Ahmed Metwally

Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing

The human microbiome has emerged as a major player in regulating human health and disease. Translational studies of the microbiome have the potential to indicate clinical applications such as fecal transplants and probiotics. However, one major issue is accurate identification of microbes constituting the microbiota. Studies of the microbiome have frequently utilized sequencing of the conserved 16S ribosomal RNA (rRNA) gene. We present a comparative study of an alternative approach using whole genome shotgun sequencing (WGS). In the present study, we analyzed the human fecal microbiome compiling a total of 194.1 × 10(6) reads from a single sample using multiple sequencing methods and platforms. Specifically, after establishing the reproducibility of our methods with extensive multiplexing, we compared: 1) The 16S rRNA amplicon versus the WGS method, 2) the Illumina HiSeq versus MiSeq platforms, 3) the analysis of reads versus de novo assembled contigs, and 4) the effect of shorter versus longer reads. Our study demonstrates that whole genome shotgun sequencing has multiple advantages compared with the 16S amplicon method including enhanced detection of bacterial species, increased detection of diversity and increased prediction of genes. In addition, increased length, either due to longer reads or the assembly of contigs, improved the accuracy of species detection.

A diverse virome in kidney transplant patients contains multiple viral subtypes with distinct polymorphisms

Recent studies have established that the human urine contains a complex microbiome, including a virome about which little is known. Following immunosuppression in kidney transplant patients, BK polyomavirus (BKV) has been shown to induce nephropathy (BKVN), decreasing graft survival. In this study we investigated the urine virome profile of BKV+ and BKV− kidney transplant recipients. Virus-like particles were stained to confirm the presence of VLP in the urine samples. Metagenomic DNA was purified, and the virome profile was analyzed using metagenomic shotgun sequencing. While the BK virus was predominant in the BKV+ group, it was also found in the BKV− group patients. Additional viruses were also detected in all patients, notably including JC virus (JCV) and Torque teno virus (TTV) and interestingly, we detected multiple subtypes of the BKV, JCV and TTV. Analysis of the BKV subtypes showed that nucleotide polymorphisms were detected in the VP1, VP2 and Large T Antigen proteins, suggesting potential functional effects for enhanced pathogenicity. Our results demonstrate a complex urinary virome in kidney transplant patients with multiple viruses with several distinct subtypes warranting further analysis of virus subtypes in immunosuppressed hosts.

Effect of the Obesity Epidemic on Kidney Transplantation: Obesity Is Independent of Diabetes as a Risk Factor for Adverse Renal Transplant Outcomes

Background Obesity is a growing epidemic in most developed countries including the United States resulting in an increased number of obese patients with end-stage renal disease. A previous study has shown that obese patients with end-stage renal disease have a survival benefit with transplantation compared with dialysis. However, due to serious comorbidities, many centers place restrictions on the selection of obese patients for transplantation. Further, due to obese patients having an increased risk of diabetes, it is unclear whether obesity can be an independent risk, independent of diabetes for increasing adverse renal transplant outcomes. Methods To investigate the role of obesity in kidney transplantation, we used the Scientific Registry of Transplant Recipients database. After filtering for subjects that had the full set of covariates including age, gender, graft type, ethnicity, diabetes, peripheral vascular disease, dialysis time and time period of transplantation for our analysis, 191,091 subjects were included in the analyses. Using multivariate logistic regression analyses adjusted for covariates we determined whether obesity is an independent risk factor for adverse outcomes such as delayed graft function, acute rejection, urine protein and graft failure. Cox regression modeling was used to determine hazard ratios of graft failure. Results Using multivariate model analyses, we found that obese patients have significantly increased risk of adverse transplant outcomes, including delayed graft function, graft failure, urine protein and acute rejection. Cox regression modeling hazard ratios showed that obesity also increased risk of graft failure. Life-table survival curves showed that obesity may be a risk factor independent of diabetes mellitus for a shorter time to graft failure. Conclusions A key observation in our study is that the risks for adverse outcome of obesity are progressive with increasing body mass index. Furthermore, pre-obese overweight recipients compared with normal weight recipients also had increased risks of adverse outcomes related to kidney transplantation.

WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial Sequences.

Background Metagenome shotgun sequencing presents opportunities to identify organisms that may prevent or promote disease. The analysis of sample diversity is achieved by taxonomic identification of metagenomic reads followed by generating an abundance profile. Numerous tools have been developed based on different design principles. Tools achieving high precision can lack sensitivity in some applications. Conversely, tools with high sensitivity can suffer from low precision and require long computation time. Methods In this paper, we present WEVOTE (WEighted VOting Taxonomic idEntification), a method that classifies metagenome shotgun sequencing DNA reads based on an ensemble of existing methods using k-mer-based, marker-based, and naive-similarity based approaches. Our evaluation on fourteen benchmarking datasets shows that WEVOTE improves the classification precision by reducing false positive annotations while preserving a high level of sensitivity. Conclusions WEVOTE is an efficient and automated tool that combines multiple individual taxonomic identification methods to produce more precise and sensitive microbial profiles. WEVOTE is developed primarily to identify reads generated by MetaGenome Shotgun sequencing. It is expandable and has the potential to incorporate additional tools to produce a more accurate taxonomic profile. WEVOTE was implemented using C++ and shell scripting and is available at www.github.com/aametwally/WEVOTE.

A Circulating MicroRNA Signature Serves as a Diagnostic and Prognostic Indicator in Sarcoidosis

&NA; MicroRNAs (miRNAs) act as post‐transcriptional regulators of gene expression. In sarcoidosis, aberrant miRNA expression may enhance immune responses mounted against an unknown antigenic agent. We tested whether a distinct miRNA signature functions as a diagnostic biomarker and explored its role as an immune modulator in sarcoidosis. The expression of miRNAs in peripheral blood mononuclear cells from subjects who met clinical and histopathologic criteria for sarcoidosis was compared with that observed in matched controls in the ACCESS (A Case Controlled Etiologic Study of Sarcoidosis) study. Signature miRNAs were determined by miRNA microarray analysis and validated by quantitative RT‐PCR. Microarray analysis identified 54 mature, human feature miRNAs that were differentially expressed between the groups. Significant feature miRNAs that distinguished subjects with sarcoidosis from controls were selected by means of probabilistic models adjusted for clinical variables. Eight signature miRNAs were chosen to verify the diagnosis of sarcoidosis in a validation cohort, and distinguished subjects with sarcoidosis from controls with a positive predictive value of 88%. We identified both novel and previously described genes and molecular pathways associated with sarcoidosis as targets of these signature miRNAs. Additionally, we demonstrate that signature miRNAs (hsa‐miR‐150‐3p and hsa‐miR‐342‐5p) are significantly associated with reduced lymphocytes and airflow limitations, both of which are known markers of a poor prognosis. Together, these findings suggest that a circulating miRNA signature serves as a noninvasive biomarker that supports the diagnosis of sarcoidosis. Future studies will test the miRNA signature as a prognostication tool to identify unfavorable changes associated with poor clinical outcomes in sarcoidosis.

Detection of Differential Abundance Intervals in Longitudinal Metagenomic Data Using Negative Binomial Smoothing Spline ANOVA

Metagenomic longitudinal studies have become a widely-used study design to investigate the dynamics of the microbial ecological systems and their temporal effects. One of the important questions to be addressed in longitudinal studies is the identification of time intervals when microbial features show changes in their abundance. We propose a statistical method that is based on a semi-parametric Smoothing Spline ANOVA and negative binomial distribution to model the time-course of the features between two phenotypes. We demonstrate the superior performance of our proposed method compared to the two currently existing methods using simulated data. We present the analysis results of our proposed method in an analysis of a longitudinal dataset that investigates the association between the development of type 1 diabetes in infants and the gut microbiome. The identified significant species and their specific time intervals reveal new information that can be used in improving intervention or treatment plans.

Using convolutional neural networks to explore the microbiome

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.

PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolution Neural Networks for Metagenomic Data

Motivation Accurate prediction of the host phenotype from a metgenomic sample and identification of the associated bacterial markers are important in metagenomic studies. We introduce PopPhy-CNN, a novel convolutional neural networks (CNN) learning architecture that effectively exploits phylogentic structure in microbial taxa. PopPhy-CNN provides an input format of 2D matrix created by embedding the phylogenetic tree that is populated with the relative abundance of microbial taxa in a metagenomic sample. This conversion empowers CNNs to explore the spatial relationship of the taxonomic annotations on the tree and their quantitative characteristics in metagenomic data. Results PopPhy-CNN is evaluated using three metagenomic datasets of moderate size. We show the superior performance of PopPhy-CNN compared to random forest, support vector machines, LASSO and a baseline 1D-CNN model constructed with relative abundance microbial feature vectors. In addition, we design a novel scheme of feature extraction from the learned CNN models and demonstrate the improved performance when the extracted features are used to train support vector machines. Conclusion PopPhy-CNN is a novel deep learning framework for the prediction of host phenotype from metagenomic samples. PopPhy-CNN can efficiently train models and does not require excessive amount of data. PopPhy-CNN facilities not only retrieval of informative microbial taxa from the trained CNN models but also visualization of the taxa on the phynogenetic tree. Contact yagndai@uic.edu Availability Source code is publicly available at https://github.com/derekreiman/PopPhy-CNN Supplementary information Supplementary data are available at Bioinformatics online.

MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies

BackgroundMicrobial longitudinal studies are powerful experimental designs utilized to classify diseases, determine prognosis, and analyze microbial systems dynamics. In longitudinal studies, only identifying differential features between two phenotypes does not provide sufficient information to determine whether a change in the relative abundance is short-term or continuous. Furthermore, sample collection in longitudinal studies suffers from all forms of variability such as a different number of subjects per phenotypic group, a different number of samples per subject, and samples not collected at consistent time points. These inconsistencies are common in studies that collect samples from human subjects.ResultsWe present MetaLonDA, an R package that is capable of identifying significant time intervals of differentially abundant microbial features. MetaLonDA is flexible such that it can perform differential abundance tests despite inconsistencies associated with sample collection. Extensive experiments on simulated datasets quantitatively demonstrate the effectiveness of MetaLonDA with significant improvement over alternative methods. We applied MetaLonDA to the DIABIMMUNE cohort (https://pubs.broadinstitute.org/diabimmune) substantiating significant early lifetime intervals of exposure to Bacteroides and Bifidobacterium in Finnish and Russian infants. Additionally, we established significant time intervals during which novel differentially relative abundant microbial genera may contribute to aberrant immunogenicity and development of autoimmune disease.ConclusionMetaLonDA is computationally efficient and can be run on desktop machines. The identified differentially abundant features and their time intervals have the potential to distinguish microbial biomarkers that may be used for microbial reconstitution through bacteriotherapy, probiotics, or antibiotics. Moreover, MetaLonDA can be applied to any longitudinal count data such as metagenomic sequencing, 16S rRNA gene sequencing, or RNAseq. MetaLonDA is publicly available on CRAN (https://CRAN.R-project.org/package=MetaLonDA).

Donor and Recipient Ethnicity Impacts Renal Graft Adverse Outcomes

Renal transplant outcomes have been shown to be impacted by ethnicity. Prior studies have evaluated the disparate transplant outcomes of Black recipients and recipients of Black donors. However, it has remained unclear whether other donor ethnicities independent of medical comorbidities can influence transplant outcomes. Utilizing the Scientific Registry of Transplant Recipients (SRTR) (with greater than 100,000 patients), we evaluated the effect of each ethnicity, Black, American Indian, Hispanic, Native Hawaiian or other Pacific Islander, and Asian as compared to White recipients on adverse kidney transplant outcomes, assessing for delayed graft function, positive urine protein, acute rejection, and graft failure. Additionally, we assessed the interplay of donor ethnicity on recipient transplant outcomes, which has not previously been comprehensively examined. Logistic regression analysis that took into consideration gender, age, comorbidities, graft type, donor ethnicity, body mass index (BMI), HLA mismatch, ever been on hemodialysis, and time on dialysis indicates that Black recipients have worse outcomes compared to Whites in all outcomes assessed. A logistic regression analysis showed that recipient ethnicity was an independent risk factor for adverse outcomes. Notably, we found that donor ethnicity also independently affects graft outcomes. Hispanic donors lead to better outcomes in Whites and Blacks, while Asian donors have the best outcomes amongst Asian recipients. Recipients of Black donors fared the worst of all ethnicity donors. These data suggest the potential importance of risk stratification for the donor allograft and developing risk calculators that include both donor and recipient ethnicity may be useful, which the current Kidney Donor Profile Index (KDPI) does not currently take into account as they give black donors a different weight, but the same score is assigned to Whites, Asians, and Hispanics.