Genome- scale metabolic models (GEMs) have become essential tools for understanding human metabolism. Here, we introduce Human2, a consensus human GEM with enhanced precision and biological relevance, which leverages large language models (LLMs) and GitHub Action checks to streamline automated, efficient, and collaborative curation. Human2 supports the reconstruction of tissue- and organ- specific models tailored to sex- and age- specific human groups. By integrating transcriptomic, proteomic, and kinetic data, we reveal distinct metabolic features across these groups, such as significant differences in arachidonic acid and leukotriene metabolism. The specific models were integrated into a dynamic whole- body framework, marking an enzyme- constrained dynamic model that simulates interorgan metabolite exchanges under varying nutritional states, from feeding to fasting. Our work highlights the transformative role of LLMs in GEM reconstruction and introduces a whole- body dynamic simulation that integrates kinetic data, offering a powerful resource for multiscale human metabolism modeling.

The energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons and fluctuations in ATP demand over time. To investigate this, we used metabolic models that estimate enzyme usage per pathway, enzyme utilization over time, and enzyme transportation to evaluate how these parameters and processes affect ATP costs for enzyme synthesis and transportation. Our models show that the total enzyme maintenance energy expenditure of the human body depends on how glycolysis and mitochondrial respiration are distributed both across and within cell types in the brain. We suggest that brain metabolism is optimized to minimize the ATP maintenance cost by distributing the different ATP generation pathways in an advantageous way across cell types and potentially also across synapses within the same cell. Our models support this hypothesis by predicting export of lactate from both neurons and astrocytes during peak ATP demand, reproducing results from experimental measurements reported in the literature. Furthermore, our models provide potential explanation for parts of the astrocyte-neuron lactate shuttle theory, which is recapitulated under some conditions in the brain, while contradicting other aspects of the theory. We conclude that enzyme usage per pathway, enzyme utilization over time, and enzyme transportation are important factors for defining the optimal distribution of ATP production pathways, opening a broad avenue to explore in brain metabolism.

The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer-associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme-constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

ABSTRACT The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availability standard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

Triple negative breast cancer (TNBC) metastases are assumed to exhibit similar functions in different organs as in the original primary tumor. However, studies of metastasis are often limited to a comparison of metastatic tumors with primary tumors of their origin, and little is known about the adaptation to the local environment of the metastatic sites. We therefore used transcriptomic data and metabolic network analyses to investigate whether metastatic tumors adapt their metabolism to the metastatic site and found that metastatic tumors adopt a metabolic signature with some similarity to primary tumors of their destinations. The extent of adaptation, however, varies across different organs, and metastatic tumors retain metabolic signatures associated with TNBC. Our findings suggest that a combination of anti-metastatic approaches and metabolic inhibitors selected specifically for different metastatic sites, rather than solely targeting TNBC primary tumors, may constitute a more effective treatment approach.

Adoptive cell therapy (ACT) based on ex vivo expanded autologous tumor-infiltrating lymphocytes (TILs) can mediate durable antitumor responses even in heavily pretreated patients. However, only a subset of patients responds to ACT; efforts to identify correlates of response have focused on profiling the tumor or the TIL but rarely in an interactive environment. Interactive profiling can provide unique insights into the clinical performance of TILs since the fate, function, and metabolism of TILs are influenced by autologous tumor-derived factors. Here, we performed a suite of cell-sparing assays dubbed holistic analysis of the bioactivity of interacting T cells and autologous tumor cells (HABITAT). HABITAT profiling of TILs used for human ACT and their autologous tumor cells included function-based single-cell profiling by timelapse imaging microscopy in nanowell grids (TIMING); multi-omics using RNA-sequencing and proteomics; metabolite inference using genome-scale metabolic modeling, and pulse-chase assays based on confocal microscopy to profile the uptake and fate of fatty acids (FA). Phenotypically, the ACT TILs from both responders (Rs) and nonresponders (NRs) were comprised of predominantly effector memory T cells (TEM cells) and did not express a high frequency of programmed death ligand-1 (PD-L1) and showed no differences in TCR diversity. Our results demonstrate that while tumor cells from both Rs and NRs are efficient at uptaking FAs, R TILs are significantly more efficient at utilizing FA through fatty acid oxidation (FAO) than NR TILs under nutrient starvation conditions. While it is likely that lipid and FA uptake is an inherent adaptation of TIL populations to lipid-rich environments, performing FAO sustains the survival of TILs and allows them to sustain antitumor cytolytic activity. We propose that metabolic plasticity enabling FAO is a desirable attribute of human TILs for ACT leading to clinical responses. ### Competing Interest Statement NV is co-founder of CellChorus. C.H. reports speaker fees from the Society for Immunotherapy of Cancer, serves as an advisory board member for Briacell and the Mesothelioma Applied Research Foundation, has received personal fees from Nanobiotix, and receives funding to the MD Anderson Cancer Center from Iovance, Sanofi, Dragonfly Therapeutics, and BTG outside the submitted work. CB is a current employee of Cell Therapy Manufacturing Center, a joint venture between MD Anderson Cancer Center and Resilience, reports having served as an advisory board member for Myst Therapeutics, having received research funding from Iovance Therapeutics while at MD Anderson Cancer Center, and currently receives research support from Obsidian Therapeutics. CB and CH are inventors of intellectual property related to TIL therapy and may be entitled to receive licensing fees. PH is on the SAB of Dragonfly and Immatics.

Energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons. Here we used metabolic models that estimate costs of enzyme usage per pathway, enzyme utilization over time, and enzyme transport to evaluate a paradigm that suggests that brain energy metabolism is optimized to minimize enzyme synthesis and transportation costs. Our models recapitulate known metabolic behaviors and provide explanation for the astrocyte-neuron lactate shuttle theory.

Recent studies highlight the importance of lipotoxic damage in aortic cells as the major pathogenetic contributor to atherosclerotic disease. Since the STE20-type kinase STK25 has been shown to exacerbate ectopic lipid storage and associated cell injury in several metabolic organs, we here investigate its role in the main cell types of vasculature. We depleted STK25 by small interfering RNA in human aortic endothelial and smooth muscle cells exposed to oleic acid and oxidized LDL. In both cell types, the silencing of STK25 reduces lipid accumulation and suppresses activation of inflammatory and fibrotic pathways as well as lowering oxidative and endoplasmic reticulum stress. Notably, in smooth muscle cells, STK25 inactivation hinders the shift from a contractile to a synthetic phenotype. Together, we provide several lines of evidence that antagonizing STK25 signaling in human aortic endothelial and smooth muscle cells is atheroprotective, highlighting this kinase as a new potential therapeutic target for atherosclerotic disease.

Recombinant protein production can cause severe stress on cellular metabolism, resulting in limited titer and product quality. To investigate cellular and metabolic characteristics associated with these limitations, we compare HEK293 clones producing either erythropoietin (EPO) (secretory) or GFP (non-secretory) protein at different rates. Transcriptomic and functional analyses indicate significantly higher metabolism and oxidative phosphorylation in EPO producers compared with parental and GFP cells. In addition, ribosomal genes exhibit specific expression patterns depending on the recombinant protein and the production rate. In a clone displaying a dramatically increased EPO secretion, we detect higher gene expression related to negative regulation of endoplasmic reticulum (ER) stress, including upregulation of ATF6B, which aids EPO production in a subset of clones by overexpression or small interfering RNA (siRNA) knockdown. Our results offer potential target pathways and genes for further development of the secretory power in mammalian cell factories.

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer's disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.

B cell responses are critical for antiviral immunity. However, a comprehensive picture of antigen-specific B cell differentiation, clonal proliferation, and dynamics in different organs after infection is lacking. Here, by combining single-cell RNA and B cell receptor (BCR) sequencing of antigen-specific cells in lymph nodes, spleen, and lungs after influenza infection in mice, we identify several germinal center ( GC) B cell subpopulations and organ-specific differences that persist over the course of the response. We discover transcriptional differences between memory cells in lungs and lymphoid organs and organ-restricted clonal expansion. Remarkably, we find significant clonal overlap between GC-derived memory and plasma cells. By combining BCR-mutational analyses with monoclonal antibody (mAb) expression and affinity measurements, we find that memory B cells are highly diverse and can be selected from both low- and high-affinity precursors. By linking antigen recognition with transcriptional programming, clonal proliferation, and differentiation, these finding provide important advances in our understanding of antiviral immunity.

Background Adoptive transfer of ex vivo expanded tumor-infiltrating lymphocytes (TIL) have shown durable responses in metastatic melanoma, yet these responses are unpredictable. Bioenergetics dictates the function and fate of adoptively transferred human T cells within the tumor microenvironment but the nature of metabolic competition leading to T-cell function and dysfunction are incompletely understood. Methods We integrated the profiling of TIL co-cultured with their autologous primary tumor cells with the aid of a suite of high-throughput single-cell functional assays, transcriptional, and proteomic assays. We validated the results of the model using flow cytometry and confocal microscopy. Association of functional features with clinical outcome was assessed. Results Timelapse Imaging Microscopy In Nanowell Grids (TIMING) demonstrated that while TIL frequencies in killing autologous tumor cells are equivalent across the donors, R-TIL had a significantly higher survival rate than NR-TILs. Tumor cells from NR patients had higher motility and showed increased elongation compared to R-tumors. RNA-sequencing (RNA-seq) and proteomics showed that NR-tumors were enriched in pathways associated with utilization of fatty acids (FAs) and adipogenesis, as well as cancer cell metastasis, cellular motility, adhesion, and migration. Candidate genes associated with ameboidal migration (MYH9, MYH2; WNT5B and SERPINE1) and FA utilization (CD36 and PPARG) were enriched in the NR-tumors. Flow cytometry and confocal microscopy confirmed that NR-tumors showed increased CD36 expression and FA uptake compared to R-tumors. To simulate metabolic competition, we co-cultured the TIL with autologous tumors and sorted TIL for RNAseq. The R-TIL were enriched in pathways related to mitochondrial and carbohydrate metabolism; fatty acid oxidation (FAO), and long-chain FAs with a direct enrichment in fatty acyl CoA biosynthesis and both peroxisomal and mitochondrial FAO. Since patient-derived TILs were limiting for metabolomics type assays, we utilized genome-scale metabolic models to infer relevant metabolic pathways by comparison to the human metabolic Atlas (HMR2). At the level of individual metabolites, the significantly enriched metabolites within R TILs were dominated by peroxisome and mitochondria derived fatty acyl-CoA: e.g. palmitoyl-CoA, linoleoyl-CoA, and oleoyl-CoA. We utilized flow cytometry and confocal microscopy to perform pulse-chase assays with FAs for validation. R-TILs showed an increased accumulation of FA into the mitochondria confirming a direct role for TIL FAO. Conclusions Efficient competition for FAs is a key attribute of T-cell efficacy in ACT. R-TILs are able to utilize FAs via FAO when in competition with autologous tumor cells whereas NR tumors effectively uptake and store FAs preventing T-cell function. Acknowledgements This abstract was supported by the NIH (R01CA174385); CPRIT (RP180466); MRA Established Investigator Award to NV (509800), Welch Foundation (E1774); NSF (1705464); CDMRP (CA160591); Owens Foundation (NV). We would like to acknowledge the MDACC Flow Cytometry and Cellular Imaging Core facility for the FACS sorting (NCI P30CA16672), UH Seq-n-edit core for RNA-seq, Intel for the loan of computing cluster, and the UH Center for Advanced Computing and Data Systems (CACDS) for high-performance computing facilities. Trial Registration protocol (2004–0069) Ethics Approval Approved by the Institutional Review Board (IRB) of the MD Anderson Cancer Center (Houston, TX) and an FDA- approved Investigational New Drug (IND) application ( NCT00338377 )

Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.

Pyranonaphthoquinones (PNQs) are important structural scaffolds found in numerous natural products. Research interest in these specialized metabolites lies in their natural occurrence and therapeutic activities. Nonetheless, research progress has thus far been hindered by the lack of analytical standards and analytical methods for both qualitative and quantitative analysis. We report here that various parts of Ventilago harmandiana are rich sources of PNQs. We developed an ultraperformance liquid chromatography–electrospray ionization multiple reaction monitoring/mass spectrometry method to quantitatively determine six PNQs from leaves, root, bark, wood, and heartwood. The addition of standards in combination with a stable isotope of salicylic acid-D6 was used to overcome the matrix effect with average recovery of 82% ± 1% (n = 15). The highest concentration of the total PNQs was found in the root (11,902 μg/g dry weight), whereas the lowest concentration was found in the leaves (28 μg/g dry weight). Except for the root, PNQ-332 was found to be the major compound in all parts of V. harmandiana, accounting for ∼48% of the total PNQs quantified in this study. However, PNQ-318A was the most abundant PNQ in the root sample, accounting for 27% of the total PNQs. Finally, we provide novel MS/MS spectra of the PNQs at different collision induction energies: 10, 20, and 40 eV (POS and NEG). For structural elucidation purposes, we propose complete MS/MS fragmentation pathways of PNQs using MS/MS spectra at collision energies of 20 and 40 eV. The MS/MS spectra along with our discussion on structural elucidation of these PNQs should be very useful to the natural products community to further exploring PNQs in V. harmandiana and various other sources.

The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays makes possible the identification of duplicated molecules, thereby facilitating the counting of distinct molecules from sequenced reads. However, we show that the naïve removal of duplicates can lead to a bias due to a “pooled amplification paradox,” and we propose an improved quantification method based on unseen species modeling. Our correction called BUTTERFLY uses a zero truncated negative binomial estimator implemented in the kallisto bustools workflow. We demonstrate its efficacy across cell types and genes and show that in some cases it can invert the relative abundance of genes.

Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE ( D own- SA mpling based V ariation E stimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. GEMs often encompass thousands of reactions, metabolites and genes, whose manipulation require complex data structures. Metabolic Atlas, through the web platform available at https://metabolicatlas.org , aims to make the entire GEM content available for easy navigation. This is achieved through both tabular and map views (2D and 3D), each suited for different usage scenarios. In addition, Metabolic Atlas aims to meet the needs of the community through the development of specific tools and features though iterative releases. Currently, Metabolic Atlas facilitates exploration and visualization of two open-source GEMs: Human1, an integration and extensive curation of the most recent human metabolic models ( Robinson et al., 2020 ), and Yeast8, a consensus metabolic model for S. cerevisiae ( Lu et al., 2019 ). The history of Metabolic Atlas starts before 2015 ( Pornputtapong et al., 2015 ). The present website has been re-developed from the ground up, following open-source standards. It was made publicly available mid 2019, and is now at version 1.7 ( Robinson et al., 2020 ). We plan to continue the development in a tic-toc method: a major release altering the foundation of the website, followed by several smaller releases. We are actively engaging the community to address the challenges in accessing GEMs for curation, analysis and biological understanding, under the guidance of FAIR principles.