Biomathematics and Statistics Scotland

The populations of dominant species within the human colonic microbiota can potentially be modified by dietary intake with consequences for health. Here we examined the influence of precisely controlled diets in 14 overweight men. Volunteers were provided successively with a control diet, diets high in resistant starch (RS) or non-starch polysaccharides (NSPs) and a reduced carbohydrate weight loss (WL) diet, over 10 weeks. Analysis of 16S rRNA sequences in stool samples of six volunteers detected 320 phylotypes (defined at >98% identity) of which 26, including 19 cultured species, each accounted for >1% of sequences. Although samples clustered more strongly by individual than by diet, time courses obtained by targeted qPCR revealed that 'blooms' in specific bacterial groups occurred rapidly after a dietary change. These were rapidly reversed by the subsequent diet. Relatives of Ruminococcus bromii (R-ruminococci) increased in most volunteers on the RS diet, accounting for a mean of 17% of total bacteria compared with 3.8% on the NSP diet, whereas the uncultured Oscillibacter group increased on the RS and WL diets. Relatives of Eubacterium rectale increased on RS (to mean 10.1%) but decreased, along with Collinsella aerofaciens, on WL. Inter-individual variation was marked, however, with >60% of RS remaining unfermented in two volunteers on the RS diet, compared to <4% in the other 12 volunteers; these two individuals also showed low numbers of R-ruminococci (<1%). Dietary non-digestible carbohydrate can produce marked changes in the gut microbiota, but these depend on the initial composition of an individual's gut microbiota.

The associations between time spent in sleep, sedentary behaviors (SB) and physical activity with health are usually studied without taking into account that time is finite during the day, so time spent in each of these behaviors are codependent. Therefore, little is known about the combined effect of time spent in sleep, SB and physical activity, that together constitute a composite whole, on obesity and cardio-metabolic health markers. Cross-sectional analysis of NHANES 2005-6 cycle on N = 1937 adults, was undertaken using a compositional analysis paradigm, which accounts for this intrinsic codependence. Time spent in SB, light intensity (LIPA) and moderate to vigorous activity (MVPA) was determined from accelerometry and combined with self-reported sleep time to obtain the 24 hour time budget composition. The distribution of time spent in sleep, SB, LIPA and MVPA is significantly associated with BMI, waist circumference, triglycerides, plasma glucose, plasma insulin (all p<0.001), and systolic (p<0.001) and diastolic blood pressure (p<0.003), but not HDL or LDL. Within the composition, the strongest positive effect is found for the proportion of time spent in MVPA. Strikingly, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior are asymmetric. For example, re-allocating 10 minutes of SB to MVPA was associated with a lower waist circumference by 0.001% but if 10 minutes of MVPA is displaced by SB this was associated with a 0.84% higher waist circumference. The proportion of time spent in LIPA and SB were detrimentally associated with obesity and cardiovascular disease markers, but the association with SB was stronger. For diabetes risk markers, replacing SB with LIPA was associated with more favorable outcomes. Time spent in MVPA is an important target for intervention and preventing transfer of time from LIPA to SB might lessen the negative effects of physical inactivity.

Consider a random sample on variables X1, …, Xv with some values of Xv missing. Selection models specify the distribution of X1 , …, XV over respondents and nonrespondents to Xv , and the conditional distribution that Xv is missing given X1 , …, Xv . In contrast, pattern-mixture models specify the conditional distribution of X 1, …, Xv given that XV is observed or missing respectively and the marginal distribution of the binary indicator for whether or not Xv is missing. For multivariate data with a general pattern of missing values, the literature has tended to adopt the selection-modeling approach (see for example Little and Rubin); here, pattern-mixture models are proposed for this more general problem. Pattern-mixture models are chronically underidentified; in particular for the case of univariate nonresponse mentioned above, there are no data on the distribution of Xv given X1 , …, XV–1 , in the stratum with Xv missing. Thus the models require restrictions or prior information to identify the parameters. Complete-case restrictions tie unidentified parameters to their (identified) analogs in the stratum of complete cases. Alternative types of restriction tie unidentified parameters to parameters in other missing-value patterns or sets of such patterns. This large set of possible identifying restrictions yields a rich class of missing-data models. Unlike ignorable selection models, which generally requires iterative methods except for special missing-data patterns, some pattern-mixture models yield explicit ML estimates for general patterns. Such models are readily amenable to Bayesian methods and form a convenient basis for multiple imputation. Some previously considered noniterative estimation methods are shown to be maximum likelihood (ML) under a pattern-mixture model. For example, Buck's method for continuous data, corrected as in Beale and Little (1975), and Brown's estimators for nonrandomly missing data are ML for pattern-mixture models with particular complete-case restrictions. Available-case analyses, where the mean and variance of Xj are computed using all cases with Xj observed and the correlation (or covariance) of Xj and Xk is computed using all cases with Xj and Xk observed, are also close to ML for another pattern-mixture model. Asymptotic theory for this class of estimators is outlined.

Weight loss diets for humans that are based on a high intake of protein but low intake of fermentable carbohydrate may alter microbial activity and bacterial populations in the large intestine and thus impact on gut health. In this study, 19 healthy, obese (body mass index range, 30 to 42) volunteers were given in succession three different diets: maintenance (M) for 3 days (399 g carbohydrate/day) and then high protein/medium (164 g/day) carbohydrate (HPMC) and high protein/low (24 g/day) carbohydrate (HPLC) each for 4 weeks. Stool samples were collected at the end of each dietary regimen. Total fecal short-chain fatty acids were 114 mM, 74 mM, and 56 mM (P < 0.001) for M, HPMC, and HPLC diets, respectively, and there was a disproportionate reduction in fecal butyrate (18 mM, 9 mM, and 4 mM, respectively; P < 0.001) with decreasing carbohydrate. Major groups of fecal bacteria were monitored using nine 16S rRNA-targeted fluorescence in situ hybridization probes, relative to counts obtained with the broad probe Eub338. No significant change was seen in the relative counts of the bacteroides (Bac303) (mean, 29.6%) or the clostridial cluster XIVa (Erec482, 23.3%), cluster IX (Prop853, 9.3%), or cluster IV (Fprau645, 11.6%; Rbro730 plus Rfla729, 9.3%) groups. In contrast, the Roseburia spp. and Eubacterium rectale subgroup of cluster XIVa (11%, 8%, and 3% for M, HPMC, and HPLC, respectively; P < 0.001) and bifidobacteria (4%, 2.1%, and 1.9%, respectively; P = 0.026) decreased as carbohydrate intake decreased. The abundance of butyrate-producing bacteria related to Roseburia spp. and E. rectale correlated well with the decline in fecal butyrate.

Sole-carbon-source tests (Biolog), designed to identify bacteria, have become very popular for metabolically fingerprinting soil microbial communities, despite disadvantages associated with the use of carbon source profiles that primarily select for fast-growing bacteria. In this paper we describe the use of an alternative method that combines the advantages of the Biolog community-level physiological profile (CLPP) method, in which microtiter-based detection plates are used, with the ability to measure carbon dioxide evolution from whole soil. This method facilitates measurement over short periods of time (4 to 6 h) and does not require the extraction and culturing of organisms. Deep-well microtiter plates are used as test wells into which soil is placed. The apparatus to fill the deep-well plates and interface it with a second removable detection plate is described. Two detection systems, a simple colorimetric reaction in absorbent alkali and scintillation counting with radioactive carbon sources, are described. The methods were compared to the Biolog-CLPP system by using soils under different vegetation types and soil treated with wastewater sludge. We aimed to test the hypothesis that using whole soil would have specific advantages over using extracts in that more immediate responses to substrates could be obtained that would reflect activity rather than growth. The whole-soil method was more rapid and gave earlier detection of C source use. Also, the metabolic fingerprints obtained could discriminate between sludge treatments.

Prebiotics are food ingredients that improve health by modulating the colonic microbiota. The bifidogenic effect of the prebiotic inulin is well established; however, it remains unclear which species of Bifidobacterium are stimulated in vivo and whether bacterial groups other than lactic acid bacteria are affected by inulin consumption. Changes in the faecal microbiota composition were examined by real-time PCR in twelve human volunteers after ingestion of inulin (10 g/d) for a 16-d period in comparison with a control period without any supplement intake. The prevalence of most bacterial groups examined did not change after inulin intake, although the low G+C % Gram-positive species Faecalibacterium prausnitzii exhibited a significant increase (10.3% for control period v. 14.5% during inulin intake, P=0.019). The composition of the genus Bifidobacterium was studied in four of the volunteers by clone library analysis. Between three and five Bifidobacterium spp. were found in each volunteer. Bifidobacterium adolescentis and Bifidobacterium longum were present in all volunteers, and Bifidobacterium pseudocatenulatum, Bifidobacterium animalis, Bifidobacterium bifidum and Bifidobacterium dentium were also detected. Real-time PCR was employed to quantify the four most prevalent Bifidobacterium spp., B. adolescentis, B. longum, B. pseudocatenulatum and B. bifidum, in ten volunteers carrying detectable levels of bifidobacteria. B. adolescentis showed the strongest response to inulin consumption, increasing from 0.89 to 3.9% of the total microbiota (P=0.001). B. bifidum was increased from 0.22 to 0.63% (P<0.001) for the five volunteers for whom this species was present.

Dietary carbohydrates have the potential to influence diverse functional groups of bacteria within the human large intestine. Of 12 Bifidobacterium strains of human gut origin from seven species tested, four grew in pure culture on starch and nine on fructo-oligosaccharides. The potential for metabolic cross-feeding between Bifidobacterium adolescentis and lactate-utilizing, butyrate-producing Firmicute bacteria related to Eubacterium hallii and Anaerostipes caccae was investigated in vitro. E. hallii L2-7 and A. caccae L1-92 failed to grow on starch in pure culture, but in coculture with B. adolescentis L2-32 butyrate was formed, indicating cross-feeding of metabolites to the lactate utilizers. Studies with [(13)C]lactate confirmed carbon flow from lactate, via acetyl coenzyme A, to butyrate both in pure cultures of E. hallii and in cocultures with B. adolescentis. Similar results were obtained in cocultures involving B. adolescentis DSM 20083 with fructo-oligosaccharides as the substrate. Butyrate formation was also stimulated, however, in cocultures of B. adolescentis L2-32 grown on starch or fructo-oligosaccharides with Roseburia sp. strain A2-183, which produces butyrate but does not utilize lactate. This is probably a consequence of the release by B. adolescentis of oligosaccharides that are available to Roseburia sp. strain A2-183. We conclude that two distinct mechanisms of metabolic cross-feeding between B. adolescentis and butyrate-forming bacteria may operate in gut ecosystems, one due to consumption of fermentation end products (lactate and acetate) and the other due to cross-feeding of partial breakdown products from complex substrates.

Butyrate-producing bacteria play an important role in the human colon, supplying energy to the gut epithelium and regulating host cell responses. In order to explore the diversity and culturability of this functional group, we designed degenerate primers to amplify butyryl-CoA:acetate CoA-transferase sequences from faecal samples provided by 10 healthy volunteers. Eighty-eight per cent of amplified sequences showed >98% DNA sequence identity to CoA-transferases from cultured butyrate-producing bacteria, and these fell into 12 operational taxonomic units (OTUs). The four most prevalent OTUs corresponded to Eubacterium rectale, Roseburia faecis, Eubacterium hallii and an unnamed cultured species SS2/1. The remaining 12% of sequences, however, belonged to 20 OTUs that are assumed to come from uncultured butyrate-producing strains. Samples taken after ingestion of inulin showed significant (P=0.019) increases in Faecalibacterium prausnitzii. Because several of the dominant butyrate producers differ in their DNA % G+C content, analysis of thermal melt curves obtained for PCR amplicons of the butyryl-CoA:acetate CoA-transferase gene provides a convenient and rapid qualitative assessment of the major butyrate producing groups present in a given sample. This type of analysis therefore provides an excellent source of information on functionally important groups within the colonic microbial community.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

SUMMARY: Tablet is a lightweight, high-performance graphical viewer for next-generation sequence assemblies and alignments. Supporting a range of input assembly formats, Tablet provides high-quality visualizations showing data in packed or stacked views, allowing instant access and navigation to any region of interest, and whole contig overviews and data summaries. Tablet is both multi-core aware and memory efficient, allowing it to handle assemblies containing millions of reads, even on a 32-bit desktop machine. AVAILABILITY: Tablet is freely available for Microsoft Windows, Apple Mac OS X, Linux and Solaris. Fully bundled installers can be downloaded from http://bioinf.scri.ac.uk/tablet in 32- and 64-bit versions.

Contact with green space in the environment has been associated with mental health benefits, but the mechanism underpinning this association is not clear. This study extends an earlier exploratory study showing that more green space in deprived urban neighbourhoods in Scotland is linked to lower levels of perceived stress and improved physiological stress as measured by diurnal patterns of cortisol secretion. Salivary cortisol concentrations were measured at 3, 6 and 9 h post awakening over two consecutive weekdays, together with measures of perceived stress. Participants (n = 106) were men and women not in work aged between 35-55 years, resident in socially disadvantaged districts from the same Scottish, UK, urban context as the earlier study. Results from linear regression analyses showed a significant and negative relationship between higher green space levels and stress levels, indicating living in areas with a higher percentage of green space is associated with lower stress, confirming the earlier study findings. This study further extends the findings by showing significant gender differences in stress patterns by levels of green space, with women in lower green space areas showing higher levels of stress. A significant interaction effect between gender and percentage green space on mean cortisol concentrations showed a positive effect of higher green space in relation to cortisol measures in women, but not in men. Higher levels of neighbourhood green space were associated with healthier mean cortisol levels in women whilst also attenuating higher cortisol levels in men. We conclude that higher levels of green space in residential neighbourhoods, for this deprived urban population of middle-aged men and women not in work, are linked with lower perceived stress and a steeper (healthier) diurnal cortisol decline. However, overall patterns and levels of cortisol secretion in men and women were differentially related to neighbourhood green space and warrant further investigation.

This present paper reviews the reliability and validity of visual analogue scales (VAS) in terms of (1) their ability to predict feeding behaviour, (2) their sensitivity to experimental manipulations, and (3) their reproducibility. VAS correlate with, but do not reliably predict, energy intake to the extent that they could be used as a proxy of energy intake. They do predict meal initiation in subjects eating their normal diets in their normal environment. Under laboratory conditions, subjectively rated motivation to eat using VAS is sensitive to experimental manipulations and has been found to be reproducible in relation to those experimental regimens. Other work has found them not to be reproducible in relation to repeated protocols. On balance, it would appear, in as much as it is possible to quantify, that VAS exhibit a good degree of within-subject reliability and validity in that they predict with reasonable certainty, meal initiation and amount eaten, and are sensitive to experimental manipulations. This reliability and validity appears more pronounced under the controlled (but more artificial) conditions of the laboratory where the signal:noise ratio in experiments appears to be elevated relative to real life. It appears that VAS are best used in within-subject, repeated-measures designs where the effect of different treatments can be compared under similar circumstances. They are best used in conjunction with other measures (e.g. feeding behaviour, changes in plasma metabolites) rather than as proxies for these variables. New hand-held electronic appetite rating systems (EARS) have been developed to increase reliability of data capture and decrease investigator workload. Recent studies have compared these with traditional pen and paper (P&P) VAS. The EARS have been found to be sensitive to experimental manipulations and reproducible relative to P&P. However, subjects appear to exhibit a significantly more constrained use of the scale when using the EARS relative to the P&P. For this reason it is recommended that the two techniques are not used interchangeably.

UNLABELLED: TOPALi v2 simplifies and automates the use of several methods for the evolutionary analysis of multiple sequence alignments. Jobs are submitted from a Java graphical user interface as TOPALi web services to either run remotely on high-performance computing clusters or locally (with multiple cores supported). Methods available include model selection and phylogenetic tree estimation using the Bayesian inference and maximum likelihood (ML) approaches, in addition to recombination detection methods. The optimal substitution model can be selected for protein or nucleic acid (standard, or protein-coding using a codon position model) data using accurate statistical criteria derived from ML co-estimation of the tree and the substitution model. Phylogenetic software available includes PhyML, RAxML and MrBayes. AVAILABILITY: Freely downloadable from http://www.topali.org for Windows, Mac OS X, Linux and Solaris.

There is growing interest in understanding how diet affects the intestinal microbiota, including its possible associations with systemic diseases such as metabolic syndrome. Here we report a comprehensive and deep microbiota analysis of 14 obese males consuming fully controlled diets supplemented with resistant starch (RS) or non-starch polysaccharides (NSPs) and a weight-loss (WL) diet. We analyzed the composition, diversity and dynamics of the fecal microbiota on each dietary regime by phylogenetic microarray and quantitative PCR (qPCR) analysis. In addition, we analyzed fecal short chain fatty acids (SCFAs) as a proxy of colonic fermentation, and indices of insulin sensitivity from blood samples. The diet explained around 10% of the total variance in microbiota composition, which was substantially less than the inter-individual variance. Yet, each of the study diets induced clear and distinct changes in the microbiota. Multiple Ruminococcaceae phylotypes increased on the RS diet, whereas mostly Lachnospiraceae phylotypes increased on the NSP diet. Bifidobacteria decreased significantly on the WL diet. The RS diet decreased the diversity of the microbiota significantly. The total 16S ribosomal RNA gene signal estimated by qPCR correlated positively with the three major SCFAs, while the amount of propionate specifically correlated with the Bacteroidetes. The dietary responsiveness of the individual's microbiota varied substantially and associated inversely with its diversity, suggesting that individuals can be stratified into responders and non-responders based on the features of their intestinal microbiota.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

MOTIVATION: Bayesian networks have been applied to infer genetic regulatory interactions from microarray gene expression data. This inference problem is particularly hard in that interactions between hundreds of genes have to be learned from very small data sets, typically containing only a few dozen time points during a cell cycle. Most previous studies have assessed the inference results on real gene expression data by comparing predicted genetic regulatory interactions with those known from the biological literature. This approach is controversial due to the absence of known gold standards, which renders the estimation of the sensitivity and specificity, that is, the true and (complementary) false detection rate, unreliable and difficult. The objective of the present study is to test the viability of the Bayesian network paradigm in a realistic simulation study. First, gene expression data are simulated from a realistic biological network involving DNAs, mRNAs, inactive protein monomers and active protein dimers. Then, interaction networks are inferred from these data in a reverse engineering approach, using Bayesian networks and Bayesian learning with Markov chain Monte Carlo. RESULTS: The simulation results are presented as receiver operator characteristics curves. This allows estimating the proportion of spurious gene interactions incurred for a specified target proportion of recovered true interactions. The findings demonstrate how the network inference performance varies with the training set size, the degree of inadequacy of prior assumptions, the experimental sampling strategy and the inclusion of further, sequence-based information. AVAILABILITY: The programs and data used in the present study are available from http://www.bioss.sari.ac.uk/~dirk/Supplements

Biodiversity is a cornerstone of human health and well-being. However, while evidence of the contributions of nature to human health is rapidly building, research into how biodiversity relates to human health remains limited in important respects. In particular, a better mechanistic understanding of the range of pathways through which biodiversity can influence human health is needed. These pathways relate to both psychological and social processes as well as biophysical processes. Building on evidence from across the natural, social and health sciences, we present a conceptual framework organizing the pathways linking biodiversity to human health. Four domains of pathways-both beneficial as well as harmful-link biodiversity with human health: (i) reducing harm (e.g. provision of medicines, decreasing exposure to air and noise pollution); (ii) restoring capacities (e.g. attention restoration, stress reduction); (iii) building capacities (e.g. promoting physical activity, transcendent experiences); and (iv) causing harm (e.g. dangerous wildlife, zoonotic diseases, allergens). We discuss how to test components of the biodiversity-health framework with available analytical approaches and existing datasets. In a world with accelerating declines in biodiversity, profound land-use change, and an increase in non-communicable and zoonotic diseases globally, greater understanding of these pathways can reinforce biodiversity conservation as a strategy for the promotion of health for both people and nature. We conclude by identifying research avenues and recommendations for policy and practice to foster biodiversity-focused public health actions.

Escherichia coli O157:H7 causes bloody diarrhea and potentially fatal systemic sequelae in humans. Cattle are most frequently identified as the primary source of infection, and E. coli O157:H7 generally colonizes the gastrointestinal tracts of cattle without causing disease. In this study, persistence and tropism were assessed for four different E. coli O157:H7 strains. Experimentally infected calves shed the organism for at least 14 days prior to necropsy. For the majority of these animals, as well as for a naturally colonized animal obtained from a commercial beef farm, the highest numbers of E. coli O157:H7 were found in the feces, with negative or significantly lower levels detected in lumen contents taken from the gastrointestinal tract. Detailed examination demonstrated that in these individuals the majority of tissue-associated bacteria were adherent to mucosal epithelium within a defined region extending up to 5 cm proximally from the recto-anal junction. The tissue targeted by E. coli O157:H7 was characterized by a high density of lymphoid follicles. Microcolonies of the bacterium were readily detected on the epithelium of this region by immunofluorescence microscopy. As a consequence of this specific distribution, E. coli O157:H7 was present predominantly on the surface of the fecal stool. In contrast, other E. coli serotypes were present at consistent levels throughout the large intestine and were equally distributed in the stool. This is a novel tropism that may enhance dissemination both between animals and from animals to humans. The accessibility of this site may facilitate simple intervention strategies.

Understanding genome organization and gene regulation requires insight into RNA transcription, processing and modification. We adapted nanopore direct RNA sequencing to examine RNA from a wild-type accession of the model plant Arabidopsis thaliana and a mutant defective in mRNA methylation (m6A). Here we show that m6A can be mapped in full-length mRNAs transcriptome-wide and reveal the combinatorial diversity of cap-associated transcription start sites, splicing events, poly(A) site choice and poly(A) tail length. Loss of m6A from 3’ untranslated regions is associated with decreased relative transcript abundance and defective RNA 3′ end formation. A functional consequence of disrupted m6A is a lengthening of the circadian period. We conclude that nanopore direct RNA sequencing can reveal the complexity of mRNA processing and modification in full-length single molecule reads. These findings can refine Arabidopsis genome annotation. Further, applying this approach to less well-studied species could transform our understanding of what their genomes encode.

Alternative splicing (AS) coupled to nonsense-mediated decay (NMD) is a post-transcriptional mechanism for regulating gene expression. We have used a high-resolution AS RT-PCR panel to identify endogenous AS isoforms which increase in abundance when NMD is impaired in the Arabidopsis NMD factor mutants, upf1-5 and upf3-1. Of 270 AS genes (950 transcripts) on the panel, 102 transcripts from 97 genes (32%) were identified as NMD targets. Extrapolating from these data around 13% of intron-containing genes in the Arabidopsis genome are potentially regulated by AS/NMD. This cohort of naturally occurring NMD-sensitive AS transcripts also allowed the analysis of the signals for NMD in plants. We show the importance of AS in introns in 5' or 3'UTRs in modulating NMD-sensitivity of mRNA transcripts. In particular, we identified upstream open reading frames overlapping the main start codon as a new trigger for NMD in plants and determined that NMD is induced if 3'-UTRs were >350 nt. Unexpectedly, although many intron retention transcripts possess NMD features, they are not sensitive to NMD. Finally, we have shown that AS/NMD regulates the abundance of transcripts of many genes important for plant development and adaptation including transcription factors, RNA processing factors and stress response genes.