In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: Bio-fluids contain various circulating cell-free RNA transcripts (ccfRNAs). The composition of 20 these ccfRNAs varies between bio-fluids and constitute tantalizing biomarker candidates for 21 several pathologies. ccfRNAs have also been demonstrated as mediators of cellular 22 communication, yet little is known about their function in physiological and developmental 23 settings and most works are limited to in-vitro studies. Here, we have developed iTAG-RNA, a 24 novel method for the unbiased tagging of RNA transcripts in mice in-vivo. We used this method 25 to isolate hepatocytes and kidney proximal epithelial cells-specific transcriptional response to a 26 dietary challenge without interfering with the tissue architecture, and to identify multiple 27 hepatocyte-secreted ccfRNAs in plasma. We also identified transfer of these hepatic derived 28 ccfRNAs to adipose tissue, where they likely serve as a buffering mechanism to maintain 29 cholesterol and lipid homeostasis. Our findings directly demonstrate in-vivo transfer of RNAs 30 between tissues and highlight its implications for endocrine signaling and homeostasis. Little is known about the biological function of circulating cell-free RNAs (ccfRNA). Found to be 34 associated with exosomes, lipoproteins, ribonucleoproteins and more, these transcripts can be 35 isolated and sequenced from multiple bio-fluids such as plasma, lymph, cerebral fluids, breast milk 36 and more [1, 2]. ccfRNAs are directly implicated in the development of several pathologies 37 including cancer and obesity [3][4][5] and are intensively studied as disease biomarkers [6, 7]. Despite 38 this, the role they play in physiological and developmental settings and in mediating cell-to-cell 39 communication remains largely unknown. In-vitro, a growing number of works demonstrate the 40 relevance of RNA based cellular communication [8][9][10][11], however in-vivo evidence is still limited. This discrepancy is partly due to the difficulties posed to tracking ccfRNAs from transcriptional 42 source to potential sites of action in-vivo. Indeed the tools available to study ccfRNAs in 43 physiological settings are limited and very few studies attempt to tackle this problem directly. One work found evidence to suggest that the majority of circulating miRNAs originate in adipose 45 tissue and that some of the adipose derived miRNAs may play a role in the regulation of liver 46 Fgf21 levels [12]. However, this work focuses on miRNA and does not directly demonstrate 47 transfer of RNAs between tissues nor directly identify adipose secreted RNAs. Transfer of miRNAs was also demonstrated between epithelial cells of the caput epididymis to 49 maturing spermatozoa, leading to a shift in sperm RNA content during its maturation [13]. This 50 study made use of 4-thiouracil-tagging (TU-tagging) [14] combined with SLAM-Seq [15] to 51 demonstrate loading of miRNAs transcribed in caput epididymis into maturing spermatozoa. TU-52 tagging entails cell-type specific expression of uracil phosphoribosyltransferase (UPRT) and 53 administration of 4-thiouracil, with the assumption that only cells expressing UPRT would 54 incorporate 4-thiouracil into transcribing RNA. Thio-RNA can then be purified and used for 55 downstream gene expression analyses, or alternatively combined with SLAM-Seq to identify 56 labeled transcripts. TU-tagging has proven useful in several additional systems [14, 16, 17], 57 however, given endogenous [18] and alternative [19] pathways for uracil incorporation, the 58 labeling specificity in this method remains unclear. In addition, as is demonstrated in Herzog et. 59 al [15] and by Sharma et. al [13], labeling with TU-tagging of PolI and PolIII transcripts is 60 inefficient, rendering tRNAs and ribosomal transcripts unlabeled. Indeed there are only a limited number of techniques enabling in-vivo targeted labeling of RNAs. 62 In addition to TU-tagging, 5-ethynylcytosine-tagging (EC-tagging) [20] is a new method, which 63 utilizes cell-type specific co-expression of cytosine deaminase (CD) with UPRT to achieve RNA 64 labeling with 5-ethynyluridine (5EU) following administration of 5-ethynylcytosine. Both TU and 65 EC tagging use cre-recombination to express the relevant enzymes in a tissue specific manner and 66 stochastic expression from the cre-promoter may lead to unwanted expression of the enzymes in 67 different tissues [21]. Finally, one recently developed method called Mime-seq allows for cell type 68 specific labeling of microRNA [22]. In this method, tissue specific expression of a plant derived 69 methyltransferase mediates a 3′-terminal 2′-O-methylation of microRNAs that, when combined 70 with a methylation dependent library construction, allows for sequencing of tissue specific 71 microRNAs. Mime-seq allows labeling of miRNAs alone leaving other RNA biotypes unlabeled. 72 Given the need for a technique that allows for a Cre-independent and unbiased labeling of total 73 RNA transcription in-vivo, we developed iTAG-RNA [For In-vivo Targeted Tagging of RNA]. 74 This method incorporates mouse genetics with a novel uridine analog and an established RNA 75 labeling chemistry to allow tagging of total RNA in target cells in-vivo. Using iTAG-RNA we are 76 able to identify transcriptional re-programming of hepatocytes in-vivo following an acute high fat 77 diet stress and to enrich for and identify hepatocyte derived plasma ccfRNAs. Moreover, we are 78 able to identify RNA-based liver-to-adipose RNA transfer. These liver derived ccfRNAs include 79 variable coding and non-coding RNAs such as miRNAs and tRNAs. Among the miRNAs 80 transferred from liver to adipose tissue we find mir-33, mir-10b and mir130a, which target major 81 regulators of cholesterol and lipid efflux and bio-synthesis such as Srebf1 [23], Abca1 [23, 24], 82 Ppara [25] and Pparg [26] respectively. 83 Our study demonstrates for the first time an unbiased technique that allows labelling, tracking and 84 quantification of variable types of ccfRNAs from their transcriptional source to downstream 85 tissues, in which they can potentially act to regulate expression of target genes. We demonstrate 86 RNA-based liver-to-adipose transfer of a myriad of RNA transcripts and their response to an 87 environmental challenge. The continued identification and characterization of RNA based 88 signaling in-vivo is imperative for the understanding of developmental, physiological and 89 pathological processes, and can aid in the future development of relevant disease biomarkers. 90 91 Results . CC-BY-NC-ND 4.0 International license is made available under a

A: In-vivo targeted tagging of RNA isolates cell specific transcriptional responses to environmental stimuli and identifies liver-to-adipose RNA transfer 2 3
****
Q: Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify ''novel'' genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method. The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (,30%) were located in genomic region unique to strain 2336 (,18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.

A: RNA-Seq Based Transcriptional Map of Bovine Respiratory Disease Pathogen ''Histophilus somni 2336
****
Q: The etiological agent of severe acute respiratory syndrome (SARS) has been identified as a novel coronavirus SARS-CoV. Similar to other coronaviruses, spike (S)-glycoprotein of the virus interacts with a cellular receptor and mediates membrane fusion to allow viral entry into susceptible target cells. Accordingly, S-protein plays an important role in virus infection cycle and is the primary target of neutralizing antibodies. To begin to understand its biochemical and immunological properties, we expressed both full-length and ectodomain of the protein in various primate cells. Our results show that the protein has an electrophoretic mobility of about 160 -170 kDa. The protein is glycosylated with high mannose and/or hybrid oligosaccharides, which account for approximately 30 kDa of the apparent protein mass. The detection of S-protein by immunoassays was difficult using human convalescent sera, suggesting that the protein may not elicit strong humoral immune response in virus-infected patients. We were able to pseudotype murine leukemia virus particles with S-protein and produce SARS pseudoviruses. Pseudoviruses infected Vero E6 cells in a pH-independent manner and the infection could be specifically inhibited by convalescent sera. Consistent with low levels of antibodies against S-protein, neutralizing activity was weak with 50% neutralization titers ranging between 1:15 to 1:25. To facilitate quantifying pseudovirus-infected cells, which are stained blue with X-Gal, we devised an automated procedure using an ELISPOT analyzer. The high-throughput capacity of this procedure and the safety of using SARS pseudoviruses should make possible large-scale analyses of neutralizing antibody responses against SARS-CoV. D

A:
Development of a safe neutralization assay for SARS-CoV and characterization of S-glycoprotein
****