The organotypic RHG consists of a reconstructed stratified gingiva epithelium on a gingiva fibroblast populated hydrogel (lamina propria). Biofilm was cultured from healthy human saliva, and consists of typical commensal genera Granulicatella and major oral microbiota genera Veillonella and Streptococcus. Biofilm was applied topically to RHG and host–microbiome interactions were studied over 7 days. Compared to unexposed RHG, biofilm exposed RHG showed increased epithelial thickness, more organized stratification and increased keratinocyte proliferation. Furthermore biofilm exposure increased production of RHG anti-microbial proteins Elafin, HBD2 and HBD3 but not HBD1, adrenomedullin or cathelicidin LL-37. Inflammatory and antimicrobial cytokine secretion  showed an immediate and sustained increase. In conclusion, exposure of RHG to commensal oral biofilm actively contributes to RHG epithelial barrier function.Since the oral mucosa is continuously exposed to abundant microbes, one of its most important defense features is a highly proliferative, thick, stratified epithelium. The cellular mechanisms responsible for this are still unknown. The aim of this study was to determine whether multi-species oral biofilm contribute to the extensive stratification and primed antimicrobial defense in epithelium. Two  The healthy oral mucosa also produces antimicrobial peptides, cytokines and chemokines which contribute to preserving the host-microbiota (multi-species biofilm) homeostasis5. The cellular mechanisms responsible for this notably high epithelial turnover and primed antimicrobial defense of the oral epithelial barrier are still unknown.The oral mucosa of the gingiva (gum) forms a competent protective barrier between the host and its environment. Since the oral mucosa is continuously exposed to a multitude of microbes, one of its most important defense features is a thick, multi-layered and keratinized epithelium. Starting from the basal layer and migrating to the outer surface of the epithelium, epithelial cells proliferate, differentiate and are finally shed into the oral cavity, allowing for effective clearance of pathogens and rapid repair after injury8. Also, gut bacteria have been shown to be necessary for immune plasticity and regulation of the adaptive immune system6. However, little is known about the beneficial effects of microbiota on the development of oral mucosa. One of the main reasons for this is the lack of human physiologically relevant in vitro models. Although conventional models combining oral cells and microbes have contributed greatly to our knowledge, they still have major shortcomings: (i) animal models do not satisfactorily represent human host-microbe interactions due to major differences in their physiology; (ii) 2D monolayer cell models do not adequately mimic the complexity of the native tissue and therefore fail to provide reliable information on morphological changes; (iii) most host-microbe interaction studies generally use mono-species, or a non-biofilm design (e.g. planktonic bacteria culture) whereas microbes in vivo form multi-species biofilm. This is important since a multi-species biofilm can form a microbial community with metabolic benefits which can better withstand environmental stress10; (iv) most co-culture models  are limited to 48 hours (or less) bacteria exposure13 or at the most 72 hours14, even though the interactions in vivo are in a lasting dynamic status.Mucosal health is based on a homeostatic balance between the tolerance of the host and colonization by microbiota. Since the oral epithelium is in close contact with the microbiota, it is possible that such interactions would have a beneficial influence on epithelial barrier properties. Indeed in the gastrointestinal system and in skin, it has been shown that some bacteria play very important roles in modulating barrier functions, such as host metabolism, tissue structure and tissue repairin vitro models: a 3D reconstructed human gingiva model (RHG) and multi-species oral bacteria representative of commensal biofilm, in order to study host – microbiome interactions over an extensive period of time (7 days). The organotypic RHG consists of a reconstructed stratified oral gingiva epithelium on a gingiva fibroblast populated collagen 1 hydrogel which serves as the lamina propria. Previously this RHG model has been extensively characterized and used to investigate cytokine secretion after wounding and after chemical sensitizer exposure, and also to study pathogenic biofilm immune evasion after short term (24 hr) exposure16. Whereas the RHG shows many characteristics of native gingiva epithelium , which distinguish it from, for example skin epidermis. However, RHG currently fails to show the highly proliferative capacity which results in the characteristic thickened epithelium compared to skin epidermis18. This suggests that extrinsic, as well as intrinsic properties of keratinocytes and fibroblasts are involved in regulating the important oral epithelium barrier properties. The aim of this study was to use the RHG model to determine whether oral microbiota contributes to oral epithelial barrier properties. The multi-species biofilm was cultured from healthy human saliva in vitro, and consisted of relevant numbers of bacterial species, the typical commensal genera Granulicatella, and predominant amounts of the major oral microbiota genera: Veillonella and Streptococcus16. This biofilm was exposed to the upper, stratified air exposed surface of RHG for 7 days and its effect on epithelial barrier properties was investigated. Epithelial stratification was assessed by quantifying the number of epithelial cell layers formed and by determining the expression of two typical proliferation markers: Ki67 and proliferating cell nuclear antigen (PCNA). Ki67 is necessary for cellular proliferation and strictly associated with active cell progression. PCNA aids DNA synthesis during DNA replication and is involved in repair after DNA damage. The expression of antimicrobial peptides 20 and the secretion of inflammatory, antimicrobial cytokines  which are known to prime the host for counteracting potential pathogens was also determined22.In this study we combined two state of the art 23, was strongly expressed within the granular layer of the gingiva epithelium where host-microbe interactions begin  was applied topically to the RHG. Already after 1 day exposure, the number of CFUs which could be retrieved from the RHG was substantially less, indicating a rapid decrease in viability with time . To our knowledge, there are no studies which investigate host-microbe interactions in vitro for more than 72 hours and no studies which were therefore able to describe long term effects on host tissue integrity.In this manuscript we show for the first time that multi-species oral biofilm has a beneficial effect on the host tissue by contributing to the unique physiological barrier properties of the epithelium found within the oral cavity. It is long known that oral mucosa has a higher turnover than for example skin and an increased thickness25, actively contributed to the increased epithelial thickness characteristic of oral mucosa. This finding can be explained by the observation that biofilm stimulated keratinocyte proliferation (number of Ki67 and PCNA positive cells) thus preventing the cell senescence which was observed in unexposed RHG at day 7. This would explain previous observations by us and others where sterile RHG maintained a relatively thinner epithelium with few proliferating basal keratinocytes, which was more comparable to skin epidermis than the relatively thicker gingiva epithelium27. It is possible that the moderate degree of inflammation caused by microbes is enough to stimulate an innate immune response which will result in secretion of inflammatory cytokines which also have mitogenic properties. Indeed, our results did clearly show that exposure to biofilm resulted in an immediate (within 24 hours) and prolonged (up to 7 days) inflammatory response by increasing secretion of IL-6, CXCL8, CXCL1 and CCL20. IL-6 and CXCL1 have been shown to be involved in epithelial cell proliferation and migration28.Our results show that exposure of RHG to the biofilm, which closely represented microbiota found in healthy saliva30. Elafin, an endogenous protease inhibitor which was upregulated in our biofilm exposed RHG, plays a critical role in homeostasis by preventing tissue damage from excessive proteolytic enzyme activity during inflammation32. Healthy subjects have been reported to exhibit higher Elafin levels compared to periodontitis patients33. Two AMPs, HBD-2 and HBD-3, were found to be expressed constitutively in normal oral tissue in healthy people without localized inflammation34 and could be induced by resident bacteria in gingival cells in vitro36. HBD-2 was also suggested to be correlated with cellular differentiation in human gingiva epithelial cell37. The reason that HBD-1, ADM and LL-37 were not up-regulated in biofilm exposed RHG could be due to the absence of cell types responsible for their induction in the current RHG model. For example, LL-37 has been described to be produced by neutrophils as well as epithelial cells in oral cavity24, the former of which are not yet incorporated into our model. Alternatively, these AMPs may only be induced above base line expression in response to pathogenic biofilm rather than commensal biofilm. Our finding that biofilm resulted in increased secretion of cytokines with inflammatory as well as antimicrobial properties  is in line with our previous study in which we showed that commensal biofilm stimulated a stronger innate immune response than gingivitis and cariogenic biofilm when exposed to RHG for 24 hours38. Here we show that this is a prolonged cytokine response for at least 7 days. Our in vitro results are in line with others who showed that indicator bacteria in healthy oral microbiota was associated with high basal levels of CXCL8 release from gingival epithelial cells obtained from healthy individuals39. Also, secretion of IL-6 and CXCL8 by cultured keratinocytes was shown to be promoted by less-pathogenic single-species bacteria40 and healthy oral microbiota39, but inhibited by toxic challenges e.g. live Porphyromonas gingivalis41.In addition to stimulating epithelial proliferation and stratification, we found that biofilm induced an antimicrobial protective response in the gingiva epithelium, showing a selective increase in the protein expression of the Elafin, and the mRNA expressions of HBD-2 and HBD-3 but not HBD-1, ADM or LL-37. In healthy gingiva the modest level of antimicrobial peptides, cytokines and chemokines induced by biofilm may be considered to be a strategy of the host to maintain homeostasis. The host tissue, immune system and complement system will therefore be primed and kept in an activated state against potential pathogens29. Evidence suggests that lipopolysaccharide  is sufficient to trigger MAPK and Toll-like receptor pathways which would suggest that viable bacteria are not required43. However, this needs further investigation as to whether lipopolysaccharide alone would result in our observed epithelial phenotypic changes.Notably, the pronounced effect on epithelial barrier properties were observed for up to 7 days after a single biofilm exposure even though the number of viable bacteria greatly decreased within the first day of exposure. This would indicate that a single bacterial trigger is enough to result in a long lasting effect or that dead bacteria on the upper surface of the epithelium are still able to trigger a response. There is accumulating evidence which suggests that host-microbiome responses are associated with cellular signaling pathways such as the mitogen-activated protein kinase (MAPK) and Toll-like receptor pathways. These in turn are widely involved in the regulation of cellular proliferation, differentiation, and immune response to inflammationin vivo oral microbiota16. However due to the methodology used to create enough biofilm to expose RHG in a reproducible manner, the intact structure of the preformed biofilm was inevitably disrupted. The most probable explanation for the loss of viability of the biofilm is that RHG were cultured under aerobic conditions whereas the biofilm used in these experiments prefers anaerobic culture conditions. Thus applying biofilm to the surface of RHG followed by culturing under aerobic conditions would be expected to result in the observed decrease in CFUs. Although challenging, in the future anaerobic biofilm conditions should be optimized for RHG aerobic exposure. Alternatively, the antimicrobial responses elicited in the RHGs, which showed significantly higher levels than in unexposed RHG, may have contributed to the decrease in viability of the biofilm. Our results could also possibly be explained in part by a selective sub-set of survivors derived from the original biofilm. However, it is beyond the scope of this manuscript to isolate and characterize the FISH positive invading bacteria. Furthermore, FISH (bacteria rRNA staining) does not guarantee viable bacteria in the epithelium since DNA can be isolated from dead bacteria. Another limitation in our study is the lack of immune cells. However we consider it important to introduce complexity where complexity is required. In this present study we aimed to determine whether biofilm had a beneficial influence on oral mucosa tissue integrity, and in particular directly on the epithelium. Therefore the experimental design was kept relatively simple. Indeed in future studies we will introduce Langerhans Cells in a similar manner to our skin models as Langerhans Cells are key antigen presenting cells in sampling pathogens44. Furthermore, additional cell types such as monocytes and neutrophils will be added in order to further compare commensal and pathogen host responses.The limitations of our study should also be noted. The biofilm was cultured from pooled healthy saliva in such a way that it maintained similar phenotypic features to the in vitro, therefore providing new insights and possibilities for studying host-microbe interactions.In conclusion, we show that in the presence of the biofilm, RHG developed both morphological and functional features similar to those of native gingiva, indicating that the healthy multi-species biofilm, to a certain extent, promotes a positive symbiosis in the host. Our results highlight the contribution of multi-species biofilm in promoting gingiva epithelium barrier integrity 45. The tissue was used in an anonymous fashion in accordance with the “Code for Proper Use of Human Tissues” as formulated by the Dutch Federation of Medical Scientific Organizations (www.fmwv.nl) and following procedures approved by the institutional review board of the VU University Medical Centre.Healthy human gingiva tissue was obtained after informed consent from patients undergoing wisdom tooth extraction as previously described47) and fibroblast  cell lines were used to construct RHG as described previously15. RHG were cultured at the air – liquid interface and were composed of a differentiated epithelium on a fibroblast-populated collagen hydrogel. Culture medium (DMEM/Ham’s F12 (3/1)  supplemented with 1% Fetal Clone III , 1% penicillin–streptomycin (Gibco), 0.1 µM insulin , 2 µM hydrocortisone (Sigma-Aldrich), 1 µM isoproterenol (Sigma-Aldrich), 10 µM carnitine (Sigma-Aldrich), 10 mM L-serine (Sigma-Aldrich), 0.4 mM L-ascorbic acid (Sigma-Aldrich), and 2 ng/mL epidermal growth factor (Sigma-Aldrich).RHG: the immortalized human gingiva keratinocyte . Informed consent was obtained from all participants. The biofilms were formed in the Amsterdam active attachment model (AAA-model48). The anaerobic colony forming units (CFU) of the biofilm were assessed as a measure of viable bacterial cell counts before use. Aliquots were frozen at −80 °C until use.Oral bacteria representative of multi-species commensal biofilm: pooled human saliva from 10 healthy donors was used as inoculum for multi-species biofilm as previously described49. A sample of biofilm was processed to determine CFU at Day 0. The remaining biofilm was used to apply to the upper surface of RHG as follows: RHG were topically exposed to approximately 1 × 107 CFU biofilm cells concentrated in a drop of 10 µl, further cultured at the air-liquid interface at 37 °C, 7.5% CO2 and 95% humidity and harvested 1, 2, 4, or 7 days hereafter. The RHGs were divided into two halves for conventional paraffin embedment or determination of CFUs.Biofilm application to RHG: The stored biofilm was thawed on ice, centrifuged and dispersed in Hanks’ balanced salt solution (Sigma-Aldrich)50. For PCNA and Ki67 labeling, positive cells were quantified from at least 8 images obtained from 3 independent experiments under a magnification of 20x.After paraffin embedment, tissue sections (5 µm) were stained with hematoxylin and eosin , or processed for immunohistochemistry to determine expression of PCNA , Ki67 (Dako), Elafin  or HBD-2  as described previouslyin situ hybridization was performed on paraffin sections according to the FISH kit instructions  with the probe EUB338 (5’ – GCTGCCTCCCGTAGGAGT). After the staining procedure, the sections were counterstained with DAPI and sealed using a mounting reagent .FISH rRNA Culture supernatants from RHG were collected at the time of harvesting and used to detect levels of IL-6, CXCX1, CXCL8, CCL20 and Elafin using enzyme-linked immunosorbent assays (ELISAs). With the exception of CXCL8  and Elafin  where ELISA kits were used, antibodies and recombinant proteins were purchased from R&D Systems, Inc.  and ELISAs performed according to recommendations of the supplier.2 SYBR® Green qPCR Mastermixes (Qiagen) with paired-primers for human beta defensin 1-3 , adrenomedullin , cathelicidin antimicrobial peptide  or housekeeping gene HPRT1 (HP200179), all purchased from OriGene Technologies, Rockville, USA. Briefly, 2 µl cDNA was added to 1 µl paired-primer, 9.5 µl nuclease-free water and 12.5 µl of SYBR green mastermix. The cycle threshold value was defined as the number of PCR cycles where the fluorescence signal exceeds the detection threshold value. Normalized by the expression of housekeeping gene HPRT1, the targeted mRNA induction was calculated by the ∆∆CT analysis method following the formula:RHG epithelium was removed from the collagen hydrogel, lysed and total RNA was isolated using a RNA/Protein preparation kit . Genomic DNA elimination and cDNA synthesis were performed using a reverse transcription kit (Qiagen) following the manufacturer’s guideline. Real-time PCR was performed using RTTo determine the biofilm viability at different co-culture time points, total CFUs were counted. For Day 0, serial dilutions of the dispersed biofilm were made and plated on tryptic soy blood agar plates. For Days 1–7, RHG were dissociated using a tissue dissociator , followed by sonication and plating on tryptic soy blood agar plates. The plates were subsequently incubated anaerobically for 7 days at 37 °C and the CFUs were counted.t-test . Comparison between time and treatment of the thickness, PCNA, Ki67, Elafin, cytokine secretion and mRNA expression were analyzed using 2-way ANOVA followed by Bonferroni’s multiple comparison. Differences were considered significant when p < 0.05. Data are represented as mean ± standard error of mean; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.Statistical analysis was performed with SPSS Statistics (version 23). RHG data were collected from at least three individual experiments, each with an intra-experiment duplicate. The thickness, PCNA, Ki67, Elafin, cytokine secretion were analyzed using an unpaired


S. cerevisiae poly(A)+ and poly(A)− RNA 3′ ends to simultaneously estimate total RNA levels, transcription, and decay rates from the same RNA sample. The transcription data generated correlate well with reported estimates and also reveal local RNA polymerase stalling and termination sites with high precision. Although the method by design uses brief metabolic labeling of newly synthesized RNA with 4-thiouracil, the results demonstrate that transcription estimates can also be gained from unlabeled RNA samples. These findings underscore the potential of the approach, which should be generally applicable to study a range of biological questions in diverse organisms.Cellular RNA levels are determined by transcription and decay rates, which are fundamental in understanding gene expression regulation. Measurement of these two parameters is usually performed independently, complicating analysis as well as introducing methodological biases and batch effects that hamper direct comparison. Here, we present a simple approach of concurrent sequencing of   •Method using RNA 3′ end sequencing of total and 4tU labeled RNA•− RNA of labeled and total RNA mark nascent transcript ends at high resolutionpA•Dataset from single RNA samples can be used to measure transcription and RNA decay + and pA− RNA 3′ end seq datasets from single RNA samples. pA− RNA 3′ ends from 4tU labeled and total RNA mark nascent transcript 3′ ends at high resolution.Schmid et al. present a method to interrogate transcription, total RNA levels, and RNA decay rates using pA S. cerevisiae to establish a genome-wide method to simultaneously monitor these parameters from the same experimental sample.RNA transcription and decay determine cellular RNA levels, and their changes are important contributors to gene expression responses during cellular transition. Here, we use S. cerevisiae transcriptome consists of stable non-coding RNAs produced by RNA polymerase (RNAP) I and III, most commonly rRNAs and tRNAs, as well as a variety of coding and non-coding transcripts produced by RNAPII. The RNAPII-derived non-coding RNAs are diverse, but among the best-characterized classes are the small nuclear and nucleolar RNAs (snRNAs and snoRNAs), the cryptic unstable transcripts (CUTs), and the stable unannotated transcripts (SUTs)  (pA) tails produced by the pA polymerase Pap1p  are in immediate proximity and frequently overlap on opposite strands. Analysis of their transcription and RNA turnover therefore relies critically on strand-specific high-resolution methodologies. In fact, this consideration applies to most biological systems given the generality of pervasive transcription  . While ase Pap1p . CUTs, ose Pap1p . TRAMP ise Pap1p . Owing tcription .Among contemporary techniques used to measure transcription activity, RNAPII chromatin immunoprecipitation (ChIP) is possibly the most broadly applied. ChIP allows for small-scale gene-specific analysis and genome-wide interrogation. In addition, a higher resolution variant termed ChIP-exo is available . HoweverA panel of more recently developed (and related) approaches analyzes the RNA constituent co-purified with RNAP and residing in the active site of the enzyme. Among these, native elongating transcript sequencing (NET-seq), developed by While ChIP sequencing (ChIP-seq), GRO-seq, NET-seq, and the CLiP/CRAC-seq variants are all considered state-of-the-art methods, allowing the measuring of transcription in a variety of settings, they all rely on advanced methodologies and expertise. Instead, a conceptually different approach is to measure RNA production in a defined time window using the metabolic labeling of live cells with 4-thiouracil (4tU), 4-thiouridine (4sU) or 5-bromouridine (5BrU) nucleotide analogs that incorporate into the nascent RNA chain. Combined with a measurement of total RNA levels, this allows for an estimate of RNA synthesis and decay rates from the same experiment and therefore provides a complete picture of gene expression . HoweverHere, we employ a 4tU-based method coupled to the direct sequencing of RNA 3′ ends to overcome these shortcomings. Exact positioning and pA-tail status determination of RNA 3′ ends allow us to interrogate transcription rates, RNA synthesis rates, and RNA half-lives independently and at high resolution from single RNA samples.Given the limitation of available 4tU-based protocols to discriminate transcriptional from post-transcriptional RNA, we reasoned that direct inspection of the 3′ end position and pA tail status of purified RNAs should provide such information. That is, their positioning away from annotated RNA 3′ ends and their lack of non-templated pA tails would suggest an origin within the catalytic center of RNAP and distinguish them from 3′ ends of mature transcripts positioned at annotated and/or experimentally determined gene 3′ ends. In the case of mRNAs and SUTs, this is the site of the pA tail A.Figure + and pA− 3′ ends from the same sample of total and newly synthesized S. cerevisiae RNA . An aliquot of all RNA samples was then subjected to pA+ RNA 3′ end sequencing (pA+ RNA 3′ end seq) based on a commercially available dT-primed reverse transcriptase (RT) reaction (E. coli pA polymerase (E-PAP), rRNA depleted, and subjected to pA+ RNA 3′ end sequencing, detecting both pA+ and pA− 3′ends . Even so, the results presented here could be achieved despite the low sequencing depth of 3–5 million reads per library  originated almost exclusively from annotated transcript end sites (TESs)  detected abundant and continuous RNA 3′ends within the respective gene bodies  was due to the presence of pA+ 3′ ends of tandem upstream genes in the compact S. cerevisiae genome . Moreover, EI and IE junction reads were dominating in the 2′ 4tU sample, whereas EE junctions were more prominent in the total sample  and gene 3′ end regions (the annotated TESs ± 200 bp), which are proposed to reflect transcriptional and post-transcriptional RNAs, respectively. Comparing gene body values to those of the above-mentioned published transcription estimates for mRNA  indicates an efficient quality control or complete decay of such species   C and S3Iappeared C. Thus, + and pA− 3′ ends from 4tU and total RNA samples, DRs could be calculated independently for polyadenylated and non-polyadenylated RNA species. As expected, DRs of polyadenylated mRNAs were overall lower than those of pA+ SUTs and pA+ CUTs . There are at least two independent ways to achieve this. First, a well-established method employs the relative amounts of 4tU-labeled compared to total RNA levels A, top. IpA+ CUTs A. Moreov samples D. This i . Moreover, this approach further allows for a precise analysis of RNAPII density along the genome, mapping for example its positions of stalling at gene 5′ ends and at roadblock terminators.The results presented in this paper demonstrate that key parameters in gene expression, transcription, RNA levels, and RNA DRs can be interrogated from the same RNA sample using a set of directly comparable 3′ end RNA-seq libraries. The presented method differs technically from major existing high-throughput transcription approaches like NET-seq, RNAPII ChIP-seq, and RNAPII CRAC in that it does not require purification of RNAPII or any RNA-protein or DNA-protein cross-linking. This increases its flexibility and applicability. The feature that distinguishes the presented approach from existing 4tU-based methods is its specific detection of RNA 3′ ends to discriminate truly nascent from newly made post-transcriptional RNA. This is critical, since even short 4tU labeling times allow the production of mature RNA indistinguishable from RNAPII-connected nascent transcripts. Consequently, any 4tU-based method using RNA-seq or microarray technology, as its downstream application, cannot directly infer transcription rates. Moreover, early post-transcriptional decay may occur before or during polyadenylation, and it is not possible to dissect such events using existing protocols. For example, we previously noted a marked decrease in net mRNA production upon depletion of the nuclear pA-binding protein Nab2p . However− 3′ends inside of gene bodies as representative of nascent transcript 3′ ends. A potential complication with this assumption is that such 3′ ends could in principle also be produced by RNA decay. Such activity occurring after RNA purification is typically conducted by contaminating RNase A enzyme, producing 3′ phosphate (3′P) ends, or by hydrolytic cleavage, leaving 3′P/2′OH or 3′OH/2′P ends .Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Torben Heick Jensen (All experiments were done with strains Mex67-AA (strain 1) and Nab2-AA (strain 2) described in  and Sch, respectS. pombe spike-in RNA was added. The spike-in RNA represented total RNA from S. pombe cells grown exponentially in YPAD medium at 30°C and labeled with 5 mM of 4tU for 10 min.The 4tU labeling and purification method was from  with min4 pH 6.8; 10 mM Tris-HCl pH 7; 0.2 M NaCl; 25 mM MgCl2; 0.1% SDS. 30 μl of magnetic beads were equilibrated in the same buffer and blocked by incubating with 200 μg of glycogen in the same buffer for 20 min at room temperature (RT). Beads were drained, the RNA solution was added to the beads and the reaction was incubated for 30 min at RT with gentle agitation. Then supernatant was removed and beads were washed 5x with 400 μl of binding buffer, 1x with 10 mM Tris-HCl pH 7.5; 0.5 mM EDTA; 1 M NaCl, and bound RNA eluted 2x for 5 min using 50 μl 0.7 M beta-mercaptoethanol at RT under gentle agitation. Eluates were combined and RNA was precipitated overnight using NaOAc and ethanol with 100 μg of glycogen as a carrier. After final washing, RNA was resuspended in 20 μl of RNA storage solution. RNA quality and concentration were measured using a Bioanalyzer and the RNA pico kit (Agilent). 4 μl (6 μg) of the total RNA aliquot and 10 μl (2 – 10 ng) of the 4tU IP samples were then used for a regular 24 μl TURBO DNase (Ambion) reaction.For biotin labeling 300 μg of total RNA was coupled using 10 μl of 5 mg/ml MTSEA-biotin in DMF (Biotium) in a 400 μl reaction buffered with 10 mM HEPES-KOH pH 7.5 and 1 mM EDTA for 30’ at 25°C in the dark. Reactions were then poured onto Zeba desalting columns , washed and drained 3x in RNA storage solution. Subsequently, an additional 130 μl of RNA storage solution was applied and the flow-through was collected. RNA was extracted once with phenol:chloroform and precipitated from the desalted reactions using NaOAc and ethanol and resuspended in 250 μl RNA storage solution. An 8 μl aliquot was collected at this step for total RNA analysis. Biotinylated RNA was then purified using magnetic streptavidin-coated beads , and 240 μl of the RNA samples were diluted to 300 μl in 0.1 M Na-POE. coli PAP  in 20 μl reactions, containing 1x reaction buffer, 2.5 mM MnCl2; 0.4 U E-PAP and 0.8 U RiboLock RNase inhibitor (ThermoFisher Scientific) for 30 min at 30°C. Reactions were then purified using a PureLink micro RNA purification kit (Ambion) and eluted using 22 μl RNase-free water. PureLink eluates were rRNA depleted using a down-scaled reaction of the Ribo-Zero Gold rRNA Removal Kit for Yeast (Illumina). That is, 18 μl of RNA was incubated with 2 μl of reaction buffer, 2 μl of removal solution and 8 μl of water for 10 min at 65°C, and put to RT for 5 min. This reaction was cleared using 65 μl of rRNA magnetic beads for 5 min at RT and 5 min at 50°C. Final samples were precipitated using NaOAc and ethanol with 20 μg of glycogen as carrier and resuspended in 10 μl of RNA storage solution.5 μl of TURBO DNase treated RNA was polyadenylated with in vitro polyadenylation were prepared from 3 independent cultures of each yeast strain. An overview of samples, read quality control and mapping statistics are provided in 1 μl of input diluted to 5 μl using water and 5 μl of IP samples were used for preparation of QuantSeq REV libraries . Libraries were prepared according to the manufacturer’s specifications and sequenced at Lexogen Vienna Biocenter Core Facilities multiplexing 32 samples on a HiSeqV4 SR50 run and using the QuantSeq REV specific primer CSP. All samples were prepared from both yeast strains, and 4tU samples subjected to https://jgi.doe.gov/data-and-tools/bbtools/) using parameters k = 13; ktrim = r; useshortkmers = t; mink = 5; qtrim = t; trimq = 10; minlength = 20; ref = /home/schmidm/annotations/common/Lexogen_adapters_with_pA.fa.gz. The Lexogen adapters file contains the common Illumina TruSeq adaptor sequences 1-27 and an additional A18 sequence to trim homopolymeric A-tails often observed in that protocol  of uniquely aligned reads were obtained using a custom python script using HTSeq . Adaptesee also . Trimmedsee also ) togethesee also ) at sett that cord filter was therefore not applied. Genomic positions fitting these criteria were collected in bed file format and positions removed from track files using bedtools function subtract  were counted using a custom script based on bedtools (see code). This was used to derive scaling factors by the sizeFactors function from the R package DESeq2 , using S. pombe-normalized signal from negative control IP samples was subtracted from 2′ 4tU samples to obtain background-subtracted (BGsub) signals and only those values used for analysis shown in the main text. Features or genomic intervals where BGsub values were ≤ 0  were omitted in all Figures. Analysis of the mock samples showed that the negative IP samples generally resembled total RNA, and not 4tU IP, samples concerning their relative coverage of gene body versus end regions, their coverage of introns and of snRNA and snoRNA TUs (data not shown). Spike-in normalized counts from 4tU IP samples were above negative control IP counts for the majority of investigated TUs. This enrichment was especially pronounced in the gene body regions in the pA+,- datasets, consistent with the transient nature of those tails.We reasoned that the short 4tU labeling time of 2 min might cause contamination of the purified fraction with unlabeled RNA (termed ‘background’ below). To address this worry, mock (negative) control IP samples were obtained by processing total RNA from cells harvested without prior incubation with 4tU in the growth medium. This sample was prepared in parallel with the corresponding experiment and, importantly, contained the same amount w/w total RNA of spike in RNA. Since the spike-ins were also partially 4tU labeled, data normalized to these yielded direct information about background. - signal at each position, we subtracted the S. pombe normalized pA+ signal from the pA+ and pA- signal. For the 2′ 4tU IPs this was done after background subtraction.To obtain an estimate of pAmRNA, SUT and CUT annotations were from  lifted tAll tracks were produced with log-scaled y-axes, except for the ChIP-tiling array data, which was shown on a linear scale.S. pombe normalized, background-subtracted pA+ or pA- bigwig track files. Counts were then analyzed using custom scripts in R. Average counts across two strains and replicates (see Gene body regions were defined to derive from the TU TSS to 200 bp upstream of the TES (TSS to TES - 200 bp). Gene end regions were defined to derive from 200 bp upstream to 200 bp downstream the annotated TES (TES ± 200 bp). Signal within each region was counted using bigWigAverageOverBed from UCSC tools (v2 ) using S∗-log, where time = 2 min, ‘RNA4tU’ represents the normalized and background subtracted reads of gene ends (either pA+ or pA-) from the 2′ 4tU samples and ‘RNAtotal’ represents the corresponding normalized reads from total RNA. For calculating DRs by comparing transcription to total RNA levels, we used gene body read densities  scaled by effective gene body length  and total RNA were normalized gene end reads from total RNA as above.DRs were derived from 4tU to total RNA ratios using the formula DR = (1/time)Heatmaps and metagene profiles were obtained from the representative replicate 1 of strain 1 using computeMatrix and plotHeatmap functions from the deepTools2 software suite v2.2.4 .S.pombe based size factors (see above) and for 2′ 4tU IP samples background from the mock control IP was subtracted. Quantification of EE, EI and IE reads was applied to a control RNA-seq dataset (SRA: SRX844447) derived from a similar strain background in unperturbed cells. Those data are presented as raw reads without further processing.Reads overlapping EE, EI and IE junctions were counted in all raw bam files using a custom python script and the module HTSeq (v0.7.2). EE reads were those reads overlapping an annotated exon-exon junction and mapping to at least 2nt on each side without mapping to intronic positions. EI and IE were those reads mapping across an exon-intron junction with at least 2 nt mapping on each side. Junction read counts were then normalized using For NET-seq, RNA-seq, ChIP-seq and ChIP-tiling experiments available coverage data were used see . For RNArpb1-1 chase)). Half-life and decay estimates were obtained from the Supplemental Information of the relevant publications. In cases where only half-life information was provided, decay-rates were obtained using the equation k = ln(2)/halflife.Transcription- and decay-rates were obtained from ’ for the spearman rank correlation values. Dendrograms were computed from spearman rank correlation matrices, transformed to distance measure using ‘ dist = (1 – rhospearman)/2 ’ and then clustered using the R built-in function ‘hclust’ with default settings.Correlation matrices depict spearman rank correlations of logGSE108550. Published datasets were obtained from the following sources: NET-seq: GEO: GSM617027; RNA-seq: GEO: GSM617028; RNAPII CRAC: GEO: GSM1706520; RNAPII ChIP-seq GEO: GSM2551210 and RNAPII ChIP-tiling: ArrayExpress:E-TABM-1033 . Junction reads analysis of regular RNA-seq are based on SRA: SRX844447. All relevant code and analysis scripts are available at GitHub (https://github.com/manschmi/MexNab_3seq).The accession number for the data from 2′ 4tU labeling experiments reported in this paper is GEO:


Drosophila Stau (dmStau) contains four dsRBDs. Existing data suggest that only dsRBDs 3–4 are necessary and sufficient for mRNA binding. Here, we show that dsRBDs 1 and 2 of mStau2 bind RNA with similar affinities and kinetics as dsRBDs 3 and 4. While RNA binding by these tandem domains is transient, all four dsRBDs recognize their target RNAs with high stability. Rescue experiments in Drosophila oocytes demonstrate that mStau2 partially rescues dmStau-dependent mRNA localization. In contrast, a rescue with mStau2 bearing RNA-binding mutations in dsRBD1–2 fails, confirming the physiological relevance of our findings. In summary, our data show that the dsRBDs 1–2 play essential roles in the mRNA recognition and function of Stau-family proteins of different species.Throughout metazoans, Staufen (Stau) proteins are core factors of mRNA localization particles. They consist of three to four double-stranded RNA binding domains (dsRBDs) and a C-terminal dsRBD-like domain. Mouse Staufen2 (mStau2)-like  The Staufen family of RNA-binding proteins are conserved microtubule dependent mRNA transporter factors. Here the authors use biochemical and functional approaches to characterize the RNA-binding properties of mouse Staufen 2 and study the mRNA binding capacity of its two domains dsRBDs 1 and 2. For active transport of mRNAs along the cellular cytoskeleton, ribonucleoprotein particles (RNPs) are formed. Such mRNA-containing RNPs (mRNPs) consist of motor proteins, RNA binding proteins, helicases, and translational regulators2.mRNA localization is an essential mechanism for a range of cellular processes, including embryonic development, cell differentiation, and migration, as well as neuronal plasticity4. For instance, dendritically localized RNAs produce proteins with synaptic functions such as Ca2+/calmodulin kinase II (CaMKII), the cytoskeletal protein Arc or microtubule-associated protein 2 (MAP2), and AMPA or NMDA receptors.In the mature nervous system, mRNA localization to pre- and postsynaptic areas followed by local translation has been implicated in memory and learningDrosophila as an mRNA transport factor required to establish the anterior–posterior axis of the embryo6. Together with proteins of the exon-junction complex (EJC) and the translational repressor Bruno it binds to oskar mRNA, which is transported from the nurse cells to the oocyte and then localized to its posterior pole7. During Drosophila neurogenesis, the asymmetric segregation of prospero mRNA into the ganglion mother cell requires Stau function as well8.The RNA-binding protein Staufen (Stau) was originally identified in 11. Whereas, mStau1 is ubiquitously expressed and required for Staufen-mediated decay (SMD) of its target mRNAs via UPF1 interaction, mStau2 expression is enriched in the heart and brain16. The two mammalian paralogs Stau1 and Stau2 were reported to bind distinct, yet overlapping sets of target mRNAs17, indicating distinct but possibly complementary functions. Consistent with this hypothesis is the observation that although both paralogs appear to mediate degradation of RNAs, only Stau2 seems to also stabilize a subset of its target mRNAs18.In mice, the two Staufen-paralogs mStau1 and 2 share about 50% protein-sequence identity and have both been implicated in mRNA localization and RNA-dependent control of gene expressionDrosophila Stau (dmStau) targets suggested certain RNA-secondary structure elements as Stau-recognized structures (SRSs)19. A subsequent study in mice used immunoprecipitation- and microarray-based experiments to identify the Regulator of G-Protein Signaling 4 (Rgs4) mRNA as an mStau2-regulated transcript and found two predicted SRS stem-loops in its 3′UTR18.A transcriptome-wide analysis of 20.All Stau-family proteins contain multiple so-called double-stranded RNA-binding domains (dsRBD). Whereas mStau1 contains three dsRBDs, a tubulin-binding domain (TBD) and one C-terminal noncanonical dsRBD-like domain, mStau2 and dmStau have four dsRBDs, followed by a tubulin-binding domain (TBD) and a C-terminal, noncanonical dsRBD-like domain21, whereas dsRBDs 1, 2, and 5 are often referred to as pseudo dsRBDs, which retained the fold but not activity of canonical dsRBDs21. The longest isoform of Stau2, Stau262, is most similar to dmStau, both possessing all five dsRBDs. Stau262 shuttles between the nucleus and cytoplasm and has been proposed to transport RNAs from the nucleus to distal dendrites22. Because Stau dsRBDs only seem to interact with the backbone of RNA23 and do not undergo recognizable sequence-specific interactions24, one of the unresolved questions is how specific RNA binding can be achieved by dsRBD 3–4.For all mammalian Stau proteins, the dsRBD3 and dsRBD4 are thought to be required and sufficient for full target mRNA bindingDrosophila as model system, we demonstrate the importance of RNA binding by dsRBDs 1–2 for Stau function in vivo and show that mStau2 can partially substitute for dmStau function during early Drosophila development. The requirement of a combination of two dsRBD-tandem domains and thus the possibility of binding to two stem loops allows recognition of combinations of secondary structure and thus a much more complex readout for specific binding. This observation might help to explain how Stau proteins can bind selectively to their RNA targets in vivo.Here, we show that in mStau2, the noncanonical dsRBDs 1 and 2 exhibit RNA-binding activity of equal affinity and kinetic properties as the known RNA-binding dsRBDs 3–4. Mutational analyses and biophysical characterization of RNA binding revealed that dsRBD 1–2 have to act in concert with dsRBD 3–4 to allow for stable, high-affinity RNA binding. Using Rgs4 mRNA, we performed in vitro binding experiments with mStau2 and the two previously predicted SRS motifs of the Rgs4 3′UTR  in the low micromolar concentration range for Rgs4 SRS1 as well as for SRS2  that contains both predicted SRSs. EMSAs with the previously reported RNA binding dsRBDs 3–4 using either wild-type Rgs4-mini RNA or a mutant version, in which SRS1 and SRS2 were deleted, showed similar affinities  in the micromolar concentration range . This elongated stem was bound by both tandem domains dsRBD 3–4 and dsRBD 1–2 with affinities similar to the original SRS2 stem-loop  experiments with biotin-labeled SRS* RNA or SRS2 + 5 RNA coupled to a streptavidin sensor chip surface were performed. For the tandem domains dsRBD 1–2 and dsRBD 3–4, rapid binding and dissociation kinetics were observed for both RNAs, already at the lowest tested concentration of 10 nM Fig. . BecauseRNA Fig. ; Table 2D1 of 18 nM for SRS2 + 5 RNA and of 9 nM for the SRS* RNA , histidine in loop 2 (H36), lysines from the conserved KKxxK motif (K59 and K60), and phenylalanine in the beta strand β2 (F40). Mutation of these residues in dsRBD3 from D. melanogaster to alanines had been shown to abolish RNA binding completely23.For further verification of the observed binding properties and to allow for functional in vivo studies of the RNA-binding activity of mStau2 dsRBD 1–2, RNA-binding mutants of the dsRBDs 1 and 2 were designed. For dsRBD1, mutations were introduced based on the NMR titration experiments and multiple sequence alignments with dmStau  Stau failed to rescue all aspects of 32 allows for the joint action of dsRBDs in trans and thus full, stable RNA binding is achieved.The fact that mStau1 lacks the first dsRBD raises the question how this paralog achieves full binding. One option is that its mode of RNA binding is different enough from mStau2 to allow for strong and stable binding even with only two or three dsRBDs. An alternative could be that the reported dimerization of mStau1Drosophila protein Loqs-PD, a member of the siRNA silencing pathway. Loqs-PD contains two canonical dsRBDs that show highly dynamic binding and involves sliding along RNA stems28. A similar activity was also reported for the human ortholog of Loqs-PD, TAR RNA-binding protein (TRBP)27. In contrast to these examples, however, mStau2 involves two tandem domains with four dsRBDs for its sequential RNA-target recognition.Sliding as an initial binding mode also occurs in other RNA binding proteins such as the Drosophila stau mutants with Stau constructs required all four dsRBDs to be functional. The presented model , according to the manufacturer’s instructions.DNA sequences of interest were amplified by polymerase chain reaction (PCR) from template plasmids. Cloning was performed with the In-Fusion HD Cloning kit (Clontech) according to the manufacturer’s protocol. Point mutations or deletions were introduced by 3-point PCR with overlap extensionD. melanogaster, rsEGFP2 was fused with mStau2, via cloning into pBlueScript-KS. First, the primers pBSKS-rsEGFP2 FW and rsEGFP + 3 C RV were used to create an rsEGFP2 sequence with a pBlueScript-KS 5′ overhang for In-Fusion cloning and a PreScission protease cleavage site as 3′ overhang, and the primers 3 C + Stau2 FW and pBSKS-mStau2 RV to create a mStau2 sequence, with a PreScission cleavage site 5′ overhang and a pBlueScript-KS 3′ overhang for In-Fusion cloning. The PCR products from these reactions served as templates for a third PCR with the primers pBSKS-rsEGFP2 FW and pBSKS-mStau2 RV to create rsEGFP2-mStau2 sequences with 5′ and 3′ overhangs for In-Fusion cloning into BamHI/XbaI-linearized pBlueScript-KS, according to the manufacturer’s protocol. The resulting plasmids served as templates for PCR with primers pUASp-rsEGFP2 FW and pUASp-mStau2 RV or pUASp-dmStau RV to amplify rsEGFP2-mStau2/dmStau sequences with 5′ and 3′ overhangs for In-Fusion cloning into the BamHI/XbaI-linearized pUASp-attB plasmid.For in vivo experiments in For details on plasmids and primer sequences, see Supplementary Tables mStau2 FL was expressed as a HisSUMO-tagged fusion protein in High Five insect cells. After cloning in pFastBacDual, recombinant baculovirus was produced with the Bac-to-Bac Expression System (Invitrogen) in Sf21 insect cells as described by the manufacturer’s protocol.E. coli Rosetta cells using autoinduction ZY-medium36.mStau2 proteins were expressed after cloning into the expression vector pOPINS3C as fusion proteins with HisSUMO-tag in 15N- or 15N,13C-labeled proteins for NMR experiments were expressed in 15N-M9 minimal medium  glucose, 1 mM MgSO4, 0.3 mM CaCl2, 1 µg per L biotin, 1 µg per L thiamine, 1× trace metals) supplemented with antibiotics. Hundred milliliters precultures were grown overnight at 37 °C, shaking at 150 rpm and used to inoculate 1 L prewarmed M9 minimal medium. Cultures were grown at 37 °C, 150 rpm to OD600nm = 0.6. Protein expression was induced with 0.25 mM IPTG, and cultures were cooled for protein expression overnight at 18 °C.Uniformly High Five cell pellets containing HisSUMO-tagged mStau2 FL were lysed by sonication in lysis buffer . The lysate was cleared by centrifugation, and the soluble protein fraction was purified by Ni-IMAC on HisTrap FF (GE). Bound protein was eluted with 200 mM imidazole after extensive washing with 15 CV lysis buffer. The protein was dialyzed in low salt buffer  overnight before further purification on a HiTrap Heparin HP column (GE) and size exclusion chromatography (SEC) on Superdex200 Increase (GE).E. coli Rosetta cell pellets containing HisSUMO-tagged fusion proteins were lysed by sonication in lysis buffer . The lysate was cleared by the centrifugation, and the soluble protein fraction was purified by Ni-IMAC on HisTrap FF (GE). Bound protein was eluted with 200 mM imidazole after extensive washing with 15 CV lysis buffer. For fusion-tag removal, the protein was digested overnight with PreScission protease upon dialysis in low-salt buffer . The protein was purified with a second, subtractive Ni-IMAC affinity chromatography, on a HiTrap Heparin HP column (GE) and size exclusion chromatography (SEC) on Superdex 75 (GE). Size-exclusion chromatography was performed in minimal buffer  or the indicated buffer required for downstream applications.RNAs for EMSAs were produced by small scale in vitro transcriptions with the MegaShortScript T7 Transcription kit (Ambion) according to the manufacturer’s protocol. HPLC-purified primers (Eurofins) were used as templates , 80 mg per mL PEG8000 and 0.5 mg per mL T7 RNA polymerase in 1× TRX buffer . The reaction was incubated for 2 h at 37 °C. The reaction was stopped by removal of precipitants by centrifugation at 48,384 g for 5 min and subsequent RNA precipitation with 0.1 V 3 M NaOAc and 3 V absolute ethanol at −20 °C overnight.SRS2 and SRS2 + 5Δloop RNAs were purchased from IBA (Göttingen). Other RNAs needed in large amounts for NMR experiments were produced by large-scale in vitro transcription. As a template, 4 µM HPLC-purified FW (T7prom) primer and 3.4 µM HPLC-purified RV primer  in 1x TBE running buffer at constant 300 V for 17–20 h. RNA bands were visualized by UV shadowing and the desired band was excised from the gel and extracted by electroelution in a Whatman Elutrap electroelution system  at constant 200 V in 1x TBE for 8 h. Eluted RNA was collected each hour. Eluted RNA was dialyzed against 5 M NaCl at 4 °C overnight and subsequently twice against RNase-free water at 4 °C overnight before drying in a Concentrator Plus SpeedVac (Eppendorf).RNA was pelleted by centrifugation at 48,384 RNAs for electrophoretic mobility shift assays (EMSA) were labeled radioactively for sensitive detection of protein–RNA interactions. In vitro transcribed RNA was 5′ dephosphorylated in 20 µL reactions containing 10 pmol RNA, 1x Tango buffer with BSA (Thermo Fisher), 2 U FastAP thermosensitive alkaline phosphatase (Thermo Fisher) and 20 U of the RNase inhibitor SUPERaseIn (Thermo Fisher). After incubation at 37 °C for 15 min, the dephosphorylated RNA was phenol/chloroform extracted and precipitated with 0.1 V 3 M NaOAc, 3 V absolute ethanol and subsequent chilling at −20 °C for ≥15 min.32P from γ-32P ATP  in a 20 µL reaction with T4 polynucleotide kinase (New England Biolabs) in 1× buffer A. The labeling reaction was incubated at 37 °C for 30 min and subsequently stopped at 72 °C for 10 min.For radioactive labeling, 10 pmol dephosphorylated RNA or chemically synthesized RNA were 5′-phosphorylated with 2O and stored at −20 °C.Remaining free nucleotides were removed by purification on a NucAway™ Spin column (Ambion) according to the manufacturer’s instructions. Eluted radiolabeled RNA was diluted to a final concentration of 100 nM in RNase-free HFor EMSAs with short RNAs (<100 nt), protein at the indicated final concentration was mixed with 5 nM radiolabeled RNA in RNase-free protein buffer supplemented with 4% glycerol and 30 µg per mL yeast tRNA as a competitor in a final volume of 20 µL. In order to allow protein–RNA complexes to form, the mixtures were incubated for >20 min at RT.Separation of protein–RNA complexes was performed by native PAGE on 6% polyacrylamide 1x TBE gels in 40 min at constant 110 V in 1x TBE running buffer. Subsequently, the gels were fixed in 30% (v/v) methanol, 10% (v/v) acetic acid for 10 min before drying in a vacuum gel drier (BioRad). Visualization of radioactivity occurred after exposure of radiograph films (Kodak) in a Protec Optimax developer (Hohmann) or by PhosphorImaging with a Fujifilm FLA-3000. Each experiment was performed as a triplicate on different days.In the case of long, unlabeled RNA (>100 nt), 10–100 nM RNAs were used, and separation of protein–RNA complexes was performed by 1–1.5% agarose gel electrophoresis. Visualization of RNA was achieved by GelRed (Biotium) staining. Fluorescence was visualized with a Fusion SL imaging system (Vilber Lourmat) by UV at 254 nm.TM RNA 3′ End Biotinylation Kit (Thermo Fisher Scientific) was used according to the manufacturer’s protocol. Fifty pmol of RNAs were used per 30 µL reaction. After extraction and precipitation, the RNA was redissolved in 100 µL RNase-free water.Ligand RNA for binding studies by Surface Plasmon Resonance was biotinylated to allow immobilization on a streptavidin-coated surface. For biotinylation, the PierceSurface Plasmon Resonance (SPR) experiments were performed with a BIACORE 3000 system . To assess protein–RNA interactions, biotinylated RNA in a volume of 60 µL was streptavidin-captured on a SA-Chip  surface at a flow rate of 10 µL per min after three consecutive 1 min conditioning injections of 50 mM NaOH, 1 M NaCl.Full-length mStau interacted strongly with the blank SA-Chip surface. Thus, to assess the interaction of RNA with mStau FL, the protein was diluted in HBS-EP buffer  and covalently amine-coupled to a CM5-Chip  according to the manufacturer’s instructions.Kinetic analysis of protein–RNA interactions was performed at a flow rate of 30 µL per min in HBS-EP buffer. Analyte protein or, in case of mStau2 FL, RNA in HBS-EP buffer at the indicated concentrations was injected for 4–5 min to allow for association, subsequent dissociation was allowed for 10 to 15 min in HBS-EP buffer. To remove any residual bound protein, two 1 min regeneration injections with 1 M NaCl were performed.n ≥ 3 experiments were averaged. Kinetic fits, if applicable, were performed with the BIAevaluation software  using the bivalent analyte model available in the software.Data were analyzed in the BIAevaluation software . After double-referencing of obtained binding curves against the signal in a ligand-free reference channel or, in the case of full-length mStau2, against a HisSUMO-coupled reference channel, and a buffer run, average values for the analyte response at equilibrium were calculated. Steady-state binding curves were obtained by plotting the response at equilibrium against analyte concentration and curve fitting with the Origin Pro 9.0 (OriginLab) software using the two-site binding or Hill1 fits available in the software. All experiments were performed at least in triplicate on different days and the results of the Circular dichroism (CD) spectra were collected with a Jasco J-715 spectropolarimeter from 260 nm to 190 nm in a continuous scanning mode with a scanning speed of 50 nm per min at a bandwidth of 1 nm. Five scans were collected per measurement and response time was 8 s. The measurements were performed at 20 °C. Spectra were analyzed with SpectraManager (Jasco).Static light scattering (SLS) experiments were performed with a 270 Dual Detector and a VE3580 RI Detector  after SEC on a 10/300 Superdex200 Increase column  at 7 °C and a flow rate of 0.5 mL per min. System calibration was performed with BSA. Data were analyzed with the OmniSEC 5.02 software .Rgs4 SRS2 were obtained in 80 mM NaCl or 80 mM KCl, 20 mM BaCl2, 40 mM Na cacodylate pH 6.5, 40% MPD, 12 mM spermine after 4 days at room temperature using the sitting-drop vapor-diffusion method. Crystals could be reproduced in 34–46% MPD and were harvested with cryogenic loops and flash-frozen in liquid nitrogen. Native datasets were collected at the Swiss Light Source (SLS) synchrotron, beamline PXIII. Anomalous data for phasing were collected at the European Synchrotron radiation Facility (ESRF) at beamline ID 23–2. Data were indexed and integrated using XDS and scaled via XSCALE. Structure factor amplitudes were obtained with Truncate (CCP4 package)37. The structure was solved by MAD phasing with Barium from the mother liquor, using the AutoRickshaw web server38. The structure was completed by iterative manual building in COOT and refinement with RefMac5 (CCP4 package)37. All crystallographic software was used from the SBGRID software bundle. Images of the crystal structure were prepared with PyMol  or CueMol .Crystallization experiments were performed at the X-ray Crystallography Platform at Helmholtz Zentrum München. Initial crystals of 2O was added for locking. Measurements were performed at 298 K on Bruker AVIII 600, AVIII 800 or AVIII 900 NMR spectrometers with cryogenic (TCI) triple resonance gradient probes. Data were processed with Topspin 3.0 or Topspin 3.5 and analyzed with Sparky 339 and CcpNmr Analysis40. RNA assignment of imino groups was based on 1H,1H-NOESY spectra; an initial protein backbone assignment was made with HNCACB spectra. Titration experiments with the single mStau2 dsRBDs 1 and 2 as well as the tandem domain dsRBD1–2 were performed at 50 µM protein concentration. After snap-cooling, the RNA ligand was added in molar ratios of 0.5, 1.0, 2.0, and 3.0 to the protein.RNA and RNA–protein complexes were dialyzed to 150 mM NaCl, 20 mM sodium phosphate buffer, pH 7.0, 5 mM DTT prior to analysis, and 5–10% DTo determine the binding interface of Stau2 dsRBD1–2 to RNA, imino signals of the unbound RNA were compared with the respective resonances of a Stau2 dsRBD1–2-RNA (1:1) complex. The bound spectrum was scaled so that imino signals, which do not show additional exchange-mediated line broadening in the complex, have the same peak height as in the free spectrum (still with larger line broadening).41  and stau[R9]5  alleles was used to generate females lacking endogenous dmstaufen. The αTub67C:GFPm6-Staufen42  transgene was used as a source of GFP-dmStau in the female germline. Expression of the UASp-GFP-mStau2 transgenes was driven with one copy of oskar-Gal443 (FBtp0083699) in the female germ line. w1118  was used as the wild-type control. All UASp-GFP-mStau2 transgenes were generated by subcloning wild-type or mutant mStau2 coding sequences 3′ to the cds of rsEGFP2 into the pUASp-attB trangenesis vector44. The UASp-rsEGFP2-mStau2 vectors were injected into embryos of y[1] M{vas-int.Dm}ZH-2A w[*]; PBac{y[ + ]-attP-3B}VK00033 (FBti0076453) females to facilitate psiC31 mediated insertion into the same locus on the 3 L chromosome arm. All stocks were raised on normal cornmeal agar at 25 °C.Heterozygous combination of the stau[D3]46, to generate osk42x53245 and bcd24x633  Triton X-100, pH 7.4) for 5 min. Ovaries were prehybridized in 200 µL 2 × HYBEC  ethylene carbonate, 1 mM EDTA, 50 µg per mL heparin, 100 µg per mL salmon sperm DNA, 1% (v/v) Triton X-100) for 10 min at 42 °C. In total, 50 µL prewarmed probe mixture  was added to the prehybridization mixture and hybridization was allowed to proceed for 2 h at 42 °C. Free probe molecules were washed out of the specimen by two washes with prewarmed HYBEC and a final wash with PBT at room temperature. Ovaries were mounted in Vectashield and processed for smFISH analysis. For further details, see refs. 46.Forty-two and 24 different ssDNA oligonucleotides were labeled enzymatically with Atto532-ddUTP and Atto633-ddUTP, respectively, as described in refs. Drosophila egg-chambers mounted onto slides in Vectashield were imaged on a Leica TCS SP8 confocal laser scanning microscope using a 20x dry (NA = 0.75) objective for imaging the RNA distribution and a 63x oil immersion (NA = 1.4) objective to obtain high-resolution images for co-localization analysis of oskar or bicoid mRNA and GFP-Stau. The outlines of the oocytes and the anteroposterior (AP) axis were manually specified, and the smFISH signal was redistributed into a 100 × 100 matrix. Each column of this matrix represents the relative amount of signal found under 1% of the AP axis length with anterior on the left (column 1) and posterior on the right (column 100). Such matrices are then averaged to obtain a mean and the variability of the RNA localization during a certain stage of oogenesis. Moreover, descriptors such as the center of mass (relative to the geometric center of the ooctye) and the amount of RNA localizing to the posterior domain  were extracted and compared statistically using a Kruskal–Wallis test followed by pairwise Mann–Whitney U test against the stau null condition. More details on the analyses of RNA distribution within stage 9 and stages 10–11 oocytes are described in ref. 48.oskar or bicoid mRNPs and GFP-Stau particles49. To determine the number of mRNA molecules in an mRNP and to normalize GFP-Stau signal intensity, we fitted multiple Gaussian functions to the corresponding signal intensity distributions taken from the nurse cells using the mixtools package in R (https://cran.r-project.org/web/packages/mixtools/index.html)50. The µ value of Gaussian fit that described the largest portion of the distribution  was taken as the signal intensity of a unit . The chosen µ value was always the smallest among the µ values of the fitted Gaussians. Raw signal intensities were normalized with the determined unit values. RNPs were clustered based on this normalized intensity under the following rule: [2i:2i+1], i ∈ [0:8], i.e., 1, 2:3, 4:7, 8:15, etc. The observed nearest neighbor colocalization frequencies were computed for each of the clusters and were compared to the expected co-localization frequencies . The slopes of the fitted lines were compared pairwise using least-squares means analysis51. All statistical analyses were carried out in R52 using RStudio (www.rstudio.com). All graphs were plotted by the ggplot2 library in R53.Colocalization between the mRNAs and GFP-Stau was assayed by image deconvolution using Huygens essential segmentation and establishment of nearest neighbor pairs between Further information on experimental design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


Using the RNA-dependent RNA polymerase (RdRp) from poliovirus (PV) as our model system, we have shown that Lys-359 in motif-D functions as a general acid in the mechanism of nucleotidyl transfer. A K359H (KH) RdRp derivative is slow and faithful relative to wild-type enzyme. In the context of the KH virus, RdRp-coding sequence evolves, selecting for the following substitutions: I331F  and P356S . We have evaluated IF-KH, PS-KH, and IF-PS-KH viruses and enzymes. The speed and fidelity of each double mutant are equivalent. Each exhibits a unique recombination phenotype, with IF-KH being competent for copy-choice recombination and PS-KH being competent for forced-copy-choice recombination. Although the IF-PS-KH RdRp exhibits biochemical properties within twofold of wild type, the virus is impaired substantially for recombination in cells. We conclude that there are biochemical properties of the RdRp in addition to speed and fidelity that determine the mechanism and efficiency of recombination. The interwoven nature of speed, fidelity, the undefined property suggested here, and recombination makes it impossible to attribute a single property of the RdRp to fitness. However, the derivatives described here may permit elucidation of the importance of recombination on the fitness of the viral population in a background of constant polymerase speed and fidelity. In spite of the substantial resources that have been allocated by the World Health Organization, the National Institute of Allergy and Infectious Diseases, the European Centre for Disease Prevention and Control, and the Centers for Disease Control and Prevention to support prediction of emerging viral pathogens , viral oBecause all RNA viruses encode an RNA-dependent RNA polymerase (RdRp) with conserved function and mechanism, this enzyme has emerged as an attractive target for development of broad-spectrum therapeutics ,3,4 and Known RdRp variants exhibiting a high-fidelity phenotype can also exhibit a reduced speed of nucleotide incorporation, at least at the biochemical level . ReplicaThe most extensively studied PV fidelity mutants encode RdRps with amino acid substitutions at sites remote from the active site. Constructing equivalent substitutions conferring equivalent phenotypes in RdRps other than PV is difficult if not impossible. Our laboratory has therefore pursued active-site-based strategies to manipulate the fidelity, speed, and/or recombination efficiency of the RdRp . We haveIn this study, we characterize a second motif-D mutant of PV (K359H). Unlike the K359R RdRp-encoding virus , K359H PAdherent monolayers of HeLa and L929 fibroblasts were grown in DMEM/F-12 media. Media was supplemented with 100 U/mL penicillin, 100 µg/mL streptomycin, and 10% heat inactivated (HI)-FBS. All cells were passaged in the presence of trypsin-EDTA. Wild-type and recombinant PV viruses were recovered after transfection of RNA produced in vitro (see below) from full-length cDNA or from the CRE-REP assay parental partners . PV typecis-acting replication element (CRE) located in the 2C-coding region and was described previously  TGTGCGCTGCCTGAATTTGATGTGA-3′ in a 7300 Real-Time qPCR System  machine. A standard curve was generated using in vitro transcribed RNA. This assay was described previously . HeLa ceThis assay was developed in the laboratory of David Evans and was used as described previously ,31. L929Purified polymerases used for biochemical analysis were prepared as described previously ,27.2, 60 μM ZnCl2, dT15 (2 μM), poly(rA) (100 μM AMP), UTP (500 μM), α-32P-UTP (0.2 μCi/μL), and PV RdRp (0.2 μM). Reactions were initiated by addition of PV RdRp and incubated at 30 °C for 5 min at which time the reactions were quenched by addition of EDTA to a final concentration of 50 mM. Reaction volumes were 50 μL. Products were analyzed by DE81 filter binding, where 10 μL of the quenched reaction was spotted onto DE81 filter paper discs and dried completely. The discs were washed three times for 10 min in 250 mL of 5% dibasic sodium phosphate and rinsed in absolute ethanol. Bound radioactivity was quantitated by liquid scintillation counting in 5 mL of Ecoscint scintillation fluid .Reactions contained 50 mM HEPES, pH 7.5, 10 mM 2-mercaptoethanol, 5 mM MgCl2, and 5 mM MgCl2. Products were analyzed by denaturing polyacrylamide gel electrophoresis, visualized using a PhosphorImager, and the transfer products quantified using ImageQuant TL software . The sym/sub assay has been described previously ,32. ElonMost studies of RdRp fidelity have benefited from the selection of derivatives that were either more or less sensitive to a mutagenic nucleoside ,36,37,38Biological studies of K359H PV were not reported, because this virus was not genetically stable. When PV is rescued from in vitro transcribed RNA, four passages are required for the genetic diversity of the viral population to come to equilibrium . During At multiplicities of infection of one or higher, the double- and triple-mutant viruses were stable for multiple passages, thus permitting us to characterize the biological properties of these viruses. Interestingly, the growth properties of the viruses were not as expected based on the experiments with the replicon. Each mutant virus exhibited the same delay relative to WT virus prior to the first detection of infectious virus after four hours post-infection (hpi) A. ThereaOur studies of RdRp fidelity mutants in cell culture have highlighted the fact that the use of the plaque-forming unit (PFU) as a measure of virus concentration will mask changes in the specific infectivity of the viral RNA . As a reA change to the specific infectivity of the viral RNA caused by increased mutational load—that is, reduced fidelity of the PS-KH RdRp, would also explain the observed reduction in the specific infectivity of PS-KH PV relative to the other mutant PVs. To test this possibility, we evaluated the sensitivity of each virus population to growth in the presence of ribavirin. We reasoned that the mutagenic activity of ribavirin would exhibit the greatest negative impact on viruses with a mutator phenotype as described previously . RelativWe have previously reported biochemical properties of the RdRps for the repertoire of mutant viruses described above . ConsistBased on the myriad RdRp fidelity mutants that have been reported to date, there appears to be a direct correlation between the rate of nucleotide addition (speed) and the fidelity of nucleotide addition ,18,23,43The current state of the art for evaluation of recombination in cell culture is based on the co-transfection of two viral (sub) genomic RNAs incapable of producing infectious virus . The donWe evaluated our panel of KH-containing PV mutants using this assay, and the outcomes were, in most cases, quite unexpected B. KH PV The one caveat of the recombination assay is that the recombinant viruses produced must be able to form plaques, which may mean that the recombinant virus must spread by a lytic mechanism. It was possible that PS-KH PV was defective for virus assembly and/or spread, at least lytic spread B. In ordTwenty years ago, we showed that template switching was the primary mechanism of product formation when oligo (dT) or oligo(rU) were used to prime poly(rU) RNA synthesis on oligo(rA) or poly(rA) RNA templates . BrieflyThe observation that only a subset of the KH mutants were competent for recombination, presumably by a copy-choice mechanism , providen + 1). Addition of the remaining nucleotides in the absence or presence of the acceptor template will yield a strong-stop RNA product. In the presence of a complementary acceptor RNA, the strong-strop RNA product (donor) will be extended, creating a transfer product.Recently, our laboratory developed an assay for forced-copy-choice recombination ,45, inspWe evaluated each KH-containing RdRp derivative in this assay. Substrate and product analysis are presented in We conclude that copy-choice and forced-copy-choice recombination use distinct mechanisms, requiring unique biochemical properties of the RdRp.For more than a decade now, our laboratory and others have published studies asserting connections between viral RdRp fidelity and viral fitness ,14,15,16Lys-359 protonates the pyrophosphate leaving group during nucleotidyl transfer ,49. The Each substitution (IF and PS) individually increased the replication efficiency of K359H PV, with the combination of the two substitutions producing an additive outcome C. Each sThe substitutions at the active site of the polymerase do not only affect replication efficiency and population diversity but also impact other stages of the lifecycle. The kinetics of virus production for the different mutants did not correspond directly to the kinetics of replication compare C. Virus We and others have observed correlations between RdRp fidelity and recombination efficiency, with higher fidelity suppressing recombination and vice versa ,22,23,24Our laboratory has had a longstanding interest in the mechanism of recombination ,23,32,45The current narrative of the characterized PV mutants impaired for recombination is that recombination acts as a mechanism to purge deleterious mutations in the viral population ,53,54. OViral fitness, virulence, and pathogenesis are determined collectively by the structure, dynamics, and activity of all virus-encoded functions. The conserved structure, dynamics, and mechanism of the viral RdRp makes this enzyme an attractive target for development of attenuated viruses by changing conserved residues capable of perturbing conserved, biochemical function ,9,11. As


Phylosymbiosis is a pattern defined as the tendency of closely related species to host microbiota whose compositions resemble each other more than host species drawn at random from the same tree. Understanding the mechanisms behind phylosymbiosis is important because it can shed light on rules governing the assembly of host-associated microbiotas and, potentially, their coevolutionary dynamics with hosts. For example, is phylosymbiosis a result of coevolution, or can it be generated by simple ecological filtering processes? Beyond qualitative theoretical models, quantitative theoretical expectations can provide new insights. For example, deviations from a simple baseline of ecological filtering may be used to test more-complex hypotheses . Here, we use simulations to provide evidence that simple host-related ecological filtering can readily generate phylosymbiosis, and we contrast these predictions with real-world data. We find that while phylosymbiosis is widespread in nature, phylosymbiosis patterns are compatible with a simple ecological model in the majority of taxa. Internal compartments of hosts, such as the animal gut, often display stronger phylosymbiosis than expected from a purely ecological filtering process, suggesting that other mechanisms are also involved. Host-associated microbiota composition can be conserved over evolutionary time scales. Indeed, closely related species often host similar microbiota; i.e., the composition of their microbiota harbors a phylogenetic signal, a pattern sometimes referred to as “phylosymbiosis.” Elucidating the origins of this pattern is important to better understand microbiota ecology and evolution. However, this is hampered by our lack of theoretical expectations and a comprehensive overview of phylosymbiosis prevalence in nature. Here, we use simulations to provide a simple expectation for when we should expect this pattern to occur and then review the literature to document the prevalence and strength of phylosymbiosis across the host tree of life. We demonstrate that phylosymbiosis can readily emerge from a simple ecological filtering process, whereby a given host trait  that varies with host phylogeny  filters preadapted microbes. We found marked differences between methods used to detect phylosymbiosis, so we proposed a series of practical recommendations based on using multiple best-performing approaches. Importantly, we found that, while the prevalence of phylosymbiosis is mixed in nature, it appears to be stronger for microbiotas living in internal host compartments  than those living in external compartments . We show that phylosymbiosis can theoretically emerge without any intimate, long-term coevolutionary mechanisms and that most phylosymbiosis patterns observed in nature are compatible with a simple ecological process. Deviations from baseline ecological expectations might be used to further explore more complex hypotheses, such as codiversification.IMPORTANCE Phylosymbiosis is a pattern defined as the tendency of closely related species to host microbiota whose compositions resemble each other more than host species drawn at random from the same tree. Understanding the mechanisms behind phylosymbiosis is important because it can shed light on rules governing the assembly of host-associated microbiotas and, potentially, their coevolutionary dynamics with hosts. For example, is phylosymbiosis a result of coevolution, or can it be generated by simple ecological filtering processes? Beyond qualitative theoretical models, quantitative theoretical expectations can provide new insights. For example, deviations from a simple baseline of ecological filtering may be used to test more-complex hypotheses . Here, we use simulations to provide evidence that simple host-related ecological filtering can readily generate phylosymbiosis, and we contrast these predictions with real-world data. We find that while phylosymbiosis is widespread in nature, phylosymbiosis patterns are compatible with a simple ecological model in the majority of taxa. Internal compartments of hosts, such as the animal gut, often display stronger phylosymbiosis than expected from a purely ecological filtering process, suggesting that other mechanisms are also involved. Rich communities of microorganisms, collectively called the microbiota, make their living in and on macro-organisms. Within a host species, microbiota compositions can vary dramatically across body sites , across Buchnera and their insect hosts, the pea aphids  to provide a simple quantitative ecological baseline for when we should expect phylosymbiosis to arise from ecological filtering, (ii) to assess the specificity and sensitivity  of the proposed phylosymbiosis approaches to detect ecological filtering, (iii) to document phylosymbiosis prevalence across diverse host species, and (iv) to compare expected and observed patterns and discuss the potential processes shaping host-associated microbiotas. Overall, we show that phylosymbiosis is widespread but often weak, that it has been measured in a variety of ways, that it can emerge from simple ecological processes, and that the phylogenetic signal of the host trait strongly determines the strength of phylosymbiosis. Finally, the proposed methods to detect phylosymbiosis differ markedly in their statistical powers, so that future studies should aim to use similar and rigorous methods.We use simulations to provide a simple quantitative ecological baseline for when we should expect phylosymbiosis to arise from ecological filtering. Simultaneously, we assess the specificity and sensitivity  of the existing, widely used, statistical approaches that measure phylosymbiosis to detect host filtering correlated with host phylogeny . We firsm = 5%); here, this translates to detecting correlations in 5% of the neutral simulations despite a lack of true signal. We find that the Mantel and the dendrogram-based approaches report false-positive correlations in ∼5% of neutral simulations  or the strength of the phylosymbiosis as quantified by Pearson’s r or Robinson-Foulds metric  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). Note that Pearson’s r and the normalized Robinson-Foulds metric should be interpreted differently with respect to the strength of phylosymbiosis; normalized Robinson-Foulds metric values close to 0 indicate strong phylosymbiosis, while Pearson’s r values near 1 indicate strong phylosymbiosis. For the correlation between microbiota composition and host trait (green curves and boxplots), the plots depict the relationship between the proportion of significant Mantel tests  or the strength of the signal as quantified by Pearson’s r  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). The strength of the simulated ecological filtering increases from the left to right panels: minimum for the left panels  and maximum for the right panels (beta.env = 1). These results are based on 1,000 simulation sets, each set containing 150 microbial species and 25 host species. Beta-diversity is quantified using the weighted UniFrac metric. Download FIG S1, EPS file, 2.3 MB.Theoretical expectation of the significance and strength of the correlation between microbiota composition and host phylogeny  or host trait for varying strength of simulated ecological filtering. The correlation between microbiota composition and host phylogeny  is measured with two methods , while the correlation between microbiota composition and host trait is measured with a Mantel test (green curve and boxplots). Note that because microbiota have been directly filtered with the host trait, we expect the correlation between microbiota composition and this host trait to be higher than with host phylogeny  which imperfectly reflects the host trait. For phylosymbiosis (dendrogram-based and Mantel methods) the plots depict the relationship between the proportion of phylosymbiosis , ∼50% (Mantel approach) and ∼20% (dendrogram-based approach) of communities have compositions that harbor phylosymbiosis . The porr between host distances and microbiota dissimilarities, while the dendrogram-based approach relies on a metric that calculates the dissimilarity of tree branching structures . We find that with a moderate-to-strong phylogenetic signal  for the host trait, Pearson’s r is ∼0.2 on average. In the same range of values for the phylogenetic signal of the host trait, the normalized Robinson-Foulds metric is ∼0.92   . In contK > 1.5) . WeighteK > 1.5) , and resK > 1.5) . ImportaK > 1.5)  to S6.10.1128/mSystems.00097-18.2FIG S2y axis, top plots) or the strength of the phylosymbiosis as a quantified by Pearson’s r or the Robinson-Foulds metric  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). Note that Pearson’s r and normalized Robinson-Foulds metric should be interpreted differently with respect to the strength of phylosymbiosis; normalized Robinson-Foulds metric values close to 0 indicate strong phylosymbiosis while Pearson’s r values near 1 indicate strong phylosymbiosis. For the correlation between microbiota composition and host trait (green curves and boxplots), the plots depict the relationship between the proportion of significant Mantel tests  or the strength of the signal as a quantified by Pearson’s r  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). The strength of the simulated ecological filtering is set to 0.5, and multiple beta-diversity metrics are tested (see the key). These results are based on 1,000 simulation sets, with each set containing 150 microbial species and 25 host species. Download FIG S2, EPS file, 2.4 MB.Theoretical expectation of the significance and strength of the correlation between microbiota composition and host phylogeny  or host trait for varying beta-diversity metrics. The correlation between microbiota composition and host phylogeny  is measured with two methods , while the correlation between microbiota composition and host trait is measured with a Mantel test (green curve and boxplots). Note that because microbiota have been directly filtered with the host trait, we expect the correlation between microbiota composition and this host trait to be higher than with host phylogeny , which imperfectly reflects the host trait. For phylosymbiosis (dendrogram-based and Mantel methods), the plots depict the relationship between the proportion of phylosymbiosis  or the strength of the phylosymbiosis as a quantified by Pearson’s r or normalized Robinson-Foulds metric  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). Note that Pearson’s r and the normalized Robinson-Foulds metric should be interpreted differently with respect to the strength of phylosymbiosis; normalized Robinson-Foulds metric values close to 0 indicate strong phylosymbiosis, while Pearson’s r values near 1 indicate strong phylosymbiosis. For the correlation between microbiota composition and host trait (green curves and boxplots), the plots depict the relationship between the proportion of significant Mantel tests  or the strength of the signal as a quantified by Pearson’s r  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). The carrying capacity  increases from the left to right panels. The simulated ecological filtering strength is set to 0.5 . These results are based on 1,000 simulation sets, with each set containing 150 microbial species and 25 host species. Download FIG S3, EPS file, 1.6 MB.Theoretical expectation of the significance and strength of the correlation between microbiota composition and host phylogeny  or host trait for varying microbiota-carrying capacities . The correlation between microbiota composition and host phylogeny  is measured with two methods , while the correlation between microbiota composition and host trait is measured with a Mantel test (green curve and boxplots). Note that because microbiota have been directly filtered with the host trait, we expect the correlation between microbiota composition and this host trait to be higher than with host phylogeny , which imperfectly reflects the host trait. For phylosymbiosis (dendrogram-based and Mantel methods), the plots depict the relationship between the proportion of phylosymbiosis  or the strength of the phylosymbiosis as quantified by Pearson’s r or the normalized Robinson-Foulds metric  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). Note that Pearson’s r and the normalized Robinson-Foulds metric should be interpreted differently with respect to the strength of phylosymbiosis; normalized Robinson-Foulds metric values close to 0 indicate strong phylosymbiosis, while Pearson’s r values near 1 indicate strong phylosymbiosis. For the correlation between microbiota composition and host trait (green curves and boxplots), the plots depict the relationship between the proportion of significant Mantel tests  or the strength of the signal as a quantified by Pearson’s r  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). The number of host species increases from the left to right panels. The simulated ecological filtering strength is set to 0.5 . These results are based on 1,000 simulation sets, with each set containing 150 microbial species. Download FIG S4, EPS file, 1.6 MB.Theoretical expectation of the significance and strength of the correlation between microbiota composition and host phylogeny  or host trait for varying numbers of host species. The correlation between microbiota composition and host phylogeny  is measured with two methods , while the correlation between microbiota composition and host trait is measured with a Mantel test (green curve and boxplots). Note that because microbiota have been directly filtered with the host trait, we expect the correlation between microbiota composition and this host trait to be higher than with host phylogeny , which imperfectly reflects the host trait. For phylosymbiosis (dendrogram-based and Mantel methods), the plots depict the relationship between the proportion of phylosymbiosis  or the strength of the phylosymbiosis as a quantified by Pearson’s r or the normalized Robinson-Foulds metric  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). Note that Pearson’s r and the normalized Robinson-Foulds metric should be interpreted differently with respect to the strength of phylosymbiosis: normalized Robinson-Foulds metric values close to 0 indicate strong phylosymbiosis, while Pearson’s r values near 1 indicate strong phylosymbiosis. For the correlation between microbiota composition and host trait (green curves and boxplots), the plots depict the relationship between the proportion of significant Mantel tests  or the strength of the signal as a quantified by Pearson’s r  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). The number of microbial species decreases from the left to right panels. The simulated ecological filtering strength is set to 0.5 . These results are based on 1,000 simulation sets, with each set containing 25 host species. Download FIG S5, EPS file, 1.5 MB.Theoretical expectation of the significance and strength of the correlation between microbiota composition and host phylogeny  or host trait for varying numbers of microbial species. The correlation between microbiota composition and host phylogeny  is measured with two methods , while the correlation between microbiota composition and host trait is measured with a Mantel test (green curve and boxplots). Note that because microbiota have been directly filtered with the host trait, we expect the correlation between microbiota composition and this host trait to be higher than with host phylogeny , which imperfectly reflects the host trait. For phylosymbiosis (dendrogram-based and Mantel methods), the plots depict the relationship between the proportion of phylosymbiosis  or the strength of the phylosymbiosis as a quantified by Pearson’s r or the normalized Robinson-Foulds metric  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). Note that Pearson’s r and the normalized Robinson-Foulds metric should be interpreted differently with respect to the strength of phylosymbiosis; normalized Robinson-Foulds metric values close to 0 indicate strong phylosymbiosis, while Pearson’s r values near 1 indicate strong phylosymbiosis. For the correlation between microbiota composition and host trait (green curves and boxplots), the plots depict the relationship between the proportion of significant Mantel tests  or the strength of the signal as a quantified by Pearson’s r  and the phylogenetic signal of the host trait that filters microbes to assemble microbiotas (x axis). The number of colonization steps used in simulations  increases from the left to right panels. The simulated ecological filtering strength is set to 0.5 . These results are based on 1,000 simulation sets, with each set containing 25 host species and 150 microbial species. Download FIG S6, EPS file, 1.2 MB.Theoretical expectation of the significance and strength of the correlation between microbiota composition and host phylogeny  or host trait for varying numbers of colonization steps. The correlation between microbiota composition and host phylogeny  is measured with two methods , while the correlation between microbiota composition and host trait is measured with a Mantel test (green curve and boxplots). Note that because microbiota have been directly filtered with the host trait, we expect the correlation between microbiota composition and this host trait to be higher than with host phylogeny , which imperfectly reflects the host trait. For phylosymbiosis (dendrogram-based and Mantel methods), the plots depict the relationship between the proportion of phylosymbiosis , the approach used to measure phylosymbiosis, the use of cofactors or intrahost species variability, and the results found. Download Copyright © 2018 Mazel et al.2018Mazel et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the r, often used in the Mantel approach (r distributions in r2 ∼ 0.4) but measured very weak signal (r2 < 0.05) when considering similarity threshold higher than 9% (91% OTUs).Overall, phylosymbiosis is widespread in nature, being observed in the plant root endosphere, in insect and mammalian guts, and in sponges  to 5. Itapproach . In somein silico), or taken into account by considering a null distance for the host phylogenetic distances between individuals of the same species  will have more power to detect the filtering process. Although we found that the differences in phylosymbiosis detection between beta-diversity metrics were not striking, we suggest that future tests of phylosymbiosis should use and report the results for multiple metrics (both abundance-based metrics and presence/absence metrics) to enable better assessment of the robustness of the results.a priori reason  to think that microbiota compositions relate to each other in a hierarchical manner. More importantly, this transformation into a dendrogram comes at a cost of data loss because distances between microbiota compositions based on the dendrograms and raw (true) distances are never perfectly correlated. This data loss might explain why the dendrogram-based approach seems to have a reduced power in our simulations. However, we found that the 10 empirical phylosymbiosis tests carried out with this approach detected phylosymbiosis, whereas the Mantel approach detected phylosymbiosis in only 30% of tests. While this result appears in contradiction with our theoretical findings, it is likely explained by the fact that most of the dendrogram-based tests used gut microbiomes, a host compartment that is known to harbor strong phylosymbiosis. In summary, our results suggest that the Mantel approach is preferable to the dendrogram-based approach to measure phylosymbiosis, although the validity of the Mantel test is also debated , but they should always be accompanied by statistical investigations. The two most commonly employed statistical procedures are the Mantel approach and the dendrogram-based approach. Our results show that the Mantel test is more powerful than dendrogram-based approaches at detecting ecological filtering. The dendrogram-based approach starts by building a community dendrogram from the raw beta-diversity values, usually using a hierarchical clustering approach such as the unweighted pair group method using average linkages (UPGMA). However, this step might not be necessary because there is no  debated , 27. In  debated  and mult debated , which hIn this paper, we focused our analyses on phylosymbiosis at the interspecific level, using only 1 individual per host species in our simulations. However, phylosymbiosis may also occur within host species, e.g., between populations. There is some empirical evidence that within-species phylogenetic distances are correlated with microbiota compositions , but alm10.1128/mSystems.00097-18.7FIG S7x coordinates of the blue points) are set to zero because there is no information on genetic distances between individuals within a species (as is often the case). While there is no signal at the interspecific scale , phylosymbiosis appears if all comparisons are included (grey line fit). However, there is no a priori effect of host phylogenetic distances but only an effect of host species identities . Download FIG S7, EPS file, 0.1 MB.The effects of intra- versus interspecific microbiota comparisons on phylosymbiosis. The figure depicts a theoretical case where intraspecific comparisons (blue points) and interspecific comparisons (red points) are included in a Mantel test. Each point represents a comparison between two samples. Here, intraspecific phylogenetic distances  on the assembly of microbiotas. If this host trait is known and measured across host species, it can be directly used to explain microbiota compositions, and the effect of host phylogeny could be measured on the remaining unexplained variability in microbiota compositions. If this additional variability is related to host phylogeny, it may represent the effect of another unmeasured  host trait. Teasing apart the effects of the measured host traits and host phylogeny can be done by conducting experiments under controlled conditions for host trait  or by iThe detection of the phylosymbiosis of microbial compositions has recently gained much attention . However(i) Empirical patterns of phylosymbiosis can be explained by simple ecological filtering. Phylosymbiosis seems widespread in nature but is clearly not universal. Even if the prevalence of phylosymbiosis that we estimated here is likely to be overestimated because of publication bias , we shoIn summary, our results suggest that the majority of phylosymbiosis patterns detected in the literature  are comp(ii) Deviations from theoretical expectations. Empirical evidence suggests that, for some types of microbiotas, phylosymbiosis can be relatively strong, such as in the case of mammalian gut microbiotas  Heterogeneity of phylosymbiosis. In this paper, we considered a measure of phylosymbiosis for the overall host phylogenetic tree. However, it is increasingly recognized that macroevolutionary patterns can vary across the phylogenetic tree  Toward explicit models of microbiota macroevolution. The seemingly high prevalence of phylosymbiosis is stimulating research on the micro- and macroevolution of host-associated microbiotas  creating host and microbial species and traits, (ii) creating host-associated microbiotas based on known assembly rules, and then (iii) measuring the phylosymbiosis in the resulting microbiotas .n hosts species and m microbial species or OTUs . Different hosts harbored different trait values , and different microbes harbored different host trait preference values . We simulated these traits as follows: first, phylogenies for both host and microbes were simulated with a pure birth model and rescaled the height of the tree to 1 so that the heights were comparable across simulations; second, we independently simulated one single continuous host trait  and one single continuous microbial trait preference . In order to vary the degree of phylogenetic signal harbored by the host trait, we used the delta model of trait evolution (Each unique simulation was based on a unique data set containing volution . An inten host microbiotas (one for each host) based on simple ecological filtering using the R package VirtualCom  and two approaches to measure correlation between host phylogeny and microbiota composition: the dendrogram approach , 10 and 10.1128/mSystems.00097-18.8FIG S8x axis) in order to compute the percentage of simulations that yielded phylosymbiosis. These results are based on 1,000 simulation sets in total, with each set containing 150 microbial species and 25 host species. Download FIG S8, EPS file, 0.2 MB.Phylosymbiosis as measured by the dendrogram-based approach with an alternative null model. This figure is equivalent to Copyright © 2018 Mazel et al.2018Mazel et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the https://github.com/FloMazel/Phylosymbiosis-Ecological-model.All analysis was carried out in R version 3.3.2 , with ex