Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale ...studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.
Crohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have ...mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.
We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.
We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.
Abstract
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.
Here, authors present a follow-up on a cohort of 22 pediatric CD patients undergoing EEN treatment to induce remission. They leverage machine learning techniques to show that the baseline microbiome with clinical metadata can predict sustained remission.
The mechanisms underlying the complications of mild traumatic brain injury, including post-concussion syndrome, post-impact catastrophic death, and delayed neurodegeneration, remain poorly ...understood. This limited pathophysiological understanding has hindered the development of diagnostic and prognostic biomarkers and has prevented the advancement of treatments for the sequelae of mild traumatic brain injury. We aimed to characterize the early electrophysiological and neurovascular alterations following repetitive mild traumatic brain injury and sought to identify new targets for the diagnosis and treatment of individuals at risk of severe post-impact complications. We combined behavioural, electrophysiological, molecular, and neuroimaging techniques in a rodent model of repetitive mild traumatic brain injury. In humans, we used dynamic contrast-enhanced MRI to quantify blood-brain barrier dysfunction after exposure to sport-related concussive mild traumatic brain injury. Rats could clearly be classified based on their susceptibility to neurological complications, including life-threatening outcomes, following repetitive injury. Susceptible animals showed greater neurological complications and had higher levels of blood-brain barrier dysfunction, transforming growth factor β signaling, and neuroinflammation compared to resilient animals. Cortical spreading depolarizations were the most common electrophysiological events immediately following mild traumatic brain injury and were associated with longer recovery from impact. Triggering cortical spreading depolarizations in mild traumatic brain injured rats (but not in controls) induced blood-brain barrier dysfunction. Treatment with a selective transforming growth factor β receptor inhibitor prevented blood-brain barrier opening and reduced injury complications. Consistent with the rodent model, blood-brain barrier dysfunction was found in a subset of human athletes following concussive mild traumatic brain injury. We provide evidence that cortical spreading depolarization, blood-brain barrier dysfunction, and pro-inflammatory transforming growth factor β signaling are associated with severe, potentially life-threatening outcomes, following repetitive mild traumatic brain injury. Diagnostic-coupled targeting of transforming growth factor β signaling may be a novel strategy in treating mild traumatic brain injury.
Abstract There has been a dramatic rise in gene×environment studies of human behavior over the past decade that have moved the field beyond simple nature versus nurture debates. These studies offer ...promise in accounting for more variability in behavioral and biological phenotypes than studies that focus on genetic or experiential factors alone. They also provide clues into mechanisms of modifying genetic risk or resilience in neurodevelopmental disorders. Yet, it is rare that these studies consider how these interactions change over the course of development. In this paper, we describe research that focuses on the impact of a polymorphism in a brain-derived neurotrophic factor (BDNF) gene, known to be involved in learning and development. Specifically we present findings that assess the effects of genotypic and environmental loadings on neuroanatomic and behavioral phenotypes across development. The findings illustrate the use of a genetic mouse model that mimics the human polymorphism, to constrain the interpretation of gene–environment interactions across development in humans.
To investigate the link between dysfunction of the blood-brain barrier (BBB) and exposure to head impacts in concussed football athletes.
This was a prospective, observational pilot study.
Canadian ...university football.
The study population consisted of 60 university football players, aged 18 to 25. Athletes who sustained a clinically diagnosed concussion over the course of a single football season were invited to undergo an assessment of BBB leakage.
Head impacts detected using impact-sensing helmets were the measured variables.
Clinical diagnosis of concussion and BBB leakage assessed using dynamic contrast-enhanced MRI (DCE-MRI) within 1 week of concussion were the outcome measures.
Eight athletes were diagnosed with a concussion throughout the season. These athletes sustained a significantly higher number of head impacts than nonconcussed athletes. Athletes playing in the defensive back position were significantly more likely to sustain a concussion than remain concussion free. Five of the concussed athletes underwent an assessment of BBB leakage. Logistic regression analysis indicated that region-specific BBB leakage in these 5 athletes was best predicted by impacts sustained in all games and practices leading up to the concussion-as opposed to the last preconcussion impact or the impacts sustained during the game when concussion occurred.
These preliminary findings raise the potential for the hypothesis that repeated exposure to head impacts may contribute to the development of BBB pathology. Further research is needed to validate this hypothesis and to test whether BBB pathology plays a role in the sequela of repeated head trauma.
Adolescence is a developmental period that entails substantial changes in risk-taking behavior and experimentation with alcohol and drugs. Understanding how the brain is changing during this period ...relative to childhood and adulthood and how these changes vary across individuals are key in predicting risk for later substance abuse and dependence.
This review discusses recent human imaging and animal work in the context of an emerging view of adolescence as characterized by a tension between early emerging "bottom-up" systems that express exaggerated reactivity to motivational stimuli and later maturing "top-down" cognitive control regions. Behavioral, clinical, and neurobiological evidences are reported for dissociating these two systems developmentally. The literature on the effects of alcohol and its rewarding properties in the brain is discussed in the context of these two systems.
Collectively, these studies show curvilinear development of motivational behavior and the underlying subcortical brain regions, with a peak inflection from 13 to 17 years. In contrast, prefrontal regions, important in top-down regulation of behavior, show a linear pattern of development well into young adulthood that parallels that seen in behavioral studies of impulsivity.
The tension or imbalance between these developing systems during adolescence may lead to cognitive control processes being more vulnerable to incentive-based modulation and increased susceptibility to the motivational properties of alcohol and drugs. As such, behavior challenges that require cognitive control in the face of appetitive cues may serve as useful biobehavioral markers for predicting which teens may be at greater risk for alcohol and substance dependence.
ABSTRACT
We searched for an isotropic stochastic gravitational wave background in the second data release of the International Pulsar Timing Array, a global collaboration synthesizing decadal-length ...pulsar-timing campaigns in North America, Europe, and Australia. In our reference search for a power-law strain spectrum of the form $h_c = A(f/1\, \mathrm{yr}^{-1})^{\alpha }$, we found strong evidence for a spectrally similar low-frequency stochastic process of amplitude $A = 3.8^{+6.3}_{-2.5}\times 10^{-15}$ and spectral index α = −0.5 ± 0.5, where the uncertainties represent 95 per cent credible regions, using information from the auto- and cross-correlation terms between the pulsars in the array. For a spectral index of α = −2/3, as expected from a population of inspiralling supermassive black hole binaries, the recovered amplitude is $A = 2.8^{+1.2}_{-0.8}\times 10^{-15}$. None the less, no significant evidence of the Hellings–Downs correlations that would indicate a gravitational-wave origin was found. We also analysed the constituent data from the individual pulsar timing arrays in a consistent way, and clearly demonstrate that the combined international data set is more sensitive. Furthermore, we demonstrate that this combined data set produces comparable constraints to recent single-array data sets which have more data than the constituent parts of the combination. Future international data releases will deliver increased sensitivity to gravitational wave radiation, and significantly increase the detection probability.
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