BioInnovation Institute

In this study, we report that inhaled nanosized elongated mineral particles (EMPs) reach the human central nervous system (CNS) via two neuronal pathways, cranial nerve I (olfactorius) and cranial nerve V (trigeminus), from deposits on the nasal mucosa. High-resolution analytical imaging of autopsied brain tissues from eleven members of a Religious Orders Study (ROS) cohort (Rush Alzheimer’s Disease Center) indicated that EMPs translocate from their nasal deposits to the brain either by the olfactory pathway (presence in the olfactory bulb (OB), olfactory tract, and amygdala) or by the trigeminal pathway (presence in the cerebellum). Sub-nanometer imaging and immunohistochemical (IHC) labeling were used to detect corpora amylacea (CA), abundant numbers of endogenous ferritin nanoparticles, and myelin damage as indicators of inflammation or oxidative stress. The majority of EMPs in the OB were identified as inorganic crystalline and amorphous SiO2 fibers. Amphibole-like fibers (Mg/Si/Fe) were present (length from 25 up to 200 nm), along with lengthened nanoplastics and metallic or carbonaceous fibers. Extensive and consistent demyelination, phosphorylation, wall thickening, and CA bodies (size ranging from 10 nm to ∼10 μm) are present in all studied brain tissues. EMPs are frequently observed inside and outside of CA bodies that occur in close proximity to neurons with myelin damage. The majority of EMPs show shedding of nanosized fiber fragments and ions from their long fiber surfaces and the formation of carbon-rich coronas (physiochemical alterations: bioprocessing). Similar to spherical nanoparticles, EMPs show a tendency to bioprocess, which involves interacting with microglia, astrocytes, and CA. In conclusion, we note that although the presence of ambient EMPs in the OB, amygdala, and cerebellum of human brains is consistent with neuronal translocation from nasal deposits of inhaled EMPs to the human CNS, it remains important to further investigate the potential contribution of nano-EMPs entering from the blood compartment by crossing the blood–brain barrier (BBB) and other potential routes to the CNS.

Abstract The presence of organelles is a hallmark distinguishing eukarya from bacteria and archaea, and this culminates in compartmentalization of cellular metabolism and subsequent metabolic specialization. Here we established a dataset encompassing over 300 absolute quantitative proteomes, the largest to date, across two yeast species under diverse experimental conditions. Leveraging big data analysis, formula fitting, and machine learning models, quantitative correlations among protein abundance, organelle-level resource distribution, and cellular phenotypes were elucidated at a system level. We found that protein resources always exhibit robust and precise distribution at the organelle level across distinct conditions. Specifically, at high specific growth rates, the protein mass fraction from some main organelles, i.e., peroxisome and nucleus, is consistently reduced to offset the increasing protein resource demand from the ribosome. Meanwhile, we found that the nutrition limitation could induce resource recycling by upregulating protein resources within the vacuole and lipid droplets to sustain stress adaptation. Importantly, our integrative analysis demonstrates that protein mass fraction from less than 4 organelles (e.g., nucleus and ribosomes) can accurately predict diverse yeast physiological parameters (e.g., specific growth rate, oxygen uptake rate), and a core set of 37 proteins could predict resource allocation among 24 main organelles and sub-organelles with high accuracy (average R^2 > 0.9). Finally, we found organelle resource allocation reflects the divergence of yeast species. For example, anaerobic conditions and respiratory suppression have less influence on Crabtree-positive yeast, i.e., Saccharomyces cerevisiae, with respect to organelle resource allocation but have a larger effect on the Crabtree-negative yeast Issatchenkia orientalis, thus suggesting that cellular resources have facilitated adaptive evolution. In summary, the high-quality, genome-scale quantitative proteomic dataset for yeast species offers an unprecedented opportunity for understanding the basic principles underlying resource allocation at the organelle level, laying theoretical foundations for precision engineering of cell factories in synthetic biology. The resource used in this study is available at https://yeast-proteome-database.streamlit.app/.

Metabolism is at the core of all functions of living cells as it provides Gibbs free energy and building blocks for synthesis of macromolecules, which are necessary for structures, growth, and proliferation. Metabolism is a complex network composed of thousands of reactions catalyzed by enzymes involving many co-factors and metabolites. Traditionally it has been difficult to study metabolism as a whole network and most traditional efforts were therefore focused on specific metabolic pathways, enzymes, and metabolites. By using engineering principles of mathematical modeling to analyze and study metabolism, as well as engineer it, that is, design and build, new metabolic features, it is possible to gain many new fundamental insights as well as applications in biotechnology. Here, we present the history and basic principles of engineering metabolism, as well as the newest developments in the field. We are using examples of applications in: (1) production of protein pharmaceuticals and chemicals; (2) basic studies of metabolism; and (3) impacting health care. We will end by discussing how engineering metabolism can benefit from advances in artificial intelligence (AI)-based models.

Cell phase engineering can significantly impact protein synthesis and cell size, potentially enhancing the production of lipophilic products. This study investigated the impact of G1 phase extension on resource allocation, metabolic functions, and the unfolded protein response (UPR) in yeast, along with the potential for enhancing the production of lipophilic compounds. In brief, the regulation of the G1 phase was achieved by deleting CLN3 (G1 cyclin) in various yeast strains. This modification resulted in a 83% increase in cell volume, a 76.9% increase in dry cell weight, a 82% increase in total protein content, a 41% increase in carotenoid production, and a 159% increase in fatty alcohol production. Transcriptomic analysis revealed significant upregulation of multiple metabolic pathways involved in acetyl-CoA (acetyl coenzyme A) synthesis, ensuring an ample supply of precursors for the synthesis of lipophilic products. Furthermore, we observed improved protein synthesis, attributed to UPR activation during the prolonged G1 phase. These findings not only enhanced our understanding and application of yeast's capacity to synthesize lipophilic compounds in applied biotechnology but also offered unique insights into cellular behavior during the modified G1 phase, particularly regarding the UPR response, for basic research. This study demonstrates the potential of G1 phase intervention to increase the yield of hydrophobic compounds in yeast, providing a promising direction for further research.

BackgroundArginase-1 (Arg1) is a trimeric metalloenzyme that plays a central immune suppressive role via regulating the availability of L-arginine for activated T cells and its overexpression has been reported in several cancers including renal cell carcinoma, pancreatic cancer, and head and neck cancer. Arg1 is produced by broad immune suppressive cell types including myeloid-derived suppressor cells and tumor-associated macrophages (TAMs). We previously reported the existence of intrinsic immunity against Arg1 in human,1 and that peptide vaccines against Arg1 (T-win ® IO112) control tumor growth and alter the M1/M2 macrophage ratio in murine models of cancer.2 In this study, we investigated whether TAMs are the direct targets of T cells against Arg1.MethodsC57BL/6 mice harboring MC38 tumor were vaccinated with Arg1 vaccine, and the impact on TAMs in vivo was assessed by multiplex gene expression analysis. Splenic CD4+ T cells from the vaccinated animals were isolated and their impact on macrophages was tested in a co-culture with in vitro M2-differentiated bone marrow-derived macrophages or ex vivo isolated F4/80+ TAMs. Similarly, the functionality of Arg1-specific T cells was assessed by co-culturing human Arg1-specific CD4+ T-cell clones with Arg1+ TAMs generated from CD14+ PBMC or the myeloid cell lines MonoMac1 and THP-1. MHC class II-restricted Arg1 peptide presentation by macrophages was confirmed by immunopeptidomics. T cell-mediated changes in the macrophage immune phenotype and cytokine microenvironment were examined using flow cytometry, RT-qPCR, multiplex immunoassay and single cell RNAseq.ResultsTumor growth control by Arg1 vaccination correlated with a reduction in multiple immunosuppressive pathways in the TAM population of the treated animals. In co-culture experiments T cells isolated from the vaccinated animals effectively reprogrammed TAMs and led to elevated production of proinflammatory cytokines IL-2 and IFNg in the microenvironment. Similarly, human CD4+ Arg1-specific T cells directly recognized Arg1+ TAMs, leading to the secretion of pro-inflammatory cytokines. The reprogramming of TAMs was confirmed by increased expression of markers such as CD80 and HLA-DR in human in vitro as well as elevated levels of CD80 and CD86.ConclusionsOur data collectively support that TAMs are directly targeted and modulated by Arg1-specific CD4+ T cells. Arg1-specific T cells effectively convert TAMs into a proinflammatory phenotype, thus altering the immune suppressive TME. These findings provide additional MoA and a strong rationale for future clinical development of T-win® IO112 (Arg1 peptide vaccine).ReferencesMartinenaite, et al. Frequent adaptive immune responses against arginase-1. Oncoimmunology 2017 Dec 26;7(3):e1404215. doi: 10.1080/2162402X.2017.1404215. Jørgensen, et al. Arginase 1-based immune modulatory vaccines induce anticancer immunity and synergize with anti-PD-1 checkpoint blockade. Cancer Immunol Res 2021 Nov;9(11):1316–1326. doi: 10.1158/2326-6066.CIR-21-0280. Epub 2021 Sep 13.Ethics ApprovalAnimal experiments were conducted following the Danish, FELASA and ARRIVE guidelines with approval from the Danish Ethics Committee on experimental animal welfare. The protocol for the use of human samples was approved by the Scientific Ethics Committee for The Capital Region of Denmark and conducted in accordance with the provisions of the Declaration of Helsinki. Written informed consent from the patients was obtained before study entry.

Establishing efficient cell factories involves a continuous process of trial and error due to the intricate nature of metabolism. This complexity makes predicting effective engineering targets a challenging task. Therefore, it is vital to learn from the accumulated successes of previous designs for advancing future cell factory development. In this study, we developed a method based on large language models (LLMs) to extract metabolic engineering strategies from research articles on a large scale. We created a database containing over 29006 metabolic engineering entries, 1210 products and 751 organisms. Using this extracted data, we trained a hybrid model combining deep learning and mechanistic approaches to predict engineering targets. Our model outperformed traditional metabolic engineering target prediction algorithms, excelled in predicting the effects of gene modifications, and generalized well to out-of-distribution products and multiple gene combinations. Our study provides a valuable dataset, a chatbot, and an engineering target prediction model for the metabolic engineering field and exemplifies an efficient method for leveraging existing knowledge for future predictions.

Abstract We aimed to investigate the use of free glycosaminoglycan profiles (GAGomes) and cfDNA in plasma to differentiate between lung cancer and benign lung disease. GAGs were analyzed using the MIRAM® Free Glycosaminoglycan Kit with ultra-high-performance liquid chromatography and electrospray ionization triple-quadrupole mass spectrometry. We detected two GAGome features, 0S chondroitin sulfate (CS) and 4S CS, with cancer-specific changes. Based on the observed GAGome changes, we devised a model to predict lung cancer. The model, named the GAGome score, could detect lung cancer with 41.2% sensitivity (95% CI: 9.2-54.2%) at 96.4% specificity (CI: 95.2-100.0%, n=113). Furthermore, we found that the GAGome score, when combined with a cfDNA test, could increase the sensitivity for lung cancer from 42.6% (95% CI: 31.7-60.6%, cfDNA alone) to 70.5% (CI: 57.4 - 81.5%) at 95% specificity (CI: 75.1-100%, n=74). Notably, the combined GAGome and cfDNA testing improved the sensitivity, especially in early stages, relative to the cfDNA alone. Our findings show that plasma GAGome profiles can enhance cfDNA testing performance, highlighting the applicability of a multiomics approach in lung cancer diagnostics.