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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2015

Efficient sequence-specific isolation of DNA fragments and chromatin by in vitro enChIP technology using recombinant CRISPR ribonucleoproteins

The clustered regularly interspaced short palindromic repeat (CRISPR) system is widely used for various biological applications, including genome editing. We developed engineered DNA-binding molecule-mediated chromatin immunoprecipitation (enChIP) using CRISPR to isolate target genomic regions from cells for their biochemical characterization. In this study, we developed \"in vitro enChIP\" using recombinant CRISPR ribonucleoproteins (RNPs) to isolate target genomic regions. in vitro enChIP has the great advantage over conventional enChIP of not requiring expression of CRISPR complexes in cells. We first demonstrate that in vitro enChIP using recombinant CRISPR RNPs can be used to isolate target DNA from mixtures of purified DNA in a sequence-specific manner. In addition, we show that this technology can be employed to efficiently isolate target genomic regions, while retaining their intracellular molecular interactions, with negligible contamination from irrelevant genomic regions. Thus, in vitro enChIP technology is of potential use for sequence-specific isolation of DNA, as well as for identification of molecules interacting with genomic regions of interest in vivo in combination with downstream analysis.

Biochemistry↗

bioRxiv2016

Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease

Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimers disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinsons disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at http://genocanyon.med.yale.edu/GenoSkyline.\n\nAuthor SummaryAfter years of community efforts, many experimental and computational approaches have been developed and applied for functional annotation of the human genome, yet proper annotation still remains challenging, especially in non-coding regions. As complex disease research rapidly advances, increasing evidence suggests that non-coding regulatory DNA elements may be the primary regions harboring risk variants in human complex diseases. In this paper, we introduce GenoSkyline-Plus, a principled annotation framework to identify tissue and cell type-specific functional regions in the human genome through integration of diverse high-throughput epigenomic and transcriptomic data. Through validation of known non-coding tissue-specific regulatory regions, enrichment analyses on 45 complex traits, and an in-depth case study of neurodegenerative diseases, we demonstrate the ability of GenoSkyline-Plus to accurately identify tissue-specific functionality in the human genome and provide unbiased, genome-wide insights into the genetic basis of human complex diseases.

Genetics↗

bioRxiv2016

The Bayesian-Laplacian Brain

We outline what we believe could be an improvement in future discussions of the brain acting as a Bayesian-Laplacian system. We do so by distinguishing between two broad classes of priors on which the brains inferential systems operate: in one category are biological priors ({beta} priors) and in the other artifactual ones ( priors). We argue that {beta} priors, of which colour categories and faces are good examples, are inherited or acquired very rapidly after birth, are highly or relatively resistant to change through experience, and are common to all humans. The consequence is that the probability of posteriors generated from {beta} priors having universal assent and agreement is high. By contrast, priors, of which man-made objects are examples, are acquired post-natally and modified at various stages throughout post-natal life; they are much more accommodating of, and hospitable to, new experiences. Consequently, posteriors generated from them are less likely to find universal assent. Taken together, in addition to the more limited capacity of experiment and experience to alter the {beta} priors compared to priors, another cardinal distinction between the two is that the probability of posteriors generated from {beta} priors having universal agreement is greater than that for priors. The two categories are not, however, always totally distinct and can merge into one another to varying extents, resulting in posteriors that draw upon both categories.

neuroscience↗

bioRxiv2016

Quantifying the relationship between pollinator behavior and plant reproductive isolation

Pollinator behavior is an important contributor to reproductive isolation in plants. Despite hundreds of years of empirical research, we lack a quantitative framework for evaluating how variation in pollinator behavior causes variation in reproductive isolation in plants. Here I present a model describing how two aspects of pollinator behavior - constancy and preference - lead to reproductive isolation in plants. This model is motivated by two empirical observations: most co-occurring plants vary in frequency over space and time and most plants have multiple pollinators that differ in behavior. These two observations suggest a need to understand how plant frequency and pollinator frequency influence reproductive isolation between co-occurring plants. My model predicts how the proportion of heterospecific matings varies over plant frequencies given pollinator preference and constancy. I find that the shape of this relationship is dependent on the strength of pollinator behavior. Additionally, my model incorporates multiple pollinators with different behaviors to predict the proportion of heterospecific matings across pollinator frequencies. I find that when two pollinators display different strength constancy the total proportion of heterospecific matings is simply the average proportion of heterospecific matings predicted for each pollinator. When pollinators vary in their preference the pollinator with the stronger preference disproportionally contributes to the predicted proportion of total heterospecific matings. I apply this model to examples of pollinator-mediated reproductive isolation in Phlox and in Mimulus to predict relationships between plant and pollinator frequency and reproductive isolation in natural systems.

evolutionary biology↗

bioRxiv2018

SSPIM: a beam shaping toolbox for structured selective plane illumination microscopy

An important aim of the development of selective plane illumination microscopy (SPIM) is to present a completely open and flexible microscope set-up for nonspecialist users. Here, we report Structured SPIM (SSPIM), which provides an open-source, user-friendly and compact toolbox for beam shaping that can generate digital patterns for a wide range of illumination beams. SSPIM represents a toolbox to produce static, spherical Gaussian, Bessel and Airy beams by simple control of a Spatial Light Modulator (SLM). In addition, it is able to produce patterns for incoherent and coherent (lattice beam) array beam formation and tiling for all types of beams supported. We demonstrate the workflow and experimental and simulation results using the SSPIM toolbox. In final, the capability of the SSPIM is investigated with 3D imaging of Drosophila embryo using three different illumination beams such as scanned/dithered Gaussian, Bessel and Lattice beam which engineered with SSPIM. SSPIM toolbox is easy to use and applicable for a wide range of applications to generate and optimize the desired beam pattern and thus can help developing adaptation of the Open SPIM system towards a wider range of biological samples.

bioinformatics↗

bioRxiv2018

Ultrastructure of salivary glands in Psammotettix alienus Dahlbom (Homoptera: Cicadellidae) infected with Taastrup virus

Taastrup virus (TV) is a novel virus belonging to Mononegavirales and with filovirus-like morphology. In adult Psammotettix alienus infected with TV, the highest concentration of virus particles was found in salivary glands, consisting of a principal gland (type I-VI-cells) and an accessory gland. Examination of thin sections revealed enveloped particles, about 1300 nm long and 62 nm in diameter, located singly or in paracrystalline arrays in canaliculi of type III- and IV-cells. In gland cells with TV particles in canaliculi, granular masses up to 15 micrometer in diameter are present in the cytoplasm. These masses are believed to be viroplasms, the sites for viral replication. TV particles were observed at the connection between a canaliculus and the salivary duct system.

cell biology↗

bioRxiv2018

Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy

Understanding living cells as integrated systems, a challenge central to modern biology, is complicated by limitations of available imaging methods. While fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and damaging to cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images.

cell biology↗

bioRxiv2018

Establishing a cell-free Vibrio natriegens expression system

The fast growing bacterium Vibrio natriegens is an emerging microbial host for biotechnology. Harnessing its productive cellular components may offer a compelling platform for rapid protein production and prototyping of metabolic pathways or genetic circuits. Here, we report the development of a V. natriegens cell-free expression system. We devised a simplified crude extract preparation protocol and achieved >260 g/mL of super-folder GFP in a small-scale batch reaction after three hours. Culturing conditions, including growth media and cell density, significantly affect translation kinetics and protein yield of extracts. We observed maximal protein yield at incubation temperatures of 26{degrees}C or 30{degrees}C, and show improved yield by tuning ions crucial for ribosomal stability. This work establishes an initial V. natriegens cell-free expression system, enables probing of V. natriegens biology, and will serve as a platform to accelerate metabolic engineering and synthetic biology applications.

synthetic biology↗

bioRxiv2018

A Bacterial Expression Vector Archive (BEVA) forflexible modular assembly of Golden Gate-compatible vectors

We present a Bacterial Expression Vector Archive (BEVA) for the modular assembly of bacterial vectors compatible with both traditional and Golden Gate cloning, utilizing the Type IIS restriction enzyme Esp3I. Ideal for synthetic biology and other applications, this modular system allows a rapid, low-cost assembly of new vectors tailored to specific tasks. To demonstrate the potential of the system three example vectors were constructed and tested. Golden Gate level 1 vectors; pOGG024, with a broad-host range and high copy number was used for gene expression in laboratory-cultured Rhizobium leguminosarum, and pOGG026, with a broad-host range a lower copy number and excellent stability, even in the absence of antibiotic selection. The application of pOGG026 is demonstrated in environmental samples by bacterial gene expression in nitrogen-fixing nodules on pea plants roots formed by R. leguminosarum. Finally, the level 2 cloning vector pOGG216 is a broad-host range, medium copy number, for which we demonstrate an application by constructing a dual reporter plasmid expressing green and red fluorescent proteins.\n\nIMPORTANCEModular assembly is powerful as it allows easy combining of different components from a library of parts. In designing a modular vector assembly system, the key constituent parts (and modules) are; an origin of plasmid replication, antibiotic resistance marker(s), cloning site(s), together with additional accessory modules as required. In an ideal vector, the size of each module would be minimized, and this we have addressed. We have designed such a vector assembly system by utilizing the Type IIS restriction enzyme Esp3I and have demonstrated its use for Golden Gate cloning in Escherichia coli. An important attribute of this modular vector assembly is that using the principles outlined here, new modules for specific applications, e.g. origin of replication for plasmids in other bacteria, can easily be designed. It is hoped that this vector construction system will be expanded by the scientific community over time by creation of novel modules through an open source approach.

microbiology↗

bioRxiv2016

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways

The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein-protein interaction network (PPIN) of infected cells to elucidate biological signaling transduction pathways affected by rabies virus. To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.

Systems Biology↗

bioRxiv2018

Multi-regional Neurodegeneration in Alzheimer’s disease: Meta-analysis and data integration of transcriptomics data

Alzheimers disease (AD) is a complex neurodegenerative disease with various deleterious perturbations in regulatory pathways of various brain regions. Thus, it would be critical to understanding the role of different regions of the brain in initiation and progression of AD, However, owing to complex and multifactorial nature of this disease, the molecular mechanism of AD has yet to be fully elucidated. To confront with this challenge, we launched a meta-analytical study of current transcriptomics data in four different regions of the brain in AD (Entorhinal, Hippocampus, Temporal and Frontal) with systems analysis of identifying involved signaling and metabolic pathways. We found different regulatory patterns in Entorhinal and Hippocampus regions to be associated with progression of AD. We also identified shared versus unique biological pathways and critical proteins among different brain regions. ACACB, GAPDH, ACLY, and EGFR were the most important proteins in Entorhinal, Frontal, Hippocampus and Temporal regions, respectively. Moreover, eight proteins including CDK5, ATP5G1, DNM1, GNG3, AP2M1, ALDOA, GPI, and TPI1 were differentially expressed in all four brain regions, among which, CDK5 and ATP5G1 were enriched in KEGG Alzheimers disease pathway as well.

systems biology↗

bioRxiv2018

Identification of Casiopeina II-gly secondary targets through a systems pharmacology approach

Casiopeinas are a group of copper-based compounds designed to be used as less toxic, more efficient chemotherapeutic agents. In this study, we analyzed the in vitro effects of Casiopeina Il-gly on the expression of canonical biological pathways. Using microarray data from HeLa cell lines treated with Casiopeina II-gly, we identified biological pathways that are perturbed after treatment. We present a novel approach integrating pathway analysis and network theory: The Pathway Crosstalk Network. We constructed a network with deregulated pathways, featuring links between those pathways that crosstalk with each other. We identified modules grouping deregulated pathways that are functionally related. Through this approach, we were able to identify three features of Casiopeina treatment: a) Perturbation of signaling pathways, related to induction of apoptosis; b) perturbation of metabolic pathways, and c) activation of immune responses. These findings can be useful to drive new experimental exploration on their role in adverse effects and efficacy of Casiopeinas.

systems biology↗

bioRxiv2018

Multi-omic and multi-view clustering algorithms: review and cancer benchmark

High throughput experimental methods developed in recent years have been used to collect large biomedical omics datasets. Clustering of such datasets has proven invaluable for biological and medical research, and helped reveal structure in data from several domains. Such analysis is often based on investigation of a single omic. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for multi-omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types.\n\nIn addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic benchmark comparison of leading multi-omics and multiview clustering algorithms. The results highlight several key questions regarding the use of single-vs. multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the rapidly increasing use of multi-omics data, these issues may be important for future progress in the field.

bioinformatics↗

bioRxiv2017

Ancestral Reconstruction of Gene Blocks using an Event-Based Method

Complexity is a fundamental attribute of life. Complex systems are made of parts that together perform functions that a single component, or subsets containing individual components, cannot. Examples of complex molecular systems include protein structures such as the F1Fo-ATPase, the ribosome, or the flagellar motor: each one of these structures requires most or all of its components to function properly. Given the ubiquity of complex systems in the biosphere, understanding the evolution of complexity is central to biology. At the molecular level, operons are a classic example of a complex system. An operons genes are co-transcribed under the control of a single promoter to a polycistronic mRNA molecule, and the operons gene products often form molecular complexes or metabolic pathways. With the large number of complete bacterial genomes available, we now have the opportunity to explore the evolution of these complex entities, by identifying possible intermediate states of operons. In this work, we developed a maximum parsimony algorithm to reconstruct ancestral operon states, and show a simple vertical evolution model of how operons may evolve from the individual component genes. We describe several ancestral states that are plausible functional intermediate forms leading to the full operon. We also offer Reconstruction of Ancestral Gene blocks Using Events or ROAGUE as a software tool for those interested in exploring gene block and operon evolution.\n\nThe software accompanying this paper is available under GPLv3 license in: https://github.com/nguyenngochuy91/Ancestral-Blocks-Reconstruction.\n\nAll figures in this paper are available in enlarged downloadable form from:https://github.com/nguyenngochuy91/Ancestral-Blocks-Reconstruction/tree/master/images

evolutionary biology↗

bioRxiv2017

Evolution of density-dependent movement during replicated experimental range expansions

Range expansions and biological invasions are prime examples of non-equilibrium systems that are likely impacted by rapid evolutionary changes. As a spatial process, range expansions are driven by dispersal and movement behaviour. While it is widely accepted that dispersal and movement may be context-dependent, for instance density-dependent, and best represented by reaction norms, the evolution of density-dependent movement during range expansions has received little experimental attention. We therefore tested current theory predicting the evolution of increased movement at low densities at range margins using highly replicated and controlled range expansion experiments across multiple genotypes of the protist model system Tetrahymena thermophila. Although rare, we found evolutionary changes during range expansions even in the absence of initial standing genetic variation. Range expansions led to the evolution of negatively density-dependent movement at range margins. In addition, we report the evolution of increased competitive ability and concurrently decreased population growth rates in range cores. Our findings highlight the importance of understanding movement and dispersal as evolving reaction norms and plastic life-history traits of central relevance for range expansions, biological invasions and the dynamics of spatially structured systems in general.\n\nData accessibilityThe data will be made available at Dryad (http://dx.doi.org/xxxxx). Video analysis scripts can be found on GitHub (https://github.com/efronhofer/analysis_script_bemovi).

evolutionary biology↗

bioRxiv2018

The thermodynamic landscape of carbon redox biochemistry

Redox biochemistry plays a key role in the transduction of chemical energy in living systems. However, the compounds observed in metabolic redox reactions are a minuscule fraction of chemical space. It is not clear whether compounds that ended up being selected as metabolites display specific properties that distinguish them from non-biological compounds. Here we introduce a systematic approach for comparing the chemical space of all possible redox states of linear-chain carbon molecules to the corresponding metabolites that appear in biology. Using cheminformatics and quantum chemistry, we analyze the physicochemical and thermodynamic properties of the biological and non-biological compounds. We find that, among all compounds, aldose sugars have the highest possible number of redox connections to other molecules. Metabolites are enriched in carboxylic acid functional groups and depleted of carbonyls, and have higher solubility than non-biological compounds. Upon constructing the energy landscape for the full chemical space as a function of pH and electron donor potential, we find that over a large range of conditions metabolites tend to have lower Gibbs energies than non-biological molecules. Finally, we generate Pourbaix phase diagrams that serve as a thermodynamic atlas to indicate which compounds are local and global energy minima in redox chemical space across a set of pH values and electron donor potentials. Our work yields insight into the physicochemical principles governing redox metabolism, and suggests that thermodynamic stability in aqueous environments may have played an important role in early metabolic processes.

systems biology↗

bioRxiv2016

Calculating biological module enrichment or depletion and visualizing data on large-scale molecular maps with ACSNMineR and RNaviCell R packages

Biological pathways or modules represent sets of interactions or functional relationships occurring at the molecular level in living cells. A large body of knowledge on pathways is organized in public databases such as the KEGG, Reactome, or in more specialized repositories, such as the Atlas of Cancer Signaling Network (ACSN). All these open biological databases facilitate analyses, improving our understanding of cellular systems. We hereby describe the R package ACSNMineR for calculation of enrichment or depletion of lists of genes of interest in biological pathways. ACSNMineR integrates ACSN molecular pathways, but can use any molecular pathway encoded as a GMT file, for instance sets of genes available in the Molecular Signatures Database (MSigDB). We also present the R package RNaviCell, that can be used in conjunction with ACSNMineR to visualize different data types on web-based, interactive ACSN maps. We illustrate the functionalities of the two packages with biological data taken from large-scale cancer datasets.

Bioinformatics↗

bioRxiv2016

Modeling multi-particle complexes in stochastic chemical systems

Large complexes of classical particles play central roles in biology, in polymer physics, and in other disciplines. However, physics currently lacks mathematical methods for describing such complexes in terms of component particles, interaction energies, and assembly rules. Here we describe a Fock space structure that addresses this need, as well as diagrammatic methods that facilitate the use of this formalism. These methods can dramatically simplify the equations governing both equilibrium and non-equilibrium stochastic chemical systems. A mathematical relationship between the set of all complexes and a list of rules for complex assembly is also identified.

Biophysics↗