Shahd ElNaggar

All Publications

• A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome. Communications medicine ElNaggar, S., Chen, W. C., Prodehl, L. M., Marumo, T. K., Khan, M. U., Mathew, C. G., Ruff, P., Jin, Z., Neugut, A. I., Rustgi, A. K., Uhlemann, A. C., Korem, T., Abrams, J. A. 2026; 6 (1)

  Abstract

  Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and limited tools for early detection. Saliva is easily accessible and its microbiome composition can serve as a marker for upper gastrointestinal tract disease. This study aims to evaluate the potential of an oral microbiome signature for classifying ESCC.In a cross-sectional study of 48 ESCC patients and 110 controls from South Africa, a region with high ESCC incidence, we studied the potential utility of an oral microbiome signature for the disease. We built models using nested cross-validation to evaluate whether this signature is generalizable to held-out samples and further evaluated generalizability in studies from China, a distinct geographic region.We find significant alterations in the oral microbiome in patients with ESCC including significantly reduced α diversity and increased abundance of Fusobacterium nucleatum. We also find that logistic regression models based on microbiome data can better classify ESCC in held-out samples (auROC=0.96) compared to clinical and demographic data (auROC = 0.69; DeLong p < 1 x 10-8). Lastly, we find that microbiome-based models trained across multiple studies can generalize well to geographically distinct studies.Our results show that the oral microbiome in individuals with ESCC is distinct from controls and that this signal can generalize across unseen samples, suggesting the potential of saliva to serve as a non-invasive screening tool for ESCC.

  View details for DOI 10.1038/s43856-026-01468-y

  View details for PubMedID 41764274

  View details for PubMedCentralID PMC13066412

• Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models. Nature microbiology Austin, G. I., Brown Kav, A., ElNaggar, S., Park, H., Biermann, J., Uhlemann, A. C., Pe'er, I., Korem, T. 2025; 10 (4): 897-911

  Abstract

  Every step in common microbiome profiling protocols has variable efficiency for each microbe, for example, different DNA extraction efficiency for Gram-positive bacteria. These processing biases impede the identification of signals that are biologically interpretable and generalizable across studies. 'Batch-correction' methods have been used to address these issues computationally with some success, but they are largely non-interpretable and often require the use of an outcome variable in a manner that risks overfitting. We present DEBIAS-M (domain adaptation with phenotype estimation and batch integration across studies of the microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using diverse benchmarks including 16S rRNA and metagenomic sequencing, classification and regression, and a variety of clinical and molecular targets, we demonstrate that using DEBIAS-M improves cross-study prediction accuracy compared with commonly used batch-correction methods. Notably, we show that the inferred bias-correction factors are stable, interpretable and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M facilitates improved modelling of microbiome data and identification of interpretable signals that generalize across studies.

  View details for DOI 10.1038/s41564-025-01954-4

  View details for PubMedID 40148567

  View details for PubMedCentralID PMC12087262

• Tradeoff between lag time and growth rate drives the plasmid acquisition cost. Nature communications Ahmad, M., Prensky, H., Balestrieri, J., ElNaggar, S., Gomez-Simmonds, A., Uhlemann, A. C., Traxler, B., Singh, A., Lopatkin, A. J. 2023; 14 (1): 2343

  Abstract

  Conjugative plasmids drive genetic diversity and evolution in microbial populations. Despite their prevalence, plasmids can impose long-term fitness costs on their hosts, altering population structure, growth dynamics, and evolutionary outcomes. In addition to long-term fitness costs, acquiring a new plasmid introduces an immediate, short-term perturbation to the cell. However, due to the transient nature of this plasmid acquisition cost, a quantitative understanding of its physiological manifestations, overall magnitudes, and population-level implications, remains unclear. To address this, here we track growth of single colonies immediately following plasmid acquisition. We find that plasmid acquisition costs are primarily driven by changes in lag time, rather than growth rate, for nearly 60 conditions covering diverse plasmids, selection environments, and clinical strains/species. Surprisingly, for a costly plasmid, clones exhibiting longer lag times also achieve faster recovery growth rates, suggesting an evolutionary tradeoff. Modeling and experiments demonstrate that this tradeoff leads to counterintuitive ecological dynamics, whereby intermediate-cost plasmids outcompete both their low and high-cost counterparts. These results suggest that, unlike fitness costs, plasmid acquisition dynamics are not uniformly driven by minimizing growth disadvantages. Moreover, a lag/growth tradeoff has clear implications in predicting the ecological outcomes and intervention strategies of bacteria undergoing conjugation.

  View details for DOI 10.1038/s41467-023-38022-6

  View details for PubMedID 37095096

  View details for PubMedCentralID PMC10126158