Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism.

Despite extensive research and aggressive therapies, glioblastoma (GBM) remains a central nervous system malignancy with poor prognosis. The varied histopathology of GBM suggests a landscape of differing microenvironments and clonal expansions, which may influence metabolism, driving tumor progression. Indeed, GBM metabolic plasticity in response to differing nutrient supply within these microenvironments has emerged as a key driver of aggressiveness. Additionally, emergent biophysical and biochemical interactions in the tumor microenvironment (TME) are offering new perspectives on GBM metabolism. Perivascular and hypoxic niches exert crucial roles in tumor maintenance and progression, facilitating metabolic relationships between stromal and tumor cells. Alterations in extracellular matrix and its biophysical characteristics, such as rigidity and topography, regulate GBM metabolism through mechanotransductive mechanisms. This review highlights insights gained from deployment of bioengineering models, including engineered cell culture and mathematical models, to study the microenvironmental regulation of GBM metabolism. Bioengineered approaches building upon histopathology measurements may uncover potential therapeutic strategies that target both TME-dependent mechanotransductive and biomolecular drivers of metabolism to tackle this challenging disease. Longer term, a concerted effort integrating in vitro and in silico models predictive of patient therapy response may offer a powerful advance toward tailoring of treatment to patient-specific GBM characteristics.

Epigenetic Changes Associated with Early Life Experiences: Saliva, A Biospecimen for DNA Methylation Signatures.

BACKGROUND: Adverse Childhood Experiences (ACEs), which include traumatic injury, are associated with poor health outcomes in later life, yet the biological mechanisms mediating this association are unknown. Neurocircuitry, immune system and hormone regulation differ from normal in adults reporting ACEs. These systems could be affected by epigenetic changes, including methylation of cytosine (5mC) in genomic DNA, activated by ACEs. Since 5mC levels influence gene expression and can be long-lasting, altered 5mC status at specific sites or throughout the genome is hypothesized to influence mental and physical outcomes after ACE(s). Human and animal studies support this, with animal models allowing experiments for attributing causality. Here we provide a lengthy introduction and background on 5mC and the impact of early life adversity. OBJECTIVE: Next we address the issue of a mixture of cell types in saliva, the most accessible biospecimen for 5mC analysis. Typical human bio-specimens for 5mC analysis include saliva or buccal swabs, whole blood or types of blood cells, tumors and post-mortem brain. In children saliva is the most accessible biospecimen, but contains a mixture of keratinocytes and white blood cells, as do buccal swabs. Even in saliva from the same individual at different time points, cell composition may differ widely. Similar issues affect analysis in blood, where nucleated cells represent a wide array of white blood cell types. Unless variations in ratios of these cells between each sample are included in the analysis, results can be unreliable. METHODS: Several different biochemical assays are available to test for site-specific methylation levels genome-wide, each producing different information, with high-density arrays being the easiest to use, and bisulfite whole genome sequencing the most comprehensive. We compare results from different assays and use high-throughput computational processing to deconvolve cell composition in saliva samples. RESULTS: Here we present examples demonstrating the critical importance of determining the relative contribution of blood cells versus keratinocytes to the 5mC profile found in saliva. We further describe a strategy to perform a reference-based computational correction for cell composition, and therefore to identify differential methylation patterns due to experience, or for the diagnosis of phenotypes that correlate between traits, such as hormone levels, trauma status and various mental health outcomes. CONCLUSION: Specific sites that respond to adversity with altered methylation levels in either blood cells, keratinocytes or both can be identified by this rigorous approach, which will then be useful as diagnostic biomarkers and therapeutic targets.

Neuropathology of JC virus infection in progressive multifocal leukoencephalopathy in remission.

AIM: To investigate the neuropathology of the brain in a rare case of remission following diagnosis of progressive multifocal leukoencephalopathy (PML). METHODS: Consent from the family for an autopsy was obtained, clinical records and radiograms were retrieved. A complete autopsy was performed, with brain examination after fixation and coronal sectioning at 1 cm intervals. Fourteen regions were collected for paraffin embedding and staining for microscopic analysis. Histologic sections were stained with Luxol blue, hematoxylin/eosin, and immunostained for myelin basic protein, neurofilament, SV40 T antigen and p53. The biopsy material was also retrieved and sections were stained with hematoxylin/eosin and immunostained for SV40 and p53. Sections were examined by American Board of Pathology certified pathologists and images captured digitally. RESULTS: Review of the clinical records was notable for a history of ulcerative colitis resulting in total colectomy in 1977 and a liver transplant in 1998 followed by immune-suppressive therapy. Neurological symptoms presented immediately, therefore a biopsy was obtained which was diagnosed as PML. Immunotherapy was adjusted and clinical improvement was noted. No subsequent progression was reported. Review of the biopsy demonstrated atypical astrocytes and enlarged hyperchromatic oligodendroglial cells consistent with JC virus infection. Strong SV40 and p53 staining was found in glial cells and regions of dense macrophage infiltration were present. On gross examination of the post-mortem brain, a lesion in the same site as the original biopsy in the cerebellum was identified but no other lesions in the brain were found. Microscopic analysis of this cerebellar lesion revealed a loss of myelin and axons, and evidence of axonal damage. This single burned-out lesion was equivocally positive for SV40 antigen with little p53 staining. Examination of thirteen other brain regions found no other occult sites. CONCLUSION: Our study reveals residual damage, rare macrophages or other inflammation and minimal evidence of persistent virus. This case demonstrates the possibility of complete remission of PML.

Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements.

Physical properties of the microenvironment influence penetration of drugs into tumors. Here, we develop a mathematical model to predict the outcome of chemotherapy based on the physical laws of diffusion. The most important parameters in the model are the volume fraction occupied by tumor blood vessels and their average diameter. Drug delivery to cells, and kill thereof, are mediated by these microenvironmental properties and affected by the diffusion penetration distance after extravasation. To calculate parameter values we fit the model to histopathology measurements of the fraction of tumor killed after chemotherapy in human patients with colorectal cancer metastatic to liver (coefficient of determination R(2) = 0.94). To validate the model in a different tumor type, we input patient-specific model parameter values from glioblastoma; the model successfully predicts extent of tumor kill after chemotherapy (R(2) = 0.7-0.91). Toward prospective clinical translation, we calculate blood volume fraction parameter values from in vivo contrast-enhanced computed tomography imaging from a separate cohort of patients with colorectal cancer metastatic to liver, and demonstrate accurate model predictions of individual patient responses (average relative error = 15%). Here, patient-specific data from either in vivo imaging or histopathology drives output of the model's formulas. Values obtained from standard clinical diagnostic measurements for each individual are entered into the model, producing accurate predictions of tumor kill after chemotherapy. Clinical translation will enable the rational design of individualized treatment strategies such as amount, frequency, and delivery platform of drug and the need for ancillary non-drug-based treatment.

Quantitative measurements and modeling of cargo-motor interactions during fast transport in the living axon.

The kinesins have long been known to drive microtubule-based transport of sub-cellular components, yet the mechanisms of their attachment to cargo remain a mystery. Several different cargo-receptors have been proposed based on their in vitro binding affinities to kinesin-1. Only two of these-phosphatidyl inositol, a negatively charged lipid, and the carboxyl terminus of the amyloid precursor protein (APP-C), a trans-membrane protein-have been reported to mediate motility in living systems. A major question is how these many different cargo, receptors and motors interact to produce the complex choreography of vesicular transport within living cells. Here we describe an experimental assay that identifies cargo-motor receptors by their ability to recruit active motors and drive transport of exogenous cargo towards the synapse in living axons. Cargo is engineered by derivatizing the surface of polystyrene fluorescent nanospheres (100 nm diameter) with charged residues or with synthetic peptides derived from candidate motor receptor proteins, all designed to display a terminal COOH group. After injection into the squid giant axon, particle movements are imaged by laser-scanning confocal time-lapse microscopy. In this report we compare the motility of negatively charged beads with APP-C beads in the presence of glycine-conjugated non-motile beads using new strategies to measure bead movements. The ensuing quantitative analysis of time-lapse digital sequences reveals detailed information about bead movements: instantaneous and maximum velocities, run lengths, pause frequencies and pause durations. These measurements provide parameters for a mathematical model that predicts the spatiotemporal evolution of distribution of the two different types of bead cargo in the axon. The results reveal that negatively charged beads differ from APP-C beads in velocity and dispersion, and predict that at long time points APP-C will achieve greater progress towards the presynaptic terminal. The significance of this data and accompanying model pertains to the role transport plays in neuronal function, connectivity, and survival, and has implications in the pathogenesis of neurological disorders, such as Alzheimer's, Huntington and Parkinson's diseases.

Integrative physical oncology.

Cancer is arguably the ultimate complex biological system. Solid tumors are microstructured soft matter that evolves as a consequence of spatio-temporal events at the intracellular (e.g., signaling pathways, macromolecular trafficking), intercellular (e.g., cell-cell adhesion/communication), and tissue (e.g., cell-extracellular matrix interactions, mechanical forces) scales. To gain insight, tumor and developmental biologists have gathered a wealth of molecular, cellular, and genetic data, including immunohistochemical measurements of cell type-specific division and death rates, lineage tracing, and gain-of-function/loss-of-function mutational analyses. These data are empirically extrapolated to a diagnosis/prognosis of tissue-scale behavior, e.g., for clinical decision. Integrative physical oncology (IPO) is the science that develops physically consistent mathematical approaches to address the significant challenge of bridging the nano (nm)-micro (µm) to macro (mm, cm) scales with respect to tumor development and progression. In the current literature, such approaches are referred to as multiscale modeling. In the present article, we attempt to assess recent modeling approaches on each separate scale and critically evaluate the current 'hybrid-multiscale' models used to investigate tumor growth in the context of brain and breast cancers. Finally, we provide our perspective on the further development and the impact of IPO.

A novel, patient-specific mathematical pathology approach for assessment of surgical volume: application to ductal carcinoma in situ of the breast.

We introduce a novel "mathematical pathology" approach, founded on a biophysical model, to identify robust patient-specific predictors of tumor growth useful in clinical practice to improve the accuracy of diagnosis/prognosis and intervention. In accordance with biological observations, our model simulates the diffusion-limited in situ tumors with a relatively short phase of fast initial growth, followed by a prolonged slow-growth phase where tumor size is constrained primarily by the relative weight of cell mitosis and death. The former phase may only last for a few months, so that at the time of diagnosis, we may assume that most tumors will have entered the phase where their size is changing slowly. Based on this prediction, we hypothesize that the volume of breast with ducts affected by in situ tumors at the time of diagnosis will be closely approximated by a model-derived mathematical function based on the ratio of tumor cell proliferation-to-apoptosis indices and on the extent of diffusion of cell nutrients (diffusion penetration length), which can be measured from immunohistochemical and morphometric analysis of patient histopathology specimens without the need for multiple-time measurements. We tested this idea in a retrospective study of 17 patients by staining breast tumor specimens containing ductal carcinoma in situ for mitosis with Ki-67 and for apoptosis with cleaved caspase-3 and counting cells positive for each marker. We also determined diffusion penetration by measuring the thickness of viable rims of tumor cells within ducts. Using the ensuing ratios, we applied the model to determine a predicted surgical volume or tumor size. We then corroborated our hypothesis by comparing the predicted size of each tumor based on our model with the actual size of the pathological specimen after tumor excision (R2 = 0.74-0.88). In addition, for the 17 cases studied, both histological grade and mammography were not found to correlate with tumor size (R2 = 0.08-0.47). We conclude that our mathematical pathology approach yields a high degree of accuracy in predicting the size of tumors based on the mitotic/apoptotic index and on diffusion penetration. By obtaining these ratios at the time of initial biopsy, pathologists can employ our model to predict the size of the tumor and thereby inform surgeons how much tissue to remove (surgical volume). We discuss how results from the model have implications concerning the current debate on recommendations for screening mammography, while the model itself may contribute to better planning of breast conservation surgery.

Physical oncology: a bench-to-bedside quantitative and predictive approach.

Cancer models relating basic science to clinical care in oncology may fail to address the nuances of tumor behavior and therapy, as in the case, discussed herein, of the complex multiscale dynamics leading to the often-observed enhanced invasiveness, paradoxically induced by the very antiangiogenic therapy designed to destroy the tumor. Studies would benefit from approaches that quantitatively link the multiple physical and temporal scales from molecule to tissue in order to offer outcome predictions for individual patients. Physical oncology is an approach that applies fundamental principles from the physical and biological sciences to explain certain cancer behaviors as observable characteristics arising from the underlying physical and biochemical events. For example, the transport of oxygen molecules through tissue affects phenotypic characteristics such as cell proliferation, apoptosis, and adhesion, which in turn underlie the patient-scale tumor growth and invasiveness. Our review of physical oncology illustrates how tumor behavior and treatment response may be a quantifiable function of marginally stable molecular and/or cellular conditions modulated by inhomogeneity. By incorporating patient-specific genomic, proteomic, metabolomic, and cellular data into multiscale physical models, physical oncology could complement current clinical practice through enhanced understanding of cancer behavior, thus potentially improving patient survival.

Chronic active herpes simplex type 2 encephalitis in an asymptomatic immunocompetent child.

A unique form of chronic, active, granulomatous herpes simplex type 2 encephalitis is described in an asymptomatic, immunocompetent 8-year-old girl who acquired the virus as a neonate. The extensive, bilateral cerebral parenchymal involvement was discovered incidentally. Diagnosis was confirmed by a combination of serial neuroimaging, brain biopsy, and quantitative polymerase chain reaction targeted to DNA sequences in the glycoprotein G gene, allowing differentiation between herpes simplex virus types 1 and 2. The clinical course over a 5-year period, treatment with intermittent intravenous steroids, and daily valacyclovir, diagnostic imaging, and laboratory studies are reviewed in detail. This form of herpes simplex virus type 2 encephalitis hasn't been described previously and is significant because of its prolonged indolent course, absence of neurological findings or suggestive history, and benign behavior in this child, who is now 14 years old. The authors believe this entity can be unsuspected and underdiagnosed in the general pediatric population, especially in those with a prior maternal history of herpes simplex virus type 2 infection.

Germination behavior, biochemical features and sequence analysis of the RACK1/arcA homolog from Phaseolus vulgaris.

Partial peptide sequence of a 36 kDa protein from common bean embryo axes showed 100% identity with a reported beta-subunit of a heterotrimeric G protein from soybean. Analysis of the full sequence showed 96.6% identity with the reported soybean G(beta)-subunit, 86% with RACK1B and C from Arabidopsis and 66% with human and mouse RACK1, at the amino acid level. In addition, it showed 85.5, 85 and 83% identities with arcA from Solanum lycopersicum, Arabidopsis (RACK1A) and Nicotiana tabacum, respectively. The amino acid sequence displayed seven WD40 domains and two sites for activated protein kinase C binding. The protein showed a constant expression level but the mRNA had a maximum at 32 h post-imbibition. Western immunoblotting showed the protein in vegetative plant tissues, and in both microsomal and soluble fractions from embryo axes. Synthetic auxin treatment during germination delayed the peak of RACK1 mRNA expression to 48 h but did not affect the protein expression level while the polar auxin transport inhibitor, naphtylphtalamic acid had no effect on either mRNA or protein expression levels. Southern blot and genomic DNA amplification revealed a small gene family with at least one member without introns in the genome. Thus, the RACK1/arcA homolog from common bean has the following features: (1) it is highly conserved; (2) it is both soluble and insoluble within the embryo axis; (3) it is encoded by a small gene family; (4) its mRNA has a peak of expression at the time point of germination stop and (5) its expression is only slightly affected by auxin but unaffected by an auxin transport blocker.

Multiparameter computational modeling of tumor invasion.

Clinical outcome prognostication in oncology is a guiding principle in therapeutic choice. A wealth of qualitative empirical evidence links disease progression with tumor morphology, histopathology, invasion, and associated molecular phenomena. However, the quantitative contribution of each of the known parameters in this progression remains elusive. Mathematical modeling can provide the capability to quantify the connection between variables governing growth, prognosis, and treatment outcome. By quantifying the link between the tumor boundary morphology and the invasive phenotype, this work provides a quantitative tool for the study of tumor progression and diagnostic/prognostic applications. This establishes a framework for monitoring system perturbation towards development of therapeutic strategies and correlation to clinical outcome for prognosis.

One at a time, live tracking of NGF axonal transport using quantum dots.

Retrograde axonal transport of nerve growth factor (NGF) signals is critical for the survival, differentiation, and maintenance of peripheral sympathetic and sensory neurons and basal forebrain cholinergic neurons. However, the mechanisms by which the NGF signal is propagated from the axon terminal to the cell body are yet to be fully elucidated. To gain insight into the mechanisms, we used quantum dot-labeled NGF (QD-NGF) to track the movement of NGF in real time in compartmentalized culture of rat dorsal root ganglion (DRG) neurons. Our studies showed that active transport of NGF within the axons was characterized by rapid, unidirectional movements interrupted by frequent pauses. Almost all movements were retrograde, but short-distance anterograde movements were occasionally observed. Surprisingly, quantitative analysis at the single molecule level demonstrated that the majority of NGF-containing endosomes contained only a single NGF dimer. Electron microscopic analysis of axonal vesicles carrying QD-NGF confirmed this finding. The majority of QD-NGF was found to localize in vesicles 50-150 nm in diameter with a single lumen and no visible intralumenal membranous components. Our findings point to the possibility that a single NGF dimer is sufficient to sustain signaling during retrograde axonal transport to the cell body.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Alteration of marrow cell gene expression, protein production, and engraftment into lung by lung-derived microvesicles: a novel mechanism for phenotype modulation.

Numerous animal studies have demonstrated that adult marrow-derived cells can contribute to the cellular component of the lung. Lung injury is a major variable in this process; however, the mechanism remains unknown. We hypothesize that injured lung is capable of inducing epigenetic modifications of marrow cells, influencing them to assume phenotypic characteristics of lung cells. We report that under certain conditions, radiation-injured lung induced expression of pulmonary epithelial cell-specific genes and prosurfactant B protein in cocultured whole bone marrow cells separated by a cell-impermeable membrane. Lung-conditioned media had a similar effect on cocultured whole bone marrow cells and was found to contain pulmonary epithelial cell-specific RNA-filled microvesicles that entered whole bone marrow cells in culture. Also, whole bone marrow cells cocultured with lung had a greater propensity to produce type II pneumocytes after transplantation into irradiated mice. These findings demonstrate alterations of marrow cell phenotype by lung-derived microvesicles and suggest a novel mechanism for marrow cell-directed repair of injured tissue.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

A peptide zipcode sufficient for anterograde transport within amyloid precursor protein.

Fast anterograde transport of membrane-bound organelles delivers molecules synthesized in the neuronal cell body outward to distant synapses. Identification of the molecular "zipcodes" on organelles that mediate attachment and activation of microtubule-based motors for this directed transport is a major area of inquiry. Here we identify a short peptide sequence (15 aa) from the cytoplasmic C terminus of amyloid precursor protein (APP-C) sufficient to mediate the anterograde transport of peptide-conjugated beads in the squid giant axon. APP-C beads travel at fast axonal transport rates (0.53 mum/s average velocity, 0.9 mum/s maximal velocity) whereas beads coupled to other peptides coinjected into the same axon remain stationary at the injection site. This transport appears physiologic, because it mimics behavior of endogenous squid organelles and of beads conjugated to C99, a polypeptide containing the full-length cytoplasmic domain of amyloid precursor protein (APP). Beads conjugated to APP lacking the APP-C domain are not transported. Coinjection of APP-C peptide reduces C99 bead motility by 75% and abolishes APP-C bead motility, suggesting that the soluble peptide competes with protein-conjugated beads for axoplasmic motor(s). The APP-C domain is conserved (13/15 aa) from squid to human, and peptides from either squid or human APP behave similarly. Thus, we have identified a conserved peptide zipcode sufficient to direct anterograde transport of exogenous cargo and suggest that one of APP's roles may be to recruit and activate axonal machinery for endogenous cargo transport.