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Isotopic Labeling-Enabled Chemical Proteomics Analysis Revealed Structural and Functional Features of Allysine Modifications in Mammalian Cells and Tissues

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Analytical Chemistry 2025年第18期97卷 9944-9952页

作者： Sin, Yi-Cheng Hosny, Nora Batti Angulski, Addeli Bez Kim, Do-Hyung Metzger, Joseph M. Chen, Yue Department of Biochemistry Molecular Biology and Biophysics University of Minnesota at Twin Cities MinneapolisMN55455 United States Department of Integrative Biology and Physiology University of Minnesota at Twin Cities MinneapolisMN55455 United States Bioinformatics and Computational Biology Program University of Minnesota at Twin Cities MinneapolisMN55455 United States

Allysine is a pivotal protein post-translational modification that regulates protein interaction and activities. It is also recognized as a marker of oxidative stress under certain metabolic and physiological conditions. In this study, we developed a capture-and-release chemical proteomics workflow with heavy isotopic labeling that enables system-wide enrichment and site-specific identification of allysine as well as other carbonylated peptides, such as peptides containing glutamic semialdehyde derived from the oxidative damage of arginine and proline, with high confidence. The streamlined workflow led to the identification of 434 allysine sites on 349 proteins in human 293T and HCT116 cells and 317 allysine sites on 157 proteins in mouse muscle tissues without any treatment with an oxidative stress-inducing chemical reagent. We identified 48 histone allysine sites, including 38 sites on core histones in human 293T cells, many of which overlapped with well-characterized histone acetylation and methylation epigenetic marks. Bioinformatic analysis revealed notable characteristics of the amino acid preferences of allysine flanking sequences and the significant depletion of allysine sites in the protein secondary structure in cultured human cells. Pathway analysis showed that allysine substrates were involved in diverse cellular processes including translation, protein folding, and RNA processing in human cells and were enriched with muscle contractile fiber proteins and metabolic enzymes in mouse muscle tissue. Thus, our integrated chemical proteomics analysis revealed the structural and functional features of allysine targets under regular growth conditions in cultured human cells and mouse tissues. © 2025 American Chemical Society.

关键词： Proteolysis

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Small extrachromosomal circular DNA in amyotrophic lateral sclerosis matter

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Neural Regeneration Research 2025年第5期20卷 1411-1413页

作者： Marcos J.Arauzo-Bravo Daniela Gerovska Matthias Schwab Alexandra Kretz Computational Biology and Systems Biomedicine Biodonostia Health Research InstituteSan SebastianSpain Basque Foundation for Science IKERBASQUEBilbaoSpainMax Planck Institute for Molecular BiomedicineComputational Biology and Bioinformatics GroupMünsterNorth Rhine-WestphaliaGermany Department of Cell Biology and Histology Faculty of Medicine and NursingUniversity of Basque Country(UPV/EHU)LeioaSpain Department of Neurology Jena University HospitalJenaThuringiaGermany

Comprehensive studies identify motor neuron spectrum disorders including amyotrophic lateral sclerosis(ALS)as globally rising fatal disorders with the highest prevalence in aging populations,influenced by ethnicity and ancestry(GBD 2016 Motor Neuron Disease Colla borators,2018).While~10% of diagnoses involve a family history(fALS),most cases are considered sporadic(sALS).However,population-based studies suggest that even cases without a common index mutation impart heritability(Ryan et al.,2019),indicating a crucial role of rare and as yet unknown genetic denominators.

关键词： amyotrophic sclerosis

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Mathematical bounds on r² and the effect size in case-control genome-wide association studies

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Theoretical Population biology 2025年 164卷 1-11页

作者： Paye, Sanjana M. Edge, Michael D. Department of Quantitative and Computational Biology University of Southern California United States University of Michigan Medical Scientist Training Program United States

Case-control genome-wide association studies (GWAS) are often used to find associations between genetic variants and diseases. When case-control GWAS are conducted, researchers must make decisions regarding how many cases and how many controls to include in the study. Connections between variants and diseases are made using association statistics, including χ². Previous work in population genetics has shown that LD statistics, including r², are bounded by the allele frequencies in the population being studied. Since varying the case fraction changes sample allele frequencies, we use the known bounds on r² to explore how the fraction of cases included in a study can affect statistical power to detect associations. We analyze a simple mathematical model and use simulations to study a quantity proportional to the χ² noncentrality parameter, which is closely related to r², under various conditions. Varying the case fraction changes the χ² noncentrality parameter, and by extension the statistical power, with effects depending on the dominance, penetrance, and frequency of the risk allele. Our framework explains previously observed results, such as asymmetries in power to detect risk vs. protective alleles, and the fact that a balanced sample of cases and controls does not always give the best power to detect associations, particularly for highly penetrant minor risk alleles that are either dominant or recessive. We show by simulation that our results can be used as a rough guide to statistical power for association tests other than χ² tests of independence. © 2025 Elsevier Inc.

关键词： Case control studies Genome-wide association studies Linkage disequilibrium Mathematical bounds Population genetics

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INFERRING MECHANISTIC PARAMETERS OF SOMATIC HYPERMUTATION USING NEURAL NETWORKS AND APPROXIMATE BAYESIAN COMPUTATION

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Annals of Applied Statistics 2025年第1期19卷 720-743页

作者： Fisher, Thayer Sung, Kevin Simon, Noah Fukuyama, Julia Matsen, Frederick A. Department of Biostatistics University of Washington United States Computational Biology Program Fred Hutchinson Cancer Center United States Department of Statistics Indiana University United States

Somatic hypermutation (SHM) is a critical enzyme-mediated process of the adaptive immune response in which antibodies acquire mutations to enhance antigen binding. Despite abundant research elucidating the biochemical basis of SHM, and substantial sequence data available for parameterization, previous computational models of SHM have not been explicitly mechanistic. In this paper we bridge this gap by developing a probabilistic latent variable model encapsulating a sequence of interacting steps, thus formulating the biochemical underpinnings of SHM in a mathematical framework. However, fitting this latent variable model is challenging. To navigate this complexity, we employ an approximate Bayesian computation strategy integrated with neural networks. We are able to estimate almost all of the parameters of the model to good accuracy but find that the parameters involving the boundaries of the nucleotide stripping process are slightly more challenging, given the type of data available. © Institute of Mathematical Statistics, 2025.

关键词： adaptive immunology Bayesian statistics computational biology likelihood-free inference

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Machine learning approaches enable the discovery of therapeutics across domains

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Molecular Therapy 2025年第5期33卷 2269-2278页

作者： Chhibbar, Prabal Das, Jishnu Centre for Systems Immunology Department of Immunology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Department of Computational and Systems Biology School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States Integrative Systems Biology PhD Program School of Medicine University of Pittsburgh Pittsburgh 15213 PA United States

Multi-modal datasets have grown exponentially in the last decade. This has created an enormous demand for machine learning models that can predict complex outcomes by leveraging cellular, molecular, and humoral profiles. Corresponding inference of mechanisms can help to uncover new therapeutic targets. Here, we discuss how biological principles guide the design of predictive models and how interpretable machine learning can lead to novel mechanistic insights. We provide descriptions of multiple learning techniques and how suited they are to domain adaptations. Finally, we talk about broad learning capabilities of foundation models on large datasets and whether they can be used to provide meaningful inference about biological datasets. © 2025 The Author(s)

关键词： deep learning foundation models interpretable machine learning therapeutics

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Glycolysis model shows that allostery maintains high ATP and limits accumulation of intermediates

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Biophysical Journal 2025年第10期124卷 1562-1586页

作者： Choe, Mangyu Einav, Tal Phillips, Rob Titov, Denis V. Department of Nutritional Sciences and Toxicology University of California Berkeley Berkeley California United States Department of Molecular and Cell Biology University of California Berkeley Berkeley California United States Center for Computational Biology University of California Berkeley Berkeley California United States Division of Biology and Biological Engineering California Institute of Technology Pasadena California United States Department of Physics California Institute of Technology Pasadena California United States Basic Sciences Division and Computational Biology Program Fred Hutchinson Cancer Research Center Seattle WA United States Center for Vaccine Innovation La Jolla Institute for Immunology La Jolla California United States

Glycolysis is a conserved metabolic pathway that produces ATP and biosynthetic precursors. It is not well understood how the control of mammalian glycolytic enzymes through allosteric feedback and mass action accomplishes various tasks of ATP homeostasis, such as controlling the rate of ATP production, maintaining high and stable ATP levels, ensuring that ATP hydrolysis generates a net excess of energy, and maintaining glycolytic intermediate concentrations within physiological levels. To investigate these questions, we developed a biophysical model of glycolysis based on enzyme rate equations derived from in vitro kinetic data. This is the first biophysical model of human glycolysis that successfully recapitulates the above tasks of ATP homeostasis and predicts absolute concentrations of glycolytic intermediates and isotope tracing kinetics that align with experimental measurements in human cells. We use the model to show that mass action alone is sufficient to control the ATP production rate and maintain the high energy of ATP hydrolysis. Meanwhile, allosteric regulation of hexokinase and phosphofructokinase by ATP, ADP, inorganic phosphate, and glucose-6-phosphate is required to maintain high ATP levels and to prevent uncontrolled accumulation of phosphorylated intermediates of glycolysis. Allosteric feedback achieves the latter by maintaining hexokinase and phosphofructokinase enzyme activity at one-half of ATP demand and, thus, inhibiting the reaction of Harden and Young, which otherwise converts glucose to supraphysiological levels of phosphorylated glycolytic intermediates at the expense of ATP. Our methodology provides a roadmap for a quantitative understanding of how metabolic homeostasis emerges from the activities of individual enzymes. © 2025 Biophysical Society

关键词： allosteric feedback glycolysis metabolism modeling

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Comparative analysis of methods for estimating genetic parameters of fruit-quality traits in apple breeding program

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Euphytica 2025年第6期221卷 1-15页

作者： Pinto, Catalina Maldonado, Carlos Testolin, Raffaele Cipriani, Guido De Mori, Gloria Institute of Agri-Food Animal and Environmental Sciences-ICA3 University of O’Higgins San Fernando Chile Bioinformatics and Computational Biology Laboratory Universidad Mayor Huechuraba Chile Department of Agricultural Food Environmental and Animal Sciences University of Udine Udine Italy

Apples, originating from Central Asia, are widely cultivated worldwide, with China producing 47.6 million tonnes annually. Despite their global importance, breeding programs often focus on a limited number of high-quality cultivars, potentially threatening genetic diversity. To mitigate this risk, advanced statistical methods could be employed to improve selection strategies. For example, the multi-trait BLUP method, which accounts for genetic correlations among traits, can reduce selection bias but increases computational complexity due to the model’s intricacy and slow convergence of the REML process. Bayesian methods, like Markov Chain Monte Carlo (MCMC), offer a solution to these challenges. This study compares single-trait and multi-trait approaches using REML/BLUP and MCMC/BLUP to estimate variance components and predict genetic values. Over two seasons, phenotypic data from 304 seedlings and 16 parents were analyzed using an animal model to predict their genetic values related to fruit quality traits. Significant progress was made in estimating genetic parameters and selecting parents for traits such as fruit weight, flesh firmness, and soluble solids content in the University of Udine’s apple breeding program. The multi-trait BLUP method improved the accuracy of predicted breeding values, particularly for fruit weight, polar diameter, flesh firmness, and soluble solids content. The multivariate repeated measures model, despite low correlations between some trait pairs, was recommended for integrating multi-season results and accounting for trait correlations. The Bayesian MCMC approach proved superior in genetic evaluations, offering higher heritabilities and genetic gains compared to Fisherian methods (REML). It effectively handles small sample sizes, captures more genetic variance, and enhances breeding value predictions, recommending traits like fruit size, firmness, and sweetness for selecting superior parents. © The Author(s) 2025.

关键词： BLUP/MCMC BLUP/REML Fruit crop Fruit genetics Malus X domestica

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Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer

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computational and Structural Biotechnology Journal 2025年 27卷 1719-1731页

作者： Tseng, Tzu-Yang Hsieh, Chiao-Hui Liu, Jie-Yu Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies. © 2025 The Authors

关键词： Drug infusion

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Development of resources for training machine learning systems for the eye

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Lachke, Salil A. Department of Biological Sciences University of Delaware Newark DE USA Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

Regulation of distinct molecular pathways in coordinately developing tissues drives eye development. This process involves factors in signaling, transcription, translation and post-transcriptional control that together determine the proteome of individual cells. Characterizing these regulatory events and their relationships with each other in distinct ocular tissues will lead to the derivation of gene regulatory networks (GRNs) for eye development, which in turn will allow building of the “developmental oculome” (Lachke, Maas (2010) Wiley Interdiscip Rev Syst Biol Med 2(3):305-323 doi: 10.1002/wsbm.59 ). Toward this goal, we generated and analyzed various omics datasets on developing eye tissues, performed functional characterization of key molecules, and developed a web resource called iSyTE (Anand, Lachke (2017) Exp Eye Res 156:22-33 PMC5026553 ; Kakrana et al (2018) Nucleic Acids Res 46(D1): D875-D885 doi: 10.1093/nar/gkx837 ; Lachke (2022) Exp Eye Res 214:108889 PMC8792301 ). To extract further insights from these omics data, it is necessary to analyze them in the context of experimentally verified data on the eye. These data can be used for training machine learning systems to develop models for eye development. The data used for training represents a an important factor in machine learning systems, i.e. the quality of these data is a key contributor that determines the robustness of the resulting models. There is a wealth of molecular functional (i.e. experimental evidence-based) data on the eye that has been generated by researchers worldwide over the past several decades. These individual regulatory data “nodes” (regulators and targets) – currently “static” in the literature with limited connectivity – hold rich potential to define detailed, and importantly, experimentally validated GRNs that can be used for training machine learning systems. As proof of principle, we have focused on ocular lens development and curated mechanistic data from individual gene p

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Integrated approach to uncover regulatory networks in eye development and disease

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Lachke, Salil A. Department of Biological Sciences University of Delaware Newark DE USA Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

It is challenging to identify/prioritize genes involved in development of the eye and its associated defects. The application of genome-level transcript profiling (transcriptomics) to examine eye tissues and cell types has facilitated ocular gene discovery. To maximally utilize the large transcriptome data, there is a need to perform meta-analysis of independently generated public data and to present it a user-friendly and freely accessible platform. Toward these goals, we developed a web-resource tool called iSyTE ( i ntegrated Sy stems T ool for E ye gene discovery; https://***/iSyTE/ ). iSyTE has effectively expedited eye gene discovery, leading to the identification of several genes ( Caprin2 , Celf1 , Elavl1 , Ell2 , Mafg , Mafk , Pvrl3 , Rbm24 , Sep15 , Tdrd7 , etc .) associated with lens defects and/or cataract and has contributed to the understanding of many other important lens regulatory pathways ( e.g. Foxe3 , Frfr2 , n-Myc , Prox1 , Sip1 (Zeb2) , CBP , p300 , etc .). To update iSyTE, we have now meta-analyzed transcriptome data from isolated lens epithelium and fiber cells as the lens develops from early embryonic stages through aging. Further, we have applied single nucleus (sn) multiomics approach using snRNA-seq and ATAC-seq to characterize early postnatal lens development. These new analysis have identified potential new genes and pathways in lens development and cataract.

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