intron value. Based on these studies, we suggested that DNase may induce the degradation of small RNA in spermatozoa during RNA isolation. This could lead to biased interpretation of sperm transcriptomic study. Therefore, later researchers need to reconsider the use of DNase during sperm RNA study.Generally, DNase is used to rule out gDNA contamination in isolated RNA. However, DNase has harmful effect not only in gDNA but also in concentrations of RNA and small RNAs2, with or without DNase RNA isolation and Oligo dT cDNA synthesis, were selected as the optimized sperm processing conditions for predicting male fertility.For the cDNA synthesis step, the effects of oligo dT and random hexamer primers were compared. The size of the PCR product with the junction primer for GAPDH was 194 base pairs, suggesting that most amplicons were from gDNA. Because of this, the Cq value of the exon primer should be significantly lower than that of the intron primer and junction primer for detecting mRNA. In case of fresh sperm sample, DNase treated RNA showed this corresponding expression pattern. The oligo dT primer cDNA, from 4 to − 80 °C snap-frozen storage and without DNase-treated isolated sperm RNA samples also showed this corresponding expression pattern. Thus, snap-frozen samples in LN. described a method for ruling out the gDNA factor in cDNA samples using an optimized gDNA-specific ValidPrime assay and gDNA reference sample59. Similarly, we directly evaluated the expression of gene intron sequences and attempted to exclude gDNA contamination. Only EQTN was correlated with male fertility when the gDNA factor was considered. To clarify the effect of gDNA factor consideration in male fertility prediction value the cut-off value of EQTN relative expression was settled at fixed litter size (13). The decreased male fertility prediction value in EQTN expression was a major limitation when considering the gDNA factor. Moreover, the losing correlation on PRDX4 and decrease in predictive value of EQTN was corresponded with coefficient of variation and quartile deviation.To assess the male fertility predicting power of mRNA markers using the optimized protocols, a correlation test was conducted to evaluate the mRNA expression of EQTN and PRDX4 could represent the male fertility data or not. EQTN and PRDX4 exon expression levels were negatively and positively correlated with litter size in isolated RNA without DNase, respectively. In contrast, no correlation between the exon expression of EQTN and PRDX4 from isolated RNA with DNA was observed. As previously noticed, in the isolated RNA with DNase treatment, the expression of the exon sequence was similar or lower than the expression of the intron sequence. In the other word, the portion of RNA in the sample was too low to indicate the correlation between gene expression and male fertility. Laurell et al2, without DNase RNA isolation, oligo dT cDNA synthesis, and RT-qPCR with exon primers. The highest exon sequence expression, male fertility prediction power, and consistency were obtained using this method. The mRNA level obtained using the optimized method showed better male fertility predicting power and consistency of data. This study will shed light on medical and industrial use of sperm RNA to evaluate infertility, animal health, and offspring phenotypes. Moreover, the optimized protocol can be used to elucidate the underlying mechanisms of life course research from spermatozoa to the next generation.In summary, we optimized the processing of sperm RNA for screening male fertility from sample storage to RT-qPCR. The optimized condition involved snap-freezing in LNAll procedures involving animals were approved by the Institutional Animal Care and Use Committee of Chung-Ang University  and performed according to the corresponding guidelines. All methods were performed in accordance with the relevant guidelines and regulations.60 and collected semen were diluted to a density of 30 × 106 sperm cells/mL in 100 mL of Beltsville thawing solution61. During sample transport, semen was maintained at 17 °C in an ice box 61. After arrival in the laboratory, each semen sample was washed with discontinuous 35 and 70% Percoll  gradient to eliminate non-sperm contents from the semen62. Thereafter, sperm pellets were snap-frozen in LN2 (− 196 °C) or incubated in RNAlater  overnight in 4 °C. Both samples were stored in 4 °C or− 80 °C for 7 days before RNA isolation. To minimize the effect of individual variation, semen samples from three boars were combined and used for method optimization with GAPDH gene. Moreover, to determine whether the optimized method can be used for accurately predicting fertility phenotype of boar spermatozoa, the mRNA expression level of semen from 20 different boars were evaluated.Yorkshire boar semen was provided by Sunjin Co. grand-grand parents farm . The sexually mature (11–23 months) and body weight over 90 kg boars were used to collect semen sample. For the laboratory work, the semen samples were collected once in summer season (June–July). The ejaculates of boar were obtained by gloved-hand technique32.Sperm motility and motion kinematics of Percoll separated spermatozoa was checked by Computer-assisted sperm analysis system . Percoll separated spermatozoa was resuspended in 1 mL of mTCM199 media and 10 μL of resuspended sample was placed on pre-heated (37 °C) Makler counting chamber . Under a phase contrast microscope, the motility, hyperactivation, curvilinear velocity, straight line velocity, average path velocity, linearity, beat cross frequency, wobble, and mean amplitude of head lateral displacement of spermatozoa were measured (Supplementary Table Sperm RNAs were isolated by using a PureLink RNA Mini Kit (Invitrogen) according to the manufacturer’s protocol with some modifications. Briefly, spermatozoa pellets were resuspended in PBS and counted under microscope with a Makler counting chamber to adjust the concentration to 40–50 × 10 6 cells/mL. Pellets (40–50 × 10 6 cells) were suspended in non-toxic guanidine-isothiocyanate lysis buffer in PureLink RNA Mini Kit containing β-mercaptoethanol  and homogenized. TRIzol (Invitrogen) and chloroform were added and centrifuged at 12,000 ×  g . The upper layer was transferred into a fresh tube and mixed with pure ethanol . Sperm RNA was attached to a spin cartridge. For DNase-treated RNA, the spin cartridge membrane was incubated in PureLink DNase mixture  for 15 min at room temperature during washing. Both isolated RNAs with and without DNase were immersed in 20 μL of nuclease-free water. The quality (260/280 ratio) and quantity of sperm RNA were checked with an Epoch Microplate Spectrophotometer . After matching of total RNA concentration among samples , cDNA was synthesized with the PrimeScript 1st strand cDNA Synthesis Kit  according to the manufacturer’s protocol. Oligo dT and random hexamer primers were compared for cDNA synthesis.63. The EQTN and PRDX4 genes were selected as target markers of male fertility39. Exon and intron primers of selected markers were designed; and for reference gene, GAPDH was used  in each well and Cq value of studied genes were analyzed with amount of cDNA  and amplified on a 7,500 fast real-time PCR system (Applied Biosystems). As a positive control and negative control, cDNA from porcine testis RNA and no template and reverse transcriptase cDNA was used, respectively. All primers were designed based on Reference genome Sscrofa11.1 Primary Assembly. For method optimization, we compared the exon, intron, and exon-exon junction primers of GAPDH, a reference gene for RT-qPCR in boar spermatozoa Table 63. The Eed Table . The ampThe size of the PCR products was checked with running gel electrophoresis on 2% agarose gel for 30 min. The gel image was gathered with a PowerShot A640  under ultraviolet light. The image brightness was controlled with PSRemote v1.6.4 software .66. The inseminated sows were managed at 20 ± 5 °C with air circulation, 2:1 light/dark cycle, and adequate feed for pregnant sow during the experimental period. AI was performed for 1 year to avoid the seasonal fluctuation and boar semen was inseminated to average 17.25 ± 1.98 sows . The average number of born piglets from AI results using boar semen was considered as the fertility outcome.Fertility data from randomly selected 20 Yorkshire boars were gathered by AI using their semen. Semen samples were collected after sexual maturation and diluted as described above. AI was performed by professional technicians from Sunjin Co. twice per estrusi.e. sensitivity, specificity, positive predictive value, and negative predictive value of each marker67. The cut-off values of relative expression in all treatments were decided based on a fixed litter size (13) to compare each treatment. Sensitivity is the percentage of boars that were true-positive when tested for mRNA expression. Specificity is the percentage of boars that tested as true-negative. The positive predictive value is the percentage of boars that tested as positive and had a true-positive litter size. The negative predictive value is the percentage of boars that tested as negative or had a true-negative litter size.The correlation between fertility and mRNA expression of EQTN and PRDX4 from 20 individual boar semen sample were analyzed with linear regression in all treatments. To estimate the male fertility predicting power of mRNA markers, receiver operating characteristic (ROC) curve analysis was utilized to set the cut-off value and assess the four key parameters t-test, one-way analysis of variance, Pearson correlation test, linear regression test, and ROC curve test were performed with SPSS v1.8 software . All data were subjected to a normality test (Shapiro–Wilk test) and homogeneity of variances test (Levene’s test). P < 0.05 was considered to indicate significantly different results. All data are expressed as the mean ± SE.The Student’s Supplementary information (DOCX 1217 kb)


To evaluate the relative enrichment of cellular RBPs on poly(A)+ RNA, we combine poly(A)+ RNA interactome capture with a whole-cell extract normalization procedure. This approach yields estimates of in vivo RNA-binding activities that identify subunits within multiprotein complexes that directly contact RNA. As validation, we trace RNA interactions of different functional modules of the 3′ end processing machinery and reveal additional contacts. Extending our analysis to different mutants of the RNA exosome complex, we explore how substrate channeling through the complex is affected by mutation. Our data highlight the central role of the RNA helicase Mtl1 in regulation of the complex and provide insights into how different components contribute to engagement of the complex with substrate RNA. In addition, we characterize RNA-binding activities of novel RBPs that have been recurrently detected in the RNA interactomes of multiple species. We find that many of these, including cyclophilins and thioredoxins, are substoichiometric RNA interactors in vivo. Because RBPomes show very good overall agreement between species, we propose that the RNA-binding characteristics we observe in fission yeast are likely to apply to related proteins in higher eukaryotes as well.Large RNA-binding complexes play a central role in gene expression and orchestrate production, function, and turnover of mRNAs. The accuracy and dynamics of RNA–protein interactions within these molecular machines are essential for their function and are mediated by RNA-binding proteins (RBPs). Here, we show that fission yeast whole-cell poly(A) A major challenge in the field of RNA regulation is to understand how large multi-subunit complexes interact with RNA. The cleavage and polyadenylation machinery, for example, is a megadalton assembly that processes the 3′ ends of RNAs transcribed by RNA polymerase II (Pol II). It consists of the cleavage and polyadenylation factor (CPF) and the accessory cleavage factors IA and IB (CFIA and CFIB). It recognizes polyadenylation sites (PAS) and auxiliary regulatory sequences, and it induces cleavage of the nascent transcript followed by polyadenylation of the 3′ end . StudiesCrosslinking of recombinant in vitro reconstituted ribonucleoprotein complexes (RNPs) has been a powerful tool to identify proteins that interact with RNA in the context of large RNPs . HoweverIn recent years, RNA–protein interactions have been systematically studied using in vivo, system-wide approaches. Crosslinking and immunoprecipitation (CLIP) has become the method of choice to identify RNAs bound by an RBP of interest. It can do so with nucleotide resolution . DespiteSchizosaccharomyces pombe and introduced a whole-cell extract (WCE)-normalization procedure to assess relative enrichment of cellular RBPs. To evaluate performance of the method, we validated RNA-binding behavior of selected RBPs experimentally and applied our analysis to RNA–protein interactions within different multi-subunit RNPs—namely, the 3′ end processing machinery and the RNA exosome complex.It was the aim of this study to develop a modified RIC analysis workflow that allows the comparison of in vivo RNA-binding activities of RBPs and enhancement of the applicability of RIC to the study of RNPs. To this end, we determined the RNA interactome of the fission yeast S. pombe poly(A)+ RNA interactome, we UV-crosslinked wild-type (WT) cells labeled with 4-thiouracil (4sU). RNPs were enriched by oligo(dT) selection and RNA-associated proteins identified by MS. We discovered 805 proteins significantly enriched over the non-crosslinked (noCL) control (P < 0.01) . As expected, Gene Ontology (GO) analysis revealed RNA-related GO terms to be significantly enriched in our data set, including “RNA metabolic process” (P = 8.63 × 10−27) and “RNA binding” (P = 4.58 × 10−63). We rediscovered 99 of 136 proteins annotated to harbor a classical RNA-binding domain (RBD) , highlighting the depth our RBPome and the presence of the expected molecular signatures . Of the proteins identified by RIC, 83.7% were also detected in the WCE, which thus had a very good coverage. Background values were imputed for proteins that were not detected in the WCE . WCE normalization revealed that many proteins with high MS signal in RIC were poorly captured considering their abundance. In contrast, many proteins with low to intermediate RIC signal were captured very efficiently and strongly overrepresented relative to statistical expectation based on their cellular abundance  the UV-crosslinkability of RBP and the bound RNA, which is determined by the geometry of the interaction and the type of residues present at the interface.Recent studies that compared RBPomes under different conditions have included WCE analysis to monitor whether changes in RNA binding were accompanied by changes in RBP abundance . We hypobundance E. We exp+ RNA based on GO term annotation .To validate our normalization method, we first considered proteins that we expected to have high RNA-binding activity. Through an optimal interaction surface, classical RBDs bind RNA with high affinity . On aver+ RNA-binding activity. To estimate the number of false-negative data points that arise from this, we searched for known mRNA binders that failed to be enriched after RIC. Ninety-nine classical RBPs were robustly detected in RIC. In contrast, 14 were detected but not significantly enriched compared to the noCL control (P > 0.01) (S. pombe (TER1) levels, which is non-poly(A) (Poor UV-crosslinkability can lead to an underestimation of the degree of RNA association for RBPs with high in vivo poly(A) > 0.01) C. Five n > 0.01) F, left oS. pombe . Rbm8  in RNA binding than was to be expected from complexes assembled on isolated PAS elements; however, further research is needed to validate this observation.Oligo(dT) selection during the RIC procedure limits our analysis to protein interactions with transcripts where cleavage and polyadenylation have already taken place. Nevertheless, we observed strong enrichment of several fission yeast CPF proteins on poly(A)+ RNA A. ConsisS. cerevisiae, the auxiliary elements are recognized through direct interaction with the RRM domains of Rna15p and Hpr1p, which are bridged by Rna14p , but to also be characterized by a submodule-typical noisiness of the data (SD). To our understanding, this “conservation of noisiness” is an intrinsic property of the RIC method. We can think of two possible explanations for this behavior: First, it appears that MS intensities of proteins that are stably associated show higher correlation between replicates than with other proteins. We consider it plausible that this reflects biological (expression level) noise or technical variability in extract preparation , which would tend to affect members of the same complexes similarly. Second, there are systematic aspects to the technical noise during MS data acquisition, which are primarily connected to signal strength. Because different subunits of stable RNA-binding complexes are likely to have comparable signals both in the WCE and in RIC, the noise of RIC/WCE ratios could be influenced by the stoichiometries of RNA–protein interactions through this as well. We infer that clustering in the SD plot is more typically observed for modules that are preferentially present on RNA as fully assembled complexes, rather than as individual components that bind in a noncorrelated fashion. We conclude that RIC-based analysis can provide insights into the organization of native RNA-regulatory complexes and help to generate functional models that can be experimentally tested in the future.As we analyzed CPF crosslinking, we noticed that subunits belonging to the same functional module tended to not only have similar RIC/WCE ratios but to also be alike in the statistical significance of their enrichment. For example, the polymerase module subunit Iss1 clustered with Pfs2 and Yth1, as did phosphatase module subunits A. To obtNext, we examined RNA interactions of the nuclear 3′-5′ RNA decay machinery. The RNA exosome regulates levels of various cellular RNAs . On its S. cerevisiae  associated with the nuclear poly(A)-binding protein Pab2, Red5 [Z3CH3] and Rmn1 [RBM26/27] that strongly resembles human Poly(A) Tail eXosome Targeting (PAXT), which targets processed transcripts , supporting a potential contribution of these proteins to different aspects of exosome targeting to substrate RNAs. Consistent with the current model for the exosome regulatory network in fission yeast, clustering in the SD plot was observed for CBC-Ars2 and MTREC, and to a lesser degree for other modules .According to our RIC data, most factors linked to the exosome by previous studies, including Red1, Mtl1, Mmi1, Iss10, Cbc1, Cbc2, Pir2 [ARS2], Pab2, and Red5  were strongly enriched on poly(A)da)  . In vivoormation +, which supports its proposed function in RNA selection . In contrast, proteins that constitute the PH ring of the core showed limited interactions with poly(A)+ RNA . We assume this to be the case because removal of the poly(A) tail during degradation prevents capture of the crosslinked complex by oligo(dT) capture. In accordance with this assumption, recovery of crosslinked exosome core subunits from HEK293 cells was reported to be more efficient when crosslinked RNPs were enriched by chemical extraction rather than poly(A)+ RNA selection election . As obseelection B.dis3-54, a mutant with reduced exonucleolytic activity + RNA with the channel, underlining the prominent role for the channel in RNA decay . Crosslinking of Mtl1 to poly(A)+ RNA was lost in the mutated protein (+ RNA with the cap region was strongly reduced (negative shift in the comparative volcano plot) . Concomitantly, nuclear RNPs marked by CBC-Ars2 were stabilized (positive shift). RNA association of the other exosome-associated helicase Mtr4 was unchanged. This suggests that a significant proportion of nuclear poly(A)+ RNA substrates depends on Mtl1 for recruitment to the exosome and supports a model in which channel-dependent RNA degradation is facilitated by the presence of functional Mtl1 + RNA than in WT + RNA in rrp6Δ ; and (3) domains found in proteins with a broad range of RIC/WCE ratios, which we refer to as “adaptive” RBDs . In the case of WD40 repeats, a common protein–protein interaction fold, the presence of basic amino acids in the binding surface was previously found to correlate with its ability to bind RNA . Cyp4 is one of several proteins that are more enriched on poly(A)+ RNA in the mtl1-1 mutant  . When comparing these to RBPs that were underrepresented , enriched proteins were more likely to be nuclear; to be involved in gene expression and DNA metabolic processes; to be annotated as RNA-, DNA-, or metal-ion-binding; or to possess transcription regulator activity + RNA: the histone deacetylase complex subunit Sap18; the argonaute protein Ago1; and the rRNA methyltransferase and potential histone H2A methyltransferase Fib1; and Spac5g10.01, a homolog of TRRAP, which is a component of the histone acetylation complex in humans. In addition, a number of DNA-binding transcription factors was highly enriched on poly(A)+ RNA, including the shuttle craft/ NFX1 homolog Spcc18.03, and a calcineurin-responsive transcription factor, Prz1 .Although RIC/WCE ratios of many nonclassical RBPs were low, 202 proteins that were not annotated with any classical or nonclassical RBD from our list had high in vivo poly(A)activity A. We weractivity . A promiactivity . In S. pSupplemental Table S5). Cdk9 (P-TEFb) is a cyclin-dependent protein kinase that controls early elongation of RNA Pol II  was recently shown to bind RNA in the nanomolar range . RNA binding of Cdk9/Pch1 was then assessed by fluorescence anisotropy assay (Kd = 47 ± 1.7 nM), confirming that our normalization approach successfully identifies RBPs that are characterized by high RNA-binding activity.Among all chromatin-associated RBPs without annotated RBD, Cdk9 was most highly enriched compared to statistical expectation  in vivo .S. pombe RNA-binding activity estimates to classify novel RBDs that were previously identified in human capture experiments  selection procedure, some of these RBPs may preferentially bind non-poly(A) RNA species. Likely examples are Nhp2 and Snu13, which are components of box H/ACA and U3 snoRNPs, respectively. Others, however, may either bind to RNA with low affinity or display “moonlighting” RNA-binding activities  RIC data, for which several methods have recently been developed  were highly enriched in S. pombe RIC, one moderately (Spt6). The cyclin Lsc1 and the histone modifier Set1 were not detected. Because all of these factors were described to bind nascent pre-mRNAs, the strong enrichment of Spt5, Cdk9, and Lsk1 on poly(A)+ RNA was unexpected. It suggests that these TEFs stay associated with mRNAs post-transcriptionally and might interact with RNA independently of the Pol II transcription machinery. In contrast, we did not detect poly(A)+ RNA association for most components of the PAF complex, nor for the histone modifier Set1, suggesting that their interactions with RNA may be limited to nascent transcripts. These potential differences in dissociation kinetics should be validated in future experiments.WCE-normalized RIC is one approach that gives a quantitative measure of the relative RNA-binding activities of different RBPs. Recently, orthogonal approaches have been developed to study RNA–protein interactions, for example R-DeeP, a density gradient centrifugation method that compares sedimentation of protein complexes before and after RNase treatment . Here, trevisiae . In cont+ RNA. Comparable poly(A)+ RNA-binding activities were only observed for the atypical protein kinases Rio1/2 . Rio kinases have a conserved role in rRNA maturation and were recently reported to be regulated by binding to rRNA  to rRNA . However to rRNA . Our datAt the outset, we were particularly interested in adapting RIC as a tool to study RNP complex topology in vivo. Using the 3′ end processing machinery as an example, we show that RIC/WCE ratios allow subunits that are directly involved in RNA binding within large RNA–protein assemblies to be pinpointed. In addition, we show that dynamic changes in the RNA-binding behavior of large RNPs can be captured by RIC. Specific RNP conformations will generally be defined by a set of conformation-specific RNA–protein interactions. Although RIC/WCE ratios derived from single-condition RIC represent average values for the ensemble of RNP states and thus do not allow easy identification of conformation-specific RNA–protein interaction signatures, one can delimit discrete topological states of dynamic RNPs by comparing between conditions and identifying covariant patterns. We apply this strategy to monitor changes in substrate access to the RNA exosome in exosome mutants. We propose that this approach can be used to identify RNP remodeling events under various conditions and thus provide insights into the function of multi-subunit RNA-regulatory complexes.S. pombe strains used in this study are listed in Supplemental Table S6. Standard methods were used for cell growth and genetic manipulations . The amounts of RNases A and T1 used to treat the elution fractions were reduced to 1/500 compared to the original protocol. A commented version of the protocol is available . WT1 and 2 data sets were merged for analysis of the WT interactome, and comparative interactomes were analyzed relative to the corresponding WT. A noCL control was included in the first experiment (WT1 + mtl1-1). Preparation of the samples for MS is described in the Supplemental Methods.Poly(A)revisiae , with mivailable . We perf2) for missing values at the level of the triplicate experiments before the data sets were merged. For the noCL-normalized interactome, raw MS intensities were used as provided in Supplemental Table S7. For WCE-normalized interactomes, raw MS intensities were normalized to median = zero for each data set before enrichment was calculated . Analysis was carried out in R . For all protein data, relative differences were tested against zero with a moderated t-test using the limma package (eBayes function), release 3.32.10, to generate P-values  were generated with p.adjust (method = “fdr” or “bonferroni”). Background values were imputed for WCE normalization if no MS intensities were available (open circles). If GO term or Pfam ID annotations are shown, proteins that were not detected in RIC were also included, as long as they were detected in the WCE; for these, background values were imputed for the interactome signal (crosses). For comparative RIC, we estimated the error for WT and mutant RIC/WCE ratios with uncertainty propagation using the propagate package and second-order Taylor expansion with the errors of mean RIC and mean WCE signals as input. For statistical analysis with limma, we simulated data with the exact statistics from the error propagation with mvrnorm  from the MASS package and tested relative differences against zero. Mutant RIC/WCE ratios were corrected by a constant factor to minimize global variance for all crosslinked proteins between WT and mutant before calculating fold-change values. GO enrichment analysis was performed with the enrichment analysis tool on the Gene Ontology Consortium server (release 20181018)  . GO terme 2.32.1 , and infe 2.32.1 . Plots we 2.32.1 .S. pombe cells were grown, 4sU-labeled, crosslinked, and lysed as for RIC. To validate RNA binding, partial CLIP was performed as described (32P]-ATP and T4 polynucleotide kinase (New England Biolabs), resolved on SDS-PAGE, and transferred to nitrocellulose membrane. RNA-bound proteins were detected by autoradiography, and total immunoprecipitated protein subsequently analyzed by anti-FLAG western blot.escribed . In brieS. pombe Pch1  and Cdk9 were cloned into pACE-BacI vectors. Bacmids were generated in DH10EMBacY cells . V1 viruses were harvested 72 h after proliferation arrest and stored at 4°C. For coexpression of Cdk9 and Pch1, 500 mL Sf9 cells (2E6/mL) were coinfected with both viruses (0.5:100 [v/v] each), cultivated for 72 h at 27°C and collected by centrifugation. Cells were harvested , washed in PBS and snap-frozen in liquid N2. All subsequent steps were performed in the cold. Cells were lysed by sonication in WB buffer  freshly supplemented with EDTA-free protease inhibitor cocktail tablets (Roche) and 2500 units SuperNuclease . The lysate was clarified by centrifugation at 230,000g for 40 min, filtered through a 0.45 μm filter, and applied to Ni-NTA resin (2 mL). After a wash with 30 mL WB buffer, proteins were eluted with 250 mM imidazole. The His-tag was cleaved with AcTEV protease (Thermo Fisher Scientific) at 4°C overnight, and proteins were run through a Superdex 200 (10/300) gel filtration column  equilibrated in buffer CB 2, 1 mM 2-Mercaptoethanol), snap-frozen in liquid N2, and stored at −80°C.cY cells , then trS. pombe Spt4/5 2, 0.51 mM ATP, 10 µM GTP, 1 µM UTP, 5 ng/µL dsDNA template, 0.5 units/µL of RNasin [Promega]), and 3.4 µg of Spt4/5 were incubated with 0.3 µg of Cdk9/Pch1 or buffer for 15 min at RT. After boiling in Laemmli buffer, 1 µg were run on a 7.5% acrylamide gel with 7.5 µM phospho-tag and 50 µM MnCl2. For the time-course experiment, Cdk9/Pch1 (40 pmol) was equilibrated in 20 mM HEPES pH 7.5, 30 mM NaCl, 1 mM DTT, 5% (v/v) glycerol, 0.01% (v/v) NP-40 and 1 mM MgCl2 in a total volume of 30 µL for 20 min at RT. In vitro kinase assays were performed in 60 μL reactions with 30 pmol Spt4/5 substrate. MnCl2, DTT, and [γ-32P]-ATP were added to a final concentration of 2.5 mM, 1 mM, and 1 μCi., respectively, and reactions incubated at RT. Ten microliters was taken for each time point. The reactions were stopped with LDS-loading buffer and heated for 5 min at 95°C before SDS-PAGE (4%–12%).Purified e Spt4/5  was used2, 0.5 mM Mg(OAc)2, 1 mM 2-Mercaptoethanol) from 1240 nM to 0.32 nM and incubating with RNA substrate for 20 min at RT. The ligand was excited with linearly polarized light at 485 nm, and emission was measured at 520 nm in parallel and perpendicular planes to the excitation plane at 25°C using a FLUOstar-Omega microplate reader (BMG-Labtech). Each data point is an average of four readings from two different experiments. Anisotropy data were fitted with SigmaPlot using a standard four-parameter logistic equation to identify Kd as follows:ymin and ymax are the minimum and maximum anisotropy values, x represents the protein concentration, and n represents the Hill slope.Fluorescence anisotropy experiments were carried out at RT with 8 nM FAM-labeled RNA of the sequence AUUAGUAAAAUAUAUGCAUAAAGACCAGGC (IDT). The RNA was heated for 5 min at 95°C before incubation with proteins. Titration of Cdk9/Pch1 was performed by serial dilution with CB buffer  (The proteomics data generated in this study have been submitted to the ProteomeXchange Consortium (nge.org)  under acThe authors declare no competing interests.


The effects of coronaviruses on the respiratory system are of great concern, but their effects on the digestive system receive much less attention. Coronaviruses that infect mammals have shown gastrointestinal pathogenicity and caused symptoms such as diarrhea and vomiting. Available data have shown that human coronaviruses, including the newly emerged SARS-CoV-2, mainly infect the respiratory system and cause symptoms such as cough and fever, while they may generate gastrointestinal symptoms. However, there is little about the relation between coronavirus and digestive system. This review specifically addresses the effects of mammalian and human coronaviruses, including SARS-CoV-2, on the digestive tract, helping to cope with the new virus infection-induced disease, COVID-19. The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in severe adverse impacts worldwide. Available data have shown that patients with COVID-2019 present respiratory symptoms and fever; however, approximately 8.8–60.6% of patients suffer from gastrointestinal symptoms, such as decreased appetite, nausea, vomiting, and diarrhea, as well as possible abnormal liver function . AlthougAlphacoronavirus, Betacoronavirus, Gammacoronavirus and Deltacoronavirus . Specifically, α- and β- coronaviruses mainly infect the human respiratory system, mammalian intestine, and central nervous system; γ- and δ- coronaviruses primarily infect birds (or poultry). The coronaviruses infecting humans can cause severe diseases. To date, seven coronaviruses (HCoVs) have been reported in humans, namely, human coronavirus 229E (HCoV-229E), human coronavirus NL63 (HCoV-NL63), human coronavirus HKU1 (HCoV-HKU1), human coronavirus OC43 (HCoV-OC43), Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and the recently identified SARS-CoV-2 [Coronaviruses (CoV) are enveloped, single-stranded, positive-sense RNA viruses that belong to the order Nidovirales. They contain the most abundant RNA genome, with lengths from 27 to 32 kb among all known viruses, and are members of the family Coronaviridae, subfamily Coronavirinae. This subfamily consists of four genera, RS-CoV-2 .Alphacoronavirus, while porcine deltacoronavirus (PDCoV) belongs to genus Deltacoronavirus. These porcine coronaviruses can infect piglets of all ages by invading the GI tract, with clinical signs of diarrhea, vomiting, and anorexia, resulting in dehydration, metabolic acidosis, and hyperkalemia and eventually leading to death. Scattered intestinal epithelial cell necrosis and villus damage are the primary pathological manifestations of porcine coronavirus infection [Porcine coronaviruses, such as porcine transmissible gastroenteritis virus (TGEV), porcine epidemic diarrhea virus (PEDV), and swine acute diarrhea syndrome coronavirus (SADS-CoV), are classified as genus nfection –8. Therenfection . In addinfection . In addinfection . Etiolognfection . In addinfection .+ T cells), as well as hemorrhage and necrosis in multiple organs  [Canine coronaviruses (CCoV) involve two serotypes, CCoV-I and CCoV-II, which belong to α-coronavirus. CCoV-II can be further divided into CCoV-IIa  and CCoV-IIb. Classical CCoV-IIa is restricted to the small intestine, causing symptoms such as decreased appetite, vomiting, watery diarrhea, and dehydration. Pantropic CCoV-IIa causes subacute bronchial pneumonia and leukopenia (mainly CD4d liver) , 13. Cand liver) .Feline coronavirus (FCoV) includes feline enteric coronavirus (FECV) and feline infectious peritonitis virus (FIPV), both of which are α-coronaviruses. FECV usually causes mild intestinal symptoms in cats but self-limiting recovery due to weak toxicity. In contrast, the toxicity of FIPV is generally strong and leads to a high fatality rate, causing systemic inflammatory diseases are known as feline infectious peritonitis (FIP). Generally, FCoV replicates in host intestinal epithelial cells and is excreted with feces , 15. At β-coronavirus, which has high similarity with human coronavirus OC43 in antigenicity and gene sequence [Lactobacillus preparations are effective in treating bovine diarrhea, but this assertion is still controversial [Bovine coronavirus (BCoV) is a member of the genus sequence . BCoV casequence . Currentoversial .α-coronaviruses HCoV-229E and HCoV-NL63 were isolated in 1965 and 2004, respectively [The ectively , 22. Epiectively . HCoV-22ectively , 25. HCoectively .β-coronavirus, which were found in 1967 and 2005, respectively [β-coronavirus, especially bovine coronavirus (BCoV) [Both HCoV-OC43 and HCoV-HKU1 belong to lineage A of the genus ectively , 28. Alts (BCoV) . HCoV-OCs (BCoV) . In addis (BCoV) , with ocs (BCoV) . Howevers (BCoV) .β-coronavirus. Fever and respiratory symptoms are the most common signs of the infection, and additional GI symptoms also appear as anorexia, nausea, vomiting, abdominal pain, and diarrhea. A retrospective study investigated 752 people with SARS-CoV infection and indicated that approximately 20% of patients had diarrhea in their initial stages [SARS-CoV was isolated and identified in 2003, belonging to lineage B of the genus l stages . Anotherl stages . AltogetThe majority of SARS-CoV enters human cells through angiotensin-converting enzyme 2 (ACE2), which is expressed on lung and gastrointestinal cells . CurrentBetacoronavirus and binds to the dipeptidyl peptidase 4 (DPP4) receptor [MERS-CoV was first detected in Saudi Arabia in September 2012, which belongs to lineage C of receptor . Based oreceptor . In a rereceptor . Anotherreceptor . Approxireceptor . Based oreceptor .Betacoronavirus was first reported in 2019 in China, which has significant genetic characteristics distinguished from those of SARS-CoV and MERS-CoV. The genome sequence of SARS-CoV-2 shares over 85% homology with that of bat SARS-CoV-like viruses (bat-SL-CoVZC45) [SARS-CoV-2 belonging to CoVZC45) . The infCoVZC45) , 7.3% [1CoVZC45) , and 49.CoVZC45)  in diffeCoVZC45) . In a laCoVZC45) . Thus, wCoVZC45) . The posCoVZC45) . A similCoVZC45) , suggestThis review describes eight coronaviruses that can infect mammals and cause gastrointestinal symptoms and seven human coronaviruses (see Table


Multivalent protein-protein and protein-RNA interactions are the drivers of biological phase separation. Biomolecular condensates typically contain a dense network of multiple proteins and RNAs, and their competing molecular interactions play key roles in regulating the condensate composition and structure. Employing a ternary system comprising of a prion-like polypeptide (PLP), arginine-rich polypeptide (RRP), and RNA, we show that competition between the PLP and RNA for a single shared partner, the RRP, leads to RNA-induced demixing of PLP-RRP condensates into stable coexisting phases—homotypic PLP condensates and heterotypic RRP-RNA condensates. The morphology of these biphasic condensates  is determined by the RNA-to-RRP stoichiometry and the hierarchy of intermolecular interactions, providing a glimpse of the broad range of multiphasic patterns that are accessible to these condensates. Our findings provide a minimal set of physical rules that govern the composition and spatial organization of multicomponent and multiphasic biomolecular condensates. Liquid ribonucleoprotein condensates typically involve a dense network of multiple proteins and RNAs. Here, the authors employ a minimal system composed of Prion-like polypeptides (PLP), Arg-rich polypeptides (RRP), and RNA to form biphasic condensates with diverse morphologies tunable via mixture stoichiometry and hierarchy of intermolecular interactions. These condensates can rapidly assemble and dissolve in response to cellular stimuli via a physical process known as liquid–liquid phase separation5. The functional specificity of biomolecular condensates as subcellular organelles is linked to selective enrichment of specific enzymes/signaling factors within6, whereas altered compositions of signaling condensates are associated with disease pathologies9. Mounting evidence now suggests that the spatial organization of biomolecules into distinct sub-compartments within biomolecular condensates  adds another layer of internal regulation of composition and plays a fundamental role in facilitating their complex biological functions. These mesoscopic multilayered structures can be qualitatively understood based on a multi-phasic condensate model, where two or more distinct types of partially immiscible condensed phases are formed by spontaneous phase separation of individual components in a multi-component mixture16. In this work, we set out to study the underlying molecular mechanisms that regulate the multiphasic condensate composition and spatial organization by employing a tractable, minimalistic ternary system.In biological cells, many multivalent ribonucleoproteins (RNPs) form biomolecular condensates that act as active or repressive hubs for intracellular storage and signaling18. Analysis of sequence features of eukaryotic biomolecular condensate proteins revealed that intrinsically disordered low-complexity domains (LCDs) are common drivers and/or regulators of RNP phase separation with and without RNAs23. The LCD sequence composition and patterning provide programmable modules for dynamic multivalent protein–protein and protein–RNA interactions29. In multi-component mixtures, these dynamic inter-chain interactions are ubiquitous and can either cooperate or compete within a dense network of LC proteins and RNAs31. The presence of multiple RNPs and RNAs within an intracellular biomolecular condensate highlights the relevance of understanding how networks of competing interactions control the condensate composition and structure32, given these very properties are intricately linked to their functional output in the cell2.The composition and spatial organization of biomolecular condensates are ultimately controlled by the nature of intermolecular interactions between RNPs and RNAs as well as their interactions with the solvent molecules34 23, whereas R-rich polypeptides bind RNAs with a broad range of sequence composition and structures35, and commonly occur as intrinsically disordered RGG domains36 . Both LCD types are highly abundant in stress granule and processing body proteins37—the two major cytoplasmic biomolecular condensates in eukaryotes. From a pathological point of view, multivalent R-rich repeat polypeptides, such as poly(GR) and poly(PR), are potent neurotoxins and are directly linked to c9orf72-derived repeat expansion disorder44. These R-rich repeat polymers can invade SGs and impair their fluid dynamics by aberrantly interacting with SG components, including PLPs and RNAs47. Within the PLP-RRP-RNA system, three interactions have the capacity to drive independent phase separation processes: PLP-PLP interactions can drive homotypic PLP phase separation48, PLP-RRP interactions can drive the co-phase separation of PLP-RRP into heterotypic condensates26, and RRP-RNA interactions can drive the formation of RRP-RNA condensates50. Therefore, the RRP within the PLP-RRP-RNA system represents a common module that can interact with both PLPs and RNAs46. As such, the PLP-RRP-RNA ternary system represents a suitable and biologically relevant triad for dissecting how networks of competing biomolecular interactions control the condensate composition and structure.To systematically explore the regulatory principles of multicomponent RNP condensation with competing protein–protein and protein–RNA interactions, here we employ a minimalistic three-component system composed of two LC disordered polypeptides: a prion-like polypeptide (PLP) and an Arg-rich polypeptide (RRP), and RNA. PLPs are typically characterized by the presence of π electron-rich and polar amino acidsTo systematically study the role of competitive protein–protein and protein–RNA interactions in controlling the organization of ternary PLP-RRP-RNA condensates, here we employ a multi-scale biophysical approach. For two-component systems (PLP-RRP and RRP-RNA), we show that the mixture composition is a key factor in controlling the phase behavior, condensate spatial organization, and client recruitment in a context-dependent manner. Specifically, we show that for PLP-RRP mixtures, RRP monotonically enhances PLP condensation. On the contrary, RRP-RNA mixtures display a reentrant phase behavior in which the RNA-to-RRP mixing ratio determines the surface organization of RRP-RNA binary condensates in a non-monotonic fashion. Within the PLP-RRP-RNA ternary system, RNA-RRP interactions dominate over PLP-RRP interactions, leading to an RNA-induced demixing of PLP-RRP condensates into stable coexisting phases (homotypic PLP condensates and heterotypic RRP-RNA condensates). The organization of these biphasic condensates  is determined by the RNA-to-RRP stoichiometry and the hierarchy of intermolecular interactions, providing a glimpse of the broad range of multiphasic patterns that are accessible to these condensates. Mechanistically, the multiphasic structuring of PLP-RRP-RNA condensates is governed by the molecular interactions at the liquid-liquid interface, which are encoded in the amino-acid sequence of the proteins and regulated by the composition of the mixture. This multi-scale regulation of inter-condensate surface interactions controls the relative interfacial tensions between the three liquid phases . Together, our findings reveal that competing intermolecular interactions in a multicomponent system represent a regulatory force for controlling the composition and structure of multiphasic biomolecular condensates.45. To quantify such an effect in our PLP-RRP binary system, we first determined the isothermal state diagram for FUSPLP in the presence of arginine/glycine-rich polypeptides  in the dense phase decreases with increasing [RRP]  simulations using a single-residue resolution coarse-grained model of an RRP from an archetypal ribonucleoprotein FUS (FUSRGG3) and a homopolymeric RNA, poly(rU)53  condensates  greater than PLP saturation concentration  with RRP-RNA condensates (prepared independently) and imaged them using a confocal microscope. We observed that PLP condensates coexist with RRP-RNA condensates in a multiphasic pattern that is reproducible irrespective of the method of sample preparation , although quite dynamic as indicated by their respective coalescence-induced condensate growth Fig. S, do not tes Fig. . Indeed,tes Fig. . To detetes Fig. . Taken ttes Fig.  and 3.67, are hallmarks of several subcellular biomolecular condensates such as the nucleolus and stress granules68. For a four-component system  that undergoes phase separation into three phases- A (condensate), B + C (condensate), and D (dispersed liquid phase), the equilibrium morphology is determined by the relative interfacial tensions  of the three liquid phases16. Based on the rank order of γA−D, γB+C]−D, three configurations are possible ; (ii) condensates partially merge in a way that they share a common interface but are still partly exposed to the dispersed liquid phase ; and (iii) condensate B + C resides within the condensate A and remains completely separated from the dispersed liquid phase .Multi-phasic structures, which stem from the coexistence of multiple immiscible liquid phasesble Fig. : (i) conθPLP) and RRP-RNA (θRRP) droplets using an image analysis approach , can be expressed as a function of the interfacial tensions of the three liquid phases as69Our experimental results in Fig. θPLP indicates that the PLP phase is engulfing the RRP-RNA phase partially and the contact area between the two droplets is significant. On the other hand, a large value of θPLP indicates that the two phases do not have a significant preference for a shared interface  as the RNA-to-RRP ratio is increased, while the RRP contact angle (θRRP) remained unchanged  increased, we observed that the droplets transitioned from a completely engulfed morphology to a partially engulfed state and subsequently to a non-engulfing state -RNA condensates. The simulated equilibrium structures  plays a central role in determining morphologies of multiphasic condensates  KRPs can phase separate with RNA via ionic interactions since lysine is expected to carry a similar charge to that of arginine28. Indeed, our state diagram analysis indicates that KRP has no impact on PLP phase separation, even at KRP concentrations that are 20 times more than the PLP concentration  do not share any interface (non-engulfment) at all the tested RNA-to-KRP mixing ratios Fig. . These ron Fig. S, restoreon Fig. S. Overallon Fig. S.PLP) and RNA-binding Arg-rich domains (FUSRBD)  showed enhanced partition into RRP-RNA condensates  across all tested RNA-to-RRP mixing ratios  resulted in a partially engulfed morphology  Fig. . The fulns Fig. S. These rios Fig. . Furtherogy Fig. . Interesbes Fig. . Taken tFL) with independent sites for RRP and RNA binding, which manifests in completely engulfed ternary morphologies in an RNA-independent manner  in an ice bath.A list of the proteins used in this study is shown in Table 86. A list of the proteins used in this study is shown in Table The cysteine-containing variants of the proteins were gene synthesized by GenScript USA Inc.  through site-directed mutagenesis. The proteins were expressed and purified using an identical protocol as described above with one modification: all buffers contained 1 mM DTT to prevent cysteine cross-linking. The protein samples were fluorescently labeled with either Alexa488 dye, Alexa594 dye, or Cy5 dye  as described in the manufacturer protocol. The His6-MBP-N10 tag was removed by the action of TEV protease (TEV: protein = 1:25 v/v) for 1 h at 30 °C. Ni-NTA beads  were used to separate the tag from the proteins. The cleaved proteins were diluted in 25 mM Tris HCl (pH 7.5), 125 mM NaCl , and stored as aliquots at −80 °C. The labeling efficiency for all samples was observed to be ≥65% (UV–Vis absorption measurements). All peptides [purchased through GenScript USA Inc. ] contained a C-terminal cysteine which was used for site-specific labeling with Alexa488 or Alexa594 dyes using the same protocol as described in our earlier work5, [KGKGG]5, [KGYGG]5, RGG-3 domain of FUS) were purchased from Genscript USA Inc. . All peptides contained a C-terminus cysteine for site-specific peptide labeling. Peptide stock solutions were prepared in RNase-free water (Santa Cruz Biotechnology) with 50 mM dithiothreitol (DTT). Polyuridylic acid , Polyadenylic acid , Poly(phosphate) , custom-synthesized RNA oligomer poly(rU)-rU40 , yeast total RNA (Sigma-Aldrich)] and FAM-labeled RNA oligomer-rU10 [Integrated DNA Technologies (IDT)] were reconstituted in RNase-free water. The concentration of all RNAs was calculated from their respective measured absorbance at 260 nm in a UV–Vis spectrophotometer (Nanodrop oneC). Both RNA and peptide stock solutions were stored at −20 °C. Before sample preparation for experiments, the RNA  stock solutions were checked for any aggregates using bright-field microscopy. Nucleic acid staining dye SYTO13 was purchased from Thermo Fisher Scientific Inc.All the peptides used  into 25 mM Tris-HCl buffer (pH 7.5) at room temperature. This is followed by the removal of His6-MBP-N10 tag using TEV protease (TEV:protein = 1:25 v/v) in a 25 mM Tris HCl (pH 7.5) buffer containing 150 mM NaCl for 1 h at 30 °C. Samples for phase diagram analyses were prepared at room temperature at the desired FUSPLP and peptide concentrations in a 25 mM Tris-HCl buffer containing 150 mM NaCl and 20 mM DTT (pH 7.5). Samples were then placed onto a Tween20-coated (20% vol/vol) microscope glass slide and loaded under a Zeiss Primovert inverted iLED microscope (×40 or ×100 objective). Images were captured using a Zeiss Axiocam 503 monochrome camera. Samples were kept covered with a glass cover to prevent concentration fluctuations due to evaporation and monitored for 2–5 min for droplet formation. The mixture was marked as LLPS or no LLPS depending on the clear existence of visible droplets throughout the microscopic field of view. The state diagram for FUSPLP-poly(rU) mixtures was also obtained similarly except the sample buffer used was 25 mM Tris HCl (pH 7.5) buffer, 150 mM NaCl without any DTT. The samples for the RRP-RNA state diagram were also prepared at room temperature in 25 mM Tris-HCl buffer containing 150 mM NaCl and 20 mM DTT (pH 7.5) and were analyzed for the existence of droplets similarly as stated above.State diagrams for all FUSPLP-peptide samples were prepared at a fixed FUSPLP concentration (280 µM) and varying peptide concentration in a buffer containing 25 mM Tris-HCl,150 mM NaCl, and 20 mM DTT (pH 7.5). These samples correspond to the FUSPLP concentrations above the homotypic phase-separation threshold for FUSPLP as shown in the FUSPLP-peptide state diagrams  of Alexa488-labeled FUSPLP  and Alexa594-labeled peptides  were used within the unlabeled protein-peptide mixtures. Samples were then placed inside a Tween20-coated (20% vol/vol) Nunc Lab-Tek Chambered Coverglass (ThermoFisher Scientific Inc.) for imaging and FRAP assays. For FRAP experiments, a circular region of interest was bleached with 100% power for ~2–6 s which was followed by an imaging scan for 300 s. The recorded Alexa488-labeled FUSPLP intensity values from the bleached ROI were then corrected for photofading, normalized, and fitted with a 2D diffusion model to obtain the recovery half time 84. The apparent diffusion coefficient (D) was calculated using the formula87R is the radius of the bleaching ROI  was used for fluorescence recovery after photobleaching (FRAP) experiments. Phase-separated FUSI Figs. S. The appPLP-peptide mixtures as described above in the FRAP section. All the confocal images were collected within 30 min of sample preparation. The partition coefficient (k) was calculated by dividing the mean intensity of Alexa488-labeled FUSPLP per unit area inside the droplet by the mean intensity per unit area in the external dilute phase PLP condensates in the presence and absence of poly(rU) RNA were collected using a laser scanning confocal microscope . Samples were prepared at concentrations mentioned in the relevant figure legends  and variable concentrations of poly(rU) RNA as mentioned in the text or the figure legends. The concentrations of poly(rU) RNA were chosen such that the RNA-to-peptide ratio maps the left, right, and peak points on the turbidity plots of respective peptide and RNA  were added to the sample mixture  25 mm × 75 mm × 0.1 mm custom-made flow chamber. All the images were collected within 1 h of sample preparation. The client recruitment was quantified using the client partition coefficient (k) within the peptide-RNA droplets. The partition coefficient (k) was calculated by dividing the mean intensity of Alexa 488-labeled client per unit area inside the droplet by the mean intensity per unit area in the external dilute phase PLP recruitment into FUSRGG3 and poly(P) droplets were performed similarly as described above for FUSPLP recruitment in FUSRGG3- poly(rU) droplets.Phase-separated samples were prepared at a fixed concentration (1.0 mg/ml) of the peptide  mixtures were prepared as described in the state diagram and the FRAP experiment sections. FUSPLP and RRP concentrations were chosen within the green/pink region in the state diagram , 150 mM NaCl, and 20 mM DTT and ~1% (labeled: unlabeled ratio) of Alexa 488-labeled FUSPLP  and 150 mM NaCl (pH 7.5). Next, the His6-MBP-N10 tag was cleaved using TEV protease (1:25 volume ratio-TEV: protein) for 1 h at 30 °C. Homotypic FUSPLP/ FUSFL droplets were formed at room temperature at concentrations  well above their respective homotypic phase-separation thresholds84. 1–2% of the labeled protein was added to the sample of unlabeled proteins. For the fluorescent labels, we used Alexa488-labeled  25 mm × 75 mm × 1 mm glass slide. The sample was then sealed with an 18-mm square coverslip of 0.1-mm thickness using double-sided tape. The resulting protein and peptide-RNA droplet samples were imaged using a laser scanning confocal microscope . All the images were collected within one hour of sample preparation. The same method of preparation and imaging was used for rU40 RNA-peptide multiphasic condensates  droplets and RRP-RNA ([RGRGG]5-poly(rU) RNA) droplets for the various samples were measured manually using the Angle Tool in Fiji-ImageJ88. Three tangent lines were drawn; (a) a tangent at the PLP-solvent interface, (b) a tangent at the interface between the RRP-RNA droplet and the solvent, and (c) a tangent at the interface between PLP droplets and RRP-RNA droplets. The angle between tangent (a) and tangent (c) was taken as the PLP contact angle (Contact angles between the co-existing PLP (FUSRGG3 and poly(rU) mixtures were prepared at a fixed FUSRGG3 concentration and variable poly(rU) concentrations in a buffer containing 25 mM Tris-HCl, 150 mM NaCl and 20 mM DTT (pH 7.5). Sample absorbance at 350 nm was measured using a spectrophotometer (Nanodrop oneC UV–Vis) with an optical path length of 1 mm. A gradual poly(rU) titration was used to record the turbidity data. Turbidity for FUSRGG3 and poly(P) mixtures was obtained in a similar manner.FUS71. Briefly, two volumes of distinct liquids are created. Each interface is given a specific value of interfacial tension y] Fig. S. Throughhttps://pypi.org/project/pycorrelate/#description). Five autocorrelation curves were averaged for each sample and plotted for comparison.Samples containing 50 nM of Alexa488-labeled PLP were injected into a Tween-coated (Tween20) 25 mm × 75 mm × 0.1 mm custom-made flow chamber and loaded onto the microscope stage  equipped with a single-photon Avalanche photodiode (sAPD). Measurements of the photon arrival times were acquired at a 100 MHz sampling rate by performing a point scan in the sample away from the glass surface. The excitation power was kept at a minimum to avoid photobleaching of the fluorophores. Each point scan was curated over 5 min. For each sample [PLP and PLP + poly(rU)], five point-scans were obtained and analyzed as follows. For each point scan, the autocorrelation function was calculated for different lag times using the pycorrelate python library  RNA) at the concentrations mentioned in the appropriate figure legend  RNA. Approximately 5 μL volume of prepared sample was placed inside the tween20-coated Nunc Lab-Tek Chambered Cover glass and imaged using the Zeiss LSM710 laser scanning confocal microscope. The sample was covered with 100–200 μL of FluorinertTM FC-770 (Sigma-Aldrich), which is a highly inert liquid and completely immiscible with water. FC-770 layer on the top of the sample helps in avoiding sample drying and preserving the sample for days.Before sample preparation, FUSnd Fig. S. ApproxiPLP was buffer exchanged in the same way as mentioned in the state-diagram analyses section. The co-existing droplet sample was prepared by mixing all the three components in a test tube  at the concentrations mentioned in the appropriate figure legend  was added 16% by volume to a final concentration of 1.6 mg/ml. A similar multi-phasic sample was prepared at the same time point to which buffer was added instead of RNase-A (16% by volume). Both samples, with and without RNase-A, were placed inside Tween-coated (Tween20) 25 mm × 75 mm × 0.1 mm custom-made flow chamber and sealed. Time-lapse images for both the samples were acquired using a confocal microscope  for comparison.Before sample preparation, FUSend Fig. . Approxit-test was used for statistical analysis. **** represents a p-value < 0.0001, *** represents a p-value between 0.0001 and 0.001 and no star represents a p-value > 0.05. The number of droplets (n) analyzed in various figures is mentioned below.A two-tailed n = 60 for partition and n = 3 for diffusion coefficient. Figure n = 100 for EWSPLP partition, n = 50 for FUSPLP partition and n = 75 for BRG1LCD partition. Figure n = 25 for contact angle measurements of each droplet type  was used for image processing. OriginPro (2018b) was used for Graphing. Adobe Illustrator CC 2019 (v23.0) was used for the figure assembly and production. ZEN  was used for image recording/processing using Zeiss Primovert microscope. Bluelake (v1.6.11) was used for image recording and processing using Lumicks C-Trap microscope. Fluid-interface modeling was performed using Surface Evolver (v2.70). MD simulation was performed using HOOMD-blue (2.7.0). MD visualization was done using VMD (v1.9.4) and OVITO (v3.2.0).Excel 2016 was used for partition calculations, MATLAB (R2018a) was used for FRAP analysis and statistical analysis. Fiji-ImageJ89 to study the phase behavior of intrinsically disordered proteins. The potential energy function contains bonded, electrostatic, and short-range pairwise interaction terms. Bonded interactions are modeled using a harmonic potential κ is the Debye screening length and D = 80, is the dielectric constant of the solvent (water). We set the Debye screening length In this study, we have employed a single residue/base resolution coarse-grained polyelectrolyte model for protein and RNA chains. For the amino acids, we employ the same coarse-grained parameters as have been employed by Dignon et al.−7 kJ/mole and force tolerance of 10−7 kJ/mole-Å. The system was then equilibrated in the canonical constant Number of particles, Pressure, and Temperature (NPT) ensemble at 298 K and 1 atm using the Nose-Hoover thermostat and barostat with a coupling time constant of 1 ps and 10 ps respectively. The equilibrated system was further stimulated in the canonical NVT ensemble at 298 K using the Langevin thermostat with a friction coefficient of 91. The equilibrium run was performed for 10 ns with a time step of 0.01 ps and the production run was performed for an additional 10 ns. The surface-recruitment simulations shown in Fig. The initial system configurations were generated by placing the protein and RNA chains randomly in the simulation box at concentrations corresponding to the experimental conditions. The system was energy-minimized with an energy tolerance of 10Further information on research design is available in the Supplementary InformationPeer Review FileSupplementary Movie 1Supplementary Movie 2Reporting SummaryDescription of additional supplementary files


Totiviridae showed a genome structure that has not yet been reported in this family. These results suggest that a number of distinct RNA viruses are widespread in a broad range of protists.Protists provide insights into the diversity and function of RNA viruses in marine systems. Among them, marine macroalgae are good targets for RNA virome analyses because they have a sufficient biomass in nature. However, RNA viruses in macroalgae have not yet been examined in detail, and only partial genome sequences have been reported for the majority of RNA viruses. Therefore, to obtain further insights into the distribution and diversity of RNA viruses associated with marine protists, we herein examined RNA viruses in macroalgae and a diatom. We report the putative complete genome sequences of six novel RNA viruses from two marine macroalgae and one diatom holobiont. Four viruses were not classified into established viral genera or families. Furthermore, a virus classified into  After carefully washing the samples with autoclaved seawater  that was passed through polycarbonate membrane filters with a pore size of 0.2 μm , excess water was removed using paper towels. Treated samples were stored at –80°C until analyzed.Macroalgae samples 1–6  were disrupted in liquid nitrogen with a mortar. Regarding dsRNA purification, samples were suspended in 2× STE  containing 0.1% (v/v) β-mercaptoethanol, and total nucleic acids were manually extracted with SDS-phenol. dsRNA was purified twice through a Poly-Prep Empty Chromatography Column (Bio-Rad) and microspin column  containing cellulose powder . To remove the remaining DNA and ssRNA, eluted dsRNA was further treated with amplification grade DNase I (Invitrogen) and S1 nuclease (Invitrogen) as described previously  according to the manufacturer’s protocol. The total ssRNA fraction was treated with amplification grade DNase I (Invitrogen) and purified using RNA Clean & Concentrator-5 (Zymo Research).cDNA libraries were constructed from purified dsRNA and ssRNA as described previously (de novo assembly using CLC GENOMICS WORKBENCH version 11.0 (CLC Bio) with the following parameters: a minimum contig length of 500, word value set to auto, and bubble size set to auto. To obtain putative complete viral RNA genomes from FLDS data, the ends of the contigs with at least 250× average coverage were extended using CLC GENOMICS WORKBENCH version 11.0, Genetyx version 14 (Genetyx), and Tablet viewer version 1.19.09.03  to the SILVA database because these sequences were absent in the original SILVA database. In the surveillance of RNA-dependent RNA polymerase (RdRp)-coding sequences from total RNA-seq data, trimmed reads were assembled as described above.The raw sequence reads obtained from total RNA-seq libraries were trimmed as described previously  database with an e-value ≤1×10The phylogenetic positions of the RNA viruses identified were analyzed based on the deduced amino acid sequences of RdRp genes using the maximum-likelihood method, specifically RAxML . RelatedDatasets supporting the results of the present study are available in the GenBank database repository (Accession Nos. DDBJ: LC521321–LC521329) and Short Read Archive database (Accession No. DDBJ: DRA009245).in silico . We found twelve viral segments and eight viral RNA genomes were reconstructed  that suggested the presence of RNA viruses were detected . We thenstructed  and S1. structed . Since tPyropia suborbiculata , Pyropia yezoensis chloroplast , Pyropia tenera , Halochlorococcum dilatatum , P. yezoensis mitochondria .To clarify the active holobiont population of samples, SSU rRNA sequences were reconstructed from the reads obtained from total RNA-seq. Their compositions were estimated based on mapped read numbers on the SSU rRNA sequences . This reEndornaviridae, and BraRV1 formed a single unclassified deep branch with St97 virga-like virus 1 (Accession: BDQD01000142)  were obtained in the brown algae holobiont A. Based 1000142) B and C. Three viral RNA genomes were obtained from the diatom and red algae holobiont: diatom RNA virus 1 (DiRV1) and diatom totivirus 1 (DiTV1) from the diatom holobiont and red algae totivirus 1 (RaTV1) from the red algae holobiont A and B. Totiviridae (Totiviridae. Similar to many viruses in the genus Victorivirus (family Totiviridae)  have been reported  and RBDV (7.7 kb), but differs from those of Bromoviridae viruses that have the three-segmented genome. These results suggest that BraRV1 is distantly related to an established RNA virus family and forms a novel RNA virus group with St97 virga-like virus 1. All known viruses in Totiviridae harbor undivided dsRNA genomes with two large ORFs  Viral RNA Genomes Identified from Marine Macroalgae and a Diatom. https://doi.org/10.1264/jsme2.ME20016Supplementary Material