Chenwei Wang

Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous hematological malignancy. Here, to better define this clinically taxing and translationally challenging malignancy, we applied a multiomics approach, consisting of 13 modalities to analyze 173 treatment-naive individuals with AML. By integrating these ‘omes’, we identified distinct AML subtypes, genotype–phenotype associations, biomarkers and pathobiological mechanisms. Across the spectrum of primitive and committed AML, we found extensive metabolomic and lipidomic reprogramming driven by divergent MYC and mTOR activity. We linked metabolic changes to striking hyperacetylation of mitochondrial proteins in CEBPA -mutant AML. Protein-centric subtyping revealed a distinct NPM1 -mutant subset characterized by outlier expression of FOXC1 and HOXB8/9. To nominate therapeutic targets across subtypes, we developed a multiomic machine-learning approach and validated MTA1 as a contributor to panobinostat resistance. Altogether our findings underscore the complex nature of AML and provide a clinically and translationally informed unified view that reveals coalescent phenotypes across multiomic layers.

Neurofibromin/NF1 is a RAS (rat sarcoma virus) GTPase-activating protein and estrogen receptor (ER) transcriptional corepressor. NF1 ^low status, identified by copy number loss or low mRNA/protein expression, is associated with endocrine therapy resistance in ~20% of ER ⁺ /HER2 ⁻ (human epidermal growth factor receptor 2) early-stage breast cancers. The identification of targeted treatments for NF1 ^low ER ⁺ /HER2 ⁻ breast cancer is therefore a priority. In this study, proteogenomic analysis of ER ⁺ /HER2 ⁻ breast cancer demonstrated that NF1 ^low tumors exhibited elevated cyclin-dependent kinase 4/6 (CDK4/6) activity. In cell lines, NF1 deletion had a dual effect on CDK4 activity: first, by promoting ER recruitment to CCND1 (cyclin D1), thereby increasing CDK4–cyclin D1 complex formation, and second, by activating C-RAF (rapidly accelerated fibrosarcoma), which drove phosphorylation of the CDK4 activation loop. Preclinical modeling demonstrated that NF1 ^low ER ⁺ cancer cells were more sensitive to fulvestrant combined with a CDK4/6 inhibitor versus fulvestrant alone, with the induction of cell death in vitro and durable tumor regressions in ER ⁺ NF1 ^low patient-derived xenograft models in vivo. Furthermore, NF1 ^low ER ⁺ /HER2 ⁻ tumors were more sensitive to neoadjuvant aromatase inhibitor (AI) plus palbociclib than to neoadjuvant AI alone, as indicated by suppression of mRNA-based proliferation scores. These data are consistent with a model whereby ER and RAS coactivation upon NF1 loss can drive CDK4/6 activity and endocrine therapy resistance but renders NF1 ^low ER ⁺ tumors susceptible to CDK4/6 inhibition. Development of clinical-grade NF1 diagnostics should be prioritized to determine whether NF1 ^low ER ⁺ breast cancers should receive adjusted adjuvant treatment recommendations that reflect increased responsiveness to CDK4/6 inhibition.

Post-translational modifications (PTMs) are critical regulators of protein function, and their disruption is a key mechanism by which missense variants contribute to disease. Accurate PTM site prediction using deep learning can help identify PTM-altering variants, but progress has been limited by the lack of large, high-quality training datasets. Here, we introduce PTMAtlas, a curated compendium of 397,524 PTM sites generated through systematic reprocessing of 241 public mass-spectrometry datasets, and DeepMVP, a deep learning framework trained on PTMAtlas to predict PTM sites for phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation. DeepMVP substantially outperforms existing tools across all six PTM types. Its application to predicting PTM-altering missense variants shows strong concordance with experimental results, validated using literature-curated variants and cancer proteogenomic datasets. Together, PTMAtlas and DeepMVP provide a robust platform for PTM research and a scalable framework for assessing the functional consequences of coding variants through the lens of PTMs.

Dysregulation of metabolites is a hallmark of cancer, yet the underlying regulatory mechanisms remain poorly understood. To systematically explore metabolic regulation across cancers, we developed an XGBoost-based machine learning pipeline, MetaSage, that integrates context-agnostic knowledge graph with multi-omics datasets. Using harmonized data from 15 cohorts spanning 11 cancer types, we identified 442 variable metabolites and found that both genes and upstream metabolites showed comparable regulatory influence. Predictable metabolites, defined by a significant correlation between predicted and measured levels, were identified using our pipeline and varied widely across cohorts-partially due to the batch effect. For each predictable metabolite, key regulatory features were determined using Shapley values. This yielded 1,146 gene features and 363 precursor metabolites as important regulators. Network analysis of 22 recurrent metabolites revealed a mix of conserved and cancer type-specific regulatory patterns. Our framework enables robust discovery of metabolite regulation and therapeutic insights in cancer.

Among different age groups, middle-aged individuals are particularly susceptible to obesity, with a 22% higher risk of all-cause mortality. However, the underlying mechanisms remain unclear. In this study, we identify adipose progenitor cells (APCs) in the white adipose tissue (WAT) of middle-aged subjects as potential causes of midlife obesity. Specifically, the extracellular vesicles (EVs) derived from APCs display an impaired ability to mitigate the inflammaging of adipose tissue macrophages (ATMs) in middle-aged individuals. Mechanistically, these EVs, lacking miR-145-5p, fail to suppress the expression of L-selectin in ATMs, thereby facilitating their M1 program via the NF-κB signaling pathway. In contrast, EVs from young APCs effectively inhibit M1 macrophage polarization. Accordingly, targeted liposomes are designed to deliver miR-145-5p mimics to ATMs, which effectively prevent the obesity in middle-aged mice. Collectively, our findings highlight the role of APC-derived EVs in midlife obesity and propose miR-145-5pas a promising therapeutic target for clinical applications.

In a groundbreaking study, Eckert and colleagues introduce DecryptE, an innovative approach to dose-resolved proteomics that significantly advances our understanding of drug effects at the proteomic level. This method integrates cutting edge sample preparation and mass spectrometry technologies, establishing a robust platform for high-throughput proteome analysis. DecryptE enables the quantification of more than 7,000 proteins per hour and was employed to study 144 clinical drugs and research compounds, generating more than 1 million dose-response curves using Jurkat acute T-cell leukemia cells as a model system. The platform demonstrates outstanding reproducibility, ensuring reliable and consistent results across multiple experiments. By providing detailed information on drug potency and efficacy, DecryptE allows the identification of subtle changes in protein expression and facilitates the clustering of drugs based on their proteomic profiles. This study not only reveals novel drug mechanisms but also creates a comprehensive resource that can be utilized by the broader research community. Furthermore, it highlights the potential of integrating proteomics—and potentially other omics modalities in the future—with dose-response analysis to advance pharmacological research and improve therapeutic strategies.

Proteostasis is fundamental for maintaining organismal health. However, the mechanisms underlying its dynamic regulation and how its disruptions lead to diseases are largely unclear. Here, we conduct in-depth propionylomic profiling in Drosophila, and develop a small-sample learning framework to prioritize the propionylation at lysine 17 of H2B (H2BK17pr) to be functionally important. Mutating H2BK17 which eliminates propionylation leads to elevated total protein level in vivo. Further analyses reveal that H2BK17pr modulates the expression of 14.7–16.3% of genes in the proteostasis network, and determines global protein level by regulating the expression of genes involved in the ubiquitin-proteasome system. In addition, H2BK17pr exhibits daily oscillation, mediating the influences of feeding/fasting cycles to drive rhythmic expression of proteasomal genes. Our study not only reveals a role of lysine propionylation in regulating proteostasis, but also implements a generally applicable method which can be extended to other issues with little prior knowledge.

Protein kinase-mediated phosphorylation plays a critical role in many biological processes. However, the identification of key regulatory kinases is still a great challenge. Here, we develop a trans-omics-based method, central kinase inference, to predict potentially key kinases by integrating quantitative transcriptomic and phosphoproteomic data. Using known kinases associated with anti-cancer drug resistance, the accuracy of our method denoted by the area under the curve is 5.2% to 29.5% higher than Kinase-Substrate Enrichment Analysis. We further use this method to analyze trans-omic data in hepatocyte maturation and hepatic reprogramming of human dermal fibroblasts, uncovering 5 kinases as regulators in the two processes. Further experiments reveal that a serine/threonine kinase, PIM1, promotes hepatic conversion and protects human dermal fibroblasts from reprogramming-induced ferroptosis and cell cycle arrest. This study not only reveals new regulatory kinases, but also provides a helpful method that might be extended to predict central kinases involved in other biological processes.

Programmed cell death (PCD) is an essential biological process involved in many human pathologies. According to the continuous discovery of new PCD forms, a large number of proteins have been found to regulate PCD. Notably, post-translational modifications play critical roles in PCD process and the rapid advances in proteomics have facilitated the discovery of new PCD proteins. However, an integrative resource has yet to be established for maintaining these regulatory proteins. Here, we briefly summarize the mainstream PCD forms, as well as the current progress in the development of public databases to collect, curate and annotate PCD proteins. Further, we developed a comprehensive database, with integrated annotations for programmed cell death (iPCD), which contained 1,091,014 regulatory proteins involved in 30 PCD forms across 562 eukaryotic species. From the scientific literature, we manually collected 6493 experimentally identified PCD proteins, and an orthologous search was then conducted to computationally identify more potential PCD proteins. Additionally, we provided an in-depth annotation of PCD proteins in eight model organisms, by integrating the knowledge from 102 additional resources that covered 16 aspects, including post-translational modification, protein expression/proteomics, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein–protein interaction, drug–target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, subcellular localization and DNA and RNA element. With a data volume of 125 GB, we anticipate that iPCD can serve as a highly useful resource for further analysis of PCD in eukaryotes.

In Saccharomyces cerevisiae, Atg9 is an important autophagy-related (Atg) protein, and interacts with hundreds of other proteins. How many Atg9-interacting proteins are involved in macroautophagy/autophagy is unclear. Here, we conducted a multi-omic profiling of Atg9-dependent molecular landscapes during nitrogen starvation-induced autophagy, and identified 290 and 256 genes to be markedly regulated by ATG9 in transcriptional and translational levels, respectively. Unexpectedly, we found most of known Atg proteins and autophagy regulators that interact with Atg9 were not significantly changed in the mRNA or protein level during autophagy. Based on a hypothesis that proteins with similar molecular characteristics might have similar functions, we developed a new method named inference of functional interacting partners (iFIP) to integrate the transcriptomic, proteomic and interactomic data, and predicted 42 Atg9-interacting proteins to be potentially involved in autophagy, including 15 known Atg proteins or autophagy regulators. We validated 2 Atg9-interacting partners, Glo3 and Scs7, to be functional in both bulk and selective autophagy. The mRNA and protein expressions but not subcellular localizations of Glo3 and Scs7 were affected with or without ATG9 during autophagy, whereas the colocalizations of the 2 proteins and Atg9 were markedly enhanced at early stages of the autophagic process. Further analyses demonstrated that Glo3 but not Scs7 regulates the retrograde transport of Atg9 during autophagy. A working model was illustrated to highlight the importance of the Atg9 interactome. Taken together, our study not only provided a powerful method for analyzing the multi-omics data, but also revealed 2 new players that regulate autophagy. Abbreviations: ALP: alkaline phosphatase; Arf1: ADP-ribosylation factor 1; Atg: autophagy-related; Co-IP: co-immunoprecipitation; Cvt: cytoplasm-to-vacuole targeting; DEM: differentially expressed mRNA; DEP: differentially expressed protein; DIC: differential interference contrast; E-ratio: enrichment ratio; ER: endoplasmic reticulum; ES: enrichment score; FC: fold change; FPKM: fragments per kilobase of exon per million fragments mapped; GAP: GTPase-activating protein; GFP: green fluorescent protein; GO: gene ontology; GSEA: gene set enrichment analysis; GST: glutathione S-transferase; HA: hemagglutinin; iFIP: inference of functional interacting partners; KO: knockout; LR: logistic regression; OE: over-expression; PAS: phagophore assembly site; PPI: protein-protein interaction; RFP: red fluorescence protein; RNA-seq: RNA sequencing; RT-PCR: real-time polymerase chain reaction; SCC: Spearman’s correlation coefficient; SD-N: synthetic minimal medium lacking nitrogen; THANATOS: The Autophagy, Necrosis, ApopTosis OrchestratorS; Vsn: variance stabilization normalization; WT: wild-type.