Network topology and community function in spatial microbial communities

Complex communities of microbes act collectively to regulate human health, provide sources of clean energy, and ripen aromatic cheese. The efficient functioning of these communities can be directly related to competitive and cooperative interactions between species. Physical constraints and local environment affect the stability of these interactions. Here we explore the role of spatial habitat and interaction networks in microbial ecology and human disease. In the first part of the dissertation, we model mutualism to understand how spatial microbial communities survive number fluctuations in physical habitats. We explicitly account for the production, consumption, and diffusion of public goods in a two-species microbial community. We show that increased sharing of nutrients breaks down coexistence, and that species may benefit from making slower-diffusing nutrients. In multi-species communities, indirect and higher order interactions may affect community function. We find that the requirement for spatial proximity severely restricts the network of possible microbial interactions. While cooperation between two species is stable, higher-order mutualism requiring three or more species succumbs easily to number fluctuations. Additional cyclic or reciprocal interactions between pairs can stabilize multi-species communities. Inter-species interactions also affect human health via the human microbiome: microbial communities in the gut, lungs and skin. In the second part of the dissertation, we use machine learning and statistics to establish links between microbiota abundance and composition, and the incidence of chronic diseases. We study the gut fungal profile to probe the effects of diet and fungal dysbiosis in a cohort of Saudi children with Crohn's disease. While statistical microbiome studies established that each disease phenotype is associated with a distinct state of intestinal dysbiosis, they often produced conflicting results and identified a very large number of microbes associated with disease. We show that a handful of taxa could drive the dynamics of ecosystem-level abundance changes due to strong inter-species interactions. Using maximum entropy methods, we propose a simple statistical approach (Direct Association Analysis or DAA) to account for interspecific interactions. When applied to the largest dataset on IBD, DAA detects a small subset of associations directly linked to the disease, avoids p-value inflation and identifies most predictive features of the microbiome

Interactions between species introduce spurious associations in microbiome studies

Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease.Comment: 4 main text figures, 15 supplementary figures (i.e appendix) and 6 supplementary tables. Overall 49 pages including reference

Fungal dysbiosis predicts the diagnosis of pediatric Crohn's disease

AIM: To investigate the accuracy of fungal dysbiosis in mucosa and stool for predicting the diagnosis of Crohn’s disease (CD). METHODS: Children were prospectively enrolled in two medical centers: one university hospital and one private gastroenterology clinic in the city of Riyadh, Kingdom of Saudi Arabia. The children with confirmed diagnosis of CD by standard guidelines were considered cases, and the others were considered non-inflammatory bowel disease controls. Mucosal and stool samples were sequenced utilizing Illumina MiSeq chemistry following the manufacturer’s protocols, and abundance and diversity of fungal taxa in mucosa and stool were analyzed. Sparse logistic regression was used to predict the diagnosis of CD. The accuracy of the classifier was tested by computing the receiver operating characteristic curves with 5-fold stratified cross-validation under 100 permutations of the training data partition and the mean area under the curve (AUC) was calculated. RESULTS: All the children were Saudi nationals. There were 15 children with CD and 20 controls. The mean age was 13.9 (range: 6.7-17.8) years for CD children and 13.9 (3.25-18.6) years for controls, and 10/15 (67%) of the CD and 13/20 (65%) of the control subjects were boys. CD locations at diagnosis were ileal (L1) in 4 and colonic (L3) in 11 children, while CD behavior was non-stricturing and non-penetrating (B1) in 12 and stricturing (B2) in 3 children. The mean AUC for the fungal dysbiosis classifier was significantly higher in stools (AUC = 0.85 ± 0.057) than in mucosa (AUC = 0.71 ± 0.067) (P < 0.001). Most fungal species were significantly more depleted in stools than mucosal samples, except for Saccharomyces cerevisiae and S. bayanus, which were significantly more abundant. Diversity was significantly more reduced in stools than in mucosa. CONCLUSION: We found high AUC of fungal dysbiosis in fecal samples of children with CD, suggesting high accuracy in predicting diagnosis of CD. Key Words: Fungiome, Mycobiome, Crohn’s disease, Inflammation, Saudi children Core tip: We found high accuracy of fungal dysbiosis in predicting diagnosis of Crohn’s disease (CD), a finding similar to bacterial dysbiosis. However, the higher area under the curve for the fungal dysbiosis classifier in stool (0.85 ± 0.057) than in mucosa (0.71 ± 0.067) (P < 0.001), contrasts with bacterial studies, suggesting higher accuracy of stool samples. Although the clinical application of this finding is limited at present by the high cost of fungal analysis, such information is important from a scientific viewpoint, to increase the understanding of the role of fungal flora in CD and to stimulate further studies.The authors extend their appreciations to the Deanship of Scientific Research at King Saud University in Riyadh, Kingdom of Saudi Arabia for funding this work through Research Group No [RGP-1436-007]. This work was also supported by a grant from the Simons Foundation [No. 409704] to Kirill Korolev) and by the startup fund from Boston University to Kirill Korolev. Simulations were carried out on Shared Computing Cluster at Boston University. Rajita Menon was partially supported by a Hariri Graduate Fellowship from Boston University. Harland Winter, MD received support from Martin Schlaff and the Diane and Dorothy Brooks Foundation. (RGP-1436-007 - King Saud University in Riyadh, Kingdom of Saudi Arabia; 409704 - Simons Foundation; Boston University; Hariri Graduate Fellowship from Boston University; Diane and Dorothy Brooks Foundation)Published versio

Investigating self-efficacy gaps in the intro-physics classrom

We employ self-efficacy measures to quantify and estimate the gender gap for students taking introductory physics. The SOSESC-P (Sources Of Self-Efficacy in Science Courses -Physics) survey was administered to a wide and diverse range of students from five different introductory physics courses to estimate self-efficacy scores. We analyzed the self-efficacy gap for a variety of student academic backgrounds and carried out a fine-grained investigation of student attitudes within physics. With our work, we provide a database for future self-efficacy studies and a basis for design and implementation of teaching interventions addressing the self-efficacy gap

Network of direct associations with Crohn’s Disease.

<p>Five species and four genera were found to be significantly associated with Crohn’s Disease (<i>q</i> < 0.05) after correcting for microbial interactions (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s002" target="_blank">S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s005" target="_blank">S4</a> Figs). The links correspond to significant interactions (<i>q</i> < 0.05) between the taxa with <i>J</i>_<i>ij</i> > 0.27 or <i>J</i>_<i>ij</i> < −0.15; the width of the arrows reflects the strength of the interactions. For comparison, the correlation-based network for directly associated taxa is shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s008" target="_blank">S7</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s006" target="_blank">S5</a> Figs, and a complete summary of correlations and interactions for all species pairs is provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s001" target="_blank">S1 Text</a>.</p

Direct associations analysis corrects p-value inflation and retains diagnostic accuracy.

<p>(<b>A</b>) The distribution of p-values in DAA closely follows the expected uniform distribution. Because conventional MWAS does not correct for microbial interactions, it yields an excess of low p-values, which is a strong signature of indirect associations. For both methods, p-values were computed using a permutation test. The expected uniform distribution was obtained by sampling from a generator of uniform random numbers. The ranked plot of p-values visualizes their cumulative distribution functions; this is a variant of a Q-Q plot. (<b>B</b>) Direct associations are a small subset of all associations with IBD (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s005" target="_blank">S4 Fig</a>), yet they retain full power in classifying samples as cases or controls. In contrast, the classification power is substantially reduced for an equally-sized subset of randomly-chosen indirect associations. In each case, we used sparse logistic regression to train a classifier on 80% of the data and tested its performance on the remaining 20% (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#sec007" target="_blank">Methods</a>). The shaded regions show one standard deviation obtained by repeated partitioning the data into training and validation sets. Identical results were obtained with a random forest [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.ref064" target="_blank">64</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.ref065" target="_blank">65</a>] and support vector machine [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.ref066" target="_blank">66</a>] classifiers (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s009" target="_blank">S8 Fig</a>)</p

Microbial interactions generate spurious associations.

<p>(<b>A</b>) A hypothetical interaction network of five species together with their dynamics in disease. Only two species (shown in color) are directly linked to host phenotype. These directly-linked species inhibit or promote the growth of the other members of the community (shown with arrows). As a result, all five species have different abundances between case and control groups. (<b>B</b>) Microbial interactions are visualized via a hierarchically-clustered correlation matrix computed from the data in Ref. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.ref021" target="_blank">21</a>]. We used Pearson’s correlation coefficient between log-transformed abundances to quantify the strength of co-occurrence for each genus pair. Dark regions reflect strong interspecific interactions that could potentially generate spurious associations. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s001" target="_blank">S1 Text</a> for the list of 47 most prevalent genera included in the plot.</p

Signatures of indirect associations in synthetic and IBD data sets.

<p>The synthetic data set was generated to match the statistical properties of the IBD data set from Ref. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.ref021" target="_blank">21</a>], but with a predefined number of 6 directly associated taxa (See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s001" target="_blank">S1 Text</a>). (<b>A</b>) In synthetic data, DAA identifies no spurious association and detects 4 out of 6 directly associated genera. All 6 genera and no false positives are detected when the sample size is increased further (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s010" target="_blank">S9 Fig</a>). In sharp contrast, a large number of spurious associations is observed for metrics that rely on changes in abundance between cases and controls and do not correct for microbial interactions. The number of false positives grows rapidly with statistical power until all taxa are reported as significantly associated with the disease. (<b>B</b>) All spurious associations show substantial differences between cases and controls and, therefore, cannot be discarded based on their effect sizes. To quantify the effect size, we estimated the magnitude of the fold change for each genus. Specifically, we first computed the difference in the mean log-abundance between cases and controls and then exponentiated the absolute value of this difference. The plot shows how the median effect size for significantly associated genera depends on the sample size. Larger samples sizes result in much higher number of associations, but only a small drop in the typical effect size. (<b>C</b>) and (<b>D</b>) are the same as (A) and (B), but for the IBD data set. The results are consistent between the two data sets suggesting that most associations detected by traditional MWAS are spurious. The complete list of indirect associations inferred from the IBD data set is shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s001" target="_blank">S1 Text</a>, and the results for different synthetic data sets are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005939#pcbi.1005939.s015" target="_blank">S14 Fig</a>.</p

Microbiota profile in new-onset pediatric Crohn’s disease: data from a non-Western population

Abstract Background The role of microbiota in Crohn’s disease (CD) is increasingly recognized. However, most of the reports are from Western populations. Considering the possible variation from other populations, the aim of this study was to describe the microbiota profile in children with CD in Saudi Arabia, a non-Western developing country population. Results Significantly more abundant genera in children with CD included Fusobacterium, Peptostreptococcus, Psychrobacter, and Acinetobacter; whereas the most significantly-depleted genera included Roseburia, Clostridium, Ruminococcus, Ruminoclostridium, Intestinibacter, Mitsuokella, Megasphaera, Streptococcus, Lactobacillus, Turicibacter, and Paludibacter. Alpha diversity was significantly reduced in stool (p = 0.03) but not in mucosa (p = 0.31). Beta diversity showed significant difference in community composition between control and CD samples (p = 0.03). Conclusion In this developing country, we found a pattern of microbiota in children with CD similar to Western literature, suggesting a role of recent dietary lifestyle changes in this population on microbiota structure