Neurofilament light chain in the vitreous humor of the eye

Background: Neurofilament light chain (NfL) is a promising biomarker of neurodegeneration in the cerebrospinal fluid and blood. This study investigated the presence of NfL in the vitreous humor and its associations with amyloid beta, tau, inflammatory cytokines and vascular proteins, apolipoprotein E (APOE) genotypes, Mini-Mental State Examination (MMSE) scores, systemic disease, and ophthalmic diseases. / Methods: This is a single-site, prospective, cross-sectional cohort study. Undiluted vitreous fluid (0.5–1.0 mL) was aspirated during vitrectomy, and whole blood was drawn for APOE genotyping. NfL, amyloid beta (Aβ), total Tau (t-Tau), phosphorylated Tau (p-Tau181), inflammatory cytokines, chemokines, and vascular proteins in the vitreous were quantitatively measured by immunoassay. The main outcome measures were the detection of NfL levels in the vitreous humor and its associations with the aforementioned proteins. Linear regression was used to test the associations of NfL with other proteins, APOE genotypes, MMSE scores, and ophthalmic and systemic diseases after adjustment for age, sex, education level, and other eye diseases. / Results: NfL was detected in all 77 vitreous samples. NfL was not found to be associated with ophthalmic conditions, APOE genotypes, MMSE scores, or systemic disease (p > 0.05). NfL levels were positively associated with increased vitreous levels of Aβ40 (p = 7.7 × 10−5), Aβ42 (p = 2.8 × 10−4), and t-tau (p = 5.5 × 10−7), but not with p-tau181 (p = 0.53). NfL also had significant associations with inflammatory cytokines such as interleukin-15 (IL-15, p = 5.3 × 10−4), IL-16 (p = 2.2 × 10−4), monocyte chemoattractant protein-1 (MCP1, p = 4.1 × 10−4), and vascular proteins such as vascular endothelial growth factor receptor-1 (VEGFR1, p = 2.9 × 10−6), Vegf-C (p = 8.6 × 10−6), vascular cell adhesion molecule-1 (VCAM-1, p = 5.0 × 10−4), Tie-2 (p = 6.3 × 10−4), and intracellular adhesion molecular-1 (ICAM-1, p = 1.6 × 10−4). / Conclusion: NfL is detectable in the vitreous humor of the eye and significantly associated with amyloid beta, t-tau, and select inflammatory and vascular proteins in the vitreous. Additionally, NfL was not associated with patients’ clinical eye condition. Our results serve as a foundation for further investigation of NfL in the ocular fluids to inform us about the potential utility of its presence in the eye

The Cyprinodon variegatus genome reveals gene expression changes underlying differences in skull morphology among closely related species

Genes in durophage intersection set at 15 dpf. This is a comma separated table of the genes in the 15 dpf durophage intersection set. Given are edgeR results for each pairwise comparison. Columns indicating whether a gene is included in the intersection set at a threshold of 1.5 or 2 fold are provided. (CSV 13 kb

Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain

Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract

Mining the Gene Wiki for functional genomic knowledge

<p>Abstract</p> <p>Background</p> <p>Ontology-based gene annotations are important tools for organizing and analyzing genome-scale biological data. Collecting these annotations is a valuable but costly endeavor. The Gene Wiki makes use of Wikipedia as a low-cost, mass-collaborative platform for assembling text-based gene annotations. The Gene Wiki is comprised of more than 10,000 review articles, each describing one human gene. The goal of this study is to define and assess a computational strategy for translating the text of Gene Wiki articles into ontology-based gene annotations. We specifically explore the generation of structured annotations using the Gene Ontology and the Human Disease Ontology.</p> <p>Results</p> <p>Our system produced 2,983 candidate gene annotations using the Disease Ontology and 11,022 candidate annotations using the Gene Ontology from the text of the Gene Wiki. Based on manual evaluations and comparisons to reference annotation sets, we estimate a precision of 90-93% for the Disease Ontology annotations and 48-64% for the Gene Ontology annotations. We further demonstrate that this data set can systematically improve the results from gene set enrichment analyses.</p> <p>Conclusions</p> <p>The Gene Wiki is a rapidly growing corpus of text focused on human gene function. Here, we demonstrate that the Gene Wiki can be a powerful resource for generating ontology-based gene annotations. These annotations can be used immediately to improve workflows for building curated gene annotation databases and knowledge-based statistical analyses.</p

Markov Chain Ontology Analysis (MCOA)

<p>Abstract</p> <p>Background</p> <p>Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data.</p> <p>Results</p> <p>In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods.</p> <p>Conclusion</p> <p>A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.</p

Leveraging Rural Energy Investment for Parasitic Disease Control: Schistosome Ova Inactivation and Energy Co-Benefits of Anaerobic Digesters in Rural China

Cooking and heating remain the most energy intensive activities among the world's poor, and thus improved access to clean energies for these tasks has been highlighted as a key requirement of attaining the major objectives of the UN Millennium Development Goals. A move towards clean energy technologies such as biogas systems (which produce methane from human and animal waste) has the potential to provide immediate benefits for the control of neglected tropical diseases. Here, an assessment of the parasitic disease and energy benefits of biogas systems in Sichuan Province, China, is presented, highlighting how the public health sector can leverage the proliferation of rural energy projects for infectious disease control. ova) counted at the influent of two biogas systems were removed in the systems when adjusted for system residence time, an approximate 1-log removal attributable to sedimentation. Combined, these inactivation/removal processes underscore the promise of biogas infrastructure for reducing parasite contamination resulting from nightsoil use. When interviewed an average of 4 years after construction, villagers attributed large changes in fuel usage to the installation of biogas systems. Household coal usage decreased by 68%, wood by 74%, and crop waste by 6%. With reported energy savings valued at roughly 600 CNY per year, 2–3 years were required to recoup the capital costs of biogas systems. In villages without subsidies, no new biogas systems were implemented.Sustainable strategies that integrate rural energy needs and sanitation offer tremendous promise for long-term control of parasitic diseases, while simultaneously reducing energy costs and improving quality of life. Government policies can enhance the financial viability of such strategies by introducing fiscal incentives for joint sanitation/sustainable energy projects, along with their associated public outreach and education programs

Combinatorial Binding in Human and Mouse Embryonic Stem Cells Identifies Conserved Enhancers Active in Early Embryonic Development

Transcription factors are proteins that regulate gene expression by binding to cis-regulatory sequences such as promoters and enhancers. In embryonic stem (ES) cells, binding of the transcription factors OCT4, SOX2 and NANOG is essential to maintain the capacity of the cells to differentiate into any cell type of the developing embryo. It is known that transcription factors interact to regulate gene expression. In this study we show that combinatorial binding is strongly associated with co-localization of the transcriptional co-activator Mediator, H3K27ac and increased expression of nearby genes in embryonic stem cells. We observe that the same loci bound by Oct4, Nanog and Sox2 in ES cells frequently drive expression in early embryonic development. Comparison of mouse and human ES cells shows that less than 5% of individual binding events for OCT4, SOX2 and NANOG are shared between species. In contrast, about 15% of combinatorial binding events and even between 53% and 63% of combinatorial binding events at enhancers active in early development are conserved. Our analysis suggests that the combination of OCT4, SOX2 and NANOG binding is critical for transcription in ES cells and likely plays an important role for embryogenesis by binding at conserved early developmental enhancers. Our data suggests that the fast evolutionary rewiring of regulatory networks mainly affects individual binding events, whereas “gene regulatory hotspots” which are bound by multiple factors and active in multiple tissues throughout early development are under stronger evolutionary constraints

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data

Investigating the effect of paralogs on microarray gene-set analysis

<p>Abstract</p> <p>Background</p> <p>In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research.</p> <p>Results</p> <p>We show that paralogs, which typically have high sequence identity and similar molecular functions, also exhibit high correlation in their expression patterns. We investigate this correlation as a potential confounding factor common to current GSA methods using Indygene <url>http://www.cbio.uct.ac.za/indygene</url>, a web tool that reduces a supplied list of genes so that it includes no pairwise paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs.</p> <p>Conclusions</p> <p>The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.</p

Phosphorylation of the Drosophila melanogaster RNA–Binding Protein HOW by MAPK/ERK Enhances Its Dimerization and Activity

Drosophila melanogaster Held Out Wings (HOW) is a conserved RNA–binding protein (RBP) belonging to the STAR family, whose closest mammalian ortholog Quaking (QKI) has been implicated in embryonic development and nervous system myelination. The HOW RBP modulates a variety of developmental processes by controlling mRNA levels and the splicing profile of multiple key regulatory genes; however, mechanisms regulating its activity in tissues have yet to be elucidated. Here, we link receptor tyrosine kinase (RTK) signaling to the regulation of QKI subfamily of STAR proteins, by showing that HOW undergoes phosphorylation by MAPK/ERK. Importantly, we show that this modification facilitates HOW dimerization and potentiates its ability to bind RNA and regulate its levels. Employing an antibody that specifically recognizes phosphorylated HOW, we show that HOW is phosphorylated in embryonic muscles and heart cardioblasts in vivo, thus documenting for the first time Serine/Threonine (Ser/Thr) phosphorylation of a STAR protein in the context of an intact organism. We also identify the sallimus/D-titin (sls) gene as a novel muscle target of HOW–mediated negative regulation and further show that this regulation is phosphorylation-dependent, underscoring the physiological relevance of this modification. Importantly, we demonstrate that HOW Thr phosphorylation is reduced following muscle-specific knock down of Drosophila MAPK rolled and that, correspondingly, Sls is elevated in these muscles, similarly to the HOW RNAi effect. Taken together, our results provide a coherent mechanism of differential HOW activation; MAPK/ERK-dependent phosphorylation of HOW promotes the formation of HOW dimers and thus enhances its activity in controlling mRNA levels of key muscle-specific genes. Hence, our findings bridge between MAPK/ERK signaling and RNA regulation in developing muscles