Nanotoxicology: a perspective and discussion of whether or not in vitro testing is a valid alternative

Despite the many proposed advantages related to nanotechnology, there are increasing concerns as to the potential adverse human health and environmental effects that the production of, and subsequent exposure to nanoparticles (NPs) might pose. In regard to human health, these concerns are founded upon the plethora of knowledge gained from research relating to the effects observed following exposure to environmental air pollution. It is known that increased exposure to environmental air pollution can cause reduced respiratory health, as well as exacerbate pre-existing conditions such as cardiovascular disease and chronic obstructive pulmonary disease. Such disease states have also been associated with exposure to the NP component contained within environmental air pollution, raising concerns as to the effects of NP exposure. It is not only exposure to accidentally produced NPs however, which should be approached with caution. Over the past decades, NPs have been specifically engineered for a wide range of consumer, industrial and technological applications. Due to the inevitable exposure of NPs to humans, owing to their use in such applications, it is therefore imperative that an understanding of how NPs interact with the human body is gained. In vivo research poses a beneficial model for gaining immediate and direct knowledge of human exposure to such xenobiotics. This research outlook however, has numerous limitations. Increased research using in vitro models has therefore been performed, as these models provide an inexpensive and high-throughput alternative to in vivo research strategies. Despite such advantages, there are also various restrictions in regard to in vitro research. Therefore, the aim of this review, in addition to providing a short perspective upon the field of nanotoxicology, is to discuss (1) the advantages and disadvantages of in vitro research and (2) how in vitro research may provide essential information pertaining to the human health risks posed by NP exposure.

Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria

The intestinal ecosystem is formed by a complex, yet highly characteristic microbial community. The parameters defining whether this community permits invasion of a new bacterial species are unclear. In particular, inhibition of enteropathogen infection by the gut microbiota ( = colonization resistance) is poorly understood. To analyze the mechanisms of microbiota-mediated protection from Salmonella enterica induced enterocolitis, we used a mouse infection model and large scale high-throughput pyrosequencing. In contrast to conventional mice (CON), mice with a gut microbiota of low complexity (LCM) were highly susceptible to S. enterica induced colonization and enterocolitis. Colonization resistance was partially restored in LCM-animals by co-housing with conventional mice for 21 days (LCM(con21)). 16S rRNA sequence analysis comparing LCM, LCM(con21) and CON gut microbiota revealed that gut microbiota complexity increased upon conventionalization and correlated with increased resistance to S. enterica infection. Comparative microbiota analysis of mice with varying degrees of colonization resistance allowed us to identify intestinal ecosystem characteristics associated with susceptibility to S. enterica infection. Moreover, this system enabled us to gain further insights into the general principles of gut ecosystem invasion by non-pathogenic, commensal bacteria. Mice harboring high commensal E. coli densities were more susceptible to S. enterica induced gut inflammation. Similarly, mice with high titers of Lactobacilli were more efficiently colonized by a commensal Lactobacillus reuteri(RR) strain after oral inoculation. Upon examination of 16S rRNA sequence data from 9 CON mice we found that closely related phylotypes generally display significantly correlated abundances (co-occurrence), more so than distantly related phylotypes. Thus, in essence, the presence of closely related species can increase the chance of invasion of newly incoming species into the gut ecosystem. We provide evidence that this principle might be of general validity for invasion of bacteria in preformed gut ecosystems. This might be of relevance for human enteropathogen infections as well as therapeutic use of probiotic commensal bacteria.

TILLING for Mutations in Model Plants and Crops

A growing world population, changing climate and limiting fossil fuels will provide new pressures on human production of food, medicine, fuels and feed stock in the twenty-first century. Enhanced crop production promises to ameliorate these pressures. Crops can be bred for increased yields of calories, starch, nutrients, natural medicinal compounds, and other important products. Enhanced resistance to biotic and abiotic stresses can be introduced, toxins removed, and industrial qualities such as fibre strength and biofuel per mass can be increased. Induced and natural mutations provide a powerful method for the generation of heritable enhanced traits. While mainly exploited in forward, phenotype driven, approaches, the rapid accumulation of plant genomic sequence information and hypotheses regarding gene function allows the use of mutations in reverse genetic approaches to identify lesions in specific target genes. Such gene-driven approaches promise to speed up the process of creating novel phenotypes, and can enable the generation of phenotypes unobtainable by traditional forward methods. TILLING (Targeting Induced Local Lesions IN Genome) is a high-throughput and low cost reverse genetic method for the discovery of induced mutations. The method has been modified for the identification of natural nucleotide polymorphisms, a process called Ecotilling. The methods are general and have been applied to many species, including a variety of different crops. In this chapter the current status of the TILLING and Ecotilling methods and provide an overview of progress in applying these methods to different plant species, with a focus on work related to food production for developing nations.

The homeobox gene HLXB9 is upregulated in a morphological subset of poorly differentiated hepatocellular carcinoma

The prognostic outcome for hepatocellular carcinoma (HCC) remains poor. Disease progression is accompanied by dedifferentiation of the carcinoma, a process that is not well understood. The aim of this study was to get more insight into the molecular characteristics of dedifferentiated carcinomas using high throughput techniques. Microarray-based global gene expression analysis was performed on five poorly differentiated HCC cell lines compared with non-neoplastic hepatic controls and a set of three cholangiolar carcinoma (CC) cell lines. The gene with the highest upregulation was HLXB9. HLXB9 is a gene of the homeobox genfamily important for the development of the pancreas. RT-PCR confirmed the upregulation of HLXB9 in surgical specimens of carcinoma tissue, suggesting its biological significance. Interestingly, HLXB9 upregulation was primary observed in poorly differentiated HCC with a pseudoglandular pattern compared with a solid pattern HCC or in moderate or well-differentiated HCC. Additional the expression of translated HLXB9, the protein HB9 (NCBI: NP_001158727), was analyzed by western blotting. Expression of HB9 was only detected in the cytoplasm but not in the nuclei of the HCC cells. For validation CC were also investigated. Again, we found an upregulation of HLXB9 in CC cells accompanied by an expression of HB9 in the cytoplasms of these tumor cells, respectively. In conclusion, homeobox HLXB9 is upregulated in poorly differentiated HCC with a pseudoglandular pattern. The translated HB9 protein is found in the cytoplasm of these HCC and CC. We therefore assume HLXB9 as a possible link in the understanding of the development of HCC and CC, respectively.

Controlled angiogenesis in the heart by cell-based expression of specific vascular endothelial growth factor levels

Vascular endothelial growth factor (VEGF) can induce normal angiogenesis or the growth of angioma-like vascular tumors depending on the amount secreted by each producing cell because it remains localized in the microenvironment. In order to control the distribution of VEGF expression levels in vivo, we recently developed a high-throughput fluorescence-activated cell sorting (FACS)-based technique to rapidly purify transduced progenitors that homogeneously express a specific VEGF dose from a heterogeneous primary population. Here we tested the hypothesis that cell-based delivery of a controlled VEGF level could induce normal angiogenesis in the heart, while preventing the development of angiomas. Freshly isolated human adipose tissue-derived stem cells (ASC) were transduced with retroviral vectors expressing either rat VEGF linked to a FACS-quantifiable cell-surface marker (a truncated form of CD8) or CD8 alone as control (CTR). VEGF-expressing cells were FACS-purified to generate populations producing either a specific VEGF level (SPEC) or uncontrolled heterogeneous levels (ALL). Fifteen nude rats underwent intramyocardial injection of 10(7) cells. Histology was performed after 4 weeks. Both the SPEC and ALL cells produced a similar total amount of VEGF, and both cell types induced a 50%-60% increase in both total and perfused vessel density compared to CTR cells, despite very limited stable engraftment. However, homogeneous VEGF expression by SPEC cells induced only normal and stable angiogenesis. Conversely, heterogeneous expression of a similar total amount by the ALL cells caused the growth of numerous angioma-like structures. These results suggest that controlled VEGF delivery by FACS-purified ASC may be a promising strategy to achieve safe therapeutic angiogenesis in the heart.

A global ”imaging” view on systems approaches in immunology

The immune system exhibits an enormous complexity. High throughput methods such as the "-omic'' technologies generate vast amounts of data that facilitate dissection of immunological processes at ever finer resolution. Using high-resolution data-driven systems analysis, causal relationships between complex molecular processes and particular immunological phenotypes can be constructed. However, processes in tissues, organs, and the organism itself (so-called higher level processes) also control and regulate the molecular (lower level) processes. Reverse systems engineering approaches, which focus on the examination of the structure, dynamics and control of the immune system, can help to understand the construction principles of the immune system. Such integrative mechanistic models can properly describe, explain, and predict the behavior of the immune system in health and disease by combining both higher and lower level processes. Moving from molecular and cellular levels to a multiscale systems understanding requires the development of methodologies that integrate data from different biological levels into multiscale mechanistic models. In particular, 3D imaging techniques and 4D modeling of the spatiotemporal dynamics of immune processes within lymphoid tissues are central for such integrative approaches. Both dynamic and global organ imaging technologies will be instrumental in facilitating comprehensive multiscale systems immunology analyses as discussed in this review.

Modulation of L-α-lysophosphatidylinositol/GPR55 mitogen-activated protein kinase (MAPK) signaling by cannabinoids

GPR55 is activated by l-α-lysophosphatidylinositol (LPI) but also by certain cannabinoids. In this study, we investigated the GPR55 pharmacology of various cannabinoids, including analogues of the CB1 receptor antagonist Rimonabant®, CB2 receptor agonists, and Cannabis sativa constituents. To test ERK1/2 phosphorylation, a primary downstream signaling pathway that conveys LPI-induced activation of GPR55, a high throughput system, was established using the AlphaScreen® SureFire® assay. Here, we show that CB1 receptor antagonists can act both as agonists alone and as inhibitors of LPI signaling under the same assay conditions. This study clarifies the controversy surrounding the GPR55-mediated actions of SR141716A; some reports indicate the compound to be an agonist and some report antagonism. In contrast, we report that the CB2 ligand GW405833 behaves as a partial agonist of GPR55 alone and enhances LPI signaling. GPR55 has been implicated in pain transmission, and thus our results suggest that this receptor may be responsible for some of the antinociceptive actions of certain CB2 receptor ligands. The phytocannabinoids Δ9-tetrahydrocannabivarin, cannabidivarin, and cannabigerovarin are also potent inhibitors of LPI. These Cannabis sativa constituents may represent novel therapeutics targeting GPR55.

An integrated nano-scale approach to profile miRNAs in limited clinical samples

Profiling miRNA expression in cells that directly contribute to human disease pathogenesis is likely to aid the discovery of novel drug targets and biomarkers. However, tissue heterogeneity and the limited amount of human diseased tissue available for research purposes present fundamental difficulties that often constrain the scope and potential of such studies. We established a flow cytometry-based method for isolating pure populations of pathogenic T cells from bronchial biopsy samples of asthma patients, and optimized a high-throughput nano-scale qRT-PCR method capable of accurately measuring 96 miRNAs in as little as 100 cells. Comparison of circulating and airway T cells from healthy and asthmatic subjects revealed asthma-associated and tissue-specific miRNA expression patterns. These results establish the feasibility and utility of investigating miRNA expression in small populations of cells involved in asthma pathogenesis, and set a precedent for application of our nano-scale approach in other human diseases. The microarray data from this study (Figure 7) has been submitted to the NCBI Gene Expression Omnibus (GEO; http://ncbi.nlm.nih.gov/geo) under accession no. GSE31030.

16S rRNA terminal restriction fragment length polymorphism for the characterization of the nasopharyngeal microbiota

A novel non-culture based 16S rRNA Terminal Restriction Fragment Length Polymorphism (T-RFLP) method using the restriction enzymes Tsp509I and Hpy166II was developed for the characterization of the nasopharyngeal microbiota and validated using recently published 454 pyrosequencing data. 16S rRNA gene T-RFLP for 153 clinical nasopharyngeal samples from infants with acute otitis media (AOM) revealed 5 Tsp509I and 6 Hpy166II terminal fragments (TFs) with a prevalence of >10%. Cloning and sequencing identified all TFs with a prevalence >6% allowing a sufficient description of bacterial community changes for the most important bacterial taxa. The conjugated 7-valent pneumococcal polysaccharide vaccine (PCV-7) and prior antibiotic exposure had significant effects on the bacterial composition in an additive main effects and multiplicative interaction model (AMMI) in concordance with the 16S rRNA 454 pyrosequencing data. In addition, the presented T-RFLP method is able to discriminate S. pneumoniae from other members of the Mitis group of streptococci, which therefore allows the identification of one of the most important human respiratory tract pathogens. This is usually not achieved by current high throughput sequencing protocols. In conclusion, the presented 16S rRNA gene T-RFLP method is a highly robust, easy to handle and a cheap alternative to the computationally demanding next-generation sequencing analysis. In case a lot of nasopharyngeal samples have to be characterized, it is suggested to first perform 16S rRNA T-RFLP and only use next generation sequencing if the T-RFLP nasopharyngeal patterns differ or show unknown TFs.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Point-of-care testing in the cardiovascular operating theatre

Point-of-care testing (POCT) remains under scrutiny by healthcare professionals because of its ill-tried, young history. POCT methods are being developed by a few major equipment companies based on rapid progress in informatics and nanotechnology. Issues as POCT quality control, comparability with standard laboratory procedures, standardisation, traceability and round robin testing are being left to hospitals. As a result, the clinical and operational benefits of POCT were first evident for patients on the operating table. For the management of cardiovascular surgery patients, POCT technology is an indispensable aid. Improvement of the technology has meant that clinical laboratory pathologists now recognise the need for POCT beyond their high-throughput areas.

Rapid and reliable genotyping of polymorphic loci modifying correct splicing of CFTR pre-mRNA using mass spectrometry

We describe a fast and unambiguous method for haplotyping the (TG)mTn repeat in IVS8 and determining three other single nucleotide polymorphisms (SNPs) in exons 10, 14a and 24 in the cystic fibrosis transmembrane conductance regulator (CFTR) gene affecting correct splicing of the CFTR pre-mRNA using primer extension and mass spectrometry. The diagnostic products are generated by primer extension (PEX) reactions, which require a single detection primer complementary to a region downstream of a target strand's variable site. On addition of a polymerase and an appropriate mixture of dNTP's and 2', 3'-dideoxynucleotide triphosphates (ddNTP's), the primer is extended through the mutation region until the first ddNTP is incorporated and the mass of the extension products determines the composition of the variable site. Analysis of patient DNA assigned the correct and unambiguous haplotype for the (TG)mTn repeat in intron 8 of the CFTR gene. Additional crucial SNPs influencing correct splicing in exon 10, 14 and 24 can easily be detected by biplexing the assay to genotype allelic variants important for correct splicing of the CFTR pre-mRNA. Different PEX reactions with subsequent mass spectrometry generate sufficient data, to enable unambiguous and easy haplotyping of the (TG)mTn repeat in the CFTR gene. The method can be easily extended to the inclusion of additional SNPs of interest by biplexing some of the PEX reactions. All experimental steps required for PEX are amenable to the high degree of automation desirable for a high-throughput diagnostic setting, facilitating the work of clinicians involved in the diagnosis of non-classic cystic fibrosis.

ASSOCIATON TESTS THAT ACCOMMODATE GENOTYPING ERRORS

High-throughput SNP arrays provide estimates of genotypes for up to one million loci, often used in genome-wide association studies. While these estimates are typically very accurate, genotyping errors do occur, which can influence in particular the most extreme test statistics and p-values. Estimates for the genotype uncertainties are also available, although typically ignored. In this manuscript, we develop a framework to incorporate these genotype uncertainties in case-control studies for any genetic model. We verify that using the assumption of a “local alternative” in the score test is very reasonable for effect sizes typically seen in SNP association studies, and show that the power of the score test is simply a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that the power to detect a true association can be substantially increased for difficult to call genotypes, resulting in improved inference in association studies.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.