Evidence of histidine and aspartic acid phosphorylation in human prostate cancer cells

Metabolomic profiles altered by erlotinib encapsulated in poly(lactide-co-glycolide) nanoparticles in non-small cell lung cancer

OBJECTIVE

This research is focused on the metabolomics and cytotoxic effects of the anticancer drug erlotinib encapsulated in poly(lactide-co-glycolide) nanoparticles on non-small cell lung cancer (NSCLC) cell lines.

METHODS

Uniform-sized nanoparticles (0.325 and 0.068 PDI) with mean diameters of 264.5 and 268.4 nm for blank and erlotinib-PLGA nanoparticles (nanodrugs-NDs) were formulated, respectively. The encapsulation efficiency of prepared nanoparticles was found to be 90.1%. 36% of erlotinib was released from PLGA nanoparticles within 24 h, and the maximum sustained release was 43% at 72 h. The metabolomic and cytotoxic effects of ND were evaluated.

RESULTS

The Bax/Bcl-2 ratio was the lowest in the nanodrug group at 72 h, showing increased apoptosis, indicating that the most effective drug formulation is the combined nanoparticle at 72 h. The metabolomic studies revealed changing amino acids, antioxidant molecules, and carbohydrate profiles. The most significant changes were obtained in pathways related to the synthesis of p-glycoprotein, which is the principal protein for drug efflux and causes drug resistance. The lowest levels of amino acids and polyamines like serine, threonine, spermine, and spermidine were obtained at 72 h with erlotinib encapsulated in poly(lactide-co-glycolide) (PLGA) nanoparticles, showing that the drug resistance may in part be overcome with this nanoparticles.

CONCLUSION

The encapsulation of erlotinib with PLGA showed effects and influenced critical metabolic pathways, especially pointing out the need to lower drug resistance and signifying it's potential use as an effective treatment strategy for NSCLC.

CaLMPhosKAN: prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov-Arnold network

MOTIVATION

The mapping from codon to amino acid is surjective due to codon degeneracy, suggesting that codon space might harbor higher information content. Embeddings from the codon language model have recently demonstrated success in various protein downstream tasks. However, predictive models for residue-level tasks such as phosphorylation sites, arguably the most studied Post-Translational Modification (PTM), and PTM sites prediction in general, have predominantly relied on representations in amino acid space.

RESULTS

We introduce a novel approach for predicting phosphorylation sites by utilizing codon-level information through embeddings from the codon adaptation language model (CaLM), trained on protein-coding DNA sequences. Protein sequences are first reverse-translated into reliable coding sequences by mapping UniProt sequences to their corresponding NCBI reference sequences and extracting the exact coding sequences from their GenBank format using a dynamic programming-based global pairwise alignment. The resulting coding sequences are encoded using the CaLM encoder to generate codon-aware embeddings, which are subsequently integrated with amino acid-aware embeddings obtained from a protein language model, through an early fusion strategy. Next, a window-level representation of the site of interest, retaining the full sequence context, is constructed from the fused embeddings. A ConvBiGRU network extracts feature maps that capture spatiotemporal correlations between proximal residues within the window. This is followed by a prediction head based on a Kolmogorov-Arnold network (KAN) using the derivative of gaussian wavelet transform to generate the inference for the site. The overall model, dubbed CaLMPhosKAN, performs better than the existing approaches across multiple datasets.

AVAILABILITY AND IMPLEMENTATION

CaLMPhosKAN is publicly available at https://github.com/KCLabMTU/CaLMPhosKAN.

Nphos: Database and Predictor of Protein N-phosphorylation

Protein N-phosphorylation is widely present in nature and participates in various biological processes. However, current knowledge on N-phosphorylation is extremely limited compared to that on O-phosphorylation. In this study, we collected 11,710 experimentally verified N-phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on protein N-phosphorylation. Upon these substantial data, we characterized the sequential and structural features of protein N-phosphorylation. Moreover, after comparing hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of human N-phosphorylation, achieving mean area under the receiver operating characteristic curve (AUC) values of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinct N-phosphosites in the human proteome. The models were also deployed in Nphos for interactive N-phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations of N-phosphorylation. It will also facilitate a deeper and more systematic understanding of protein N-phosphorylation modification by providing a data and technical foundation. Nphos is freely available at http://www.bio-add.org/Nphos/ and http://ppodd.org.cn/Nphos/.

Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics

Introduction

We aimed to investigate the feasibility of metabolomics to explain the underlying biological implications of radiomics features obtained from magnetic resonance imaging (MRI) preceding carbon ion radiotherapy (CIRT) in patients with prostate cancer and to further explore the clinical significance of radiomics features on the prognosis of patients, based on their biochemical recurrence (BCR) status.

Methods

Metabolomic results obtained using high-performance liquid chromatography coupled with tandem mass spectrometry of urine samples, combined with pre-RT radiomic features extracted from MRI images, were evaluated to investigate their biological significance. Receiver operating characteristic (ROC) curve analysis was subsequently conducted to examine the correlation between these biological implications and clinical BCR status. Statistical and metabolic pathway analyses were performed using MetaboAnalyst and R software.

Results

Correlation analysis revealed that methionine alteration extent was significantly related to four radiomic features (Contrast, Difference Variance, Small Dependence High Gray Level Emphasis, and Mean Absolute Deviation), which were significantly correlated with BCR status. The area under the curve (AUC) for BCR prediction of these four radiomic features ranged from 0.704 to 0.769, suggesting that the higher the value of these four radiomic features, the greater the decrease in methionine levels after CIRT and the lower the probability of BCR. Pre-CIRT MRI radiomic features were associated with CIRT-suppressed metabolites.

Discussion

These radiomic features can be used to predict the alteration in the amplitude of methionine after CIRT and the BCR status, which may contribute to the optimization of the CIRT strategy and deepen the understanding of PCa.

Hollow Core-Shell Metal Oxide Heterojunctions for the Urinary Metabolic Fingerprint-Based Noninvasive Diagnostic Strategy

Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.

pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties

Background

Protein histidine phosphorylation (pHis) plays critical roles in prokaryotic signal transduction pathways and various eukaryotic cellular processes. It is estimated to account for 6–10% of the phosphoproteome, however only hundreds of pHis sites have been discovered to date. Due to the inherent disadvantages of experimental methods, it is an urgent task for developing efficient computational approaches to identify pHis sites.

Results

Here, we present a novel tool, pHisPred, for accurately identifying pHis sites from protein sequences. We manually collected the largest number of experimental validated pHis sites to build benchmark datasets. Using randomized tenfold CV, the weighted SVM-RBF model shows the best performance than other four commonly used classification models (LR, KNN, RF, and MLP). From ten thousands of features, 140 and 150 most informative features were individually selected out for eukaryotic and prokaryotic models. The average AUC and F1-score values of pHisPred were (0.81, 0.40) and (0.78, 0.46) for tenfold CV on the eukaryotic and prokaryotic training datasets, respectively. In addition, pHisPred significantly outperforms other tools on testing datasets, in particular on the eukaryotic one.

Conclusion

We implemented a python program of pHisPred, which is freely available for non-commercial use at https://github.com/xiaofengsong/pHisPred. Moreover, users can use it to train new models with their own data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04938-x.

Identification of characteristic metabolic panels for different stages of prostate cancer by 1H NMR-based metabolomics analysis

Background

Prostate cancer (PCa) is the second most prevalent cancer in males worldwide, yet detecting PCa and its metastases remains a major challenging task in clinical research setups. The present study aimed to characterize the metabolic changes underlying the PCa progression and investigate the efficacy of related metabolic panels for an accurate PCa assessment.

Methods

In the present study, 75 PCa subjects, 62 PCa patients with bone metastasis (PCaB), and 50 benign prostatic hyperplasia (BPH) patients were enrolled, and we performed a cross-sectional metabolomics analysis of serum samples collected from these subjects using a ¹H nuclear magnetic resonance (NMR)-based metabolomics approach.

Results

Multivariate analysis revealed that BPH, PCa, and PCaB groups showed distinct metabolic divisions, while univariate statistics integrated with variable importance in the projection (VIP) scores identified a differential metabolite series, which included energy, amino acid, and ketone body metabolism. Herein, we identified a series of characteristic serum metabolic changes, including decreased trends of 3-HB and acetone as well as elevated trends of alanine in PCa patients compared with BPH subjects, while increased levels of 3-HB and acetone as well as decreased levels of alanine in PCaB patients compared with PCa. Additionally, our results also revealed the metabolic panels of discriminant metabolites coupled with the clinical parameters (age and body mass index) for discrimination between PCa and BPH, PCaB and BPH, PCaB and PCa achieved the AUC values of 0.828, 0.917, and 0.872, respectively.

Conclusions

Overall, our study gave successful discrimination of BPH, PCa and PCaB, and we characterized the potential metabolic alterations involved in the PCa progression and its metastases, including 3-HB, acetone and alanine. The defined biomarker panels could be employed to aid in the diagnosis and classification of PCa in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03478-5.

HisPhosSite: A comprehensive database of histidine phosphorylated proteins and sites

Histidine phosphorylation is critically important in a variety of cellular processes including signal transduction, cell cycle, proliferation, differentiation, and apoptosis. It is estimated to account for 6% of all phosphorylated amino acids. However, due to the acid lability of the PN bond, the study of pHis lags far behind that of pSer, pThr, and pTyr. Recently, the development and use of pHis-specific antibodies and methodologies have led to a resurgence in the study of histidine phosphorylation. Although a considerable number of pHis proteins and sites have been discovered, most of them have not been manually curated and integrated to any databases. There is a lack of a data repository for pHis, and such work is expected to help further systemic studies of pHis. Thus, we present a comprehensive resource database of histidine phosphorylation (HisPhosSite) by curating experimentally validated pHis proteins and sites and compiling putative pHis sites with ortholog search. HisPhosSite contains 776 verified pHis sites and 2702 verified pHis proteins in 38 eukaryotic and prokaryotic species and 15,378 putative pHis sites and 10,816 putative pHis proteins in 1366 species. HisPhosSite provides rich annotations of pHis sites and proteins and multiple search engines (including motif search and BLAST search) for users to locate pHis sites of interest. HisPhosSite is available at http://reprod.njmu.edu.cn/hisphossite. SIGNIFICANCE: Histidine phosphorylation is involved in a variety of cellular processes as well as cancers, and it has been proved to be more common than previously thought. The HisPhosSite database was developed to collect pHis data from published literatures with experimental evidences. Unification of the identified pHis proteins and sites will give researchers an informative resource for histidine phosphorylation. HisPhosSite has a user-friendly interface with multiple search engines for users to locate pHis sites of interest. In addition, the database provides rich structural and functional annotations. HisPhosSite will help future studies and elucidation of the functions of histidine phosphorylation.

Protein phosphatase 1 in tumorigenesis: is it worth a closer look?

Cancer cells take advantage of signaling cascades to meet their requirements for sustained growth and survival. Cell signaling is tightly controlled by reversible protein phosphorylation mechanisms, which require the counterbalanced action of protein kinases and protein phosphatases. Imbalances on this system are associated with cancer development and progression. Protein phosphatase 1 (PP1) is one of the most relevant protein phosphatases in eukaryotic cells. Despite the widely recognized involvement of PP1 in key biological processes, both in health and disease, its relevance in cancer has been largely neglected. Here, we provide compelling evidence that support major roles for PP1 in tumorigenesis.

Role of nm23H1 in predicting metastases in prostatic carcinoma

Background

Non-metastatic nm23H1 gene is thought to play a critical role in cell proliferation. Studies of nm23H1 have been done in many other malignancies. But none of these studies took up nm23H1 gene as predictor in the metastases of prostatic carcinoma.

Aims and Objectives

To study the expression of nm23H1 in prostatic lesion and to correlate nm23H1 expression with presence of metastases, tumour stage, tumour grade and with PSA level serum.

Setting and Design

Tertiary hospital based retrospective and prospective study done in a period of one year from thirty patients having prostatic lesion confirmed by biopsy.

Material and Methods

Immunohistochemistry for nm23H1 was performed on unstained coated sections of prostatic lesions to study the relation with prostatic lesion and their correlation with age, PSA level, tumour stage, grading. Clinical data was collected from medical records.

Statistical Analysis

SPSS Version 15 analysis software was used. The value were presented in number(%) and Mean ± SD.

Results

Majority of patients belong to age group 61 to 70yrs.Gleason score >7 were seen in 55% of patients of adenocarcinoma with and without metastasis. The difference in PSA levels between BPH and adenocarcinoma was significant (P < 0.001). IHC expression for nm23H1 gene showed positive findings in all the cases (P = 1). PSA values >20ng/ml showed maximum % mean expression (98.64%) as compared to PSA levels <10 ng/ml (96.91%).

Conclusion

IHC expression of nm23H1 is not an effective tool to distinguish among the cases of BPH, adenocarcinoma of prostate with and without metastasis. Hence nm23H1 gene does not behave like an antimetastatic gene in prostatic lesions.

Advances in development of new tools for the study of phosphohistidine

Protein phosphorylation is an important post-translational modification that is an integral part of cellular function. The O-phosphorylated amino-acid residues, such as phosphoserine (pSer), phosphothreonine (pThr) and phosphotyrosine (pTyr), have dominated the literature while the acid labile N-linked phosphorylated amino acids, such as phosphohistidine (pHis), have largely been historically overlooked because of the acidic conditions routinely used in amino-acid detection and analysis. This review highlights some misinterpretations that have arisen in the existing literature, pinpoints outstanding questions and potential future directions to clarify the role of pHis in mammalian signalling systems. Particular emphasis is placed on pHis isomerization and the hybrid functionality for both pHis and pTyr of the proposed τ-pHis analogue bearing the triazole residue.

pHisphorylation: the emergence of histidine phosphorylation as a reversible regulatory modification

Histidine phosphorylation is crucial for prokaryotic signal transduction and as an intermediate for several metabolic enzymes, yet its role in mammalian cells remains largely uncharted. This is primarily caused by difficulties in studying histidine phosphorylation because of the relative instability of phosphohistidine (pHis) and lack of specific antibodies and methods to preserve and detect it. The recent synthesis of stable pHis analogs has enabled development of pHis-specific antibodies and their use has started to shed light onto this important, yet enigmatic posttranslational modification. We are beginning to understand that pHis has broader roles in protein and cellular function including; cell cycle regulation, phagocytosis, regulation of ion channel activity and metal ion coordination. Two mammalian histidine kinases (NME1 and NME2), two pHis phosphatases (PHPT1 and LHPP), and a handful of substrates were previously identified. These new tools have already led to the discovery of an additional phosphatase (PGAM5) and hundreds of putative substrates. New methodologies are also being developed to probe the pHis phosphoproteome and determine functional consequences, including negative ion mode mass spectroscopy and unnatural amino acid incorporation. These new tools and strategies have the potential to overcome the unique challenges that have been holding back our understanding of pHis in cell biology.

Monoclonal 1- and 3-Phosphohistidine Antibodies: New Tools to Study Histidine Phosphorylation

Histidine phosphorylation (pHis) is well studied in bacteria; however, its role in mammalian signaling remains largely unexplored due to the lack of pHis-specific antibodies and the lability of the phosphoramidate (P-N) bond. Both imidazole nitrogens can be phosphorylated, forming 1-phosphohistidine (1-pHis) or 3-phosphohistidine (3-pHis). We have developed monoclonal antibodies (mAbs) that specifically recognize 1-pHis or 3-pHis; they do not cross-react with phosphotyrosine or the other pHis isomer. Assays based on the isomer-specific autophosphorylation of NME1 and phosphoglycerate mutase were used with immunoblotting and sequencing IgG variable domains to screen, select, and characterize anti-1-pHis and anti-3-pHis mAbs. Their sequence independence was determined by blotting synthetic peptide arrays, and they have been tested for immunofluorescence staining and immunoaffinity purification, leading to putative identification of pHis-containing proteins. These reagents should be broadly useful for identification of pHis substrates and functional study of pHis using a variety of immunological, proteomic, and biological assays.