Molecular omics最新文献

Mice lacking caveolin-1 (Cav1), a major protein of the lipid raft of plasma membrane, show deregulated cellular proliferation of the mammary gland and an abnormal fetoplacental communication during pregnancy. This study leverages a multi-omics approach to test the hypothesis that the absence of Cav1 elicits a coordinated crosstalk of genes among the mammary gland, placenta and fetal brain in pregnant mice. Integrative analysis of metabolomics and transcriptomics data of mammary glands showed that the loss of Cav1 significantly impacted specific metabolites and metabolic pathways in the pregnant mice. Next, gene expression changes of the deregulated metabolic pathways of the mammary gland were compared with the gene expression changes of the placenta and fetus. The analysis showed that genes associated with specific metabolic and signaling pathways changed in a coordinated manner in the placenta, mammary gland and fetal brain of Cav1-null mice. The cytokine signaling pathway emerged as a key player of the molecular crosstalk among these tissues. By interrogating the single-nuclei gene expression data of placenta and fetal brain previously generated from Cav1-null mice, the study further revealed that these metabolic and signaling genes were differentially regulated in specific cell types of the placenta and fetal brain. Though a causal effect of the mammary gland on the placenta and/or fetal brain can’t be inferred from this study, the findings show that the mammary gland, placenta and fetal brain show a coordinated molecular crosstalk in response to the absence of Cav1 in mice.

The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular states and their interactions. However, integrating these high-dimensional and heterogeneous datasets poses significant computational challenges, including batch effects, sparsity, and modality alignment. Deep learning has shown great promise in addressing these issues through neural network-based frameworks, including variational autoencoders (VAEs) and graph neural networks (GNNs). In this Review, we examine cutting-edge deep learning methodologies for integrating single-cell multimodal data, discussing their architectures, applications, and limitations. We highlight key tools such as sciCAN, scJoint, and scMaui, which use deep learning techniques to harmonize various omics layers, improve feature extraction, and improve downstream biological analyses. Despite significant advancements, it remains challenging to ensure model interpretability, scalability, and generalizability across different datasets. Future directions of research in this field include the development of self-supervised learning strategies, transformer-based architectures, and federated learning frameworks to enhance the robustness and reproducibility of single-cell multi-omics integration.

Oral cancer (OC) is a malignant tumour with high morbidity and mortality. Significant contributory factors include alcohol and tobacco abuse that dysregulate the proteome and metabolome. We assessed saliva as a noninvasive bio-sample to understand the changes in proteome and metabolome in OC, tobacco abusers (TA), and controls. OC, TA, and control samples (n = 22, 21, and 21, respectively) were subjected to LFQ-proteomics and NMR-based metabolomics analyses individually and integrated using systems biology; 292 out of 758 proteins with two or more unique peptides were significantly differently regulated. Functional annotation revealed that differentially expressed proteins are involved in important cellular metabolic processes. PLS-DA in metabolomics separated OC from the control and TA, and K-means clustering of proteomics and metabolomics profiles revealed distinguishing proteins and metabolites in OC, TA, and the control. Integrated analysis revealed convergence on molecules like transketolase (TKTT), transaldolase (TALDO), kallikrein 1 (KLK1), enolase A (ENOA), glucose-6-phosphate isomerase (G6PI), and aldolase A and C (ALDOA and ALDOC). Finally, the characteristic discriminatory features of several clusters between OC, TA, and the control remain valid only among high tobacco abusers. The results reveal metabolites that could serve as early indicators for OC, especially among chewing tobacco abusers, and therefore establish the basis for larger cohort studies to develop them as predictive OC biomarkers.

Glycans are recognized as biomarkers and therapeutic targets. However, these molecules remain a critical blind spot in understanding post-translational modifications, particularly in vertebrate species inhabiting diverse habitats. The glycans present in tears play a crucial role in eye protection and may be one of the key factors in adapting to direct environmental contact. This study aimed to describe and compare the glycomic profiles of roadside hawk (Rupornis magnirostris), broad-snouted caiman (Caiman latirostris), and loggerhead sea turtle (Caretta caretta) tears, thereby one avian and two reptilian species. Samples were collected from 10 healthy roadside hawks, 70 broad-snouted caimans, and 10 loggerhead sea turtles to determine N- and O-glycan compounds. The compounds were released from tear glycoproteins and enriched by solid-phase extraction (SPE). Then, the glycans were eluted based on size and polarity. SPE fractions were analyzed using high-resolution mass spectrometry. 155 N-glycans (56% sialylated) and 259 O-glycans (37% sialylated) were detected in roadside hawk tears; 127 N-glycans (55% sialylated) and 263 O-glycans (35% sialofucosylated) in broad-snouted caiman tears; and 85 N-glycans (36% fucosylated) and 84 O-glycans (89% fucosylated) in loggerhead sea turtle tears. The marine habitat has a significant impact on the tear's glycans. The high presence of fucosylated glycans can represent a shield mechanism potentially related to its adhesion to glycocalyx, and interaction with the immune system, also serving as an environmental biomarker. Tears are composed of various biologically active substances, and this description can help in further studies on the identification of novel ocular surface biomarkers and in the differentiation of glycan profiles in healthy and non-healthy animals.

The development of acquired resistance to tamoxifen poses a significant clinical challenge in breast cancer treatment. Tumour heterogeneity has emerged as a primary reason for the clinical implications of resistance, yet we still lack actionable targets to address this issue. Repurposing existing drugs has become an emerging trend to tackle demanding medical indications. Therefore, we aim to study the efficacy of the antipsychotic drug Thioridazine against both parental and tamoxifen-resistant breast cancer cells. In this study, we have demonstrated that Thioridazine induces phospholipid accumulation, followed by necroptosis in both parental and tamoxifen-resistant breast cancer cell lines. We have shown thioridazine-mediated cytostatic effects through analyses of cell viability, cell count, caspase activation, cell cycle, and p21 expression levels. Moreover, employing a pharmacometabolomics approach, we identified that Thioridazine induces phospholipid accumulation in breast cancer cells. We established that Thioridazine promotes the accumulation of phospholipids rather than neutral lipids in cells via lipid-specific fluorescent quantification and imaging analysis. The phospholipid accumulation triggers necroptosis, which was evaluated through a propidium iodide uptake assay. Thioridazine activates RIP signalling, facilitating the subsequent translocation of pore-forming MLKL to the plasma membrane to initiate necroptosis. The formation of MLKL-induced membrane pores was confirmed using scanning electron microscopy for cell surface visualisation. Furthermore, thioridazine co-treatment enhances the efficacy of tamoxifen in resistant breast cancer cells, augmenting its potential for combinatorial treatment. Altogether, Thioridazine induces phospholipid accumulation followed by necroptosis in both parental and tamoxifen-resistant breast cancer cell lines, highlighting its potential application in breast cancer treatment.

In proteomics research, samples are frequently stored at −20 °C and −80 °C for extended periods, and assessing protein stability under these conditions is essential. We evaluated protein stability in healthy and diseased mice liver tissues stored at 4 °C, −20 °C, and −80 °C for 0, 7, 30, 90, and 180 days. A 10% variation in protein concentrations (by day 90, p < 0.001) was observed via BCA assay across all conditions. Untargeted proteomic analysis was performed using in-solution trypsin digestion and LC-Q-Orbitrap-MS/MS, with data processed using Proteome Discoverer 2.5. Proteins were shortlisted based on ≥2 unique peptides, FDR < 1%, and abundance ratio p ≤ 0.001. Differentially expressed proteins were identified using log 2 FC ± 2, p-adj ≤ 0.05. Protein degradation varied with storage conditions. In healthy tissues, 24, 11, and 8 proteins completely degraded at 4 °C, −20 °C, and −80 °C, respectively, after 7 days, compared to 8, 2, and 3 proteins in diseased tissues. The total number of significant proteins consistently identified across all time points in healthy samples was 2570, 2711, and 2617, and in diseased samples it was 2124, 2414, and 2353 at 4 °C, −20 °C, and −80 °C, respectively. RNA-binding proteins, such as La ribonucleoprotein 1B, Reticulophagy regulator 3, and Telomerase RNA component interacting RNase, were particularly prone to degradation across all conditions within 7 days. Notably, 18 degraded proteins were reported as biomarkers in disease conditions. Although −20 °C and −80 °C provided better preservation, residual instability persisted. Optimizing storage conditions is essential to prevent degradation, particularly for biomarker discovery studies.

Understanding metabolic alterations in CKD is crucial, as serum creatinine-based diagnosis lacks precision, affecting key clinical decisions. In this study, a ¹H NMR-based metabolomics approach was employed to distinguish between advanced-stage CKD (ASCKD) patients and healthy controls (HC), as well as within the ASCKD stages (stage 4 and stage 5). Serum samples from 52 ASCKD (S4, S5) and 25 HC were analyzed. Multivariate and univariate analysis revealed distinct metabolic patterns across groups, providing insights into CKD pathophysiology and associated pathway alterations. Compared to HC, six metabolites were significantly altered in both stage 4 and 5 CKD patients with upregulated creatinine, urea, myoinositol, choline, N,N-dimethylglycine, and downregulated tyrosine, showing potential as biomarkers with AUC above 0.8 in ROC analysis. Additionally, myo-inositol, dimethylamine, N,N-dimethylglycine, and choline correlate positively with creatinine while tyrosine correlates negatively. Amino acid metabolism was downregulated in S5 indicating more severity. Within ASCKD patients, significant alterations were observed in metabolites such as glutamate, glutamine, alanine, threonine, myo-inositol, dimethylamine, citrulline, urea, citrate, and betaine. Pathway analysis identified five distinct metabolic pathways associated with CKD progression. Consequently, we propose a panel of serum metabolites which should be monitored along with creatinine for following CKD progression. Markers of oxidative stress, inflammation, and gut dysbiosis were evident in the perturbed metabolic profile due to the systemic impact of CKD.

This proteomic study provides a nuanced mechanistic understanding of the signaling processes upon agonist binding to the melanocortin-3 receptor (MC3R). Utilizing thermal proteome profiling (TPP) combined with LC–MS, we uncovered the distinct influences of the endogenous agonists adrenocorticotropic hormone (ACTH), α-melanocyte-stimulating hormone (α-MSH), and γ-melanocyte-stimulating hormone (γ-MSH) on protein thermal stability and pathway activation. In our 2D-TPP study, transfected HEK293 cells for expression of MC3R were exposed to the three endogenous MC3R-ligands across several concentrations followed by incubation at several temperatures, centrifugation and LC–MS analysis of the resulting supernatants. This enabled us to assess the effects of type of ligand and concentration on the thermal stability of proteins in these cells. We employed a combination of multivariate analysis, differential expression, TPP and pathway analysis to deeply characterize the impact of MC3R activation on molecular mechanisms. All three ligands affected signaling pathways related to the immune system and energy homeostasis. While α-MSH significantly modulated the IL-6 pathway via STAT3, and γ-MSH prominently activated interferon signaling, ACTH uniquely affected NADPH-related proteins. All ligands shared involvement in the cAMP-PKA-CREB and varied impacts on PI3K and ERK pathways, crucial for energy metabolism. All proteomic data are available under DOI: https://doi.org/10.6019/PXD039945.

Colorectal cancer (CRC) is currently a global health burden, with staggering worldwide prevalence. CRC is ranked as the third most common and second deadliest cancer worldwide. With rising life expectancy population growth, CRC incidence and mortality are projected to increase, particularly among individuals under 50. This underscores the need to improve early detection of CRC. Although colonoscopy remains the preferred diagnostic technique, due to its high sensitivity and specificity for CRC its invasive nature and cost result in low adherence rates. Consequently, the scientific community is actively exploring alternative diagnostic methods, primarily through biomarkers, molecules exhibiting dysregulated levels associated with specific diseases. Lipidomics has become crucial in cancer research, as lipids play key roles in metabolic pathways driving cancer development. Recent investigations have revealed decreased levels of lipid classes such as lysophosphatidylcholine (LPC) in CRC patients compared to healthy controls, alongside an increase in specific sphingolipid species across multiple studies. In the context of CRC progression, triglycerides (TGs) stand out as the lipids that display the most pronounced differentiation among different disease stages. These lipid dysregulations present promising avenues for identifying potential therapeutic targets and innovative diagnostic methods, however, a comprehensive understanding of these processes requires further exploration.

Dairy cows are susceptible to negative energy balance, which can lead to metabolic disorders such as ketosis. Negative energy balance (NEB) often occurs in early lactation, but can also be due to food scarcity. Its quantification is difficult and prone to error, justifying the need to identify biomarkers instead. The effect of NEB on milk composition is known to be directly related to its intensity, impacting major and minor milk constituents. As such, one promising approach may be to identify non-invasive biomarkers in milk. To identify potential biomarkers of NEB, we performed an integrative multi-omic study of milk production and composition in two feed restriction trials of different lengths and intensities. Multivariate data integration using a redundancy analysis enabled an exploration of the linear relationships between variation in energy balance and milk production and composition. A highly correlated multi-omic signature of NEB was then identified using a multi-block partial least squares discriminant analysis. Early and late integration of data from the two feed restriction trials enabled the identification of a robust multi-omic panel of biomarkers of NEB. Taken together, these analyses showed that feed restrictions led to consistent decreases in milk yield, lactose content and uric acid concentration, as well as increased isocitrate and serotransferrin concentrations and differentially abundant microRNAs in both whole milk and milk fat globules. These findings are promising for the development of a panel of non-invasive biomarkers for monitoring animal energy status, and enhance our understanding of adaptations to NEB.