The solute carrier SLC7A1 may act as a protein transporter at the blood-brain barrier.

Despite extensive research, targeted delivery of substances to the brain still poses a great challenge due to the selectivity of the blood-brain barrier (BBB). Most molecules require either carrier- or receptor-mediated transport systems to reach the central nervous system (CNS). These transport systems form attractive routes for the delivery of therapeutics into the CNS, yet the number of known brain endothelium-enriched receptors allowing the transport of large molecules into the brain is scarce. Therefore, to identify novel BBB targets, we combined transcriptomic analysis of human and murine brain endothelium and performed a complex screening of BBB-enriched genes according to established selection criteria. As a result, we propose the high-affinity cationic amino acid transporter 1 (SLC7A1) as a novel candidate for transport of large molecules across the BBB. Using RNA sequencing and in situ hybridization assays, we demonstrated elevated SLC7A1 gene expression in both human and mouse brain endothelium. Moreover, we confirmed SLC7A1 protein expression in brain vasculature of both young and aged mice. To assess the potential of SLC7A1 as a transporter for larger proteins, we performed internalization and transcytosis studies using a radiolabelled or fluorophore-labelled anti-SLC7A1 antibody. Our results showed that SLC7A1 internalised a SLC7A1-specific antibody in human colorectal carcinoma (HCT116) cells. Moreover, transcytosis studies in both immortalised human brain endothelial (hCMEC/D3) cells and primary mouse brain endothelial cells clearly demonstrated that SLC7A1 effectively transported the SLC7A1-specific antibody from luminal to abluminal side. Therefore, here in this study, we present for the first time the SLC7A1 as a novel candidate for transport of larger molecules across the BBB.

A single nuclear transcriptomic characterisation of mechanisms responsible for impaired angiogenesis and blood-brain barrier function in Alzheimer's disease.

Brain perfusion and blood-brain barrier (BBB) integrity are reduced early in Alzheimer's disease (AD). We performed single nucleus RNA sequencing of vascular cells isolated from AD and non-diseased control brains to characterise pathological transcriptional signatures responsible for this. We show that endothelial cells (EC) are enriched for expression of genes associated with susceptibility to AD. Increased ß-amyloid is associated with BBB impairment and a dysfunctional angiogenic response related to a failure of increased pro-angiogenic HIF1A to increased VEGFA signalling to EC. This is associated with vascular inflammatory activation, EC senescence and apoptosis. Our genomic dissection of vascular cell risk gene enrichment provides evidence for a role of EC pathology in AD and suggests that reducing vascular inflammatory activation and restoring effective angiogenesis could reduce vascular dysfunction contributing to the genesis or progression of early AD.

RNA sequencing reveals MMP2 and TGFB1 downregulation in LRRK2 G2019S Parkinson's iPSC-derived astrocytes.

Non-neuronal cell types such as astrocytes can contribute to Parkinson's disease (PD) pathology. The G2019S mutation in leucine-rich repeat kinase 2 (LRRK2) is one of the most common known causes of familial PD. To characterize its effect on astrocytes, we developed a protocol to produce midbrain-patterned astrocytes from human induced pluripotent stem cells (iPSCs) derived from PD LRRK2 G2019S patients and healthy controls. RNA sequencing analysis revealed the downregulation of genes involved in the extracellular matrix in PD cases. In particular, transforming growth factor beta 1 (TGFB1), which has been shown to inhibit microglial inflammatory response in a rat model of PD, and matrix metallopeptidase 2 (MMP2), which has been shown to degrade α-synuclein aggregates, were found to be down-regulated in LRRK2 G2019S astrocytes. Our findings suggest that midbrain astrocytes carrying the LRRK2 G2019S mutation may have reduced neuroprotective capacity and may contribute to the development of PD pathology.

Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study.

Reproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long-term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounders such as passaging effects and progenitor storage. Single-cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis-generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.

Multiplex High-Throughput Targeted Proteomic Assay To Identify Induced Pluripotent Stem Cells.

Induced pluripotent stem cells have great potential as a human model system in regenerative medicine, disease modeling, and drug screening. However, their use in medical research is hampered by laborious reprogramming procedures that yield low numbers of induced pluripotent stem cells. For further applications in research, only the best, competent clones should be used. The standard assays for pluripotency are based on genomic approaches, which take up to 1 week to perform and incur significant cost. Therefore, there is a need for a rapid and cost-effective assay able to distinguish between pluripotent and nonpluripotent cells. Here, we describe a novel multiplexed, high-throughput, and sensitive peptide-based multiple reaction monitoring mass spectrometry assay, allowing for the identification and absolute quantitation of multiple core transcription factors and pluripotency markers. This assay provides simpler and high-throughput classification into either pluripotent or nonpluripotent cells in 7 min analysis while being more cost-effective than conventional genomic tests.

Reduced neonatal mortality in Meishan piglets: a role for hepatic fatty acids?

The Meishan pig breed exhibits increased prolificacy and reduced neonatal mortality compared to commercial breeds, such as the Large White, prompting breeders to introduce the Meishan genotype into commercial herds. Commercial piglets are highly susceptible to hypoglycemia, hypothermia, and death, potentially due to limited lipid stores and/or delayed hepatic metabolic ability. We therefore hypothesized that variation in hepatic development and lipid metabolism could contribute to the differences in neonatal mortality between breeds. Liver samples were obtained from piglets of each breed on days 0, 7, and 21 of postnatal age and subjected to molecular and biochemical analysis. At birth, both breeds exhibited similar hepatic glycogen contents, despite Meishan piglets having significantly lower body weight. The livers from newborn Meishan piglets exhibited increased C18∶1n9C and C20∶1n9 but lower C18∶0, C20∶4n6, and C22∶6n3 fatty acid content. Furthermore, by using an unsupervised machine learning approach, we detected an interaction between C18∶1n9C and glycogen content in newborn Meishan piglets. Bioinformatic analysis could identify unique age-based clusters from the lipid profiles in Meishan piglets that were not apparent in the commercial offspring. Examination of the fatty acid signature during the neonatal period provides novel insights into the body composition of Meishan piglets that may facilitate liver responses that prevent hypoglycaemia and reduce offspring mortality.

Quantitative, genome-wide analysis of the DNA methylome in sporadic pituitary adenomas.

DNA methylation is one of the several epigenetic modifications that together with genetic aberrations are hallmarks of tumorigenesis including those emanating from the pituitary gland. In this study, we examined DNA methylation across 27 578 CpG sites spanning more than 14 000 genes in the major pituitary adenoma subtypes. Genome-wide changes were first determined in a discovery cohort comprising non-functioning (NF), growth hormone (GH), prolactin (PRL)-secreting and corticotroph (CT) adenoma relative to post-mortem pituitaries. Using stringent cut-off criteria, we validated increased methylation by pyrosequencing in 12 of 16 (75%) genes. Overall, these criteria identified 40 genes in NF, 21 in GH, six in PRL and two in CT that were differentially methylated relative to controls. In a larger independent cohort of adenomas, for genes in which hypermethylation had been validated, different frequencies of hypermethylation were apparent, where the KIAA1822 (HHIPL1) and TFAP2E genes were hypermethylated in 12 of 13 NF adenomas whereas the COL1A2 gene showed an increase in two of 13 adenomas. For genes showing differential methylation across and between adenoma subtypes, pyrosequencing confirmed these findings. In three of 12 genes investigated, an inverse relationship between methylation and transcript expression was observed where increased methylation of EML2, RHOD and HOXB1 is associated with significantly reduced transcript expression. This study provides the first genome-wide survey of adenoma, subtype-specific epigenomic changes and will prove useful for identification of biomarkers that perhaps predict or characterise growth patterns. The functional characterisation of identified genes will also provide insight of tumour aetiology and identification of new therapeutic targets.

Identification of DNA methylation biomarkers from Infinium arrays.

Epigenetic modifications of DNA, such as cytosine methylation are differentially abundant in diseases such as cancer. A goal for clinical research is finding sites that are differentially methylated between groups of samples to act as potential biomarkers for disease outcome. However, clinical samples are often limited in availability, represent a heterogeneous collection of cells or are of uncertain clinical class. Array-based methods for identification of methylation provide a cost-effective method to survey a proportion of the methylome at single base resolution. The Illumina Infinium array has become a popular and reliable high throughput method in this field and are proving useful in the identification of biomarkers for disease. Here, we compare a commonly used statistical test with a new intuitive and flexible computational approach to quickly detect differentially methylated sites. The method rapidly identifies and ranks candidate lists with greatest inter-group variability whilst controlling for intra-group variability. Intuitive and biologically relevant filters can be imposed to quickly identify sites and genes of interest.

A comparative approach to understanding tissue-specific expression of uncoupling protein 1 expression in adipose tissue.

The thermoregulatory function of brown adipose tissue (BAT) is due to the tissue-specific expression of uncoupling protein 1 (UCP1) which is thought to have evolved in early mammals. We report that a CpG island close to the UCP1 transcription start site is highly conserved in all 29 vertebrates examined apart from the mouse and xenopus. Using methylation sensitive restriction digest and bisulfite mapping we show that the CpG island in both the bovine and human is largely un-methylated and is not related to differences in UCP1 expression between white and BAT. Tissue-specific expression of UCP1 has been proposed to be regulated by a conserved 5' distal enhancer which has been reported to be absent in marsupials. We demonstrate that the enhancer, is also absent in five eutherians as well as marsupials, monotremes, amphibians, and fish, is present in pigs despite UCP1 having become a pseudogene, and that absence of the enhancer element does not relate to BAT-specific UCP1 expression. We identify an additional putative 5' regulatory unit which is conserved in 14 eutherian species but absent in other eutherians and vertebrates, but again unrelated to UCP1 expression. We conclude that despite clear evidence of conservation of regulatory elements in the UCP1 5' untranslated region, this does not appear to be related to species or tissues-specific expression of UCP1.

Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs.

While previous studies have shed light on the link between the structure of metabolism and its transcriptional regulation, the extent to which transcriptional regulation controls metabolism has not yet been fully explored. In this work, we address this problem by integrating a large number of experimental data sets with a model of the metabolism of Escherichia coli. Using a combination of computational tools including the concept of elementary flux patterns, methods from network inference and dynamic optimization, we find that transcriptional regulation of pathways reflects the protein investment into these pathways. While pathways that are associated to a high protein cost are controlled by fine-tuned transcriptional programs, pathways that only require a small protein cost are transcriptionally controlled in a few key reactions. As a reason for the occurrence of these different regulatory strategies, we identify an evolutionary trade-off between the conflicting requirements to reduce protein investment and the requirement to be able to respond rapidly to changes in environmental conditions.

Comparison of clustering methods for investigation of genome-wide methylation array data.

The use of genome-wide methylation arrays has proved very informative to investigate both clinical and biological questions in human epigenomics. The use of clustering methods either for exploration of these data or to compare to an a priori grouping, e.g., normal versus disease allows assessment of groupings of data without user bias. However no consensus on the methods to use for clustering of methylation array approaches has been reached. To determine the most appropriate clustering method for analysis of illumina array methylation data, a collection of data sets was simulated and used to compare clustering methods. Both hierarchical clustering and non-hierarchical clustering methods (k-means, k-medoids, and fuzzy clustering algorithms) were compared using a range of distance and linkage methods. As no single method consistently outperformed others across different simulations, we propose a method to capture the best clustering outcome based on an additional measure, the silhouette width. This approach produced a consistently higher cluster accuracy compared to using any one method in isolation.

Cooperation and cheating in microbial exoenzyme production--theoretical analysis for biotechnological applications.

The engineering of microorganisms to produce a variety of extracellular enzymes (exoenzymes), for example for producing renewable fuels and in biodegradation of xenobiotics, has recently attracted increasing interest. Productivity is often reduced by "cheater" mutants, which are deficient in exoenzyme production and benefit from the product provided by the "cooperating" cells. We present a game-theoretical model to analyze population structure and exoenzyme productivity in terms of biotechnologically relevant parameters. For any given population density, three distinct regimes are predicted: when the metabolic effort for exoenzyme production and secretion is low, all cells cooperate; at intermediate metabolic costs, cooperators and cheaters coexist; while at high costs, all cells use the cheating strategy. These regimes correspond to the harmony game, snowdrift game, and Prisoner's Dilemma, respectively. Thus, our results indicate that microbial strains engineered for exoenzyme production will not, under appropriate conditions, be outcompeted by cheater mutants. We also analyze the dependence of the population structure on cell density. At low costs, the fraction of cooperating cells increases with decreasing cell density and reaches unity at a critical threshold. Our model provides an estimate of the cell density maximizing exoenzyme production.