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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A cobalamin-dependent pathway of choline demethylation from the human gut acetogen Eubacterium limosum

Elevated serum levels of trimethylamine N-oxide (TMAO) are reported to promote the development of atherosclerosis. TMAO is produced by hepatic oxidation of trimethylamine (TMA) produced by the gut microbiome from dietary quaternary amines such as choline. Net TMA production in the gut depends on microbial enzymes that either produce or consume TMA and its precursors. Here we report the elucidation of a novel microbial pathway consuming choline without TMA production. The human gut acetogen Eubacterium limosum grows by demethylating choline to N-N-dimethylaminoethanol. Quantitative mass spectral analysis of the proteome revealed a multi-protein choline to tetrahydrofolate (THF) methyltransferase system present only in choline-grown cells. The components are encoded in a gene cluster on the genome and include MthB, an MttB superfamily member; MthC, homologous to methylotrophic cobalamin-binding proteins; MthA, homologous to cobalamin:THF methyltransferases; and MthK, a protein related to serine kinases. Together, MthB, MthC, and MthA methylate THF with phosphocholine, but not choline or other quaternary amines. MthB specifically methylates Co(I)-MthC with phosphocholine. MthK acts as a bifunctional choline kinase which can utilize ATP or the MthB demethylation product, N,N-dimethylaminoethanol phosphate, to phosphorylate choline. Together, MthK, MthB, MthC, and MthA are proposed to carry out the methylation of THF with choline. These results outline a THF methylation pathway in which choline is first activated with ATP to phosphocholine prior to demethylation to form N,N-dimethylaminoethanol phosphate. Furthermore, the latter can be recycled by MthK to form more phosphocholine without expending additional ATP, thus minimizing energy utilization during choline-dependent acetogenesis.

acetogenesis↗
DOE OSTI2025

The Elements of Life, Photosynthesis and Genomics

I am a Professor of Biochemistry, Biophysics and Structural Biology and Plant and Microbial Biology at the University of California in Berkeley. I was born and raised in India, emigrated to the United States to attend university, earning a B.S. in Molecular Biology and a Ph.D. in Biochemistry at the University of Wisconsin in Madison. Following post-doctoral studies with Lawrence Bogorad at Harvard University where I became interested in genetic control of trace element quotas, I joined the department of Chemistry and Biochemistry at UCLA. One of the first to appreciate essential trace metals as potential regulators of gene expression, I articulated the details of the nutritional Cu regulon in Chlamydomonas. In parallel, I used genetic approaches to discover the genes governing missing steps in tetrapyrrole metabolism, including the attachment of heme to apocytochromes in the thylakoid lumen and the factors catalyzing the formation of ring V in chlorophyll. After biochemistry and classical genetics, I embraced genomics, taking a leadership role on the Joint Genome Institute’s efforts on the Chlamydomonas genome and more recently, contributing to high quality assemblies of several genomes in the green algal radiation, and large transcriptomic and proteomic datasets — focusing on the diel metabolic cycle in synchronized cultures and acclimation to key environmental and nutritional stressors — that are well-used and appreciated by the community. Finally, a new venture in Berkeley is the promotion of Auxenochlorella protothecoides as the true “green yeast” and as a platform for engineering algae to produce useful bioproducts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Contributions of major tau kinase activation and phospho-tau accumulation to cortical and hippocampal tangle formation and cognition in older adults

Aberrant activation of tau kinases (tauK) has been proposed as a major step in tau hyperphosphorylation and misfolding, and subsequent formation of neurofibrillary tangles (NFT) in Alzheimer's disease (AD). However, evidence of tauK hyperactivation in actual AD brains is scarce and inconsistent, and their role in age-related cognitive decline remains undocumented. We evaluated activated/inhibited species of CDK5/p35/p25, GSK3a/ß, and ERK1/2 as well as ten tau/phospho-tau (ptau) peptides (mapping Ser 202 , Thr 217 , Ser 262 , Ser 305 , and Ser 404 phospho-residues) by Western blot or selected reaction monitoring proteomics, respectively, in postmortem dorsolateral prefrontal cortex (DLPFC) and hippocampal samples of 150 participants from the Rush Memory and Aging Project (MAP). Regression models and mediation analyses assessed the contributions of these variables to tau phosphorylation, NFT deposition and antemortem cognitive status of MAP participants. Surprisingly, greater p25 and p35 (indices for CDK5 activation) and lower pSer 21/9 -GSK3a/ß (inhibited species) immunodensities were associated with lower ptau peptide amounts. Individuals with higher p25 cortical densities displayed better cognitive outcomes, particularly working memory. Statistical mediation analyses indicated that the beneficial effect of CDK5/p25 on cognition was mediated by lower densities of phospho-Thr 217 -tau and NFT deposition in DLPFC, and also identified Thr 217 and Ser 262 as the ptau sites with greatest influence in both NFT accumulation and cognitive impairment. The present data suggest that tau hyperphosphorylation, tangle deposition, and the subsequent cognitive impairment do not rely on aberrant activation of major tauKs. Additionally, novel evidence was provided for the beneficial contribution of cortical CDK5/p25 to the maintenance of working memory.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

The temporal onset of associations of cortical proteins with cognitive resilience vary during late life

Background: Cortical proteins associated with cognitive resilience have been identified but their temporal onset in older adults is unknown. We present a multistage approach to first identify cortical proteins associated with cognitive resilience and then examine their associated temporal onset. Methods: We used data from a subset of 1088 decedents from two cohort-studies who had selected reaction monitoring proteomics from the dorsolateral prefrontal cortex, and at least 3 cognitive assessments. Cognition was assessed using a composite derived from 19 tests. We first used linear mixed-effects models to identify cortical proteins associated with cognitive resilience. We then used functional mixed-effects models to examine non-linear associations between proteins and cognitive resilience to identify their temporal onset. Results: Mean age at death was 90 years (SD = 6.4); 69 % were female. On average, cognition started to decline at around 15 years before death, with accelerated decline in the last 7 years. We identified 40 proteins associated with cognitive resilience, of which 17 proteins also showed non-linear associations. Non-linear associations indicated that higher levels of 10 proteins were associated with slower cognitive decline between 23 and 4 years before death. In contrast, higher levels of 7 proteins were associated with faster decline only within the last 7 years before death. Conclusions: Cognitive resilience proteins are differentially related to late-life cognitive aging; the onset of proteins that maintain cognition may begin many years before the onset of proteins that hasten cognitive decline. The temporal onset of cognitive resilience proteins may be crucial for timing efficacious interventions.

Zammit, Andrea↗
DOE OSTI2025

Spatial metabolomics and lipidomics in kidney disease

Kidney disease is a global health issue that affects over 850 million people, and early detection is key to preventing severe disease and complications. Kidney diseases are associated with complex dysregulation of lipid metabolism. Spatial metabolomics through mass spectrometry imaging (MSI) enables spatial mapping of the lipids in tissue and includes a variety of techniques that can be used to image lipids. In the kidney MSI studies often seek to resolve individual functional tissue units such as glomeruli and proximal tubules. Several different MSI techniques such as matrix-assisted laser desorption/ionization and desorption electrospray ionization have been used to characterize lipids and small molecules in chronic kidney disease, acute kidney injury, genetic kidney disease, and cancer. In this review we provide several examples of how spatial metabolomics data can provide critical information concerning localization of changes in disease states. Additionally, when combined with pathology, measurements, transcriptomics, or proteomics, the metabolomic changes can illuminate underlying mechanisms and provide new clinical insights.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The oleaginous yeast Rhodosporidium toruloides engineered for biomass hydrolysate-derived (E)-α-bisabolene production

The oleaginous yeast Rhodosporidium toruloides has been exploited for many bioproducts, including several terpenes, owing to its oleaginous nature and biomass inhibitor tolerance. Here, we built upon previous (E)-a-bisabolene work by iteratively stacking the complete mevalonate pathway from Saccharomyces cerevisiae onto a multicopy bisabolene synthase parent strain. Metabolomics and proteomics verified heterologous pathway expression and identified metabolic bottlenecks at three intermediate steps, with candidate feedback-resistant mevalonate kinases screening improving titers 15%. Subtle differences in codon optimization, and preliminary attenuation of competing flux toward lipids resulted in 6-fold, 7-fold higher titers relative to controls, respectively. Media optimization led to modest improvements, with zinc identified as the most promising at 10% titer improvement. Ultimately, high-performance strains were cultivated with corn-stover biomass hydrolysate in microtiter plates at 300g/L total sugar, achieving 20.8g/L bisabolene, the highest reported titer in the literature. A 2L glucose minimal medium bioreactor achieved 19.3 g/L bisabolene and a literature-high productivity of 0.11 g/L/h.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Disruption of acyl-acyl carrier protein (acyl-ACP) synthetase in cyanobacteria impairs lipid remodeling as revealed by acyl-ACP measurements

Free fatty acid (FFA) production in bacteria is a key target for metabolic engineering. The knockout of the acyl-ACP synthetase (AAS) prevents reincorporation of FFA into the fatty acid biosynthetic cycle and is widely used to enhance their secretion. However, the role of AAS in membrane lipid remodeling under environmental stress, such as altered temperature, remains poorly understood. In cyanobacteria, temperature shifts are known to affect fatty acid desaturation and membrane fluidity, yet it is unclear whether AAS contributes to these adaptive responses through re-esterification of membrane-released acyl chains. We elucidated unique aspects of fatty acid metabolism in response to temperature changes in biotechnologically relevant microbes with the development of an efficient method for quantifying acyl-ACP intermediates using anion exchange chromatography (AEX). In Escherichia coli, which performs desaturation during fatty acid biosynthesis, we detected saturated and unsaturated acyl-ACPs that confirm biosynthetic pathway operation. In the cyanobacteria, Picosynechococcus sp. PCC 7002 and the Δaas strain, changes between two temperatures were interpreted with support from proteomic and lipidomic analyses and indicated that the AAS is tied to membrane lipid remodeling. Further, polyunsaturated acyl-ACPs were detected in the Δaas strain, which was unexpected because fatty acid synthesis does not produce polyunsaturates in cyanobacteria, suggesting the presence of alternative acyl-activating enzymes or unknown acyl-ACP desaturases. This study highlights the possible link between acyl chain recycling and lipid remodeling in cyanobacteria and demonstrates the utility of AEX-based acyl-ACP profiling in dissecting fatty acid metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Metabolic flux and resource balance in the oleaginous yeast Rhodotorula toruloides

The yeast Rhodotorula toruloides is a promising bioproduction organism due to its high lipid yields and ability to grow on cheap and abundant substrates. Quantitative, systems-level assessment of its metabolic activity is accordingly merited. Resource-balance analysis (RBA) models capture not only reaction stoichiometry but also enzyme requirements for catalysis, providing valuable tools for understanding metabolic trade-offs and optimizing metabolic engineering strategies. Here, in this work, we present systems-level measurements of R. toruloides metabolic flux based on isotope tracing and metabolic flux analysis. In combination with new proteomic measurements, these flux data are used to parameterize a genome-scale resource balance model rtRBA. We find that S. cerevisiae and R. toruloides grow at nearly indistinguishable rates using similar biosynthetic but dramatically different central metabolic programs. R. toruloides consumes one-fifth as much glucose, which it metabolizes primarily via the pentose phosphate pathway and TCA cycle unlike primarily glycolysis in S. cerevisiae . Overall, across these two divergent yeasts, protein abundances aligned more closely than metabolic flux. Resource balance modeling of these metabolic programs predicts superior theoretical yields but lower productivities in R. toruloides than S. cerevisiae for industrial chemicals, highlighting the value of rapid glucose uptake for productivity but respiratory metabolism for yields.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Seasonal Controls on Microbial Depolymerization and Oxidation of Organic Matter in Floodplain Soils

Floodplain soils are vast reservoirs of organic carbon often attributed to anaerobic conditions that impose metabolic constraints on organic matter degradation. What remains elusive is how such metabolic constraints respond to dynamic flooding and drainage cycles characteristic of floodplain soils. Here, in this work, we show that microbial depolymerization and respiration of organic compounds, two rate-limiting steps in decomposition, vary spatially and temporally with seasonal flooding of mountainous floodplain soils (Gothic, Colorado, USA). Combining metabolomics and -proteomics, we found a lower abundance of oxidative enzymes during flooding coincided with the accumulation of aromatic, high-molecular weight compounds, particularly in surface soils. In subsurface soils, we found that a lower oxidation state of carbon coincided with a greater abundance of chemically reduced, energetically less favorable low-molecular weight metabolites, irrespective of flooding condition. Our results suggest that seasonal flooding temporarily constrains oxidative depolymerization of larger, potentially plant-derived compounds in surface soils; in contrast, energetic constraints on microbial respiration persist in more reducing subsurface soils regardless of flooding. Our work underscores that the potential vulnerability of these distinct anaerobic carbon storage mechanisms to changing flooding dynamics should be considered, particularly as climate change shifts both the frequency and extent of flooding in floodplains globally.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Molecular Modeling and Molecular Dynamics Simulation of a Packed and Intact Bacterial Microcompartment

Bacterial microcompartments (BMCs) are protein-bound organelles found in some bacteria which encapsulate enzymes for enhanced catalytic activity. These compartments spatially sequester enzymes within semipermeable shell proteins and are packed full of enzyme cargoes and metabolites as they fulfill their function. Coupling together recent SAXS and proteomics work, it is possible to develop molecular models for these microcompartments and interrogate enzyme and metabolite dynamics within. Our primary goal of this study is to quantify the permeability of metabolite glyceraldehyde-3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP) across the BMC shell through classical molecular dynamics simulation. The Haliangium ochraceum model of BMC shell (PDB: 6MZX) was used to model an intact BMC of approximately 10 million atoms. Working at this scale presented its own challenges in managing large data sets, with multiple challenges and hardware advances discussed that facilitated this work. Over approximately 750 ns of aggregate simulation, we see multiple permeation events for these metabolites that were added at high concentration through the pores present within BMC shell tiles. When compared to independent permeability estimates for the same metabolites determined through replica exchange umbrella sampling simulations, the permeabilities varied by approximately 3 orders of magnitude. Regardless, the permeability coefficients for both G3P and DHAP are highly similar and very high, such that only very small concentration gradients can be maintained across the BMC shell between the cytosol and BMC interior. The large simulation systems also facilitated comparisons for molecular diffusivity in the crowded environment within the BMC shell. By our estimates, the viscosity within a packed BMC shell is at least 10-fold higher than it would be in neat solution and is the real driver for varying permeability estimates we obtained through simulation. These findings will be used as design inputs for future bioengineering efforts to make products from BMCs, highlighting how permeable BMC shells can be.

Diffusion↗
DOE OSTI2024

Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows

Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Post-acquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: 1) mapping the IM axis to the LC axis for IM-MS data, 2) converting the drift time vs. m/z space to CCS/z vs m/z space, and 3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. Finally, these new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Iron rescues glucose-mediated photosynthesis repression during lipid accumulation in the green alga Chromochloris zofingiensis

Energy status and nutrients regulate photosynthetic protein expression. The unicellular green alga Chromochloris zofingiensis switches off photosynthesis in the presence of exogenous glucose (+Glc) in a process that depends on hexokinase (HXK1). Here, we show that this response requires that cells lack sufficient iron (–Fe). Cells grown in –Fe+Glc accumulate triacylglycerol (TAG) while losing photosynthesis and thylakoid membranes. However, cells with an iron supplement (+Fe+Glc) maintain photosynthesis and thylakoids while still accumulating TAG. Proteomic analysis shows that known photosynthetic proteins are most depleted in heterotrophy, alongside hundreds of uncharacterized, conserved proteins. Photosynthesis repression is associated with enzyme and transporter regulation that redirects iron resources to (a) respiratory instead of photosynthetic complexes and (b) a ferredoxin-dependent desaturase pathway supporting TAG accumulation rather than thylakoid lipid synthesis. Combining insights from diverse organisms from green algae to vascular plants, we show how iron and trophic constraints on metabolism aid gene discovery for photosynthesis and biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Genome-guided isolation of the hyperthermophilic aerobe Fervidibacter sacchari reveals conserved polysaccharide metabolism in the Armatimonadota

Few aerobic hyperthermophilic microorganisms degrade polysaccharides. Here, we describe the genome-enabled enrichment and optical tweezer-based isolation of an aerobic polysaccharide-degrading hyperthermophile, Fervidibacter sacchari, previously ascribed to candidate phylum Fervidibacteria. F. sacchari uses polysaccharides and monosaccharides for growth at 65–87.5°C and expresses 191 carbohydrate-active enzymes (CAZymes) according to RNA-Seq and proteomics, including 31 with unusual glycoside hydrolase domains (GH109, GH177, GH179). Fluorescence in-situ hybridization and nanoscale secondary ion mass spectrometry confirmed rapid assimilation of 13 C-starch in spring sediments. Purified GHs were optimally active at 80–100°C on ten different polysaccharides. Finally, we propose reassigning Fervidibacteria as a class within phylum Armatimonadota, along with 18 other species, and show that a high number and diversity of CAZymes is a hallmark of the phylum, in both aerobic and anaerobic lineages. Our study establishes Fervidibacteria as hyperthermophilic polysaccharide degraders in terrestrial geothermal springs and suggests a broad role for Armatimonadota in polysaccharide catabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Computational ranking identifies Plexin-B2 in circulating tumor cell clustering with monocytes in breast cancer metastasis

Abstract Multicellular circulating tumor cell (CTC) clusters can be up to 50 times more efficient than single CTCs in mediating viable metastasis. Here, combining computational ranking and functional determination, we identify the transmembrane protein Plexin-B2 (PLXNB2) as one of the top molecular targets associated with unfavorable distant metastasis-free survival, showing enriched expression in CTC clusters versus single CTCs from patients with advanced breast cancer (mostly female). Loss of PLXNB2 (Plxnb2) reduces the formation of homotypic tumor cell clusters and heterotypic tumor-myeloid cell clusters, reducing spontaneous metastases in female mice bearing human (mouse) breast cancer. Interactions of PLXNB2 with its ligands SEMA4C on tumor cells and SEMA4A on myeloid cells (monocytes) promote homotypic and heterotypic CTC cluster formation, respectively, thereby driving lung metastasis. Global proteomic analysis reveals downstream effectors of the PLXNB2 pathway associated with tumor cell clustering. Thus, PLXNB2 is a therapeutic target for preventing new metastasis in breast cancer.

Science & Technology - Other Topics↗
DOE OSTI2025

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Carruthers, David N↗
DOE OSTI2026

Probing the limits of genetic recoding using multi-omics-guided evolution

Engineering the genetic code—by reassigning multiple of the 64 natural codons—enables making organisms resistant to all viruses, preventing genetic information exchange, and allowing the biosynthesis of genetically encoded unnatural polymers. However, synonymous codon replacement—recoding—is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using genome synthesis, directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple synthetic Escherichia coli genomes. We construct six partially recoded E. coli strains bearing up to 45.8% of a synthetic genome with a deleterious 57-codon genetic code. As our analyses revealed widespread defects—including unassigned codons in Syn61 and Syn57—we apply multi-omics to revise our genome design and mitigate defects. Using multi-omics, we show that recoding induces transcriptional and translational changes leading to fitness defects under hundreds of conditions. Finally, we develop a multi-omics-guided evolution strategy that rapidly restores fitness, enabling genome synthesis with radical changes.

Nyerges, Akos [Harvard Medical School, Boston, MA ↗
DOE OSTI2025

Addressing genome scale design tradeoffs in Pseudomonas putida for bioconversion of an aromatic carbon source

Genome-scale metabolic models (GSMM) are commonly used to identify gene deletion sets that result in growth coupling and pairing product formation with substrate utilization and can improve strain performance beyond levels typically accessible using traditional strain engineering approaches. However, sustainable feedstocks pose a challenge due to incomplete high-resolution metabolic data for non-canonical carbon sources required to curate GSMM and identify implementable designs. Here we address a four-gene deletion design in the Pseudomonas putida KT2440 strain for the lignin-derived non-sugar carbon source, p-coumarate (p-CA), that proved challenging to implement. We examine the performance of the fully implemented design for p-coumarate to glutamine, a useful biomanufacturing intermediate. In this study glutamine is then converted to indigoidine, an alternative sustainable pigment and a model heterologous product that is commonly used to colorimetrically quantify glutamine concentration. Through proteomics, promoter-variation, and growth characterization of a fully implemented gene deletion design, we provide evidence that aromatic catabolism in the completed design is rate-limited by fumarase hydratase (FUM) enzyme activity in the citrate cycle and requires careful optimization of another fumarate hydratase protein (PP_0897) expression to achieve growth and production. A double sensitivity analysis also confirmed a strict requirement for fumarate hydratase activity in the strain where all genes in the growth coupling design have been implemented. Metabolic cross-feeding experiments were used to examine the impact of complete removal of the fumarase hydratase reaction and revealed an unanticipated nutrient requirement, suggesting additional functions for this enzyme. While a complete implementation of the design was achieved, this study highlights the challenge of completely inactivating metabolic reactions encoded by under-characterized proteins, especially in the context of multi-gene edits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fibrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI’s cellular dynamics analysis, and the use of the fibrotic cocktail-treated human precision-cut lung slices confirms UNAGI’s predictions that nifedipine, an antihypertensive drug, may have anti-fibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including COVID, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fibrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.

Neural Network↗