BACKGROUND: This study aimed to investigate the characteristics of gray matter volume loss in hippocampal subfields and alterations in whole-brain functional connectivity among patients with diabetic retinopathy (DR), and to examine the correlations between these neural changes and neuropsychological scale scores as well as clinical indicators. METHODS: Structural and functional magnetic resonance imaging (MRI) data, along with neurocognitive assessments, were acquired from 32 patients with diabetic retinopathy (DR) and 38 age- and sex-matched healthy controls using a Siemens 3.0T MRI scanner. Hippocampal subfield volumes were segmented and extracted using the Anatomy toolbox and Restplus (v1.25) within the MATLAB R2022a environment. Subregions demonstrating significant volumetric alterations were identified through subsequent between-group comparisons and defined as regions of interest (ROIs) for seed-based whole-brain functional connectivity (FC) analysis. The relationships between gray matter volume, functional connectivity values, neuropsychological test scores, and clinical indices were investigated using partial correlation analysis. RESULTS: Compared to healthy controls, patients with diabetic retinopathy (DR) exhibited significantly reduced bilateral entorhinal cortex (EC) volumes and increased volume in bilateral dentate gyrus (DG) (P < 0.05, FDR-corrected). Using these volume-altered subregions as regions of interest (ROIs), seed-based functional connectivity (FC) analysis revealed significantly increased FC strength between the left EC and the left caudate nucleus, alongside significantly decreased FC strength between the right DG and the right middle occipital gyrus (GRF correction: voxel-level p < 0.001, cluster-level p < 0.05, two-tailed). Furthermore, partial correlation analyses demonstrated significant positive correlations between the FC strength of the right hippocampal DG subfield and the right middle occipital gyrus, and scores on both the Montreal Cognitive Assessment (MoCA) (r = 0.560, p = 0.001) and the Mini-Mental State Examination (MMSE) (r = 0.541, p = 0.002). CONCLUSIONS: Patients with diabetic retinopathy (DR) demonstrate structural alterations in hippocampal subfields and widespread functional dysconnectivity across the brain. Notably, the strength of functional connectivity between the hippocampus and the visual cortex was significantly and positively correlated with cognitive function. This study provides neuroimaging evidence supporting the mechanisms underlying central nervous system complications in DR, suggesting that hippocampal subfield volumes and specific functional connectivity patterns hold potential as neuroimaging biomarkers for early intervention.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

MOTIVATION: Transcriptomics dose-response analysis is a promising new approach method for toxicity testing. While international regulatory agencies have spent substantial effort establishing a standardized statistical approach, existing software that follows this approach is computationally inefficient and must be locally installed. RESULTS: FastBMD is a web-based tool that implements standardized methods for transcriptomics benchmark dose-response analysis in R. It is >60 times faster than the current leading software, supports transcriptomics data from 13 species, and offers a comprehensive analytical pipeline that goes from processing and normalization of raw gene expression values to interactive exploration of pathway-level benchmark dose results. AVAILABILITY AND IMPLEMENTATION: FastBMD is freely available at www.fastbmd.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: The MetaboAnalyst web application has been widely used for metabolomics data analysis and interpretation. Despite its user-friendliness, the web interface has presented its inherent limitations (especially for advanced users) with regard to flexibility in creating customized workflow, support for reproducible analysis, and capacity in dealing with large data. To address these limitations, we have developed a companion R package (MetaboAnalystR) based on the R code base of the web server. The package has been thoroughly tested to ensure that the same R commands will produce identical results from both interfaces. MetaboAnalystR complements the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data. Availability and implementation: MetaboAnalystR is freely available from https://github.com/xia-lab/MetaboAnalystR.

SUMMARY: Gene expression or metabolomics data generated from clinical settings are often associated with multiple metadata (i.e. diagnosis, genotype, gender, etc.). It is of great interest to analyze and to visualize the data in these contexts. Here, we introduce INVEX-a novel web-based tool that integrates the server-side capabilities for data analysis with the browse-based technology for data visualization. INVEX has two key features: (i) flexible differential expression analysis for a wide variety of experimental designs; and (ii) interactive visualization within the context of metadata and biological annotations. INVEX has built-in support for gene/metabolite annotation and a fully functional heatmap builder. AVAILABILITY AND IMPLEMENTATION: Freely available at http://www.invex.ca.

SUMMARY: Time-series and multifactor studies have become increasingly common in metabolomic studies. Common tasks for analyzing data from these relatively complex experiments include identification of major variations associated with each experimental factor, comparison of temporal profiles across different biological conditions, as well as detection and validation of the presence of interactions. Here we introduce MetATT, a web-based tool for time-series and two-factor metabolomic data analysis. MetATT offers a number of complementary approaches including 3D interactive principal component analysis, two-way heatmap visualization, two-way ANOVA, ANOVA-simultaneous component analysis and multivariate empirical Bayes time-series analysis. These procedures are presented through an intuitive web interface. At the end of each session, a detailed analysis report is generated to facilitate understanding of the results. AVAILABILITY: Freely available at http://metatt.metabolomics.ca CONTACT: [email protected].

UNLABELLED: MetPA (Metabolomics Pathway Analysis) is a user-friendly, web-based tool dedicated to the analysis and visualization of metabolomic data within the biological context of metabolic pathways. MetPA combines several advanced pathway enrichment analysis procedures along with the analysis of pathway topological characteristics to help identify the most relevant metabolic pathways involved in a given metabolomic study. The results are presented in a Google-map style network visualization system that supports intuitive and interactive data exploration through point-and-click, dragging and lossless zooming. Additional features include a comprehensive compound library for metabolite name conversion, automatic generation of analysis report, as well as the implementation of various univariate statistical procedures that can be accessed when users click on any metabolite node on a pathway map. MetPA currently enables analysis and visualization of 874 metabolic pathways, covering 11 common model organisms. AVAILABILITY: Freely available at http://metpa.metabolomics.ca.

BACKGROUND: One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts. There are several software packages that support compound identification and quantification via 1D 1H NMR by spectral fitting techniques. Because 1D 1H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification. However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies. RESULTS: We describe a standalone graphics software tool, called MetaboMiner, which can be used to automatically or semi-automatically identify metabolites in complex biofluids from 2D NMR spectra. MetaboMiner is able to handle both 1H-1H total correlation spectroscopy (TOCSY) and 1H-13C heteronuclear single quantum correlation (HSQC) data. It identifies compounds by comparing 2D spectral patterns in the NMR spectrum of the biofluid mixture with specially constructed libraries containing reference spectra of approximately 500 pure compounds. Tests using a variety of synthetic and real spectra of compound mixtures showed that MetaboMiner is able to identify >80% of detectable metabolites from good quality NMR spectra. CONCLUSION: MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra. Using comprehensive reference libraries coupled with robust algorithms for peak matching and compound identification, the program greatly simplifies the process of metabolite identification in complex 2D NMR spectra.