ProtoCode: Leveraging large language models (LLMs) for automated generation of machine-readable PCR protocols from scientific publications.

Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each experimental procedure. Recent advancements in Large Language Models (LLMs) offer a promising solution to interpret and curate knowledge from complex scientific literature. In this work, we develop ProtoCode, a tool leveraging fine-tune LLMs to curate protocols into intermediate representation formats which can be interpretable by both human and machine interfaces. Our proof-of-concept, focused on polymerase chain reaction (PCR) protocols, retrieves information from PCR protocols at an accuracy ranging 69-100 % depending on the information content. In all tested protocols, we demonstrate that ProtoCode successfully converts literature-based protocols into correct operational files for multiple thermal cycler systems. In conclusion, ProtoCode can alleviate labor intensive curation and standardization of life science protocols to enhance research reproducibility by providing a reliable, automated means to process and standardize protocols. ProtoCode is freely available as a web server at https://curation.taxila.io/ProtoCode/.

IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data.

The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems.

Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease.

BACKGROUND: Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. METHODS: In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. RESULTS: We applied our computational framework to prioritize novel putative target genes for Alzheimer's disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). CONCLUSIONS: Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

Lenvatinib plus anti-PD-1 antibody combination treatment activates CD8+ T cells through reduction of tumor-associated macrophage and activation of the interferon pathway.

Lenvatinib is a multiple receptor tyrosine kinase inhibitor targeting mainly vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) receptors. We investigated the immunomodulatory activities of lenvatinib in the tumor microenvironment and its mechanisms of enhanced antitumor activity when combined with a programmed cell death-1 (PD-1) blockade. Antitumor activity was examined in immunodeficient and immunocompetent mouse tumor models. Single-cell analysis, flow cytometric analysis, and immunohistochemistry were used to analyze immune cell populations and their activation. Gene co-expression network analysis and pathway analysis using RNA sequencing data were used to identify lenvatinib-driven combined activity with anti-PD-1 antibody (anti-PD-1). Lenvatinib showed potent antitumor activity in the immunocompetent tumor microenvironment compared with the immunodeficient tumor microenvironment. Antitumor activity of lenvatinib plus anti-PD-1 was greater than that of either single treatment. Flow cytometric analysis revealed that lenvatinib reduced tumor-associated macrophages (TAMs) and increased the percentage of activated CD8+ T cells secreting interferon (IFN)-γ+ and granzyme B (GzmB). Combination treatment further increased the percentage of T cells, especially CD8+ T cells, among CD45+ cells and increased IFN-γ+ and GzmB+ CD8+ T cells. Transcriptome analyses of tumors resected from treated mice showed that genes specifically regulated by the combination were significantly enriched for type-I IFN signaling. Pretreatment with lenvatinib followed by anti-PD-1 treatment induced significant antitumor activity compared with anti-PD-1 treatment alone. Our findings show that lenvatinib modulates cancer immunity in the tumor microenvironment by reducing TAMs and, when combined with PD-1 blockade, shows enhanced antitumor activity via the IFN signaling pathway. These findings provide a scientific rationale for combination therapy of lenvatinib with PD-1 blockade to improve cancer immunotherapy.

Modeling signaling-dependent pluripotency with Boolean logic to predict cell fate transitions.

Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The mechanisms that maintain pluripotency, or that cause its destabilization to initiate development, are complex and incompletely understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to input signals. Our strategy used random asynchronous Boolean simulations (R-ABS) to simulate single-cell fate transitions and strongly connected components (SCCs) strategy to represent population heterogeneity. This framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) and used to simulate cellular responses to combinations of five signaling pathways. Our simulations predicted experimentally verified cell population compositions and input signal combinations controlling specific cell fate transitions. Extending the model to PSC differentiation, we predicted a combination of signaling activators and inhibitors that efficiently and robustly generated a Cdx2+Oct4- cells from naïve mESCs. Overall, this platform provides new strategies to simulate cell fate transitions and the heterogeneity that typically occurs during development and differentiation.

CD24 tracks divergent pluripotent states in mouse and human cells.

Reprogramming is a dynamic process that can result in multiple pluripotent cell types emerging from divergent paths. Cell surface protein expression is a particularly desirable tool to categorize reprogramming and pluripotency as it enables robust quantification and enrichment of live cells. Here we use cell surface proteomics to interrogate mouse cell reprogramming dynamics and discover CD24 as a marker that tracks the emergence of reprogramming-responsive cells, while enabling the analysis and enrichment of transgene-dependent (F-class) and -independent (traditional) induced pluripotent stem cells (iPSCs) at later stages. Furthermore, CD24 can be used to delineate epiblast stem cells (EpiSCs) from embryonic stem cells (ESCs) in mouse pluripotent culture. Importantly, regulated CD24 expression is conserved in human pluripotent stem cells (PSCs), tracking the conversion of human ESCs to more naive-like PSC states. Thus, CD24 is a conserved marker for tracking divergent states in both reprogramming and standard pluripotent culture.

Dynamic simulation and metabolome analysis of long-term erythrocyte storage in adenine-guanosine solution.

Although intraerythrocytic ATP and 2,3-bisphophoglycerate (2,3-BPG) are known as direct indicators of the viability of preserved red blood cells and the efficiency of post-transfusion oxygen delivery, no current blood storage method in practical use has succeeded in maintaining both these metabolites at high levels for long periods. In this study, we constructed a mathematical kinetic model of comprehensive metabolism in red blood cells stored in a recently developed blood storage solution containing adenine and guanosine, which can maintain both ATP and 2,3-BPG. The predicted dynamics of metabolic intermediates in glycolysis, the pentose phosphate pathway, and purine salvage pathway were consistent with time-series metabolome data measured with capillary electrophoresis time-of-flight mass spectrometry over 5 weeks of storage. From the analysis of the simulation model, the metabolic roles and fates of the 2 major additives were illustrated: (1) adenine could enlarge the adenylate pool, which maintains constant ATP levels throughout the storage period and leads to production of metabolic waste, including hypoxanthine; (2) adenine also induces the consumption of ribose phosphates, which results in 2,3-BPG reduction, while (3) guanosine is converted to ribose phosphates, which can boost the activity of upper glycolysis and result in the efficient production of ATP and 2,3-BPG. This is the first attempt to clarify the underlying metabolic mechanism for maintaining levels of both ATP and 2,3-BPG in stored red blood cells with in silico analysis, as well as to analyze the trade-off and the interlock phenomena between the benefits and possible side effects of the storage-solution additives.

A metabolic model of human erythrocytes: practical application of the E-Cell Simulation Environment.

The human red blood cell (RBC) has long been used for modeling of complex biological networks, for elucidation of a wide variety of dynamic phenomena, and for understanding the fundamental topology of metabolic pathways. Here, we introduce our recent work on an RBC metabolic model using the E-Cell Simulation Environment. The model is sufficiently detailed to predict the temporal hypoxic response of each metabolite and, at the same time, successfully integrates modulation of metabolism and of the oxygen transporting capacity of hemoglobin. The model includes the mechanisms of RBC maintenance as a single cell system and the functioning of RBCs as components of a higher order system. Modeling of RBC metabolism is now approaching a fully mature stage of realistic predictions at the molecular level and will be useful for predicting conditions in biotechnological applications such as long-term cold storage of RBCs.

Paradoxical ATP elevation in ischemic penumbra revealed by quantitative imaging mass spectrometry.

Local responses of energy metabolism during brain ischemia are too heterogeneous to decipher redox distribution between anoxic core and adjacent salvageable regions such as penumbra. Imaging mass spectrometry combined by capillary electrophoresis/mass spectrometry providing quantitative metabolomics revealed spatio-temporal changes in adenylates and NADH in a mouse middle-cerebral artery occlusion model. Unlike the core where ATP decreased, the penumbra displayed paradoxical elevation of ATP despite the constrained blood supply. It is noteworthy that the NADH elevation in the ischemic region is clearly demarcated by the ATP-depleting core. Results suggest that metabolism in ischemic penumbra does not respond passively to compromised circulation, but actively compensates energy charges.

T-state stabilization of hemoglobin by nitric oxide to form alpha-nitrosyl heme causes constitutive release of ATP from human erythrocytes.

Upon hypoxia, erythrocytes utilize hemoglobin (Hb) to trigger activation of glycolysis through its interaction with band 3. This process contributes to maintenance of ATP, a portion of which is released extracellularly to trigger endothelium-dependent vasorelaxation. However, whether the ATP release results either from metabolic activation of the cells secondarily or from direct regulation of the gating through Hb allostery remains unknown. This study aimed to examine if stabilization of T-state Hb could induce steady-state and hypoxia-induced alterations in glycolysis and the ATP release from erythrocytes. Treatment of deoxygenated erythrocytes with a nitric oxide (NO) donor generated alpha-NO Hb that is stabilized T-state allostery. Under these circumstances, the release of ATP was significantly elevated even under normoxia and not further enhanced upon hypoxia. These events did not coincide with activation of glycolysis of the cells, so far as judged by the fact that intracellular ATP was significantly decreased by the NO treatment. Collectively, the present study suggests that hypoxia-induced ATP release is triggered through mechanisms involving R-T transition of Hb, and the gating process might occur irrespective of hypoxia-responsive regulation of glycolysis.

Hypotaurine is an Energy-Saving Hepatoprotective Compound against Ischemia-Reperfusion Injury of the Rat Liver.

Metabolome analyses assisted by capillary electrophoresis-mass spectrometry (CE-MS) have allowed us to systematically grasp changes in small molecular metabolites under disease conditions. We applied CE-MS to mine out biomarkers in hepatic ischemia-reperfusion. Rat livers were exposed to ischemia by clamping of the portal inlet followed by reperfusion. Metabolomic profiling revealed that l contents of taurine in liver and plasma were significantly increased. Of interest is an elevation of hypotaurine, collectively suggesting significance of hypotaurine/taurine in post-ischemic responses. Considering the anti-oxidative capacity of hypotaurine, we examined if supplementation of the compound or its precursor amino acids could affect hepatocellular viability and contents of taurine in liver and plasma. Administration of hypotaurine, N-acetylcysteine or methionine upon reperfusion comparablly attenuated the post-ischemic hepatocellular injury but with different metabolomic profiling among groups: rats treated with methionine or N-acetylcysteine but not those treated with hypotaurine, exhibited significant elevation of hepatic lactate generation without notable recovery of the energy charge. Furthermore, the group treated with hypotaurine exhibited elevation of the plasma taurine, suggesting that the exogenously administered compound was utilized as an antioxidant. These results suggest that taurine serves as a surrogate marker for ischemia-reperfusion indicating effectiveness of hypotaurine as an energy-saving hepatoprotective amino acid.

In silico modeling and metabolome analysis of long-stored erythrocytes to improve blood storage methods.

There is currently no effective method for preventing ATP and 2,3-bisphosphoglycerate (2,3-BPG) depletion during long-term erythrocyte storage in the cold, although these metabolites are strongly associated with cell viability and oxygen delivery after transfusion. Metabolite reduction is caused by whole metabolic networks in the cell, which are regulated by various physical or chemical factors. Mathematical modeling is a powerful tool for integrating such complex and dynamic systems. Here, we developed a mathematical model to predict metabolism in erythrocytes preserved with a mannitol-adenine-phosphate solution (MAP) at 4 degrees C, by modifying a published model of large-scale erythrocyte metabolism. Our model successfully reproduced the reported decreases in ATP and 2,3-BPG during storage. Analysis of our model identified several enzymatic reactions and factors related to ATP and 2,3-BPG depletions, which may serve as possible targets for improving blood storage methods. We also performed metabolome analysis of laboratory-made MAP-stored erythrocytes using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), which provided a comprehensive view of the metabolism dynamics. Alterations in the metabolic intermediate concentrations after long storage were qualitatively predicted by the model. Finally, through further systematic analysis, we also discuss the usability of our model.