Identification of the differentially expressed protein biomarkers in rat blood plasma in response to gamma irradiation

SEPDB: a database of secreted proteins

Detecting changes in the dynamics of secreted proteins in serum has been a challenge for proteomics. Enter secreted protein database (SEPDB), an integrated secretory proteomics database offering human, mouse and rat secretory proteomics datasets collected from serum, exosomes and cell culture media. SEPDB compiles secreted protein information from secreted protein database, UniProt and Human Protein Atlas databases to annotate secreted proteomics data based on protein subcellular localization and disease markers. SEPDB integrates the latest predictive modeling techniques to measure deviations in the distribution of signal peptide structures of secreted proteins, extends signal peptide sequence prediction by excluding transmembrane structural domain proteins and updates the validation analysis pipeline for secreted proteins. To establish tissue-specific profiles, we have also created secreted proteomics datasets associated with different human tissues. In addition, we provide information on heterogeneous receptor network organizational relationships, reflective of the complex functional information inherent in the molecular structures of secreted proteins that serve as ligands. Users can take advantage of the Refreshed Search, Analyze, Browse and Download functions of SEPDB, which is available online at https://sysomics.com/SEPDB/. Database URL:  https://sysomics.com/SEPDB/.

Screening radiation-differentially expressed circular RNAs and establishing dose classification models in the human lymphoblastoid cell line AHH-1

PURPOSE

In the event of a large-scale radiological accident, rapid and high-throughput biodosimetry is the most vital basis in medical resource allocation for the prompt treatment of victims. However, the current biodosimeter is yet to be rapid and high-throughput. Studies have shown that ionizing radiation modulates expressions of circular RNAs (circRNAs) in healthy human cell lines and tumor tissue. circRNA expressions can be quantified rapidly and high-throughput. However, whether circRNAs are suitable for early radiation dose classification remains unclear.

METHODS

We employed transcriptome sequencing and bioinformatics analysis to screen for radiation-differentially expressed circRNAs in the human lymphoblastoid cell line AHH-1 at 4 h following exposure to 0, 2, and 5 Gy 60Co γ-rays. The dose-response relationships between differentially expressed circRNA expressions and absorbed doses were investigated using real-time polymerase chain reaction and linear regression analysis at 4 h, 24 h, and 48 h post-exposure to 0, 2, 4, 6, and 8 Gy. Six distinct dose classification models of circRNA panels were established and validated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 11 radiation-differentially expressed circRNAs were identified and validated. Based on dose-response effects, those circRNAs changed in a dose-responsive or dose-dependent manner were combined into panels A through F at 4 h, 24 h, and 48 h post-irradiation. ROC curve analysis showed that panels A through C had the potential to effectively classify exposed and non-exposed conditions, which area under the curve (AUC) of these three panels were all 1.000, and the associate p values were .009. Panels D through F excellently distinguished between different dose groups (AUC = 0.963-1.000, p < .05). The validation assay showed that panels A through F demonstrated consistent excellence in sensitivity and specificity in dose classification.

CONCLUSIONS

Ionizing radiation can indeed modulate the circRNA expression profile in the human lymphoblastoid cell line AHH-1. The differentially expressed circRNAs exhibit the potential for rapid and high-throughput dose classification.

Protein biomarkers for radiation injury and testing of medical countermeasure efficacy: promises, pitfalls, and future directions

INTRODUCTION

Radiological/nuclear accidents, hostile military activity, or terrorist strikes have the potential to expose a large number of civilians and military personnel to high doses of radiation resulting in the development of acute radiation syndrome and delayed effects of exposure. Thus, there is an urgent need for sensitive and specific assays to assess the levels of radiation exposure to individuals. Such radiation exposures are expected to alter primary cellular proteomic processes, resulting in multifaceted biological responses.

AREAS COVERED

This article covers the application of proteomics, a promising and fast developing technology based on quantitative and qualitative measurements of protein molecules for possible rapid measurement of radiation exposure levels. Recent advancements in high-resolution chromatography, mass spectrometry, high-throughput, and bioinformatics have resulted in comprehensive (relative quantitation) and precise (absolute quantitation) approaches for the discovery and accuracy of key protein biomarkers of radiation exposure. Such proteome biomarkers might prove useful for assessing radiation exposure levels as well as for extrapolating the pharmaceutical dose of countermeasures for humans based on efficacy data generated using animal models.

EXPERT OPINION

The field of proteomics promises to be a valuable asset in evaluating levels of radiation exposure and characterizing radiation injury biomarkers.

Screening of Lipids for Early Triage and Dose Estimation after Acute Radiation Exposure in Rat Plasma Based on Targeted Lipidomics Analysis

Rapid early triage and dose estimation is vital for limited medical resource allocation and treatment of a large number of the wounded after radiological accidents. Lipidomics has been utilized to delineate biofluid lipid signatures after irradiation. Here, high-coverage targeted lipidomics was employed to screen radiosensitive lipids after 0, 1, 2, 3, 5, and 8 Gy total body irradiation at 4, 24, and 72 h postirradiation in rat plasma. Ultra-performance liquid chromatography-tandem mass spectrometry with a multiple reaction monitoring method was utilized. In total, 416 individual lipids from 18 major classes were quantified and those biomarkers altered in a dose-dependent manner constituted panel A-panel D. Receiver operator characteristic curve analysis using combined lipids showed good to excellent sensitivity and specificity in triaging different radiation exposure levels (area under curve = 0.814-1.000). The equations for dose estimation were established by stepwise regression analysis for three time points. A novel strategy for radiation early triage and dose estimation was first established and validated using panels of lipids. Our study suggests that it is feasible to acquire quantitative lipid biomarker panels using targeted lipidomics platforms for rapid, high-throughput triage, which can provide further insights in developing lipidomics strategies for radiation biodosimetry in humans.

Radioprotection and Radiomitigation: From the Bench to Clinical Practice

The development of protective agents against harmful radiations has been a subject of investigation for decades. However, effective (ideal) radioprotectors and radiomitigators remain an unsolved problem. Because ionizing radiation-induced cellular damage is primarily attributed to free radicals, radical scavengers are promising as potential radioprotectors. Early development of such agents focused on thiol synthetic compounds, e.g., amifostine (2-(3-aminopropylamino) ethylsulfanylphosphonic acid), approved as a radioprotector by the Food and Drug Administration (FDA, USA) but for limited clinical indications and not for nonclinical uses. To date, no new chemical entity has been approved by the FDA as a radiation countermeasure for acute radiation syndrome (ARS). All FDA-approved radiation countermeasures (filgrastim, a recombinant DNA form of the naturally occurring granulocyte colony-stimulating factor, G-CSF; pegfilgrastim, a PEGylated form of the recombinant human G-CSF; sargramostim, a recombinant granulocyte macrophage colony-stimulating factor, GM-CSF) are classified as radiomitigators. No radioprotector that can be administered prior to exposure has been approved for ARS. This differentiates radioprotectors (reduce direct damage caused by radiation) and radiomitigators (minimize toxicity even after radiation has been delivered). Molecules under development with the aim of reaching clinical practice and other nonclinical applications are discussed. Assays to evaluate the biological effects of ionizing radiations are also analyzed.

Redefining Tumor-Associated Macrophage Subpopulations and Functions in the Tumor Microenvironment

The immunosuppressive status of the tumor microenvironment (TME) remains poorly defined due to a lack of understanding regarding the function of tumor-associated macrophages (TAMs), which are abundant in the TME. TAMs are crucial drivers of tumor progression, metastasis, and resistance to therapy. Intra- and inter-tumoral spatial heterogeneities are potential keys to understanding the relationships between subpopulations of TAMs and their functions. Antitumor M1-like and pro-tumor M2-like TAMs coexist within tumors, and the opposing effects of these M1/M2 subpopulations on tumors directly impact current strategies to improve antitumor immune responses. Recent studies have found significant differences among monocytes or macrophages from distinct tumors, and other investigations have explored the existence of diverse TAM subsets at the molecular level. In this review, we discuss emerging evidence highlighting the redefinition of TAM subpopulations and functions in the TME and the possibility of separating macrophage subsets with distinct functions into antitumor M1-like and pro-tumor M2-like TAMs during the development of tumors. Such redefinition may relate to the differential cellular origin and monocyte and macrophage plasticity or heterogeneity of TAMs, which all potentially impact macrophage biomarkers and our understanding of how the phenotypes of TAMs are dictated by their ontogeny, activation status, and localization. Therefore, the detailed landscape of TAMs must be deciphered with the integration of new technologies, such as multiplexed immunohistochemistry (mIHC), mass cytometry by time-of-flight (CyTOF), single-cell RNA-seq (scRNA-seq), spatial transcriptomics, and systems biology approaches, for analyses of the TME.