The transcription factor CREB3L1 is expressed in a wide variety of tissues including cartilage, pancreas, and bone. It is located in the endoplasmic reticulum and upon stimulation is transported to the Golgi where is proteolytically cleaved. Then, the N-terminal domain translocates to the nucleus to activate gene expression. In thyroid follicular cells, CREB3L1 is a downstream effector of thyrotropin (TSH), promoting the expression of proteins of the secretory pathway along with an expansion of the Golgi volume. Here, we analyzed the role of CREB3L1 as a TSH-dependent transcriptional regulator of the expression of the sodium/iodide symporter (NIS), a major thyroid protein that mediates iodide uptake. We show that overexpression and inhibition of CREB3L1 induce an increase and decrease in the NIS protein and mRNA levels, respectively. This, in turn, impacts on NIS-mediated iodide uptake. Furthermore, CREB3L1 knockdown hampers the increase the TSH-induced NIS expression levels. Finally, the ability of CREB3L1 to regulate the promoter activity of the NIS-coding gene (Slc5a5) was confirmed. Taken together, our findings highlight the role of CREB3L1 in maintaining the homeostasis of thyroid follicular cells, regulating the adaptation of the secretory pathway as well as the synthesis of thyroid-specific proteins in response to TSH stimulation.

Protein secretion is an essential process of mammalian cells. In biomanufacturing, this process can be optimized to enhance production yields and biotherapeutic quality. While cell line engineering and bioprocess optimization have yielded high protein titers for some recombinant proteins, many remain difficult to express. Here, we investigated factors influencing protein expression in Chinese hamster ovary (CHO) cells, expressing 2,135 Human Secretome Project proteins. While the abundance of mRNA from recombinant proteins explained less than 1% of observed variation in secretion titers, analysis of 218 biochemical and biophysical descriptors uncovered intrinsic protein features that account for ~15% of secretion variability, pinpointing key drivers such as molecular weight, cysteine content, and N-linked glycosylation, and establishing a roadmap for rational design of difficult-to-express proteins. We subsequently analyzed RNA-Seq data from 95 CHO cell cultures, each expressing a distinct recombinant protein, spanning a wide range of titers. Host cell transcriptomic signatures showed strong correlations with titer, thereby providing insights into cellular processes that covary with expression. Cells failing to produce proteins exhibited increased ubiquitin-mediated proteasomal degradation, including ER-associated degradation; whereas high-producing cells demonstrated enhanced lipid metabolism and a stronger response to oxidative stress, suggesting these factors may support successful recombinant protein productions. Together, using this resource, we quantified the contributions of various protein and cellular factors that correlate with the expression of diverse recombinant human proteins in a heterologous host, thereby providing insights for next-generation CHO cell engineering.

Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present i CHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, i CHO3K includes 3489 protein structures and structural descriptors for >70% of its 7377 metabolites, enabling deeper exploration of the link between molecular structure and cellular metabolism. We used i CHO3K to contextualize transcriptomics and metabolomics data from a CHO cell line in which lactate secretion is abolished. We found the reduced glycolytic flux and enhanced TCA cycle flux were accompanied by an elevated NADH and PEP levels in these cells, consistent with experimental measurements. Leveraging i CHO3K’s structural annotations, we identified candidate binding interactions of NADH and PEP with glycolytic enzymes showing model-predicted differential flux, suggesting novel allosteric regulation associated with the observed decrease in glucose uptake and glycolysis. Overall, i CHO3K offers a valuable framework for systematic integration of omics data, improved flux predictions, and structure-guided insights, thus advancing CHO cell engineering and enhancing biomanufacturing efficiency.

The secretory pathway processes >30% of mammalian proteins, orchestrating their synthesis, modification, trafficking, and quality control. However, its complexity— spanning multiple organelles and dependent on coordinated protein interactions—limits our ability to decipher how protein secretion is controlled in biomedical and biotechnological applications. To advance such research, we present secRecon—a comprehensive reconstruction of the mammalian secretory pathway, comprising 1,127 manually curated genes organized within an ontology of 77 secretory process terms, annotated with functional roles, subcellular localization, protein interactions, and complex composition. Using secRecon to integrate multi-omics data, we identified distinct secretory topologies in antibody-producing plasma cells compared to CHO cells. Genes within proteostasis, translocation, and N-glycosylation are deficient in CHO cells, highlighting them as potential engineering targets to boost secretion capacity. Applying secRecon to single-cell transcriptomics and SEC-seq data, we uncovered secretory pathway signatures underlying secretion diversity among IgG-secreting plasma cells. Different transcriptomic clusters had unique secretory phenotypes characterized by variations in the unfolded protein response (UPR), endoplasmic reticulum-associated degradation (ERAD), and vesicle trafficking pathways. Additionally, we discovered specific secretory machinery genes as new markers for plasma cell differentiation. These findings demonstrate secRecon can identify mechanisms regulating protein secretion and guide diverse studies in biomedical research and biotechnology. Graphical

Genome-scale metabolic models (M-models) provide mechanistic insight into intracellular metabolism by simulating fluxes subject to nutrient and energy resource constraints. However, they cannot account for a major component of resource allocation, since they do not explicitly account for the cost of producing and maintaining enzymes. Genome-scale models of metabolism and gene expression (ME-Models) address this by including gene expression reactions, but these have only been developed for prokaryotes due to the additional complexity and challenges of modeling eukaryotes. Here, we present the human ME-Model, which encodes transcription, translation, complex formation, and turnover reactions for all enzymes catalyzing metabolic reactions, and couples these processes to constrain metabolic fluxes. We introduce humanME, a Python package to build and analyze human ME-Models. With this, we constructed 16 cancer cell line ME-Models. We found that resource constraints improve growth-rate predictions, and that ME-Model flux predictions are more biologically plausible and efficient. Moreover, transcriptional fluxes recapitulate RNA-Seq expression levels, with discrepancies revealing potential trade-offs involving multiple cellular objectives. Finally, the ME-Model recapitulates the Warburg effect, with increasing growth rate inducing glycolytic shifts, in part due to machinery costs of the electron transport chain. Altogether, we show ME-modeling can mechanistically link gene expression, resource allocation, and metabolism in human cells, substantially expanding the predictive scope of constraint-based models.