Integration Of Proteomics Research Articles

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-treated Pancreatic Beta Cells.

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse post-translational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated β-cells. We observed a swift response in global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-κB signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of a complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple co-localized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

Oncogenic cholesterol rewires lipid metabolism in hepatocellular carcinoma via the CSNK2A1-IGF2R Ser2484 axis.

Alcohol consumption and hepatitis B virus (HBV) infection are common risk factors for hepatocellular carcinoma (HCC). However, few studies have focused on elucidating the mechanisms of HCC with combined alcohol and HBV etiology. We aimed to investigate the molecular features of alcohol and HBV on HCC and to seek out potential therapeutic strategies. Two independent cohorts of HCC patients (n = 539 and n = 140) were included to investigate HCC with synergetic alcohol and HBV (AB-HCC) background. Patient-derived cell lines, organoids, and xenografts were used to validate the metabolic fragile. High-throughput drug screening (1181 FDA-approved anticancer drugs) was leveraged to explore the potential therapeutic agents. Here, we delineated AB-HCC as a distinctive metabolic subtype, hallmarked by oncogenic cholesterol, through the integration of clinical cohorts, proteomics, phosphoproteomics, and spatial transcriptome. Mechanistically, our findings revealed that cholesterol directly binds to CSNK2A1 (Casein Kinase 2 Alpha 1), augmenting its kinase activity and leading to phosphorylation of IGF2R (Insulin-Like Growth Factor 2 Receptor) at Ser2484. This cascade rewires lipid-driven mitochondrial oxidative phosphorylation, spawns reactive oxygen species measured by malondialdehyde assay, and perpetuates a positive feedback loop for cholesterol biosynthesis, ultimately culminating in tumorigenesis. Initial transcriptional activation of CSNK2A1 is driven by upregulation of RAD21 in AB-HCC. Our cholesterol profiling exposes AB-HCC's compensatory mechanism of AB-HCC, which capitalizes on both uptake and biosynthesis of cholesterol to confer survival edge. Moreover, high-throughput drug screening coupled with in vivo validation has uncovered the susceptibilities of AB-HCC, which can be effectively addressed by a combination of dietary cholesterol restriction and oral administration of Fostamatinib. The CSNK2A1-mediated cholesterol biosynthesis pathway has been implicated in various cancers characterized by cholesterol metabolism. These findings not only pinpoint the oncogenic metabolite cholesterol as a hidden culprit in AB-HCC subtype, but also enlighten a novel combination strategy to rejuvenate tumor metabolism.

The abundance change of age-regulated secreted proteins affects lifespan of C. elegans

Proteome integrity is vital for survival and failure to maintain it results in uncontrolled protein abundances, misfolding and aggregation which cause proteotoxicity. In multicellular organisms, proteotoxic stress is communicated among tissues to maintain proteome integrity for organismal stress resistance and survival. However, the nature of these signalling molecules and their regulation in extracellular space is largely unknown. Secreted proteins are induced in response to various stresses and aging, indicating their roles in inter-tissue communication. To study the fates of age-regulated proteins with potential localization to extracellular, we analysed publicly available age-related proteome data of C. elegans. We found that abundance of majority of the proteins with signal peptides (SP) increases with age, which might result in their supersaturation and subsequent aggregation. Intriguingly, these changes are differentially regulated in the lifespan mutants. A subset of these SP proteins is also found in the cargo of extracellular vesicles. Many of these proteins are novel and functionally uncharacterized. Reducing levels of a few extracellular proteins results in increasing lifespan. This suggests that uncontrolled levels of extracellular proteins might disturb proteostasis and limit the lifespan. Overall, our findings suggest that the age-induced secreted proteins might be the potential candidates to be considered as biomarkers or for mitigating age-related pathological conditions.

Integrative Proteomic and Phosphoproteomic Profiling Reveals the Salt-Responsive Mechanisms in Two Rice Varieties (Oryza Sativa subsp. Japonica and Indica).

Salinity stress induces ionic and osmotic imbalances in rice plants that in turn negatively affect the photosynthesis rate, resulting in growth retardation and yield penalty. Efforts have, therefore, been carried out to understand the mechanism of salt tolerance, however, the complexity of biological processes at proteome levels remains a major challenge. Here, we performed a comparative proteome and phosphoproteome profiling of microsome enriched fractions of salt-tolerant (cv. IR73; indica) and salt-susceptible (cv. Dongjin/DJ; japonica) rice varieties. This approach led to the identification of 5856 proteins, of which 473 and 484 proteins showed differential modulation between DJ and IR73 sample sets, respectively. The phosphoproteome analysis led to the identification of a total of 10,873 phosphopeptides of which 2929 and 3049 phosphopeptides showed significant differences in DJ and IR73 sample sets, respectively. The integration of proteome and phosphoproteome data showed activation of ABA and Ca2+ signaling components exclusively in the salt-tolerant variety IR73 in response to salinity stress. Taken together, our results highlight the changes at proteome and phosphoproteome levels and provide a mechanistic understanding of salinity stress tolerance in rice.

The Role of Heat Shock Factor 1 in Preserving Proteomic Integrity During Copper-Induced Cellular Toxicity.

Copper is crucial for many physiological processes across mammalian cells, including energy metabolism, neurotransmitter synthesis, and antioxidant defense mechanisms. However, excessive copper levels can lead to cellular toxicity and "cuproptosis", a form of programmed cell death characterized by the accumulation of copper within mitochondria. Tumor cells are less sensitive to this toxicity than normal cells, the mechanism for which remains unclear. We address this important issue by exploring the role of heat shock factor 1 (HSF1), a transcription factor that is highly expressed across several types of cancer and has a crucial role in tumor survival, in protecting against copper-mediated cytotoxicity. Using pancreatic ductal adenocarcinoma cells, we show that excessive copper triggers a proteotoxic stress response (PSR), activating HSF1 and that overexpressing HSF1 diminishes intracellular copper accumulation and prevents excessive copper-induced cell death and amyloid fibrils formation, highlighting HSF1's role in preserving proteasomal integrity. Copper treatment decreases the lipoylation of dihydrolipoamide S-acetyltransferase (DLAT), an enzyme necessary for cuproptosis, induces DLAT oligomerization, and induces insoluble DLAT formation, which is suppressed by overexpressing HSF1, in addition to enhancing the interaction between HSF1 and DLAT. Our findings uncover how HSF1 protects against copper-induced damage in cancer cells and thus represents a novel therapeutic target for enhancing copper-mediated cancer cell death.

Proteomic and genomic evaluation of plasma protein biomarkers for atrial fibrillation

Abstract Background Atrial fibrillation (AF) is a common arrhythmia and an important cause of ischemic stroke. Several plasma proteins have been suggested as biomarkers for the risk of AF and subsequent stroke in previous studies, but validation in independent cohorts is needed. Large-scale integration of proteomics with genetic and clinical data holds potential for refinement of disease prediction and risk stratification. Purpose This study aimed to evaluate previously suggested plasma protein biomarkers of AF and ischemic stroke using plasma protein measurements in a large cohort. Methods Ten tentative protein biomarkers previously associated with AF or ischemic stroke in AF patients, were evaluated in a nested case-control study comprising 49,757 individuals from the UK biobank (UKB). Plasma proteins were measured by normalized protein expression on the Olink platform. Outcomes were incident AF and ischemic stroke. The proteins were evaluated in Cox regression models adjusted for sex, age, and clinical risk factors. To assess whether the proteins were independent predictors, they were evaluated in sensitivity analyses with additional adjustment for NT-proBNP levels. Polygenic risk scores were calculated for plasma protein concentrations using the PRS-CS-auto algorithm and tested for association with AF or ischemic stroke in an independent subset of the UKB cohort without plasma protein measurements (n=379,392). Results All ten plasma proteins were associated with incident AF in the primary model, and eight were associated with incident ischemic stroke (P-values < 0.0025 for all). Adjusted for NT-proBNP levels, the proteins interleukin-6, GDF15, angiopoietin 2, and Troponin-I remained independently associated with AF, and the proteins GDF15, Cystatin-C, interleukin-6, ADAMTS13, and FGF23 remained independently associated with ischemic stroke. Polygenic risk scores for NT-proBNP and angiopoietin 2 were associated with increased rates of AF. No polygenic scores were significantly associated with ischemic stroke. Conclusion Large-scale proteomic evaluation validated ten plasma proteins associated AF and found eight to be associated with ischemic stroke. Seven proteins were associated with AF or ischemic stroke independently of NT-proBNP levels. Additional genomic evaluation provided evidence of associations between incident AF and genetically predicted plasma levels of angiopoietin 2 and NT-proBNP. As proteomic analyses of the plasma proteome become cheaper and more readily available, these findings could contribute to future risk stratification and selection of individuals for AF screening programs.Proteins associated with AFProteins associated with ischemic stroke

Current Technologies in Snake Venom Analysis and Applications

This comprehensive review explores the cutting-edge advancements in snake venom research, focusing on the integration of proteomics, genomics, transcriptomics, and bioinformatics. Highlighting the transformative impact of these technologies, the review delves into the genetic and ecological factors driving venom evolution, the complex molecular composition of venoms, and the regulatory mechanisms underlying toxin production. The application of synthetic biology and multi-omics approaches, collectively known as venomics, has revolutionized the field, providing deeper insights into venom function and its therapeutic potential. Despite significant progress, challenges such as the functional characterization of toxins and the development of cost-effective antivenoms remain. This review also discusses the future directions of venom research, emphasizing the need for interdisciplinary collaborations and new technologies (mRNAs, cryo-electron microscopy for structural determinations of toxin complexes, synthetic biology, and other technologies) to fully harness the biomedical potential of venoms and toxins from snakes and other animals.

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning.

Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples. We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples. We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5. We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Integration of four-dimensional proteomics and network pharmacology to reveal molecular mechanisms of multi-components multi-targets effects of Sini decoction on myocardial infarction

Sini Decoction (SND) has been proven to be an effective formula to alleviate cardiac injury of myocardial infarction (MI). However, the potential mechanism of SND remains unclear. In this study, the MI rat model was established by ligating the left anterior descending coronary artery. A total of 17 SND-distributed components in heart were identified by using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOFMS). The combination of four-dimensional (4D) proteomics and network pharmacology was employed to find the potential targets for therapeutic intervention, and molecular docking and cellular thermal shift assay (CETSA) were used to reveal the interactions between the potential targets and the potential active components distributed in heart of SND. 33 SND-effected proteins were identified by 4D proteomics, which was involved in carbon metabolism, fatty acid metabolism, valine, leucine and isoleucine degradation, tricarboxylic acid (TCA) cycle and PPAR signaling pathway. 17 potential SND-targeted direct proteins were screened by comparing SND-effected proteins generated from 4D proteomics with the MI-related proteins obtained from disease database. The potential relationships between 17 components and 17 potential SND-targeted direct proteins were established by molecular docking analysis, in which songorine, benzoylhypaconine, hypaconine, formononetin, and liquiritigenin could be bound to the surrounding amino acid residues in the binding pocket of Mtor, Parp1, Acadm, Crat, and Aldh2. Then, CETSA analysis further confirmed that songorine and benzoylhypaconine could increase the heat stability of Mtor and Parp1 in cardiac tissue lysate, respectively, which suggested that there existed direct interactions between songorine and Mtor, and benzoylhypaconine and Parp1. In summary, this work concluded that SND produced cardioprotective effects mainly through preserving energy metabolism, also demonstrated that the combination of 4D proteomics and network pharmacology was a promising tool for uncovering the molecular mechanisms of multi-components multi-targets effects of TCM.

Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson's Disease.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD's complex biochemical networks underlying neurodegeneration.

Lysine succinylation precisely controls normal erythropoiesis.

Lysine succinylation (Ksu) has recently emerged as a protein modification that regulates diverse functions in various biological processes. However, the systemically and precise role of lysine succinylation in erythropoiesis remains to be fully elucidated. In this study, we noted a prominent increase of succinyl-CoA and lysine succinylation during human erythroid differentiation. To explore the functional significance of succinylation, we inhibited succinylation by either knock downing key succinyltransferases or overexpressing desuccinylases. Succinylation inhibition led to suppressed cell proliferation, increased apoptosis, and disrupted erythroid differentiation. In vivo overexpression of the desuccinylases SIRT5 delayed erythroid differentiation. Furthermore, integrative proteome and succinylome analysis identifies 939 succinylated proteins with 3,562 Ksu sites, distributed across various cellular compartments and involved in multiple cellular processes. Significantly, inconsistencies between protein expression levels and succinylation levels were observed, indicating that the succinylation of certain proteins may function independently of expression. Mechanistically, we implicated KAT2A-mediated succinylation of histone H3 K79, leading to chromatin remodeling and subsequently erythropoiesis regulation. Specially, we identified CYCS as a key regulator of erythropoiesis, which depends on its succinylation sites K28/K40. Taken together, our comprehensive investigation of the succinylation landscape during erythropoiesis provides valuable insights into its regulatory role and offer potential implications for erythroid-related diseases.

Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer

This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E–03 in training cohort; 0.75 vs 0.69, P = 5.49E–04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening.

Integrative Omics Strategies for Understanding and Combating Brown Planthopper Virulence in Rice Production: A Review

The brown planthopper (Nilaparvata lugens, BPH) is a serious insect pest responsible for causing immense economic losses to rice growers around the globe. The development of high-throughput sequencing technologies has significantly improved the research on this pest, and its genome structure, gene expression profiles, and host–plant interactions are being unveiled. The integration of genomic sequencing, transcriptomics, proteomics, and metabolomics has greatly increased our understanding of the biological characteristics of planthoppers, which will benefit the identification of resistant rice varieties and strategies for their control. Strategies like more optimal genome assembly and single-cell RNA-seq help to update our knowledge of gene control structure and cell type-specific usage, shedding light on how planthoppers adjust as well. However, to date, a comprehensive genome-wide investigation of the genetic interactions and population dynamics of BPHs has yet to be exhaustively performed using these next-generation omics technologies. This review summarizes the recent advances and new perspectives regarding the use of omics data for the BPH, with specific emphasis on the integration of both fields to help develop more sustainable pest management strategies. These findings, in combination with those of post-transcriptional and translational modifications involving non-coding RNAs as well as epigenetic variations, further detail intricate host–brown planthopper interaction dynamics, especially regarding resistant rice varieties. Finally, the symbiogenesis of the symbiotic microbial community in a planthopper can be characterized through metagenomic approaches, and its importance in enhancing virulence traits would offer novel opportunities for plant protection by manipulating host–microbe interactions. The concerted diverse omics approaches collectively identified the holistic and complex mechanisms of virulence variation in BPHs, which enables efficient deployment into rice resistance breeding as well as sustainable pest management.

Integrative microbiomics, proteomics and lipidomics studies unraveled the preventive mechanism of Shouhui Tongbian Capsules on cerebral ischemic stroke injury

Ethnopharmacological relevanceCerebral ischemic stroke (CIS) is one of the most important factors leading to death and disability, which seriously threaten the survival and health of patients. The intentional flora and its derived metabolites are demonstrated to play vital roles in the physiology and onset of CIS. Shouhui Tongbian Capsules (SHTB), a Traditional Chinese Medicine, could regulate gut microbiota and metabolites. Study has found that SHTB has protective effect on CIS, but the mechanism is still unclear. Aim of studyThis study was designed to evaluate the preventive effects and the mechanism of SHTB on CIS injury. Materials and methodsThe rats were pretreated with SHTB for 5 days, then the middle cerebral artery occlusion/reperfusion (MCAO/R) was established. Neurological deficit score, TTC staining, brain water content, H&E and Nissl staining were preformed to evaluate the preventive effects of SHTB on CIS. The Occludin and ZO-1 were analyzed to evaluate the blood-brain barrier (BBB). 16S rDNA sequencing and LC-ESI-MS/MS-based metabolomics profiling were performed to analyze the gut microbiota composition and short chain fatty acids (SCFAs) profile in gut. Serum lipopolysaccharide specific IgA antibody (LPS-SIgA) and diamine oxidase (DAO), as well as colon Claudin 5 and ZO-1 were analyzed to evaluate the intestinal barrier. Proteomics was used to evaluated the proteins profile in brain. Lipidomics were used to evaluate the brain SCFAs as well as medium and long chain fatty acids (MCFAs and LCFAs). Malondialdehyde (MDA), Total Superoxide dismutase (T-SOD), Glutathione (GSH), Glutathione peroxidase (GSH-Px), Catalase (CAT) and reactive oxygen species (ROS) were assayed to evaluate the oxidative stress in brain. Western blot was performed to evaluate the expression of PPARγ, Nrf2, SLC3A2, SCL7A11, GPX4, ACSL4 and LOX. ResultsSHTB prevented rats from MCAO/R injury, which was confirmed by lower cerebral infarct rate, brain water content, neurological deficit score and nissl body loss, and improved brain pathology. Meanwhile, SHTB upregulated the expression of ZO-1 and Occludin to maintain the integrity of BBB. 16S rDNA sequencing and LC-ESI-MS/MS-based targeted metabolomics found that SHTB increased the abundance of gut microbiota, regulated the numbers of intestinal bacteria to increase the production of Acetic acid, Propionic acid, and Butyric acid, as well as decrease the production of Valeric acid and Hexanoic acid in the gut. Meanwhile, SHTB improved the intestinal barrier by upregulating the protein levels of Claudin 5 and ZO-1, which was confirmed by low concentrations of LPS-SIgA and DAO in serum. Multi omics and spearman correlation analysis indicated that SHTB regulated the abundance of Escherichia-Shigella and Lactobacillus to increase Acetic acid, Propionic acid, and Butyric acid to induce the expression of PPARγ, thereby regulating fatty acid metabolism and degradation, improving lipid metabolism disorders, downregulating lipid oxidative stress, inhibiting ferroptosis, and alleviating brain injury. ConclusionThis study confirmed that SHTB improved the disturbance of fatty acid metabolism in brain tissue by regulating gut microbiota and the production of fecal SCFAs to inhibit ferroptosis caused by lipid oxidative stress and prevent CIS injury, which provided a potential candidate drug for the prevention of CIS.

Integration of bile proteomics and metabolomics analyses reveals novel insights into different types of gallstones in a high-altitude area

BackgroundTo explore the pathogenesis of different subtypes of gallstones in high-altitude populations from a molecular perspective.MethodsWe collected bile samples from 20 cholesterol gallstone disease (CGD) patients and 20 pigment gallstone disease (PGD) patients. Proteomics analysis was performed by LC/MS DIA, while metabolomics analysis was performed by UPLC- Q-TOF/MS.ResultsWe identified 154 up-regulated and 196 down-regulated differentially expressed proteins, which were significantly enriched in neurodegenerative diseases, energy metabolism, amino acid metabolism etc. In metabolomics analysis, 20 up-regulated and 63 down-regulated differentially expressed metabolites were identified, and they were significantly enriched in vitamin B6 metabolism. Three pathways of integrated proteomics and metabolomics were significantly enriched: porphyrin and chlorophyll metabolism, riboflavin metabolism and aminoacyl-tRNA biosynthesis. Remarkably, 7 differentially expressed proteins and metabolites showed excellent predictive performance and were selected as potential biomarkers.ConclusionThe findings of our metabolomics and proteomics analyses help to elucidate the underlying mechanisms of gallstone formation in high-altitude populations.

Proteogenomic characterization of skull-base chordoma.

Skull-base chordoma is a rare, aggressive bone cancer with a high recurrence rate. Despite advances in genomic studies, its molecular characteristics and effective therapies remain unknown. Here, we conduct integrative genomics, transcriptomics, proteomics, and phosphoproteomics analyses of 187 skull-base chordoma tumors. In our study, chromosome instability is identified as a prognostic predictor and potential therapeutic target. Multi-omics data reveals downstream effects of chromosome instability, with RPRD1B as a putative target for radiotherapy-resistant patients. Chromosome 1q gain, associated with chromosome instability and upregulated mitochondrial functions, lead to poorer clinical outcomes. Immune subtyping identify an immune cold subtype linked to chromosome 9p/10q loss and immune evasion. Proteomics-based classification reveals subtypes (P-II and P-III) with high chromosome instability and immune cold features, with P-II tumors showing increased invasiveness. These findings, confirmed in 17 paired samples, provide insights into the biology and treatment of skull-base chordoma.

Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma

Glioblastoma is characterized by a pronounced resistance to therapy with dismal prognosis. Transcriptomics classify glioblastoma into proneural (PN), mesenchymal (MES) and classical (CL) subtypes that show differential resistance to targeted therapies. The aim of this study was to provide a viable approach for identifying combination therapies in glioblastoma subtypes. Proteomics and phosphoproteomics were performed on dasatinib inhibited glioblastoma stem cells (GSCs) and complemented by an shRNA loss-of-function screen to identify genes whose knockdown sensitizes GSCs to dasatinib. Proteomics and screen data were computationally integrated with transcriptomic data using the SamNet 2.0 algorithm for network flow learning to reveal potential combination therapies in PN GSCs. In vitro viability assays and tumor spheroid models were used to verify the synergy of identified therapy. Further in vitro and TCGA RNA-Seq data analyses were utilized to provide a mechanistic explanation of these effects. Integration of data revealed the cell cycle protein WEE1 as a potential combination therapy target for PN GSCs. Validation experiments showed a robust synergistic effect through combination of dasatinib and the WEE1 inhibitor, MK-1775, in PN GSCs. Combined inhibition using dasatinib and MK-1775 propagated DNA damage in PN GCSs, with GCSs showing a differential subtype-driven pattern of expression of cell cycle genes in TCGA RNA-Seq data. The integration of proteomics, loss-of-function screens and transcriptomics confirmed WEE1 as a target for combination with dasatinib against PN GSCs. Utilizing this integrative approach could be of interest for studying resistance mechanisms and revealing combination therapy targets in further tumor entities.

Identifying new biomarkers and potential therapeutic targets for breast cancer through the integration of human plasma proteomics: a Mendelian randomization study and colocalization analysis.

The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer. Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets. Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer. The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts.

Integrating DIA Single-Cell Proteomics Data with the DiagnoMass Proteomic Hub for Biological Insights.

Single-cell proteomics has emerged as a powerful technology for unraveling the complexities of cellular heterogeneity, enabling insights into individual cell functions and pathologies. One of the primary challenges in single-cell proteomics is data generation, where low mass spectral signals often preclude the triggering of MS2 events. This challenge is addressed by Data Independent Acquisition (DIA), a data acquisition strategy that does not depend on peptide ion isotopic signatures to generate an MS2 event. In this study, we present data generated from the integration of DIA single-cell proteomics with a version of the DiagnoMass Proteomic Hub that was adapted to handle DIA data. DiagnoMass employs a hierarchical clustering methodology that enables the identification of tandem mass spectral clusters that are discriminative of biological conditions, thereby reducing the reliance on search engine biases for identifications. Nevertheless, a search engine (in this work, DIA-NN) can be integrated with DiagnoMass for spectral annotation. We used single-cell proteomic data from iPSC-derived neuroprogenitor cell cultures as a test study of this integrated approach. We were able to differentiate between control and Rett Syndrome patient cells to discern the proteomic variances potentially contributing to the disease's pathology. Our research confirms that the DiagnoMass-DIA synergy significantly enhances the identification of discriminative proteomic signatures, highlighting critical biological variations such as the presence of unique spectra that could be related to Rett Syndrome pathology.

Integrative transcriptomics and proteomics analysis provide a deep insight into goose astrovirus-host interactions during GAstV infection

Goose astrovirus (GAstV) is a newly discovered astrovirus. GAstV causes gout and death in 4- to -16-day-old goslings. For the past few years, fatal gout, the cardinal clinical symptom of gosling infected with GAstV, has been spreading rapidly in some goose Chinese farms, which caused continuous economic losses to the goose breeding industry in China. Currently, several underlying mechanisms involved in viral replication, inflammatory reaction, virions release, and viral pathogenesis of GAstV remain to be elucidated. In this study, we explored the mechanisms of GAstV-host interactions, the transcriptome and proteome profiles of GAstV-infected LMH cells were sequenced by RNA-seq and data-independent acquisition (DIA) techniques, respectively, and followed using an integrative analysis. Compared with uninfected LMH cells, a total of 322 differentially expressed genes (DEG) (195 up-regulated, 127 down-regulated) and 36 differentially expressed proteins (DEP) (31 up-regulated, 5 down-regulated) were detected. Nine DEGs were randomly selected for further validation by quantitative real-time polymerase chain reaction (qPCR). Through GO and KEGG enrichment analysis, DEG and DEP were significantly enriched in several important cellular signaling pathways, including MAPK, PI3K-Akt, cAMP, chemokine, calcium, phospholipase D, Ras, TNF, IL-17, Rap1, NF-kappa B signaling pathways, indicating that GAstV affects cell growth and immune signaling. This study provided an overview of changes in transcriptome and proteome profiles of GAstV-infected LMH cells, therefore, providing a crucial basis to further explore the mechanisms of GAstV-host interactions.