Research

Systematic investigation of metabolic reprogramming in different cancers based on multi-omics data

Cancer is a heterogeneous disease. Characterization of such heterogeneity at different scales and resolutions is not well established. Our research strategy consists in leveraging cutting-edge sequencing techniques and bioinformatic approaches to ultimately understand various aspect of tumors.

We have developped a computational pipeline to study metabolic programs in single cells (https://github.com/zhengtaoxiao/Single-Cell-Metabolic-Landscape). By applying this workflow to two representative human cancers, melanoma and head and neck, we enable to explore the metabolic features of different cell type/subtypes and to define principles of the tumor microenvironment (Xiao et al. 2019. Nat. Commun.).

This study has been selected as top10 cancer research publication by the Board of the European Association for Cancer Research (https://magazine.eacr.org/the-eacrs-top-10-cancer-research-publications-november-2019/8/).

Metabolic effects on chromatin modifications and structures

Chromatin and associated epigenetic marks provide important platforms for gene regulation in response to metabolic changes associated with environmental exposures, including physiological stress, nutritional deprivation, and starvation. The fluctuations of key metabolites can influence chromatin modifications, but their effects on chromatin structure (e.g. chromatin compaction, nucleosome arrangement, and chromatin loops) and how they appropriately deposit specific chemical modification on chromatin are largely unknown. We are seeking to investigate the metabolic effects on chromatin modifications and structure, as well as consequences on gene regulation (Xiao & Locasale 2021. Current Opinion in Chemical Biology).

Identification of genome-wide translation dysregulations and novel translated peptides with ribosome profiling data

By capturing and sequencing the RNA fragments protected by translating ribosomes, ribosome profiling provides snapshots of translation at subcodon resolution. Specialized for the ribosome profiling data, we developped a computational piple for comprehensively and accurately identifing differentially translated genes in pairwise comparisons (Xiao et al. 2016. Nat. Commun.) and another piple for de novo annotating and charaterizing the translatome (Xiao et al. 2018. NAR.). We provided step-by-step instructions for identifying the actively translated open reading frames (ORFs) and evaluating the translation rates of the predicted ORFs with ribosome profiling data (Zhu et al. 2022. JoVE).