The incidence rate of lung large cell neuroendocrine carcinoma (LCNEC) in lung cancer is low, but the malignancy is high and the prognosis is poor. We used the Surveillance, Epidemiology, and End Results (SEER) database to determine the population distribution of organ metastasis in LCNEC, conduct survival analysis, judge prognostic factors, and provide direction for follow-up diagnosis and treatment.By logging into the SEER database, the data of lung LCNEC were retrieved and the target population was selected. According to the presence or absence of organ metastasis , we divided the target population into the no organ metastasis group  and the organ metastasis group (n = 870). By analyzing the clinicopathological data of patients and using the survival function, the corresponding median survival time was obtained, and the influencing factors of each group were analyzed. Then, the significant influencing factors were analyzed by multivariate Cox regression analysis to screen out the independent influencing factors.In the overall sample group, multivariate Cox regression analysis showed that sex, age, primary site surgery, bone metastasis, brain metastasis, liver metastasis, radiotherapy, and chemotherapy were independent prognostic factors. The 1-year survival rate was 13.8% in the bone metastasis group, 19.1% in the brain metastasis group, 13.8% in the liver metastasis group, and 20.3% in the intrapulmonary metastasis group. In the organ metastasis group, multivariate Cox regression analysis showed that sex, chemotherapy, radiotherapy sequence with surgery, primary site surgery, liver metastasis, and age at diagnosis were independent factors affecting the prognosis.In the overall sample of LCNEC, bone metastasis, brain metastasis, and liver metastasis all reduced the overall survival time, while the effect of intrapulmonary metastasis on the overall survival time was not statistically significant. Sex, chemotherapy, radiotherapy sequence with surgery, primary site surgery, liver metastasis, and age were independent factors affecting the prognosis of the LCNEC organ metastasis group. Women, chemotherapy, and radiotherapy sequence with surgery were favorable factors, while old age, liver metastasis, and male were unfavorable factors. Its biological behavior is similar to that of small cell lung cancer, but it belongs to a variant of large cell carcinoma . The conpositive . It is apositive . Howeverwww.seer.cancer.gov; download the SEER * Stat version 8.3.9.2 software (Apply for an account at (1) The location of the primary cancer is in the lung, (2) the period of diagnosis is 2010–2015, and (3) the code of the histological type of lung cancer is 8013 (lung LCNEC number).(1) The primary site is located in the bronchus, (2) the follow-up survival status is unclear, and (3) the four common metastatic sites are unclear.A total of 2,072 cases were included. According to the presence or absence of organ metastasis , we formed the cases without metastasis of four organs into the no organ metastasis group  and the cases with one or more organ metastasis into the organ metastasis group (n = 870). This study has been reviewed by the ethics committee of our hospital.P < 0.05 showed a significant difference. The survival curve was drawn one by one by using the survival function Kaplan–Meier to obtain the median survival time. Log rank  was used to test the significant difference. The factors with P < 0.05 were the factors affecting the prognosis. The factors with significant differences were analyzed by multivariate Cox regression analysis.The clinicopathological data of patients were analyzed by the SPSS21 software. The basic pathological data of each group were analyzed by cross-table chi-square test. Based on the analysis of the basic pathological data of 2,072 cases, it can be seen that the proportion of men in the two groups is higher than that of women, of which 57.7% are men in the organ metastasis group. The proportion of people over 60 years old is significantly higher than that of people under 60 years old, of which 72.2% are over 60 years old in the organ metastasis group. In the race comparison, whites are significantly higher than other races, of which 85.1% are whites in the organ metastasis group. The proportion of death cases in both groups was high, of which the death in the organ metastasis group accounted for 94.8%. In the organ metastasis group, according to the frequency of metastasis, the order from high to low is liver, brain, bone, and lung. The most common metastasis of two organs is liver + bone (P < 0.05 are the prognostic factors (P 0.000), primary site (P 0.000), histological grade (P 0.000), laterality (P 0.000), T (P 0.000), N (P 0.000), M (P 0.000), primary site surgery (P 0.000), radiotherapy (P 0.000), insurance (P 0.008), organ metastasis (P 0.000) (P 0.000), brain metastasis (P 0.000), bone metastasis (P 0.000), intrapulmonary metastasis (P 0.000), and age at diagnosis (P 0.000) were all factors affecting survival time. The P value of chemotherapy was 0.055, close to 0.05. We carried out multivariate Cox regression analysis with the factors listed in P 0.000) (P 0.003) (P 0.002), N (P 0.005), primary site surgery (P 0.000), radiotherapy (P 0.000) (P 0.000) (P 0.01), and liver metastasis (P 0.000)  test for significant difference. Factors with  factors Table 3.The overall median survival time of the disease was 10 months Table 6,In the overall sample of LCNEC, bone metastasis, brain metastasis, and liver metastasis all reduced the overall survival time, while the effect of intrapulmonary metastasis on the overall survival time was not statistically significant. In the organ metastasis group, the 1-year survival rate of lung LCNEC after organ metastasis was only 16.4%. The 3-year survival rate was only 1.9%. The median survival time was only 4 months. The median survival time of liver metastasis was the shortest: only 3 months. Sex, chemotherapy, radiotherapy sequence with surgery, primary site surgery, liver metastasis, and age were independent factors affecting the prognosis of the LCNEC organ metastasis group. Women, chemotherapy, and radiotherapy sequence with surgery were favorable factors, while old age, liver metastasis, and male were unfavorable factors.Lung LCNEC is a rare disease with high malignancy and poor prognosis. In order to master its basic clinical characteristics and the impact of related treatment on its prognosis, we use the American SEER database established in 1973. Many cases of LCNEC were recorded in this database. We selected 2,072 cases (including 870 cases of organ metastasis) for study and analyzed their basic clinicopathological data. It was found that age, sex, primary site surgery, radiotherapy, chemotherapy, bone metastasis, brain metastasis, and liver metastasis were independent factors affecting the prognosis of LCNEC. The independent factors affecting the prognosis of the LCNEC organ metastasis group were sex, chemotherapy, radiotherapy sequence with surgery, primary site surgery, liver metastasis, and age. More men are suffering from LCNEC than women, and the survival time of men is significantly shorter than that of women. In the whole sample, the median survival time is 9 months for men and 13 months for women. In the organ metastasis group, the median survival time is 4 months for men and 5 months for women. The older the age, the more patients with LCNEC, and the survival time of people over 60 years old is significantly shorter than that of people under 60 years old. In the whole sample, the median survival time is 12 months for people under the age of 60 and 10 months for people over the age of 60. In the organ metastasis group, the median survival time is 6 months for people under the age of 60 and 4 months for people over the age of 60. According to the analysis, it may be related to male smoking and the length of smoking time, since smoking is the main risk factor of lung cancer . Some scThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.C-fL: resources, data curation, software, formal analysis, funding acquisition, writing—original draft, and project administration. Y-jT: validation and writing—review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


X-ray crystallography remains a powerful method to gain atomistic insights into the catalytic and regulatory functions of RNA molecules. However, the technique requires the preparation of diffraction-quality crystals. This is often a resource- and time-consuming venture because RNA crystallization is hindered by the conformational heterogeneity of RNA, as well as the limited opportunities for stereospecific intermolecular interactions between RNA molecules. The limited success at crystallization explains in part the smaller number of RNA-only structures in the Protein Data Bank. Several approaches have been developed to aid the formation of well-ordered RNA crystals. The majority of these are construct-engineering techniques that aim to introduce crystal contacts to favor the formation of well-diffracting crystals. A typical example is the insertion of tetraloop–tetraloop receptor pairs into non-essential RNA segments to promote intermolecular association. Other methods of promoting crystallization involve chaperones and crystallization-friendly molecules that increase RNA stability and improve crystal packing. In this review, we discuss the various techniques that have been successfully used to facilitate crystal packing of RNA molecules, recent advances in construct engineering, and directions for future research in this vital aspect of RNA crystallography. Th. Th95]. 6.The molecular surface of RNA is dominated by repetitive anionic backbone phosphates. The limited chemical diversity and charge repulsion reduce the chances that the RNA molecule can pack in a unique and consistent manner. RNA also tends to be more conformationally heterogeneous. These factors impede the crystallizability of RNAs compared to proteins. To promote favorable crystal packing of RNAs, one strategy is to introduce peptides or proteins to bridge the RNA molecules.6.1.The spliceosomal protein U1A is the first and most widely used protein module for crystallizing RNAs. The N-terminal 98-residue RNA recognition motif of U1A (U1A-RRM) recognizes stem loop II of the U1 snRNA. The 1.9 Å crystal structure of U1A bound to a fragment of stem loop II of U1 snRNA shows how the U1A-RRM interacts with the 10 unpaired nucleotides (AUUGCACUCC) on the stem loop . By engi6.2.E. coli culture [Fragments of antibody (Fabs) selected to bind RNA by phage display have been utilized to create an interface for crystal contacts. Compared to U1A, Fabs are more efficient at creating crystal contacts as they are larger (50 kDa compared to 11 kDa) and thus have more surface area available for potential crystal contacts . In addi culture . In addi6.3.If introducing a large artificial protein is undesirable, one could consider utilizing a synthetic molecule to form specific WC-like base pairs with the RNA. Peptide nucleic acid (PNA) is a synthetic molecule comprising of repeating N-(2-aminoethyl) glycine units, essentially resembling a peptide backbone attached to nucleobases . PNA was7.glmS ribozyme-riboswitch crystals improved diffraction from ~3.0 Å to 1.7 Å) [2 with 40 mM SrCl2 in the presence of a high concentration of precipitant successfully improved the diffraction limit from ~8 to ~3 Å of the crystals of the ternary complex of the Stem I domain of a T-box riboswitch, its cognate tRNA, and the RNA-binding protein YbxF [+ metal cations allowed the RNA components to shift as rigid bodies and repack more optimally, thereby contributing to the remarkable improvement in diffraction quality [When incorporating interacting modules to promote crystal packing, it is important to try to place them along with several WC bp insertions to fine-tune the orientation. Each bp insertion will add a rotation of 32.7° and a rise of 2.6 Å along the helical axis . If a cro 1.7 Å) . Finallyo 1.7 Å) . Replaciein YbxF . Structu quality . Barium 8.When prior knowledge crystallization modules and rationale design approaches fail, a stochastic process might succeed. One example is the exciting “in crystallo” selection in which error-prone PCR generates a pool of 10 million mutant DNA templates (each DNA containing 0–2 mutants). Next, the templates are transcribed with Phage T7 RNA polymerase, gel purified, and then used in crystallization experiments using conditions known to give crystals of the wild-type RNA . The larAnother under-exploited area is the enhancement of the RNA structure stability by introducing X bonds between halogen atoms in bases and the backbone oxygen atoms. These interactions have been better characterized in DNA . BromineAnother rapidly emerging structure determination technique of possible relevance to RNA crystallography is microcrystal electron diffraction (MicroED). These experiments are conducted with a transmission electron microscope in diffraction mode. The advantage of this method is that high-resolution diffraction data can often be obtained from nanocrystals  that are a billionth of the volume considered suitable for X-ray diffraction studies . The dis9.With the remarkable advancement in structural prediction algorithms and Cryo-EM, one may ask if there is still relevance in designing crystal contacts for nucleic acid structure determination. While the accuracy of AlphaFold2 is remarkable for predicting protein structures ,112, it With the recent “resolution revolution” in Cryo-EM, Cryo-EM has yielded several low-resolution structures of single-stranded RNAs as small as 40 kDa, suggesting that Cryo-EM will generally apply to large and medium-sized RNA structures ,115. It The crystal packing strategies described in this review can reduce the flexibility of the interacting regions. Some of these crystal packing modules generate symmetry, which should promote crystallization because proteins with molecular symmetry are known to crystallize more readily than those without molecular symmetry . For exa10.Over 98% of the human genome codes for noncoding RNAs . In vivo


Candida auris affects people with weakened immune systems, particularly those who are hospitalized or are in health care facilities. Extracellular vesicles (EVs) are lipid bilayer structures released by organisms from all domains of life. EVs can deliver functional molecules to target cells, including proteins and nucleic acids, especially RNA molecules. EVs from several pathogenic fungi species play diverse biological roles related to cell-cell communication and pathogen-host interaction. In this study, we describe a data set which we produced by sequencing the RNA content of EVs from C. auris under normal growth conditions and in the presence of the antifungal caspofungin, a first-line drug to treat this fungus. To generate a more complete data set for future comparative studies, we also sequenced the RNA cellular content of EVs under the same conditions. This data set addresses a previously unexplored area of fungal biology regarding cellular small RNA and EV RNA. Our data will provide a molecular basis for the study of the aspects associated with antifungal treatment, gene expression response, and EV composition in C. auris. These data will also allow the exploration of small RNA content in the fungal kingdom and might serve as an informative basis for studies on the mechanisms by which molecules are directed to fungal EVs.Emerging and reemerging pathogens are a worldwide concern, and it is predicted that these microbes will cause severe outbreaks. IMPORTANCECandida auris, a relevant emerging human-pathogenic yeast, is the first fungus to be called a global public health threat by the WHO. This is because of its rapid spread on all inhabited continents, together with its extremely high frequency of drug and multidrug resistance. In our study, we generated a large data set for 3 distinct strains of C. auris and obtained cellular small RNA fraction as well as extracellular vesicle RNA (EV-RNA) during normal growth conditions and after treatment with caspofungin, the first-line drug used to treat C. auris infection. Candida, mainly non-albicans species, are responsible for more than 50% of all blood infections .ospitals . Pan-resospitals , 5 and aospitals , 7. Due C. auris, little is known about its mechanisms of pathogenicity and virulence  starvation. The transcriptional regulator Pho4 activates the transcription of intragenic and antisense RNA molecules, leading to the downregulation of complementary mRNAs , circular RNAs (circRNAs), asRNAs, and long ncRNAs (lncRNAs) . They plry mRNAs . The lncll cycle . In S. ced mRNAs .C. auris or the mechanisms linked to intrinsic multiresistance. The role of EVs in this process is also unknown. In this work, we generated a descriptive data set providing detailed information on cellular small RNA content and EV-associated RNA. In addition, we report on the molecular response to the antifungal caspofungin, expanding information on how this compound affects C. auris. Finally, these data can provide useful material for the study of small RNA content in the fungal kingdom, and for the exploration of the mechanisms that direct molecules to EVs.Despite the abundance of cellular and extracellular small RNA molecules in fungi, they have been poorly explored. In addition, there is little availability of large data sets for sRNA analysis in these organisms. For this scenario, an overview of our study design is illustrated in C. auris EVs and to further compare it with the cellular RNA content, we extracted and sequenced RNA molecules from both fungal cells and EVs. To ensure the accuracy of the cell-EV compositional comparisons, we used three distinct strains belonging to the clades I (B8441 and MMC1) and III (B11244). In addition, we also evaluated EV production and composition in response to caspofungin. For this, C. auris cells were grown in the presence of subinhibitory concentrations of caspofungin as previously described , 0.5 µg/mL (B11244), and 2 µg/mL (MMC1) , 34. ForThe isolated RNAs from the cells and the EVs were quantified by fluorometry , and on a 2100 Agilent Bioanalyzer (Agilent). We also assessed the RNA integrity number (RIN) of the cellular samples used for the small RNA library preparation using the Eukaryote Total RNA Pico Chip . A summaC. auris genome, and the mapping statistics are listed in Cellular and EV RNAs were sequenced in experimental triplicates for each strain as well as for control conditions or after caspofungin treatment. The small RNA-seq data were deposited in the SRA repository (NCBI). After sequencing 55 bp, the reads were trimmed to remove the internal adapter and most of the sequences varied from 50 to 55 nt . Next, wIn order to corroborate the RNA-seq data, real-time quantitative PCR (qPCR) was applied to the strains under the control condition and with caspofungin treatment. We arbitrary selected 6 transcripts with variable expression levels in EVs and in cells. Based on the expression levels from the RNA-seq described as TPM (transcripts per million), and the comparison with the quantitative real-time PCR, it was possible to observe a correlation in mRNA levels between the different techniques .C. auris cell morphology , small nuclear RNA (snRNAs) and tRNA fragments (tfRNAs) . Other rolecules . In Pichrphology . Histoplachinery . In addiachinery , 42. As achinery , 42. ThiC. auris, the mechanisms linked to intrinsic multiresistance, and the role of EVs within this process. Our data set provides useful information about the yeast cell small RNA content in cells and EVs, as well as small RNA content in the presence of caspofungin, and can be used to investigate antifungal response and targets for therapy and treatment. These data can be used to develop new information about C. auris biology and ncRNAs in fungi in general, an area that needs to be better explored to improve our capacity for preventing and treating global infectious threats.In summary, this is the first study that associates data on small RNA sequencing in the cell with the RNAs present in EVs in fungi. In addition, little is known about the regulation of gene expression in C. auris B8441, B11244, and MMC1 stains were maintained at −80°C. The B8441 and B11244 strains were obtained from the CDC and the MMC1 strain was isolated from a patient in Montefiore Medical Center in New York (C. auris were performed with or without the presence of caspofungin (Sigma-Aldrich) at subinhibitory concentrations of 100 ng/mL (B8441), 10 ng/mL (B11244), or 12.5 ng/mL (MMC1). The assays were performed in technical and biological triplicates in a 96-well flat-bottomed translucent plate to a final volume of 200 μl per well and 5 × 105 cells/mL. The cells were incubated in a microplate reader (Synergy Biotek) for 72h at 30°C, and optical density (OD) readings were taken every 1 h with a wavelength of 540 nm, with shaking of the plate for 30 s prior to each reading. A nonlinear model, the Gompertz curve, was used to analyze growth curves, and significance was calculated by analysis of variance (ANOVA) comparing control versus treatment. The results represent the mean ± standard deviation of three independent experiments. The curves and analyzes were performed using the GraphPad Prism 8.0 program.New York . After t6 cells/mL in a total volume of 400 mL, with or without the addition of caspofungin, under the same conditions as described for fungal growth. After 24 h at 30°C (200 rpm), the cells were pelleted  and the supernatant was centrifuged at 14,000 rpm for 15 min at 4°C. Then, the resulting supernatant was filtered through a 0.45-μm membrane (Millipore) followed by concentration on an Amicon ultrafiltration system with a 100-kDa cutoff. The concentrated supernatant was ultracentrifuged for 1 h at 150,000g at 4°C, washed with phosphate-buffered saline (PBS), and ultracentrifuged again under similar conditions. The supernatant was discarded and the resulting EVs were suspended in 100 µL of PBS.One colony from each strain was inoculated into Sabouraud broth and incubated for 24 h at 30°C, with shaking at 200 rpm. The cell density was adjusted to 10C. auris, using a Nanosight LM-10 . EV samples were diluted in filtered PBS prior to injection, and the dilution factor used varied from 1:10 to 1:50 so that reading concentrations could be within the ideal range of 1 × 108 to 1 × 109 particles/mL, as recommended  was isolated using a miRNeasy kit (Qiagen) with modifications. A 1:1 volume of glass beads was added to the TRIzol buffer, followed by 10 rounds of vortex agitation  in order to disrupt the cell wall. After centrifugation, small RNAs were collected according to the manufacturer’s instructions. For the EV, the RNA was isolated with the miRNeasy kit (Qiagen) according to the manufacturer’s instructions. The DNA cleanup step was performed with all samples using the RNase-free DNase protocol (Qiagen). For RNA quantification and integrity analysis, we used a Qubit fluorometer (Thermo Fisher) and an Agilent 2100 Bioanalyzer (Agilent Technologies). For the cellular small RNA and the EV RNA, the libraries were constructed with 100 ng of RNA (cell and EV) using the TruSeq small RNA kit (Illumina). Libraries were prepared according to the manufacturer’s instructions with slight adaptations. For the step of size-selecting the samples by using the 6% acrylamide gel, instead of cutting between custom markers, we cut the molecular sizes corresponding to 140- to 500-nt long . The sequences were first mapped using the corresponding C. auris genome for strain B8441 (GCA_002759435.2 V2). For the ncRNA, we used the ncRNA from Candida genome databases and the reference available at the RNA central database: C_auris_B8441_version_sXX-mYY-rZZ_other_features_no_introns.fasta.gz and candida_auris_gca_001189475.ASM118947v1.ncrna.1. The use of two distinct references was to improve the ncRNA annotation, so that it could be present in one reference and absent in the other. The following parameters were used for the alignments: mismatch cost (2), insertion cost (3), deletion cost (3), length fraction (0.8), and similarity fraction (0.8). Only uniquely mapped reads were considered in the analysis. For mRNA identification in the EVs, we combined the differential expression with the read coverage, so we performed the map reads to reference (C_auris_B8441_version_s01-m01-r10_genomic and C_auris_B8441_version_s01-m01-r10_other_features_plus_intergenic) using the following parameters: no masking, match score (1), mismatch cost (2), linear insertion cost (3), deletion cost (3), length fraction (0.6), similarity fraction (0.8), and global alignment. For the validation of RNA-seq data, we performed differential gene expression analysis. The statistical test applied was the DGE  test using the RNA-seq package with CLC Genomics Workbench v 20.0 (Qiagen). The expression values for the transcripts were registered in TPM (transcripts per million), and TMM  was used as a normalization method .We sequenced 36 samples in total, and the sequences in fastq format were analyzed by CLC Genomics Workbench v 20.0.03 (Qiagen). The first analysis performed was the adapter trimming to remove the internal adapter that might have been present in the shorter sequences. The TruSeq small RNA adapter sequence was 7C. auris cells using the miRNeasy RNA isolation kit (Qiagen) with adaptations. A 1:1 volume of glass beads was added to the lysis buffer along with the yeast cells, and the mixture was subjected to 10 rounds of vortex agitation  in order to disrupt the fungal cell wall. After centrifugation, total RNA was isolated according to the manufacturer’s instructions and quantified using a Qubit fluorometer RNA HS kit (Thermo Fisher), and RNA integrity was assessed with a Bioanalyzer RNA PICO 6000 (Agilent). After isolation, 100 ng of cellular RNA and 20 ng of EV RNA was treated with 1 U of DNase I RNase-free  according to the manufacturer’s instructions. After this, the cDNA was synthesized from 1 µg of cellular RNA or from 20 ng of vesicular RNA as templates. For the reverse transcriptase reactions 0.3 µM random primer (Invitrogen) and 1 µL of reverse transcriptase , according to the manufacturers’ instructions. PCR was performed with 40 ng of cDNA for the cell and 1.6 ng of cDNA for the EV as the template and GoTaq master mix according to the manufacturer’s instructions (Promega). The oligonucleotides were designed with PRIMER-BLAST using the following parameters: PCR product size maximum of 250 nt, mT varying of 60°C, RefSeq mRNA as a database, and Candida auris as the organism. The primer sets used for PCR are described below. The qPCR was performed in three technical replicates for each sample. The following program was used in the Lightcycler 480 (Roche) equipment: initial denaturation at 95°C for 15 min, and 45 cycles of 95°C for 15 s, 60°C for 20 s, and 72°C for 45 s. The primers used in this study are listed, and the reference gene used was a guanine nucleotide-binding protein subunit beta-like protein  guidelines . Total R protein .SRP295539 BioProject: PRJNA682185). Mapping statistics are available (RNA-seq raw data (fastq files) related to this study were submitted to the NCBI repository SRA (SRA: vailable .


This Reflections article is focused on the 5 years while I was a graduate student (1964–1969). During this period, I made some of the most significant discoveries of my career. I have written this article primarily for a protein biochemistry audience, my colleagues who shared this exciting time in science, and the many scientists over the last 50 years who have contributed to our knowledge of transcriptional machinery and their regulation. It is also written for today’s graduate students, postdocs, and scientists who may not know much about the discoveries and technical advances that are now taken for granted, to show that even with methods primitive by today’s standards, we were still able to make foundational advances. I also hope to provide a glimpse into how fortunate I was to be a graduate student over 50 years ago in the golden age of molecular biology. I grew up in Seattle, where my parents allowed me to do chemistry experiments in my bedroom, with only an occasional stink bomb or explosion. I was always curious about how things work, a personal trait I have never outgrown. I attended Caltech as a chemistry major from 1960 to 1964 but found biochemistry more exciting than chemistry. I received a wonderful education with teachers including Linus Pauling, John Roberts, Richard Feynman, Ray Owens, Giuseppi Attardi, Norman Davidson, Bob Edgar, and James Bonner. I even played varsity basketball. I undertook my first research project in my senior year in Robert Sinsheimer’s lab working on the new bacteriophage M13. I loved this experience, and it convinced me to go to graduate school. Having grown up and attended college on the West coast, I decided to head East.I arrived at Harvard in September 1964, to enter the new graduate program in Biochemistry and Molecular Biology, and began working with James D. Watson, in the Watson-Gilbert research group. Jim had recently been awarded the 1962 Nobel Prize for his discovery in 1953 with Francis Crick of the double-helical structure of DNA. I was delighted to be able to join his lab. Wally Gilbert had been a Physics Professor at Harvard but was attracted to molecular biology, and by 1964, he was fully involved in biological research and had joined Jim Watson to form the Watson–Gilbert research group with shared equipment, research labs, offices, seminars, and the essential daily afternoon tea. By the end of 1965, the graduate students in this group included Ray Gesteland, Peter Moore, Mario Capecchi, Jerry Adams, John Richardson, Dick Roblin, Joan Argetsinger (Steitz), Gary Gussin, Albey Reiner, Jeff Roberts, Jan Pero, Volker Vogt, Roger Hendrix, Bob Kamen, and Nancy Axelrod. I was fortunate to be a part of such an outstanding group of colleagues. The Watson–Gilbert lab was an exciting place to be. I remember vividly that all of us felt we were working in the best molecular biology lab in the world. We all had a large dose of self-confidence (arrogance?) and believed we could find the answer first, no matter who our scientific competitors were.I started research working in a lab at the opposite end of the third floor of the Harvard Biological Laboratories (the Biolabs) from most of the Watson–Gilbert group. I shared a large room with Matt Meselson’s graduate student Garrett Ihler and three of Guido Guidotti's students, Pierre Henkart, John Pringle, and Steve Rosenberg. This was not a modern lab. In fact, I remember that several of the lab benches were equipped with beautiful old brass hand-cranked centrifuges. In retrospect, these relics of the past seem symbolic of the state of technology in the early 1960’s. The advances in equipment and especially methodology during the next 5 years played a big part in the progress I was to make.Much of my time those first few months was taken up with the flurry of starting classes, taking cumulative exams and two foreign language exams, learning my way around the Biolabs, playing intramural basketball, attending Boston Symphony Orchestra concerts, and getting to know my fellow graduate student and future wife, Ann Baker.Escherichia coli DNA-dependent RNA polymerase, had finished his research and was starting to write his thesis. Jim suggested that I determine the subunit structure of the enzyme. He showed me recently published electron micrographs, supposedly of RNA polymerase, that showed an apparent hexagonal shape with a 20 Å hole in the middle  and on transcription  all focused on things other than subunit structure at that time, thus creating an opportunity for a beginning graduate student who was slowly developing research skills and maturity to tackle this crucial research problem.E. coli RNA polymerase, had published the Chamberlin–Berg purification method in 1962 (It is important to look briefly at what was known about RNA polymerase and regulation of transcription at this time (early 1965). RNA polymerase activity had been discovered in 1959 by Sam Weiss in the rat liver and soon after by Audrey Stevens in bacteria. Mike Chamberlin, working with Paul Berg at Stanford on At that time, the Jacob and Monod model of the lactose operon , dominated thinking about the molecular biology of transcriptional regulation . This elE. coli RNA polymerase comprises almost 1% of the total cell protein and is quite large and negatively charged, it proved to be a challenge to purify in the 100-mg quantities that I needed for my work. At that time, RNA polymerase had the reputation of being quite unstable, and its purification required working 12 h or more in the cold room. I hated the cold room. The cold rooms in the Harvard Biolabs were small, crowded, and generally filthy. If Wally Gilbert had been working in it, it reeked of cigar smoke. He was the only person I ever saw who could mouth pipette and smoke a cigar at the same time. My first effort to reproduce the Chamberlin–Berg procedure was a disaster, partly due to my inexperience and partly because the batch of protamine sulfate from Eli Lilly that was available to me did not work like the batch used in the Chamberlin and Berg article. I got liters of gelatinous glop that couldn’t be clarified by centrifugation, and I ended up with an enzyme that had a final specific activity lower than that of the crude extract! It was a thoroughly discouraging maiden voyage into the realm of protein purification, and it made it very clear to me that I needed a better purification method. Perhaps this experience is what drove me to continue improving on the purification of this enzyme for the rest of my career, developing at least six successively better methods for purification.I set out to purify RNA polymerase from cell lysates. Although E. coli for Wally’s ultimately successful attempts to purify the lactose repressor. Wally took the material that precipitated from the cell extract with 33% saturated ammonium sulfate and left the 33% supernatant for me to use on RNA polymerase preparations. This was a rare luxury that was key to refining my procedures.I next tried John Richardson’s method, which involved chromatography on a hydroxyapatite column . I had dIn the summer of 1965, I attended the Physiology course at the Marine Biological Laboratory at Woods Hole on Cape Cod for 6 weeks. Jim Watson often sent off early graduate students to summer courses; I suspect to allow him to host visiting scientists to spend time in his lab using the space vacated by the student. While I was gone from Harvard, Lionel and Elizabeth Crawford from Edinburgh came to Jim’s lab to carry out electron microscopy on RNA polymerase and its binding to polyoma virus DNA. On my return, I was shocked to find that they had used up the entire batch of pure enzyme that I had worked months to prepare.in vitro protein synthesis. I was fortunate to have two outstanding mentors in protein biochemistry. The first was Guido Guidotti, a dynamic and irreverent Italian who had been hired at Harvard when John Edsall retired. He was my go-to guy for advice on almost every aspect of protein purification and handling. Later, Klaus Weber arrived as a senior scientist in Watson’s group. Klaus, a friendly and very earnest German, was also extremely experienced in protein chemistry. While I often got very different advice from these mentors, I soon learned that there could be several successful ways to solve a problem. I am greatly indebted to Guido and Klaus for helping me develop my protein biochemistry skills and intuition.During this time, I was almost the only person in the lab doing protein biochemistry. Most were studying the genetics and molecular biology of viruses and I was always looking for better ways to stabilize RNA polymerase. It did me no good to end a procedure with an enzyme that was impure, inactive, denatured, degraded, or precipitated. At that time, the usual way to keep an enzyme stable was to include a buffer, like Tris, to maintain a pH that is tolerated by the enzyme as well as a chelating agent, such as EDTA, to absorb any contaminating metal ions, such as iron or copper, that could accelerate the oxidation of the protein or inhibit the enzyme. Even so, many enzymes are not very stable in solution, especially RNA polymerase. I found that glycerol and DTT helped tremendously to stabilize it.I found the stabilizing effect of glycerol pretty much by accident. At that time, I was using 10% to 30% glycerol density gradient centrifugation to fractionate RNA polymerase from smaller proteins. One Friday, I centrifuged three identical tubes, collected fractions that afternoon from two of them, and stored them in the refrigerator. I must have become distracted, because I accidently left one tube out on my bench over the weekend. On Monday, I found the tube, dripped it, assayed it, and found, amazingly, that no activity had been lost. The fact that the enzyme activity had not been lost after sitting at room temperature for 2.5 days suggested that the glycerol was stabilizing the enzyme. From that time on, I used 5% glycerol in all my buffers and included 50% glycerol in my storage buffer, to maintain the stability of the purified material. I had not been aware of earlier work on the protective effect of glycerol , 7 becauAbout this same time, Mo Cleland at the University of Wisconsin-Madison published an article describing a new reducing agent called DTT, also known as Cleland’s reagent . ReducinNormally, one does not do the same fractionation step twice, but in this case, it was very effective. My first new purification procedure took advantage of the property of RNA polymerase to exist as a monomer at high salt concentrations and to form a dimer at low salt concentrations . CentrifIn late 1966 and the Spring of 1967, I was fortunate to have Scott Keller, a bright Harvard undergraduate, working with me on some of my experiments. We were trying to develop an even better method to purify large amounts of RNA polymerase. I had been using column chromatography with diethylaminoethyl cellulose, a positively charged column resin that binds negatively charged proteins such as RNA polymerase. However, it also bound other proteins that coeluted with the polymerase, meaning that the purification achieved was not very large. One day in March, I decided to try a column of phosphocellulose (PC), a negatively charged resin containing phosphate groups that binds positively charged proteins. I decided to try my usual pH 7.9 buffer. We prepared a crude extract, carried out a few other steps, and then applied the sample to the column. Most of the protein did not bind and passed directly through the column, the “flowthrough.” We washed the column with a low-salt buffer to remove any remaining unbound material and then eluted with a gradient of increasing KCl concentration and collected fractions. Most of the protein, as measured by UV absorption at 280 nm, was in the flowthrough. One sharp peak eluted rather late in the salt gradient. To our great surprise, this peak had all the RNA polymerase activity. We had removed tremendous amounts of unwanted proteins and obtained an excellent yield of polymerase activity in this one small peak  present in the GG enzyme.The development of PAGE had a huge effect on protein biochemistry in the mid-1960s. These gels allowed one to easily and quickly assess the purity and also to determine the size of a polypeptide. In 1964, Ornstein and Davis published two highly important articles , 11. UntIn late 1965, I remember Dave Hogness, a well-known biochemist from Stanford, visiting our lab and informally telling us about this new technique. I was eager to try it to see whether RNA polymerase was composed of several different subunits that might be able to be separated by PAGE. I believe I was one of the first people to try PAGE at Harvard. I tried both native and urea gels and found that, yes indeed, I could see several different bands on urea gels. However, there were several problems. First, it was not clear which bands were part of RNA polymerase and which were contaminating proteins. Second, the stain that was commonly used at that time, amido black, was not a very sensitive stain and one needed about 5 to 10 μg of protein in a band to see it. Minor components could not be detected. In late 1966, I read an article by Fazekius de St Groth that reported a new protein stain called Coomassie Brilliant Blue that seemed to be much more sensitive . I wroteThe next year, Shapiro, Vinuela, and Maizel  showed tJournal of Biological Chemistry where he thought “protein biochemists would see it.” Keith Dunker and Roland Rueckert at the University of Wisconsin-Madison published a similar study a month later, also in the JBC , and I had the students analyze ATCase along with some molecular weight markers. Results from the gels suggested that the subunits were much smaller than the currently accepted estimates. Klaus said, “See, I told you it didn’t work.” A few months later, he finished the amino acid sequence and discovered that the gels were correct. He became a convert and went on to write with Mary Osborne a very highly cited article  about SD the JBC . Both arAfter I had developed the PC column purification procedure and could prepare large amounts of pure RNA polymerase (PC enzyme), I set out to determine its subunit structure, the original goal of my thesis work. Until that time, most studied proteins that contained more than one subunit had been found to be multimers of identical subunits. A few, such as hemoglobin and ATCase, were known to have two kinds of subunits. I was not aware of any protein complex that had more than two different subunits, although many suspected that the ribosome might contain many different protein subunits. Then, as now, one usually defined a subunit as a polypeptide component of an enzyme or protein complex that copurified with the enzymatic activity through several fractionation steps.After I saw several bands when analyzing denatured PC enzyme using both urea and SDS gels, I set out to fractionate the polypeptides under denaturing conditions. I succeeded in separating subunits by several chromatography methods. I separated the mixture of what I later called beta prime  and beta  from alpha  and from omega  using gel filtration in the presence of SDS. I then separated β′ from β by anion exchange chromatography on diethylaminoethyl cellulose in the presence of 8 M urea .2ω and a molecular weight of ∼390 kDa  enzyme with the subunit composition of β′βα∼390 kDa . This reGuido Guidotti, a member of my thesis committee, had argued that maybe the subunits I had begun calling α and β'/β were just monomer and tetramer, respectively, of the same subunit. Guido bet me a bottle of fine Italian wine  that they were, but my effort convinced him the subunits were distinct. I collected on the bet, Ann and I enjoyed this delicious wine, and I gained his eventual approval of my thesis.In the early stages of developing the PC purification, SDS gel results suggested that there were two main subunits, one about 40 kDa and one about 160 kDa. Based on the example of hemoglobin, I named them α and β, respectively. I gave a 10-min talk at the FASEB meeting in Atlantic City in April 1968 where I described the new PC purification method and my evidence for multiple subunits in RNA polymerase. This was my first public talk, and I found it very difficult to cram all I had to say into a mere 10 min. In my abstract, I used the terms α and β but during my talk I reported that recent higher-resolution SDS gels showed that β was really a doublet, which I named β′ and β. Later, I decided that the consistent presence of a 10-kDa band on SDS gels merited a subunit name, and because it was so small, I called it omega (ω).In June 1968, I attended the Gordon Conference on Nucleic Acids in New Hampton, New Hampshire. I was not scheduled to give a talk, but I told many people about my GG and PC column purification procedures. As was common and collegial in those days, I sent a detailed draft of the PC purification procedure to researchers who requested it, including Ekke Bautz (Rutgers) and Ben Hall (University of Washington). Their labs independently used this method to find sigma.My research progress was disrupted for 3 weeks in February 1968 by the filming of an ABC TV program called “The Scientist–Race for the Repressor” that took place in the Watson-Gilbert lab. This program focused on the competition between Wally Gilbert and Benno Mueller-Hill on one hand trying to isolate and characterize the lac operon repressor and Mark Ptashne and Nancy Hopkins on the other working to isolate the lambda phage repressor. While this was interesting and often amusing, the omnipresent film crew as well as the huge cables running down the halls and into the cold room to energize Klieg lights were major distractions to research. Once, while a bacterial air shaker filled with flasks of swirling, colored liquids was being filmed, my wife Ann told the film crew that this was unrealistic and that scientists would know it was staged. They replied, “Scientists do not have color TVs.” I had the honor of being the “typical graduate student” and responding to Wally’s questions about my SDS gels of RNA polymerase displayed on a light box. The best part was the huge lab party that Jim held to celebrate the airing of the program and our film debuts.In January 1968, I had received a Helen Hay Whitney Fellowship for my upcoming postdoctoral training with Alfred Tissieres at the Institute of Molecular Biology in Geneva, Switzerland. I was eager to shift from a $2400/year graduate stipend to a $7000/year postdoc stipend; however, I could not start until after defending my thesis. As a result, I was trying hard to focus on writing. A rapid series of events conspired to delay my thesis by about 5 months but resulted in the most exciting and productive time in my scientific career.in vitro. For at least a year, he had been using my GG enzyme for his transcription studies, and it had worked very well. During September, he ran out of the enzyme and asked me for some more. I was very proud of my pure and well-characterized PC enzyme and gave him a big portion. This PC enzyme was very active on calf thymus DNA, but Jeff shocked me by announcing that it was not active in transcribing high-quality, intact phage lambda DNA. This was unexpected because I considered it to be the best enzyme I ever made.In September 1968, Andrew Travers arrived from Cambridge, England, as a new postdoc in the Watson Lab. He was a soft-spoken, creative Englishman looking for a project. Because it was a large lab, I did not pay much attention to him at first, not knowing that he would play a very important role in the coming months. My fellow lab mate in room 388, Jeff Roberts, was studying transcription of lambda phage DNA in vitro transcription experiments by Andrew Travers showing that phage T4 DNA also was not transcribed by PC enzyme, I decided to do one last RNA polymerase preparation before writing my thesis. I needed to understand the difference between the GG and PC enzyme. I planned to prepare a fresh batch of the GG enzyme and pass it over a PC column to see whether something that is required for activity on intact phage DNA is lost during this step. I would do all this very carefully and monitor each step and fraction with enzyme assays and SDS gels. Anne-Marie Piret, my technician, had been working full time with me since the previous November, and she and I were at the top of our games. I had spent almost 4 years making mistakes and learning lessons the hard way and was now well prepared to design, execute, and analyze several major experiments a day.In early October, based on Jeff’s concerns and some preliminary On October 24, we ran a PC column on the freshly made GG enzyme  18). On. On18). I had trouble sleeping those first few nights. I was very excited over the realization that we had made a major breakthrough in understanding RNA polymerase function and specificity. It now seemed obvious that RNA polymerase core could not bind and correctly and efficiently initiate transcription on an intact DNA template without a positive factor to direct it to a promoter. Sigma seemed to be providing the specificity for promotor binding. It seemed clear that at least one positive transcription factor, sigma, existed, and we knew how it worked. Perhaps, there would be multiple sigma factors, each with a different promoter specificity? Perhaps, this was a mechanism for the general regulation of classes of genes? We had much exciting work to do.in vitro and was using my method for making the PC enzyme. He had also discovered that the PC enzyme did not work well on an intact template.On October 30, we heard from Ray Gesteland, who had just stopped by after giving a seminar at Rutgers, that John Dunn, a graduate student with Ekke Bautz at Rutgers, had very similar results. John was studying T4 phage transcription At Jim Watson's suggestion, we called Ekke and agreed that our two labs would write up our work as one article and submit it to Nature. On Tuesday, November 5 (I remember it was the election day when Nixon beat Humphrey), Ekke and John came to Harvard and we planned the article. It ended up containing mostly my and Andrew's data because they were clearer and more complete, but it was definitely a joint effort. Andrew and I continued to actively pursue our investigation of sigma and its role in transcription initiation. The manuscript was received at Nature on December 2, a mere 5 weeks after our initial discovery. On January 4, 1969, the article, “Factor Stimulating Transcription by RNA Polymerase” appeared in Nature .I was amazed at the publicity our publication generated. There were several long “News and Views” columns published in Nature commenting on the discovery and even a feature article in the Boston Globe.In retrospect, the excitement created by sigma overshadowed my subunit work, which had actually resulted in the discovery of one of the first protein complexes containing three or more different subunits. RNA polymerase was one of the first characterized molecular machines that now are known to be ubiquitous, central players in transcription, replication, translation, signal transduction, protein degradation, mRNA splicing, apoptosis, and much more.Many people have asked me why Jim Watson’s name was not on the article. Jim never put his name on articles from his lab unless he did some of the work himself. As a result, I got much more notoriety as the first author than I normally would have. Jim was very pleased by our results and describes in his 2007 book “Avoid Boring People” . In March, I traveled to Europe to give talks about my recent work for Benno Mueller-Hill's Phage Course in Cologne, Wolfram Zillig in Munich, Alfred Tissieres in Geneva, Francois Gros in Paris, Heinz Schaller in Tubingen, and Guido Hartmann in Wurzburg. It was thrilling to be greeted as a visiting dignitary.In February 1969, I defended my PhD. thesis, “Subunit Structure of When I returned to the Biolabs from this trip, Jim asked me if I had told everyone about Jeff’s discovery of the termination factor rho. I said, “No, it’s his work and his story to tell, not mine.” Jim was mad at me (the only time I remember this happening) for my failure to communicate this new information. While I felt comfortable with my actions, it was clear that Jim felt very strongly that exciting science should be shared with others openly and immediately.“If sigma itself determines the specificity of initiation, the interesting possibility arises that several similar factors could exist, each with a specificity for a different type of initiation site.”During late 1968 and early 1969, Andrew and I continued to study the details of sigma action. It was very clear that the enzyme lacking sigma  was capable of RNA synthesis but requires sigma to form the holoenzyme that binds to specific DNA sites, the promoters of Jacob and Monod. It had not escaped our attention that it was very plausible that there could be several sigmas that recognized different types of promoters. We hinted at this multiple sigma hypothesis in our first article :“If sigmThis hypothesis turned out to be true.In May, Andrew and I published a second article in Nature, “Cyclic Re-use of the RNA Polymerase Sigma Factor” . This esE. coli RNA Polymerase: Purification, Subunit Structure, and Factor Requirements”  that added considerably to our growing knowledge of bacterial RNA polymerase and transcription factor sigma.Gordon Hager, a graduate student with Ben Hall in Seattle, was also studying B. subtilis. Jan and her student Tom Fox found the first alternative sigmas, those involved in phage SP01 late transcription, in 1974 , flagellar synthesis (sigmaF), adaptation to starvation and stationary phase (sigmaS), nitrogen (sigmaN/sigma54) and iron (sigmaFecI) metabolism, and response to extreme temperatures (sigmaE) and extracytoplasmic events (sigmaE and sigmaFecI).Ultimately, seven different sigmas were discovered in It took another 50 years to fill out our understanding of the structure and function of the various parts of the transcription machinery. Major progress has been made in my lab in McArdle Laboratory for Cancer Research at the University of Wisconsin-Madison and in many labs around the world. Below are some of these areas of progress, illustrated in many cases by representative work from my lab, including some of the method developments that made this research possible.in vitro transcription of phage DNA .Technical advances and early adoption such as improved protein stabilizers, unconventional use of PC chromatography, and PAGE, especially SDS gels, made a huge impact on my progress during my graduate research career. As I established my research program at UW-Madison in the Fall of 1971, I continued to focus on better understanding the transcription machinery, RNA polymerases, and regulatory factors. But I also was keenly aware that developing a new technique or significantly improving an existing technique for use in our polymerase research could have a broad impact in many other fields that use protein biochemistry. As a result, I spent significant time, resources, and creativity on method development in the area of protein purification and characterization. An enjoyable and rewarding phase of my career has been in disseminating and teaching these methods to new generations of protein biochemists through organizing meetings, editing books , 85, 86,The last 50 years have witnessed an explosion of research activity around the world on the machinery of transcription and transcriptional regulation in bacteria, archaea, and eukaryotes. The incredible complexity that has been revealed is astounding. I was fortunate to have entered the field in simpler times when we merely wanted to know what was in the black box called RNA polymerase. I was also fortunate to have been both a player and a spectator in that exciting time when molecular biology, biochemistry, and genetics combined with new technical capabilities to produce a truly wonderful and illuminating time in biological science research.The author declares that he has no conflicts of interest with the contents of this article.


Complex RNA–RNA interactions are increasingly known to play key roles in numerous biological processes from gene expression control to ribonucleoprotein granule formation. By contrast, the nature of these interactions and characteristics of their interfaces, especially those that involve partially or wholly structured RNAs, remain elusive. Herein, we discuss different modalities of RNA–RNA interactions with an emphasis on those that depend on secondary, tertiary, or quaternary structure. We dissect recently structurally elucidated RNA–RNA complexes including RNA triplexes, riboswitches, ribozymes, and reverse transcription complexes. These analyses highlight a reciprocal relationship that intimately links RNA structure formation with RNA–RNA interactions. The interactions not only shape and sculpt RNA structures but also are enabled and modulated by the structures they create. Understanding this two-way relationship between RNA structure and interactions provides mechanistic insights into the expanding repertoire of noncoding RNA functions, and may inform the design of novel therapeutics that target RNA structures or interactions. Biomolecules have evolved to function in coordination with each other in crowded intracellular environments in which new molecular interfaces and connections develop continuously. Direct protein–protein interactions likely mediate the bulk of cellular communication and signaling pathways in contemporary organisms. In the primordial RNA world, RNA–RNA and RNA–metabolite interactions were responsible for executing and regulating most if not all aspects of the metabolism. Not all of these RNA interfaces have been lost. RNA–metabolite interactions remain key pathways that regulate bacterial metabolism in the form of diverse riboswitches . RNA–RNAIn contrast to our more detailed understanding of protein–protein interfaces and their specificities, the physical picture is much less clear for how RNA–RNA interactions occur. Even for relatively simple RNA–RNA interactions that are primarily driven by base pairing, it is generally not clear how one RNA finds its target RNA partner. Is either RNA partially structured prior to and during the encounter? The vast majority of RNA–RNA interactions are multivalent, as they involve more than one base pair and frequently more than one interface. In what order are these individual contacts established, how do they propagate, and which among the multiple contacts determine the specificity and affinity?To address these fundamental questions about the origin and characteristics of RNA–RNA interactions such as affinity and specificity, we first explore the chemical basis and underlying forces that drive RNA–RNA contacts. We then examine three primary classes of RNA–RNA interactions—those between single-stranded RNAs (ssRNAs), between ssRNA and structured RNAs, and between structured RNAs. Using specific examples from triplexes to ribozymes, with particular emphasis on those with high-resolution structural information, we derive general trends and themes that are common to these diverse systems. These insights could help understand other RNA–RNA interaction systems whose structures remain unknown, and further inform the design of strategies or therapeutics that modulate RNA–RNA interactions important for cellular physiology or pathology.2+ or polyamines are needed to partially shield the charge of the phosphate backbone to allow such juxtaposition. Both the overall trajectory and local structure of the RNA phosphate backbone are more frequently recognized by basic amino acid residues of sequence-non-specific RNA-binding proteins. The ribose 2′-hydroxyl imparts significant structural rigidity over DNA strands and enforces A-form geometry in both double-stranded RNAs (dsRNA) and RNA–DNA hybrids. It also plays key roles in RNA recognition and ribozyme catalysis ,5655,56]Another paradigm of ssRNA interaction with structured RNA is the small nucleolar RNAs (snoRNAs) d. SnoRNAPartially or wholly structured RNAs can also recognize each other to mediate a number of cellular transactions from gene regulation to RNA processing and modification. These include localized interactions between compact RNA structural motifs as well as larger, multivalent interactions that involve several discreet interfaces ,64. CertKd of ~5 nm . U. U44]. UThe T-box system was among the first RNA-centric gene-regulatory mechanisms discovered in bacteria almost three decades ago . Due to T-box riboswitches share a common core sensory and regulatory mechanism ,120. TheSeveral recently reported T-box–tRNA complex structures jointly paint a picture of multivalent, multimodal tRNA–mRNA interactions ,51,114. Xanthomonas campestris pv. campestris (Xcc) is controlled through its 5′ UTR region encoding a SAM-I riboswitch at the level of translation initiation. This composite riboswitch is proposed to receive dual inputs of SAM binding to the canonical SAM-I portion of the RNA, as well as uncharged initiator tRNA binding to its 3′ region adjacent to the ribosome binding site [Recently, a novel tRNA-binding RNA element has been proposed in a group of SAM-I riboswitches . SAM-I bing site . CuriousFrom the examples discussed above and other analyses, we derive the following conclusions, speculations, and projections towards an in-depth understanding of complex RNA–RNA interactions.Although complementary ssRNA strands can in principle hybridize and interact with each other without assistance, biological ssRNA–ssRNA interactions in cells are generally facilitated by a third party. The facilitator can be adjacent structured RNA segments that help present the ssRNAs so that their intended base edges are exposed and pre-arranged for base-pairing formation with an incoming partner RNA. This is exemplified by the pre-organization of the tRNA anticodon trinucleotide structure . By pre-Besides adjacent RNA structure, RNA-binding proteins frequently act to assist with RNA–RNA interactions in diverse ways. Protein binding to ssRNA prevents non-native, undesired self-structure formation such as self-hairpins and dimers, and extends the ssRNA to provide the topology closer to the bound form. In addition, RNA-binding proteins such as the L7Ae-superfamily can bind and stabilize RNA architectural elements such as K-turns, to support the intended global structure ,123. FurssRNA–ssRNA base-pairing produces dsRNA segments that are immediately flanked by adjacent ssRNA elements linking them to additional dsRNA segments. Although these flanking ssRNA residues are generally imagined as flexible tethers or linkers, they are in fact frequently observed as immobile elements that participate directly in the interactions. These ssRNA residues, especially purines, frequently act as helix caps that stack across the terminal base pair imparting necessary stabilizing energy, especially when the helical segment is short . This isAnother frequently underappreciated RNA–RNA interaction is between the termini of dsRNA segments. Topologically juxtaposed dsRNA helices are strongly attracted to each other to hide their hydrophobic base planes from the solvent. In both the HIV-1 PBS–tRNA and full-length T-box–tRNA complexes, we note a general tendency for the stems to concatenate via coaxial stacking to form long spines. This solution behavior likely gives rise to the formation of end-to-end stacked pseudo-infinite helices in the majority of RNA crystals ,67. In lThe ability to predictably and reliably perturb functionally important RNA structures or interactions using small, deliverable RNA devices is highly desirable for the design of effective RNA therapeutics. The recent structural elucidation of several ENE-polyA triplexes and discovery of the CCR5-miRNA triplexes suggest ample potential to leverage the controllable stability of RNA triplexes to modulate gene expression and cellular metabolic programs beyond RNA stability. For example, triplex elements can be exploited to conditionally control the formation or exposure of long dsRNA segments that trigger or dampen innate immune responses, or to regulate translation initiation. Considering the fact that most dsRNA-binding proteins access the shallow and wide minor groove, and the frequent occurrence of A-minor interactions, it seems there is the prospect to observe and create additional stable minor groove RNA triplexes beyond the BWYV triplex. Another notable difference is that minor groove triplexes seem to form with existing dsRNA segments, rather than by invading and reorganizing internal U-rich loops. Thus, major groove triplexes such as the ENE rely on substantial enthalpic gains to overcome large entropic costs of triplex formation, whereas minor groove triplexes such as the CCR5 and BWYV pseudoknots likely form with much reduced enthalpic drivers and entropic barriers. Conceptionally, this distinction may have a major impact on their thermodynamic and kinetic behavior and may further explain their different biological roles. It is possible that robust, high-barrier major groove triplexes are better suited for RNA protection against repeated probing by destructive enzymes, while more malleable minor groove triplexes function better as bistable regulators that can propagate an input signal of limited energetic signature (such as metabolite binding) to effect an RNA conformational change.Taken together, our analyses highlight and dissect the symbiotic, reciprocal relationship between RNA structure and RNA–RNA interactions. The same set of operating principles and underlying forces enable ssRNA, dsRNA, triplexes, and other structural motifs to engage each other with affinity and selectivity, imparting diverse functionalities to noncoding RNAs as scaffolds, sensors, regulators, catalysts, and more.


DDX3 is an RNA chaperone of the DEAD-box family that regulates translation. Ded1, the yeast ortholog of DDX3, is a global regulator of translation, whereas DDX3 is thought to preferentially affect a subset of mRNAs. However, the set of mRNAs that are regulated by DDX3 are unknown, along with the relationship between DDX3 binding and activity. Here, we use ribosome profiling, RNA-seq, and PAR-CLIP to define the set of mRNAs that are regulated by DDX3 in human cells. We find that while DDX3 binds highly expressed mRNAs, depletion of DDX3 particularly affects the translation of a small subset of the transcriptome. We further find that DDX3 binds a site on helix 16 of the human ribosomal rRNA, placing it immediately adjacent to the mRNA entry channel. Translation changes caused by depleting DDX3 levels or expressing an inactive point mutation are different, consistent with different association of these genetic variant types with disease. Taken together, this work defines the subset of the transcriptome that is responsive to DDX3 inhibition, with relevance for basic biology and disease states where DDX3 is altered. Previoual delay , which aal delay ,18 and oal delay ,20 also Inactivation of Ded1 in yeast leads to polysome collapse and global downregulation of translation ,22. Morein vitro and cellular reporter systems to conclude that decreases in ribosome occupancy upon DDX3 depletion are driven by 5′ UTRs. Taken together, our results support a model for DDX3 function where interactions with the small ribosomal subunit facilitate translation on messages with structured 5′ UTRs, which, when inactivated, pathologically deregulates protein synthesis.Here, we depleted DDX3 protein levels and measured alterations to translation and RNA abundance using ribosome profiling and RNA-seq. We also characterized DDX3 binding by PAR-CLIP, using the presence of T>C mutations as a diagnostic hallmark of protein–RNA interactions. We observed robust interactions between DDX3 and transcript 5′ UTRs, as well as a specific and conserved site on the 18S ribosomal rRNA. We found that transcripts with structured 5′ UTRs are preferentially affected by DDX3. We used Ribo-seq fastq files were stripped of the adapter sequences using cutadapt. UMI sequences were removed and reads were collapsed to fasta format. Reads were first aligned against rRNA (accession number U13369.1), and to a collection of snoRNAs, tRNAs and miRNA (retrieved using the UCSC table browser) using bowtie2  in the ‘De-novo splice junction discovery was disabled for all datasets. Only the best alignment per each read was retained. Read counts for all libraries are in Remaining reads were mapped to the hg38 version of the genome (without scaffolds) using STAR 2.6.0a  suppliedPeak calling for PAR-CLIP reads was performed with PARalyzer v1.5  in the ‘BANDWIDTH = 3CONVERSION = T>CMINIMUM_READ_COUNT_PER_GROUP = 5MINIMUM_READ_COUNT_PER_CLUSTER = 5MINIMUM_READ_COUNT_FOR_KDE = 5MINIMUM_CLUSTER_SIZE = 8MINIMUM_CONVERSION_LOCATIONS_FOR_CLUSTER = 1MINIMUM_CONVERSION_COUNT_FOR_CLUSTER = 3MINIMUM_READ_COUNT_FOR_CLUSTER_INCLUSION = 5MINIMUM_READ_LENGTH = 18MAXIMUM_NUMBER_OF_NON_CONVERSION_MISMATCHES = 0Peaks with >10 reads were retained for subsequent analysis.Coverage-normalized T>C conversions on rRNA for positions with 2000 reads or more  were mapGenomicFeatures, GenomicFiles and GenomicAlignments  as an additional covariate. Translationally regulated genes were defined using an FDR cutoff of 0.05 from a likelihood ratio test, using a reduced model without the assay type covariate, e.g. assuming no difference between RNA-seq and Ribo-seq counts , using the Fisher test with default parameters.For both RNA-seq and Ribo-seq, only genes with BaseMean >8 or more than the bottom 10% of the library were used. GO enrichment analysis was performed with the randomForest package  with default parameters. Lasso regression was performed on scaled variables using the glmnet package ;Baseline TE levels, defined as ratio of Ribo to RNA reads;Baseline RNA mature levels, defined as length-normalized ratio of RNA-seq reads in introns versus exons;GC content, length  and ribosome density in: 5′ UTRs, a window of 25nt around start and stop codons, CDS regions, non-coding internal exons, introns, and 3′ UTRs;Biostrings package) for the following motifs, partially taken from : ‘[CU][CU][CU][CU][U][CU][CU][CU]’- TISU(Translator Initiator of Short 5′-UTR): ‘[CG][A][A][CG][A][U][G][G][C][G][G][C]’- CERT (cytosine-enriched regulator of translation): ‘[CG][CGU][CGU][CG][CGU][C][CGU][C][CA][GU][C][CGU][CGUA][CG][C]’pqsfinder R package  and correlation between predicted and measured test data were calculated on a 10-fold cross-validation scheme.PARalyzer peaks (and peaks from the POSTAR2 repository ) were made novo motif finding, using PAR-CLIP peaks from the POSTAR2 repository, selecting peaks from HEK 293 cells called with PARalyzer as above, and selecting for peaks in UTRs and intronic regions in protein-coding genes. The following parameters were used:The STREME algorithm  was used–rna –minw 5 –maxw 15 –pvt 0.05 –totallength 1e7 –time 18000 –patience 6Ribo-seQC. Counts on 5′ UTR and CDS were first averaged between replicates. The ratio 5′ UTR to CDS of these counts were calculated for each gene, in the siRNA and controls condition. The log2 of the ratio siDDX3/control for those values represents the skew of counts towards 5′ UTR in the siDDX3 condition:To compare read mapping locations within transcripts, a window of 25nt around the start codon was subtracted from annotated 5′ UTRs and CDS. 5′ UTRs and CDS regions in genomic and transcriptomic space were retrieved using in silico folding was performed on 5′ UTRs sequences using RNAlfold (ConversionEventCount / (ConversionEventCount + NonConversionEventCount)) for each PAR-CLIP peak were derived using the clusters.csv output file from PARalyzer. Gviz was used to plot tracks for RNA-seq, Ribo-seq and PAR-CLIP over different transcripts. The Wilcoxon rank sum was used for statistical testing and Cliff's delta was used to calculate effect sizes as described  Triton X-100, 25 U/ml TurboDNase (Ambion), harvested, centrifuged at 20 000 g for 4 min at 4°C and supernatants were stored at –80°C. Thawed lysates were treated with RNase I (Ambion) at 2.5 U/μl for 45 min at room temperature with slow agitation. Further RNase activity was stopped by addition of SUPERase:In (Ambion). Next Illustra MicroSpin Columns S-400 HR  were used to enrich for ribosome complexes. RNA was extracted from column flow throughs with TRIzol (Ambion) reagent. Precipitated nucleic acids were further purified and concentrated with Zymo-Spin IIC column (Zymo Research). Obtained RNA was depleted of rRNAs with Ribo-Zero Gold Kit (Human/Mouse/Rat) kit (Illumina), separated in 17% urea gel and stained with SYBR Gold (Invitrogen). Gel slices containing nucleic acids 27–30 nucleotides long were excised and incubated in a thermomixer with 0.3 M NaCl at 4°C overnight with constant agitation to elute RNA. After precipitation nucleic acids were treated with T4 polynucleotide kinase (Thermo Scientific). Purified RNA was ligated to 3′ and 5′ adapters, reverse transcribed and PCR amplified. The amplified cDNA was sequenced on a HiSeq2000 (Illumina).Knockdown RP: Flp-In T-REx HEK 293 cells transfected with control siPool and with DDX3X-mAID tagged HCT 116  cells expressing OsTIR1 were transfected with either wild-type DDX3X or DDX3X R326H. 24 hours post-transfection, media was changed and fresh media with 500 μM indole 3-acetic acid (IAA) was added to cells. Un-transfected cells were treated with either DMSO or IAA. Forty-eight hours after auxin addition, cells were treated with 100 μg/ml cycloheximide (CHX) for two minutes to trap ribosomes and harvested and lysed as described in  Triton X-100, 25 U/ml TurboDNase (Ambion). 240 μl lysate was treated with 6 μl RNase I  for 45 min at RT with gentle agitation and further digestion halted by addition of SUPERase:In (Ambion). Illustra Microspin Columns S-400 HR  were used to enrich for monosomes, and RNA was extracted from the flow-through using Direct-zol kit (Zymo Research). Gel slices of nucleic acids between 24 and 32 nts long were excised from a 15% urea–PAGE gel. Eluted RNA was treated with T4 PNK and preadenylated linker was ligated to the 3′ end using T4 RNA Ligase 2 truncated KQ . Linker-ligated footprints were reverse transcribed using Superscript III (Invitrogen) and gel-purified RT products circularized using CircLigase II . rRNA depletion was performed using biotinylated oligos as described in  NP-40, 0.5 mM DTT, complete EDTA-free protease inhibitor cocktail (Roche)), incubated 10 min on ice and centrifuged at 13000 rpm for 15 min at 4°C. Supernatants were filtered through 5 μm syringe filter. Next, lysates were treated with RNase I (Thermo-Fisher Scientific) at final concentration of 0.25 U/μl for 10 min at room temperature. Immunoprecipitation of the DDX3/RNA complexes was performed with FLAG magnetic beads (Sigma). After IP and washing, the protein-bound RNAs were 3′ de-phosphorylated and 5′-end phosphorylated using T4 PNK with 0.01% Triton X-100, and the NIR fluorescent adaptor (5′-OH-AGATCGGAAGAGCGGTTCAGAAAAAAAAAAAA/iAzideN/AAAAAAAAAAAA/3Bio/-3′) was ligated to the RNA using truncated RNA ligase 2 K227Q (NEB) overnight at 16°C, shaking at 1600 rpm. Crosslinked protein–RNA complexes were resolved on a 4–12% NuPAGE gel (Thermo-Fisher Scientific) and transferred to a nitrocellulose membrane. Protein–RNA complex migrating at an expected molecular weight were excised, and RNA by proteinase K (Roche) treatment and phenol–chloroform extraction. Purified RNA was further ligated to 5′ adapters, reverse transcribed and PCR amplified. The amplified cDNA was sequenced on a NextSeq 500 device (Illumina).Flp-In T-REx HEK 293 cells expressing FLAG/HA-tagged DDX3X  were labDDX3X ,47. BrieAnnotated 5′ UTRs for selected transcripts were cloned upstream of Renilla Luciferase (RLuc) under the control of a T7 promoter, with 60 adenosine nucleotides downstream of the stop codon to mimic polyadenylation. 5′ UTR sequences are in pA60 txn rev: TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT TTT CTG CAGpA60 txn fwd: CGG CCA GTG AAT TCG AGC TCT AAT ACG ACT CAC TAT AGGin vitro transcription reactions were set up at room temperature with 1–5 μg of purified template, 7.5 mM ACGU ribonucleotides, 30 mM Tris–Cl pH 8.1, 125 mM MgCl2, 0.01% Triton X-100, 2 mM spermidine, 110 mM DTT, T7 polymerase and 0.2 U/μl units of Superase-In RNase inhibitor (Thermo-Fisher Scientific). Transcription reactions were incubated in a PCR block at 37°C for 1 h. 1 μl of 1 mg/ml pyrophosphatase (Roche) was added to each reaction, and the reactions were subsequently incubated in a PCR block at 37°C for 3 h. 1 unit of RQ1 RNase-free DNase (Promega) was added to each reaction followed by further incubation for 30 minutes. RNA was precipitated by the addition of 200 μl 0.3 M NaOAc pH 5.3, 15 μg GlycoBlue co-precipitant (Thermo-Fisher Scientific) and 750 μl 100% EtOH. Precipitated RNA was further purified over the RNA Clean & Concentrator-25 columns (Zymo Research). Glyoxal gel was run to assess the integrity of the RNA before subsequent capping and 2′ O-methylation.100 μl S-adenosylmethionine (SAM), 20 units of Vaccinia capping enzyme (New England Biolabs), 100 units of 2′-O-Me-transferase (New England Biolabs) and 25 units RNasin Plus RNase inhibitor (Promega). The reactions were incubated at 37°C for 1 h, followed by purification over the RNA Clean & Concentrator-25 columns (Zymo Research) and elution in DEPC H2O. Glyoxal gel was run to assess the integrity of the RNA before proceeding to in vitro translation reactions.20 μg of total RNA was used in a 40 μl capping reaction with 0.5mM GTP, 0.2 mM HEK 293T cells in 150mM plates were transfected with 20 μl of siRNA . 40 μg of plasmids were transfected into HCT 116 cells using Lipofectamine 2000 (Thermo Fisher Scientific), following manufacturer's instructions. Cells were treated with 500 μM indole-3-acetic acid (IAA) 24 h post-transfection and harvested for preparation of cellular extracts after a further 48 h.2, 5 mM DTT, and 1 tablet Complete mini EDTA free protease inhibitor (Sigma) per 10 ml) was added to the cell pellet and was incubated on ice for 45 min. The pellet was homogenized by trituration through a 26G needle attached to a 1 ml syringe 13–15 times. Efficiency of disruption was checked by trypan blue staining (>95% disruption target). The lysate was cleared by centrifugation at 14000g for 1 min at 4°C, 2–5 μl was reserved for western blot analysis, and the remainder was aliquoted and flash frozen in liquid nitrogen.Three to five 150 mm plates of HEK 293T or HCT 116 cells were trypsinized and pelleted at 1000g, 4°C. One cell-pellet volume of lysis buffer  as described (5 μl escribed . ReactioDDX3X-mAID tagged HCT-116 cells expressing OsTIR1 were plated into 96-well plates at ∼80% confluency. Cells were pre-treated with DMSO or 500 μM indole 3-acetic acid (IAA) for 48 h. Cells were transfected with a mixture of 10 ng in vitro transcribed firefly luciferase RNA with a minimal 5′ UTR (FLuc) and between 50–200 ng renilla luciferase RNA (RLuc) downstream of selected 5′ UTRs using the TransIT mRNA Transfection Kit (Mirus Bio) per manufacturer's instructions. Twenty-four hours post transfection, cells were lysed and expression of the reporters were measured using the Dual-Luciferase Reporter Assay System (Promega) on a GloMax Explorer plate reader (Promega). The Renilla Luciferase signal in each well was normalized to the Firefly Luciferase signal to control for cell number, transfection efficiency, and basal translation levels.DDX3X is an essential gene . We then made translation extracts from HEK 293T cells transfected with a nontargeting control siRNA or a DDX3 siRNA  was able to moderately predict the translation changes upon DDX3 knockdown . The two methods largely agreed in pinpointing relevant features, with the random forest slightly outperforming the lasso . The feaHMBS ribosome occupancy is shown in Figure We measured the enrichment of ribosomes in transcript 5′ UTRs, under the hypothesis that depletion of DDX3 might lead to defective scanning and ribosome accumulation , or seleThe above ribosome profiling and RNA-seq experiments identified the set of transcripts that are affected by DDX3 depletion, but these transcripts could be affected by direct or indirect mechanisms. Therefore, to better define the set of transcripts that are direct targets of DDX3, we measured DDX3 binding sites with high specificity using PAR-CLIP Figure . PreviouHigh-throughput sequencing of RNA fragments crosslinked to DDX3 identified a binding site for DDX3 on the 18S ribosomal RNA Figure . It is pODC1  on the human 40S ribosome. This is similar to binding sites identified previously using other CLIP approaches , confirmDespite primarily affecting translation, some genes exhibited changes in steady-state RNA abundance upon DDX3 depletion. RNA-level changes could be mediated by indirect effects of a DDX3-dependent translation target, or reflect additional mechanisms of post-transcriptional gene regulation, possibly mediated by RNA structural features, codon composition, or other RNA decay pathways . Moreovein cis by binding to the 40S ribosome and facilitating translation initiation on the associated mRNA (Figure in trans by activating an mRNA prior to 43S complex loading. Future work defining the binding site of DDX3 on the ribosome could enable separation of cis and trans functions to test these two models, although we note that the functional consequences of DDX3 depletion we have observed here are independent of its functioning in cis or trans.DDX3 is an abundant protein, with approximately 1.4 million copies per HeLa cell , or abouA Figure . It is aDDX3 is altered in numerous human diseases, including cancers and developmental disorders . Some dihttps://github.com/lcalviell/DDX3X_RPCLIP.Processed datasets and code to reproduce the main figures can be found here: Sequencing data can be retrieved using GEO accession numbers GSE125114 and GSE157063.gkab287_Supplemental_FilesClick here for additional data file.