Molecular Annotation Research Articles

Molecular annotation of G protein variants in a neurological disorder

Heterotrimeric G proteins transduce extracellular chemical messages to generate appropriate intracellular responses. Point mutations in GNAO1, encoding the G protein αo subunit, have been implicated in a pathogenic condition characterized by seizures, movement disorders, intellectual disability, and developmental delay (GNAO1 disorder). However, the effects of these mutations on G protein structure and function are unclear. Here, we report the effects of 55 mutations on Gαo conformation, thermostability, nucleotide binding, and hydrolysis, as well as interaction with Gβγ subunits, receptors, and effectors. Our effort reveals four functionally distinct groups of mutants, including one group that sequesters receptors and another that sequesters Gβγ, both acting in a genetically dominant manner. These findings provide a more comprehensive understanding of disease-relevant mutations and reveal that GNAO1 disorder is likely composed of multiple mechanistically distinct disorders that will likely require multiple therapeutic strategies.

PATH-35. SHIFTS IN GLIOMA PROGNOSTIC MOLECULAR FEATURES ACROSS THE LIFESPAN

Abstract INTRODUCTION Epidemiological studies examining glioma prognostic features have consistently identified older age as a negative predictor of survival. Accumulation and co-occurrence of favorable or deleterious molecular alterations across the lifespan likely plays a role in the age-dependent variability seen in glioma patient outcomes. METHODS We assembled 4,297 molecularly and clinically annotated glioma patients from multi-institutional datasets and TCGA, with IDH1/2-mutation, MGMT-methylation, and chromosome 1p19q codeletion status assayed. Patient age was stratified: pediatric (< 20 years), young adult (20-39 years), middle aged (40-64 years), and older adults ( >65 years). Pairwise proportions were tested to study distribution of alterations across age as well as variability in assaying for MGMT-methylation. RESULTS 4,297 patients with glioma were identified with molecular annotation of IDH1/2-mutation status (40.1% IDH1/2-mutant). Of assayed gliomas, 44.3% were MGMT-methylated and 13.9% had chromosome 1p19q codeletion. IDH1/2-mutation incidence decreased with age across all gliomas (85% in young adults, 35% middle aged, and 8% older adults) and in grade 4 gliomas (64% IDH1/2-mutation in young adults, 9% middle aged, and 2% older adults). 1p19q codeletion also decreased in incidence with increasing age (young adult 22%, middle aged 15%, older adults 6%). MGMT-methylation remained consistent across adult patients, between 43-50% across all gliomas. Interestingly, amongst the few patients >65 years old with IDH1/2-mutation or 1p19q codeletion, 87% possessed co-occurring MGMT-methylation, significantly greater than young adults (54%, p< 0.001). Consistent with this, 82% of patients >65yrs with grade 4 IDH1/2-mutant gliomas had co-occurring MGMT-methylation. Despite the treatment response implication of MGMT-methylation, 72.2% of low-grade gliomas were not assayed for MGMT. CONCLUSIONS There is an age-dependent pattern of molecular alterations in gliomas, with the incidence of certain favorable prognostic signatures decreasing over time. Given the stability of MGMT-methylation across the age spectrum and grade, more widespread MGMT profiling may be beneficial to guide treatment decisions.

RIZ2 at the crossroad of the EGF/EGFR signaling in colorectal cancer

BackgroundColorectal cancer (CRC) is the third most deadly and fourth most diagnosed cancer worldwide. Despite the progress in early diagnosis and advanced therapeutic options, CRC shows a poor prognosis with a 5 year survival rate of ~ 45%. PRDM2/RIZ, a member of PR/SET domain family (PRDM), expresses two main molecular variants, the PR-plus isoform (RIZ1) and the PR-minus (RIZ2). The imbalance in their expression levels in favor of RIZ2 is observed in many cancer types. The full length RIZ1 has been extensively investigated in several cancers where it acts as a tumor suppressor, whereas few studies have explored the RIZ2 oncogenic properties. PRDM2 is often target of frameshift mutations and aberrant DNA methylation in CRC. However, little is known about its role in CRC.MethodsWe combined in-silico investigation of The Cancer Genome Atlas (TCGA) CRC datasets, cellular and molecular assays, transcriptome sequencing and functional annotation analysis to assess the role of RIZ2 in human CRC.ResultsOur in-silico analysis on TCGA datasets confirmed that PRDM2 gene is frequently mutated and transcriptionally deregulated in CRC and revealed that a RIZ2 increase is highly correlated with a significant RIZ1 downregulation. Then, we assayed several CRC cell lines by qRT-PCR analysis for the main PRDM2 transcripts and selected DLD1 cell line, which showed the lowest RIZ2 levels. Therefore, we overexpressed RIZ2 in these cells to mimic TCGA datasets analysis results and consequently to assess the PRDM2/RIZ2 role in CRC. Data from RNA-seq disclosed that RIZ2 overexpression induced profound changes in CRC cell transcriptome via EGF pathway deregulation, suggesting that RIZ2 is involved in the EGF autocrine regulation of DLD1 cell behavior. Noteworthy, the forced RIZ2 expression increased cell viability, growth, colony formation, migration and organoid formation. These effects could be mediated by the release of high EGF levels by RIZ2 overexpressing DLD1 cells.ConclusionsOur findings add novel insights on the putative RIZ2 tumor-promoting functions in CRC, although additional efforts are warranted to define the underlying molecular mechanism.

Metabolic pathways engineering for drought or/and heat tolerance in cereals.

Drought (D) and heat (H) are the two major abiotic stresses hindering cereal crop growth and productivity, either singly or in combination (D/+H), by imposing various negative impacts on plant physiological and biochemical processes. Consequently, this decreases overall cereal crop production and impacts global food availability and human nutrition. To achieve global food and nutrition security vis-a-vis global climate change, deployment of new strategies for enhancing crop D/+H stress tolerance and higher nutritive value in cereals is imperative. This depends on first gaining a mechanistic understanding of the mechanisms underlying D/+H stress response. Meanwhile, functional genomics has revealed several stress-related genes that have been successfully used in target-gene approach to generate stress-tolerant cultivars and sustain crop productivity over the past decades. However, the fast-changing climate, coupled with the complexity and multigenic nature of D/+H tolerance suggest that single-gene/trait targeting may not suffice in improving such traits. Hence, in this review-cum-perspective, we advance that targeted multiple-gene or metabolic pathway manipulation could represent the most effective approach for improving D/+H stress tolerance. First, we highlight the impact of D/+H stress on cereal crops, and the elaborate plant physiological and molecular responses. We then discuss how key primary metabolism- and secondary metabolism-related metabolic pathways, including carbon metabolism, starch metabolism, phenylpropanoid biosynthesis, γ-aminobutyric acid (GABA) biosynthesis, and phytohormone biosynthesis and signaling can be modified using modern molecular biotechnology approaches such as CRISPR-Cas9 system and synthetic biology (Synbio) to enhance D/+H tolerance in cereal crops. Understandably, several bottlenecks hinder metabolic pathway modification, including those related to feedback regulation, gene functional annotation, complex crosstalk between pathways, and metabolomics data and spatiotemporal gene expressions analyses. Nonetheless, recent advances in molecular biotechnology, genome-editing, single-cell metabolomics, and data annotation and analysis approaches, when integrated, offer unprecedented opportunities for pathway engineering for enhancing crop D/+H stress tolerance and improved yield. Especially, Synbio-based strategies will accelerate the development of climate resilient and nutrient-dense cereals, critical for achieving global food security and combating malnutrition.

Rapid discovery of two unprecedented meroterpenoids from Daphne altaica Pall. using molecular networking integrated with MolNetEnhancer and Network Annotation Propagation

Under the guidance of the approach which integrates molecular networking, MolNetEnhancer and Network Annotation Propagation (NAP), daphnaltaicanoids A and B (1 and 2) with unprecedented 9-oxa-tetracyclo[6.6.1.02,6.08,13]pentadecane and tetracyclo[5.3.0.12,5.24,11]tridecane central frameworks were isolated from Daphne altaica Pall., representing two types of unparalleled meroterpenoid cores. Their structures were elucidated by extensive spectroscopic analysis, nuclear magnetic resonance (NMR) calculations, DP4+ analysis and electronic circular dichroism (ECD) calculations. The plausible biosynthetic pathways for 1 and 2 were postulated. Biologically, 2 exerted potent neuroprotective activities which were superior to trolox at 12.5 and 25 µmol/L. Moreover, 1 and 2 exhibited more noticeable acetylcholinesterase inhibitory activities than donepezil. Molecular docking simulations were performed to explore the intermolecular interaction of compounds 1 and 2 with acetylcholinesterase. The bioactivity evaluation results highlight the prospects of 1 and 2 as a novel category of neurological agents.

Protein Sequencing and Multi-Omics Data Analysis of Human Insulin

Abstract: Advancements in molecular biology have led to a deeper understanding of complex biological processes through the analysis of multi-omics data. In this study, we focused on the protein sequencing and multi-omics data analysis of human insulin, a pivotal hormone with critical roles in regulating glucose metabolism. In this study, we delved into the intricate realm of human insulin, a critical hormone regulating glucose metabolism, using advanced molecular biology techniques. Leveraging the Protein Data Bank (PDB) and the WorldWide Protein Data Bank (wwPDB), we explored 3D protein structures through sources like X-ray Crystallography and NMR spectrometry. RasMol software aided dynamic visualization of molecular structures, allowing manipulation and annotation. The Molecular Modelling Database (MMDB) facilitated structure identification and cross-species sequence alignment. The National Centre for Biotechnology Information (NCBI) provided genetic and biomedical insights, enhancing our understanding of insulin's molecular intricacies. Computational tools, including the FASTA format, accelerated sequence analysis, while BLAST unveiled evolutionary relationships and functional conservation. COBALT enabled incremental protein sequence alignment. Python-based PyMOL enriched analyses with specialized plugins, promoting efficient complex research.

Identification of Ecdysteroid Sinapate Esters with COX-2 Inhibitory Effects from Fibraurea recisa Using Molecular Networking and MS2LDA.

The roots of Fibraurea recisa are recognized as a rich source of protoberberine and aporphine alkaloids, but the non-alkaloidal metabolites in this plant are underexplored. The present study investigated the chemical composition of the plant roots using untargeted metabolomics-based molecular networking and MS2LDA motif annotation, revealing the presence of a characteristic fragment motif related to several sinapoyl-functionalized metabolites. Guided by the targeted motif, two new sinapic acid-ecdysteroid hybrids, named 3-O-sinapoyl makisterone A (1) and 2-O-sinapoyl makisterone A (2), were isolated. The structures of these compounds, including their absolute configuration, were elucidated by HR-ESIQTOFMS, MS2 fragmentation, NMR spectroscopy, and chemical degradation coupled with optical rotation measurements. Although neither compound inhibited nitric oxide (NO) production or inducible nitric oxide synthase (iNOS) protein expression on lipopolysaccharide-induced RAW 264 cells, 2 significantly suppressed cyclooxygenase 2 (COX-2) protein expression at 1-30 μM. Additionally, decreased expression of COX-2 protein was barely observed after treatment with methyl sinapate or makisterone A, the steroid skeleton of 1 and 2. These results indicated that the presence of the sinapoyl moiety at C-2 on the C28-ecdysteroid skeleton played a key role in the selectivity for the suppression of the COX-2 protein expression.

Three-dimensional molecular architecture of mouse organogenesis

Mammalian embryos exhibit sophisticated cellular patterning that is intricately orchestrated at both molecular and cellular level. It has recently become apparent that cells within the animal body display significant heterogeneity, both in terms of their cellular properties and spatial distributions. However, current spatial transcriptomic profiling either lacks three-dimensional representation or is limited in its ability to capture the complexity of embryonic tissues and organs. Here, we present a spatial transcriptomic atlas of all major organs at embryonic day 13.5 in the mouse embryo, and provide a three-dimensional rendering of molecular regulation for embryonic patterning with stacked sections. By integrating the spatial atlas with corresponding single-cell transcriptomic data, we offer a detailed molecular annotation of the dynamic nature of organ development, spatial cellular interactions, embryonic axes, and divergence of cell fates that underlie mammalian development, which would pave the way for precise organ engineering and stem cell-based regenerative medicine.

A curated gene and biological system annotation of adverse outcome pathways related to human health

Adverse outcome pathways (AOPs) are emerging as a central framework in modern toxicology and other fields in biomedicine. They serve as an extension of pathway-based concepts by depicting biological mechanisms as causally linked sequences of key events (KEs) from a molecular initiating event (MIE) to an adverse outcome. AOPs guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation. While AOPs model the systemic mechanisms at various levels of biological organisation, toxicogenomics provides the means to study the molecular mechanisms of chemical exposures. Systematic integration of these two concepts would improve the application of AOP-based knowledge while also supporting the interpretation of complex omics data. Hence, we established this link through rigorous curation of molecular annotations for the KEs of human relevant AOPs. We further expanded and consolidated the annotations of the biological context of KEs. These curated annotations pave the way to embed AOPs in molecular data interpretation, facilitating the emergence of new knowledge in biomedicine.

The prognostic effect of immune cell infiltration depends on molecular subtype in Endometrioid Ovarian Carcinomas.

Endometrioid ovarian carcinoma (ENOC) is the second most-common type of ovarian carcinoma, comprising 10-20% of cases. Recently, the study of ENOC has benefitted from comparisons to endometrial carcinomas (EC) including defining ENOC with four prognostic molecular subtypes. Each subtype suggests differential mechanisms of progression, though tumor initiating events remain elusive. There is evidence that the ovarian microenvironment may be critical to early lesion establishment and progression. However, while immune infiltrates have been well studied in high-grade serous ovarian carcinoma, studies in ENOC are limited. We report on 210 ENOC, with clinical follow-up and molecular subtype annotation. Using multiplex immunohistochemistry and immunofluorescence, we examine the prevalence of T cell lineage, B cell lineage, macrophages, and populations with PD-1 or PD-L1 across subtypes of ENOC. Immune cell infiltrates in tumor epithelium and stroma showed higher densities in ENOC subtypes with known high mutation burden (POLEmut and MMRd). While molecular subtypes were prognostically significant, immune infiltrates were not (OS p>0.2). Analysis by molecular subtype revealed that immune cell density was prognostically significant in only the no specific molecular subtype (NSMP) subtype, where immune infiltrates lacking B cells (TILB minus) had inferior outcome (DSS HR:4.0 CI 1.1-14.7, p<0.05). Similar to EC, molecular subtype stratification was generally superior to immune response in predicting outcomes. Subtype stratification is critical for better understanding of ENOC, in particular the distribution and prognostic significance of immune cell infiltrates. The role of B cells in the immune response within NSMP tumors warrants further study.

Single-cell transcriptomic profiling of dorsal root ganglion: an overview.

The somatosensory neurons in the dorsal root ganglion (DRG) are responsible to detect peripheral physical and noxious stimuli, and then transmit these inputs into the central nervous system. DRG neurons are composed of various subpopulations, which are suggested to respond to different stimuli, such as mechanical, thermal, and cold perception. For a long time, DRG neurons were classified based on anatomical criteria. Recently, single-cell (scRNA-seq) and single-nucleus RNA-sequencing (snRNA-seq) has advanced our understanding of the composition and functional heterogeneity of both human and rodent DRG neurons at single-cell resolution. In this review, we summarized the current literature regarding single-cell transcriptomic profiling of DRG to provide an integral understanding in the molecular transcriptomes, cell types, and functional annotations of DRG neurons in humans and rodents.

Targeted Isolation of Dolabellane Diterpenoids from the Soft Coral Clavularia viridis Using Molecular Networking.

LC-MS/MS-based molecular networking annotation coupled 1H NMR detection allowed the depiction of the soft coral Clavularia viridis to produce a profile of dolabellane-type diterpenoids. Chromatographic separation of the EtOAc fraction resulted in the isolation of 12 undescribed dolabellane-type diterpenoids, namely, clavirolides J-U (1-12). Their structures were characterized by the extensive analysis of the spectroscopic data, including the calculated ECD and X-ray diffraction for the configurational assignments. Clavirolides J-K are characterized by a 1,11- and 5,9-fused tricyclic tetradecane scaffold fused with a α,β-unsaturated-δ-lactone, and clavirolide L possesses a 1,11- and 3,5-fused tricyclic tetradecane scaffold, which extend the dolabellane-type scaffolds. Clavirolides L and G showed significant inhibition against HIV-1 without RT enzyme inhibition, providing additional non-nucleosides with different mechanisms from efavirenz.

Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development.

The brain is a complex tissue whose function relies on coordinated anatomical and molecular features. However, the molecular annotation of the spatial organization of the brain is currently insufficient. Here, we describe microfluidic indexing-based spatial assay for transposase-accessible chromatin and RNA-sequencing (MISAR-seq), a method for spatially resolved joint profiling of chromatin accessibility and gene expression. By applying MISAR-seq to the developing mouse brain, we study tissue organization and spatiotemporal regulatory logics during mouse brain development.

Development of an object-based image analysis tool for mass spectrometry imaging ion classification.

Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.

Assortative mixing in micro-architecturally annotated brain connectomes

The wiring of the brain connects micro-architecturally diverse neuronal populations, but the conventional graph model, which encodes macroscale brain connectivity as a network of nodes and edges, abstracts away the rich biological detail of each regional node. Here, we annotate connectomes with multiple biological attributes and formally study assortative mixing in annotated connectomes. Namely, we quantify the tendency for regions to be connected based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species, and consider a range of molecular, cellular, and laminar annotations. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections and find that the arrangement of connections with respect to biological annotations is associated to patterns of regional functional specialization. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation.

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer.

To assess whether integrative radiomics and transcriptomics analyses could provide novel insights for radiomic features' molecular annotation and effective risk stratification in non-small cell lung cancer (NSCLC). A total of 627 NSCLC patients from three datasets were included. Radiomics features were extracted from segmented 3-dimensional tumour volumes and were z-score normalized for further analysis. In transcriptomics level, 186 pathways and 28 types of immune cells were assessed by using the Gene Set Variation Analysis (GSVA) algorithm. NSCLC patients were categorized into subgroups based on their radiomic features and pathways enrichment scores using consensus clustering. Subgroup-specific radiomics features were used to validate clustering performance and prognostic value. Kaplan-Meier survival analysis with the log-rank test and univariable and multivariable Cox analyses were conducted to explore survival differences among the subgroups. Three radiotranscriptomics subtypes (RTSs) were identified based on the radiomics and pathways enrichment profiles. The three RTSs were characterized as having specific molecular hallmarks: RTS1 (proliferation subtype), RTS2 (metabolism subtype), and RTS3 (immune activation subtype). RTS3 showed increased infiltration of most immune cells. The RTS stratification strategy was validated in a validation cohort and showed significant prognostic value. Survival analysis demonstrated that the RTS strategy could stratify NSCLC patients according to prognosis (p = 0.009), and the RTS strategy remained an independent prognostic indicator after adjusting for other clinical parameters. This radiotranscriptomics study provides a stratification strategy for NSCLC that could provide information for radiomics feature molecular annotation and prognostic prediction. • Radiotranscriptomics subtypes (RTSs) could be used to stratify molecularly heterogeneous patients. • RTSs showed relationships between molecular phenotypes and radiomics features. • The RTS algorithm could be used to identify patients with poor prognosis.

RNA-Seq Based Transcriptomic Analysis of Bud Sport Skin Color in Grape Berries

The most common bud sport trait in grapevines is the change in color of grape berry skin, and the color of grapes is mainly developed by the composition and accumulation of anthocyanins. Many studies have shown that MYBA is a key gene regulates the initiation of bud sport color and anthocyanin synthesis in grape peels. In the current study, we used berry skins of ‘Italia’, ‘Benitaka’, ‘Muscat of Alexandria’, ‘Flame Muscat’, ‘Rosario Bianco’, ‘Rosario Rosso’, and ‘Red Rosario’ at the véraison stage (10 weeks post-flowering and 11 weeks post-flowering) as research materials. The relative expressions of genes related to grape berry bud sport skin color were evaluated utilizing RNA-Seq technology. The results revealed that the expressions of the VvMYBA1/A2 gene in the three red grape varieties at the véraison stage were higher than in the three white grape varieties. The VvMYBA1/A2 gene is known to be associated with UFGT in the anthocyanin synthesis pathway. According to the results, VvMYBA1/A2 gene expression could also be associated with the expression of LDOX. In addition, a single gene (gene ID: Vitvi19g01871) displayed the highest expressions in all the samples at the véraison stage for the six varieties. The expression of this gene was much higher in the three green varieties compared to the three red ones. GO molecular function annotation identified it as a putative metallothionein-like protein with the ability to regulate the binding of copper ions to zinc ions and the role of maintaining the internal stable state of copper ions at the cellular level. High expression levels of this screened gene may play an important role in bud sport color of grape berry skin at the véraison stage.

Mechanism of Huangjing Qianshi Decoction in treatment of prediabetic mice based on transcriptome sequencing

Based on transcriptome sequencing technology, the mouse model of prediabetes treated with Huangjing Qianshi Decoction was sequenced to explore the possible mechanism of treating prediabetes. First of all, transcriptome sequencing was performed on the normal BKS-DB mouse group, the prediabetic model group, and the Huangjing Qianshi Decoction treatment group(treatment group) to obtain differentially expressed genes in the skeletal muscle samples of mice. The serum biochemical indexes were detected in each group to screen out the core genes of Huangjing Qianshi Decoction in prediabetes. Gene Ontology(GO) database and Kyoto Encyclopedia of Genes and Genomes(KEGG) database were used to conduct signaling pathway enrichment analysis of differentially expressed genes, and real-time quantitative polymerase chain reaction(RT-qPCR) was used to verify them. The results showed that the levels of fasting blood glucose(FBG), fasting insulin(FINS), insulin resistance index(HOMA-IR), total cholesterol(TC), triglycerides(TG), and low-density lipoprotein cholesterol(LDL-C) in the mouse model were significantly decreased after treatment with Huangjing Qianshi Decoction. In the results of differential gene screening, there were 1 666 differentially expressed genes in the model group as compared with the normal group, and there were 971 differentially expressed genes in the treatment group as compared with the model group. Among them, interleukin-6(IL-6) and NR3C2 genes, which were closely related to the regulation of insulin resis-tance function, were significantly up-regulated between the model group and the normal group, and vascular endothelial growth factor A(VEGFA) genes were significantly down-regulated between the model group and the normal group. However, the expression results of IL-6, NR3C2, and VEGFA genes were adverse between the treatment group and the model group. GO functional enrichment analysis found that the biological process annotation mainly focused on cell synthesis, cycle, and metabolism; cell component annotation mainly focused on organelles and internal components; and molecular function annotation mainly focused on binding molecular functions. KEGG pathway enrichment analysis found that it involved the protein tyrosine kinase 6(PTK6) pathway, CD28-dependent phosphoinositide 3-kinase/protein kinase B(PI3K/AKT) pathway, p53 pathway, etc. Therefore, Huangjing Qianshi Decoction can improve the state of prediabetes, and the mechanism may be related to cell cycle and apoptosis, PI3K/AKT pathway, p53 pathway, and other biological pathways regulated by IL-6, NR3C2, and VEGFA.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.