Honors & Awards
2nd place grand prize award at Stanford Health++ Hackathon, Stanford (2018)
American Thoracic Society Scholarship (Pediatric Assembly), ATS (2018)
Impact Scholar, UIC (2018)
Predoctoral Education for Clinical and Translational Scientists Fellowship (UL1TR002003) ($33,000), NIH (2017)
2nd place poster winner, GLBIO'17, ISCB (2017)
Scientific Excellence Award, Department of Medicine, UIC (2017)
Chancellor's Graduate Research Award ($8,000), UIC (2015)
1st place award at Research Forum among participants from Engineering, CS, Math, and Statistics, UIC (2016)
Travel awards, NSF, ACM, IEEE, ISCB, UIC, UIUC, ICTP, LinkSCEEM (2013-2018)
Distinctive honor and ranked 1st among undergraduate class, Cairo University (2010)
Helmholtz-Zentrum Geesthacht Fellowship, DAAD (2009)
Boards, Advisory Committees, Professional Organizations
Board member and Global Student Representative, IEEE Engineering in Medicine and Biology Society (EMBS) (2017 - 2019)
Member, IEEE Engineering in Medicine and Biology Society (EMBS) (2009 - Present)
Member, American Thoracic Society (ATS) (2017 - Present)
Member, Association for Computing Machinery (ACM) (2015 - Present)
Member, International Society for Computational Biology (ISCB) (2013 - Present)
Ph.D. in Bioinformatics, University of Illinois at Chicago (2018)
M.Sc. in Computer Science, University of Illinois at Chicago (2018)
M.Sc. in Biomedical Engineering, Cairo University (2014)
B.Sc. in Biomedical Engineering, Cairo University (2010)
Michael Snyder, Postdoctoral Faculty Sponsor
Longitudinal multi-omics of host-microbe dynamics in prediabetes.
2019; 569 (7758): 663–71
Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2Dbetter, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.
View details for DOI 10.1038/s41586-019-1236-x
View details for PubMedID 31142858
Taxonomic Classification at the Strain Level using a Species-of-Interest k-mer Database
View details for Web of Science ID 000508002200084
Identifying Appropriate Probabilistic Models for Sparse Discrete Omics Data
View details for Web of Science ID 000508002200123
MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies
2018; 6: 32
Microbial 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.We 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.MetaLonDA 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 ).
View details for DOI 10.1186/s40168-018-0402-y
View details for Web of Science ID 000425442200001
View details for PubMedID 29439731
View details for PubMedCentralID PMC5812052
A Circulating MicroRNA Signature Serves as a Diagnostic and Prognostic Indicator in Sarcoidosis
AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY
2018; 58 (1): 40–54
Micro-RNAs (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. Expression of miRNAs from peripheral blood mononuclear cells from subjects that met clinical and histopathologic criteria for sarcoidosis were compared to those from matched controls in the ACCESS study. Signature miRNAs were determined by miRNA microarray analysis and validated by quantitative reverse transcription PCR (RT-qPCR). Microarray analysis identified 54 differentially expressed feature mature human miRNAs between groups. Significant feature miRNAs that distinguish sarcoidosis from controls were selected by use of probabilistic models adjusted for clinical variables. Eight signature miRNAs were chosen to verify the diagnosis of sarcoidosis in a validation cohort and distinguished 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, known markers of poor prognosis. Together, these findings suggest that a circulating miRNA signature serves as a non-invasive biomarker that supports the diagnosis of sarcoidosis. Future studies will test the miRNA signature as a prognostication tool associated with poor clinical outcomes in sarcoidosis.
View details for DOI 10.1165/rcmb.2017-0207OC
View details for Web of Science ID 000419123200009
View details for PubMedID 28812922
View details for PubMedCentralID PMC5941311
Bronchiolitis obliterans syndrome susceptibility and the pulmonary microbiome.
The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
Lung transplantation outcomes remain complicated by bronchiolitis obliterans syndrome (BOS), a major cause of mortality and retransplantation for patients. A variety of factors linking inflammation and BOS have emerged, meriting further exploration of the microbiome as a source of inflammation. In this analysis, we determined features of the pulmonary microbiome associated with BOS susceptibility.Bronchoalveolar lavage (BAL) samples were collected from 25 patients during standard of care bronchoscopies before BOS onset. Microbial DNA was isolated from BAL fluid and prepared for metagenomics shotgun sequencing. Patient microbiomes were phenotyped using k-means clustering and compared to determine effects on BOS-free survival.Clustering identified 3 microbiome phenotypes: Actinobacteria dominant (AD), mixed, and Proteobacteria dominant. AD microbiomes, distinguished by enrichment with Gram-positive organisms, conferred reduced odds and risks for patients to develop acute rejection and BOS compared with non-AD microbiomes. These findings were independent of treatment models. Microbiome findings were correlated with BAL cell counts and polymorphonuclear cell percentages.In some populations, features of the microbiome may be used to assess BOS susceptibility. Namely, a Gram-positive enriched pulmonary microbiome may predict resilience to BOS.
View details for DOI 10.1016/j.healun.2018.04.007
View details for PubMedID 29929823
Donor and Recipient Ethnicity Impacts Renal Graft Adverse Outcomes.
Journal of racial and ethnic health disparities
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.
View details for DOI 10.1007/s40615-017-0447-9
View details for PubMedID 29270842
Using Convolutional Neural Networks to Explore the Microbiome
IEEE. 2017: 4269–72
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.
View details for Web of Science ID 000427085304175
View details for PubMedID 29060840
- Detection of Differential Abundance Intervals in Longitudinal Metagenomic Data Using Negative Binomial Smoothing Spline ANOVA ASSOC COMPUTING MACHINERY. 2017: 295–304
Effect of the Obesity Epidemic on Kidney Transplantation: Obesity Is Independent of Diabetes as a Risk Factor for Adverse Renal Transplant Outcomes
2016; 11 (11): e0165712
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.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.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.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.
View details for DOI 10.1371/journal.pone.0165712
View details for Web of Science ID 000387909300023
View details for PubMedID 27851743
View details for PubMedCentralID PMC5112887
WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial Sequences
2016; 11 (9): e0163527
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.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.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.
View details for DOI 10.1371/journal.pone.0163527
View details for Web of Science ID 000384171400044
View details for PubMedID 27683082
View details for PubMedCentralID PMC5040256
A diverse virome in kidney transplant patients contains multiple viral subtypes with distinct polymorphisms
2016; 6: 33327
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.
View details for DOI 10.1038/srep33327
View details for Web of Science ID 000383332400001
View details for PubMedID 27633952
View details for PubMedCentralID PMC5025891
Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing
BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
2016; 469 (4): 967–77
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.
View details for DOI 10.1016/j.bbrc.2015.12.083
View details for Web of Science ID 000369353000029
View details for PubMedID 26718401
View details for PubMedCentralID PMC4830092
Distributed Suffix Array Construction Algorithms: Comparison of Two Algorithms
IEEE. 2016: 27–30
View details for Web of Science ID 000400192500007
Cloud-based Parallel Suffix Array Construction based on MPI
IEEE. 2014: 334–37
View details for Web of Science ID 000353351700079