Evaluation of Radiosensitization and Cytokine Modulation by Differentially PEGylated Gold Nanoparticles in Glioblastoma Cells.

Glioblastoma (GBM) is known as the most aggressive type of malignant brain tumour, with an extremely poor prognosis of approximately 12 months following standard-of-care treatment with surgical resection, radiotherapy (RT), and temozolomide treatment. Novel RT-drug combinations are urgently needed to improve patient outcomes. Gold nanoparticles (GNPs) have demonstrated significant preclinical potential as radiosensitizers due to their unique physicochemical properties and their ability to pass the blood-brain barrier. The modification of GNP surface coatings with poly(ethylene) glycol (PEG) confers several therapeutic advantages including immune avoidance and improved cellular localisation. This study aimed to characterise both the radiosensitizing and immunomodulatory properties of differentially PEGylated GNPs in GBM cells in vitro. Two GBM cell lines were used, U-87 MG and U-251 MG. The radiobiological response was evaluated by clonogenic assay, immunofluorescent staining of 53BP1 foci, and flow cytometry. Changes in the cytokine expression levels were quantified by cytokine arrays. PEGylation improved the radiobiological efficacy, with double-strand break induction being identified as an underlying mechanism. PEGylated GNPs also caused the greatest boost in RT immunogenicity, with radiosensitization correlating with a greater upregulation of inflammatory cytokines. These findings demonstrate the radiosensitizing and immunostimulatory potential of ID11 and ID12 as candidates for RT-drug combination in future GBM preclinical investigations.

Using AI-Based Evolutionary Algorithms to Elucidate Adult Brain Tumor (Glioma) Etiology Associated with IDH1 for Therapeutic Target Identification.

Adult brain tumors (glioma) represent a cancer of unmet need where standard-of-care is non-curative; thus, new therapies are urgently needed. It is unclear whether isocitrate dehydrogenases (IDH1/2) when not mutated have any role in gliomagenesis or tumor growth. Nevertheless, IDH1 is overexpressed in glioblastoma (GBM), which could impact upon cellular metabolism and epigenetic reprogramming. This study characterizes IDH1 expression and associated genes and pathways. A novel biomarker discovery pipeline using artificial intelligence (evolutionary algorithms) was employed to analyze IDH-wildtype adult gliomas from the TCGA LGG-GBM cohort. Ninety genes whose expression correlated with IDH1 expression were identified from: (1) All gliomas, (2) primary GBM, and (3) recurrent GBM tumors. Genes were overrepresented in ubiquitin-mediated proteolysis, focal adhesion, mTOR signaling, and pyruvate metabolism pathways. Other non-enriched pathways included O-glycan biosynthesis, notch signaling, and signaling regulating stem cell pluripotency (PCGF3). Potential prognostic (TSPYL2, JAKMIP1, CIT, TMTC1) and two diagnostic (MINK1, PLEKHM3) biomarkers were downregulated in GBM. Their gene expression and methylation were negatively and positively correlated with IDH1 expression, respectively. Two diagnostic biomarkers (BZW1, RCF2) showed the opposite trend. Prognostic genes were not impacted by high frequencies of molecular alterations and only one (TMTC1) could be validated in another cohort. Genes with mechanistic links to IDH1 were involved in brain neuronal development, cell proliferation, cytokinesis, and O-mannosylation as well as tumor suppression and anaplerosis. Results highlight metabolic vulnerabilities and therapeutic targets for use in future clinical trials.

Novel deep learning-based solution for identification of prognostic subgroups in liver cancer (Hepatocellular carcinoma).

BACKGROUND: Liver cancer (Hepatocellular carcinoma; HCC) prevalence is increasing and with poor clinical outcome expected it means greater understanding of HCC aetiology is urgently required. This study explored a deep learning solution to detect biologically important features that distinguish prognostic subgroups. A novel architecture of an Artificial Neural Network (ANN) trained with a customised objective function (LRSC) was developed. The ANN should discover new data representations, to detect patient subgroups that are biologically homogenous (clustering loss) and similar in survival (survival loss) while removing noise from the data (reconstruction loss). The model was applied to TCGA-HCC multi-omics data and benchmarked against baseline models that only use a reconstruction objective function (BCE, MSE) for learning. With the baseline models, the new features are then filtered based on survival information and used for clustering patients. Different variants of the customised objective function, incorporating only reconstruction and clustering losses (LRC); and reconstruction and survival losses (LRS) were also evaluated. Robust features consistently detected were compared between models and validated in TCGA and LIRI-JP HCC cohorts. RESULTS: The combined loss (LRSC) discovered highly significant prognostic subgroups (P-value = 1.55E-77) with more accurate sample assignment (Silhouette scores: 0.59-0.7) compared to baseline models (0.18-0.3). All LRSC bottleneck features (N = 100) were significant for survival, compared to only 11-21 for baseline models. Prognostic subgroups were not explained by disease grade or risk factors. Instead LRSC identified robust features including 377 mRNAs, many of which were novel (61.27%) compared to those identified by the other losses. Some 75 mRNAs were prognostic in TCGA, while 29 were prognostic in LIRI-JP also. LRSC also identified 15 robust miRNAs including two novel (hsa-let-7g; hsa-mir-550a-1) and 328 methylation features with 71% being prognostic. Gene-enrichment and Functional Annotation Analysis identified seven pathways differentiating prognostic clusters. CONCLUSIONS: Combining cluster and survival metrics with the reconstruction objective function facilitated superior prognostic subgroup identification. The hybrid model identified more homogeneous clusters that consequently were more biologically meaningful. The novel and prognostic robust features extracted provide additional information to improve our understanding of a complex disease to help reveal its aetiology. Moreover, the gene features identified may have clinical applications as therapeutic targets.

The ancient Britons: groundwater fauna survived extreme climate change over tens of millions of years across NW Europe.

Global climate changes during the Cenozoic (65.5-0 Ma) caused major biological range shifts and extinctions. In northern Europe, for example, a pattern of few endemics and the dominance of wide-ranging species is thought to have been determined by the Pleistocene (2.59-0.01 Ma) glaciations. This study, in contrast, reveals an ancient subsurface fauna endemic to Britain and Ireland. Using a Bayesian phylogenetic approach, we found that two species of stygobitic invertebrates (genus Niphargus) have not only survived the entire Pleistocene in refugia but have persisted for at least 19.5 million years. Other Niphargus species form distinct cryptic taxa that diverged from their nearest continental relative between 5.6 and 1.0 Ma. The study also reveals an unusual biogeographical pattern in the Niphargus genus. It originated in north-west Europe approximately 87 Ma and underwent a gradual range expansion. Phylogenetic diversity and species age are highest in north-west Europe, suggesting resilience to extreme climate change and strongly contrasting the patterns seen in surface fauna. However, species diversity is highest in south-east Europe, indicating that once the genus spread to these areas (approximately 25 Ma), geomorphological and climatic conditions enabled much higher diversification. Our study highlights that groundwater ecosystems provide an important contribution to biodiversity and offers insight into the interactions between biological and climatic processes.

Transcriptional Profiling of a Patient-Matched Cohort of Glioblastoma (IDH-Wildtype) for Therapeutic Target and Repurposing Drug Identification.

Glioblastoma (GBM) is the most prevalent and aggressive adult brain tumor. Despite multi-modal therapies, GBM recurs, and patients have poor survival (~14 months). Resistance to therapy may originate from a subpopulation of tumor cells identified as glioma-stem cells (GSC), and new treatments are urgently needed to target these. The biology underpinning GBM recurrence was investigated using whole transcriptome profiling of patient-matched initial and recurrent GBM (recGBM). Differential expression analysis identified 147 significant probes. In total, 24 genes were validated using expression data from four public cohorts and the literature. Functional analyses revealed that transcriptional changes to recGBM were dominated by angiogenesis and immune-related processes. The role of MHC class II proteins in antigen presentation and the differentiation, proliferation, and infiltration of immune cells was enriched. These results suggest recGBM would benefit from immunotherapies. The altered gene signature was further analyzed in a connectivity mapping analysis with QUADrATiC software to identify FDA-approved repurposing drugs. Top-ranking target compounds that may be effective against GSC and GBM recurrence were rosiglitazone, nizatidine, pantoprazole, and tolmetin. Our translational bioinformatics pipeline provides an approach to identify target compounds for repurposing that may add clinical benefit in addition to standard therapies against resistant cancers such as GBM.

A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer.

Introduction: Cancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific tests are urgently required. Methods: The molecular profile of HGSC during disease progression was investigated in a unique patient cohort. A bespoke data browser was developed to analyse gene expression and DNA methylation datasets for biomarker discovery. The Ovarian Cancer Data Browser (OCDB) is built in C# with a.NET framework using an integrated development environment of Microsoft Visual Studio and fast access files (.faf). The graphical user interface is easy to navigate between four analytical modes (gene expression; methylation; combined gene expression and methylation data; methylation clusters), with a rapid query response time. A user should first define a disease progression trend for prioritising results. Single or multiomics data are then mined to identify probes, genes and methylation clusters that exhibit the desired trend. A unique scoring system based on the percentage change in expression/methylation between disease stages is used. Results are filtered and ranked using weighting and penalties. Results: The OCDB's utility for biomarker discovery is demonstrated with the identified target OSR2. Trends in OSR2 repression and hypermethylation with HGSC disease progression were confirmed in the browser samples and an independent cohort using bioassays. The OSR2 methylation biomarker could discriminate HGSC with high specificity (95%) and sensitivity (93.18%). Conclusions: The OCDB has been refined and validated to be an integral part of a unique biomarker discovery pipeline. It may also be used independently to aid identification of novel targets. It carries the potential to identify further biomarker assays that can reduce type I and II errors within clinical diagnostics.

Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression.

PURPOSE: Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNAseq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq-based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing. METHODS: In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness. RESULTS: Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss. CONCLUSION: Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.

Determining the migration duration of rice leaf folder (Cnaphalocrocis medinalis (Guenée)) moths using a trajectory analytical approach.

Many moths finish their long distance migration after consecutive nights, but little is known about migration duration and distance. This information is key to predicting migration pathways and understanding their evolution. Tethered flight experiments have shown that ovarian development of rice leaf folder (Cnaphalocrocis medinalis [Guenée]) moths was accelerated and synchronized by flight in the first three nights, whereby most females were then matured for mating and reproduction. Thus, it was supposed that this moth might fly three nights to complete its migration. To test this hypothesis, 9 year's field data for C. medinalis was collected from Nanning, Guangxi Autonomous Region in China. Forward trajectories indicated that most moths arrived at suitable breeding areas after three nights' flight. Thus, for C. medinalis this migration duration and distance was a reasonable adaptation to the geographic distribution of suitable habitat. The development of female moth ovaries after three consecutive night flights appears to be a well-balanced survival strategy for this species to strike between migration and reproduction benefits. Hence, an optimum solution of migration-reproduction trade-offs in energy allocation evolved in response to the natural selection on migration route and physiological traits.