iPSC-derived organ-on-a-chip models for personalized human genetics and pharmacogenomics studies

Genome-wide association studies (GWAS) have now correlated hundreds of genetic variants with complex genetic diseases and drug efficacy. Functional characterization of these factors remains challenging, particularly because of the lack of human model systems. Molecular and nanotechnological advances, in particular the ability to generate patient-specific PSC lines, differentiate them into diverse cell types, and seed and combine them on microfluidic chips, have led to the establishment of organ-on-a-chip (OoC) platforms that recapitulate organ biology. OoC technology thus provides unique personalized platforms for studying the effects of host genetics and environmental factors on organ physiology. In this review we describe the technology and provide examples of how OoCs may be used for disease modeling and pharmacogenetic research.     

Critical choices for modeling breast cancer in transgenic mouse models

Modeling breast cancer in the mouse has helped to better define the heterogeneity of human breast cancer. In the recent past, it has become evident that some limitations have restricted the potential benefits that can be achieved with this approach. In this review, we highlight some key points that should be taken into account when the mouse is used, with special emphasis on transgenic models.     

HLA expression in cancer: implications for T cell-based immunotherapy

HLA class I expression is altered in a significant fraction of the tumor types reviewed here, reflecting either immune pressure or, simply, the accumulation of pathological changes and alterations. However, in all tumor types analyzed, a majority of the tumors express HLA class I. with a general tendency for the more severe alterations to be found in later-stage and less differentiated tumors. These results are encouraging for the development of specific immunotherapies, especially considering that (1) the relatively low sensitivity of immunohistochemical techniques might underestimate HLA expression in tumors, (2) class I expression can be induced in tumor cells as a result of local inflammation and lymphokine release, and (3) class I-negative cells would be predicted to be sensitive to Iysis by natural killer cells.     

VirtualLeaf: an open-source framework for cell-based modeling of plant tissue growth and development

Plant organs, including leaves and roots, develop by means of a multilevel cross talk between gene regulation, patterned cell division and cell expansion, and tissue mechanics. The multilevel regulatory mechanisms complicate classic molecular genetics or functional genomics approaches to biological development, because these methodologies implicitly assume a direct relation between genes and traits at the level of the whole plant or organ. Instead, understanding gene function requires insight into the roles of gene products in regulatory networks, the conditions of gene expression, etc. This interplay is impossible to understand intuitively. Mathematical and computer modeling allows researchers to design new hypotheses and produce experimentally testable insights. However, the required mathematics and programming experience makes modeling poorly accessible to experimental biologists. Problem-solving environments provide biologically intuitive in silico objects ("cells", "regulation networks") required for setting up a simulation and present those to the user in terms of familiar, biological terminology. Here, we introduce the cell-based computer modeling framework VirtualLeaf for plant tissue morphogenesis. The current version defines a set of biologically intuitive C++ objects, including cells, cell walls, and diffusing and reacting chemicals, that provide useful abstractions for building biological simulations of developmental processes. We present a step-by-step introduction to building models with VirtualLeaf, providing basic example models of leaf venation and meristem development. VirtualLeaf-based models provide a means for plant researchers to analyze the function of developmental genes in the context of the biophysics of growth and patterning. VirtualLeaf is an ongoing open-source software project (http://virtualleaf.googlecode.com) that runs on Windows, Mac, and Linux.     

Outcome modeling techniques for prostate cancer radiotherapy: Data,models, and validation

Prostate cancer is a frequently diagnosed malignancy worldwide and radiation therapy is a first-line approach in treating localized as well as locally advanced cases. The limiting factor in modern radiotherapy regimens is dose to normal structures, an excess of which can lead to aberrant radiation-induced toxicities. Conversely, dose reduction to spare adjacent normal structures risks underdosing target volumes and compromising local control. As a result, efforts aimed at predicting the effects of radiotherapy could invaluably optimize patient treatments by mitigating such toxicities and simultaneously maximizing biochemical control. In this work, we review the types of data, frameworks and techniques used for prostate radiotherapy outcome modeling. Consideration is given to clinical and dose-volume metrics, such as those amassed by the QUANTEC initiative, and also to newer methods for the integration of biological and genetic factors to improve prediction performance. We furthermore highlight trends in machine learning that may help to elucidate the complex pathophysiological mechanisms of tumor control and radiation-induced normal tissue side effects.     

Microwell bioreactor system for cell-based high throughput proliferation and cytotoxicity assays

Cell-based high throughput proliferation and cytotoxicity assays are increasingly used in drug screening and bioprocess development. However, online monitoring of cell proliferation, pH, and dissolved oxygen (DO) has been a challenge in 3D cell-based assays. In this work, a 40-microwell bioreactor (40-MBR) system was developed from a 384-well plate for real-time, noninvasive monitoring of pH, DO, and cell proliferation in 3D microenvironments. Chinese hamster ovary (CHO) and MCF-07 breast cancer cells cultured in 40-MBR confirmed that the 40-MBR was capable of simultaneously monitoring DO and cell proliferation based on culture fluorescence and pH by measuring the absorbance of phenol red. Cytotoxicity studies of sodium butyrate on CHO cells demonstrated that 40-MBR with dynamic background fluorescence correction gave more reliable and highly reproducible growth kinetic data compared to conventional multiwells with static background correction. Furthermore, the dosage effects of two new anticancer drug candidates, 5,7-dihydroxy-2-(4-hydroxyphenyl)-8-[(E)-2-phenylethenyl]-3,4-dihydro-2H-1-benzopyran-4-one (DH-8P-DB) and 5,7-dihydroxy-2-(4-hydroxyphenyl)-6-[(E)-2-phenylethenyl]-3,4-dihydro-2H-1-benzopyran-4-one (DH-6P-DB), on HT-29 colon cancer cells were assessed using the 40-MBR, and the results indicated that DH-6P-DB would be a more potent drug in treating colon cancer than DH-8P-DB. These studies demonstrated that 40-MBR could serve as a high throughput platform for screening potential cancer drugs in early-stage drug discovery.     

Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models

MOTIVATION: The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model. Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time. As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change. RESULTS: We developed a model for genetic sequence and polymorphism data, and used XML Schema to specify the elements and attributes required for this model. We implemented this model as an ontology in a frame-based representation and as a relational model in a database system. We collected genetic data from two pharmacogenetics resequencing studies, and formulated queries useful for analysing these data. We compared the ontology and relational models in terms of query complexity, performance, and difficulty in changing the information model. Our results demonstrate benefits of evolving the schema for storing pharmacogenetics data: ontologies perform well in early design stages as the information model changes rapidly and simplify query formulation, while relational models offer improved query speed once the information model and types of queries needed stabilize.     

Intelligent cell-based therapies for cancer and autoimmune disorders

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Drug resistance, predictive markers and pharmacogenomics in colorectal cancer

Resistance to chemotherapy limits the effectiveness of current cancer therapies, including those used to treat colorectal cancer, which is the second most common cause of cancer death in Europe and the United States. 5-Fluorouracil-based chemotherapy regimens are the standard treatment for colorectal cancer in both the adjuvant and advanced disease settings. Drug resistance is thought to cause treatment failure in over 90% of patients with metastatic cancer, while drug resistant micrometastic tumour cells may also reduce the impact of adjuvant chemotherapy treatment. The identification of panels of biomarkers that not only identify those patients most likely to benefit from chemotherapy treatment, but also which chemotherapies to use, would be a major advance. In this review, we describe molecular mechanisms of drug resistance that may be relevant to colorectal cancer. We also describe the results of predictive biomarker studies in this disease. Finally, we discuss how pharmacogenomics and other high through-put technologies may impact on the clinical management of colorectal cancer in the future.     

High-throughput 3-D cell-based proliferation and cytotoxicity assays for drug screening and bioprocess development

We have designed, built and tested a three-dimensional (3-D) cell culture system on modified microplates for high-throughput, real-time, proliferation and cytotoxicity assays. In this 3-D culture system, cells expressing the enhanced green fluorescent protein (EGFP) were cultured in nonwoven polyethylene terephthalate (PET) fibrous scaffolds. Compared to 2-D cultures in conventional microplates, 3-D cultures gave more than 10-fold higher fluorescence signals with significantly increased signal-to-noise ratio (SNR), thus extending the application of conventional fluorescence microplate readers for online monitoring of culture fluorescence. The 3-D system was successfully used to demonstrate the effects of fetal bovine serum, fibronectin coating of PET fibers, and cytotoxicity of dexamethasone on recombinant murine embryonic stem D3 cells. The dosage effects of 5-fluorouracil and gemcitabine on high-density colon cancer HT-29 cells were also tested. These studies demonstrated that the 3-D culture microplate system with EGFP expressing cells can be used as a high-throughput system in drug discovery and bioprocess development.     

Mixed effects models with bivariate and univariate association parameters for longitudinal bivariate binary response data

When two binary responses are measured for each study subject across time, it may be of interest to model how the bivariate associations and marginal univariate risks involving the two responses change across time. To achieve such a goal, marginal models with bivariate log odds ratio and univariate logit components are extended to include random effects for all components. Specifically, separate normal random effects are specified on the log odds ratio scale for bivariate responses and on the logit scale for univariate responses. Assuming conditional independence given the random effects facilitates the modeling of bivariate associations across time with missing at random incomplete data. We fit the model to a dataset for which such structures are feasible: a longitudinal randomized trial of a cardiovascular educational program where the responses of interest are change in hypertension and hypercholestemia status. The proposed model is compared to a naive bivariate model that assumes independence between time points and univariate mixed effects logit models.     

Cancer pharmacogenomics: role of DNA repair genetic polymorphisms in individualizing cancer therapy

The evolving field of cancer pharmacogenomics uses genetic profiling to predict the response of tumor and normal tissue to therapy. The narrow therapeutic index and heterogeneity of patient responses to chemotherapy and radiotherapy implies that the efficacy of these treatments could, potentially, be significantly enhanced by improving our understanding of the genetic bases for interindividual differences in their effects. The cytotoxicity of both chemotherapy and radiotherapy is to a large extent directly related to their ability to induce DNA damage. The ability of cancer cells to recognize and repair this damage contributes to therapeutic resistance. On the other hand, suboptimal DNA repair in normal tissue may negatively impact on normal tissue tolerance.More than 130 genes have been identified that are associated with human DNA repair, and single nucleotide polymorphisms of several of the DNA repair genes have been described recently. In this article, we present the current evidence implicating variations within DNA repair genes as important predictive and prognostic markers in cancer. We review evidence suggesting DNA repair genetic polymorphisms may significantly influence the clinical response to chemotherapy and radiotherapy, and may influence normal tissue tolerance to cancer treatments.     

Differential effects of polyunsaturated fatty acids on sterol synthesis rates in adult and fetal tissues of the hamster: consequence of altered sterol balance

Cholesterol is necessary for the proper growth and development of the fetus. Consequently, disruptions in cholesterol biosynthesis lead to abnormal fetal development. It has been shown that in cells exposed to polyunsaturated fatty acids (PUFA), the expressions of genes and activities of enzymes involved in cholesterol synthesis are reduced. Similarly, we found that adult male hamsters fed PUFA-enriched diets had an approximately 60% reduction in in vivo hepatic sterol synthesis rates. If fetal tissues respond to PUFA in the same manner as do adult livers, then maternal dietary PUFA could lead to a reduction in fetal sterol synthesis rates and possibly abnormal development. To investigate the impact of maternal dietary fatty acids on fetal sterol synthesis rates, female hamsters were fed diets enriched in various fatty acids before and throughout gestation. In vivo sterol synthesis rates were measured in fetuses at mid- and late gestation. At both gestational stages, dietary PUFA had no effect on fetal sterol synthesis rates. This lack of effect was not a consequence of a lack of PUFA enrichment in fetal fatty acids or the lack of PUFA receptor expression in the fetus. We hypothesize that the fetus may experience a dysregulation of sterol synthesis as the result of the fetus being in a negative sterol balance; the PUFA-induced suppression of sterol synthesis in the adult male hamster liver was ablated by creating a net negative sterol balance across the adult hepatocyte.     

Behavioural models of population growth rates: implications for conservation and prediction

Conservation biologists often wish to predict how vertebrate populations will respond to local or global changes in conditions such as those resulting from sea-level rise, deforestation, exploitation, genetically modified crops, global warming, human disturbance or from conservation activities. Predicting the consequences of such changes almost always requires understanding the population growth rate and the density dependence. Traditional means of directly measuring density dependence are often extremely difficult and have the problem that if the environment changes then it is necessary to remeasure the density dependence. We describe an alternative approach that does not require such long datasets and can be used to predict the density dependence under novel conditions. Game theory can be used to describe behavioural decisions that individuals make in response to interference, prey depletion, territorial behaviour or social dominance, and the resultant fitness consequences. It is then possible to predict how survival or reproductive output changes with population size. From this we can then make predictions about the responses of populations to environmental changes. We will illustrate how this can be applied to a range of species and a range of applied problems.     

Induced pluripotent stem cell-based disease modeling and prospective immune therapy for coronavirus disease 2019

The emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic poses a never before seen challenge to human health and the economy. Considering its clinical impact, with no streamlined therapeutic strategies in sight, it is crucial to understand the infection process of SARS-CoV-2. Our limited knowledge of the mechanisms underlying SARS-CoV-2 infection impedes the development of alternative therapeutics to address the pandemic. This aspect can be addressed by modeling SARS-CoV-2 infection in the human context to facilitate drug screening and discovery. Human induced pluripotent stem cell (iPSC)-derived lung epithelial cells and organoids recapitulating the features and functionality of the alveolar cell types can serve as an in vitro human model and screening platform for SARS-CoV-2. Recent studies suggest an immune system asynchrony leading to compromised function and a decreased proportion of specific immune cell types in coronavirus disease 2019 (COVID-19) patients. Replenishing these specific immune cells may serve as useful treatment modality against SARS-CoV-2 infection. Here the authors review protocols for deriving lung epithelial cells, alveolar organoids and specific immune cell types, such as T lymphocytes and natural killer cells, from iPSCs with the aim to aid investigators in making relevant in vitro models of SARS-CoV-2 along with the possibility derive immune cell types to treat COVID-19.