Neutrophil diversity is associated with T-cell immunity and clinical relevance in patients with thyroid cancer.

Neutrophil heterogeneity is involved in autoimmune diseases, sepsis, and several cancers. However, the link between neutrophil heterogeneity and T-cell immunity in thyroid cancer is incompletely understood. We investigated the circulating neutrophil heterogeneity in 3 undifferentiated thyroid cancer (UTC), 14 differentiated thyroid cancer (DTC) (4 Stage IV, 10 Stage I-II), and healthy controls (n = 10) by transcriptomic data and cytometry. Participants with UTC had a significantly higher proportion of immature high-density neutrophils (HDN) and lower proportion of mature HDN in peripheral blood compared to DTC. The proportion of circulating PD-L1+ immature neutrophils were significantly increased in advanced cancer patients. Unsupervised analysis of transcriptomics data from circulating HDN revealed downregulation of innate immune response and T-cell receptor signaling pathway in cancer patients. Moreover, UTC patients revealed the upregulation of glycolytic process and glutamate receptor signaling pathway. Comparative analysis across tumor types and stages revealed the downregulation of various T-cell-related pathways, such as T-cell receptor signaling pathway and T-cell proliferation in advanced cancer patients. Moreover, the proportions of CD8+ and CD4+ T effector memory CD45RA+ (TEMRA) cells from peripheral blood were significantly decreased in UTC patients compared to DTC patients. Finally, we demonstrated that proportions of tumor-infiltrated neutrophils were increased and related with poor prognosis in advanced thyroid cancer using data from our RNA-seq and TCGA (The Cancer Genome Atlas) data. In conclusion, observed prevalence of circulating immature high-density neutrophils and their immunosuppressive features in undifferentiated thyroid cancers underscore the importance of understanding neutrophil dynamics in the context of tumor progression in thyroid cancer.

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.

Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.

Sustainable metabolic engineering requires a perfect trifecta.

The versatility of cellular metabolism in converting various substrates to products inspires sustainable alternatives to conventional chemical processes. Metabolism can be engineered to maximize the yield, rate, and titer of product generation. However, the numerous combinations of substrate, product, and organism make metabolic engineering projects difficult to navigate. A perfect trifecta of substrate, product, and organism is prerequisite for an environmentally and economically sustainable metabolic engineering endeavor. As a step toward this endeavor, we propose a reverse engineering strategy that starts with product selection, followed by substrate and organism pairing. While a large bioproduct space has been explored, the top-ten compounds have been synthesized mainly using glucose and model organisms. Unconventional feedstocks (e.g. hemicellulosic sugars and CO2) and non-model organisms are increasingly gaining traction for advanced bioproduct synthesis due to their specialized metabolic modes. Judicious selection of the substrate-organism-product combination will illuminate the untapped territory of sustainable metabolic engineering.

Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes.

Complete understanding of a biological system requires quantitation of metabolic fluxes that reflect its dynamic state. Various analytical chemistry tools, enzyme-based probes, and microscopy enable flux measurement. However, any method alone falls short of comprehensive flux quantitation. Here we show that integrating these techniques results in a systems-level quantitative map of absolute metabolic fluxes that constitute an indispensable dimension of characterizing phenotypes. Stable isotopes, mass spectrometry, and NMR spectroscopy reveal relative pathway fluxes. Biochemical probes reveal the physical rate of environmental changes. FRET-based and SRS-based microscopy reveal targeted metabolite and chemical bond formation. These techniques are complementary and can be computationally integrated to reveal actionable information on metabolism. Integrative metabolic flux analysis using various quantitative techniques advances biotechnology and medicine.