Cell-based, mathematical modeling of collective cell behavior has become a prominent tool in developmental biology. Cell-based models represent individual cells as single particles or as sets of interconnected particles and predict the collective cell behavior that follows from a set of interaction rules. In particular, vertex-based models are a popular tool for studying the mechanics of confluent, epithelial cell layers. They represent the junctions between three (or sometimes more) cells in confluent tissues as point particles, connected using structural elements that represent the cell boundaries. A disadvantage of these models is that cell–cell interfaces are represented as straight lines. This is a suitable simplification for epithelial tissues, where the interfaces are typically under tension, but this simplification may not be appropriate for mesenchymal tissues or tissues that are under compression, such that the cell–cell boundaries can buckle. In this paper, we introduce a variant of VMs in which this and two other limitations of VMs have been resolved. The new model can also be seen as on off-the-lattice generalization of the Cellular Potts Model. It is an extension of the open-source package VirtualLeaf, which was initially developed to simulate plant tissue morphogenesis where cells do not move relative to one another. The present extension of VirtualLeaf introduces a new rule for cell–cell shear or sliding, from which cell rearrangement (T1) and cell extrusion (T2) transitions emerge naturally, allowing the application of VirtualLeaf to problems of animal development. We show that the updated VirtualLeaf yields different results than the traditional vertex-based models for differential adhesion-driven cell sorting and for the neighborhood topology of soft cellular networks.The online version of this article (10.1007/s11538-019-00599-9) contains supplementary material, which is available to authorized users. How cells form tissues, organs, and organisms remains one of the most intriguing and most central questions of biology. Recent theoretical approaches to study collective cell behavior are taking a prominent role in addressing these questions. Theoretical approaches provide deeper intuition about processes that typically are unfamiliar to the researchers by testing the physical plausibility of speculative hypotheses or by making predictions that can be tested experimentally. Theory can aid the analysis of data-rich time-lapse images of cell movements during development  and the vertex-based model (VM). The CPM represents cells as  domains of lattice sites on a regular lattice. Cells move on the lattice by randomly extending or retracting their domain to adjacent lattice sites, according to a Hamiltonian energy function that describes the contractile and viscoelastic structures that form each cell, the physical adhesive interactions between cells, and in some cases, extracellular materials. Alternatively, structural elements have been used to describe the cell boundaries and cross-linking elements for the cell interior , the actual area of cell c, and e taken from E, the set of all edges in the simulation, and VirtualLeaf represents confluent tissues in two dimensions as a set of interconnected polygonal cells. A cell mentclass2pt{minimT, setting the amount of random cell motility or ‘noise’ added in this way.We update the model using Metropolis dynamics: We iteratively select a random node sliding operator to further reduce the Hamiltonian  involves cycling over all nodes Independent of this Hamiltonian description of cell mechanics and cell motility, additional rules motivated by the biological problem can be included, including cell growth, cell division, and subcellular models describing the genetic or metabolic networks regulating cell behavior using differential equations  differential adhesion cell sorting  and Following previous Cellular Potts and vertex-based approaches , which predicts that the least cohesive cells move to the aggregate’s periphery, see, e.g., the CPM  in Case I agree, with only minor differences, with those reported in Farhadifar et al.  or cortical tension (Case II) straightens cell boundaries, such that boundary flexibility has no effect. In Case III, the specific topology of ‘soft networks’ is due to the boundaries’ resistance to compression by adjacent cells. Adding additional nodes to the membranes makes them flexible and allows membranes to buckle ) Fig. b–d. In tT, increases the speed of cell sorting Supplementary material 2 (mp4 37576 KB)Supplementary material 3 (mp4 34910 KB)Supplementary material 4 (mp4 37676 KB)Supplementary material 5 (mp4 32852 KB)Supplementary material 6 (mp4 37483 KB)Supplementary material 7 (mp4 37775 KB)Supplementary material 8 (mp4 38046 KB)Supplementary material 9 (mp4 25093 KB)Supplementary material 10 (mp4 24505 KB)Supplementary material 11 (mp4 28179 KB)Supplementary material 12 (mp4 18065 KB)Supplementary material 13 (mp4 16682 KB)Supplementary material 14 (mp4 23781 KB)Supplementary material 15 (mp4 56338 KB)Below is the link to the electronic supplementary material.


Several wastes can be instrumental in the improvement of the mechanical properties of medium carbon steel when quenched. The quenching media employed such as coconut water (CW), pap water (PW) and spent engine oil (SPE) have been largely considered as wastes. The data in this article are related to the research article titled “Mechanical Properties Improvement Evaluation of Medium Carbon Steels Quenched in Different Media”   The chemical composition of the medium carbon steel under consideration is as shown in The hardness value, yield strength and ultimate tensile strength obtained when quenched in CW, PW and SPE are as shown in 2Latitude: 8°08׳18.85" N, Longitude: 5°06׳09.36" E). The chemical composition of the medium carbon steel was determined using optical emission spectrophotometer. The quenching media (considered as wastes) utilized for the quenching include coconut water (CW), pap water (PW) and spent engine oil (SPE). Medium size lathe machine was used in preparing the samples for tensile test as shown in The medium carbon steel used for this experiment was obtained from a local market in Omu-Aran, Kwara State (


IDH1/2 mutations are early drivers present in diverse human cancer types arising in various tissue sites. IDH1/2 mutation is known to induce a global hypermethylator phenotype. However, the effects on DNA methylation across IDH mutant cancers and functionally different genome regions, remain unknown. We analyzed DNA methylation data from IDH1/2 mutant acute myeloid leukemia, oligodendroglioma, astrocytoma, solid papillary breast carcinoma with reverse polarity, sinonasal undifferentiated carcinoma and cholangiocarcinoma, which clustered by their embryonal origin. Hypermethylated common probes affect predominantly gene bodies while promoters in IDH1/2 mutant cancers remain unmethylated. Enhancers showed global hypermethylation, however commonly hypomethylated enhancers were associated with tissue differentiation and cell fate determination. We demonstrate that some chromosomes, chromosomal arms and chromosomal regions are more affected by IDH1/2 mutations while others remain resistant to IDH1/2 mutation induced methylation changes. Therefore IDH1/2 mutations have different methylation effect on different parts of the genome, which may be regulated by different mechanisms. IDH) gene family is composed of three genes, IDH1, IDH2 and IDH3. IDH enzymes catalyze oxidative decarboxylation of isocitrate to alpha-ketoglutarate (α-KG) in the citric acid cycle. While IDH1 is located in the cytosol and peroxisome, IDH2/3 are located in mitochondria. Mutations in the IDH1 and IDH2 genes have been found in various tumor types and catalyze reduction of a-KG into a structurally similar oncometabolite 2-hydroxyglutarate (2-HG), which probably functions as an α-KG antagonist in numerous metabolic processes2. 2-HG is normally produced at a low level (<300 µM) by errors in catalysis during phosphoglycerate dehydrogenase, hydroxyl oxoacetic transferase and malate dehydrogenase reactions. It has no known metabolic function in mammals, and is rapidly cleared via conversion to αKG by chirality-specific dehydrogenases (D-2HGDH or L-2HGDH) in physiologic conditions. In cancers with IDH1/2 mutations, 2-HG levels can reach millimolar concentrations saturating normal clearing mechanisms.The Isocitrate dehydrogenase 11. In addition, a cohort of normal non-neoplastic blood samples of patients evaluated for leukocytosis (n = 32) was used for comparisons with IDH mutant AML. DNA methylation data from our previously published studies on solid papillary breast carcinoma with reverse polarity (N = 5)16, and sinonasal undifferentiated carcinoma 12, as well as DNA methylation profiles of IDH mutant oligodendroglioma (N = 20) and astrocytoma (N = 31) profiled at NYU Langone Health from 2015 to 2018. IDH1/2 mutant brain tumors were compared with IDH wild-type glioblastoma from our previously published cohort33. A cohort of proneural  GBMs was used for comparison with the oligodendroglioma cohort and mix of classic and mesenchymal GBMs (n = 10 for each subtype) was used for comparison with IDH mutant astrocytoma.In this study we incorporated DNA methylation data from Cancer Genome Atlas (TCGA) profiled IDH mutant and wild-type acute myeloid leukemia IDH1/2 mutated tumors with biologically relevant IDH wild-type tumors when available. DNA methylation of IDH mutated AML was compared both with normal non-neoplastic blood DNA sample (leukocytes) and IDH wild-type AML  and Mesenchymal glioblastoma (GBM)  Fig.  and oligBM) Fig. . The anaers Fig.  compareders Fig.  and Oligers Fig. , suggestIDH1 and IDH2 genes lead to global hypermethylation which readily distinguishes mutated tumors from their wild-type counterparts34  showed the most prominent hypermethylation phenotype with 122,648 hypermethylated probes and the highest level of tumor specific hypermethylation . Gene bodies located on chromosomal arms 4p, 5p, 7p, 10p, 13q and chromosome 16 showed the highest fraction of hypermethylated probes, while chromosomal arms 5q, 9p, 18p and 20p showed the lowest ratio of hypermethylated gene body probes. Chromosomal arms 4q, 9p, 10p and 13q showed the highest ratio of hypermethylated enhancer probes across all IDH1/2 mutant cancers. Considering that chromosomal arm 13p has virtually no methylation probe coverage on either 450k or EPIC array  karyogram and historically labeled as heterochromatin  terms associated with basic cellular functions such as RNA processing, regulation of mitosis, and ribosome biogenesis suggesting that genes likely critical in cellular functions are not silenced by global DNA hypermethylation. In contrary, GO associated with hypermethylated promoters were enriched (6/10 top GO terms) for genes involved in meiosis and gamete generation, likely playing no role in cancer. Hypermethylation of gene bodies and enhancers was enriched for GO terms associated with central nervous system function, such as axonogenesis, dendrite development, and neuronal differentiation. This observation could be due to a somewhat larger number of IDH mutant brain tumors in our cohort skewing the analysis towards cellular processes critical for brain development. Other GO terms affected by gene body and enhancer hypermethylation included Ras signaling, cell morphogenesis, GTPase activity, and cell migration  requires IDH mutation status for accurate diagnosis and prognosis. We show that there are disease specific regions as well as common regions of DNA/chromosomes that are preferentially affected across all IDH mutant cancers. IDH mutations do not directly cause hypermethylation, but rather lead to inhibition of demethylases and may preserve previously established tissue/cell-specific differences in DNA methylation patterns occurring before the mutation event. Nevertheless, our results show that enhancers associated with tissue differentiation and cell fate remain unmethylated. It is possible that the three dimensional structure of chromatin renders some parts of the chromatin more susceptible than others to IDH mutation induced hypermethylation.IDH mutant cancers. Both gene body and enhancer hypermethylation pathway analysis showed enrichment in the MAPK pathway suggesting a common signaling pathway in these tumors. Interestingly, although enhancers showed global hypermethylation, the enrichment of GO terms for hypomethylated enhancers was for processes involved in cellular and tissue differentiation. This is concordant with previous observations that IDH mutations “lock” cells in undifferentiated states and prevent terminal differentiation25. Our data suggest that this process may be mediated via hypomethylation of tissue specific enhancers. Interestingly, we observed that specific parts of the genome, coinciding with dark bands on chromosomal banding, show lack of common hyper- or hypomethylated probes for gene bodies, enhancers and promoters. This suggests that these particular parts of the chromatin may not be affected by IDH1/2 mutation induced methylation changes, and their epigenetic regulation may depend on mechanisms other than DNA methylation47. While we have observed a striking predisposition, as well as a resistance, of specific chromosomal arms and regions to hypermethylation in IDH1/2 mutant cancers, the major limitation of our study is the two-dimensional nature of our data which prohibits us to examine whether the affected regions interact with each other on a three-dimensional level. Our study identifies common effects of IDH1/2 mutations on DNA methylation across six cancer types, derived from all three embryological layers. However it is limited by the fact that only in three cancers, AML, astrocytomas and oligodendroglioma we were able to perform a comparison with biologically relevant wild-type tumors and only in AML we compared the IDH1/2 mutated tumors with the normal tissue.Interestingly, although methylation of promoters and gene bodies showed almost the opposite direction genome-wide, the common hypomethylated probes in gene promoters and bodies were both enriched for GO terms for genes involved in basic cellular functions such as RNA processing and RNA and protein catabolism, suggesting that other epigenetic mechanisms may play a role in regulating critical cell functions in IDH mutation in gliomas leads to loss of insulation between topological domains and aberrant gene activation48. Whether there are also three-dimensional chromatin interactions that are common across all IDH1/2 mutant cancers remains to be explored. While it is well known that oncometabolite 2-HG leads to global DNA hypermethylation, it is currently not clear whether hypermethylation affects DNA indiscriminately or whether functional or topographic parts of the genome are differentially methylated. Here we show that tumors with IDH1/2 mutations can still be distinguished based on the tissue of origin suggesting that IDH1/2 mutation induced methylation acts on the background of developmental epigenetic changes. In addition to previously described DNA hypermethylation phenotype, IDH1/2 mutant tumors show surprisingly high level of DNA hypomethylation, which is particularly prominent across gene promoters. In striking contrast, DNA hypermethylation predominantly affects gene body regions and enhancers across all cancers; with exception of enhancers involved in terminal cell and tissue differentiation, which remain unmethylated. This suggests that the CpG hypermethylator phenotype induced by IDH1/2 mutations affects predominantly non-promoter CpG islands with enhancer activity. Lastly, DNA methylation varies between chromosomes and chromosomal regions, which appear to be preferentially affected while other regions remain protected from the hypermethylation in IDH mutant setting. In summary, our findings provide new insight into topographic and functional effects of IDH1/2 induced DNA methylation.We and others have previously shown that 6 and TCGA-CHOL11 projects on The Cancer Genome Atlas (TCGA). See also Supplemental File 1. DNA methylation data from IDH mutated breast carcinoma (N = 5) with altered nuclear polarity and sinonasal undifferentiated carcinomas (N = 8) were obtained from our previously published cohorts16. In addition, DNA was extracted from the de-identified tumors of 20 patients with IDH mutated oligodendroglioma and 31 patients with IDH mutated astrocytoma who had been treated at the New York University Langone Medical Center (NYULMC). DNA was extracted from archival formalin-fixed paraffin-embedded (FFPE) tissue from tumor biopsies using automated Maxwell Promega system, and methylation profiling was performed at the NYU Department of Molecular Pathology using the Illumina Infinium Human Methylation 450 Bead-Chip (450 K array) or Illumina EPIC array according to the manufacturer’s instructions as described previously49. All NYU brain tumor samples had IDH1/2 status identified by clinically validated CLIA certified NYU NGS50 IonTorrent sequencing panel and had the methylation subgroup confirmed using CNS tumor classifier tool as described previously33. All DNA methylation profiles across cancer types were analyzed together regardless of the mutated IDH1 or IDH2 gene, or mutation hotspot. The research was performed in accordance with institutional and federal guidelines and regulations pertaining to the discarded tissue research. The study was approved by the NYU Institutional Review Board and the waiver of consent was granted .Raw methylation data from IDH-mutated acute myeloid leukemia  and cholangiocarcinoma (N = 9) were obtained from TCGA-LAMLMinfi, which was used to subset for overlapping probes among the two different methylation array platforms via the CombineArrays function. Minfi was used to check for the quality of samples by calculating the mean detection p-values. Samples with mean p < 0.01 were considered for further downstream analysis. Sex probes were removed, and all samples passed quality control. All sample probes were quantile normalized, adjusted for background signal, and processed for identifying the differentially methylated CpG sites. Stratified quantile normalization (preprocessQuantile) was chosen to control for differences between type I and type II probes. The number of overlapping probes between the two arrays after quality control and filtering was 363,700.Raw methylation data were then analyzed with the R Bioconductor package Minfi to display global methylation pattern differences and similarities between samples. Average M-values were calculated in R for each probe across each tumor type. For each tumor type, hypomethylated probes were then extracted from the full list of overlapping probes by subsetting for a mean M-value cutoff of −2, and hypermethylated probes were extracted by subsetting for a mean M-value cutoff of +2. These values correspond to beta values of 0.2 and 0.8, respectively50. Mean global methylation levels were determined for each tumor type. Hypermethylated and hypomethylated probe lists for each tumor type were further subsetted into enhancer regions, regions within 200 base pairs of a transcriptional start site , and gene body regions. This was done using the gene region information provided by the University of California Santa-Cruz for each probe on the reference annotation files of the Illumina arrays. If probes were associated with more than one gene region, they were included in the lists for each region.M-values, which are calculated using the log2 ratio of the intensities of methylated probes versus unmethylated probes, were used as they have been suggested to be more statistically valid than beta values for differential methylation analysis. The differential methylation analysis sorted the probes in order of smallest to largest q-values, so that the top probes represent the sources of largest variation between sample groups. Heatmaps were generated from the top 10,000 probes from this list using hierarchical clustering via InteractiVenn online tool was used to generate Venn diagrams that display both the overlapping and unique hypermethylated or hypomethylated probes between the six tumor types, using default settings. R was used find the intersections between the lists of hypermethylated probes for each tumor type and between the lists of hypomethylated probes for each tumor type. This resulted in lists of probes either commonly hypomethylated or commonly hypermethylated across tumor types. The setdiff function then allowed for identification of probes that were uniquely hypomethylated or uniquely hypermethylated for each tumor type.The Using these lists, the karyoploteR R package was used to plot the distribution of hypermethylated and hypomethylated probes for each tumor type and gene region across the entire genome, versus mean methylation in M-values. These probes therefore all already met the criteria of a minimum cutoff M-value of +2 for hypermethylation, and a maximum M-value cutoff of −2 for hypomethylation. The karyoploteR package was also used to examine probe coverage across the genome as a control to identify any regions that did not have any probes to begin with. This was done by plotting all overlapping probes between 450 K and 850 K arrays based on their position alone , which included samples from a Classic subtype  and Mesenchymal subtype (n = 10). IDH mutated oligodendrogliomas were compared with Proneural (RTKI) GBMs (n = 10). IDH mutated cholangiocarcinomas were compared with wild-type cholangiocarcinomas (n = 7) and IDH mutated AML samples were compared with IDH wild-type AML (n = 100) and a cohort of normal non-neoplastic blood samples of patients evaluated for leukocytosis (n = 32). Raw methylation data for IDH wild-type cholangiocarcinoma and AML samples were downloaded from TCGA, and methylation data for IDH wild-type GBM samples were obtained from our previously published cohorts that were processed on the Illumina Infinium Human Methylation 450 Bead-Chip (450 K array) or Illumina EPIC arrayDifferential methylation was performed using Minfi for each of the pairs using the same settings described previously, and selecting only probes with an FDR < 0.05. The total number of differentially expressed genes with an FDR < 0.05 varied as follows: 271,300 (IDH Oligodendroglioma vs GBM RTKI), 243,435 , 980 (IDH vs wild-type Cholangiocarcinoma), and 254,537 (IDH vs wild-type AML).Due to the low number of probes that passed FDR < 0.05, cholangiocarcinoma comparison was excluded from further analysis. Heatmaps were generated via hierarchical clustering for each pair using only the top 10,000 most differentially methylated probes. Average M-values of −2 and +2 across all samples within a given tumor type were used as cutoffs for extracting hypomethylated and hypermethylated probes, respectively, from each list of significant differentially methylated probes. Identified tumor specific hyper- and hypomethylated probes for IDH mutated tumors were then separated into gene body regions, TSS regions, and enhancer regions using the UCSC gene region information provided for each probe by Illumina. KaryoploteR was used to plot the unique hypermethylated and hypomethylated probe distributions between each pair for each gene region, across all autosomal chromosomes. The y-axis scale  for each pair had a range of −/+1.9 to −/+5. Bar plots were plotted using the number of probes in different regions and two-tailed t-tests assuming unequal variances were performed to obtain p-value for each pair comparison.ClusterProfiler. The bar plots represent the number of genes (x-axis) involved in specific GO terms (y-axis) based on the biological processes. The color of the bar shows the significance level of each term. Cytoscape was used to generate the network plots using the results of GO enrichment. Network plots show the top 10 genes in each GO term that are interconnected by certain common genes. The size of the node represents the number of genes involved in each GO category (as represented in bar plot). KEGG enrichment from ClusterProfiler was used to find the signaling pathways based on each set of probes. The dot plots represent ratio of genes (x-axis) involved in each signaling pathway (y-axis) of KEGG database. Size of the dots shows the gene counts and the color denotes the significance level.Common hypermethylated and hypomethylated probes across all the disease types were used to determine the GO terms and pathways they were involved in. Common hypermethylated and hypomethylated probes were categorized as promoters, enhancers and gene bodies based on the Illumina annotation. Each set of probes were run through the Gene Ontology enrichment analysis using R package Supplementary information


Aegilops mutica showed a less inhibition, indicative of their positive contribution to submergence tolerance, whereas cytoplasms of Aegilops umbellulata and related species caused a greater inhibition. Superoxide dismutase (SOD) activity showed a marked increase accompanied by retardation of seedling growth in a susceptible NC hybrid. The observation suggested that the elevated SOD activity was resulted from a high level of reactive oxygen species accumulated and remained in susceptible seedlings. Taken together, our results point to the usefulness of NC hybrids in further studies needed to clarify molecular mechanisms underlying the nucleus-cytoplasm interaction regulating submergence stress response in wheat.Genetic diversity in cytoplasmic and nuclear genomes and their interaction affecting adaptive traits is an attractive research subject in plants. We addressed submergence stress response of wheat that has become increasingly important but remained largely uninvestigated. Our primary aim was to disclose cytoplasmic diversity using nucleus-cytoplasm (NC) hybrids possessing a series of heterologous cytoplasms in a common nuclear background. Effects of submergence on seedling emergence and growth from imbibed seeds were studied and compared with euplasmic lines. Marked phenotypic variabilities were observed among both lines, demonstrating divergent cytoplasmic and nuclear effects on submergence response. NC hybrids with cytoplasm of  A vast majority of genetic information is stored in the nuclear genomes, whereas small numbers of protein-coding as well as non-coding genes reside in the two cytoplasmic genomes. Although small in size, owing to their essential roles in energy metabolisms, unique evolution through endosymbiosis and complex functional interactions with nuclear genomes, cytoplasmic genomes have provided researchers with many attractive subjects in plant science5. In contrast to nuclear genomes exhibiting Mendelian inheritance, cytoplasmic genomes compartmentalized in mitochondria and chloroplasts are generally inherited through maternal lineage in a majority of plant species. Taking advantage of this characteristic unilateral mode of inheritance, cytoplasms of related genera and species can be combined with given nuclei through repeated substitution backcrosses with recurrent paternal parents. To study intergeneric and interspecific diversity among cytoplasms in Triticum (wheat) and Aegilops (goatgrass) species, Kihara (1951) produced nucleus-cytoplasm substitution lines or nucleus-cytoplasm hybrids  and demonstrated that cytoplasm of Ae. caudata induced male sterility when it was introduced into common wheat (T. aestivum)6. This was the first demonstration of cytoplasmic male sterility in wheat. Ever since, a number of NC hybrids have been produced and utilized as valuable experimental materials in studying phylogeny and evolution of the tribe Triticeae. Particularly in the Triticum-Aegilops complex that comprises a series of diploid, allotetraploid and allohexaploid species evolved through hybridization followed by amphidiploidization between ancestral species7, a collection of 552 lines of NC hybrids have been produced by combining 12 different nuclear genomes of common wheat with 46 distinct cytoplasmic genomes covering all representative cytoplasms in the complex8. Based on extensive and systematic studies of cytoplasmic effects on fourteen vegetative and seven reproductive traits exhibited in these NC hybrids together with their chloroplast and mitochondrial DNA variations, cytoplasms of the Triticum-Aegilops complex were classified into 18 major types plus five subtypes10. NC hybrids thus contributed to establish the evolutional history in Triticum and Aegilops and greatly increased our knowledge of genetic architecture of both nuclear and cytoplasmic genomes10.Plants possess three interacting and coevolving genomes in three intracellular organelles, 13, growth vigor11, viability14, inter-crossability and speciation16, photosynthesis and respiration18, transcriptomes and metabolomes19, and other agronomically important traits22. In such studies, hexaploid and tetraploid NC hybrids with cytoplasms of Triticum, Aegilops, Agropyron, Haynaldia and a wild relative of barley Hordeum chilense have been used. In maize also, NC hybrids with cytoplasms from teosinte including distantly related Zea species exhibited differences in morphological, physiological and developmental traits23. In the last decade, studies of a model plant species, Arabidopsis thaliana, have demonstrated intraspecific diversity of cytoplasms and nucleus-cytoplasm interactions both affecting a wide range of plant phenotypes. Studies using reciprocal F2 families or recombinant inbred lines showed variations in cytoplasmic genomes and cytonuclear interactions (synonymous with nucleus-cytoplasm interactions) that greatly affected natural variations in germination capacity and metabolomes25. Intraspecific cytonuclear interactions affecting various adaptive phenotypes in the fields have also been reported in a study using 56 cytolines produced by complete diallele crosses among eight natural Arabidopsis accessions26.Studies of interspecific and intergeneric diversity in cytoplasms and nucleus-cytoplasm interactions have been extended to various phenotypes including male-sterility31. Submergence (complete inundation) and waterlogging (saturation of soil with water) caused by floods and heavy rainfalls have become serious threats in wheat production due to global climate changes38. Submergence together with waterlogging were reported to cause 15–20% yield reduction of wheat in the world, causing ever-increasing loss particularly in Asian countries where rice-wheat rotation is in a wide practice33. However, as compared with semiaquatic rice, our current knowledge of submergence/waterlogging stress response of wheat is quite limited. Because of the importance of this trait in rice, a large volume of research has been conducted and advanced our understanding of genetic, physiological, morphological and molecular mechanisms controlling the trait42. Two major strategies, i.e., “quiescent strategy” and “elongation or escape strategy”, have been known to be responsible for controlling submergence tolerance in rice. SUB1A, a gene encoding an ethylene responsive transcription factor (AP2/ERF), plays a key role in the operation of “quiescent strategy” by repressing cellular gibberellin levels39. By contrast, SNORKEL 1, 2, also encoding ethylene responsive transcription factors, play a key role in “elongation strategy” in deep-water or floating rice by enhancing gibberellin responses to induce rapid internode elongation40.As above, inter- and intraspecific and intergeneric diversity of cytoplasms and nucleus-cytoplasm interactions affecting various phenotypes have been well investigated and documented in plants. However, contribution and role of cytoplasms in regulating complex adaptive traits, particularly responses to adverse environmental stresses in cereal crops, remain largely uninvestigated, although cytoplasmic genomes and nucleus-cytoplasm genome interaction have been known to play important roles in stress tolerance, signaling and adaptation in plantsTriticum-Aegilops complex combined with a common nucleus of a paternal donor parent, and compared their response to that of wheat accessions covering a wide range of genetic diversity. Also, we compared superoxide dismutase activity as a major antioxidant enzyme among a nuclear donor and tolerant and susceptible NC hybrids and discussed a possible involvement of cytoplasmic substitution in redox balance affecting submergence stress response. We herein report results providing the first experimental evidence for a large genetic diversity in both cytoplasmic and nuclear genomes affecting submergence stress response in wheat. Also, we point to the invaluable usefulness of NC hybrids as materials in further studies to elucidate molecular mechanisms underlying nucleus-cytoplasm interactions that regulate submergence stress response in wheat.We focused on submergence stress response in wheat and initiated a study of cytoplasmic genetic diversity affecting seedling emergence and growth after subjecting imbibed seeds to submergence stress. To do this, we used a series of NC hybrids possessing heterologous cytoplasms derived from the 43 was adopted with modifications for wheat. Before studying cytoplasmic effects, we examined suitability of the bioassay method for evaluation of submergence stress response in wheat using a nuclear donor of standard common wheat cultivar Chinese Spring (abbreviated as CS). A time course of seedling growth without submergence was first studied using seeds imbibed for 1 day. Seed germination was 100% and seedlings continued to grow until the end of the incubation period in test tubes  reached peaks after 1 to 3 days of submergence and thereafter declined gradually over time /μa: relative rate of growth increment compared with mean of a. μa-c and μb-c: growth inhibition by 3-day submergence compared with mean of a and b, respectively. (μa-c)/μa and (μb-c)/μb: relative rate of growth inhibition by 3-day submergence compared with mean of a and b, respectively.We studied effects of cytoplasmic genome substitution on seed germination and seedling growth after treating imbibed seeds with and without submergence using 37 lines of NC hybrids, each possessing one of 24 distinct cytoplasm types derived from the ex Table . Three bex Table . SubmergIn all three experimental schemes, germination rates were considerably high in all lines, irrespective of submergence treatment Fig. . Germinath day of incubation, a majority of NC hybrids showed smaller values of variable a, except for C13, C31 and C35, all of which showed equivalent values compared with CS  data in the following analyses  between the 10th and 13th day was larger in C01mf and C30 than CS and other NC hybrids /μa between the 10th and 13th day were significantly larger in C01mf, C14 and all eight NC hybrids possessing either U or U’ cytoplasms than in CS and other NC hybrids /μa were greater in many NC hybrids than in CS, but in C14 both were smaller /μb, respectively  showed several unique features Fig. . NC hybr10. Two variable b and c were measured, and their sensitivity to submergence was evaluated based on two derivative variables μb-c and (μb-c)/μb. A large variability existed among these euplasmic wheat lines  that was used to make the first NC hybrid6, red winter wheat variety Jones Fife (JF), solid-stemmed spring variety S615, and hulled and non-free threshing T. spelta (Splt), showed significantly smaller values of both variables, indicating the higher submergence tolerance than CS. We then compared coefficients of variation (CV) among the hexaploid wheat lines and those of NC hybrids using these two variables, μb-c and (μb-c)/μb , which showed contrasting levels of submergence tolerance/susceptibility. Triplicate samples of whole seedlings each grown in three different incubation conditions  were used. When compared among seedling emerged and grown under the same incubation conditions without submergence (2di + 7dg and 2di + 10dg), SOD activity showed some significant differences among all lines , Ae. triuncialis (C26), Ae. biuncialis , Ae. triaristata  and Ae. columnaris (C30) caused greater rates of growth inhibition in all NC hybrids carrying them 52. De-submergence is also detrimental because oxidative stress disturbs physiological processes producing ROS upon exposure to aerial oxygen42. It is crucial to understand how heterologous cytoplasms regulate submergence stress response. Therefore, as the first step, we studied the activity of superoxide dismutase (SOD), which acts in the conversion of superoxide radicals to molecular oxygen and hydrogen peroxide. SOD is composed of metalloenzymes Fe-SOD localized in chloroplasts, Mn-SOD in mitochondria and peroxisomes, and Cu/Zn-SOD in many cellular compartments including the three organelles. We measured a total SOD activity by adopting experimental schemes, in which effects of hypoxic stress on imbibed seeds during prolonged submergence and possible oxidative stress upon and during de-submergence could simultaneously be studied  were imbibed individually for one day in test tubes  filled with 15 ml of deionized water (10 cm in depth). Water was drained out at indicated time points, and imbibed seeds were kept with embryo side up and incubated for up to 17 days. Care was taken to maintain an adequate amount of water by pipetting every day or every other day. Sensitivity of seedlings was studied by adding 15 ml of deionized water to cover growing seedlings at indicated time points and keeping them submerged for additional 7 days. Sensitivity of imbibed seeds was also studied by immersing 15 seeds at each time point continuously for up to 11 days followed by incubation under de-submergence conditions for additional 10 days for germination and subsequent seedling growth. Incubation conditions of seeds and seedlings were adjusted at day/night temperatures of 15 °C/10 °C with a photoperiod of 12hL/12hD under LED lumps at a light-intensity of ca. 120 μmol m−2 s−1 in a walk-in incubator. These temperature conditions were close to those in November when wheat seedlings were at the early stage of growth in the Kansai area of Japan. Germination was judged based on protrusion of a radicle with two seminal roots and a coleoptile length longer than 15 mm. Seedling growth was assessed by shoot length and fresh weight, root length and fresh weight, and total seedling fresh weight.Test tube bioassay developed and successfully used for QTL mapping of submergence tolerance and analysis of gene expression associated with submergence in ricea, b and c and six derivative variables measured based on the five traits were used for assessing seedling growth. Fifteen seeds each of wheat lines and NC hybrid lines were subjected to each experiment, and all experiments were repeated three times, except for CS that was repeated six times (for variable b and c), C05 four times (for variable a), C02 twice (for variables a and b), and C55 twice (for variable a).Seeds of 37 NC hybrid lines and 12 wheat lines (Table 63. Magnitude of variabilities within and between NC hybrids and wheat lines were compared based on coefficients of variations (CV). Distance matrix was calculated based on Euclidean method using the nine variables, and clustering by Unweighted Pair Group Method with Arithmetic mean (UPGMA) was performed using R package “fastcluster”64.Pair-wise comparisons between CS and NC lines were made by Steel test, and multiple rank sum comparisons among all NC hybrids and hexaploid wheat lines including CS were made by Steel-Dwass test, both using a software “EZR”Activity of total superoxide dismutase (SOD) was measured using a nuclear donor CS and three NC lines of C13, C14 and C26 showing contrasting levels of submergence response. Seedings grown for 7 days after 3-days submergence (2di + 3ds + 7dg) and ones grown for 7 and 10 days without submergence (2di + 7dg and 2di + 10dg) were used for the measurements. Whole seedlings were frozen with liquid nitrogen, ground using TissueLyser II (Qiagen) and suspended in 0.1 M phosphate buffer saline (pH 7.4). Measurements of SOD activity (Unit/mg fresh weight of whole seedlings) were made in triplicate samples using SOD Assay Kit-WST . SOD activity was defined as the amount of enzyme that inhibited the production of soluble formazan from tetrazolium salt WST-1 by 50% according to the supplier’s instruction. Mean comparison among the lines was made by Tukey’s test. a, b, c in the upper corner indicate significant difference at the 1% level.Figure S1, S2, S3 and S4. Table S1, S2, S3, S4 and S5


Streptococcus, Succinivibrio and unclassified Myxococcales were enriched in the rumen microbiota of bloated steers, whereas Fibrobacter and Ruminococcus were overrepresented in the rumen contents of non-bloated steers. Our results provide novel insights into bloat-associated shifts in the composition and predicted functional properties of the rumen microbiota of cattle grazing alfalfa pasture.Frothy bloat is an often fatal digestive disorder of cattle grazing alfalfa pastures. The aim of this study was to investigate ruminal and fecal microbiota dynamics associated with development of alfalfa-induced frothy bloat and to further explore how bloat prevention strategies influence the composition of these microbial communities. In a 3 × 3 crossover experiment, twelve rumen-cannulated steers were sequentially subjected to: (1) pure alfalfa pasture, (2) pure alfalfa pasture supplemented with the pluronic detergent ALFASURE, and (3) alfalfa – sainfoin mixed pasture. Eleven out of 12 steers in pure alfalfa pasture developed clinical bloat, whereas ALFASURE treatment prevented the development of bloat in all 12 steers and alfalfa – sainfoin prevented bloat in 5 out of 11 steers. Development of bloat was associated with considerable shifts in the microbiota profile of rumen contents. In particular, the microbiota of solid rumen contents from bloated steers contained higher species richness and diversity.  Although ruminants themselves lack the required enzymatic repertoire to harvest energy from lignocellulose-rich plant material, microbial symbionts inhabiting the rumen ecosystem can sequentially breakdown and ferment plant cell wall carbohydrates and thus support the energy requirement of ruminants2. Legume forages contain high concentrations of digestible proteins, which upon release into the rumen result in the rapid proliferation of ruminal microbes and an increase in fermentative activities and gas production1. This rapid proliferation of microbes, in particular the formation of bacterial biofilms, results in the production of an excessive amount of bacterial slime (i.e. exopolysaccharides) which increase the viscosity of the rumen contents and entrap fermentation gases in a stable foam that cannot be expelled by eructation3. In addition, legumes also possess glycosides (i.e. saponins) with foaming properties that can play a secondary role in development of bloat4. Among all North American forage legumes, alfalfa (Medicago sativa) has been the most widely adopted due to its high nutritive quality and yield. However, the occurrence of bloat is a major limitation to its greater use in grazing systems5. Several preventive strategies have been deployed in an attempt to mitigate bloat in cattle grazing alfalfa pastures. These include breeding for bloat-reduced alfalfa (AC Grazeland6), the use of alcohol ethoxylate/pluronic detergents, such as ALFASURE7, and the inclusion of non-bloating legumes such as sainfoin (Onobrychis viciifolia) in mixed legume pastures9.Frothy bloat is an often-fatal digestive disorder of cattle grazing on highly digestible legumes or wheat pastureset al.10 proposed that highly digestible high-protein forages, such as alfalfa, clover, and vegetative wheat result in the proliferation of certain ruminal microbial populations1 that promote bloat. To date, most of our knowledge regarding the contribution of rumen microbes to bloat is limited to either culture-based studies12, or targeted amplification and quantification (qPCR) of classical rumen bacteria14. However, the majority of microbes inhabiting the rumen ecosystem are unculturable15 or lack the genomic information for targeted amplification and therefore have likely been overlooked as important components within the rumen microbiome that contribute to frothy bloat. More recently, Pitta et al.16 used high-throughput sequencing to explore the association of the rumen microbiome with development of frothy bloat in steers grazing vegetative wheat pastures. They observed that wheat-induced frothy bloat was associated with an enrichment of several bacterial genera within the phylum Firmicutes, including Clostridium, Eubacterium, and Butyrivibrio, and an underrepresentation of genes encoding for oligosaccharidases. While this study generated valuable insight into the role of the rumen microbiome in development of wheat-induced frothy bloat, there remains a knowledge gap regarding the contribution of the rumen microbiota to alfalfa-induced frothy bloat.Understanding the contribution of rumen microbiota to the development of frothy bloat is of high importance for implementing effective preventive strategies against this digestive disorder. Howarth The main objectives of our study were to (a) determine changes in the composition and functional properties of the rumen microbiota underlying the development of alfalfa-induced frothy bloat, (b) explore the response of rumen microbiota to dietary interventions to prevent bloat, including the grazing of alfalfa-sainfoin mixed pastures and the addition of the pluronic detergent ALFASURE, and (c) to investigate the degree that fecal microbiota can be influenced by these bloat prevention strategies.Following introduction to pure alfalfa pasture, 11 out of 12 steers developed clinical bloat. In contrast, ALFASURE completely prevented bloat and alfalfa – sainfoin was moderately effective as 5 out of 11 steers bloated , 45,541, and 42,544 high quality sequences per rumen liquid, rumen solid, and fecal sample, respectively. At 97% similarity threshold, alignment of non-singletone OTUs to the Greengenes database resulted in classification of OTUs into 17 and 15 different bacterial phyla for rumen and fecal samples, respectively (Supplementary Table P < 005) than that of steers that did not bloat while grazing alfalfa – sainfoin pasture  = 0.001) or grazing alfalfa – sainfoin (p(PERMANOVA) = 0.004). Microbiota of solid rumen content of bloated steers were also phylogentically more dispersed (p(PERMDISP) = 0.01) compared to non-bloated steers receiving ALFASURE. Within the liquid fraction of the rumen content, microbiota composition only differed between steers that bloated while grazing alfalfa and those that did not bloat with ALFASURE (p(PERMANOVA) = 0.026). Within both solid and liquid fractions of the rumen, no clustering pattern was observed between the microbiota of steers that did not bloat as a result of receiving ALFASURE or grazing alfalfa – sainfoin  = 0.003 and p(rumen-solid) < 0.001) and period (p(rumen-liquid) = 0.004 and p(rumen-solid) < 0.001), the interaction of treatment and period was not significant for microbiota in either fraction of the rumen content (p(rumen-liquid) = 0.447 and p(rumen-solid) = 0.072; Supplementary Table Comparisons of alpha-diversity indices were performed at an even depth of 22,000 sequences per sample. Figure ure Fig. . There woin Fig. . To testp(PERMANOVA) = 0.001) from the microbiota of rumen samples collected during the baseline adaptation phase . The same analysis also revealed distinct clustering patterns (p(PERMANOVA) = 0.01) between the microbiota of solid and liquid fractions of rumen contents  revealed that the microbiota of rumen samples from steers that bloated as result of grazing pure alfalfa clustered distinctly , Proteobacteria , and Tenericutes (Anaeroplasma) were overrepresented, whereas other genera within phyla Bacteroidetes , Fibrobacteres (genus Fibrobacter), and Firmicutes (genus Ruminococcus) were overrepresented in the rumen of steers during the baseline adaptation phase , and Streptococcus (phylum Firmicutes), whereas Fibrobacter and Ruminococcus were overrepresented in the rumen of non-bloated steers. With regards to predicted functional pathways, in general, carbohydrate metabolism pathways  were significantly enriched within the rumen microbiota of bloated steers, whereas amino acid metabolism pathways, including cysteine and methionine metabolism were enriched within the rumen microbiota of non-bloated steers , and Proteobacteria (unclassified Alphaproteobacteria) were significantly overrepresented within the microbiota of the liquid fraction of rumen contents  = 0.03; Fig. Buleidia, unclassified Ruminococcaceae, and unclassified Colostridiales, within the rumen microbiota of steers that did not bloat, whereas Prevotella was significantly overrepresented in the baseline rumen microbiota of bloated steers  = 0.005) between the microbiota of steers that bloated grazing alfalfa and those that received ALFASURE. In addition, unsupervised clustering analysis based on the proportions of main bacterial genera (>0.1% of community) revealed a distinct clustering pattern between the microbiota of fecal samples collected during the baseline adaptation phase and those that bloated while grazing alfalfa (p(PERMANOVA) < 0.001; Fig. Phascolarctobacterium and unclassified Peptococcaceae within the phylum Firmicutes, unclassified Bacteroidetes belonging to families Rikenellaceae, S24-7, and RF16 were overrepresented within the fecal microbiota of bloated steers grazing alfalfa, whereas Fibrobacter, Prevotella, Selenomonas and unclassified Lachnospiraceae were overrepresented within the fecal microbiota of samples collected during the baseline adaptation phase.Unlike rumen microbiota, the richness and diversity of fecal microbiota was not affected by treatments Fig. . With re17. In beef cattle, grazing of wheat and alfalfa pastures has been associated with frothy bloat9, with recent evidence suggesting that this condition may be linked to a perturbed rumen microbiome in cattle grazing wheat pastures18. In the present study, we showed that transition from alfalfa hay to alfalfa pasture resulted in rapid development of frothy bloat in the rumen ecosystem of steers. We further demonstrated that bloat preventive strategies, in particular supplementation of drinking water with ALFASURE reduces the development of alfalfa-induced frothy bloat. ALFASURE is a pluronic detergent known to prevent pasture bloat via reducing rumen fluid viscosity and disrupting the stability of the froth that causes bloat. This may be in part due to the ability of pluronic detergents to prevent the formation of micelle within the rumen ecosystem, which in turn can disrupt gas bubbles and prevent them from coalescing19. Sainfoin is a bloat-safe forage that contains condensed tannins20. Tannins are a family of polyphenolic compounds capable of binding to soluble proteins, polysaccharides and other macromolecules21. Thus, by binding to plant proteins and preventing them from being solubilized into the ruminal fluid, condensed tannins of sainfoin can prevent the formation of proteinaceous gas-trapping foam within rumen contents and therefore prevent frothy bloat. It has been demonstrated that inclusion of sainfoin with alfalfa at a concentration of 10–12% (DM basis) reduces the incidence of bloat23.Composition and functionality of rumen microbiota can be influenced by a wide array of host and environmental factors among which diet appears to play a central role in shaping the overall profile of rumen microbial ecosystem25. Availability of a diverse genetic pool allows associated microbial consortia to form structured communities - embedded in extracellular matrices - where end products produced by one colony can be sequentially used by closely associated colonies26. In the current study, increased diversity of the rumen microbiota of bloated steers was concomitant with enrichment of predicted bacterial genes that are responsible for metabolism of complex carbohydrates. This might be indicative of the robustness of ruminal biofilm for degrading complex plant polysaccharides and production of the exopolysaccharide slime that is associated with bloat1. Further, we observed that increased microbiota diversity in bloated animals was more pronounced in the solid fraction of rumen contents. One possible explanation could be that in the bloated rumen ecosystem, increased viscosity results in entrapment of end products (metabolites) of the enzymatic degradation of plant polysaccharides surrounding solid particles, which in turn may result in uncontrolled proliferation of solid-associated biofilms.Transition from alfalfa hay to alfalfa pasture resulted in increased richness and diversity of the rumen microbiota. Increased microbial diversity is an intrinsic characteristic of biofilm formation and proliferation; a principle referred to as “insurance hypothesis” and known to be the predominant growth mode for microbial communities in natural environmentsStreptococcus, Selenomonas, Shuttleworthia and unclassified Clostridiaceae - were enriched within the rumen contents of bloated steers; whereas other genera such as Ruminococcus and Fibrobacter were associated with baseline alfalfa hay diet. Ruminococcus spp.  and Fibrobacter succinogenes are commonly regarded as the main cellulose-degrading species within the rumen ecosystem27 and are predominantly present in high fiber diets28. In contrast, Streptococcus and Selenomonas are dominant bacterial genera within rumen microbiota of ruminants receiving high soluble carbohydrate diets29 or high quality fresh forage diets with high levels of soluble protein30. Some species of these genera, including Streptococcus bovis and Selenomonas ruminantium, are known to produce and store high amounts of reserve polysaccharides in their cytoplasm which can serve as precursors for extracellular polymeric substances of biofilms32. Previously, Min et al.33 reported the in vitro ability of S. bovis to actively produce biofilms. This same study also revealed increased in vivo biofilm production in rumen ecosystem of steers exposed to wheat forage. Unfortunately, the DGGE approached used by Min et al.33 lacked the required resolution for accurate classification of bacterial genera and their association with bloat.Transition from hay to alfalfa-pasture also resulted in sharp compositional shifts in the rumen microbiota of grazing steers. In particular, our associative analysis revealed that a group of co-occurring bacterial genera within the phylum Firmicutes - including et al.18 observed that Ruminococcus, Lactobacillus, and Prevotella were overrepresented in normal rumen contents, whereas Clostridium, Eubacterium, and Butyrivibrio were enriched within the rumen contents of bloated cattle. The authors concluded that the latter group of bacteria can utilize the oligosaccharides trapped in the biofilm, which they hypothesized could be related to the high proportion of genes within these Firmicutes that contribute to carbohydrate metabolism. In the present study, we observed that bloat-associated enrichment of Streptococcus, Selenomonas, Shuttleworthia and unclassified Clostridiaceae was associated with increased proportion of predicted microbial genes that were annotated to carbohydrate metabolism pathways. These findings led us to speculate that bloat-associated biofilms naturally select for genera that have higher potential for rapid metabolism of soluble carbohydrates trapped within exopolysaccharide slime. Ironically, Pitta et al.18 reported that the abundance and diversity of Carbohydrate Active Enzymes (CAZy) was greatly reduced in the rumen content of bloated steers on wheat pasture. Discrepancies between our observations and those of Pitta et al.18 might have been in part due to possible differences in the etiology and pathogenesis of alfalfa-induced versus wheat-induced frothy bloat, or perhaps because of methodological differences between the two studies. However, it should be noted that metagenomics approaches, either based on whole-genome sequencing or predicted from marker genes, face fundamental limitations to directly reflect the functional activities of microbial communities. Indeed, parallel metagenomics and metatransciptomics investigation of human gut microbiome has revealed that certain microbial clades possess metabolic activities that tend to be consistently overexpressed ; whereas other genes can be consistently under expressed within microbial communities . This demonstrates that the translational activity of a given metagenome is highly dynamic and largely regulated by metabolome profile (i.e. availability/deficiency of certain metabolites in the ecosystem)34.By exploring the dynamics of rumen microbiome during development of wheat-induced frothy bloat, Pitta Streptococcus, Succinivibrio, and Unclass. Myxococcales were consistently enriched in the rumen ecosystem of bloated steers, whereas, Fibrobacter and Ruminococcus were overrepresented in the rumen contents of non-bloated steers. Previously, Pitta et al.18 also observed that the proportion of Ruminococcus was decreased in the rumen content of bloated steers grazing wheat pasture. This, together with our observation regarding simultaneous decreases in proportions of Fibrobacter and Ruminococcus, suggests that development of frothy bloat disfavors the growth of the main fiber-degrading bacterial lineages. On the other hand, we observed that members of the phylum Proteobacteria, including Succinivibrio and Myxococcales were enriched in the rumen content of bloated steers. Proteobacteria have been reported to be enriched during adaptation to high-grain diets36 and appear to be more tolerant of low rumen pH37. Pitta et al.18 also observed increased proportion of the phylum Proteobacteria in the rumen content of bloated steers on wheat pastures. Simultaneous DNA and RNA analyses of rumen microbiome of steers fed grain-based diet38 suggested that metabolic contributions of Proteobacteria are greater than their proportion in rumen community. The metabolic activity of family Myxococcales in the rumen ecosystem is unknown. However, Succinivibrio spp. have been identified as part of the core-rumen microbiota of cattle which predominate during adaptation to high starch diets39. Succinivibrio dextrinosolvens, is a predominant rumen dweller that contributes to fermentation of a variety of carbohydrates and its end products (succinate and formate) can serve as intermediates of rumen fermentation that are further used by other microorganisms40. Our results suggest that Proteobacteria play a central role in development of alfalfa-induced frothy bloat, likely via contribution to rapid metabolism of complex polysaccharides.Multiple comparisons among the composition of rumen microbiota of bloated steers with those that did not develop bloat following dietary interventions led us to identify key bacterial genera that were consistently associated with development of frothy bloat. In particular, we observed that Prevotella was significantly higher in the baseline rumen microbiota of bloat-sensitive steers. Prevotella spp. are predominant rumen dwellers42 which possess extensive amylolytic and proteolytic activities44. Overrepresentation of Prevotella in the rumen microbiota of bloat-sensitive steers led us to speculate that the metabolic activity and end/by products of this bacterial lineage may accelerate the formation and proliferation of bloat-associated biofilms.An important caveat of the present study was the relatively short washout phase (i.e. 7 days) between different treatment periods of our crossover experimental design, which might have resulted in carryover effect of previous treatments and limit the ability of different groups of microbes to proliferate and establish stable populations following introduction to new dietary regimens. Despite this shortcoming, our results suggested that some steers were more susceptible to development of frothy bloat when grazing mixed alfalfa – sainfoin pastures. Comparison of the baseline rumen microbiota of this group of steers with those that were resistant to development of bloat when grazing on mixed alfalfa – sainfoin pastures revealed distinct clustering patterns. In particular, we observed that the proportion of 47. In the present study, transition from baseline diet to alfalfa pasture was associated with moderate shifts in the composition of fecal microbiota of steers. More specifically, we observed an underrepresentation of fiber degrading bacterial lineages such as Fibrobacter and unclassified Ruminococcaceae in the fecal microbiota of bloated steers, which might be indicative of disrupted fermentation profile of the hindgut ecosystem as a result of frothy bloat. However, we failed to observe any notable influence of dietary interventions on the fecal microbiota of bloated versus non-bloated steers grazed on alfalfa pastures. Comparison of baseline fecal microbiota between bloat-sensitive steers with those that did not develop bloat on mixed alfalfa – sainfoin pastures also did not reveal any significant differences. Overall, our data suggest that rapid screening of fecal microbiota does not offer a viable strategy to identify steers that may be susceptible to development of frothy bloat on alfalfa pastures.The bypass of undigested rumen contents to the intestines can modify fermentation profile and microbiota composition of the hindgut ecosystemIn the present study, we described bloat-associated shifts in the composition of rumen and fecal bacterial communities of steers grazing alfalfa pastures. Whereas only slight differences existed between the fecal microbiota of bloated versus non-bloated steers, we observed that the development of frothy bloat was associated with dramatic shifts in the diversity of rumen microbiota, particularly those associated with the solid fraction of rumen contents. Increased species-richness and diversity of fiber-associated microbiota suggested that development of frothy bloat might be in part due to the rapid proliferation of fiber-adherent biofilms. In particular, bacterial genera and predicted functional pathways that are associated with metabolism of complex plant polysaccharides were enriched within the rumen content of bloated steers.Protocol (1214) for this experiment was reviewed and approved by the Animal Care Committee (ACC) of the Lethbridge Research Centre (LRC), Lethbridge, Alberta. Cattle were cared for according to the guidelines of the Canadian Council on Animal Care .−1; Rafter 8 Products Inc., Calgary, AB, Canada) to prevent development of bloat; and (3) steers grazed for 6 h on mixed sainfoin – alfalfa pastures in which a minimum of 15% of the standing pasture dry matter was composed of sainfoin. Within this group, we had two subgroups of (a) steers that did not experience bloat, and (b) steers that did experience clinical bloat. A primary adaptation period (baseline phase) of 3 weeks preceded the experiment during which all steers were fed dry alfalfa hay.Twelve mature (3–4 y old) ruminally-fistulated Angus steers were allocated to a 3 × 3 crossover experimental design, subjecting all animals to three different dietary treatments evenly distributed across three time periods. The treatments included: (1) pure alfalfa pasture; steers grazed on pure stands of alfalfa for 6 h where bloat occurred; (2) steers grazed on pure stands of alfalfa for 6 h but treated with the pluronic detergent ALFASURE , 1 = Slight , 2 = Marked:  look when walking away from the observer), and 3 = Severe . A single steer bloating on one day was counted as one case of bloat. If the assessment indicated that the sum of bloat incidences reached 9 or more in any of the three groups within the sampling phase, then steers were crossed over between pastures and the experiment was repeated until an additional 9 cases of bloat were obtained. For the purpose of this study, rumen and fecal samples collected from steers with a bloat score of 0 were considered as non-bloated and those collected from steers with a bloat score between 1–3 were considered as bloated.The crossover experiment was conducted in 3 periods each divided into an adaptation/washout phase  and sampling phase (4 days: steers grazed on pastures). At the start of the experiment, all groups were released at 0830 into either pure vegetative alfalfa (with and without ALFASURE) or mixed vegetative alfalfa – sainfoin pastures. The steers were allowed to freely graze for 6 h and at 1430 all steers were removed from the pasture and retained in a pen overnight without feed, but with free access to water. On pasture, all steers had access to water, but those steers on the ALFASURE treatment had access only to water that contained 0.25 mL/L of ALFASURE, which on average delivered 9.5 g/d of poloxalene to each animal. Steers were closely monitored every 30 min during grazing and for 2 h after grazing to record the incidences of clinical bloat according to the procedure described by Majak Prior to the grazing experiment, rumen digesta  and fecal samples were collected from each steer at the end of the primary adaptation period (baseline samples). In period 1, after the steers were introduced to the pasture, rumen digesta and fecal samples were collected from steers on 4 consecutive days, immediately after they were removed from their assigned paddock in the afternoon. The experimental procedure for period 2 and 3 were exactly the same as period 1, but steers were crossed among treatment paddocks.Approximately 50 g of ruminal digesta was collected from each of the cranial and caudal ventral sacs of the rumen. Digesta was transferred into a heavy-walled 250-mL beaker and squeezed with a Bodum coffee maker plunger . Aliquots of fluid digesta (5 mL) were placed in aluminum foil dishes and flash-frozen in liquid nitrogen and stored at −80 °C. Solid residue was suspended in 30 mL cold (4 °C) grinding buffer . The suspension was placed in a shallow aluminum foil dish and flash frozen in liquid nitrogen. All samples were stored at -80 °C until further processing.et al.36. Amplicons were verified by agarose gel electrophoresis.To extract genomic DNA, each frozen sample was coarsely ground under liquid nitrogen in a precooled porcelain mortar. Samples were then transferred into a precooled Retsch RM 100 Mortar Grinder equipped with a stainless-steel mortar bowl and pestle  and ground for a further 5 min under liquid nitrogen. Liquid nitrogen was added to the mortar bowl during grinding as needed to maintain the grinding mixture in a semi-fluid state. The ground samples were transferred to a 200-mL wide-mouth centrifuge bottle. The sample was then slowly poured into a 50 mL falcon tube. Genomic DNA was then extracted from 150–250 mg of each sample using QIAamp DNA Stool Mini Kit . The extracted DNA was quality checked using agarose gel electrophoresis and quantified by Picogreen dsDNA . DNA samples were then normalized to 20 ng/µL, and quality checked by PCR amplification of the 16S rRNA gene using universal primers 27F (5′-GAAGAGTTTGATCATGGCTCAG-3′) and 342R (5′-CTGCTGCCTCCCGTAG-3′) as described by Khafipour et al49. PCR reaction for each sample was performed in duplicate and contained 1.0 µL of pre-normalized DNA, 1.0 µL of each forward and reverse primers (10 µM), 12 µL HPLC grade water  and 10 µL 5 Prime Hot MasterMix . Reactions consisted of an initial denaturing step at 94 °C for 3 min followed by 35 amplification cycles at 94 °C for 45 sec, 50 °C for 60 sec, and 72 °C for 90 sec; finalized by an extension step at 72 °C for 10 min in an Eppendorf Mastercycler pro . PCR products were then purified using ZR-96 DNA Clean-up Kit  to remove primers, dNTPs and reaction components. The V3-V4 library was then generated by pooling 200 ng of each sample as quantified by Picogreen dsDNA . This was followed by multiple dilution steps using pre-chilled hybridization buffer (HT1)  to bring the pooled amplicons to a final concentration of 5 pM, as measured by a Qubit 2.0 Fluorometer . Finally, 15% of PhiX control library was spiked into the amplicon pool to improve the unbalanced and biased base composition, a known characteristic of low diversity 16S rRNA libraries. Customized sequencing primers were synthesized and purified by polyacrylamide gel electrophoresis  and added to the MiSeq Reagent Kit V3 (600-cycle) . The 300 paired-end sequencing reaction was performed on a MiSeq platform  at the Gut Microbiome and Large Animal Biosecurity Laboratories, Department of Animal Science, University of Manitoba, Canada. The sequencing data are uploaded into the Sequence Read Archive (SRA) of NCBI (http://www.ncbi.nlm.nih.gov/sra) and can be accessed through accession numbers SRR7350213-SRR7350413. Metadata used for bioinformatics and statistical analyses can be found in Supplementary Table The V3-V4 region of 16S rRNA gene was targeted for PCR amplification using modified primers as described by Derakhshani 50 was used to merge and fix the overlapping paired-end Illumina fastq files. All the sequences with low quality base calling scores as well as those containing uncalled bases (N) in the overlapping region were discarded. The output fastq file was then analyzed by downstream computational pipelines of the open source software package QIIME51. Assembled reads were demultiplexed according to the barcode sequences, chimeric reads were filtered using UCHIME52 and sequences were assigned to Operational Taxonomic Units (OTU) using the QIIME implementation of UCLUST53 at a 97% pairwise identity threshold. Taxonomies were assigned to the representative sequence of each OTU using the RDP classifier54 and aligned with the Greengenes Core reference database55 using PyNAST algorithms56. A phylogenetic tree was built with FastTree 2.1.3.57 for further comparisons between microbial communities. Within community richness (Chao 1 estimator of species richness) and diversity (Shannon’s index) were calculated using QIIME at an even depth of 22,000 and 8,000 sequences per rumen and fecal samples, respectively. To compare microbial composition among samples, β-diversity was measured by calculating the weighted and unweighted UniFrac distances58. Principal coordinate analysis (PCoA) was applied on resulting distance matrices to generate two-dimensional plots using PRIMER v6 software59. Finally, open source software PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states)60 was used to predict the functional genes of the classified members of the rumen microbiota, and assign them to corresponding KEGG61 orthologs (KOs) and pathways (level 2 and 3).The PANDAseq assembler62. The resultant data were visualized over the heatmap of abundance matrix using the R “complexheatmap” package63. Genera were also clustered based on their Spearman’s correlation coefficient using R “complexheatmap” package.To test for discrete clustering pattern of samples, the relative abundance of the OTUs were binned into genus-level taxonomic groups and filtered to retain the most abundant genera across all samples . The resulting relative abundance table was normalized  to correct for compositionality and also aide in the heatmap-visualization of differentially abundant genera. The dissimilarity of samples were calculated based on Bray–Curtis measure using the R “vegan” package and the resulting matrix was subjected to unsupervised hierarchical clustering using the R “dendextend” package64 was used to calculate P-values and test for significant differences of β-diversity  among treatment groups. Label permutations were used in PERMANOVA to estimate the distribution of test statistics under the null hypothesis that within-group distances are not significantly different from between-group distances65.The UNIVARIATE procedure of SAS  was used to test the normality of residuals for alpha biodiversity data. Non-normally distributed data were log transformed and then used to assess the effect of MAP infection using the MIXED procedure of SAS. All pairwise comparisons among the groups were tested using Tukey’s studentized range distribution. Permutational multivariate analysis of variance (PERMANOVA)66) was used to determine significant associations of bacterial genera/functional pathways with treatment groups. MaAsLin included a general linear model with treatment groups as categorical predictor variables and arcsine-square root transformed relative abundances of bacterial genera/functional pathways as the response variable. In addition, MaAsLin also accounted for other potential confounders (covariates) that could affect the profile of microbiota, including rumen fraction (liquid and solid), period, and subject (steers). Multiple hypotheses were adjusted by Benjamini and Hochberg false discovery rate (FDR). Unless otherwise indicated, significant associations were considered below a q-value threshold of 0.05.Multivariate analysis with linear modeling (MaAsLinSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4