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Mallick, Himel

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Mallick

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Himel

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Mallick, Himel
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    Multi-Omics of the Gut Microbial Ecosystem in Inflammatory Bowel Diseases
    (Springer Science and Business Media LLC, 2019-05) Lloyd-Price, Jason; Arze, Cesar; Schirmer, Melanie; Andrews, Elizabeth; Ajami, Nadim J.; Brislawn, Colin J.; Courtney, Holly; Gonzalez, Antonio; Graeber, Thomas G.; Hall, A. Brantley; Mallick, Himel; Rahnavard, Gholamali; Sauk, Jenny; Shungin, Dmitry; Vázquez-Baeza, Yoshiki; White, Richard A.; Braun, Jonathan; Denson, Lee A.; Jansson, Janet K.; Knight, Robert; Kugathasan, Subra; McGovern, Dermot P. B.; Stappenbeck, Thaddeus S.; Vlamakis, Hera; Huttenhower, Curtis; Ananthakrishnan, Ashwin; Avila-Pacheco, Julian; Poon, Tiffany; Bonham, Kevin; Casero, David; Lake, Kathleen; Landers, Carol; Plichta, Damian; Prasad, Mahadev; Winter, Harland; Clish, Clary; Franzosa, Eric; Xavier, Ramnik; Petrosino, Joseph
    Inflammatory bowel diseases (IBD), which include Crohn’s disease (CD) and ulcerative colitis (UC), affect several million individuals worldwide. CD and UC are complex diseases and heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Extensive study has focused on individual contributing factors. As part of the Integrative Human Microbiome Project (HMP2), 132 subjects were followed one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each, in total 2,965 stool, biopsy, and blood specimens). These provide a comprehensive view of the gut microbiome’s functional dysbiosis during IBD activity, showing a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (e.g. among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and host serum antibody levels. Disease was also marked by greater temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to the dysregulation. The study’s infrastructure resources, results, and data, available through the Inflammatory Bowel Disease Multi'omics Database (http://ibdmdb.org), provide the most comprehensive description to date of host and microbial activities in IBD.
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    A Bayesian method for detecting pairwise associations in compositional data
    (Public Library of Science, 2017) Schwager, Emma; Mallick, Himel; Ventz, Steffen; Huttenhower, Curtis
    Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.
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    EM Adaptive LASSO—A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes
    (Frontiers Media S.A., 2016) Mallick, Himel; Tiwari, Hemant K.
    Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice.
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    Negative binomial mixed models for analyzing microbiome count data
    (BioMed Central, 2017) Zhang, Xinyan; Mallick, Himel; Tang, Zaixiang; Zhang, Lei; Cui, Xiangqin; Benson, Andrew K.; Yi, Nengjun
    Background: Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. Results: In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. Conclusions: We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data.
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    Global Chemical Impact of the Microbiome Includes Novel Bile Acid Conjugations
    (Springer Nature, 2020-02-26) Quinn, Robert; Melnik, Alexey; Vrbanac, Alison; Patras, Kathryn; Christy, Mitchell; Zsolt, Bodai; Belda-Ferre, Pedro; Tripathi, Anupriya; Chung, Lawton; Quinn, Melissa; Humphrey, Greg; Panitchpakdi, Morgan; Weldon, Kelly; Aksenov, Alexander; da Silva, Ricardo; Avila-Pacheco, Julian; Clish, Clary; Bae, Sena; Mallick, Himel; Franzosa, Eric; Lloyd-Price, Jason; Bussell, Robert; Thron, Taren; Nelson, Andrew; Wang, Mingxun; Leszczynski, Eric; Vargas, Fernando; Gauglitz, Julia; Meehan, Michael; Gentry, Emily; Arthur, Timothy; Downes, Michael; Fu, Ting; Welch, Ryan; Komor, Alexis; Poulsen, Orit; Boland, Brigid; Chang, John; Sandborn, William; Lim, Meerana; Garg, Neha; Lumeng, Julie; Xavier, Ramnik; Kazmierczak, Barbara; Jain, Ruchi; Egan, Marie; Rhee, Kyung; Ferguson, David; Evans, Ronald; Raffatellu, Manuela; Vlamakis, Hera; Haddad, Gabriel; Siegel, Dionicio; Huttenhower, Curtis; Mazmanian, Sarkis; Nizet, Victor; Knight, Rob; Dorrestein, Pieter
    A mosaic of cross-phyla chemical interactions occurs between all metazoans and their microbiomes. In humans, the gut harbors the heaviest microbial load, but many organs, particularly those with a mucosal surface, associate with highly adapted and evolved microbial consortia. The microbial residents within these organ systems are increasingly well characterized, yielding a good understanding of human microbiome composition. However, we have yet to elucidate the full chemical impact the microbiome exerts on an animal and the breadth of the chemical diversity it contributes. A number of molecular families are known to be shaped by the microbiome including short-chain fatty acids, indoles, complex polysaccharides, host sphingolipids and bile acids. These metabolites profoundly affect host physiology and are being explored for their roles in both health and disease. Considering the diversity of the human microbiome, numbering over 40,000 operational taxonomic units, a plethora of molecular diversity remains to be discovered. In this study we used novel mass spectrometry informatics and visualization approaches to provide an untargeted assessment of the chemical contributions of the microbiome to an entire mammal by comparing germ-free (GF) and specific-pathogen free (SPF) animals. We found that the microbiome affected the chemistry of all murine organs. These affects were highlighted by novel amino acid conjugations of host bile acids that have evaded characterization despite the extensive research on bile acid chemistry. These new bile acid conjugates were enriched in dysbiotic disease states and directly agonized the farnesoid X receptor (FXR) resulting in changes in host bile acid metabolism.
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    Experimental design and quantitative analysis of microbial community multiomics
    (BioMed Central, 2017) Mallick, Himel; Ma, Siyuan; Franzosa, Eric; Vatanen, Tommi; Morgan, Xochitl C.; Huttenhower, Curtis
    Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.