The gastrointestinal antibiotic resistome in pediatric leukemia and lymphoma patients.

Introduction: Most children with leukemia and lymphoma experience febrile neutropenia. These are treated with empiric antibiotics that include ß-lactams and/or vancomycin. These are often administered for extended periods, and the effect on the resistome is unknown. Methods: We examined the impact of repeated courses and duration of antibiotic use on the resistome of 39 pediatric leukemia and lymphoma patients. Shotgun metagenome sequences from 127 stool samples of pediatric oncology patients were examined for abundance of antibiotic resistance genes (ARGs) in each sample. Abundances were grouped by repeated courses (no antibiotics, 1-2 courses, 3+ courses) and duration (no use, short duration, long and/or mixed durationg) of ß-lactams, vancomycin and "any antibiotic" use. We assessed changes in both taxonomic composition and prevalence of ARGs among these groups. Results: We found that Bacteroidetes taxa and ß-lactam resistance genes decreased, while opportunistic Firmicutes and Proteobacteria taxa, along with multidrug resistance genes, increased with repeated courses and/or duration of antibiotics. Efflux pump related genes predominated (92%) among the increased multidrug genes. While we found ß-lactam ARGs present in the resistome, the taxa that appear to contain them were kept in check by antibiotic treatment. Multidrug ARGs, mostly efflux pumps or regulators of efflux pump genes, were associated with opportunistic pathogens, and both increased in the resistome with repeated antibiotic use and/or increased duration. Conclusions: Given the strong association between opportunistic pathogens and multidrug-related efflux pumps, we suggest that drug efflux capacity might allow the opportunistic pathogens to persist or increase despite repeated courses and/or duration of antibiotics. While drug efflux is the most direct explanation, other mechanisms that enhance the ability of opportunistic pathogens to handle environmental stress, or other aspects of the treatment environment, could also contribute to their ability to flourish within the gut during treatment. Persistence of opportunistic pathogens in an already dysbiotic and weakened gastrointestinal tract could increase the likelihood of life-threatening blood borne infections. Of the 39 patients, 59% experienced at least one gastrointestinal or blood infection and 60% of bacteremia's were bacteria found in stool samples. Antimicrobial stewardship and appropriate use and duration of antibiotics could help reduce morbidity and mortality in this vulnerable population.

Why are whales big? Genes behind ocean giants.

Gigantism is prevalent in animals, but it has never reached more extreme levels than in aquatic mammals such as whales, dolphins, and porpoises. A new study by Silva et al. has uncovered five genes underlying this gigantism, a phenotype with important connections to aging and cancer suppression in long-lived animals.

Successful Dietary Therapy in Paediatric Crohn's Disease is Associated with Shifts in Bacterial Dysbiosis and Inflammatory Metabotype Towards Healthy Controls.

BACKGROUND AND AIMS: Nutritional therapy with the Crohn's Disease Exclusion Diet + Partial Enteral Nutrition [CDED+PEN] or Exclusive Enteral Nutrition [EEN] induces remission and reduces inflammation in mild-to-moderate paediatric Crohn's disease [CD]. We aimed to assess if reaching remission with nutritional therapy is mediated by correcting compositional or functional dysbiosis. METHODS: We assessed metagenome sequences, short chain fatty acids [SCFA] and bile acids [BA] in 54 paediatric CD patients reaching remission after nutritional therapy [with CDED + PEN or EEN] [NCT01728870], compared to 26 paediatric healthy controls. RESULTS: Successful dietary therapy decreased the relative abundance of Proteobacteria and increased Firmicutes towards healthy controls. CD patients possessed a mixture of two metabotypes [M1 and M2], whereas all healthy controls had metabotype M1. M1 was characterised by high Bacteroidetes and Firmicutes, low Proteobacteria, and higher SCFA synthesis pathways, and M2 was associated with high Proteobacteria and genes involved in SCFA degradation. M1 contribution increased during diet: 48%, 63%, up to 74% [Weeks 0, 6, 12, respectively.]. By Week 12, genera from Proteobacteria reached relative abundance levels of healthy controls with the exception of E. coli. Despite an increase in SCFA synthesis pathways, remission was not associated with increased SCFAs. Primary BA decreased with EEN but not with CDED+PEN, and secondary BA did not change during diet. CONCLUSION: Successful dietary therapy induced correction of both compositional and functional dysbiosis. However, 12 weeks of diet was not enough to achieve complete correction of dysbiosis. Our data suggests that composition and metabotype are important and change quickly during the early clinical response to dietary intervention. Correction of dysbiosis may therefore be an important future treatment goal for CD.

Community-level evolutionary processes: Linking community genetics with replicator-interactor theory.

Understanding community-level selection using Lewontin's criteria requires both community-level inheritance and community-level heritability, and in the discipline of community and ecosystem genetics, these are often conflated. While there are existing studies that show the possibility of both, these studies impose community-level inheritance as a product of the experimental design. For this reason, these experiments provide only weak support for the existence of community-level selection in nature. By contrast, treating communities as interactors (in line with Hull's replicator-interactor framework or Dawkins's idea of the "extended phenotype") provides a more plausible and empirically supportable model for the role of ecological communities in the evolutionary process.

Antibiotic and antifungal use in pediatric leukemia and lymphoma patients are associated with increasing opportunistic pathogens and decreasing bacteria responsible for activities that enhance colonic defense.

Due to decreased immunity, both antibiotics and antifungals are regularly used in pediatric hematologic-cancer patients as a means to prevent severe infections and febrile neutropenia. The general effect of antibiotics on the human gut microbiome is profound, yielding decreased diversity and changes in community structure. However, the specific effect on pediatric oncology patients is not well-studied. The effect of antifungal use is even less understood, having been studied only in mouse models. Because the composition of the gut microbiome is associated with regulation of hematopoiesis, immune function and gastrointestinal integrity, changes within the patient gut can have implications for the clinical management of hematologic malignancies. The pediatric population is particularly challenging because the composition of the microbiome is age dependent, with some of the most pronounced changes occurring in the first three years of life. We investigated how antibiotic and antifungal use shapes the taxonomic composition of the stool microbiome in pediatric patients with leukemia and lymphoma, as inferred from both 16S rRNA and metagenome data. Associations with age, antibiotic use and antifungal use were investigated using multiple analysis methods. In addition, multivariable differential abundance was used to identify and assess specific taxa that were associated with multiple variables. Both antibiotics and antifungals were linked to a general decline in diversity in stool samples, which included a decrease in relative abundance in butyrate producers that play a critical role in host gut physiology (e.g., Faecalibacterium, Anaerostipes, Dorea, Blautia),. Furthermore, antifungal use was associated with a significant increase in relative abundance of opportunistic pathogens. Collectively, these findings have important implications for the treatment of leukemia and lymphoma patients. Butyrate is important for gastrointestinal integrity; it inhibits inflammation, reinforces colonic defense, mucosal immunity. and decreases oxidative stress. The routine use of broad-spectrum anti-infectives in pediatric oncology patients could simultaneously contribute to a decline in gastrointestinal integrity and colonic defense while promoting increases in opportunistic pathogens within the patient gut. Because the gut microbiome has been linked to both short-term clinical outcomes, and longer-lasting health effects, systematic characterization of the gut microbiome in pediatric patients during, and beyond, treatment is warranted.

Novel Application of Survival Models for Predicting Microbial Community Transitions with Variable Selection for Environmental DNA.

Survival analysis is a prolific statistical tool in medicine for inferring risk and time to disease-related events. However, it is underutilized in microbiome research to predict microbial community-mediated events, partly due to the sparsity and high-dimensional nature of the data. We advance the application of Cox proportional hazards (Cox PH) survival models to environmental DNA (eDNA) data with feature selection suitable for filtering irrelevant and redundant taxonomic variables. Selection methods are compared in terms of false positives, sensitivity, and survival estimation accuracy in simulation and in a real data setting to forecast harmful cyanobacterial blooms. A novel extension of a method for selecting microbial biomarkers with survival data (SuRFCox) reliably outperforms other methods. We determine that Cox PH models with SuRFCox-selected predictors are more robust to varied signal, noise, and data correlation structure. SuRFCox also yields the most accurate and consistent prediction of blooms according to cross-validated testing by year over eight different bloom seasons. Identification of common biomarkers among validated survival forecasts over changing conditions has clear biological significance. Survival models with such biomarkers inform risk assessment and provide insight into the causes of critical community transitions. IMPORTANCE In this paper, we report on a novel approach of selecting microorganisms for model-based prediction of the time to critical microbially modulated events (e.g., harmful algal blooms, clinical outcomes, community shifts, etc.). Our novel method for identifying biomarkers from large, dynamic communities of microbes has broad utility to environmental and ecological impact risk assessment and public health. Results will also promote theoretical and practical advancements relevant to the biology of specific organisms. To address the unique challenge posed by diverse environmental conditions and sparse microbes, we developed a novel method of selecting predictors for modeling time-to-event data. Competing methods for selecting predictors are rigorously compared to determine which is the most accurate and generalizable. Model forecasts are applied to show suitable predictors can precisely quantify the risk over time of biological events like harmful cyanobacterial blooms.

Evolution of Amino Acid Propensities under Stability-Mediated Epistasis.

Site-specific amino acid preferences are influenced by the genetic background of the protein. The preferences for resident amino acids are expected to, on average, increase over time because of replacements at other sites-a nonadaptive phenomenon referred to as the "evolutionary Stokes shift." Alternatively, decreases in resident amino acid propensity have recently been viewed as evidence of adaptations to external environmental changes. Using population genetics theory and thermodynamic stability constraints, we show that nonadaptive evolution can lead to both positive and negative shifts in propensities following the fixation of an amino acid, emphasizing that the detection of negative shifts is not conclusive evidence of adaptation. By examining propensity shifts from when an amino acid is first accepted at a site until it is subsequently replaced, we find that ≈50% of sites show a decrease in the propensity for the newly resident amino acid while the remaining sites show an increase. Furthermore, the distributions of the magnitudes of positive and negative shifts were comparable. Preferences were often conserved via a significant negative autocorrelation in propensity changes-increases in propensities often followed by decreases, and vice versa. Lastly, we explore the underlying mechanisms that lead propensities to fluctuate. We observe that stabilizing replacements increase the mutational tolerance at a site and in doing so decrease the propensity for the resident amino acid. In contrast, destabilizing substitutions result in more rugged fitness landscapes that tend to favor the resident amino acid. In summary, our results characterize propensity trajectories under nonadaptive stability-constrained evolution against which evidence of adaptations should be calibrated.

Gut bacterial gene changes following pegaspargase treatment in pediatric patients with acute lymphoblastic leukemia.

Treatment of pediatric acute lymphoblastic leukemia (ALL) with pegaspargase exploits ALL cells dependency on asparagine. Pegaspargase depletes asparagine, consequentially affecting aspartate, glutamine and glutamate. The gut as a confounding source of these amino acids (AAs) and the role of gut microbiome metabolism of AAs has not been examined. We examined asparagine, aspartate, glutamine and glutamate in stool samples from patients over pegaspargase treatment. Microbial gene-products, which interact with these AAs were identified. Stool asparagine declined significantly, and 31 microbial genes changed over treatment. Changes were complex, and included genes involved in AA metabolism, nutrient sensing, and pathways increased in cancers. While we identified changes in a gene (iaaA) with limited asparaginase activity, it lacked significance after correction leaving open other mechanisms for asparagine decline, possibly including loss from gut to blood. Understanding pathways that change AA availability, including by microbes in the gut, could be useful in optimizing pegaspargase therapy.

Shifts in amino acid preferences as proteins evolve: A synthesis of experimental and theoretical work.

Amino acid preferences vary across sites and time. While variation across sites is widely accepted, the extent and frequency of temporal shifts are contentious. Our understanding of the drivers of amino acid preference change is incomplete: To what extent are temporal shifts driven by adaptive versus nonadaptive evolutionary processes? We review phenomena that cause preferences to vary (e.g., evolutionary Stokes shift, contingency, and entrenchment) and clarify how they differ. To determine the extent and prevalence of shifted preferences, we review experimental and theoretical studies. Analyses of natural sequence alignments often detect decreases in homoplasy (convergence and reversions) rates, and variation in replacement rates with time-signals that are consistent with temporally changing preferences. While approaches inferring shifts in preferences from patterns in natural alignments are valuable, they are indirect since multiple mechanisms (both adaptive and nonadaptive) could lead to the observed signal. Alternatively, site-directed mutagenesis experiments allow for a more direct assessment of shifted preferences. They corroborate evidence from multiple sequence alignments, revealing that the preference for an amino acid at a site varies depending on the background sequence. However, shifts in preferences are usually minor in magnitude and sites with significantly shifted preferences are low in frequency. The small yet consistent perturbations in preferences could, nevertheless, jeopardize the accuracy of inference procedures, which assume constant preferences. We conclude by discussing if and how such shifts in preferences might influence widely used time-homogenous inference procedures and potential ways to mitigate such effects.

The role of purifying selection in the origin and maintenance of complex function.

Fitness contribution alone should not be the criterion of 'function' in molecular biology and genomics. Disagreement over the use of 'function' in molecular biology and genomics is still with us, almost eight years after publicity surrounding the Encyclopedia of DNA Elements project claimed that 80.4% of the human genome comprises "functional elements". Recent approaches attempt to resolve or reformulate this debate by redefining genomic 'function' in terms of current fitness contribution. In its favour, this redefinition for the genomic context is in apparent conformity with predominant experimental practices, especially in biomedical research, and with ascription of function by selective maintenance. We argue against approaches of this kind, however, on the grounds that they could be seen as non-Darwinian, and fail to properly account for the diversity of non-adaptive processes involved in the origin and maintenance of genomic complexity. We examine cases of molecular and organismal complexity that arise neutrally, showing how purifying selection maintains non-adaptive genomic complexity. Rather than lumping different sorts of genomic complexity together by defining 'function' as fitness contribution, we argue that it is best to separate the heterogeneous contributions of preaptation, exaptation and adaptation to the historical processes of origin and maintenance for complex features.

Investigating the gut microbial community and genes in children with differing levels of change in serum asparaginase activity during pegaspargase treatment for acute lymphoblastic leukemia.

Asparaginase (ASNase) is an effective treatment of pediatric acute lymphoblastic leukemia (ALL). Changes in ASNase activity may lead to suboptimal treatment and poorer outcomes. The gut microbiome produces metabolites that could impact ASNase therapy, however, remains uninvestigated. We examined gut-microbial community and microbial-ASNase and asparagine synthetase (ASNS) genes using 16SrRNA and metagenomic sequence data from stool samples of pediatric ALL patients. Comparing ASNase activity between consecutive ASNase-doses, we found microbial communities differed between decreased- and increased-activity samples. Escherichia predominated in the decreased-activity community while Bacteroides and Streptococcus predominated in the increased-activity community. In addition microbial ASNS was significantly (p=.004) negatively correlated with change in serum ASNase activity. These preliminary findings suggest microbial communities prior to treatment could affect serum ASNase levels, although the mechanism is unknown. Replication in an independent cohort is needed, and future research on manipulation of these communities and genes could prove useful in optimizing ASNase therapy.

Consequences of Stability-Induced Epistasis for Substitution Rates.

Do interactions between residues in a protein (i.e., epistasis) significantly alter evolutionary dynamics? If so, what consequences might they have on inference from traditional codon substitution models which assume site-independence for the sake of computational tractability? To investigate the effects of epistasis on substitution rates, we employed a mechanistic mutation-selection model in conjunction with a fitness framework derived from protein stability. We refer to this as the stability-informed site-dependent (S-SD) model and developed a new stability-informed site-independent (S-SI) model that captures the average effect of stability constraints on individual sites of a protein. Comparison of S-SI and S-SD offers a novel and direct method for investigating the consequences of stability-induced epistasis on protein evolution. We developed S-SI and S-SD models for three natural proteins and showed that they generate sequences consistent with real alignments. Our analyses revealed that epistasis tends to increase substitution rates compared with the rates under site-independent evolution. We then assessed the epistatic sensitivity of individual site and discovered a counterintuitive effect: Highly connected sites were less influenced by epistasis relative to exposed sites. Lastly, we show that, despite the unrealistic assumptions, traditional models perform comparably well in the presence and absence of epistasis and provide reasonable summaries of average selection intensities. We conclude that epistatic models are critical to understanding protein evolutionary dynamics, but epistasis might not be required for reasonable inference of selection pressure when averaging over time and sites.

Re-evaluating the relationship between missing heritability and the microbiome.

Human genome-wide association studies (GWASs) have recurrently estimated lower heritability estimates than familial studies. Many explanations have been suggested to explain these lower estimates, including that a substantial proportion of genetic variation and gene-by-environment interactions are unmeasured in typical GWASs. The human microbiome is potentially related to both of these explanations, but it has been more commonly considered as a source of unmeasured genetic variation. In particular, it has recently been argued that the genetic variation within the human microbiome should be included when estimating trait heritability. We outline issues with this argument, which in its strictest form depends on the holobiont model of human-microbiome interactions. Instead, we argue that the microbiome could be leveraged to help control for environmental variation across a population, although that remains to be determined. We discuss potential approaches that could be explored to determine whether integrating microbiome sequencing data into GWASs is useful. Video abstract.

Bacterial Taxa and Functions Are Predictive of Sustained Remission Following Exclusive Enteral Nutrition in Pediatric Crohn's Disease.

BACKGROUND: The gut microbiome is extensively involved in induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN). In this follow-up study of pediatric CD patients undergoing treatment with EEN, we employ machine learning models trained on baseline gut microbiome data to distinguish patients who achieved and sustained remission (SR) from those who did not achieve remission nor relapse (non-SR) by 24 weeks. METHODS: A total of 139 fecal samples were obtained from 22 patients (8-15 years of age) for up to 96 weeks. Gut microbiome taxonomy was assessed by 16S rRNA gene sequencing, and functional capacity was assessed by metagenomic sequencing. We used standard metrics of diversity and taxonomy to quantify differences between SR and non-SR patients and to associate gut microbial shifts with fecal calprotectin (FCP), and disease severity as defined by weighted Pediatric Crohn's Disease Activity Index. We used microbial data sets in addition to clinical metadata in random forests (RFs) models to classify treatment response and predict FCP levels. RESULTS: Microbial diversity did not change after EEN, but species richness was lower in low-FCP samples (<250 µg/g). An RF model using microbial abundances, species richness, and Paris disease classification was the best at classifying treatment response (area under the curve [AUC] = 0.9). KEGG Pathways also significantly classified treatment response with the addition of the same clinical data (AUC = 0.8). Top features of the RF model are consistent with previously identified IBD taxa, such as Ruminococcaceae and Ruminococcus gnavus. CONCLUSIONS: Our machine learning approach is able to distinguish SR and non-SR samples using baseline microbiome and clinical data.

The relationship between fecal bile acids and microbiome community structure in pediatric Crohn's disease.

Gut microbiome community structure is associated with Crohn's disease (CD) development and response to therapy. Bile acids (BAs) play a central role in modulating intestinal immune responses, and changes in gut bacterial communities can profoundly alter the intestinal BA pool. The liver synthesizes and conjugates primary bile acids (priBAs) that are then deconjugated, epimerized, and dehydroxylated by gut bacteria to produce secondary bile acids (secBAs). We investigated the relationship between the gut microbiome and the fecal BA pool in stool samples obtained from a well-characterized cohort of pediatric CD patients undergoing nutritional therapy to induce disease remission. We found that fecal BA composition was altered in a sub-group of CD patients who did not sustain remission. The microbial community structures associated with priBA and secBA-dominant profiles were distinct. In addition, the fecal BA concentrations were correlated with the abundance of distinct bacterial taxonomic groups. Finally, priBA dominant samples were associated with community-level decreases in enzymes for dehydroxylation but not deconjugation.

A Phenotype-Genotype Codon Model for Detecting Adaptive Evolution.

A central objective in biology is to link adaptive evolution in a gene to structural and/or functional phenotypic novelties. Yet most analytic methods make inferences mainly from either phenotypic data or genetic data alone. A small number of models have been developed to infer correlations between the rate of molecular evolution and changes in a discrete or continuous life history trait. But such correlations are not necessarily evidence of adaptation. Here, we present a novel approach called the phenotype-genotype branch-site model (PG-BSM) designed to detect evidence of adaptive codon evolution associated with discrete-state phenotype evolution. An episode of adaptation is inferred under standard codon substitution models when there is evidence of positive selection in the form of an elevation in the nonsynonymous-to-synonymous rate ratio $\omega$ to a value $\omega > 1$. As it is becoming increasingly clear that $\omega > 1$ can occur without adaptation, the PG-BSM was formulated to infer an instance of adaptive evolution without appealing to evidence of positive selection. The null model makes use of a covarion-like component to account for general heterotachy (i.e., random changes in the evolutionary rate at a site over time). The alternative model employs samples of the phenotypic evolutionary history to test for phenomenological patterns of heterotachy consistent with specific mechanisms of molecular adaptation. These include 1) a persistent increase/decrease in $\omega$ at a site following a change in phenotype (the pattern) consistent with an increase/decrease in the functional importance of the site (the mechanism); and 2) a transient increase in $\omega$ at a site along a branch over which the phenotype changed (the pattern) consistent with a change in the site's optimal amino acid (the mechanism). Rejection of the null is followed by post hoc analyses to identify sites with strongest evidence for adaptation in association with changes in the phenotype as well as the most likely evolutionary history of the phenotype. Simulation studies based on a novel method for generating mechanistically realistic signatures of molecular adaptation show that the PG-BSM has good statistical properties. Analyses of real alignments show that site patterns identified post hoc are consistent with the specific mechanisms of adaptation included in the alternate model. Further simulation studies show that the covarion-like component of the PG-BSM plays a crucial role in mitigating recently discovered statistical pathologies associated with confounding by accounting for heterotachy-by-any-cause. [Adaptive evolution; branch-site model; confounding; mutation-selection; phenotype-genotype.].

Introduction to Genome Biology and Diversity.

Organisms display astonishing levels of cell and molecular diversity, including genome size, shape, and architecture. In this chapter, we review how the genome can be viewed as both a structural and an informational unit of biological diversity and explicitly define our intended meaning of genetic information. A brief overview of the characteristic features of bacterial, archaeal, and eukaryotic cell types and viruses sets the stage for a review of the differences in organization, size, and packaging strategies of their genomes. We include a detailed review of genetic elements found outside the primary chromosomal structures, as these provide insights into how genomes are sometimes viewed as incomplete informational entities. Lastly, we reassess the definition of the genome in light of recent advancements in our understanding of the diversity of genomic structures and the mechanisms by which genetic information is expressed within the cell. Collectively, these topics comprise a good introduction to genome biology for the newcomer to the field and provide a valuable reference for those developing new statistical or computation methods in genomics. This review also prepares the reader for anticipated transformations in thinking as the field of genome biology progresses.

Looking for Darwin in Genomic Sequences: Validity and Success Depends on the Relationship Between Model and Data.

Codon substitution models (CSMs) are commonly used to infer the history of natural section for a set of protein-coding sequences, often with the explicit goal of detecting the signature of positive Darwinian selection. However, the validity and success of CSMs used in conjunction with the maximum likelihood (ML) framework is sometimes challenged with claims that the approach might too often support false conclusions. In this chapter, we use a case study approach to identify four legitimate statistical difficulties associated with inference of evolutionary events using CSMs. These include: (1) model misspecification, (2) low information content, (3) the confounding of processes, and (4) phenomenological load, or PL. While past criticisms of CSMs can be connected to these issues, the historical critiques were often misdirected, or overstated, because they failed to recognize that the success of any model-based approach depends on the relationship between model and data. Here, we explore this relationship and provide a candid assessment of the limitations of CSMs to extract historical information from extant sequences. To aid in this assessment, we provide a brief overview of: (1) a more realistic way of thinking about the process of codon evolution framed in terms of population genetic parameters, and (2) a novel presentation of the ML statistical framework. We then divide the development of CSMs into two broad phases of scientific activity and show that the latter phase is characterized by increases in model complexity that can sometimes negatively impact inference of evolutionary mechanisms. Such problems are not yet widely appreciated by the users of CSMs. These problems can be avoided by using a model that is appropriate for the data; but, understanding the relationship between the data and a fitted model is a difficult task. We argue that the only way to properly understand that relationship is to perform in silico experiments using a generating process that can mimic the data as closely as possible. The mutation-selection modeling framework (MutSel) is presented as the basis of such a generating process. We contend that if complex CSMs continue to be developed for testing explicit mechanistic hypotheses, then additional analyses such as those described in here (e.g., penalized LRTs and estimation of PL) will need to be applied alongside the more traditional inferential methods.

Crohn's Disease Exclusion Diet Plus Partial Enteral Nutrition Induces Sustained Remission in a Randomized Controlled Trial.

BACKGROUND & AIMS: Exclusive enteral nutrition (EEN) is recommended for children with mild to moderate Crohn's disease (CD), but implementation is challenging. We compared EEN with the CD exclusion diet (CDED), a whole-food diet coupled with partial enteral nutrition (PEN), designed to reduce exposure to dietary components that have adverse effects on the microbiome and intestinal barrier. METHODS: We performed a 12-week prospective trial of children with mild to moderate CD. The children were randomly assigned to a group that received CDED plus 50% of calories from formula (Modulen, Nestlé) for 6 weeks (stage 1) followed by CDED with 25% PEN from weeks 7 to 12 (stage 2) (n = 40, group 1) or a group that received EEN for 6 weeks followed by a free diet with 25% PEN from weeks 7 to 12 (n = 38, group 2). Patients were evaluated at baseline and weeks 3, 6, and 12 and laboratory tests were performed; 16S ribosomal RNA gene (V4V5) sequencing was performed on stool samples. The primary endpoint was dietary tolerance. Secondary endpoints were intention to treat (ITT) remission at week 6 (pediatric CD activity index score below 10) and corticosteroid-free ITT sustained remission at week 12. RESULTS: Four patients withdrew from the study because of intolerance by 48 hours, 74 patients (mean age 14.2 ± 2.7 years) were included for remission analysis. The combination of CDED and PEN was tolerated in 39 children (97.5%), whereas EEN was tolerated by 28 children (73.6%) (P = .002; odds ratio for tolerance of CDED and PEN, 13.92; 95% confidence interval [CI] 1.68-115.14). At week 6, 30 (75%) of 40 children given CDED plus PEN were in corticosteroid-free remission vs 20 (59%) of 34 children given EEN (P = .38). At week 12, 28 (75.6%) of 37 children given CDED plus PEN were in corticosteroid-free remission compared with 14 (45.1%) of 31 children given EEN and then PEN (P = .01; odds ratio for remission in children given CDED and PEN, 3.77; CI 1.34-10.59). In children given CDED plus PEN, corticosteroid-free remission was associated with sustained reductions in inflammation (based on serum level of C-reactive protein and fecal level of calprotectin) and fecal Proteobacteria. CONCLUSION: CDED plus PEN was better tolerated than EEN in children with mild to moderate CD. Both diets were effective in inducing remission by week 6. The combination CDED plus PEN induced sustained remission in a significantly higher proportion of patients than EEN, and produced changes in the fecal microbiome associated with remission. These data support use of CDED plus PEN to induce remission in children with CD. Clinicaltrials.gov no: NCT01728870.

Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates.

BACKGROUND: An excess of nonsynonymous substitutions, over neutrality, is considered evidence of positive Darwinian selection. Inference for proteins often relies on estimation of the nonsynonymous to synonymous ratio (ω = dN/dS) within a codon model. However, to ease computational difficulties, ω is typically estimated assuming an idealized substitution process where (i) all nonsynonymous substitutions have the same rate (regardless of impact on organism fitness) and (ii) instantaneous double and triple (DT) nucleotide mutations have zero probability (despite evidence that they can occur). It follows that estimates of ω represent an imperfect summary of the intensity of selection, and that tests based on the ω > 1 threshold could be negatively impacted. RESULTS: We developed a general-purpose parametric (GPP) modelling framework for codons. This novel approach allows specification of all possible instantaneous codon substitutions, including multiple nonsynonymous rates (MNRs) and instantaneous DT nucleotide changes. Existing codon models are specified as special cases of the GPP model. We use GPP models to implement likelihood ratio tests for ω > 1 that accommodate MNRs and DT mutations. Through both simulation and real data analysis, we find that failure to model MNRs and DT mutations reduces power in some cases and inflates false positives in others. False positives under traditional M2a and M8 models were very sensitive to DT changes. This was exacerbated by the choice of frequency parameterization (GY vs. MG), with rates sometimes > 90% under MG. By including MNRs and DT mutations, accuracy and power was greatly improved under the GPP framework. However, we also find that over-parameterized models can perform less well, and this can contribute to degraded performance of LRTs. CONCLUSIONS: We suggest GPP models should be used alongside traditional codon models. Further, all codon models should be deployed within an experimental design that includes (i) assessing robustness to model assumptions, and (ii) investigation of non-standard behaviour of MLEs. As the goal of every analysis is to avoid false conclusions, more work is needed on model selection methods that consider both the increase in fit engendered by a model parameter and the degree to which that parameter is affected by un-modelled evolutionary processes.