Anemia affects approximately 30% of children all over the world. Acute respiratory tract infections (ARTI), urinary tract infections (UTI) and gastroenteritis (GE) are common infectious entities in children. Here, we assessed the association between anemia and development of recurrent ARTI, UTI, and GE in children. This was a case-control study in hospitalized 2–5 years old children in Professorial Pediatric Unit at Teaching Hospital Anuradhapura, Sri Lanka. An 18-month follow up was done to assess the risk factors for the development of recurrent ARTI, GE, UTI, and control presented without infections. Further, 6-month follow up done after 3-month iron supplementation to assess the occurrence of recurrences. Blood Hb concentration was measured using Drabking’s reagent. Logistic regression was used to find the risk factors for the development of recurrences. In ARTI, 121/165 (73.3%), GE, 88/124 (71%), UTI 46/96 (47.9%) and control 40/100 (40%) were having anemia. Initial ARTI group, recurrent ARTI was 24 ; initial GE group: recurrent GE was 14 , recurrent ARTI was 11 ; initial UTI group, development of; recurrent UTI was 8 ; control, recurrent ARTI was 11 . Following 3-month iron supplementation reduction of recurrences was significant: initial ARTI recurrent ARTI in 90%, recurrent GE in 77.7%; initial GE recurrent GE in 83.3%, recurrent ARTI in 80%; initial UTI recurrent ARTI in 71.4% and control recurrent ARTI in 88.8%. Iron deficiency is a major type of anemia and anemic children are more prone to develop recurrent ARTI and GE. Once iron deficiency being corrected the rate of recurrent ARTI and GE was reduced. This would be a boost for policy developers to implement strategies at the community level to prevent iron deficiency in children to reduce ARTI and GE recurrences. Acute respiratory tract infections (ARTI), urinary tract infections (UTI) and gastroenteritis (GE) are such common infectious entities5. In the globe, infections following bacteria and viruses play a significant role while parasites and fungi are emerging and threatening7.Acute infective episodes in children are quite common and are associated with high morbidity and mortality1. In 2014, 18% of mortality for children younger than 5 years of age was caused by ARTI while the diarrheal disease is the next greatest9. UTI in children <5 years of age the associated burden is 4%. Further the overall conclusion one out of 20 girls and one out of 50 boys will have a UTI by the age of 5 years, with a predominance of boys during the neonatal period and early infancy10. Overall burden following these 3 major acute childhood infections is substantial thus impact on the globe and the country economy is enormous11.The disease burden following childhood ARTI is greater than that of any other cause of disease14. Hemoglobin (Hb) concentration is a parameter that reflects the chronic nutritional status and also blood oxygen carrying capacity16. Children are at a rapid growth state thus demand the nutrition is enormous17. Simultaneously, the tendency to develop under-nutrition is also high. In such instances, the risk for development of infection is high and the vicious cycle continues leads to poor-nutrition18.Following entry of microorganism to the organ vicinity several factors concurrently contribute for the development of the infection. When considering host-parasite interface nutritional status of the host is one of the key contributory factors for invasion and development of infections20. Several factors are contributed to anemia. In childhood, nutritional anemia including iron, vitamin B-12, and folate deficiency is the commonest21. In addition to the nutritional, hereditary type of anemia including thalassemia, sickle cell anemia and aplastic variety following bone marrow suppression is observed22. Irrespective of the etiology following anemia, child suffers fatigability and its negative effect on growth is great. Iron deficiency anemia in children occurs most frequently between the age of 6 months and 3 years, the period of age when repeated infections occur23. Anemia associated lower ARTI occurs more commonly in children than in adults.Anemia is affecting approximately 30% of children all over the world25. Recurrent infective episodes invariably lead to undernutrition26. Perhaps, anemia is a well-known risk factor for recurrent infective episodes27. In addition, studies on hemoglobin level and development of multiple episodes of otitis media in children been discussed28. In a cross-sectional case-control study anemia and occurrence of acute gastroenteritis also been discussed among children in Gaza strip29. In contrast, anemia and development of recurrent UTI are not been well described in the literature. Another hand if a child develops recurrent infections possibility of immunodeficiency need to be excluded30.Recurrent ARTI and UTI are common in children and following development of recurrences the associated burden would worsen31. This study would assess the Hb status and development of acute as well as recurrent ARTI, UTI, and GE in children.Irrespective to the etiology of anemia, the relation between low hemoglobin (Hb) level and occurrence of infections has not been fully evaluated, and only a few reports are availableThis was a case-control study in hospitalized 2–5 years old children with ARTI, UTI, and GE over March 2014 to August 2014. To participate in the study informed written consent obtained from the legal guardians/parents. As a control, children presented to the outpatient department to seek treatment for traumatic surgical cases who were having past 6-month period free of any acute or chronic infections were included. Children having pulmonary, cardiac, gastrointestinal and urogenital structural and functional anomalies were excluded from the study. Patients who undergo repeated blood transfusions were excluded from the study. Further, children with known immunodeficiencies were also excluded from the study. The study was performed at the pediatric ward, Professorial Unit, Teaching Hospital Anuradhapura, Sri Lanka. Participants including controls were followed up 18 months to assess the occurrence of recurrent ARTI, GE and UTI and the risk factors. This was done following weekly telephone conversations with the guardians. In each episode they were asked to admit to the Professorial Unit, Teaching Hospital Anuradhapura, Sri Lanka. Further, a period of 6 months was followed up following iron supplementation (3-month period) to assess the development of recurrent infections. They were followed up in similar manner. All methods and protocols were performed in accordance with the relevant approved guidelines and regulations.*Brian Yang Merritt’s Haemoglobin concentration (http://emedicine.medscape.com/article/2085614-overview) below 2 standard deviation (SD)32.ARTI cases with Severe Acute Respiratory Illness (SARI) defined by WHO were included. A child has a fever with dysuria (crying while micturition) and or hematuria was included as UTI. Sudden onset of diarrhea and/or vomiting, usually three or more bouts of diarrhea or vomiting were taken as GE. A hundred patients in age 2–5 years who visited the outpatient department for surgical problems were taken as a control. Definition of recurrent ARTI is arbitrary, too generic, restrictive and for our study: recurrent infectious rhinitis as more than five episodes per year and recurrent pharyngitis or tonsillitis more than three episodes within 12 months. For the lower respiratory tract, we have taken ≥3 episodes per 12 months. Similarly, for recurrent GE we have taken ≥3 episodes per 12 months while for the clarity cases with chronic diarrhea were excluded. Recurrent urinary tract infection (UTI) refers to ≥2 infections in six months or ≥3 infections in one year. For anemia when hemoglobin level was considered age-specific 13- ≤ 24 months [mean 12.0 g/dL (−2SD: 11.0 g/dL)] and 25- ≤ 60 months [mean 12.5 g/dL (−2SD: 11.5 g/dL)] ™ (United Kingdom)33, respiratory screening reagents for 8 respiratory viruses and viral typing was done for each of RSV, adeno, parainfluenza 1, 2 & 3, influenza A & B and Human metapneumovirus (hMPV) viruses using monoclonal antibodies DAKO IMAGEN™ (United Kingdom). Children with UTI were having significant (single isolate or mixed > 105) and culture growth from clean caught urine was taken as having bacterial UTI. In GE stool full report was having a significant number of pus cells considered. The collected human fecal samples were tested in duplicate for Group A rotavirus and Adenovirus using a commercially available qualitative enzyme immunoassay , following the manufacturer’s instructions.Children with ARTI nasopharyngeal aspirates (NPAs) collected with the help of recommended mucus extractor by the pediatrician/prior trained medical doctor (research candidate). Indirect immunofluorescence assay was performed by DAKO IMAGENShigella spp. strains, after an 18 h incubation at 37 °C. The suspicious colony was plated onto CHROMagar™ Salmonella medium  and cultivated at 37 °C for 18 h. Each stool sample was directly inoculated onto alkaline peptone water  at 37 °C for 18 h to examine for Vibrio cholera, Vibrio parahaemolyticus, Aeromonas spp., and Plesiomonas spp., and was then plated onto thiosulfate-citrate-bile salts-sucrose agar  at 37 °C for 18 h. Suspicious colonies were selected to conduct the oxidase experiment. If the oxidase test resulted in a positive reading, the systematic biochemical identification for these suspicious colonies was confirmed.Each stool was inoculated into selenite brilliant green sulfa enrichment broth  at 37 °C for 18 h, and was then plated onto Salmonella-Shigella agar  and xylose lysine deoxycholate agar  to detect non typhoidal salmonellosis and Stool iodine staining, wet smear, and microscopy were performed to assess amoebic cysts, oocytes, and other helminth oocytes and larvae. In addition to that fecal reducing substances, the level was taken to exclude lactose intolerance and malabsorption syndromes. Blood Hb concentration from all participants was measured using Drabking’s reagent using a spectrophotometer. Blood picture analysis and serum ferritin levels were measured to define the etiology for anemia.An investigator administered questionnaire was used to collect patients’ demography, nutritional status, clinical presentation, and past medical history. For iron deficiency anemia following a period of 3-month of oral iron supplementation (weight/based) the subjects were further followed up over 6-month to observe the development of recurrent ARTI, GE and UTI. Children with hemoglobin 9–10 g/dL were supplemented with 60–120 mg of iron. Hemoglobin concentration, blood picture (normochromic and normocytic) and assessment of serum ferritin level was done to confirm the cure of iron deficiency anemia.® Excel and compared and cleaned for wrong entries. Statistical analysis was done using SAS version 9.1 34. Association of each of the categorical variable with response variable was assessed by Chi-square test. Variables showing statistically significant association in univariate analysis with the outcome variable were considered as a risk factor. Only those variables were subjected to multivariate analysis. Logistic regression method was used to find the risk factor for the development of recurrent ARTI, UTI, and GE. In multivariate analysis, variables showing P < 0.05 were considered to be statistically significant. Continuous variables were expressed as a measure of central tendency.Data obtained were double entered into a spreadsheet database prepared with MicrosoftEthical approval for all experimental protocol/s were approved by ethical review and publication committee, University of Peradeniya, Sri Lanka and to participate in the study informed written consent obtained from the legal guardians/parents.Over the period of the initial 18 months, children with clinically suspected 165 cases of ARTI, 124 cases of GE, 96 cases of UTI and 100 control were enrolled in the study. Children with suspected ARTI, 65 (39.3%) cases of viral ARTI was detected based on IFA, mean age of presentation was 2.4 ± 0.5 years and mean hospital stay was 4 ± 2 days while 58% were males and 42% females. Children with GE, 62 (50%) viral, 12 (10%) bacterial cases detected based on viral ELISA and stool bacterial cultures respectively. The mean age of presentation of GE was 2.5 ± 0.8 years and mean hospital stay was 5 ± 2.5 days while 57% were males and 43% females. Children with UTI, 52 (54.1%) had culture-positive UTI mean age of presentation was 2.45 ± 0.7 years and mean hospital stay was 3 ± 2.4 days while 55% were males and 45% females. In control, 52% were males and 48% were females. In all including the control, male predominance (p = 0.03) with no significant difference between mean age and mean hospital stay was detected.Out of 165 children with ARTI, 121 were anemics (73.3%). Eighty-eight children out of 124 with GE and (71%) and 46 out of 96 children with UTI were having anemia (47.9%). In control subjects, 40 of them were having anemia (40%). When compared the diseased groups and the control, children with ARTI and GE were having anemia significantly (p = 0.03 and 0.04 respectively) whiles children with UTI, anemia was not significant (P > 0.05)  of them were found to have iron deficiency anemia, 2 of them with megaloblastic anemia and 1 with asymptomatic sickle SD disease. Whiles in initial GE group 86 (71.0%) were found to have iron deficiency anemia, 2 of them with megaloblastic anemia. In UTI group 44 (47.9%) were found to have iron deficiency anemia and 2 of them were thalassemia trait. Further in control 38 (95%) were found to have iron deficiency anemia and 2 of them were thalassemia traits. In the control group, 35 (35%) found to have iron deficiency anemia. We have excluded patients having repeated blood transfusions. Therefore, patients having thalassemia major and other hemoglobinopathies demanding blood transfusions were not considered for the analysis. Based on blood picture analysis, iron deficiency anemia was further confirmed by serum ferritin assay. In initial ARTI patients, serum ferritin level in patients with anemia was 4.5 ± 0.3 ng/ml. In initial GE patients, it was 4.5 ± 0.4 ng/ml, in initial UTI patients it was 4.5 ± 0.35 ng/ml and in control with anemia it was 4.5 ± 0.3 ng/ml. It was significantly (p = 0.03) below the age-specific lower limit of the reference range . In between anemic children in ARTI, GE, UTI groups and control the serum ferritin values were not significantly differed (p > 0.05). Further, no peripheral stigmata of iron deficiency anemia were observed. All of the subjects were on anti-helminth treatment once in six months. Further patients with GE, non-of them were having amoebic oocytes, cysts and any oocytes related to soil and non-soil inhabiting helminths in wet stool mounts. Also, on iodine staining, all tested diarrheal stool was negative for parasite cyst or ova.Anemia was a risk factor for the development of ARTI with an odds ratio of 3.08 with 95% interval confidence of 2.03–4.80 (P = 0.004) Further anemia was a risk factor for the development of GE with an odds Ratio of 2.98 with 95% interval confidence of 1.93–4.40 (P = 0.01). In UTI group anemia was not either risk or a protective factor as the odds ratio of 1.03 (p = 0.09) with 95% interval confidence of 0.78–1.40.Over the 18 months follow up period development of recurrent ARTI, GE and UTI in study groups and the control as follows. Among initial ARTI group, development of recurrent ARTI was 24 ; recurrent GE was 11  and recurrent UTI was 0. Among initial GE group, development of; recurrent GE was 14 , recurrent ARTI was 11  and recurrent UTI was 4 . Among initial UTI group, development of; recurrent UTI was 8 , recurrent ARTI was 4  and recurrent GE was 0. Among control, development of; recurrent ARTI was 11 , recurrent GE was 6  and recurrent UTI was 2 . For the risk factor analysis, only significant (p < 0.05) recurrent infections among study groups was included  and day care attendee, having congenital anomalies including cyanotic heart diseases, cystic fibrosis, structural anomalies in gastro-intestinal tract familial syndromes, having gastro-esophageal reflux and bronchial asthma.None of these factors were not associated significantly (p > 0.05) with recurrences in test subjects and the control. Birth weight (<2500 g), maturity (<36 weeks period of amenorrhea), mode of delivery , weight for age (<−2 SD), type of drinking water source , use of boiled cooled water, having daily bath and frequent (>2/day) body wash, exclusive breast feeding in first 4 months, family monthly income <30,000 rupees, mothers’/caregivers’ education (only up to primary level), proper waste disposal, birth order (3Following a period of 3-month of iron supplementation (60–120 mg daily), the subjects were further followed up period of 6-month to observe the development of recurrent ARTI and GE. Meanwhile, advice on hand hygiene was also given. Recurrent infection among anemics as follow: initial ARTI subjects recurrent ARTI was detected in 20 (16.5%), recurrent GE was detected in 9 (7.4%); initial GE subjects recurrent GE was detected in 12 (10%), recurrent ARTI was detected in 10 (12%); initial UTI subjects development of recurrent ARTI was detected in 7 (16%) and control subjects development of recurrent ARTI was detected in 9 (25%). Following 3-month oral iron supplementation hemoglobin concentration was increased significantly and (mean ± SD) mg/Dl as follows: initial ARTI- 11.3 ± 0.2 (p = 0.03), GE- 11.6 ± 0.4 (P = 0.03), UTI- 11.4 ± 0.3 (P = 0.03) and controls- 11.9 ± 0.3 (p = 0.03). serum ferritin was increased significantly and (mean ± SD) ng/ml as follows: initial ARTI- 12.6 ± 0.8 (p = 0.02), GE- 12.6 ± 0.4 (P = 0.02), UTI- 12.6 ± 0.8 (P = 0.02) and controls- 12.2 ± 0.7 (p = 0.02). Recurrent infection rates were reduced significantly.Initial ARTI subjects recurrent ARTI was detected in 2 1.6%), recurrent GE was detected in 2 (1.6%); initial GE subjects recurrent GE was detected in 2 (10%), recurrent ARTI was detected in 2 (12%); initial UTI subjects recurrent ARTI was detected in 2 (16%) and control subjects recurrent ARTI was detected in 1 (25%). Reduction of recurrences over 6-month follow up as follows: Initial ARTI subjects recurrent ARTI was reduced 90%, recurrent GE was reduced in 77.7%; initial GE subjects recurrent GE was reduced in 83.3%, recurrent ARTI was reduced in 80%; initial UTI subjects recurrent ARTI was reduced in 71.4% and control subjects recurrent ARTI was reduced in 88.8% , recurrent GE was 2  and recurrent UTI was 0. Among initial GE group, development of; recurrent GE was 2 , recurrent ARTI was 1  and recurrent UTI was 0 . Among initial UTI group, development of; recurrent UTI was 1 , recurrent ARTI was 0  and recurrent GE was 0. Among control, development of; recurrent ARTI was 2 , recurrent GE was 1  and recurrent UTI was 1 . All were not significant (p > 0.05).E. coli (64%) was commonly detected in children with UTI. Etiology of initial ARTI, GE and UTI groups and subsequent recurrences over the follow-up period following oral iron supplementation was displayed on Table In initial ARTI patients out of 121 children with anemia viral etiology was detected in 52 43%) cases. Respiratory syncytial virus (RSV) was commonly detected in the virus (60%) in children with ARTI. In initial GE patients out of 86, children with anemia etiology  were detected in 56 (65%) cases. Rotavirus (57%) was commonly detected in children with GE. In initial UTI patients out of 44 children with anemia, etiology was detected in 36 (82%) cases. % cases. Further, primary or secondary immunodeficiency was not detected in any of the subjects including the control.16. The world health organization estimates that globally around 293 million young children suffer from anemia, among which 50% are estimated to be attributable to iron deficiency. Iron deficiency anemia can be present at early age and also in well-nourished children21. Iron deficiency is one of the most common micronutrient deficiencies in the world22.Childhood nutritional anemia would reflect the status of chronic malnutrition11. Such infections could be associated with a low level of immunity. Frequent exposure and low level of hygienic practices are associated with recurrences35. Once recurrent infections are associated with childhood under-nutrition the outcome would worsen often ended up with frequent infections in early life. This is a modifiable risk factor for the development of infections18. While adherence to hygienic practices, consumption of nutritionally adequate diet would lead to alleviating the burden35.Children are vulnerable for various infections specially ARTI, UTI and GE26. Mourad et al. and Ramakrishnan et al. shows that iron deficiency anemic children were two times and five times more susceptible to lower respiratory tract infection compared to the control group, respectively36. Adequate iron is important for proliferation and maturation of immune cells, particularly lymphocytes, for generation of specific response to infection37. Further the observed risk could be due to low oxygen carrying capacity in pulmonary vasculature and parenchyma leading to the low level of protectively towards invading pathogens. Among viral ARTI, recurrence was common following RSV. Is the most prevalent virus among childhood ARTI and frequent exposure would lead to recurrences. RSV is considered one of the earliest stimuli for recurrent wheezing in children38. The supplementation of iron in healthy childhood community has reduced upper respiratory tract infections significantly40.In our study, the risk of childhood ARTI was significantly associated with iron deficiency anemia. Blood iron deficiency is a risk factor for the development of recurrent ARTIs42. Further cumulatively low level of nutrition leads to low immunity. Rotavirus was detected as commonest etiology for childhood GE as well as recurrent GE. Frequent exposure would lead to recurrences43. In here, the stool was tested only in single sample helminth infestation cannot be excluded. Continuous use of anti-helminth thus leads to the low incidence of helminth infestation.Iron deficiency is a risk factor for the development of GE. Also, GE would be associated with malnutrition. Since gastric epithelium having a high turnover rate, it requires well nourish status for maintenance of mucosal integrity and absorptive capability44.Interestingly, anemia is neither a risk factor nor a protective factor for the development of childhood UTI. Level of immunity perhaps with low in mal-nutrition but low level of hydration is key to the development of UTI. In addition to that structural malformations and anatomical anomalies act simultaneously for the acquisition of childhood UTI46. Meanwhile, advice on hand hygiene and sanitary practices were also given as health education.Following iron supplementation hemoglobin concentration rose, became normal for age and sex. The increase in serum ferritin reflects the correction of iron storages. Over the follow-up period, recurrent ARTI and GE among tested groups were significantly reduced thus indicating replenishing iron in blood plays a significant protective role in childhood recurrent ARTI and GE. World Health Organization advice to supplement iron to prevent iron deficiency in the population to minimize infections48. Poverty will be a key factor46. Also feeding mal-practices and lack of knowledge on nutritive food materials would aggravate it. It is important that implementation of ground-level education on nutrition and supplementation of macro and micro-nutrients on a regular basis. This would reduce the level of childhood infections and associated burden.Sri Lanka comprises well spread public health facilities with great awareness but the majority of children suffer anemia. Although we did not collect data on dietary intake of infants (except breast milk intake and introduction of solid/semisolid foods), evidence from other studies from rural Sri Lanka suggests that dietary diversity is low and might also be responsible for anemiaHere, we haven’t measured the level of adherence to hand hygiene and sanitary practices. This could be a confounding factor on the reduction of infections and the recurrences.Children are vulnerable for developing various infections specially ARTI, UTI and GE. Iron deficient children are more prone to develop recurrent ARTI, GE and iron deficiency anemia would worsen the associated burden. Once iron deficiency being corrected the rate of recurrent ARTI and GE was reduced. This would be a boost for policy developers to implement strategies at the community level to prevent iron deficiency in children to reduce ARTI and GE recurrences.


Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins. Interactions between proteins and RNA are an important mechanism for post-transcriptional regulation, but predicting these interactions is difficult. Through a deep learning approach, here the authors predict RNA-binding sites and binding preference based on the local physicochemical properties of the protein surface. In the latter case, the RBP interacts with backbone or non-Watson-Crick (WC) edges of the bases leaving WC-edges for target recognition. For example, in core enzymes of RNA interference  and gene-editing complexes , selective loading of a guide-RNA (gRNA) into the RBP is a prerequisite to activate the enzyme; target D/RNA recognition is then mediated through the WC edges of gRNA while other parts of the gRNA remain in contact with the RBP. Therefore, deciphering the specificity and mechanisms in RNA-protein interactions is of fundamental importance to understanding the functions of RBPs, identifying RBPs, and designing RNAs for RBP recognition and regulation.After transcription, mRNAs undergo a series of intertwining processes before being finally translated into functional proteins. These post-transcriptional regulations, which provide cells an extended option to fine-tune their proteomes, are in general mediated through interactions between RNAs and RNA-binding proteins (RBPs). In cells, RNAs are largely regulated by two modes of specific interactions – either by direct recognition of RNA motifs on the RBP surface or by an indirect RNA-guided manner. In the former case, the RBP makes direct contact with the bases of RNA. For instance, the Pumilio/FBF (PUF) family can control translations via direct base-protein contact, e.g., with UGUR motifs on RNA transcripts2 and in vitro selection assays such as HT-SELEX3 and RNAcompete4 are among the most successful technologies. In general, specificity patterns obtained from these methods can be expressed as the logo diagram for each RBP or as analytical scores for individual RNA sequences. Through structure elucidation techniques, binding mechanisms for many of these characterized RBPs, e.g., hnRNP, Nova and PAZ, have also been clarified7. However, despite such remarkable achievements, experimental assays are constrained by reactivity, detection, and scalability limits. For instance, UV-crosslinking assays prefer uridine-rich sequences, because pyrimidines are more photoactivatable than purines8. Although arguably the chemical origin of these assayed specificities can be validated by ribonucleoprotein co-crystals, single or a few such co-crystals could hardly explain the genuinely ambiguous patterns on logo diagrams .To approach systematic mapping of these interactions, various experimental and computational techniques have been developed. In the experimental genre, in vivo UV-crosslinking immunoprecipitation assays such as CLIP-HITS9, can integrate and learn over assay data collected for an RBP to infer the specificity pattern that is consistent with large-scale assays. There are also less explored structure-10 and sequence-based11 computational approaches. Typically, in these latter approaches, given a three-dimensional protein structure or its amino acid sequence, local protein sequence context among other structural information  can be extracted in units of residues and used to train models in reference to RNA-RBP structures in the Protein Data Bank (PDB). As such, the demand for experimental data to start with is relaxed from assay-based methods. However, due to the highly limited amount of available features, their predictive power is restricted to distinction of RNA-binding sites from non-sites, i.e., binary predictions made over locations or indices of protein residues without suggesting the preferred base/sequence nor any informative interaction modes . Nevertheless, computational approaches are scalable and cost-efficient, thus are important complements to experimental techniques.To this end, computational approaches can enhance experimental results. In this genre, the body of sampled experimental knowledge, assays, and structures, can be refined to uncover previously mis-/un-acknowledged specificity patterns. Exemplary assay-based computational approaches, e.g., DeepBind and variants12, which encodes physicochemical properties on protein surfaces as high-dimensional feature vectors. This rich vector space not only has covered most features developed in other programs, but can also account for subtle differences in local topologies via its discrete radial distribution setup. Importantly, learning from these high-dimensional feature space is nontrivial, therefore a deep residual network is proposed and trained for this purpose.In this work, we introduce NucleicNet, a structure-based computational framework, which addresses topical challenges presented above: (i) we developed ways to learn efficiently from the PDB such that we can predict interaction modes for different RNA constituents – Phosphate (P), Ribose (R), Adenine (A), Guanine (G), Cytosine (C), Uracil (U), and non-site – and visualize them on any protein surface; (ii) NucleicNet requires no external assay input to derive logo diagrams consistent with assay data, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark; (iii) the logo diagrams or position weight matrices (PWMs) obtained from NucleicNet can be used to score the binding potential of individual RNA sequences; (iv) NucleicNet can generalize across different families of RBPs and be potentially used to identify new RBPs and their binding pockets/preferences. Our pipeline is founded upon the FEATURE vector framework11, we show that NucleicNet can effectively outperform all available sequence-based methods in differentiating RNA-binding sites and non-sites on protein surfaces; and (ii) when compared to our own carefully constructed non-redundant 7-class dataset, we show that NucleicNet can resolve RNA constituents with a class-averaged AUROC of 0.77 with respect to all 6 RNA constituents and non-sites, and of 0.66 with respect to the 4 bases. For in vitro data, the RNAcompete (RNAC) assay is adopted to assess the accuracy of our NucleicNet PWMs in dealing with RBPs that directly recognize RNA motifs on their surfaces. In all eight available examples, we show that, without any training on the assay data, NucleicNet PWMs are comparable to RNAC PWMs in identifying best binding 7-mers from all possible 7-mer sequences. Finally, we also explored downstream applications relevant to in vivo RNAi experiments. We show that the NucleicNet score is capable of explaining in vivo asymmetry in the guide strand loading of human Argonaute 2 (hAgo2) as well as the varied knockdown levels in different siRNA sequences.We benchmark NucleicNet from three different data sources – structural, in vitro, and in vivo experiments. For structural data, two tests were done: (i) in reference to an external benchmark12  of a protein’s surface, whether the physicochemical environment presented on-site is fit to bind with an RNA and, if affirmative, the binding preference to each type of RNA constituent – Phosphate (P), Ribose (R), Adenine (A), Guanine (G), Cytosine (C), and Uracil (U) – that binds to the location. Computationally, we cast the problem as a supervised seven-class classification problem. Accordingly, we formulate the end-to-end training of NucleicNet as follows , Ribose (R), Adenine (A), Guanine (G), Cytosine (C) and Uracil (U). A 3-fold cross-validation was performed over a carefully selected and curated non-redundant dataset from all protein-RNA complex structures from PDB (see Methods), which consists of 158 complex structures, resulting in about 280,000 grid points in the dataset. We divided the 158 proteins into three folds. Each time, two folds of them were used for training and one fold for testing. Between folds, BLASTClust sequence homology of ≥90% was disallowed (see Methods). Notice that the granularity of this cross-validation is individual proteins, instead of grid points, which eliminates bias in the size of proteins. Table One strength that structure-based methods offer is their potential to reveal and visualize binding sites on protein surfaces. While previous structure-based methods concern only binary classifications (sites and non-sites), our method can illustrate further on all six common RNA constituents – ‘Phosphate’ (P), ‘Ribose’ (R), ‘Adenine’ (A), ‘Guanine’ (G), ‘Cytosine’ (C), and ‘Uracil’ (U). We demonstrate this unique power of our method via three exemplary RBPs: Fem-3-binding-factor 2  is one of the best-characterized PUF family proteins. In Fig. 17. In addition, NucleicNet also correctly captures the modest preference for A or U (A > U > G) at base 9 consistent with the consensus reported by yeast three-hybrid assays18, even though the crystal-bound native base at that position is a C. This therefore suggests that NucleicNet is able to reveal underlying sequence specificity patterns unseen in crystal structures and in the absence of third-party assay data.The PUMILIO/Fem-3-binding-factor (PUF) family of RBPs are important post-transcriptional regulators. In a typical PUF–mRNA interaction, the PUM-HD domain, common among all PUFs, will bind to the 3′ untranslated region of mRNA that contains a conserved UGUR sequence motif. The strong sequence specificity is mediated through direct interactions (aromatic stacking and hydrogen bonds) made between protein surface residues and RNA nucleobases19. Two attributing factors were identified – (1) weakening of the base pair at one of the 5′ ends, this decides which strand will unwind at its 5′ end and subsequently enter the RISC complex19; (2) guiding RNA-hAgo2 interactions at base 1  is an exemplary RBP that operates in an RNA-guided manner, where the guiding RNA strand can be a small interfering RNA (siRNA) or a micro RNA (miRNA). In cells, both of these RNAs pre-exist as a duplex of complementary single-strands. However, during assembly of the RNA-induced silencing complex (RISC), often one of the strands is preferentially loaded into hAgo2 to guide cleavage of the target RNAs. This asymmetric behavior is heavily affected by small changes in RNA sequences of the precursor duplexe 1 Fig. , these i20. For other binding preferences in the seed region, only structural evidence is available and it is scattered among different PDB entries containing different seed sequences. For example, in the PDB entry 4f3t, A2 and G5 interact with N562 and Q757 respectively; in PDB entries 5js1/5t7b, U2 interacts with N562. These results are consistent with the logo diagram provided by NucleicNet  is a domain that widely occurs among double-stranded RNA-specific endoribonucleases, including the Aa-RNase III presented here. Originally, recognition of RNAs in dsRBDs were thought to be shape-dependent rather than sequence-specific. However, recent structural evidence confirms that this domain can recognize bases by interacting with the minor groove25. RNAC is a large-scale in vitro experiment that uses the epitope-tagged RBP to competitively select RNA sequences from a designed pool. For each RBP, 7-mer RNA-binding profiles obtained can be summarized as a Z-score for the individual RNA sequence or as a PWM by aligning the top 10 scoring sequences. Higher Z-score indicates better binding. We tested NucleicNet on all the RBPs for which both RNAC data and PDB structures are available . In all cases ses Fig. , Table 2Interestingly, NucleicNet is able to predict binding preference that is beyond structural biology information in PDB. For example, all the three PDB entries for protein PTBP1  are bound with the RNA sequence CUCUCU, which deviates from the RNAC suggested sequence YUUUYU Table , which iQ with quantitative results from two types of in vivo experiments – immunoprecipitation assay and siRNA knockdown; Q is derived from analysis of a hAgo2 structure (PDBID: 4f3t) and alignment with a trinucleotide conformation library  can lead to variable consequences in RISC assembly and thereafter affect siRNA knockdown efficiency. Therefore, knowing how guide-hAgo2 interaction and loading efficiency are correlated is crucial towards development of efficient RNA-induced silencing tools. To assess NucleicNet’s ability in predicting asymmetry in gRNA loading, we compared the NucleicNet score e PDBID: f3t and aQ can differentiate between guide and passenger sequences from the same precursor miRNA duplexes determined by Ago2 IP followed by small RNA sequencing from different cell lines, namely four from human  and one from mouse (neuroblastoma N2a30). In each dataset, a strand is considered the guide in the duplex when its reads per million (RPM) supersedes its complement by at least 2 orders of magnitude in an Ago2-RIP-Seq experiment (Ago2-RNA Immunoprecipitation and Sequencing)31. Duplexes with guide strand having less than 25 RPM are also discarded resulting in a total of 222 duplexes under evaluation  and tested for their correlations with the NucleicNet score , Ribose (R), Adenine (A), Guanine (G), Cytosine (C), and Uracil (U) – binding to the location Fig. . In the The surface locations that are 2.5–5.0 Å away from any protein residue are established by three-dimensional coordinates of grid points on a cubic lattice spaced at 1 Å. Relevant locations are selected from the surface locations by considering the topology of the local protein surface and also their bound RNA constituent labels. Non-redundant locations are retrieved by removing grid points associated with homologous proteins from the determined relevant locations. This strict strategy in collecting a relevant non-redundant dataset assures that the training and testing datasets are disjoint under cross validation  structures classified by their bound nucleotides  (selection criteria of the PDB structures are covered in the SI). To define surface locations (surface grid points) on these PDB structures, Fpocket38, an alpha-sphere based external program, is adopted to mark out grid points on both buried and solvent-exposed protein surfaces. To provide positive examples of RNA constituent binding sites, geometric centroids of heavy atoms from each constituent are labeled. Surface grid points within 3 Å of these labeled centroids and at most 5 Å away from protein are considered positive relevant locations; each positive relevant location is labeled by a bound RNA constituent. Next, we consider locations where RNA-binding is unlikely. These negative relevant locations are provided by surface grid points selected randomly from space excluded by volumes within 3 Å of any RNA atoms as well as alpha spheres from Fpocket. Note that the number of positive and negative relevant locations are balanced at ratio 2:1 after the removal of redundant locations.To determine relevant surface locations, all ribonucleoprotein structures are retrieved from NPIDBTo remove redundant locations, data collected from the PDB are saturated with redundancy. Multiple copies of the same RNA-binding protein chain can exist within the same PDB entry due to the formation of homo- or hetero-multimeric complexes. Homologous chains can also be shared among different PDB entries dedicated to different bound RNA sequences, quality of resolved proteins, and mutants, etc. We define the former situation as internal redundancy and the latter as external redundancy. Often, these homologous chains can share, to large extent, common RNA-binding configurations and physicochemical environments. Using redundancy-inclusive data for training and testing could introduce large bias to the evaluation and overstate the generalizability power of a model. Therefore, redundancy must be removed from the data.To remove external redundancy, PDB entries are clustered into groups where each entry is linked with another that shares at least one RNA-bound chain with ≥90% BLASTClust sequence homology , but the origin and strengths of these interactions are determined by a varied spatial arrangement of chemical components and atoms on the protein surface . These complicated topological features, which we summarized as physicochemical environments, can be maneuvered into a feature vector, and by leveraging the power of deep learning, to predict RNA-binding partners on protein surface locations – this is the foundation that underlies our NucleicNet method.12 is adopted to perceive physicochemical environments on three-dimensional protein surfaces. Previously, this framework have been applied to predict cation42 and ligand/fragment binding sites45. In those studies, it has been shown as an effective implementation to describe similar binding sites shared by proteins with little structural or sequence resemblance. In contrast to other vector frameworks used by preceding structure46-/sequence-based11 studies, where physical/structural features  are accounted in units of residue regardless of their spatial distribution, our physicochemical features are accounted in units of atoms and their discrete radial distribution over a location12 , Ribose (R), Adenine (A), Guanine (G), Cytosine (C), Uracil (U), and Non-binding site (X). However, as positive examples of backbone constituents (P and R) are 4–5 times more abundant than that of the nucleobases , straightforward deep learning model training suffers from the serious class imbalance problem47 Fig. . To alle47 Fig.  that bal48 to cutting-edge architectures, such as residual networks (ResNet)49 and generative adversarial nets (GAN)50, each of these architectures was designed to push forward the limit of prediction accuracy and resolve specific problems encountered in training on specific categories of data. In this work, considering the complexity of the problem and the convergence rate of the model, ResNet is chosen as our basic unit architecture due to its ability in handling the gradient vanishing problem, which obstructs extensive training of baseline multi-layer convolutional neural network models when deep networks are compiled. Our model is comprised of 16 residual blocks, a fully connected (FC) layer and a final Softmax layer to make a 4-class probability prediction. The residual blocks are considered as the feature extractor and the FC-Softmax is the classifier. In total, 32 convolutional layers are compiled, where each residual block contains 2 convolutional layers. The input tensor from the FEATURE program is of the shape 1 × 6 shells × 80 physicochemical properties. In the convolutional operation, a shared filter of size 1 × 2 × 80 slides across the input, generating an inner product at each position as an intermediate output, which then goes through batch normalization (BN) and element-wise non-linear activation, in our case, rectified linear units (ReLUs)29, to produce the intermediate output. The use of the BN layer mitigates the internal covariate shift problem51. In total, 80 filters are used. Note that to enable a consistent size in the output tensor (1 × 6 × 80), the input tensor is zero-padded. In each residual block, an identical shortcut is added to allow learning of the residual between the input and the second intermediate output. The output from the final residual block is later flattened and fed to a fully connected layer to make four-class probability prediction in the final Softmax layer. All parameters in the network are optimized, with weight decay, under Adam using categorical cross-entropy as the loss function. Training is implemented with TensorFlow. In general, it takes 4 days to train the model at all levels on a Titan X GPU. In Supplementary Discussion, we also compared alternatives to ResNet in the NucleicNet predictor, e.g., shallow machine learning methods and neural networks that do not consider spatial information. We find that on the grid level prediction, the proposed model, NucleicNet, outperforms all the shallow methods as well as other deep learning architectures under the same experimental setting. Alternative machine learning strategies were also considered, e.g., MAX-AUC52 and ensemble learning with data sampling54, though issues in run time and overfitting were experienced.Architectures of neural networks have been evolving along the development of the deep learning field. From the legendary AlexNeti of centroid for the corresponding nucleobase. As such, the NucleicNet score vectors predicted on grid points within 3 Å of each base centroid are averaged to produce an averaged binding probability pi . Class P, R and X, corresponding to Phosphate, Ribose and Non-RNA Binding Sites, are omitted from the logo diagram. Note that a gap is automatically assigned when location i is ≥5 Å away from the protein.A sequence logo diagram can then be generated by proportioning the information content Ξ55, the results of NucleicNet for individual protein surfaces can be summarized as an equation Q to score an arbitrary RNA letter sequence input:Similar to the idea of applying position weight matrix scores (PWM scores) to study DNA sequence-specific binding of transcription-factorspi and a transition probability Ti,i+1. Our goal is to assimilate NucleicNet outputs via pi and Ti,i+1 and to consider geometric constraints put forward by the covalent bond network and the torsional space of genuine RNA strands. The hidden states are locations indexed by i of bases relevant to a continuous RNA strand bound to the RBP with the letter sequence of length N. The emission probability pi(b), referring to the binding probability of base bon the RNA sequence, is obtained by averaging the NucleicNet output within 3 Å of the base location i  here is normalized among the bases. The transition probability Ti,i+1 refers to the transition probability between bases i and i + 1 on a continuous RNA strand from 5′ to 3′ end. In case, base locations are predetermined by ribonucleoprotein co-crystals, transitions between consecutive bases as well as their locations i are certain, then Ti,i+1 = 1 and pi(b) can be deduced by averaging the NucleicNet output on locations just as we generate logo diagrams. The equation Q is then reduced to an ordinary PWM scoring function; RNA string sequences of length N are then evaluated by sliding across the co-crystal-native RNA strand locations to obtain a maximum in Q, which is implemented to calculate the NucleicNet score for comparison with the RNAC score , is comprised of an emission probability ore Fig. .Q cannot be easily generated as in the case of co-crystals because those hidden locations and their transition probabilities Ti,i+1 are unknown, even though pi(b) can still be calculated from the NucleicNet outputs around location i once locations are approximated. In the next section, we outline how these unknowns can be efficiently estimated by aligning top predicted binding sites of RNA constituents with a conformational library of RNA trinucleotides. In this case, the score Q can then be obtained by maximizing over all possible i, i + 1 transition paths, when an RNA letter sequence of length N is enquired.We also investigate the situation where base locations referring to a continuous RNA strand are unknown but NucleicNet predicted RNA-binding sites are clearly directed by a phosphate-ribose backbone  on each node indexed by a location i referring to the location of a base b on an RNA strand bound to an RBP. In case, where these locations are hidden, the transition probability Ti,i+1 is unknown. However, these transitions are certainly constrained, irrespective of the strand length, by the covalent bonds and the torsional space of the RNA57. Therefore, they can be estimated by screening a database of RNA geometries that are tolerated by series of predicted RNA-binding sites on the RBP surface. In particular, for cases where predicted RNA-binding sites are clearly directed by a phosphate-ribose backbone , this trail of backbone-binding sites and intermittent base binding sites are visually indicative for a continuous RNA strand  resulting in some 9-nodes coarse-grained models ready to be aligned with the top binding sites. The clique-alignment process is done with a Bron-Kerbosch algorithm59, where only ≥7-cliques that show no atomic clash with the protein are retained. The 7-clique is chosen such that transition between consecutive i, i + 1 bases  must be guided by at least five backbone constituents. These criteria assure that the proposed binding sites are geometrically feasible. To systematically assess how these aligned 3-mers can contribute to a continuous strand, we formulate the problem as a fixed HMM. Hypothetical base locations are the hidden states. To propose these locations, the Euclidean space covered by the aligned Base nodes is partitioned into multiple Voronoi cells seeded by k-means centers. To express the identity of the base, these Voronoi cells, each represent a hypothetical base location, are characterized by emission probabilities pi averaged from grid points within 3 Å of a k-means center. Then, transitions, regarding consecutive bases within the same aligned clique, between different Voronoi cells are counted and symmetrized as an estimate of transition probability Ti,i+1. In case of Ago2, since it is ascertained that the 5′ location is situated in the Mid domain20, a certain starting probability of one is assigned to a cell located in the Mid domain that is furthest away from any other cells. The 5′ to 3′ direction of transition is then ascertained by the ranking distance to this starting Voronoi cell; direction of the edge on the transition graph allows only transition from a high rank to a low one. Details of the HMM are presented in Supplementary Figs. pi and Ti,i+1 affixed, score Q can then be calculated using the equation presented above.A continuous RNA strand may be considered as a transition graph between locations of consecutive bases, where base identities can be expressed by an emission probability Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Nicotiana benthamiana led to increased TBSV accumulation and RH30 knockout lines of Arabidopsis supported higher level accumulation of turnip crinkle virus. We show that RH30 DEAD-box helicase interacts with p33 and p92pol replication proteins of TBSV, which facilitates targeting of RH30 from the nucleus to the large TBSV replication compartment consisting of aggregated peroxisomes. Enrichment of RH30 in the nucleus via fusion with a nuclear retention signal at the expense of the cytosolic pool of RH30 prevented the re-localization of RH30 into the replication compartment and canceled out the antiviral effect of RH30. In vitro replicase reconstitution assay was used to demonstrate that RH30 helicase blocks the assembly of viral replicase complex, the activation of the RNA-dependent RNA polymerase function of p92pol and binding of p33 replication protein to critical cis-acting element in the TBSV RNA. Altogether, these results firmly establish that the plant DDX17-like RH30 DEAD-box helicase is a potent, effector-type, restriction factor of tombusviruses and related viruses. The discovery of the antiviral role of RH30 DEAD-box helicase illustrates the likely ancient roles of RNA helicases in plant innate immunity.Positive-stranded RNA viruses replicate inside cells and depend on many co-opted cellular factors to complete their infection cycles. To combat viruses, the hosts use conserved restriction factors, such as DEAD-box RNA helicases, which can function as viral RNA sensors or as effectors by blocking RNA virus replication. In this paper, we have established that the plant DDX17-like RH30 DEAD-box helicase conducts strong inhibitory function on tombusvirus replication when expressed in plants and yeast surrogate host. The helicase function of RH30 was required for restriction of tomato bushy stunt virus (TBSV) replication. Knock-down of RH30 levels in  Positive-stranded RNA viruses are important and emerging pathogens that greatly depend on the host during infection. The host uses conserved innate and cell-intrinsic restriction factors as a first line of defense to combat viruses. Among the most intriguing host restriction factors are the family of DEAD-box RNA helicases, which can function as viral RNA sensors or directly as effectors by inhibiting RNA virus replication. RNA helicases are involved in cellular metabolism and perform RNA duplex unwinding and remodeling of RNA-protein complexes in cells. The authors demonstrate that the plant DDX17-like RH30 DEAD-box helicase acts as a strong restriction factor of tombusviruses by blocking multiple steps in the viral replication process. Overall, the findings presented open up a new avenue based on DEAD-box RNA helicases to improve the resistance of plants against viral infections. Positive-stranded (+)RNA viruses replicate inside cells and depend on many co-opted cellular factors to complete their infection cycle. These viruses build elaborate membranous viral replication compartments, consisting of viral replication proteins, viral RNAs and recruited host factors, in the cytosol of the infected cells. The hijacked host factors participate in all steps of RNA virus replication, including the assembly of membrane-bound viral replicase complexes (VRCs), viral RNA-dependent RNA polymerase (RdRp) activation and viral RNA synthesis. The growing list of co-opted host factors facilitating VRC assembly includes translation initiation and elongation factors, protein chaperones, RNA-modifying enzymes, SNARE and ESCRT proteins, actin network, and lipids –9. Many The host utilizes cellular proteins to sense viral pathogenicity factors and block virus replication with the help of cell-intrinsic restriction factors (CIRFs) as an early line of defense ,10–12. TTomato bushy stunt virus (TBSV), a tombusvirus infecting plants, based on yeast (Saccharomyces cerevisiae) surrogate host [pol, and a replicon (rep)RNA leads to efficient viral replication. p92pol is the RdRp [Viral RNA replication is intensively studied with ate host –19. Exprthe RdRp ,21, wherthe RdRp .TBSV, which does not code for its own helicase, usurps several yeast and plant ATP-dependent DEAD-box RNA helicases as host factors promoting TBSV RNA replication. The yeast DDX3-like Ded1p and the p68-like Dbp2p, and the plant DDX3-like RH20, DDX5-like RH5 and the eIF4AIII-like RH2 DEAD-box proteins were shown as pro-viral factors, which affect plus- and minus-strand synthesis, maintenance of viral genome integrity and RNA recombination in TBSV –29.DEAD-box helicases are the largest family of RNA helicases and are known to be involved in cellular metabolism –32, and However, cellular RNA helicases also act as antiviral restriction factors, including functioning as viral RNA sensors  or directly inhibiting RNA virus replication as effectors –47. For pol replication proteins. Several in vitro assays provide evidence that RH30 inhibits tombusvirus replication through blocking several steps in the replication process, including VRC assembly, viral RdRp activation and the specific interaction between p33 replication protein and the viral (+)RNA. RH30 knockout lines of Arabidopsis supported increased accumulation level for the related turnip crinkle virus, confirming the restriction factor function of RH30 against a group of plant viruses. This is the first identification and characterization of a plant helicase with an effector type restriction factor function against plant viruses. Since plant genomes codes for over 100 RNA helicases, it is likely that additional helicases have CIRF function against plant viruses.In this work, we find that the plant DDX17-like RH30 DEAD-box helicase plays a strong restriction factor function against tombusviruses and related plant viruses. RH30 DEAD-box helicase is expressed in all plant organs, but its cellular function is not known yet . We findArabidopsis RH30 using agroinfiltration in Nicotiana benthamiana plants. Interestingly, expression of AtRH30 blocked TBSV replication by ~90% in the inoculated leaves  in N. bentamiana via agroinfiltration. Mutation of the highly conserved F residue within the helicase core domain (see 416L) (To learn if the putative helicase function of RH30 is required for its cell intrinsic restriction factor (CIRF) function, we expressed a motif IV helicase core mutant of RH30 and RFP-tagged H2B, which is a nuclear marker protein. We detected the re-localization of GFP-RH30 into the large p33 containing replication compartment from the nucleus during CNV replication   to enricthamiana . Confocathamiana . Infectioculated . Altoget6-tagged RH30 with Flag-tagged p33 and Flag-p92pol replication proteins and the TBSV repRNA in yeast, followed by Flag-affinity purification of p33/p92pol from the detergent-solubilized membrane fraction of yeast, which is known to harbor the tombusvirus replicase [6-RH30  also bound to p33 replication protein as efficiently as the wt RH30  experiments in  RFP-SKL . Altogetin vitro in a TBSV replicase reconstitution assay, which is based on yeast cell-free extract [in vitro production of double-stranded repRNA replication intermediate was also inhibited by ~10-fold by RH30, indicating that RH30 likely inhibits an early step, such as the VRC assembly during TBSV replication.To gain insight into the mechanism of CIRF function of RH30 helicase, we affinity-purified the recombinant RH30 and tested its activity  extract ,62. Addiin vitro data support the model that the inhibitory role of RH30 is performed during or prior to the VRC assembly step, but RH30 is ineffective at the latter stages of TBSV replication.We then used a step-wise TBSV replicase reconstitution assay ,29, in win vitro RdRp activation assay based on the purified recombinant TBSV p92 RdRp, which is inactive and requires Hsp70 chaperone and the viral (+)RNA template to become an active polymerase [We also utilized an lymerase . Additioin vitro assay based on wheat germ extract [in vitro translation assay inhibited slightly the production of p36 replication protein from the gRNA when RH30 was used in high amount  and (-)repRNA  and RII(-), whereas RI(+), RIV(+) and RI(-), RIV(-) also become competitive when added in high amounts SL, which is involved in p33-mediated recruitment of the TBSV (+)RNA template [in vitro. Interestingly, RH30 bound to RII(+)SL in the absence of added ATP repRNA . Since e amounts . Becausetemplate , and thetemplate , we testdded ATP . Howeverdded ATP , as also416L) (416L) might come from its RNA binding and RNA chaperone activity with the TBSV RNA substrates. Additional biochemical assays will be needed to test if the partial activity of RH30 in the absence of added ATP is due to the possibly copurified residual ATP bound to RH30.To test the RNA helicase function of RH30, we performed strand separation assays, where parts of the TBSV repRNA was double-stranded as shown schematically in 416L) helicase core mutant still has antiviral activity, we performed a TBSV replicase reconstitution assay with yeast cell-free extract [416L) to the replicase reconstitution assay led to minor inhibition of TBSV repRNA replication  sequence of the TBSV RNA, which represents RII(+)-SL RNA recognition element required for template recruitment into replication by p33 replication protein [in vitro results suggest that one of the mechanisms by which RH30 helicase inhibits tombusvirus replication is to inhibit the binding of p33 to the critical RII(+)-SL RNA recognition element required for template recruitment into replication. This inhibition is likely due to local unwinding RII(+)-SL, because the presence of ATP enhanced the inhibitory effect of RH30.To further characterize the restriction function of RH30 during tombusvirus replication, we tested if RH30 helicase could inhibit the selective binding of p33 replication protein to the viral RNA template  protein . Moreove protein . The bio protein . After a protein . RH30 wain vitro data, we suggest that RH30 helicase could replace the RNA-bound p33C by likely remodeling the RNA-p33 complex in an ATP-dependent manner.In another set of experiments, we first incubated biotin-labeled RII(+) RNA with p33C, followed by capturing the RNA-p33 complex with streptavidin-coated magnetic beads and then, the addition of RH30 helicase to the beads . Here weN. benthamiana. The TBSV repRNA carried six copies of the RFP-tagged coat protein recognition sequence from bacteriophage MS2 in either plus or minus polarity [We also tested the localization of RH30 helicase in comparison with the viral repRNA in polarity . CNV serpolarity , was co-N. benthamiana plants led to complete block of TCV gRNA accumulation and ~4-fold reduction in RCNMV RNA1 accumulation  dianthovirus, both of which belong to the Tombusviridae family. Expression of AtRH30 in mulation . On the o 2-fold .The Arabidopsis-TCV system was also used to estimate if TCV infection could induce RH30 gene transcription. RT-PCR analysis revealed induction of RH30 mRNA transcription in TCV-infected versus mock-inoculated plants . All theN. benthamiana and measured the accumulation of the unrelated tobacco mosaic tobamovirus (TMV). We observed a ~3-fold reduction in TMV RNA accumulation in N. benthamiana leaves expressing the WT RH30, but not in those leaves expressing the helicase core mutant of RH30(F416L) (416L) helicase core mutant, also inhibited the accumulation of the insect-infecting Nodamura virus (NoV) by ~3-fold in yeast  . Expressin yeast . Interesin yeast . Based oN. benthamiana or the related TCV in RH30 knock-out lines of Arabidopsis. On the other hand, the helicase core mutant RH30 can only partially inhibit tombusvirus replication in plants or in vitro, suggesting that the helicase function of RH30 is needed for its full antiviral activity.DEAD-box RNA helicases are the most numerous among RNA helicases ,37. Theypol replication proteins based on co-purification experiments of the viral replicase complex, a pull down assay, and BiFC in N. benthamiana. We propose that the interaction of RH30 helicase with the viral replication proteins might be important for the targeting of RH30 into the viral replication compartment  the specific recognition of the critical RII(+)-SL cis-acting element in the viral (+)RNA by p33 replication protein, which is absolutely required for template recruitment into VRCs, (ii) the activation of the viral p92 RdRp, and (iii) the assembly of the VRCs [in vitro assays provide plentiful data on the direct inhibitory effect of RH30 helicase on TBSV replication, indicating that RH30 functions as an effector-type, not signaling-type, DEAD-box helicase, which detect viral RNA and send signals to downstream components of the innate immunity network [RH30 also binds to the viral RNA, including the 5’ UTR  and RII internal sequence present within the p92g region . Using ithe VRCs ,26,73. Mthe VRCs . Therefothe VRCs . Altoget network . Future A recently emerging concept in innate immunity is the significant roles of DEAD-box helicases expressed by host cells that greatly reduce virus replication and facilitate combating viruses and making the induced and passive innate immune responses more potent. Many of the identified yeast DEAD-box helicases with restriction functions against TBSV are conserved in plants and mammals. Altogether, the genome-wide screens performed with animal viruses have shown that helicases are the largest group of host proteins affecting RNA virus replication. For example, in case of HIV, the involvement of several cellular helicases has been demonstrated, including DDX17 and DDX3 ,76,77. Ypol and binding of p33 replication protein to critical cis-acting element in the TBSV RNA  in a total of 50 μl reaction volume. The interaction assay was performed with 3.8 μM of recombinant MBP-RH30 and 1.9 μM of MBP-p33C along with 0.1 μg of biotinylated RNA, 0.1 μl of tRNA (1 mg/ml), 2 U RNase inhibitor, and 1 mM ATP in the presence of biotin-RNA binding buffer  in a 10 μl reaction mixture. Non-biotinylated RII of DI-72(+) RNA or absence of ATP was used as controls.Biotinylated RII RNA of DI-72(+) was synthesized by Assay #1: Recombinant MBP-RH30 was incubated first with biontinylated RII(+) RNA at 25°C for 15 min. Then, the recombinant MBP-p33C was added to the reaction and incubated for another 15 min. Assay #2: Recombinant MBP-RH30 and MBP-p33C were co-incubated simultaneously with biontinylated RII(+) RNA at 25°C for 30 min. The reaction mixtures were incubated with 20 μl of Promega Streptavidin MagneSphere Paramagnetic Particles (VWR) at room temperature for 20 min. The particles were collected in a magnetic stand and washed with binding buffer for five times. The protein-RNA complexes were then eluted with 20 μl of SDS loading dye containing β-mercaptoethanol by boiling for 15 min. The eluted samples were analyzed by Western blot with anti-p33 antibody.Assay #3: For the detection of p33 released from protein-biotinylated RNA complex, 1.9 μM of recombinant MBP-p33C was incubated with 0.1 μg of biontinylated RII of DI-72(+) RNA at 25°C for 15 min, followed by the addition of 20 μl of Promega Streptavidin MagneSphere Paramagnetic Particles for another 30 min incubation at room temperature. After collection of the beads and washing with biotin-RNA binding buffer for five times, the particles were incubated with either 0.95 or 3.8 μM of MBP-RH30 or MBP (used as control) in the presence of biotin-RNA binding buffer containing 1 mM ATP at 25°C for 15 min. The supernatant of the mixture was collected after collecting the particles in a magnetic stand and was analyzed by Western blot with anti-p33 antibody.32P-labeled RNA probes along with different concentrations  of purified recombinant MBP-fusion proteins or MBP in the presence of RNA binding buffer , 2 U of RNase inhibitor, as well as 0.1 μg of tRNA in a total of 10 μl reaction volume. Two different amounts (2 and 4 pmol) of unlabeled RNAs together with 5.7 μM of either MBP-RH30 or MBP were used for template competition.The conditions for the EMSA experiments were described previously . Brieflyin vitro transcription. The 32P-labeled single-stranded RI(-) or RII(+) RNAs were synthesized by T7-based in vitro transcription using 32P-labeled UTP. To prepare partial dsRNA duplexes, consisting of either RI(-)/DI-72 (+) or RII(+)/DI-72 (-) (see 32P -labeled RI(-) or RII(+) were annealed to 6 pmol of unlabeled DI-72(+) or DI-72 (-) in STE buffer  by slowly cooling down the samples  from 94°C to 25°C in 30 min. To test if the purified recombinant proteins could separate the partial dsRNA duplex, 1.9 and 5.7 μM purified MBP fusion proteins or MBP as a negative control were added separately to the partial dsRNA duplex in the RNA binding buffer  along with 1mM ATP, followed by incubation at 25°C for 25 min. The reaction mixtures were then treated with Proteinase K (2 μg/per reaction) at 37°C for 20 min, followed by loading onto 5% nondenaturing polyacrylamide gel with 200V for 1 h. Additional methods can be found in To study if purified proteins could unwind partial dsRNA duplex, the dsRNA strand-separation assay was performed as described . Firstly (-) see , 2 pmol S1 Text(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 Fig6-RH30. Viral replication proteins, termed protA, were expressed with a Flag-tag from plasmids from the GAL1 promoter, while the viral RNA templates were also expressed from the GAL1 promoter. His6-RH30 was expressed from a plasmid. Middle panel: Northern blot with 18S ribosomal RNA specific probe was used as a loading control. Bottom images: Western blot analyses of the level of Flag-protA with anti-Flag antibody and His6-RH30 with anti-His antibody are shown in the bottom panels with total protein extracts from yeast stained with Coomassie Blue.(A-B) Top panel: Northern blot analysis of NoV (panel A) or FHV (panel B) RNA1 and the subgenomic RNA3 using a 3’ end specific probe shows the reduced accumulation of NoV but not FHV RNAs in WT yeast strain expressing His(EPS)Click here for additional data file.


Although there is growing evidence of the relevance of self-compassion for understanding health outcomes, few studies have examined self-compassion in relation to self-reported physical health status, also known as self-rated health (SRH). This study addressed this gap by examining the associations between self-compassion and SRH across multiple samples and after accounting for the contributions of positive and negative affect.N = 6127), comprised of 6 university student, 16 community adult, and 4 chronic illness samples, were included in the current analyses. Participants in each sample completed a survey including measures of self-compassion and SRH. Thirteen samples also completed a measure of positive and negative affect. The associations between self-compassion and SRH were statistically meta-analysed. Moderator analyses were conducted to test whether the associations varied as a function of sample type, age or participant sex. Semipartial correlations were calculated controlling for positive and negative affect in 13 samples and meta-analysed.Data from 26 samples . The associations did not however vary significantly across sample types, or as a function of participant sex or age. The meta-analyses of the adjusted effects found that self-compassion remained significantly associated with higher SRH after accounting the contributions of positive  and negative  affect.Findings indicated that self-compassion was significantly associated with higher SRH across the 26 samples (The current study demonstrated that self-compassion is robustly associated with higher SRH across 26 samples and that this association remained significant after adjusting for the influence of positive and negative affect in 13 samples. Further longitudinal and experimental research is needed to verify the causal direction between self-compassion and SRH suggested by theory and the current findings. A growing body of evidence has highlighted the significance of self-compassion for understanding health trajectories and outcomes. Defined as taking a kind, non-judgmental, connected, and mindful stance towards oneself during times of failure and difficulty , self-coSelf-compassion has been conceptualized as including six key components organized along three bipolar dimensions , each ofN = 3252) self-compassion was positively associated with an index reflecting more frequent practice of a variety of health-promoting behaviours, including healthy eating and regular exercise , and thirteen samples  completed the short 12-item version of this scale SCS-SF; 45]. The SCS assesses the three main components of self-compassion and their negative counterparts, Self-Kindness (Self-judgment), Common Humanity (Isolation), and Mindfulness (Over-identification). The SCS includes both positively  and negatively  worded items reflecting the six components of self-compassion. Research with diverse and international samples indicates that the subscales are best explained by a general overall factor of self-compassion , Qmodel (1) = 0.54, p = .46, Qresidual (24) = 26.05, p = .35.The next moderator analyses focused on the role of age. The meta-regression testing the potential influence of participant age on the unadjusted effects for self-compassion and SRH was non-significant for the unadjusted correlations, b = − 0.14 , Qmodel (1) = 2.68, p = .10, Qresidual (24) = 25.15, p = .40. This indicated that the percentage of females in the samples did not have a significant influence on the magnitude of the associations between self-compassion and SRH.The final moderator analysis examined the influence of participant sex on the associations of PC with SRH. The results of the meta-regression were not significant, N analysis of the unadjusted effects of self-compassion and SRH indicated that an additional 2363 studies with non-significant results would need to be included in the set of studies that were statistically meta-analysed to reduce the p value below .05. This Failsafe N was well above the 5 k + 10 studies cutoff (140) recommended by Rosenthal (1979).The Failsafe The current study provides the most comprehensive analysis of the relationship of self-compassion and SRH to date. Across 26 samples comprised of community adults, university students, and individuals with chronic illness, analysis revealed a small to moderate sized association of self-compassion with higher SRH. Consistent with the hypotheses, self-compassion remained, on average, significantly associated with better SRH after adjusting for the contributions of positive and negative affect in the 13 samples analysed. However, the magnitudes of the adjusted effects were reduced in size from the unadjusted effects. The subgroup analysis of the unadjusted effects revealed that the associations of self-compassion with SRH did not vary significantly as a function of sex, age or the sample type supporting the robustness of self-compassion for SRH across diverse samples.A growing evidence base supports self-compassion as an important epidemiological factor for understanding health trajectories and outcomes. The current findings add to this research in several important ways. A significant limitation within previous research on self-compassion and health is that there have been few studies examining self-compassion in relation to self-rated health, a reliable measure of health status. Among those studies that have, results were inconsistent perhaps owing to the different samples examined . In addition, the potential contributions of positive and negative affect were not controlled for in previous studies examining self-compassion and SRH. The current research addressed these important gaps by finding that in 26 diverse samples, self-compassion was associated with SRH, a robust and reliable measure of physical health status that is linked to a variety of objective measures of physical health . In thisBy statistically meta-analysing the associations across the samples in the current study, it was possible to test the extent to which the associations between self-compassion and SRH varied as a function of contextual variables posited to influence SRH by the Cognitive Process model . The metPrevious research on self-compassion and physical health has not accounted for the role of affective states, which are known to confound self-reports of health . Using aAlthough self-compassion can arguably be viewed as an affective state, the current findings support the notion that it is not simply a form of high positive affect and low negative affect. Rather, self-compassionate responding reflects a process of transforming and harnessing the negative affect experienced during suffering into a positive affective state, and specifically compassion, and as a means to motivate self-improvement . ConsistIn addition to the theoretical contributions for understanding how self-compassion relates to physical health, the current findings also have important practical implications. Self-compassion is quality that can be cultivated through training programs such as the Mindful Self-Compassion program , CompassThe current findings, though novel, should be considered in light of several limitations. The samples used in the analyses, although reflecting diverse populations, were convenience samples and therefore may not be representative of the wider populations from which they were drawn. It is therefore unclear as to whether similar findings would be obtained with more representative samples. Nonetheless, including 26 samples with over 6000 participants provides some initial evidence for a robust relationship between self-compassion and SRH. The current study used an aggregated data (AD) meta-analysis rather than an individual participant data (IPD) approach. It is therefore possible that the findings from a IPD analyses may differ from those obtained in the current study. However, the choice was made based on limited available resources to conduct IPD analyses, and previous work that found that the same data subjected to AD and IPD yielded estimates that were roughly the same for main effects .The cross-sectional designs of the studies analysed preclude making any firm conclusions about the direction of association between self-compassion and SRH. For example, it is possible that it is easier to be self-compassionate when one has better SRH. However, according to theory, self-compassion is activated and therefore most beneficial when people experience difficulties and suffering, helping them to manage their suffering . ResearcA notable strength of the current research is the demonstration of consistent associations between self-compassion and higher SRH across 26 diverse samples that overall show a small to moderate effect size. This increases confidence that the results will replicate. This comprehensive analysis also provides more conclusive evidence regarding how self-compassion is linked to SRH given the inconsistent findings in the small number of studies that previously examined the association of self-compassion to SRH. In addition, by situating this association within the conceptual framework of the Cognitive Process model of SRH , the curThe current research makes an important contribution to our understanding of the potential benefits of self-compassion for physical health by finding that self-compassion was associated with higher SRH across 26 diverse samples, and that this average association remained significant when adjusted for the contributions of positive and negative affect in separate analyses with 13 samples. Importantly, the associations between self-compassion and SRH did not vary as a function of sample type, participant sex or age. Longitudinal and experimental research is needed to verify the causal direction between self-compassion and SRH suggested by theory and the current findings to gain further insights into the role of self-compassion for physical health. Such research, if found to support a temporal link from self-compassion to SRH, would be particularly valuable for informing interventions targeted at improving physical health in both general and clinical populations.