Molecular insights into the proteomic composition of porcine treated dentin matrix.

Background: Human-treated dentin matrix (hTDM) has recently been studied as a natural extracellular matrix-based biomaterial for dentin pulp regeneration. However, porcine-treated dentin matrix (pTDM) is a potential alternative scaffold due to limited availability. However, there is a dearth of information regarding the protein composition and underlying molecular mechanisms of pTDM.Methods: hTDM and pTDM were fabricated using human and porcine teeth, respectively, and their morphological characteristics were examined using scanning electron microscopy. Stem cells derived from human exfoliated deciduous teeth (SHEDs) were isolated and characterized using flow cytometry and multilineage differentiation assays. SHEDs were cultured in three-dimensional environments with hTDM, pTDM, or biphasic hydroxyapatite/tricalcium phosphate. The expression of odontogenesis markers in SHEDs were assessed using real-time polymerase chain reaction and immunochemical staining. Subsequently, SHEDs/TDM and SHEDs/HA/TCP complexes were transplanted subcutaneously into nude mice. The protein composition of pTDM was analyzed using proteomics and compared to previously published data on hTDM.Results: pTDM and hTDM elicited comparable upregulation of odontogenesis-related genes and proteins in SHEDs. Furthermore, both demonstrated the capacity to stimulate root-related tissue regeneration in vivo. Proteomic analysis revealed the presence of 278 protein groups in pTDM, with collagens being the most abundant. Additionally, pTDM and hTDM shared 58 identical proteins, which may contribute to their similar abilities to induce odontogenesis. Conclusions: Both hTDM and pTDM exhibit comparable capabilities in inducing odontogenesis, potentially owing to their distinctive bioactive molecular networks.

Interplay of tourism renewable energies, tourism institutional quality, and political risk in OECD economies.

It is only possible to achieve the aims of reversing the impacts of the resource constraint and attaining sustainable growth if there is a rise in tourism organizational efficacy and tourism and a decrease in political instability. This is because these factors can influence the demand for energy by causing changes in the amount of power consumed. These factors may affect energy demand via changes in energy transitions. In light of this, the objective of this study is to investigate the impact that critical measures of institutional efficiency, tourism, and policy instability have on the utilization of renewable energy sources in a dataset consisting of 32 countries that are members of the Organization for Economic Co-operation and Development between the years 1997 and 2019. The article uses descriptive statistics and correlation models (cross-section dependency test and autoregressive distributed lag (ARDL) model) using panel data from 32 Organization for Economic Co-operation and Development (OECD) nations from 1997 to 2019. Insight into the matter aids in our selection of appropriate econometric methods. The following methods are briefly explained. Evidence shows that as a society's average wealth and standard of life grow, so does its utilization of renewable energy sources. In addition, the economic globalization process and the danger it entails are adversely associated with a long-term reliance on renewable energy sources. Policymakers in countries that are members of the OECD should investigate the role that institutional effectiveness and policy instability play in the demand function for renewable energy to ensure a cleaner natural environment over the long term.

PertOrg 1.0: a comprehensive resource of multilevel alterations induced in model organisms by in vivo genetic perturbation.

Genetically modified organisms (GMOs) can be generated to model human genetic disease or plant disease resistance, and they have contributed to the exploration and understanding of gene function, physiology, disease onset and drug target discovery. Here, PertOrg (http://www.inbirg.com/pertorg/) was introduced to provide multilevel alterations in GMOs. Raw data of 58 707 transcriptome profiles and associated information, such as phenotypic alterations, were collected and curated from studies involving in vivo genetic perturbation (e.g. knockdown, knockout and overexpression) in eight model organisms, including mouse, rat and zebrafish. The transcriptome profiles from before and after perturbation were organized into 10 116 comparison datasets, including 122 single-cell RNA-seq datasets. The raw data were checked and analysed using widely accepted and standardized pipelines to identify differentially expressed genes (DEGs) in perturbed organisms. As a result, 8 644 148 DEGs were identified and deposited as signatures of gene perturbations. Downstream functional enrichment analysis, cell type analysis and phenotypic alterations were also provided when available. Multiple search methods and analytical tools were created and implemented. Furthermore, case studies were presented to demonstrate how users can utilize the database. PertOrg 1.0 will be a valuable resource aiding in the exploration of gene functions, biological processes and disease models.

DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development.

Drug-likeness is a vital consideration when selecting compounds in the early stage of drug discovery. A series of drug-like properties are needed to predict the drug-likeness of a given compound and provide useful guidelines to increase the likelihood of converting lead compounds into drugs. Experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved small-molecule drugs from multiple text-based unstructured data resources have been manually assembled, curated, further digitized and processed into structured data, which are deposited in the Database of Digital Properties of approved Drugs (DDPD). DDPD 1.0 contains 30 212 drug property entries, including 2250 approved drugs and 32 properties, in a standardized value/unit format. Moreover, two analysis tools are provided to examine the drug-likeness features of given molecules based on the collected property data of approved drugs. Additionally, three case studies are presented to demonstrate how users can utilize the database. We believe that this database will be a valuable resource for the drug discovery and development field. Database URL:  http://www.inbirg.com/ddpd.

A panel of selected serum protein biomarkers for the detection of aggressive prostate cancer.

Background: Current PSA-based tests used to detect prostate cancer (PCa) lack sufficient specificity, leading to significant overdetection and overtreatment. Our previous studies showed that serum fucosylated PSA (Fuc-PSA) and soluble TEK receptor tyrosine kinase (Tie-2) had the ability to predict aggressive (AG) PCa. Additional biomarkers are needed to address this significant clinical problem. Methods: A comprehensive Pubmed search followed by multiplex immunoassays identified candidate biomarkers associated with AG PCa. Subsequently, multiplex and lectin-based immunoassays were applied to a case-control set of sera from subjects with AG PCa, low risk PCa, and non-PCa (biopsy negative). These candidate biomarkers were further evaluated for their ability as panels to complement the prostate health index (phi) in detecting AG PCa. Results: When combined through logistic regression, two panel of biomarkers achieved the best performance: 1) phi, Fuc-PSA, SDC1, and GDF-15 for the detection of AG from low risk PCa and 2) phi, Fuc-PSA, SDC1, and Tie-2 for the detection of AG from low risk PCa and non-PCa, with noticeable improvements in ROC analysis over phi alone (AUCs: 0.942 vs 0.872, and 0.934 vs 0.898, respectively). At a fixed sensitivity of 95%, the panels improved specificity with statistical significance in detecting AG from low risk PCa (76.0% vs 56%, p=0.029), and from low risk PCa and non-PCa (78.2% vs 65.5%, p=0.010). Conclusions: Multivariate panels of serum biomarkers identified in this study demonstrated clinically meaningful improvement over the performance of phi, and warrant further clinical validation, which may contribute to the management of PCa.

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

Proteogenomic analysis prioritises functional single nucleotide variants in cancer samples.

Massively parallel DNA sequencing enables the detection of thousands of germline and somatic single nucleotide variants (SNVs) in cancer samples. The functional analysis of these mutations is often carried out through in silico predictions, with further downstream experimental validation rarely performed. Here, we examine the potential of using mass spectrometry-based proteomics data to further annotate the function of SNVs in cancer samples. RNA-seq and whole genome sequencing (WGS) data from Jurkat cells were used to construct a custom database of single amino acid variant (SAAV) containing peptides and identified over 1,000 such peptides in two Jurkat proteomics datasets. The analysis enabled the detection of a truncated form of splicing regulator YTHDC1 at the protein level. To extend the functional annotation further, a Jurkat phosphoproteomics dataset was analysed, identifying 463 SAAV containing phosphopeptides. Of these phosphopeptides, 24 SAAVs were found to directly impact the phosphorylation event through the creation of either a phosphorylation site or a kinase recognition motif. We identified a novel phosphorylation site created by a SAAV in splicing factor SF3B1, a protein that is frequently mutated in leukaemia. To our knowledge, this is the first study to use phosphoproteomics data to directly identify novel phosphorylation events arising from the creation of phosphorylation sites by SAAVs. Our study reveals multiple functional mutations impacting the splicing pathway in Jurkat cells and demonstrates potential benefits of an integrative proteogenomics analysis for high-throughput functional annotation of SNVs in cancer.

Phylogenetic Analysis Using Protein Mass Spectrometry.

Through advances in molecular biology, comparative analysis of DNA sequences is currently the cornerstone in the study of molecular evolution and phylogenetics. Nevertheless, protein mass spectrometry offers some unique opportunities to enable phylogenetic analyses in organisms where DNA may be difficult or costly to obtain. To date, the methods of phylogenetic analysis using protein mass spectrometry can be classified into three categories: (1) de novo protein sequencing followed by classical phylogenetic reconstruction, (2) direct phylogenetic reconstruction using proteolytic peptide mass maps, and (3) mapping of mass spectral data onto classical phylogenetic trees. In this chapter, we provide a brief description of the three methods and the protocol for each method along with relevant tools and algorithms.

A quantitative proteomics approach identifies ETV6 and IKZF1 as new regulators of an ERG-driven transcriptional network.

Aberrant stem cell-like gene regulatory networks are a feature of leukaemogenesis. The ETS-related gene (ERG), an important regulator of normal haematopoiesis, is also highly expressed in T-ALL and acute myeloid leukaemia (AML). However, the transcriptional regulation of ERG in leukaemic cells remains poorly understood. In order to discover transcriptional regulators of ERG, we employed a quantitative mass spectrometry-based method to identify factors binding the 321 bp ERG +85 stem cell enhancer region in MOLT-4 T-ALL and KG-1 AML cells. Using this approach, we identified a number of known binders of the +85 enhancer in leukaemic cells along with previously unknown binders, including ETV6 and IKZF1. We confirmed that ETV6 and IKZF1 were also bound at the +85 enhancer in both leukaemic cells and in healthy human CD34+ haematopoietic stem and progenitor cells. Knockdown experiments confirmed that ETV6 and IKZF1 are transcriptional regulators not just of ERG, but also of a number of genes regulated by a densely interconnected network of seven transcription factors. At last, we show that ETV6 and IKZF1 expression levels are positively correlated with expression of a number of heptad genes in AML and high expression of all nine genes confers poorer overall prognosis.

FluClass: A novel algorithm and approach to score and visualize the phylogeny of the influenza virus using mass spectrometry.

A novel computer algorithm FluClass has been developed to facilitate the phylogenetic classification of influenza virus using mass spectral data. FluClass accepts a DNA or protein-based phylogenetic tree as input and generates theoretical peptide mass lists for each node. An experimental mass spectrum from an influenza virus protein digest is then placed onto the phylogenetic tree using a novel random resampling function (Z-score) that allows the scoring of spectrum against both internal and leaf nodes. Testing of the algorithm using hemagglutinin protein sequences from human-host influenza viruses showed that the Z-score performs comparably to the Profound scoring method for the scoring of leaf nodes and is substantially better at scoring internal nodes. Scoring of internal nodes allows colorizations of nodes of the phylogenetic tree enabling the classification of the query spectrum to be rapidly visualized. Finally we demonstrate the utility of FluClass on experimental spectra from six strains. Given that mass spectrometry data can be generated rapidly for influenza virus proteins, FluClass provides a fast and direct method for phylogenetic analysis of influenza proteins.