Horizontal Cell Biology: Monitoring Global Changes of Protein Interaction States with the Proteome-Wide Cellular Thermal Shift Assay (CETSA).

The cellular thermal shift assay (CETSA) is a biophysical technique allowing direct studies of ligand binding to proteins in cells and tissues. The proteome-wide implementation of CETSA with mass spectrometry detection (MS-CETSA) has now been successfully applied to discover targets for orphan clinical drugs and hits from phenotypic screens, to identify off-targets, and to explain poly-pharmacology and drug toxicity. Highly sensitive multidimensional MS-CETSA implementations can now also access binding of physiological ligands to proteins, such as metabolites, nucleic acids, and other proteins. MS-CETSA can thereby provide comprehensive information on modulations of protein interaction states in cellular processes, including downstream effects of drugs and transitions between different physiological cell states. Such horizontal information on ligandmodulation in cells is largely orthogonal to vertical information on the levels of different proteins and therefore opens novel opportunities to understand operational aspects of cellular proteomes.

Modulation of Protein-Interaction States through the Cell Cycle.

Global profiling of protein expression through the cell cycle has revealed subsets of periodically expressed proteins. However, expression levels alone only give a partial view of the biochemical processes determining cellular events. Using a proteome-wide implementation of the cellular thermal shift assay (CETSA) to study specific cell-cycle phases, we uncover changes of interaction states for more than 750 proteins during the cell cycle. Notably, many protein complexes are modulated in specific cell-cycle phases, reflecting their roles in processes such as DNA replication, chromatin remodeling, transcription, translation, and disintegration of the nuclear envelope. Surprisingly, only small differences in the interaction states were seen between the G1 and the G2 phase, suggesting similar hardwiring of biochemical processes in these two phases. The present work reveals novel molecular details of the cell cycle and establishes proteome-wide CETSA as a new strategy to study modulation of protein-interaction states in intact cells.

IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data.

IMPRINTS-CETSA (Integrated Modulation of Protein Interaction States-Cellular Thermal Shift Assay) provides a highly resolved means to systematically study the interactions of proteins with other cellular components, including metabolites, nucleic acids and other proteins, at the proteome level, but no freely available and user-friendly data analysis software has been reported. Here, we report IMPRINTS.CETSA, an R package that provides the basic data processing framework for robust analysis of the IMPRINTS-CETSA data format, from preprocessing and normalization to visualization. We also report an accompanying R package, IMPRINTS.CETSA.app, which offers a user-friendly Shiny interface for analysis and interpretation of IMPRINTS-CETSA results, with seamless features such as functional enrichment and mapping to other databases at a single site. For the hit generation part, the diverse behaviors of protein modulations have been typically segregated with a two-measure scoring method, i.e. the abundance and thermal stability changes. We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app, respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134.

nQuant Enables Precise Quantitative N-Glycomics.

N-glycosylation is a highly heterogeneous post-translational modification that modulates protein function. Defects in N-glycosylation are directly linked to various human diseases. Despite the importance of quantifying N-glycans with high precision, existing glycoinformatics tools are limited. Here, we developed nQuant, a glycoinformatics tool that enables label-free and isotopic labeling quantification of N-glycomics data obtained via LC-MS/MS, ensuring a low false quantitation rate. Using the label-free quantification module, we profiled the N-glycans released from purified glycoproteins and HEK293 cells as well as the dynamic changes of N-glycosylation during mouse corpus callosum development. Through the isotopic labeling quantification module, we revealed the dynamic changes of N-glycans in acute promyelocytic leukemia cells after all-trans retinoic acid treatment. Taken together, we demonstrate that nQuant enables fast and precise quantitative N-glycomics.

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling.

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

Deep Profiling of the Proteome Dynamics of Pseudomonas aeruginosa Reference Strain PAO1 under Different Growth Conditions.

As one of the most common bacterial pathogens causing nosocomial infections, Pseudomonas aeruginosa is highly adaptable to survive under various conditions. Here, we profiled the abundance dynamics of 3489 proteins across different growth stages in the P. aeruginosa reference strain PAO1 using data-independent acquisition-based quantitative proteomics. The proteins differentially expressed during the planktonic growth exhibit several distinct patterns of expression profiles and are relevant to various biological processes, highlighting the continuous adaptation of the PAO1 proteome during the transition from the acceleration phase to the stationary phase. By contrasting the protein expressions in a biofilm to planktonic cells, the known roles of T6SS, phenazine biosynthesis, quorum sensing, and c-di-GMP signaling in the biofilm formation process were confirmed. Additionally, we also discovered several new functional proteins that may play roles in the biofilm formation process. Lastly, we demonstrated the general concordance of protein expressions within operons across various growth states, which permits the study of coexpression protein units, and reversely, the study of regulatory components in the operon structure. Taken together, we present a high-quality and valuable resource on the proteomic dynamics of the P. aeruginosa reference strain PAO1, with the potential of advancing our understanding of the overall physiology of Pseudomonas bacteria.

Network embedding framework for driver gene discovery by combining functional and structural information.

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

Nanocrystal Ordering Enhances Thermal Transport and Mechanics in Single-Domain Colloidal Nanocrystal Superlattices.

Colloidal nanocrystal (NC) assemblies are promising for optoelectronic, photovoltaic, and thermoelectric applications. However, using these materials can be challenging in actual devices because they have a limited range of thermal conductivity and elastic modulus, which results in heat dissipation and mechanical robustness challenges. Here, we report thermal transport and mechanical measurements on single-domain colloidal PbS nanocrystal superlattices (NCSLs) that have long-range order as well as measurements on nanocrystal films (NCFs) that are comparatively disordered. Over an NC diameter range of 3.0-6.1 nm, we observe that NCSLs have thermal conductivities and Young's moduli that are up to ∼3 times higher than those of the corresponding NCFs. We also find that these properties are more sensitive to NC diameter in NCSLs relative to NCFs. Our measurements and computational modeling indicate that stronger ligand-ligand interactions due to enhanced ligand interdigitation and alignment in NCSLs account for the improved thermal transport and mechanical properties.

Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data.

BACKGROUND: The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method. RESULTS: In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method. CONCLUSIONS: Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods.

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis.

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Photothermal Nanozymatic Nanoparticles Induce Ferroptosis and Apoptosis through Tumor Microenvironment Manipulation for Cancer Therapy.

It is highly desirable to design a single modality that can simultaneously trigger apoptosis and ferroptosis to efficiently eliminate tumor progression. Herein, a nanosystem based on the intrinsic properties of tumor microenvironment (TME) is designed to achieve tumor control through the simultaneous induction of ferroptosis and apoptosis. CuCP molecules are encapsulated in a liposome-based nanosystem to assemble into biocompatible and stable CuCP nanoparticles (CuCP Lipo NPs). This nanosystem intrinsically possesses nanozymatic activity and photothermal characteristics due to the property of Cu atoms and the structure of CuCP Lipo NPs. It is demonstrated that the synergistic strategy increases the intracellular lipid-reactive oxides species, induces the occurrence of ferroptosis and apoptosis, and completely eradicates the tumors in vivo. Proteomics analysis further discloses the key involved proteins (including Tp53, HMOX1, Ptgs2, Tfrc, Slc11a2, Mgst2, Sod1, and several GST family members) and pathways (including apoptosis, ferroptosis, and ROS synthesis). Conclusively, this work develops a strategy based on one nanosystem to synergistically induce ferroptosis and apoptosis in vivo for tumor suppression, which holds great potential in the clinical translation for tumor therapy.

Photothermal Synergistic Effect of Pt1/CuO-CeO2 Single-Atom Catalysts Significantly Improving Toluene Removal.

Photothermal synergistic catalytic oxidation of toluene over single-atom Pt catalysts was investigated. Compared with the conventional thermocatalytic oxidation in the dark, toluene conversion and CO2 yield over 0.39Pt1/CuO-CeO2 under simulated solar irradiation (λ = 320-2500 nm, optical power density = 200 mW cm-2) at 180 °C could be increased about 48%. An amount of CuO was added to CeO2 to disperse single-atom Pt with a maximal Pt loading of 0.83 wt %. The synergistic effect between photo- and thermocatalysis is very important for the development of new pollutant treatment technology with high efficiency and low energy consumption. Both light and heat played an important role in the present photothermal synergistic catalytic oxidation. 0.39Pt1/CuO-CeO2 showed good redox performance and excellent optical properties and utilized the full-spectrum solar energy. Light illumination induced the generation of reactive oxygen species (•OH and •O2-), which accelerated the transformation of intermediates, promoted the release of active sites on the catalyst surface, and improved the oxidation reaction.

Predicting miRNA-disease associations via layer attention graph convolutional network model.

BACKGROUND: MiRNA is a class of non-coding single-stranded RNA molecules with a length of approximately 22 nucleotides encoded by endogenous genes, which can regulate the expression of other genes. Therefore, it is very important to predict the associations between miRNA and disease. Predecessors developed a new prediction method of drug-disease association, and it achieved good results. METHODS: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. First, we integrate three associations into a heterogeneous network, such as the known miRNA-disease association, miRNA-miRNA similarities and disease-disease similarities, next we apply graph convolution network to learn the embedding of miRNA and disease. We use an attention mechanism to combine embedding from multiple convolution layers. Unobserved miRNA-disease associations are scored based on integrated embedding. RESULTS: After fivefold cross-validations, the value of AUC is reached 0.9091, which is higher than other prediction methods and baseline methods. CONCLUSIONS: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. LAGCN has achieved good performance in predicting miRNA-disease associations, and it is superior to other association prediction methods and baseline methods.

Electronically Engineering Water Resistance in Methane Combustion with an Atomically Dispersed Tungsten on PdO Catalyst.

Improving the low-temperature water-resistance of methane combustion catalysts is of importance for industrial applications and it is challenging. A stepwise strategy is presented for the preparation of atomically dispersed tungsten species at the catalytically active site (Pd nanoparticles). After an activation process, a Pd-O-W1 -like nanocompound is formed on the PdO surface with an atomic scale interface. The resulting supported catalyst has much better water resistance than the conventional catalysts for methane combustion. The integrated characterization results confirm that catalytic combustion of methane involves water, proceeding via a hydroperoxyl-promoted reaction mechanism on the catalyst surface. The results of density functional theory calculations indicate an upshift of the d-band center of palladium caused by electron transfer from atomically dispersed tungsten, which greatly facilitates the adsorption and activation of oxygen on the catalyst.

IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier.

BACKGROUND: Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. RESULTS: Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. CONCLUSIONS: We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

Recent advances in proteome-wide label-free target deconvolution for bioactive small molecules.

Small-molecule drugs modulate biological processes and disease states through engagement of target proteins in cells. Assessing drug-target engagement on a proteome-wide scale is of utmost importance in better understanding the molecular mechanisms of action of observed beneficial and adverse effects, as well as in developing next generation tool compounds and drugs with better efficacies and specificities. However, systematic assessment of drug-target engagement has been an arduous task. With the continuous development of mass spectrometry-based proteomics instruments and techniques, various chemical proteomics approaches for drug target deconvolution (i.e., the identification of molecular target for drugs) have emerged. Among these, the label-free target deconvolution approaches that do not involve the chemical modification of compounds of interest, have gained increased attention in the community. Here we provide an overview of the basic principles and recent biological applications of the most important label-free methods including the cellular thermal shift assay, pulse proteolysis, chemical denaturant and protein precipitation, stability of proteins from rates of oxidation, drug affinity responsive target stability, limited proteolysis, and solvent-induced protein precipitation. The state-of-the-art technical implications and future outlook for the label-free approaches are also discussed.

A review on partial-wave dynamics with chiral effective field theory and dispersion relation.

The description of strong interaction physics of low-lying resonances is out of the valid range of perturbative QCD. Chiral effective field theories (EFTs) have been developed to tackle the issue. Partial wave dynamics is the systematic tool to decode the underlying physics and reveal the properties of those resonances. It is extremely powerful and helpful for our understanding of the non-perturbative regime, especially when dispersion techniques are utilized simultaneously. Recently, plenty of exotic/ordinary hadrons have been reported by experiment collaborations, e.g. LHCb, Belle, and BESIII, etc. In this review, we summarize the recent progress on the applications of partial wave dynamics combined with chiral EFTs and dispersion relations, on related topics, with emphasis onππ,πK,πNandK̄Nscatterings.

High Selectivity to HCl for the Catalytic Removal of 1,2-Dichloroethane Over RuP/3DOM WOx: Insights into the Effects of P-Doping and H2O Introduction.

Ru-based catalysts for catalytic combustion of high-toxicity Cl-containing volatile organic compounds are inclined to produce Cl2 instead of ideal HCl due to the Deacon reaction. We herein reported that the three-dimensionally ordered macroporous (3DOM) WOx-supported RuP nanocatalyst greatly improved HCl selectivity (at 400 °C, increased from 66.0% over Ru/3DOM WOx to 96.4% over RuP/3DOM WOx) and reduced chlorine-containing byproducts for 1,2-dichloroethane (1,2-DCE) oxidation. P-doping enhanced the number of structural hydroxyl groups and Brønsted acid sites. The isotopic 1,2-DCE temperature-programmed desorption experiment in the presence of H218O indicated the generation of a new active oxygen species 16O18O that participated in the reaction. Generally, P-doping and H2O introduction could promote the exchange reaction between Cl and hydroxyl groups, rather than oxygen defects, and then benefit the production of HCl and reduce the generation of other chlorine species or Cl2, via the reaction processes of C2H3Cl → alcohol → aldehyde → carboxylic acids.

Nomogram for perinatal prediction of intrapartum fever: a retrospective case-control study.

OBJECTIVE: To explore the risk factors for intrapartum fever and to develop a nomogram to predict the incidence of intrapartum fever. METHODS: The general demographic characteristics and perinatal factors of 696 parturients who underwent vaginal birth at the Affiliated Hospital of Xuzhou Medical University from May 2019 to April 2020 were retrospectively analysed. Data was collected from May 2019 to October 2019 on 487 pregnant women who formed a training cohort. A multivariate logistic regression model was used to identify the independent risk factors associated with intrapartum fever during vaginal birth, and a nomogram was developed to predict the occurrence. To verify the nomogram, data was collected from January 2020 to April in 2020 from 209 pregnant women who formed a validation cohort. RESULTS: The incidence of intrapartum fever in the training cohort was found in 72 of the 487 parturients (14.8%), and the incidence of intrapartum fever in the validation cohort was 31 of the 209 parturients (14.8%). Multivariate logistic regression analysis showed that the following factors were significantly related to intrapartum fever: primiparas (odds ratio [OR] 2.43; 95% confidence interval [CI] 1.15-5.15), epidural labour analgesia (OR 2.89; 95% CI 1.23-6.82), premature rupture of membranes (OR 2.37; 95% CI 1.13-4.95), second stage of labour ≥ 120 min (OR 4.36; 95% CI 1.42-13.41), amniotic fluid pollution degree III (OR 10.39; 95% CI 3.30-32.73), and foetal weight ≥ 4000 g (OR 7.49; 95% CI 2.12-26.54). Based on clinical experience and previous studies, the duration of epidural labour analgesia also appeared to be a meaningful factor for intrapartum fever; therefore, these seven variables were used to develop a nomogram to predict intrapartum fever in parturients. The nomogram achieved a good area under the ROC curve of 0.86 and 0.81 in the training and in the validation cohorts, respectively. Additionally, the nomogram had a well-fitted calibration curve, which also showed excellent diagnostic performance. CONCLUSION: We constructed a model to predict the occurrence of fever during childbirth and developed an accessible nomogram to help doctors assess the risk of fever during childbirth. Such assessment may be helpful in implementing reasonable treatment measures. TRIAL REGISTRATION: Clinical Trial Registration: ( www.chictr.org.cn ChiCTR2000035593 ).