Sara Brin Rosenthal

Visual sensory networks and effective information transfer in animal groups

Social transmission of information is vital for many group-living animals, allowing coordination of motion and effective response to complex environments. Revealing the interaction networks underlying information flow within these groups is a central challenge. Previous work has modeled interactions between individuals based directly on their relative spatial positions: each individual is considered to interact with all neighbors within a fixed distance (metric range), a fixed number of nearest neighbors (topological range), a shell of near neighbors (Voronoi range), or some combination (Figure 1A). However, conclusive evidence to support these assumptions is lacking. Here, we employ a novel approach that considers individual movement decisions to be based explicitly on the sensory information available to the organism. In other words, we consider that while spatial relations do inform interactions between individuals, they do so indirectly, through individuals detection of sensory cues. We reconstruct computationally the visual field of each individual throughout experiments designed to investigate information propagation within fish schools (golden shiners, Notemigonus crysoleucas). Explicitly considering visual sensing allows us to more accurately predict the propagation of behavioral change in these groups during leadership events. Furthermore, we find that structural properties of visual interaction networks differ markedly from those of metric and topological counterparts, suggesting that previous assumptions may not appropriately reflect information flow in animal groups.

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion.

Significance We know little about the nature of the evolved interaction networks that give rise to the rapid coordinated collective response exhibited by many group-living organisms. Here, we study collective evasion in schooling fish using computational techniques to reconstruct the scene from the perspective of the organisms themselves. This method allows us to establish how the complex social scene is translated into behavioral response at the level of individuals and to visualize, and analyze, the resulting complex communication network as behavioral change spreads rapidly through groups. Thus, we can map, for any moment in time, the extent to which each individual is socially influential during collective evasion and predict the magnitude of such behavioral epidemics before they actually occur. Coordination among social animals requires rapid and efficient transfer of information among individuals, which may depend crucially on the underlying structure of the communication network. Establishing the decision-making circuits and networks that give rise to individual behavior has been a central goal of neuroscience. However, the analogous problem of determining the structure of the communication network among organisms that gives rise to coordinated collective behavior, such as is exhibited by schooling fish and flocking birds, has remained almost entirely neglected. Here, we study collective evasion maneuvers, manifested through rapid waves, or cascades, of behavioral change (a ubiquitous behavior among taxa) in schooling fish (Notemigonus crysoleucas). We automatically track the positions and body postures, calculate visual fields of all individuals in schools of ∼150 fish, and determine the functional mapping between socially generated sensory input and motor response during collective evasion. We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted, directed, and heterogeneous. By studying these interaction networks, we reveal the (complex, fractional) nature of social contagion and establish that individuals with relatively few, but strongly connected, neighbors are both most socially influential and most susceptible to social influence. Furthermore, we demonstrate that we can predict complex cascades of behavioral change at their moment of initiation, before they actually occur. Consequently, despite the intrinsic stochasticity of individual behavior, establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.

Perturbed functional networks in Alzheimers Disease reveal opposing roles for TGIF and EGR3

While Alzheimer’s disease (AD) is the most prevalent cause of dementia, complex combinations of the underlying pathologies have led to evolved concepts in clinical and neuropathological criteria in the past decade. Pathological AD can be decomposed into subsets of individuals with significantly different antemortem cognitive decline rates. Using transcriptome as a proxy for functional state, we preselected 414 expression profiles of clinically and neuropathologically confirmed AD subjects and age matched non-demented controls sampled from a large community based neuropathological study. By combining brain tissue specific protein interactome with gene network, we identify functionally distinct composite clusters of genes which reveal extensive changes in expression levels in AD. The average global expression for clusters corresponding to synaptic transmission, metabolism, cell cycle, survival and immune response were downregulated while the upregulated cluster had a large set of uncharacterized pathways and processes that may constitute an AD specific phenotypic signature. We identified four master regulators across all clusters of differentially expressed genes by enrichment analysis including TGIF1 and EGR3. These transcription factors have previously not been associated with AD and were validated in brain tissue samples from an independent AD cohort. We identify TGIF1, a transcriptional repressor as being neuroprotective in AD by activating co-repressors regulating genes critical for DNA repair, maintaining homeostasis and arresting cell cycle. In addition, we show that loss of EGR3 regulation, mediates synaptic deficits by targeting the synaptic vesicle cycle. Collectively, our results highlight the utility of integrating protein interactions with gene perturbations to generate a comprehensive framework for characterizing the alterations in molecular network as applied to AD.

Map of synthetic rescue interactions for the Fanconi anemia DNA repair pathway identifies USP48

Defects in DNA repair can cause various genetic diseases with severe pathological phenotypes. Fanconi anemia (FA) is a rare disease characterized by bone marrow failure, developmental abnormalities, and increased cancer risk that is caused by defective repair of DNA interstrand crosslinks (ICLs). Here, we identify the deubiquitylating enzyme USP48 as synthetic viable for FA-gene deficiencies by performing genome-wide loss-of-function screens across a panel of human haploid isogenic FA-defective cells (FANCA, FANCC, FANCG, FANCI, FANCD2). Thus, as compared to FA-defective cells alone, FA-deficient cells additionally lacking USP48 are less sensitive to genotoxic stress induced by ICL agents and display enhanced, BRCA1-dependent, clearance of DNA damage. Consequently, USP48 inactivation reduces chromosomal instability of FA-defective cells. Our results highlight a role for USP48 in controlling DNA repair and suggest it as a potential target that could be therapeutically exploited for FA.Fanconi anemia is a rare disease caused by defective DNA interstrand crosslink repair. Here the authors observe that USP48 deficiencies reduce chromosomal instability in FA-defective cells, suggesting it might be a potential therapeutic target.

Characteristics Of Likely Precision Medicine Initiative Participants Drawn From A Large Blood Donor Population

A goal of the Precision Medicine Initiative All of Us Research Program (AoURP) is recruitment of participants who reflect the diversity of the US. Recruitment from among blood bank donors, which may better reflect the demographic makeup of local communities, is one proposed strategy. We evaluated this strategy by analyzing the results of a survey of San Diego Blood Bank donors conducted in Novemberxa02015. Whites were more likely than nonwhites to respond to the survey (7.1xa0percent versus 3.9xa0percent). However, race was not a significant predictor of interest in participating in precision medicine research. Using census data linked to donors ZIP codes, we also found that people who indicated interest in research participation were more likely to come from regions with higher educational attainment. Although blood banks represent a viable recruitment strategy for AoURP, our findings indicate that bias toward inclusion of whites and more highly educated people persists.

Interactive network visualization in Jupyter notebooks: visJS2jupyter

MotivationnNetwork biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research.nnnResultsnThe tool provides functions for performing and visualizing useful network operations in biology, including network overlap, network propagation around a focal set of genes, and co-localization of two sets of seed genes. visJS2jupyter uses the JavaScript library vis.js to create interactive networks displayed within Jupyter notebook cells with features including drag, click, hover, and zoom. We demonstrate the functionality of visJS2jupyter applied to a biological question, by creating a network propagation visualization to prioritize risk-related genes in autism.nnnAvailability and implementationnThe visJS2jupyter package is distributed under the MIT License. The source code, documentation and installation instructions are freely available on GitHub at https://github.com/ucsd-ccbb/visJS2jupyter. The package can be downloaded at https://pypi.python.org/pypi/visJS2jupyter.nnnContactnsbrosenthal@ucsd.edu.nnnSupplementary informationnSupplementary data are available at Bioinformatics online.

On entropy and information in gene interaction networks

Motivation: Modern biological experiments often produce candidate lists of genes presumably related to the studied phenotype. One can ask if the gene list as a whole makes sense in the context of existing knowledge: Are the genes in the list reasonably related to each other or do they look like a random assembly? There are also situations when one wants to know if two or more gene sets are closely related. Gene enrichment tests based on counting the number of genes two sets have in common are adequate if we presume that two genes are related only when they are in fact identical. If by related we mean well connected in the interaction network space, we need a new measure of relatedness for gene sets. Results: We derive entropy, interaction information and mutual information for gene sets on interaction networks, starting from a simple phenomenological model of a living cell. Formally, the model describes a set of interacting linear harmonic oscillators in thermal equilibrium. Because the energy function is a quadratic form of the degrees of freedom, entropy and all other derived information quantities can be calculated exactly. We apply these concepts to estimate the probability that genes from several independent genome‐wide association studies are not mutually informative; to estimate the probability that two disjoint canonical metabolic pathways are not mutually informative; and to infer relationships among human diseases based on their gene signatures. We show that the present approach is able to predict observationally validated relationships not detectable by gene enrichment methods. The converse is also true; the two methods are therefore complementary. Availability and implementation: The functions defined in this paper are available in an R package, gsia, available for download at https://github.com/ucsd‐ccbb/gsia.

Serum metabolomic profiling predicts synovial gene expression in rheumatoid arthritis

BackgroundMetabolomics is an emerging field of biomedical research that may offer a better understanding of the mechanisms of underlying conditions including inflammatory arthritis. Perturbations caused by inflamed synovial tissue can lead to correlated changes in concentrations of certain metabolites in the synovium and thereby function as potential biomarkers in blood. Here, we explore the hypothesis of whether characterization of patients’ metabolomic profiles in blood, utilizing 1H-nuclear magnetic resonance (NMR), predicts synovial marker profiling in rheumatoid arthritis (RA).MethodsNineteen active, seropositive patients with RA, on concomitant methotrexate, were studied. One of the involved joints was a knee or a wrist appropriate for arthroscopy. A Bruker Avance 700xa0MHz spectrometer was used to acquire NMR spectra of serum samples. Gene expression in synovial tissue obtained by arthroscopy was analyzed by real-time PCR. Data processing and statistical analysis were performed in Python and SPSS.ResultsAnalysis of the relationships between each synovial marker-metabolite pair using linear regression and controlling for age and gender revealed significant clustering within the data. We observed an association of serine/glycine/phenylalanine metabolism and aminoacyl-tRNA biosynthesis with lymphoid cell gene signature. Alanine/aspartate/glutamate metabolism and choline-derived metabolites correlated with TNF-α synovial expression. Circulating ketone bodies were associated with gene expression of synovial metalloproteinases. Discriminant analysis identified serum metabolites that classified patients according to their synovial marker levels.ConclusionThe relationship between serum metabolite profiles and synovial biomarker profiling suggests that NMR may be a promising tool for predicting specific pathogenic pathways in the inflamed synovium of patients with RA.

Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia

OBJECTIVEnAntipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel druggable pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product.nnnMETHODnThe protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328).nnnRESULTSnSchizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms.nnnCONCLUSIONSnThe network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.