Dr. Shengtong Han

Dr. Shengtong Han

Marquette University, Milwaukee

Publications

Journal Articles

+ co-first authors; * corresponding author

22. Liu, Q.+; Yang, B.+; Li, T.; Garcia, R.; Han, S.*; Jin, V.X*. Genome-Wide Identification of a Chromatin-Splicing Regulatory Axis Driven by DOT1L in MLL-Rearranged Acute Myeloid Leukemia. Genes 2026, 17, 857. https://doi.org/10.3390/genes17080857
[ Abstract ]
Abstract: Background/Objectives: Aberrant H3K79 dimethylation (H3K79me2) by DOT1L is a defining feature of MLL-rearranged (MLLr) acute myeloid leukemia (AML), but whether this modification influences alternative splicing is unclear. We examined the relationship between H3K79me2 and exon skipping in primary MLLr AML. Methods: We performed H3K79me2 ChIP-seq and RNA-seq on primary samples from 24 MLLr AML patients, 4 wild-type MLL AML patients, and 4 healthy bone marrow donors, with matched profiling before and after DOT1L inhibition using EPZ5676. DOT1L co-immunoprecipitation with mass spectrometry (IP-MS) was performed in MV-4-11 and MOLM-14 cells, and an aggregate splicing score was evaluated in the TCGA-AML cohort. Results: A subset of exon skipping (SE) events was enriched at H3K79me2-occupied loci in MLLr samples. EPZ5676 remodeled SE patterns, and many switched events showed concurrent loss of local H3K79me2. Genes harboring these events were enriched for RNA processing, DNA repair, and apoptosis functions. Core spliceosomal and hnRNP proteins were prominent in the DOT1L interactome and were reduced after EPZ5676, and SRSF2 and hnRNPA1 binding motifs were enriched near the regulated exons. About two-thirds of the common switched events overlapped a local H3K79me2 peak, and an aggregate splicing score derived from these events stratified patients by overall survival in the TCGA-AML cohort, with higher scores associated with shorter survival. Conclusions: These data support a model in which H3K79me2 contributes to alternative splicing regulation in MLLr AML, linking the core epigenetic lesion of this disease to aberrant RNA processing with potential prognostic relevance.

21. Guentsch, A.*, and S. Han. Augmented Reality Improves Accuracy of Dynamic Computer-Assisted Implant Surgery: An In Vitro Analysis. Clin Oral Invest 37, 7 : 896–904 (2026). [link]

20. Han S+*, Sun X+, Sloofman L+, Satterstrom FK, Xu X, Liang L, Knoblauch N, Sheng W, Zhao S, Nguyen TH, Wang G; Autism Sequencing Consortium; Buxbaum J, He X*. MIRAGE: a Bayesian rare variant association analysis method incorporating functional information of variants. Am J Hum Genet., Volume 113, Issue 1, 2026, Pages 168-183, ISSN 0002-9297, https://doi.org/10.1016/j.ajhg.2025.11.013. [link] [R package] [PDF]

MIRAGE paper figure
[ Abstract ]
Summary: Rare-variant analysis is commonly used in whole-exome or genome sequencing studies. Compared to common variants, rare variants tend to have larger effect sizes and often directly point out causal genes. These potential benefits make association analysis with rare variants a priority for human genetics researchers. To improve the power of such studies, numerous methods have been developed to aggregate information of all variants of a gene. However, these gene-based methods often make unrealistic assumptions, e.g., the commonly used burden test effectively assumes that all variants chosen in the analysis have the same effects. In practice, current methods are often underpowered. We propose a Bayesian method: mixture-model-based rare-variant analysis on genes (MIRAGE). MIRAGE analyzes summary statistics (i.e., variant counts from inherited variants in trio sequencing or from ancestry-matched case-control studies). MIRAGE captures the heterogeneity of variant effects by treating all variants of a gene as a mixture of risk and non-risk variants and uses external information of variants to model the prior probabilities of being risk variants. We demonstrate, in both simulations and analysis of an exome-sequencing dataset of autism, that MIRAGE significantly outperforms current methods for rare-variant analysis. The top genes identified by MIRAGE are highly enriched with known or plausible autism-risk genes.

19. Gousias, C., Alsuwaiyan, Z., Fial, A., Han, S., Tatakis, D.N., and Kofina, V.* Pre-Emptive Analgesia for Periodontal and Implant-Related Surgery: A Systematic Review and Meta-Analysis. J Clin Periodontol (2025). [link]

Meta-analysis paper figure

18. Vrisiis Kofina*, Juan Valencia Rincon, Swati Y. Rawal, Andrew R. Dentino, Shengtong Han, Dimitris N. Tatakis. Implant placement-associated tissue swelling: a digital three-dimensional and patient-based assessment. Clin Oral Invest 29, 212 (2025). [link]

17. Shengtong Han*, Marieke Gilmartin, Wenhui Sheng, Victor X. Jin. Integrating rare variant genetics and brain transcriptome data implicates novel schizophrenia putative risk genes. Schizophrenia Research, Volume 276, 2025, Pages 205–213, ISSN 0920-9964. [link] [PDF]

Schizophrenia paper figure

16. Gonzalez C, Badr Z, Güngör HC, Han, S , Hamdan MD. Identifying primary proximal caries lesions in pediatric patients from bitewing radiographs using artificial intelligence. Pediatr Dent, 2024:46(5). [link]

15. Han, S*. Bayesian Rare Variant Analysis Identifies Novel Schizophrenia Putative Risk Genes. Journal of Personalized Medicine, 2024, 14, 822. https://doi.org/10.3390/jpm14080822 [link]

MIRAGE-SCZ paper figure

14. Vidyalakshmi Subramanian, Howard W. Roberts, Shengtong Han , Stephanie J. Sidow, David W. Berzins. Electrochemical Properties of Nickel-Titanium Rotary Endodontic Instruments, Journal of Endodontics, 2024,ISSN 0099-2399, https://doi.org/10.1016/j.joen.2024.05.004. [PDF] [link]

13. Al-Bitar KM, Garcia JM, Han S , Guentsch A. Association between periodontal health status and quality of life: a cross-sectional study. Front Oral Health. 2024 Jan 25;5:1346814. doi: 10.3389/froh.2024.1346814. PMID: 38333564; PMCID: PMC10850382. [link]

12. Zaid Badr, Manal Hamdan, Shengtong Han , Taiseer A Sulaiman. Effect of surface finish and resin cement on the bond strength to CAD/CAM ceramics for interim resin-bonded prostheses . The Journal of Prosthetic Dentistry, 2024, ISSN 0022-3913, https://doi.org/10.1016/j.prosdent.2023.12.006. [PDF][link]

11. Manal Hamdan, Zaid Badr, Jennifer Bjork, Reagan Saxe, Francesca Malensek, Caroline Miller, Rakhi Shah, Shengtong Han , Hossein Mohammad-Rahimi. (2023), Detection of dental restorations using no-code artificial intelligence . Journal of Dentistry . 2023, 104768, ISSN 0300-5712, https://doi.org/10.1016/j.jdent.2023.104768 [PDF][link]

10. Katherine C. Schacherl, Shengtong Han , John A. Schacherl, Andrew Dentino. (2024), Unstable Periodontal Disease and its Association with Sleep-Disordered Breathing (SDB) . Gen Dent. 2024 Jan-Feb;72(1):16-25. PMID: 38117637. [PDF] [link]

9. Guentsch, A., Bjork, J., Saxe, R., Han, S. and Dentino, A.R. (2023), An in-vitro Analysis of the Accuracy of different Guided Surgery Systems – They are not all the same . Clin Oral Impl Res. 2023 May;34(5):531-541. doi: 10.1111/clr.14061. Epub 2023 Mar 17. PMID: 36892499. [Link]

8. Gaussian Bayesian network comparisons with graph ordering unknown
Hongmei Zhang, Xianzheng Huang, Shengtong Han, Faisal I. Rezwan, Wilfried Karmaus, Hasan Arshad, John W. Holloway. Computational Statistics and Data Analysis, 2021, 157, 107156. [PDF] [Link][R-package]

7. Jump-seq: genome-wide capture and amplification of 5hmC sites
Lulu Hu+, Yuwen Liu+, Shengtong Han+, Lei Yang+, Xiaolong Cui, Yawei Gao, Qing Dai, Xingyu Lu, Xiaochen Kou, Yanhong Zhao, Wenhui Sheng, Shaorong Gao, Xin He, and Chuan He. Journal of the American Chemical Society, 2019, 141(22), 8694–8697. [PDF] [Link]

Jump-seq 5hmC distribution figure

6. The nested joint clustering via Dirichlet process mixture model
Shengtong Han*, Hongmei Zhang, Wenhui Sheng, Hasan Arshad. Journal of Statistical Computation and Simulation, 2019, 89, 815-830. [PDF][Link]

5. Adjusting background noise in cluster analysis of longitudinal data
Shengtong Han, Hongmei Zhang, Wilfried Karmaus, Graham Roberts, Hasan Arshad. Computational Statistics and Data Analysis, 2017, 109, 93-104. [PDF][Link]

4. An efficient Bayesian approach for Gaussian Bayesian network structure learning
Shengtong Han, Hongmei Zhang, Wilfried Karmaus, Graham Roberts, Hasan Arshad. Communications in Statistics-Simulation and Computation, 2017, 46, 5070-5084. [PDF][Link]

3. Identifying heterogeneous transgenerational DNA Methylation sites via clustering in Beta regression
Shengtong Han, Hongmei Zhang, Gabrielle A. Lockett, Nandini Mukherjee, John W. Holloway, Wilfried J.J. Karmaus. The Annals of Applied Statistics, 2015, 9, 2052-2072. [PDF][Link]

2. A Full Bayesian Approach for Boolean Genetic Network Inference
Shengtong Han, Raymond K. W. Wong, Thomas C. M. Lee, Linghao Shen, Shuo-Yen R. Li, Xiaodan Fan. PLoS ONE, 2014, 9(12): e115806. doi:10.1371/journal.pone. 0115806. [PDF][Link]

1. Smoothing Spline Estimation for Partially Linear Single-index Models
Bartholomew Leung, Heung Wong, Riquan Zhang, Shengtong Han. Communications in Statistics-Simulation and Computation, 2010, 39, 1953-1961. [PDF][Link]

Book Chapters

1. A Nested Clustering Method to Detect and Cluster Transgenerational DNA Methylation Sites Via Beta Regressions
Jiajing Wang, Hongmei Zhang, Shengtong Han. Modern Statistical Methods for Health Research, Springer, 2021. [Link]