Biology subjects

Pauling, J. K.

Publications and source records attributed to Pauling, J. K..

9 recordsLinked to original sources

bioRxiv2025

Navigating the Lipid Universe with LipidLibrarian: A Cross-Linked Database for Lipidomics Data Integration

There are numerous public resources and guidelines available for lipidomics research, including standard nomenclatures, classification systems, and lipid databases. However, these resources are not always aligned with one another, making it difficult to find and compare information on the same lipid across different databases. To tackle these challenges we present LipidLibrarian, a lipid search engine that enables a combined search of all major lipid databases by aggregating the available information and presenting it in a unified manner. The three main sources of information that build the foundation of LipidLibrarian as a comprehensive search-engine are SwissLipids, LIPID MAPS and ALEX123. Furthermore, various secondary resources such as LION/web, LINEX, LipidLynxX, and Goslin were incorporated to enhance the results and conduct name and hierarchy conversions. LipidLibrarian is accessible via a user-friendly website, allowing the user to query lipids using their trivial names, shorthand notations, database identifiers, or their masses. Alternatively, LipidLibrarian can be accessed as a Python package for integration into high-throughput lipidomics pipelines. The output of a LipidLibrarian query is split into multiple categories, such as nomenclature, database identifiers, masses, adducts, fragments, ontology terms, and reactions. For each of these categories, LipidLi-brarian aggregates the results from all databases and provides the source from which each value originates. This enables the user to quickly assess if the databases contain differing or conflicting information. In summary, LipidLibrarian provides an effortless, comprehensive and automated search for lipid information, thereby accelerating the research workflow and making it a meaningful tool for the scientific community.

bioinformatics↗

bioRxiv2025

Systematic Evaluation of Normalization Approaches in Tandem Mass Tag and Label-Free Protein Quantification Data Using PRONE

Despite the significant progress in accuracy and reliability in mass spectrometry technology, as well as the development of strategies based on isotopic labeling or internal standards in recent decades, systematic biases originating from non-biological factors remain a significant challenge in data analysis. In addition, the wide range of available normalization methods renders the choice of a suitable normalization method challenging. We systematically evaluated 17 normalization and two batch effect correction methods, originally developed for pre-processing DNA microarray data but widely applied in proteomics, on six publicly available spike-in and three label-free and tandem mass tag datasets. Opposed to state-of-the-art normalization practice, we found that a reduction in intragroup variation is not directly related to the effectiveness of the normalization methods. Furthermore, our results demonstrated that the methods RobNorm and Normics, specifically developed for proteomics data, in line with LoessF performed consistently well across the spike-in datasets, while EigenMS exhibited a high false positive rate. Finally, based on experimental data, we show that normalization substantially impacts downstream analyses, and the impact is highly dataset-specific, emphasizing the importance of use-case-specific evaluations for novel proteomics datasets. For this, we developed the PROteomics Normalization Evaluator (PRONE), a unifying R package enabling comparative evaluation of normalization methods, including their impact on downstream analyses, while offering considerable flexibility, acknowledging the lack of universally accepted standards. PRONE is available on Bioconductor with a web application accessible at https://exbio.wzw.tum.de/prone/.

bioinformatics↗

bioRxiv2024

Smile-to-Bert: A BERT architecture trained for physicochemical properties prediction and SMILES embeddings generation

Molecular property prediction is crucial for drug discovery. Over the years, deep learning models have been widely used for these tasks; however, large datasets are often needed to achieve strong performances. Pre-training models on vast unlabeled data has emerged as a method to extract contextualized embeddings that boost performance on smaller datasets. The Simplified Molecular Input Line Entry System (SMILES) encodes molecular structures as strings, making them suitable for natural language processing. Transformers, known for capturing long-range dependencies, are well suited for processing SMILES. One such transformer-based architecture is Bidirectional Encoder Representations from Transformers (BERT), which only uses the encoder part of the Transformer and performs classification and regression tasks. Pre-trained transformer-based architectures using SMILES have significantly improved predictions on smaller datasets. Public data repositories such as PubChem, which provide SMILES, among other data, are essential for pre-training these models. SMILES embeddings that combine chemical structure and physicochemical property information could further improve performance on tasks such as Absorption, Distribution, Metabolism, Excretion, and Toxicity prediction. To this end, we introduce Smile-to-Bert, a pre-trained BERT architecture designed to predict 113 RDKit-computed molecular descriptors from PubChem SMILES. This model generates embeddings that integrate both molecular structure and physicochemical properties. We evaluate Smile-to-Bert on 22 datasets from the Therapeutics Data Commons and compare its performance with that of the 2-encoder model and a Transformer model. Smile-to-Bert achieves the best result on one dataset, while the combination of Smile-to-Bert with the other models leads to improved performance on 8 datasets. Additionally, the state-of-the-art Transformer is applied to Absorption, Distribution, Metabolism, Excretion, and Toxicity prediction for the first time, achieving the best performance on the Therapeutics Data Commons leaderboard of one dataset. Scientific Contribution: We present Smile-to-Bert, a model pre-trained to predict 113 molecular descriptors directly from SMILES representations. We evaluate our model on 22 property prediction datasets and compare the performance with other language models. We demonstrate that integrating our model with two other pre-trained models improves the performance in 8 datasets.

bioinformatics↗

bioRxiv2024

MeNu GUIDE - a metabolite nutrition graph to uncover interactions with disease etiology

AO_SCPLOWBSTRACTC_SCPLOWThe relationship between diet and disease is well-documented, yet the complex interactions among foods, metabolites, and genetics makes research challenging. This study explores the potential insights offered by a knowledge graph that connects nutrition and diseases on a metabolic level. Ten ontologies and data from six databases were merged, resulting in a graph with over 25 million triple statements, stored in a Turtle file and added to a GraphDB repository. SPARQL queries revealed biases towards specific foods and conditions within the integrated databases. Despite these biases, this knowledge graph serves as a proof-of-concept, demonstrating the feasibility of integrating information from diverse resources to yield valuable insights and enabling the drawing of meaningful conclusions. The graph allows efficient identification of disease-related compounds and their food sources and enables the exploration of changes in metabolite concentrations, such as those occurring during food processing. Researchers could use such a knowledge graph to identify biomarkers, help generate new hypotheses, and improve experimental designs. Expanding the graph with automated text-mining and recipe data would further enhance its utility for nutrition research. Such a resource could advance understanding of the molecular mechanisms behind diet-disease relationships, guiding more targeted interventions.

bioinformatics↗

bioRxiv2024

LipiDetective - a deep learning model for the identification of molecular lipid species in tandem mass spectra

Lipids are involved in many vital processes within the cell, and alterations in lipid homeostasis have been associated with various diseases such as cancer or type 2 diabetes. Confidently identifying lipids in samples is a prerequisite for understanding the multiple functions lipids fulfill in health and disease. However, the accurate identification of molecular lipid species based on tandem mass spectrometry data is still a key challenge in lipidomics. Most current approaches rely on using a custom pipeline to process and match the measured spectra against an in-house spectra reference library, which hinders the comparability of results. To address this challenge, a transformer model called LipiDetective was developed and trained on a dataset composed of reference spectra measured from lipid standards, spectra from databases, and published experiments, utilizing both shotgun as well as liquid-chromatography mass spectrometry. LipiDetective demonstrates, for the first time, that artificial neural networks can learn the characteristic lipid fragmentation patterns to automatically and accurately annotate molecular lipids species in tandem mass spectra independently of the experimental setup. The model can even correctly predict lipid species for which it has never seen a spectrum before as it is able to generalize the learned lipid fragmentation patterns. Analysis of the integrated gradients reveals that LipiDetective focuses on relevant peaks that can be matched to known fragments and are thus humanly interpretable. Therefore, LipiDetective has the potential to be a valuable tool to aid in the lipid identification process and support the comparability of results from different sources. Aside from Lipidetective as a "ready-to-use" application, this work primarily offers a deeper understanding of how the model functions and how future deep learning models for lipid identification in mass spectra could be improved.

bioinformatics↗

bioRxiv2023

ATF6 activation alters colonic lipid metabolism causing tumor-associated microbial adaptation

Endoplasmic reticulum unfolded protein responses (UPRER) contribute to cancer development and the activating transcription factor 6 (ATF6) is involved in microbiota-dependent tumorigenesis. Here, we substantiate the clinical relevance of ATF6 in early-onset and late colorectal cancer patient cohorts. Transcriptional analysis in intestinal epithelial cells (IEC) of ATF6 transgenic mice (nATF6IEC) identified bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics and isotype-labeling confirmed ATF6-related enrichment of long chain fatty acids in colonic tissue of patients, mice and organoid cultures. FASN inhibition and microbiota transfer in germ-free nATF6IEC mice confirmed the causal involvement of ATF6-induced lipid alterations in tumorigenesis. The selective expansion of tumor-relevant microbial taxa was mechanistically linked to long chain fatty acid exposure, using bioorthogonal non-canonical amino acid tagging (BONCAT) and growth analysis of Desulfovibrio isolates. We postulate chronic ATF6 signaling in the epithelium to select for tumor-promoting microbiota by altering lipid metabolism. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/565267v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@be3756org.highwire.dtl.DTLVardef@231da4org.highwire.dtl.DTLVardef@16f3aaeorg.highwire.dtl.DTLVardef@1758fe3_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Chronic ATF6 signaling in the colonic epithelium alters lipid metabolism to select a tumor-promoting microbiota C_FIG O_LIBiallelic expression of activated ATF6 (p50 nuclear fragment) in intestinal epithelial cells (nATF6IEC) induces spontaneous colon tumors in SPF but not GF mice C_LIO_LIMechanistically, biallelic SPF nATF6IEC mice alter colonic lipid metabolism, including the upregulation of LCFAs and Fasn C_LIO_LIInhibition of FASN prevents colon tumor formation in mice, and reduces the tumor-promoting potential of the intestinal microbiota (FMT) C_LIO_LIExposure of fl/fl control mouse microbiota to LCFAs ex vivo translationally activates tumor-associated bacteria, including Desulfovibrio fairfieldensis C_LIO_LIHuman CRC patients show ATF6 upregulation, FASN co-occurrence and increased LCFAs in T tissue C_LIO_LIATF6 activity links with CRC-associated microbiota in patients, including Desulfovibrio C_LI Created with BioRender.com nATF6: activated activating transcription factor 6; LCFA: long-chain fatty acids; SAFA: saturated fatty acids; Fasn: fatty acid synthase; C75 i.p.: intraperitoneal injection of the Fasn inhibitor C75.

cancer biology↗

bioRxiv2023

Identification and Integration of Key-Metabolic Reactions from Untargeted Metabolomics Data

Metabolomics has become increasingly popular in biological and biomedical research, especially for multi-omics studies, due to the many associations of metabolism with diseases. This development is driven by improvements in metabolite identification and generating large amounts of data, increasing the need for computational solutions for data interpretation. In particular, only few computational approaches directly generating mechanistic hypotheses exist, making the biochemical interpretation of metabolomics data difficult. We present mantra, an approach to estimate how metabolic reactions change their activity between biological conditions without requiring absolute quantification of metabolites. Starting with a data-specific metabolic network we utilize linear models between substrates and products of a metabolic reaction to approximate deviations in activity. The obtained estimates can subsequently be used for network enrichment and integration with other omics data. By applying mantra to untargeted metabolomics measurements of Triple-Negative Breast Cancer biopsies, we show that it can accurately pinpoint biomarkers. On a dataset of stool metabolomics from Inflammatory Bowel Disease patients, we demonstrate that predictions on our proposed reaction metric generalize to an independent validation cohort and that it can be used for multi-omics network integration. By allowing mechanistic interpretation we facilitate knowledge extraction from metabolomics experiments.

bioinformatics↗

bioRxiv2022

Lipid network and moiety analyses reveal enzymatic dysregulation and altered mechanisms from lipidomics

Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present LINEX2 (Lipid Network Explorer), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases we generate dataset-specific lipid interaction networks. To aid interpretation of these networks we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/479101v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@10d722dorg.highwire.dtl.DTLVardef@1c3256borg.highwire.dtl.DTLVardef@cd9deforg.highwire.dtl.DTLVardef@13befc4_HPS_FORMAT_FIGEXP M_FIG C_FIG LINEX2 (Lipid Network Explorer) is a framework to visualize and analyze quantitative lipidomics data. The included algorithms offer new perspectives on the lipidome and can propose potential mechanisms of dysregulation. O_LIUsing the Reactome and Rhea databases, a comprehensive set of lipid class reactions is included and utilized to map the lipidome on custom data-specific networks. C_LIO_LIWith a novel network enrichment method, enzymatic dysregulation can be recovered from lipidomics data. C_LIO_LIWe validate its usability on data with a central lipid enzymatic deficiency. C_LIO_LILINEX2 is the first tool capable of such analysis and includes complimentary analysis options for structural lipid analysis. It is freely available as a web service (https://exbio.wzw.tum.de/linex2). C_LI

systems biology↗

bioRxiv2021

MoSBi: Automated signature mining for molecular stratification and subtyping

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de-novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (Molecular Signature identification using Biclustering), an automated multi-algorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We evaluated the performance of MoSBi on transcriptomics, proteomics and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multi-algorithm integration, MoSBi identified robust group and disease specific signatures across all scenarios overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that support biological hypothesis generation. MoSBi is available as an R package and web-service to make automated biclustering analysis accessible for application in molecular sample stratification.

bioinformatics↗