Multiparametric magnetic resonance imaging discerns glioblastoma immune microenvironmental heterogeneity.

RATIONALE AND OBJECTIVE: Poor clinical outcomes for patients with glioblastoma are in part due to dysfunction of the tumor-immune microenvironment. An imaging approach able to characterize immune microenvironmental signatures could provide a framework for biologically based patient stratification and response assessment. We hypothesized spatially distinct gene expression networks can be distinguished by multiparametric Magnetic Resonance Imaging (MRI) phenotypes. MATERIALS AND METHODS: Patients with newly diagnosed glioblastoma underwent image-guided tissue sampling allowing for co-registration of MRI metrics with gene expression profiles. MRI phenotypes based on gadolinium contrast enhancing lesion (CEL) and non-enhancing lesion (NCEL) regions were subdivided based on imaging parameters (relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC)). Gene set enrichment analysis and immune cell type abundance was estimated using CIBERSORT methodology. Significance thresholds were set at a p-value cutoff 0.005 and an FDR q-value cutoff of 0.1. RESULTS: Thirteen patients (eight men, five women, mean age 58 ± 11 years) provided 30 tissue samples (16 CEL and 14 NCEL). Six non-neoplastic gliosis samples differentiated astrocyte repair from tumor associated gene expression. MRI phenotypes displayed extensive transcriptional variance reflecting biological networks, including multiple immune pathways. CEL regions demonstrated higher immunologic signature expression than NCEL, while NCEL regions demonstrated stronger immune signature expression levels than gliotic non-tumor brain. Incorporation of rCBV and ADC metrics identified sample clusters with differing immune microenvironmental signatures. CONCLUSION: Taken together, our study demonstrates that MRI phenotypes provide an approach for non-invasively characterizing tumoral and immune microenvironmental glioblastoma gene expression networks.

Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

BACKGROUND: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification. METHODS: We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model. RESULTS: We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature. CONCLUSIONS: Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma.

Annot: a Django-based sample, reagent, and experiment metadata tracking system.

BACKGROUND: In biological experiments, comprehensive experimental metadata tracking - which comprises experiment, reagent, and protocol annotation with controlled vocabulary from established ontologies - remains a challenge, especially when the experiment involves multiple laboratory scientists who execute different steps of the protocol. Here we describe Annot, a novel web application designed to provide a flexible solution for this task. RESULTS: Annot enforces the use of controlled vocabulary for sample and reagent annotation while enabling robust investigation, study, and protocol tracking. The cornerstone of Annot's implementation is a json syntax-compatible file format, which can capture detailed metadata for all aspects of complex biological experiments. Data stored in this json file format can easily be ported into spreadsheet or data frame files that can be loaded into R ( https://www.r-project.org/ ) or Pandas, Python's data analysis library ( https://pandas.pydata.org/ ). Annot is implemented in Python3 and utilizes the Django web framework, Postgresql, Nginx, and Debian. It is deployed via Docker and supports all major browsers. CONCLUSIONS: Annot offers a robust solution to annotate samples, reagents, and experimental protocols for established assays where multiple laboratory scientists are involved. Further, it provides a framework to store and retrieve metadata for data analysis and integration, and therefore ensures that data generated in different experiments can be integrated and jointly analyzed. This type of solution to metadata tracking can enhance the utility of large-scale datasets, which we demonstrate here with a large-scale microenvironment microarray study.

zRICH, a protein induced during optic nerve regeneration in zebrafish, promotes neuritogenesis and interacts with tubulin.

Mammals do not regenerate axons in their central nervous system (CNS) spontaneously. This phenomenon is the cause of numerous medical conditions after damage to nerve fibers in the CNS of humans. The study of the mechanisms of nerve regeneration in other vertebrate animals able to spontaneously regenerate axons in their CNS is essential for understanding nerve regeneration from a scientific point of view, and for developing therapeutic approaches to enhance nerve regeneration in the CNS of humans. RICH proteins are a novel group of proteins implicated in nerve regeneration in the CNS of teleost fish, yet their mechanisms of action are not well understood. A number of mutant versions of the zebrafish RICH (zRICH) protein were generated and characterized at biochemical and cellular levels in our laboratory. With the aim of understanding the effects of RICH proteins in neuronal axon outgrowth, stable transfectants derived from the neuronal model PC12 cell line expressing zRICH Wild-Type or mutant versions of zRICH were studied. Results from differentiation experiments suggest that RICH proteins enhance neuronal plasticity by facilitating neurite branching. Biochemical co-purification results have demonstrated that zRICH binds to the cytoskeletal protein tubulin. The central domain of the protein is sufficient for tubulin binding, but a mutant version of the protein lacking the terminal domains, which cannot bind to the plasma membrane, was not able to enhance neurite branching. RICH proteins may facilitate axon regeneration by regulating the axonal cytoskeleton and facilitating the formation of new neurite branches.

Identification of a motif in BMRP required for interaction with Bcl-2 by site-directed mutagenesis studies.

Bcl-2 is an anti-apoptotic protein that inhibits apoptosis elicited by multiple stimuli in a large variety of cell types. BMRP (also known as MRPL41) was identified as a Bcl-2 binding protein and shown to promote apoptosis. Previous studies indicated that the amino-terminal two-thirds of BMRP contain the domain(s) required for its interaction with Bcl-2, and that this region of the protein is responsible for the majority of the apoptosis-inducing activity of BMRP. We have performed site-directed mutagenesis analyses to further characterize the BMRP/Bcl-2 interaction and the pro-apoptotic activity of BMRP. The results obtained indicate that the 13-17 amino acid region of BMRP is necessary for its binding to Bcl-2. Further mutagenesis of this motif shows that amino acid residue aspartic acid (D) 16 of BMRP is essential for the BMRP/Bcl-2 interaction. Functional analyses conducted in mammalian cells with BMRP site-directed mutants BMRP(13Ala17) and BMRP(D16A) indicate that these mutants induce apoptosis through a caspase-mediated pathway, and that they kill cells slightly more potently than wild-type BMRP. Bcl-2 is still able to counteract BMRP(D16A)-induced cell death significantly, but not as completely as when tested against wild-type BMRP. These results suggest that the apoptosis-inducing ability of wild-type BMRP is blocked by Bcl-2 through several mechanisms.

Deletion mutational analysis of BMRP, a pro-apoptotic protein that binds to Bcl-2.

Bcl-2 is an anti-apoptotic member of the Bcl-2 family of proteins that protects cells from apoptosis induced by a large variety of stimuli. The protein BMRP (MRPL41) was identified as a Bcl-2 binding partner and shown to have pro-apoptotic activity. We have performed deletion mutational analyses to identify the domain(s) of Bcl-2 and BMRP that are involved in the Bcl-2/BMRP interaction, and the region(s) of BMRP that mediate its pro-apoptotic activity. The results of these studies indicate that both the BH4 domain of Bcl-2 and its central region encompassing its BH1, BH2, and BH3 domains are required for its interaction with BMRP. The loop region and the transmembrane domain of Bcl-2 were found to be dispensable for this interaction. The Bcl-2 deletion mutants that do not interact with BMRP were previously shown to be functionally inactive. Deletion analyses of the BMRP protein delimited the region of BMRP needed for its interaction with Bcl-2 to the amino-terminal two-thirds of the protein (amino acid residues 1-92). Further deletions at either end of the BMRP(1-92) truncated protein resulted in lack of binding to Bcl-2. Functional studies performed with BMRP deletion mutants suggest that the cell death-inducing domains of the protein reside mainly within its amino-terminal two-thirds. The region of BMRP required for the interaction with Bcl-2 is very relevant for the cell death-inducing activity of the protein, suggesting that one possible mechanism by which BMRP induces cell death is by binding to and blocking the anti-apoptotic activity of Bcl-2.