Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects DOI Creative Commons
Dmitrii Kriukov,

Ekaterina Kuzmina,

Evgeniy Efimov

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 4, 2023

Abstract Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework consider from uncertainty perspective. Our analysis reveals that DNA methylation profiles across reprogramming poorly represented in data train clock models, thus introducing high epistemic estimations. Moreover, different published inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning possibility reversal, we show challenges reliability observed vitro before pluripotency and throughout embryogenesis. Conversely, our method a significant increase after vivo recommend including estimation future models avoid risk misinterpreting results prediction.

Language: Английский

The polyamine-regulating enzyme SSAT1 impairs tissue regulatory T cell function in chronic cutaneous inflammation DOI Creative Commons

Teresa Neuwirth,

Daniel Malzl,

Katja Knapp

et al.

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

Published: Feb. 1, 2025

Language: Английский

Citations

3

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer DOI Creative Commons
Daniel Borràs, Sara Verbandt,

Markus Außerhofer

et al.

Cell Discovery, Journal Year: 2023, Volume and Issue: 9(1)

Published: Nov. 15, 2023

Abstract CD8 + T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against stable (MSS) CRC limited. Little known about most critical features cells that together determine diverse landscapes and contrasting ICB responses. Hence, we pursued a deep single mapping on transcriptomic receptor (TCR) repertoire levels patient cohort, with additional surface proteome validation. This revealed dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) antigen-specificities, environmental cues like gut microbiome or colon tissue-specific ‘self-like’ features. MSI showed tumor-specific reminiscent canonical ‘T hot’ tumors, whereas MSS exhibited unspecific bystander-like was accompanied inflammation ‘pseudo-T tumors. Consequently, overlapping phenotypic differed dramatically their antigen-specificities. Given high discriminating potential for features/specificities, used tumor-reactive signaling modules to build bulk transcriptome classification “Immune Subtype Classification” (ISC) successfully distinguished various tumoral prognostic value predicted responses Thus, deliver unique map drives novel landscape classification, relevance decision-making.

Language: Английский

Citations

38

A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data DOI Creative Commons

Lulu Pan,

Qian Gao,

Kecheng Wei

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(1), P. e1012739 - e1012739

Published: Jan. 10, 2025

Transfer learning aims to integrate useful information from multi-source datasets improve the performance of target data. This can be effectively applied in genomics when we learn gene associations a tissue, and data other tissues integrated. However, heavy-tail distribution outliers are common data, which poses challenges effectiveness current transfer approaches. In this paper, study problem under high-dimensional linear models with t-distributed error (Trans-PtLR), estimation prediction by borrowing source offering robustness accommodate complex heavy tails outliers. oracle case known transferable datasets, algorithm based on penalized maximum likelihood expectation-maximization is established. To avoid including non-informative sources, propose select sources cross-validation. Extensive simulation experiments as well an application demonstrate that Trans-PtLR demonstrates better exist compared for regression model normal distribution. Data integration, Variable selection, T distribution, Expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.

Language: Английский

Citations

2

Assessing and mitigating batch effects in large-scale omics studies DOI Creative Commons
Ying Yu,

Yuanbang Mai,

Yuanting Zheng

et al.

Genome biology, Journal Year: 2024, Volume and Issue: 25(1)

Published: Oct. 3, 2024

Language: Английский

Citations

9

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects DOI Creative Commons
Dmitrii Kriukov,

Ekaterina Kuzmina,

Evgeniy Efimov

et al.

Aging Cell, Journal Year: 2024, Volume and Issue: 23(11)

Published: July 28, 2024

Abstract Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework consider from uncertainty perspective. Our analysis reveals that DNA methylation profiles across reprogramming poorly represented in data train clock models, thus introducing high epistemic estimations. Moreover, different published inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning possibility reversal, we show challenges reliability observed vitro before pluripotency and throughout embryogenesis. Conversely, our method a significant increase after vivo recommend including estimation future models avoid risk misinterpreting results prediction.

Language: Английский

Citations

6

MetaX: A peptide centric metaproteomic data analysis platform using Operational Taxa-Functions (OTF) DOI

Qing Wu,

Zhibin Ning, Ailing Zhang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 24, 2024

Abstract Metaproteomics analyzes the functional dynamics of microbial communities by identifying peptides and mapping them to most likely proteins taxa. The challenge in this field lies seamlessly integrating taxonomic annotations accurately represent contributions individual taxa diversity. We introduce MetaX, a comprehensive tool for analyzing taxa-function relationships metaproteomics their lowest common ancestors assigning functions based on proportional thresholds, ensuring accurate peptide-level mappings. Importantly, MetaX introduces Operational Taxa-Functions (OTF), new conceptual unit exploring roles interactions within ecosystems. Additionally, extends traditional classification adding genome level below species level, enhancing accuracy function attribution specific genomes. demonstrated reanalyzing metaproteomic data from gut microbiomes exposed various sweeteners, achieving results similar protein analysis. Furthermore, using peptide-centric approach OTF, we observed that Parabacteroides distasonis significantly responds certain highlighting its role modifying metabolic functions. With intuitive, user-friendly interface, facilitates detailed study complex between metaproteomics. It enhances our understanding ecosystems health.

Language: Английский

Citations

5

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts DOI Creative Commons
Pratik Kamat, Nicolas C. Macaluso, Yukang Li

et al.

Science Advances, Journal Year: 2025, Volume and Issue: 11(17)

Published: April 25, 2025

Cellular senescence, a hallmark of aging, reveals context-dependent phenotypes across multiple biological length scales. Despite its mechanistic importance, identifying and characterizing senescence cell populations is challenging. Using primary dermal fibroblasts, we combined single-cell imaging, machine learning, several induced conditions, protein biomarkers to define functional subtypes. Single-cell morphology analysis revealed 11 distinct clusters. Among these, identified three as bona fide subtypes (C7, C10, C11), with C10 exhibiting the strongest age dependence within an aging cohort. In addition, observed that donor’s burden subtype composition were indicative susceptibility doxorubicin-induced senescence. Functional subtype-dependent responses senotherapies, C7 being most responsive combination dasatinib quercetin. Our framework, SenSCOUT, enables robust identification classification subtypes, offering applications in next-generation senotherapy screens, potential toward explaining heterogeneous based on presence

Language: Английский

Citations

0

ComBat models for harmonization of resting-state EEG features in multisite studies DOI Creative Commons
Alberto Jaramillo‐Jimenez, Diego Tovar, Yorguin-José Mantilla-Ramos

et al.

Clinical Neurophysiology, Journal Year: 2024, Volume and Issue: 167, P. 241 - 253

Published: Sept. 24, 2024

Language: Английский

Citations

3

Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis DOI Creative Commons
Miaoru Han, Yi Wang, Xiaoyan Huang

et al.

European journal of medical research, Journal Year: 2023, Volume and Issue: 28(1)

Published: Nov. 16, 2023

Abstract Background Membranous nephropathy (MN) is a chronic glomerular disease that leads to nephrotic syndrome in adults. The aim of this study was identify novel biomarkers and immune-related mechanisms the progression MN through an integrated bioinformatics approach. Methods microarray data were downloaded from Gene Expression Omnibus (GEO) database. differentially expressed genes (DEGs) between normal samples identified analyzed by Ontology analysis, Kyoto Encyclopedia Genes Genomes analysis Set Enrichment Analysis (GSEA) enrichment. Hub hub screened weighted gene co-expression network (WGCNA) least absolute shrinkage selection operator (LASSO) algorithm. receiver operating characteristic (ROC) curves evaluated diagnostic value genes. single-sample GSEA infiltration degree several immune cells their correlation with Results We total 574 DEGs. enrichment showed metabolic functions pathways significantly enriched. Four modules obtained using WGCNA. candidate signature intersected DEGs then subjected LASSO obtaining 6 ROC indicated associated high value. CD4 + T cells, CD8 B infiltrated correlated Conclusions six ( ZYX , CD151 N4BP2L2-IT2 TAPBP FRAS1 SCARNA9 ) as for MN, providing potential targets diagnosis treatment.

Language: Английский

Citations

6

Thinking points for effective batch correction on biomedical data DOI Creative Commons
Harvard Wai Hann Hui, Weijia Kong, Wilson Wen Bin Goh

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(6)

Published: Sept. 23, 2024

Abstract Batch effects introduce significant variability into high-dimensional data, complicating accurate analysis and leading to potentially misleading conclusions if not adequately addressed. Despite technological algorithmic advancements in biomedical research, effectively managing batch remains a complex challenge requiring comprehensive considerations. This paper underscores the necessity of flexible holistic approach for selecting effect correction algorithms (BECAs), advocating proper BECA evaluations consideration artificial intelligence–based strategies. We also discuss key challenges correction, including importance uncovering hidden factors understanding impact design imbalance, missing values, aggressive correction. Our aim is provide researchers with robust framework effective management enhancing reliability data analyses.

Language: Английский

Citations

2