The DISC1 Pathway Modulates Expression of Neurodevelopmental, Synaptogenic and Sensory Perception Genes

Genetic and biological evidence supports a role for DISC1 across a spectrum of major mental illnesses, including schizophrenia and bipolar disorder. There is evidence for genetic interplay between variants in DISC1 and in biologically interacting loci in psychiatric illness. DISC1 also associates with normal variance in behavioral and brain imaging phenotypes.Here, we analyze public domain datasets and demonstrate correlations between variants in the DISC1 pathway genes and levels of gene expression. Genetic variants of DISC1, NDE1, PDE4B and PDE4D regulate the expression of cytoskeletal, synaptogenic, neurodevelopmental and sensory perception proteins. Interestingly, these regulated genes include existing targets for drug development in depression and psychosis.Our systematic analysis provides further evidence for the relevance of the DISC1 pathway to major mental illness, identifies additional potential targets for therapeutic intervention and establishes a general strategy to mine public datasets for insights into disease pathways

Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments

Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition, and data analysis. Broadly accepted community guidelines and standardized metrics will enhance rigor, data quality, and alignment between laboratories. Here we propose best practices, quality controls, and data reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics.Comment: Supporting website: https://single-cell.net/guideline

Moving teaching away from transmitting facts to co-constructing conceptual understandings: a cognitively activating instructional approach

The recent science education reforms in Germany have stressed that biology teaching should move away from transmitting isolated facts to engaging students in co-constructing interconnected and conceptual level knowledge. It is known that cognitively activating instruction can warrant a deep level engagement with the subject matter. Cognitively activating instruction includes three key dimensions: teaching interconnected and complex subject matter knowledge, use of challenging tasks, and a thoughtful-constructive discourse. There is evidence that cognitively activating instruction can enhance students’ cognitive as well as affective outcomes. However, the research in this field has primarily relied on multi-dimensional rating manuals to measure the overall cognitive potential of the lessons. It remains to be investigated how the individual dimensions of this construct affect student outcomes. It also remains unclear how teachers could include the three dimensions of cognitively activating instruction in their regular lessons. Within the scope of this doctoral work, we addressed these research gaps by focusing on the following three research objectives: 1) describing German biology lessons based on two of the three key dimensions of cognitively activating instruction: teachers’ use of challenging tasks and teachers’ use of focus questions to initiate and direct thoughtful discourse; 2) ascertaining the influence of these teaching features on students’ topic-related knowledge structure; and 3) proposing a lesson-design model that supports teachers in planning and implementing cognitively activating biology lessons. A pre-selected sample of 30 out of 47 biology lessons (45 minutes each) on the common theme of ‘blood and circulatory system, from a previous quasi-experimental pre-post study were re-analyzed in this doctoral study. Additionally, we collaborated with one 11th-grade biology teacher to demonstrate how the explanation oriented teaching approach could be used to plan cognitively activating biology lessons. A descriptive analysis of biology lessons revealed that teachers mostly used lower cognitive level and lower content complexity tasks to orchestrate content-related interactions. This analysis also revealed that very few teachers used focus questions to highlight the purpose of the lesson; moreover, even fewer teachers used explanation-oriented specific and challenging focus questions to orchestrate meaning-making discussions. A multilevel analysis depicted a small magnitude positive effect of high-level cognitive processing tasks on students’ topic-related knowledge structure; however, we did not find any effect of higher content complexity tasks on this outcome variable. Furthermore, while the teachers’ use of specific and challenging focus questions predicted students’ topic-related knowledge structure, there was no significant effect of teachers’ use of non-specific or simple focus questions on the outcome variable. Additionally, the collaborative lesson-design work with the grade 11 biology teacher demonstrated how the scientific practice of constructing explanations could be used as a vehicle to plan and implement cognitively activating biology lessons. In conclusion, while the descriptive findings revealed that the teacher-centered, fact-driven instructional practices were prominent in German biology lessons, the correlational findings demonstrated a small magnitude positive effect of cognitively activating instructional features on students’ knowledge structure. Additionally, the explanation-oriented teaching model provided insights into planning cognitive activating biology lessons. Overall, the results obtained from this doctoral thesis advocate the use of cognitively activating instructional model in regular biology teaching in order to reform biology education

Elastic Network Models in Biology: From Protein Mode Spectra to Chromatin Dynamics

Biomacromolecules perform their functions by accessing conformations energetically favored by their structure-encoded equilibrium dynamics. Elastic network model (ENM) analysis has been widely used to decompose the equilibrium dynamics of a given molecule into a spectrum of modes of motions, which separates robust, global motions from local fluctuations. The scalability and flexibility of the ENMs permit us to efficiently analyze the spectral dynamics of large systems or perform comparative analysis for large datasets of structures. I showed in this thesis how ENMs can be adapted (1) to analyze protein superfamilies that share similar tertiary structures but may differ in their sequence and functional dynamics, and (2) to analyze chromatin dynamics using contact data from Hi-C experiments, and (3) to perform a comparative analysis of genome topology across different types of cell lines. The first study showed that protein family members share conserved, highly cooperative (global) modes of motion. A low-to-intermediate frequency spectral regime was shown to have a maximal impact on the functional differentiation of families into subfamilies. The second study demonstrated the Gaussian Network Model (GNM) can accurately model chromosomal mobility and couplings between genomic loci at multiple scales: it can quantify the spatial fluctuations in the positions of gene loci, detect large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. The third study revealed close similarities between chromosomal dynamics across different cell lines on a global scale, but notable cell-specific variations in the spatial fluctuations of genomic loci. It also called attention to the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation. Together, these studies provide a comprehensive view of the versatility and utility of the ENMs in analyzing spatial dynamics of biomolecules, from individual proteins to the entire chromatin

Hierarchical graphs for rule-based modeling of biochemical systems

<p>Abstract</p> <p>Background</p> <p>In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system.</p> <p>Results</p> <p>For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR) complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm.</p> <p>Conclusions</p> <p>Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for specifying rule-based models, such as the BioNetGen language (BNGL). Thus, the proposed use of hierarchical graphs should promote clarity and better understanding of rule-based models.</p

Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity

Eigenvector localization as a tool to study small communities in online social networks

We present and discuss a mathematical procedure for identification of small "communities" or segments within large bipartite networks. The procedure is based on spectral analysis of the matrix encoding network structure. The principal tool here is localization of eigenvectors of the matrix, by means of which the relevant network segments become visible. We exemplified our approach by analyzing the data related to product reviewing on Amazon.com. We found several segments, a kind of hybrid communities of densely interlinked reviewers and products, which we were able to meaningfully interpret in terms of the type and thematic categorization of reviewed items. The method provides a complementary approach to other ways of community detection, typically aiming at identification of large network modules