Molecular Omics

Gastroschisis is a congenital abdominal wall defect in which fetal intestines herniate into the amniotic cavity. Despite 97% surgical repair success rate, 40% of affected infants require hospital readmission due to gastrointestinal complications, where underlying mechanisms remain poorly characterized. We hypopthesized that the cord blood metabolome of neonates with gastroschisis differs systematically from controls and may reveal pathway-level alterations relevant to neonatal physiology. Cord blood plasma collected at delivery (23 samples each group) was analyzed using ultra-performance liquid chromatography coupled with tandem mass spectrometry. Unsupervised principal component analysis and hierarchical clustering demonstrated significant separation between groups (PERMANOVA pseudo-F = 4.632, R{superscript 2} = 0.095, p = 0.001). 53 metabolites met criteria for differential abundance, 75% were lipids. Key alterations included reduced free fatty acids, increased fatty acid amides and ceramides, disrupted steroid and bile acid metabolism, and decreased biliverdin and bilirubin isomers. Our findings provide insight into gastroschisis pathophysiology and identify potential biomarkers for future investigation.

Induced pluripotent stem cells (iPSCs) are widely used as patient-specific disease models, yet substantial unexplained variability in molecular and functional readouts limits their reliability. Here, we systematically investigated the sources of variation in iPSC-derived neurons for three rare genetic disorders: Myotonic Dystrophy Type 1, chromodomain-DNA-helicase-binding protein 2-related disorder and N-acetylneuraminic acid synthase deficiency. This was performed by profiling multi-omics layers: genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics. Our study found that clonal variability was comparable to inter-patient differences and that neuronal differentiation state and nutrient-driven metabolic activity emerged as dominant contributors to variability observed across omics layers. Clonal differences could partly be attributed to stochastic differences in DNA methylation established during reprogramming. By modeling and correcting the observed variation, we improved the detection of disease-associated molecular signatures. Our study provides guidelines for improved study design and data analysis to minimize variability, enabling robust biomarker discovery and reliable iPSC-based disease modeling.

Type 2 diabetes (T2D) is a prevalent metabolic disorder affecting millions worldwide, characterized by insulin resistance and impaired glucose homeostasis. While mammalian models are widely used, Drosophila melanogaster provides a powerful alternative due to its conserved insulin signaling pathways, genetic tractability, and suitability for high throughput studies. In addition to glucose dysregulation, lipid metabolism plays a crucial role in T2D pathophysiology, as alterations in lipid composition contribute to insulin resistance and metabolic dysfunction. Lipidomic studies have emerged as an essential approach to identify metabolic signatures and potential biomarkers for disease progression and therapeutic targeting. In this study, T2D like model was established by inducing insulin resistance through knockdown of the insulin receptor in brain insulin-producing cells using the dilp2-Gal4>UAS-InRRNAi system. This genetic manipulation resulted in significant metabolic dysregulation, including elevated glucose, trehalose, and triacylglyceride levels, along with increased oxidative stress indicators. Additionally, mRNA expression analysis of key insulin signaling components, including insulin receptor substrate 1, dilp2, dilp3, dilp5, and phosphorylated Akt, further validated the model. To further investigate metabolic alterations, Lipid profiling was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in non targeted LC-MS-based metabolomics approach to identify lipid biomarkers associated with T2D. Multivariate statistical analyses, including PCA and PLS-DA, revealed distinct lipid signatures between wild-type and T2D flies. Notably, specific phosphatidylglycerol species PG 34:0, PG 34:4, PA 38:3, PIP 38:1, PIP2 38:6, and LPS 24:0 demonstrated an area under the curve (AUC) of 1, indicating their strong reliability as lipid biomarkers for T2D diagnosis.

Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.

Neuronal differentiation requires coordinated regulation across chromatin organization, gene expression, protein abundance, and post-translational modifications. Using the LUHMES human dopaminergic neuronal differentiation model, we integrated proteome and phosphoproteome profiling with previously generated enhancer-promoter interaction maps from NET-CAGE and HiCap and transcriptomic analysis across three consecutive differentiation stages. Differentiation was accompanied by increased abundance and phosphorylation of proteins involved in axon guidance, cytoskeletal organization, and synaptic signaling, alongside repression of the cell cycle, DNA replication, and chromatin-associated programs. Phosphoproteome analysis further revealed extensive remodeling of signaling networks associated with neuronal maturation. Enhancer-promoter interaction analysis revealed substantially greater rewiring at enhancers than promoters and identified master and relay transcription factors regulated across multiple molecular layers. siRNA-mediated knockdown showed that transcription factors MYT1, ISL2, and NHLH2 are crucial for proper neuronal maturation, whereas LCOR acts as a negative regulator of differentiation. Integrative network reconstruction further nominated MEOX2 as a candidate enhancer-associated regulator of late dopaminergic maturation. Together, these findings provide a multi-layered view of regulatory networks governing human neuronal differentiation.

Krabbe disease is a rare autosomal recessive lysosomal disease caused by deficiency of galactocerebrosidase (GALC), leading to accumulation of galactosylceramide and formation of the toxic metabolite galactosylsphingosine (psychosine). While psychosine accumulation is well-established as a primary pathogenic mechanism, the broader metabolic consequences of GALC deficiency remain incompletely understood. In this study, we used stable isotope tracing to comprehensively characterize metabolic perturbations in a human oligodendrocellular Krabbe disease model. This approach revealed elevated de novo ceramide synthesis in GALC knock-out cells, characterized by increased incorporation of glucose-derived serine into ceramide biosynthetic pathways. This enhanced ceramide production was amenable to pharmacological intervention by tezacaftor, an inhibitor of sphingolipid {Delta}4-desaturate (DEGS); tezacaftor administration also normalized psychosine levels, raising the possibility of its use as substrate reduction therapy. Additionally, we identified significant disruption of UDP-hexose metabolism, manifesting as an overabundance of truncated and hypogalactosylated glycans. These findings suggest impaired protein glycosylation as a previously unrecognized pathogenic mechanism in Krabbe disease. Our findings reveal novel metabolic dysregulation in Krabbe disease extending beyond established psychosine toxicity. The identification of enhanced de novo ceramide synthesis presents a new therapeutic target, while the discovery of galactose-deficient glycosylation defects supports galactose supplementation as a potential therapeutic intervention. These metabolic insights provide new mechanistic understanding and therapeutic opportunities for this devastating neurodegenerative disorder.

Human endogenous retroviruses (HERVs) are mobile genetic sequences derived from ancient retroviral infections. While typically silenced, their reactivation has been implicated in gene dysregulation, aging, and immune-related transcriptional pathogenesis of some neurodegenerative diseases. Parkinson's disease (PD) is the second most common neurodegenerative disorder, yet its etiology and HERV reactivation remain poorly understood. This study investigates locus-specific HERV expression in early-stage PD, including genetic and non-genetic cases (all PD), idiopathic PD without a known genetic cause (iPD), and PD driven by leucine-rich repeat kinase 2 mutations (LRRK2 PD). We analyzed RNA-seq whole-blood samples from 492 individuals (358 all PD, 256 were iPD, 63 LRRK2 PD, and 134 healthy controls (HC)). We identified 20 significantly dysregulated HERV loci in all PD versus HC. Five HERV loci were shared with iPD analysis, and one HERV locus was shared with LRRK2 PD. Notably, these shared loci included HERV-H and ERVLE elements, indicating robust disease-associated retroviral signals independent of disease subtype. We found that genes proximal to these HERVs revealed pathways implicated in PD pathogenesis. Immune cell deconvolution showed increased neutrophil abundance and decreased resting CD4+ memory T cells proportions across the PD cohorts when compared to HC, consistent with neutrophil-lymphocyte ratio observed in previous peripheral immunity studies. Transcriptomic HERV alterations are present in whole blood across PD populations and are associated with dysregulation of fundamental cellular pathways and peripheral immune remodeling. Our findings motivate experimental validation of locus-specific HERV expression as a candidate blood-based signature with potential to inform PD neuroinflammatory and neurodegenerative processes.

Neurodegenerative diseases are complex disorders characterised by progressive neuronal loss and widespread transcriptomic dysregulation; however, the coordinated interactions among coding and non-coding RNAs that contribute to disease progression remain incompletely understood. In this study, RNA-seq datasets from disease-relevant neuronal populations and brain regions representing Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) were analysed using an integrative network-based framework. Differential expression analysis coupled with weighted gene co-expression network analysis identified modules significantly correlated with disease and prioritised highly connected hub genes. Integration of these hub genes with curated RNA interaction database enabled the construction of candidate lncRNA-miRNA-mRNA regulatory networks. Functional enrichment analysis revealed Gene Ontology biological processes associated with synaptic signalling, mitochondrial function, RNA metabolism and neuroinflammatory responses across neurodegenerative conditions. The inferred regulatory networks suggested both disease-specific and shared post-transcriptional regulatory modules involving key hub genes and non-coding RNAs. Additionally, putative sequence variants were identified within untranslated regions of selected hub genes, suggesting potential alterations in miRNA-mediated regulations. Therefore, this study provides a systems-level view of transcriptomic dysregulation across major neurodegenerative diseases and identifies candidate regulatory interactions and molecular targets for future functional investigation

Myotonic Dystrophy type 1 (DM1) is an autosomal multisystem disorder manifested due to unstable CTG nucleotide repeat expansion within the 3'-untranslated region of the dystrophia myotonica protein kinase (DMPK) gene. Although progress towards understanding of molecular pathogenesis in muscle and heart has been made, the pathways that affect the brain in DM1 is fundamentally unknown. In addition, the congenital DM1 manifest even more complicated brain abnormalities. Despite the wealth of existing cellular and animal models, iPSCs based studies are being fostered as they replicate the human model more closely to the disease. In view of this context, we set out to characterize the differentiation potential of congenital DM1 patient derived iPSC lines towards neuronal cells. Using neurogenin2 (NGN2) induced direct reprogramming of iPSCs into neurons and chemically defined media-induced neural induction protocol, we find that congenital DM1 mutant iPSC derived neurons exhibited precocious differentiation, as evidenced by their expression of pan-neuronal markers TUJ1 and Map2, along with increased processes extension and neurite length. Moreover, unbiased RNA sequencing analyses and qPCR validation revealed precocious and enhanced expression of several neurogenic transcription factors including, Ascl1, NeuroG2, and NeuroD1. Furthermore, immunofluorescence imaging of MBNL1 and MBNL2, RNA-splicing factors, displayed enhanced nuclear aggregations, a hallmark of the DM1 disease, in the mutant lines. Moreover, investigation of RNA splicing events identified mis-splicing in many important genes/transcripts including RMST, ANK3 and MBD1 during the neural conversion of congenital DM1 lines. These studies reveal novel paradigms that may contribute to neurological pathogenesis in CDM1 patients. These studies also provide a strong foundation for future mechanistic investigation aimed at understanding CDM1 pathology and may open new avenues for the development of gene therapy approaches for individuals with DM1.

BackgroundAcute myocardial infarction (AMI) and periodontitis (PD) have been epidemiologically linked, but the molecular mechanisms underlying this association remain elusive. We aimed to elucidate shared pathogenic signatures between AMI and PD using a comprehensive bioinformatics approach. MethodsWe integrated transcriptomic data from multiple Gene Expression Omnibus datasets, including an AMI cohort profiled from enriched circulating endothelial cells (GSE66360) with whole-blood validation (GSE48060), and PD gingival tissue cohorts (GSE16134 and GSE10334). Weighted gene co-expression network analysis, protein-protein interaction network analysis, and LASSO-based feature selection were applied. Functional enrichment, immune deconvolution, transcription factor-gene regulatory network analysis, and single-cell RNA sequencing analysis were performed to characterize shared molecular features. ResultsWe identified 95 shared differentially expressed genes (DEGs) between AMI and PD. By intersecting the shared upregulated DEGs with disease-associated WGCNA modules, we obtained 46 candidate shared genes. LASSO-based feature selection further highlighted FCN1 and LYN as overlapping candidates, which showed good discriminative performance in both training and external validation cohorts in ROC analyses. Enrichment analyses suggested that the shared signature was mainly related to myeloid cell migration, phagocytosis, and neutrophil-related inflammatory pathways (e.g., neutrophil extracellular trap formation). Immune deconvolution in PD gingival tissues suggested increased plasma cells and neutrophils and decreased resting memory CD4+ T cells; immune deconvolution results in the AMI cohort were interpreted cautiously due to the CEC-enriched sample source. Single-cell analysis revealed that FCN1 and LYN were predominantly expressed in macrophage/monocyte-derived populations. ConclusionsOur study suggests a shared inflammatory and immune-mediated transcriptomic program linking AMI and PD, and identifies FCN1 and LYN as candidate shared immune markers. These findings provide a molecular rationale for the oral-cardiovascular association and are hypothesis-generating, warranting future experimental and prospective validation.

Equitable access to large sample cohorts for robust, high-throughput proteomics for biomarker discovery is a major barrier to widescale clinical implementation. Dried blood spots (DBS) offer a minimally invasive alternative to venous blood draws, enabling at-home microsampling (<50 {micro}L) for centralised analysis, thus enhancing research participation. This approach is particularly relevant for under-represented groups, including children, the elderly, minority backgrounds and those with long-term health conditions such as chronic kidney disease (CKD), where disease fluctuations may occur outside the clinic, and vein preservation is critical. Proteomic analysis has demonstrated great utility in monitoring disease progression, and for biomarker/therapeutic target discovery. However, liquid chromatography-tandem mass spectrometry (LC-MS/MS) of whole blood is hindered by the wide dynamic range and the relatively high abundance of proteins such as haemoglobin, compromising biomarker discovery. Here, we establish an optimised workflow for protein extraction and haemoglobin depletion from microsamples obtained using DBS, enabling sensitive and high-throughput proteomic analysis. We demonstrate that haemoglobin depletion increases protein identifications by [~]50%, mitigating ion suppression and dynamic range effects, enabling the identification of putative biomarkers from patients with stage 5 CKD on dialysis. We also evaluated a commercial cell-free DBS device which yielded a sample more representative of plasma compared to traditional DBS and enabled greater depletion of haemoglobin compared to traditional DBS with haemoglobin depletion methods. Our findings offer a scalable approach for biomarker discovery, facilitating remote, longitudinal clinical studies.

Microglial inflammatory activation is accompanied by extensive molecular remodeling, yet proteomic, lipidomic, and metabolomic responses are often analyzed independently. Here, we applied an integrated mass spectrometry-based multiomic workflow to characterize proteomic, lipidomic, and polar metabolomic remodeling from matched BV-2 biological samples following stimulation with interferon-{gamma} and lipopolysaccharide (IFN-{gamma} and LPS). Inflammatory activation was confirmed by increased nitrite accumulation, elevated TNF- and IL-6 secretion, and treatment-associated morphological changes. Discovery proteomics quantified 8,676 proteins and identified 562 significantly altered proteins, including 344 increased and 218 decreased proteins. Increased proteins were enriched for interferon-responsive, innate immune, inflammatory effector, and antigen-associated pathways, whereas decreased proteins were associated with cellular organization, protein biogenesis, vesicular trafficking, and metabolic regulation. Targeted lipidomics identified 237 significantly altered lipid features out of 356 measured lipids, including increased triacylglycerols and diacylglycerols and broad remodeling of glycerophospholipids, lysophospholipids, and sphingolipid-related species. Targeted polar metabolomics identified 75 significantly altered metabolites out of 98 measured metabolites, including changes in nucleotide/NAD-related metabolism, amino acid metabolism, methylation-associated metabolites, acylcarnitine abundance, phospholipid precursors, polyamine metabolism, arginine/nitric oxide-associated metabolism, and redox-associated metabolites. Process-level integration of significant features revealed coordinated remodeling of inflammatory protein programs with lipid storage, membrane remodeling, nucleotide metabolism, amino acid availability, phospholipid precursor abundance, nitric oxide-associated metabolism, and redox/osmolyte pathways. These findings demonstrate that IFN-{gamma} and LPS-induced activation of BV-2 cells involves integrated immune, lipid, and metabolic adaptation rather than isolated induction of canonical inflammatory mediators. This integrated multiomic framework provides a resource for investigating how lipid and metabolic remodeling regulate microglial inflammatory states. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/741024v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@11dd431org.highwire.dtl.DTLVardef@155f107org.highwire.dtl.DTLVardef@1430e89org.highwire.dtl.DTLVardef@16f4a25_HPS_FORMAT_FIGEXP M_FIG C_FIG

Immortalized cell lines are widely used in biological research despite their known differences from their tissues and cell types of origin. Such cell lines are especially popular for testing hypotheses regarding the activity of cis-regulatory elements (CREs) that regulate gene expression. Previous investigations of blood and skin cell lines revealed many differences between the transcriptional regulatory networks of the cell lines and the associated primary cells. Similar comparisons for other tissues have been limited. Here, we used ATAC-seq to profile CREs in four immortalized liver cell lines and found many differences between each cell lines CREs and primary liver tissue, including differences in the transcription factors that are likely to bind them and differences in the genes that they are likely to regulate. Modifying cell culture conditions based on recommendations in the literature did not improve the similarity with primary liver tissue. Our results suggest that differences between the transcriptional regulatory networks in cell lines and primary tissue should be considered when designing and interpreting cell line experiments.

Dried blood spot (DBS) sampling is becoming a popular alternative to traditional blood sampling approaches, offering advantages such as convenience of collection, transportation, and storage, as well as lower biohazard risk. N-glycosylation, a major post-translational modification of proteins associated with numerous biological and pathological functions, is one area of interest for DBS analysis. In this study, we utilize a protocol for N-glycosylation profiling of DBS by ultra-high-performance liquid chromatography based on hydrophilic interactions and fluorescence detection (HILIC-UHPLC-FLR). The protocol includes DBS cutting, protein extraction and enzymatic digestion, labeling with 2-aminobenzamide, followed by cleanup and HILIC-UHPLC-FLR measurement. We compare DBS with plasma and demonstrate the stability of DBS N-glycosylation profile when DBS are prepared from fresh blood, frozen whole blood, or a combination of separated frozen blood cells and corresponding frozen plasma. Additionally, we compared DBS N-glycans from pre- and diabetic subjects. Fucosylation, bisection, and galactosylation showed a statistically non-significant increasing trend in diabetes, whereas sialylation showed a statistically non-significant decreasing trend in diabetes. The main advantage of this method is the ability to repurpose samples, which were initially not intended for biomarker N-glycan analysis, such as frozen whole blood. Additionally, DBS N-glycan profiling is the easier, cheapest and the least invasive approach to conventional plasma in pre-diabetes and diabetes patients' diagnostics and monitoring.

Highly reactive aldehydes are generated during metabolic processes in the body, and their detoxification is essential for maintaining cellular homeostasis. Hexadecenal, a long-chain fatty aldehyde, is formed during the sphingolipid degradation pathway from the lipid mediator sphingosine-1-phosphate (S1P). However, the cytotoxicity resulting from dysregulation of hexadecenal metabolism is still unclear. To elucidate the effects of impaired hexadecenal metabolism, we analyzed the function of ALDH3A2, an aldehyde dehydrogenase in humans. Our results revealed that ALDH3A2 enzymatic activity is crucial for the suppression of DNA damage, particularly interstrand DNA crosslinks, upon S1P exposure. Furthermore, we demonstrated that hexadecenal accumulation promotes cell death accompanied by the activation of cellular stress responses and morphological abnormalities in the endoplasmic reticulum. These findings suggest that ALDH3A2 functions as a metabolic safeguard to suppress DNA damage and cell death in response to the enhanced metabolic flux of hexadecenal.

BackgroundInduced pluripotent stem cell (iPSC)-derived brain cells are widely utilized as in vitro models for several neuropsychiatric disorders, as they retain the donors genetic profile, offering a unique opportunity to study living human brain cells and perform controlled experimental manipulations. In this study, we conducted whole transcriptome sequencing of cortical networks (co-cultures of neurons and astrocytes) derived from 12 participants with bipolar disorder (BD) and 12 participants without a history of mental health disorders. We aimed to identify new molecular mechanisms underlying the pathophysiology of bipolar disorder. MethodsiPSCs were generated by reprogramming peripheral blood mononuclear cells using episomal vectors. They were then differentiated into neural progenitor cells and matured into cortical networks that express markers of neurons and astrocytes. Whole transcriptome data were obtained using the Illumina NovaSeq X sequencing platform. ResultsDifferential expression analysis was performed using DESeq2 in R, and the identified genes were used for gene set enrichment analysis, which identified 191 enriched pathways in BD. Of these, the toll-like signalling pathway, which is downregulated in BD, was further investigated. ConclusionOur results suggest a profound immune dysregulation in BD, particularly highlighting the immune systems role as a complex signalling network.

Background: Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by FMR1 premutation CGG repeat expansions (55-200 repeats). The epigenetic landscape of the FXTAS brain remains uncharacterized. We performed genome-wide DNA methylation profiling of postmortem prefrontal cortex tissue to identify differentially methylated positions (DMPs) and candidate genes, and sought protein-level support for a neuroinflammatory signal. Methods: DNA methylation was profiled in postmortem prefrontal cortex (Brodmann area 9) from 27 male FXTAS cases and 29 male controls using the Illumina MethylationEPIC array (EPICv1 and EPICv2 platforms), merging 721,802 common probes. Surrogate variable analysis (SVA) controlled for confounders. DMPs were defined by p-value and FDR < 0.05; exploratory Reactome 2024 pathway analysis was performed on the DMP-associated gene list. Targeted proteomic profiling was performed in the same brain region using the Olink (proximity extension assay) Inflammation panel in 9 FXTAS cases and 12 controls, with SVA-adjusted differential abundance analysis, and concordance assessment against a prior mass spectrometry dataset. Results: We identified 108 significant cg-type DMPs mapping to 80 genes (50 hypermethylated, 58 hypomethylated in FXTAS). The strongest signal was CYP2E1 (7 concordant hypomethylated DMPs), an oxidative stress gene also implicated in Parkinsons disease. FTCD, a one-carbon cycle enzyme, carried 5 hypermethylated DMPs. A cluster of DMP-associated genes with established roles in innate immune and NF-kB signaling, TRAF3 (the single most significant DMP among the inflammation genes, hypermethylated), BATF, RCOR1, and MSI2; they pointed toward neuroinflammatory dysregulation. Additional genes included LINGO1 (myelination inhibitor), SYT3 (synaptic vesicle), and SLC39A4 (zinc transporter). Exploratory Reactome enrichment using the DMP-associated gene set nominated themes including neuroinflammation resolution, axonal growth inhibition, zinc homeostasis, and CYP2E1 metabolism at nominal significance (p<0.05); however, the gene-to-pathway mapping rate was low and no pathway survived correction for multiple testing. Olink proteomic analysis independently identified 60 significantly altered inflammation proteins (59 downregulated), including CXCL8, CXCL10, IL6, IL15, IL18, TLR3, IRAK1/4, and complement C1QA, which were directionally concordant with prior mass spectrometry data. Conclusions: This integrated study reveals a genome-wide epigenetic signature in the FXTAS prefrontal cortex implicating oxidative stress, myelination failure, zinc dysregulation, one-carbon cycle disruption, and most notably a coordinated set of epigenetically altered genes governing innate immune and NF-kB signaling. Convergence of TRAF3 hypermethylation with independent downregulation of TLR3 and NF-kB-pathway proteins at the protein level supports a coherent, cross-platform model of dysregulated neuroinflammatory signaling in FXTAS, identified here through individual gene- and protein-level convergence rather than formal pathway enrichment. FTCD hypermethylation proposes a self-reinforcing epigenetic loop via SAM depletion. These multi-omic findings establish FXTAS as a disorder of pervasive epigenetic reprogramming and nominate candidate genes for future mechanistic and therapeutic investigation.

Cell confluency is a major determinant of cell-cell communication, protein interactions, access to nutrients, and signalling dynamics, thereby significantly impacting biological outcomes. Lung cancer cells like A549 are widely used as screening models for scientific studies wherein their growth in vitro progress from non-confluent to confluent growth. In this study, we investigated the transcriptomic adaptations associated with the transition of A549 cells from baseline non-confluent to confluent growth. Comparative transcriptomic analysis between confluent and cells at baseline identified 815 upregulated and 671 downregulated transcripts. Pathway enrichment analysis of deregulated transcripts in confluent cells revealed enhanced cholesterol and sterol biosynthetic pathways, along with suppression of chromosomal segregation and mitotic pathways. At confluency, an increased expression of glucose transporters (SLC2, SLC60, and SL37 families) and glycolytic pathways, and a decrease in amino acid transporters (SLC1, SLC7, SLC38, and SLC36) and amino acid metabolic pathways is observed. A reduced one-carbon metabolic signature (SHMT2, DHFR, and MTHFD2) and enhanced fatty acid precursor synthesis (HMGCLL1, ALDH6A1, and AASS) were also observed at confluency. 1H NMR profiling of culture media revealed higher glucose and glutamine utilisation with lactate accumulation during culture maturation. Collectively, the data suggest transcriptome-level rewiring in A549 cells with preferential biosynthesis of lipids and sterols at confluency and underscore the importance of considering culture maturity in cancer biology, metabolism, and therapeutic studies.

The limited understanding of transporter substrate spectra constrains our ability to interpret cell and membrane function, highlighting the need for methods that enable transporter deorphanization and characterization of promiscuous transport activities. Here, we present a Xenopus oocyte-based platform for unbiased transporter substrate discovery. Oocytes expressing heterologous solute carrier proteins (SLC) were incubated in human blood serum, a chemically complex metabolite library containing thousands of endogenous metabolites and xenobiotics, followed by paired untargeted LC-MS/MS profiling of intracellular extracts and surrounding medium to capture metabolite exchange events. Across the five human SLC transporters, viz. SLC10A2, SLC10A6, SLC13A2, SLC16A10, and SLC46A1, metabolite exchange signatures were detected, and automated feature annotation was refined by manual chromatographic peak inspection. The workflow recovered known substrates of SLC10A2 and SLC16A10 and identified additional transported metabolites with MS/MS confirmation. This method provides a scalable framework for transporter substrate profiling and prioritization of candidates for targeted validation.

N-glycolylneuraminic acid (Neu5Gc) has been repeatedly associated with human cancer, but reliable detection has remained elusive, generating controversy regarding its presence in human samples. To address this, matrix-matched calibration curves, which have been pioneered in proteomics and metabolomics for assessing changes in complex mixtures, were measured of released N-glycans at four orders of magnitude dynamic range in defined mixtures, systematically benchmarking Neu5Gc-containing N-glycan detection across multiple LC-MS platforms and sites. Orthogonally, the gold-standard analytical method, consisting of fluorescence detection of labelled monosaccharides separated by LC, was applied to the same samples, yielding absolute concentrations of Neu5Gc. LC-MS demonstrated an extended detection range of three or more orders of magnitude while retaining intact N-glycan measurement, improving assay specificity and enabling detection of the variety of Neu5Gc-bearing N-glycans. By combining orthogonal dimensions of evidence, including chromatographic separation, isotopic distribution matching, and composition-confirming MS/MS, LC-MS confidently resolved Neu5Gc signals from noise, even at low abundance. In comparison, DMB-LC-FLR was limited to two orders of magnitude dynamic range, insufficient for detection of Neu5Gc in commercially available pooled human sera. These findings strongly support that DMB-LC-FLR assay specificity and sensitivity are insufficient for Neu5Gc detection in human samples due to noise overwhelming the Neu5Gc signal. By establishing a reusable benchmarking framework for future glycomic studies, we aim to use LC-MS to improve the measurement of Neu5Gc in clinical samples.