Transcriptional response to 131I exposure of rat thyroid gland

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction

Background

Molecular radiation biomarkers are an emerging tool in radiation research with applications for cancer radiotherapy, radiation risk assessment, and even human space travel. However, biomarker screening in genome-wide expression datasets using conventional tools is time-consuming and underlies analyst (human) bias. Machine Learning (ML) methods can improve the sensitivity and specificity of biomarker identification, increase analytical speed, and avoid multicollinearity and human bias.

Aim

To develop a resource-efficient ML framework for radiation biomarker discovery using gene expression data from irradiated normal tissues. Further, to identify biomarker panels predicting radiation dose with tissue specificity.

Methods

A strategic search in the Gene Expression Omnibus database identified a transcriptomic dataset (GSE44762) for normal tissues radiation responses (murine kidney cortex and medulla) suited for biomarker discovery using an ML approach. The dataset was pre-processed in R and separated into train and test data subsets. High computational cost of Genetic Algorithm/k-Nearest Neighbor (GA/KNN) mandated optimization and 13 ML models were tested using the caret package in R. Biomarker performance was evaluated and visualized via Principal Component Analysis (PCA) and dose regression. The novelty of ML-identified biomarker panels was evaluated by literature search.

Results

Caret-based feature selection and ML methods vastly improved processing time over the GA approach. The KNN method yielded overall best performance values on train and test data and was implemented into the framework. The top-ranking genes were Cdkn1a, Gria3, Mdm2 and Plk2 in cortex, and Brf2, Ccng1, Cdkn1a, Ddit4l, and Gria3 in medulla. These candidates successfully categorized dose groups and tissues in PCA. Regression analysis showed that correlation between predicted and true dose was high with R² of 0.97 and 0.99 for cortex and medulla, respectively.

Conclusion

The caret framework is a powerful tool for radiation biomarker discovery optimizing performance with resource-efficiency for broad implementation in the field. The KNN-based approach identified Brf2, Ddit4l, and Gria3 mRNA as novel candidates that have been uncharacterized as radiation biomarkers to date. The biomarker panel showed good performance in dose and tissue separation and dose regression. Further training with larger cohorts is warranted to improve accuracy, especially for lower doses.

Age and sex effects across the blood proteome after ionizing radiation exposure can bias biomarker screening and risk assessment

Molecular biomarkers of ionizing radiation (IR) exposure are a promising new tool in various disciplines: they can give necessary information for adaptive treatment planning in cancer radiotherapy, enable risk projection for radiation-induced survivorship diseases, or facilitate triage and intervention in radiation hazard events. However, radiation biomarker discovery has not yet resolved the most basic features of personalized medicine: age and sex. To overcome this critical bias in biomarker identification, we quantitated age and sex effects and assessed their relevance in the radiation response across the blood proteome. We used high-throughput mass spectrometry on blood plasma collected 24 h after 0.5 Gy total body irradiation (15 MV nominal photon energy) from male and female C57BL/6 N mice at juvenile (7-weeks-old) or adult (18-weeks-old) age. We also assessed sex and strain effects using juvenile male and female BALB/c nude mice. We showed that age and sex created significant effects in the proteomic response regarding both extent and functional quality of IR-induced responses. Furthermore, we found that age and sex effects appeared non-linear and were often end-point specific. Overall, age contributed more to differences in the proteomic response than sex, most notably in immune responses, oxidative stress, and apoptotic cell death. Interestingly, sex effects were pronounced for DNA damage and repair pathways and associated cellular outcome (pro-survival vs. pro-apoptotic). Only one protein (AHSP) was identified as a potential general biomarker candidate across age and sex, while GMNN, REG3B, and SNCA indicated some response similarity across age. This low yield advocated that unisex or uniage biomarker screening approaches are not feasible. In conclusion, age- and sex-specific screening approaches should be implemented as standard protocol to ensure robustness and diagnostic power of biomarker candidates. Bias-free molecular biomarkers are a necessary progression towards personalized medicine and integral for advanced adaptive cancer radiotherapy and risk assessment.

Age-related long-term response in rat thyroid tissue and plasma after internal low dose exposure to 131I

131I is used clinically for therapy, and may be released during nuclear accidents. After the Chernobyl accident papillary thyroid carcinoma incidence increased in children, but not adults. The aims of this study were to compare 131I irradiation-dependent differences in RNA and protein expression in the thyroid and plasma of young and adult rats, and identify potential age-dependent biomarkers for 131I exposure. Twelve young (5 weeks) and twelve adult Sprague Dawley rats (17 weeks) were i.v. injected with 50 kBq 131I (absorbed dose to thyroid = 0.1 Gy), and sixteen unexposed age-matched rats were used as controls. The rats were killed 3-9 months after administration. Microarray analysis was performed using RNA from thyroid samples, while LC-MS/MS analysis was performed on proteins extracted from thyroid tissue and plasma. Canonical pathways, biological functions and upstream regulators were analysed for the identified transcripts and proteins. Distinct age-dependent differences in gene and protein expression were observed. Novel biomarkers for thyroid 131I exposure were identified: (PTH), age-dependent dose response (CA1, FTL1, PVALB (youngsters) and HSPB6 (adults)), thyroid function (Vegfb (adults)). Further validation using clinical samples are needed to explore the role of the identified biomarkers.

EANM position paper on the role of radiobiology in nuclear medicine

With an increasing variety of radiopharmaceuticals for diagnostic or therapeutic nuclear medicine as valuable diagnostic or treatment option, radiobiology plays an important role in supporting optimizations. This comprises particularly safety and efficacy of radionuclide therapies, specifically tailored to each patient. As absorbed dose rates and absorbed dose distributions in space and time are very different between external irradiation and systemic radionuclide exposure, distinct radiation-induced biological responses are expected in nuclear medicine, which need to be explored. This calls for a dedicated nuclear medicine radiobiology. Radiobiology findings and absorbed dose measurements will enable an improved estimation and prediction of efficacy and adverse effects. Moreover, a better understanding on the fundamental biological mechanisms underlying tumor and normal tissue responses will help to identify predictive and prognostic biomarkers as well as biomarkers for treatment follow-up. In addition, radiobiology can form the basis for the development of radiosensitizing strategies and radioprotectant agents. Thus, EANM believes that, beyond in vitro and preclinical evaluations, radiobiology will bring important added value to clinical studies and to clinical teams. Therefore, EANM strongly supports active collaboration between radiochemists, radiopharmacists, radiobiologists, medical physicists, and physicians to foster research toward precision nuclear medicine.

Long-term transcriptomic and proteomic effects in Sprague Dawley rat thyroid and plasma after internal low dose 131I exposure

Background

Radioiodide (¹³¹I) is commonly used to treat thyroid cancer and hyperthyroidis.¹³¹I released during nuclear accidents, have resulted in increased incidence of thyroid cancer in children. Therefore, a better understanding of underlying cellular mechanisms behind ¹³¹I exposure is of great clinical and radiation protection interest. The aim of this work was to study the long-term dose-related effects of ¹³¹I exposure in thyroid tissue and plasma in young rats and identify potential biomarkers.

Materials and methods

Male Sprague Dawley rats (5-week-old) were i.v. injected with 0.5, 5.0, 50 or 500 kBq ¹³¹I (D_thyroid ca 1–1000 mGy), and killed after nine months at which time the thyroid and blood samples were collected. Gene expression microarray analysis (thyroid samples) and LC-MS/MS analysis (thyroid and plasma samples) were performed to assess differential gene and protein expression profiles in treated and corresponding untreated control samples. Bioinformatics analyses were performed using the DAVID functional annotation tool and Ingenuity Pathway Analysis (IPA). The gene expression microarray data and LC-MS/MS data were validated using qRT-PCR and ELISA, respectively.

Results

Nine ¹³¹I exposure-related candidate biomarkers (transcripts: Afp and RT1-Bb, and proteins: ARF3, DLD, IKBKB, NONO, RAB6A, RPN2, and SLC25A5) were identified in thyroid tissue. Two dose-related protein candidate biomarkers were identified in thyroid (APRT and LDHA) and two in plasma (DSG4 and TGM3). Candidate biomarkers for thyroid function included the ACADL and SORBS2 (all activities), TPO and TG proteins (low activities). ¹³¹I exposure was shown to have a profound effect on metabolism, immune system, apoptosis and cell death. Furthermore, several signalling pathways essential for normal cellular function (actin cytoskeleton signalling, HGF signalling, NRF2-mediated oxidative stress, integrin signalling, calcium signalling) were also significantly regulated.

Conclusion

Exposure-related and dose-related effects on gene and protein expression generated few expression patterns useful as biomarkers for thyroid function and cancer.

The IRI-DICE hypothesis: ionizing radiation-induced DSBs may have a functional role for non-deterministic responses at low doses

Low-dose ionizing radiation (IR) responses remain an unresolved issue in radiation biology and risk assessment. Accurate knowledge of low-dose responses is important for estimation of normal tissue risk in cancer radiotherapy or health risks from occupational or hazard exposure. Cellular responses to low-dose IR appear diverse and stochastic in nature and to date no model has been proposed to explain the underlying mechanisms. Here, we propose a hypothesis on IR-induced double-strand break (DSB)-induced cis effects (IRI-DICE) and introduce DNA sequence functionality as a submicron-scale target site with functional outcome on gene expression: DSB induction in a certain genetic target site such as promotor, regulatory element, or gene core would lead to changes in transcript expression, which may range from suppression to overexpression depending on which functional element was damaged. The DNA damage recognition and repair machinery depicts threshold behavior requiring a certain number of DSBs for induction. Stochastically distributed persistent disruption of gene expression may explain-in part-the diverse nature of low-dose responses until the repair machinery is initiated at increased absorbed dose. Radiation quality and complexity of DSB lesions are also discussed. Currently, there are no technologies available to irradiate specific genetic sites to test the IRI-DICE hypothesis directly. However, supportive evidence may be achieved by developing a computational model that combines radiation transport codes with a genomic DNA model that includes sequence functionality and transcription to simulate expression changes in an irradiated cell population. To the best of our knowledge, IRI-DICE is the first hypothesis that includes sequence functionality of different genetic elements in the radiation response and provides a model for the diversity of radiation responses in the (very) low dose regimen.

Dissecting the Role of Thyrotropin in the DNA Damage Response in Human Thyrocytes after 131I, γ Radiation and H2O2

BACKGROUND

The early molecular events in human thyrocytes after 131I exposure have not yet been unravelled. Therefore, we investigated the role of TSH in the 131I-induced DNA damage response and gene expression in primary cultured human thyrocytes.

METHODS

Following exposure of thyrocytes, in the presence or absence of TSH, to 131I (β radiation), γ radiation (3 Gy), and hydrogen peroxide (H2O2), we assessed DNA damage, proliferation, and cell-cycle status. We conducted RNA sequencing to profile gene expression after each type of exposure and evaluated the influence of TSH on each transcriptomic response.

RESULTS

Overall, the thyrocyte responses following exposure to β or γ radiation and to H2O2 were similar. However, TSH increased 131I-induced DNA damage, an effect partially diminished after iodide uptake inhibition. Specifically, TSH increased the number of DNA double-strand breaks in nonexposed thyrocytes and thus predisposed them to greater damage following 131I exposure. This effect most likely occurred via Gα q cascade and a rise in intracellular reactive oxygen species (ROS) levels. β and γ radiation prolonged thyroid cell-cycle arrest to a similar extent without sign of apoptosis. The gene expression profiles of thyrocytes exposed to β/γ radiation or H2O2 were overlapping. Modulations in genes involved in inflammatory response, apoptosis, and proliferation were observed. TSH increased the number and intensity of modulation of differentially expressed genes after 131I exposure.

CONCLUSIONS

TSH specifically increased 131I-induced DNA damage probably via a rise in ROS levels and produced a more prominent transcriptomic response after exposure to 131I.

Transcriptional effects of 177Lu-octreotate therapy using a priming treatment schedule on GOT1 tumor in nude mice

BACKGROUND

¹⁷⁷Lu-octreotate is used for therapy of somatostatin receptor expressing neuroendocrine tumors with promising results, although complete tumor remission is rarely seen. Previous studies on nude mice bearing the human small intestine neuroendocrine tumor, GOT1, have shown that a priming injection of ¹⁷⁷Lu-octreotate 24 h before the main injection of ¹⁷⁷Lu-octreotate resulted in higher ¹⁷⁷Lu concentration in tumor, resulting in increased absorbed dose, volume reduction, and time to regrowth. To our knowledge, the cellular effects of a priming treatment schedule have not yet been studied. The aim of this study was to identify transcriptional changes contributing to the enhanced therapeutic response of GOT1 tumors in nude mice to ¹⁷⁷Lu-octreotate therapy with priming, compared with non-curative monotherapy.

RESULTS

RNA microarray analysis was performed on tumor samples from GOT1-bearing BALB/c nude mice treated with a 5 MBq priming injection of ¹⁷⁷Lu-octreotate followed by a second injection of 10 MBq of ¹⁷⁷Lu-octreotate after 24 h and killed after 1, 3, 7, and 41 days after the last injection. Administered activity amounts were chosen to be non-curative, in order to facilitate the study of tumor regression and regrowth. Differentially regulated transcripts (RNA samples from treated vs. untreated animals) were identified (change ≥ 1.5-fold; adjusted p value < 0.01) using Nexus Expression 3.0. Analysis of the biological effects of transcriptional regulation was performed using the Gene Ontology database and Ingenuity Pathway Analysis. Transcriptional analysis of the tumors revealed two stages of pathway regulation for the priming schedule (up to 1 week and around 1 month) which differed distinctly from cellular responses observed after monotherapy. Induction of cell cycle arrest and apoptotic pathways (intrinsic and extrinsic) was found at early time points after treatment start, while downregulation of pro-proliferative genes were found at a late time point.

CONCLUSIONS

The present study indicates increased cellular stress responses in the tumors treated with a priming treatment schedule compared with those seen after conventional ¹⁷⁷Lu-octreotate monotherapy, resulting in a more profound initiation of cell cycle arrest followed by apoptosis, as well as effects on PI3K/AKT-signaling and unfolded protein response.

Deconvolution of expression microarray data reveals 131I-induced responses otherwise undetected in thyroid tissue

High-throughput gene expression analysis is increasingly used in radiation research for discovery of damage-related or absorbed dose-dependent biomarkers. In tissue samples, cell type-specific responses can be masked in expression data due to mixed cell populations which can preclude biomarker discovery. In this study, we deconvolved microarray data from thyroid tissue in order to assess possible bias from mixed cell type data. Transcript expression data [GSE66303] from mouse thyroid that received 5.9 Gy from ¹³¹I over 24 h (or 0 Gy from mock treatment) were deconvolved by cell frequency of follicular cells and C-cells using csSAM and R and processed with Nexus Expression. Literature-based signature genes were used to assess the relative impact from ionizing radiation (IR) or thyroid hormones (TH). Regulation of cellular functions was inferred by enriched biological processes according to Gene Ontology terms. We found that deconvolution increased the detection rate of significantly regulated transcripts including the biomarker candidate family of kallikrein transcripts. Detection of IR-associated and TH-responding signature genes was also increased in deconvolved data, while the dominating trend of TH-responding genes was reproduced. Importantly, responses in biological processes for DNA integrity, gene expression integrity, and cellular stress were not detected in convoluted data–which was in disagreement with expected dose-response relationships–but upon deconvolution in follicular cells and C-cells. In conclusion, previously reported trends of ¹³¹I-induced transcriptional responses in thyroid were reproduced with deconvolved data and usually with a higher detection rate. Deconvolution also resolved an issue with detecting damage and stress responses in enriched data, and may reduce false negatives in other contexts as well. These findings indicate that deconvolution can optimize microarray data analysis of heterogeneous sample material for biomarker screening or other clinical applications.

Differential gene and lncRNA expression in the lower thoracic spinal cord following ischemia/reperfusion-induced acute kidney injury in rats

We used high-throughput RNA sequencing to analyze differential gene and lncRNA expression patterns in the lower thoracic spinal cord during ischemia/reperfusion (I/R)-induced acute kidney injury (AKI) in rats. We observed that of 32662 mRNAs, 4296 out were differentially expressed in the T8-12 segments of the spinal cord upon I/R-induced AKI. Among these, 62 were upregulated and 34 were downregulated in response to I/R (FDR < 0.05, |log₂FC| > 1). Further, 52 differentially expressed lncRNAs (35 upregulated and 17 downregulated) were identified among 3849 lncRNA transcripts. The differentially expressed mRNAs were annotated as “biological process,” “cellular components” and “molecular functions” through gene ontology enrichment analysis. KEGG pathway enrichment analysis showed that cell cycle and renin-angiotensin pathways were upregulated in response to I/R, while protein digestion and absorption, hedgehog, neurotrophin, MAPK, and PI3K-Akt signaling were downregulated. The RNA-seq data was validated by qRT-PCR and western blot analyses of select mRNAs and lncRNAs. We observed that Bax, Caspase-3 and phospho-AKT were upregulated and Bcl-2 was downregulated in the spinal cord in response to renal injury. We also found negative correlations between three lncRNAs (TCONS_00042175, TCONS_00058568 and TCONS_00047728) and the degree of renal injury. These findings provide evidence for differential expression of lncRNAs and mRNAs in the lower thoracic spinal cord following I/R-induced AKI in rats and suggest potential clinical applicability.