Pollutants in Microenvironmental Cellular Interactions During Liver Inflammation Cancer Transition and the Application of Multi-Omics Analysis DOI Creative Commons
Yulun Jian, Yuhan Li, Yanfeng Zhou

et al.

Toxics, Journal Year: 2025, Volume and Issue: 13(3), P. 163 - 163

Published: Feb. 25, 2025

This study categorizes pollutant-induced inflammation–cancer transition into three stages: non-alcoholic fatty liver disease (NAFLD), fibrosis, and hepatocellular carcinoma (HCC). It systematically reveals the temporal heterogeneity of damage. The findings indicate that pollutants not only directly damage hepatocytes but also modulate key cells in immune microenvironment, such as hepatic stellate (HSCs) Kupffer cells, thereby amplifying inflammatory fibrotic responses, ultimately accelerating progression HCC. Mechanistically, early stage primarily cause hepatocyte injury through oxidative stress lipid metabolism dysregulation. During fibrosis stage, promote by inducing extracellular matrix accumulation, while HCC they drive tumorigenesis via activation Wnt/β-catenin pathway p53 inactivation. Through multi-omics analyses, this identifies critical pathogenic molecules signaling pathways regulated pollutants, providing new insights their mechanisms, potential biomarkers, therapeutic targets. These offer valuable guidance for development diagnostic strategies diseases formulation environmental health risk prevention measures.

Language: Английский

Hybrid Physics-Informed Metabolic Cybergenetics: Process Rates Augmented with Machine-Learning Surrogates Informed by Flux Balance Analysis DOI
Sebastián Espinel‐Ríos, José L. Avalos‬

Industrial & Engineering Chemistry Research, Journal Year: 2024, Volume and Issue: 63(15), P. 6685 - 6700

Published: April 8, 2024

Metabolic cybergenetics is a promising concept that interfaces gene expression and cellular metabolism with computers for real-time dynamic metabolic control. The focus on control at the transcriptional level, serving as means to modulate intracellular fluxes. Recent strategies in this field have employed constraint-based models process optimization, control, estimation. However, results bilevel optimization problems, which pose considerable numerical conceptual challenges. In study, we present an alternative hybrid physics-informed modeling framework cybergenetics, aimed simplifying estimation tasks. By utilizing machine-learning surrogates, our approach effectively embeds physics of networks into rates structurally simpler macrokinetic coupled expression. These informed by flux balance analysis, link domains manipulatable enzymes exchange This ensures critical knowledge captured system's network preserved. resulting can be integrated cybergenetic schemes involving single-level optimizations. Additionally, maintains number system states necessary minimum, easing burden monitoring Our demonstrated using computational case study optogenetically assisted production itaconate Escherichia coli.

Language: Английский

Citations

7
How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach DOI Creative Commons
Aurore Cournoyer,

Mathieu Bazinet,

Jean-Pierre Clément

et al.

Food Research International, Journal Year: 2024, Volume and Issue: 200, P. 115417 - 115417

Published: Nov. 28, 2024

Language: Английский

Citations

6
AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics DOI
Yulin Li,

Qingzu He,

Huan Guo

et al.

Journal of Proteome Research, Journal Year: 2024, Volume and Issue: 23(2), P. 834 - 843

Published: Jan. 22, 2024

In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of PSMs identified are incorrect, therefore various postprocessing software have been developed reranking peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance shallow models, limited effectiveness. this work, we propose AttnPep, deep learning model rescoring PSM scores that utilizes Self-Attention module. This module helps neural network focus features relevant to classification ignore irrelevant features. allows AttnPep analyze output different engines improve discrimination accuracy. We considered be correct if it achieves q-value <0.01 compared with existing mainstream PeptideProphet, Percolator, proteoTorch. The results indicated found an average increase in 9.29% relative other methods. Additionally, was able better distinguish between incorrect more synthetic peptides complex SWATH data set.

Language: Английский

Citations

4
Spatial MS multiomics on clinical prostate cancer tissues DOI
Jacob X. M. Truong, Sushma R. Rao, Feargal J. Ryan

et al.

Analytical and Bioanalytical Chemistry, Journal Year: 2024, Volume and Issue: 416(7), P. 1745 - 1757

Published: Feb. 7, 2024

Language: Английский

Citations

4
Gut dysbiosis and neurological modalities: An engineering approach via proteomic analysis of gut-brain axis DOI
Meenakshi Kandpal, Nidhi Varshney,

Kunal Sameer Rawal

et al.

Advances in protein chemistry and structural biology, Journal Year: 2024, Volume and Issue: unknown, P. 199 - 248

Published: Jan. 1, 2024

Language: Английский

Citations

4
From Nanozymes to Multi‐Purpose Nanomaterials: The Potential of Metal–Organic Frameworks for Proteomics Applications DOI
Siene Swinnen, Francisco de Azambuja, Tatjana N. Parac‐Vogt

et al.

Advanced Healthcare Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

Abstract Metal‐organic frameworks (MOFs) have the potential to revolutionize biotechnological and medical landscapes due their easily tunable crystalline porous structure. Herein, study presents MOFs' impact on proteomics, unveiling diverse roles MOFs can play boost it. Although are excellent catalysts in other scientific disciplines, role as proteomics applications remains largely underexplored, despite protein cleavage being of crucial importance protocols. Additionally, discusses evolving MOF materials that tailored for showcasing structural diversity functional advantages compared types used similar applications. be developed seamlessly integrate into workflows features, contributing separation, peptide enrichment, ionization mass spectrometry. This review is meant a guide help bridge gap between material scientists, engineers, chemists side researchers biology or bioinformatics working proteomics.

Language: Английский

Citations

4
Facile Fabrication of Monodisperse Vinyl Hybrid Core–Shell Silica Microsphere with Short Range Radial Channel in bi‐phase System DOI Open Access

Chenyang Wang,

Tiantian Guo, Ruizhi Tang

et al.

Small, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Abstract The development of monodisperse hybrid silica microspheres with highly regular pore structure and uniform distribution functional groups have significant value in the biomolecular separation field. In this work, short range ordered channels are precisely constructed onto non‐porous microsphere surface by a bi‐phase assembly method, cylindrical channel introduced plethora vinyl “one‐pot” co‐condensation to form shell. As hydrophilic interaction chromatography (HILIC) stationary phase, core–shell is simply modified zwitterion glutathione (SiO 2 @SiO ‐GSH), which HILIC enrichment process significantly shortened due its specific porous characteristics. Most importantly, SiO ‐GSH can enrich 2186 N‐glycopeptides from rat liver protein digest within min, mapped 806 glycoproteins. Compared result 1 h, glycoproteins glycopeptides overlap 88.3% 79.1%, performing excellent reproducibility. exhibit mass transfer efficiency, so developed method expected design more advanced materials for other urgently fields.

Language: Английский

Citations

0
Descriptive analysis of protein expression variations during pupal development of Chrysomya megacephala (Diptera: Calliphoridae) using label-free proteomic techniques DOI Creative Commons

Ren Long,

C. N. Luo,

Peng Zhang

et al.

Forensic Sciences Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Abstract Age estimation is a critical aspect of forensic entomology, especially in the examination pupae. The use proteins as means for age identification shows great promise. In this study, proteomic techniques were employed to investigate differentially expressed (DEPs) during intrapuparial stage Chrysomya megacephala. Specimens sampled at four distinct time points: 0 h (Group A), 24 B), 48 C), and 72 D). Our analysis uncovered 56 DEPs between Groups B A, 116 C total 152 D A. These categorized into nine clusters based on their expression patterns. Cluster 1 exhibited an increasing trend protein expression, while 4 displayed opposite pattern. Clusters 2, 6, 9 showed initial rise followed by decline, whereas 3 demonstrated reverse trend. 8 indicated rise, subsequent drop, another 7 decrease, increase minor decrease. Notably, C-type lectin domain-containing (CTLD) Failed axon connections (Fax) consistently upward two selected validation using parallel reaction monitoring technique (PRM)-targeted proteomics, confirming trends observed analysis. summary, study highlights potential reliable biomarkers estimating pupal age.

Language: Английский

Citations

0
Evolution of the umbilical cord blood proteome across gestational development DOI Creative Commons
Leena B. Mithal, Nicola Lancki, Ted Ling-Hu

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 17, 2025

Neonatal health is dependent on early risk stratification, diagnosis, and timely management of potentially devastating conditions, particularly in the setting prematurity. Many these conditions are poorly predicted real-time by clinical data current diagnostics. Umbilical cord blood may represent a novel source molecular signatures that provides window into state fetus at birth. In this study, we comprehensively characterized proteome infants born between 25 to 42 weeks using untargeted mass spectrometry functional enrichment analysis. We determined birth varies significantly across gestational development. Proteins function structural development growth (e.g., extracellular matrix organization, lipid particle remodeling, vessel development) more abundant earlier gestation. later gestations, proteins with increased abundance immune response inflammatory pathways, including complements calcium-binding proteins. These contribute knowledge physiologic neonates age, which crucial understand as strive best support postnatal preterm infants, determine mechanisms pathology causing adverse outcomes, develop biomarkers help tailor our diagnosis therapeutics for critical neonatal conditions.

Language: Английский

Citations

0
Irreversible Inhibitions DOI
Narayan S. Punekar

Published: Jan. 1, 2025

Language: Английский

Citations

0