Biology subjects

Qiang, H.

Publications and source records attributed to Qiang, H..

5 recordsLinked to original sources

bioRxiv2026

Intrinsic immune receptor triggering mechanism informs rational chimeric receptor design

The T cell receptor (TCR) couple antigen recognition to T-cell activation and immune homeostasis. As an eight-subunit assembly, TCR activation operates as a highly coordinated, multi-subunit signaling event. However, which subunit serves as the primary trigger upon ligand engagement still remains unresolved. we identify CD3{varepsilon} as a triggering subunit whose intracellular domain (ICD) is released from the plasma membrane during TCR activation. Elevated membrane tension facilitates this transition from a membrane-proximal to a membrane-distal conformation. The released ICD forms condensates through phase separation with free or co-receptor-associated Lck. These condensates recruit additional CD3{varepsilon} ICDs from neighboring TCRs, triggering resting receptors and promoting further recruitment of TCRs and Lck to amplify proximal signaling. The CD3{varepsilon} ICD alone support robust T cell activation independently of its own ITAM. We observe a similar triggering mechanism in several activating receptors in T and nature killer (NK) cell. These findings support a unifying model in which designated subunits initiate multi-subunit immune receptor activation through phase separation with key kinases. Guided by this model, we engineered triggering molecules (TRIG) that lack canonical intracellular signaling modules. These TRIG molecules recruit endogenous immune receptors in a ligand-dependent manner and efficiently activate both T cells and NK cells. This mechanism provides a basis for designing chimeric receptors that activate immune cells through endogenous receptor signaling.

immunology↗

bioRxiv2026

Engaging the host adaptive response to nutrient limitation delays cancer cachexia

Cancer cachexia is a debilitating wasting syndrome associated with involuntary weight loss, reduced appetite and food intake, adipose and muscle atrophy, and systemic inflammation with induction of a myriad of candidate cachexia factors. Using a multi-omic approach in a genetically engineered mouse model of lung cancer cachexia, we found that early circulating metabolic alterations preceded overt weight loss. Although both cachexia and fasting are characterized by a reduction in food intake, we show that the systemic metabolic changes in cachexia are distinct, indicating a lack of induction in the normal protective response to negative energy balance during cachexia. Cancer cachexia causes profound hepatic reprogramming, and activation of inflammatory programs, alongside muscle atrophy and elevation of circulating cytokines. However, targeting individual candidate cachectic factors such as IL-6, TNF-, GDF15, CCL2, LIF, and IL17A failed to rescue weight loss or prolong survival. In contrast, caloric restriction initiated prior to tumor induction elicited a program of protection during tumor progression that attenuated multi-tissue inflammatory and catabolic transcriptional responses, preserved lean mass, and prolonged survival. Together, these findings suggest that metabolic preconditioning broadly dampens systemic inflammation and delays the onset and progression of cancer cachexia.

cancer biology↗

bioRxiv2026

Mapping the mammalian dark metabolome by in vivo isotope tracing

Despite decades of biochemical study, a comprehensive map of the mammalian metabolome remains elusive. Mass spectrometry-based metabolomics detects thousands of small molecule-associated signals in mammalian tissues, but it is currently unclear how many of these reflect products of endogenous metabolism. Here, we leverage systematic in vivo isotope tracing to infer the biosynthetic origins of unidentified metabolites. We administered 26 different isotopically labelled nutrients to mice, measured circulating and tissue metabolite labelling by mass spectrometry, and developed a statistical framework to infer the number of carbon atoms incorporated from each of these precursors into more than 4,000 putative metabolites. We show this information can be harnessed for biosynthesis-aware structure elucidation using a multimodal AI model that co-embeds isotopic labelling patterns with chemical structures. This approach revealed several previously unrecognized families of mammalian metabolites, including cysteine-derived alkylthiazolidines, dithioacetal mercapturic acid derivatives, short-chain N-acyltaurines, acylglycyltaurines, and N-oxidized taurines. It further uncovered a family of mevalonate-derived isoprenoid metabolites that includes 2,3-dihydrofarnesoic acid, which is markedly depleted in both mouse and human aging. Age-related depletion of these isoprenoids is driven by impaired coenzyme A synthesis. Our work establishes the biosynthetic precursors for thousands of unidentified metabolites and reveals multiple previously unrecognized branches of mammalian metabolism.

biochemistry↗

bioRxiv2024

Language model-guided anticipation and discovery of unknown metabolites

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

bioinformatics↗

bioRxiv2023

Phospholipid isotope tracing reveals β-catenin-driven suppression of phosphatidylcholine metabolism in hepatocellular carcinoma

Background and AimsActivating mutations in the CTNNB1 gene encoding {beta}-catenin are among the most frequently observed oncogenic alterations in hepatocellular carcinoma (HCC). HCC with CTNNB1 mutations show profound alterations in lipid metabolism including increases in fatty acid oxidation and transformation of the phospholipidome, but it is unclear how these changes arise and whether they contribute to the oncogenic program in HCC. MethodsWe employed untargeted lipidomics and targeted isotope tracing to quantify phospholipid production fluxes in an inducible human liver cell line expressing mutant {beta}-catenin, as well as in transgenic zebrafish with activated {beta}-catenin-driven HCC. ResultsIn both models, activated {beta}-catenin expression was associated with large changes in the lipidome including conserved increases in acylcarnitines and ceramides and decreases in triglycerides. Lipid flux analysis in human cells revealed a large reduction in phosphatidylcholine (PC) production rates as assayed by choline tracer incorporation. We developed isotope tracing lipid flux analysis for zebrafish and observed similar reductions in phosphatidylcholine synthesis flux accomplished by sex-specific mechanisms. ConclusionsThe integration of isotope tracing with lipid abundances highlights specific lipid class transformations downstream of {beta}-catenin signaling in HCC and suggests future HCC-specific lipid metabolic targets. SynopsisIn this work, we show by lipid specific isotope tracing that mutations in the oncogene CTNNB1 leads to conserved changes in lipid metabolism in hepatocellular carcinoma. These include the stimulation of fatty acid oxidation and a suppression of phosphorylcholine synthesis.

cancer biology↗