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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 235 records · Page 13Linked to original sources

bioRxiv2018

Ordered arrangement of dendrites within a C. elegans sensory nerve bundle

Biological systems are organized into well-ordered structures and can evolve new patterns when perturbed. To identify principles underlying biological order, we turned to C. elegans for its simple anatomy and powerful genetics. We developed a method to quantify the arrangement of three dendrites in the main sensory nerve bundle, and found that they exhibit a stereotyped arrangement throughout larval growth. Dendrite order does not require prominent features including sensory cilia and glial junctions. In contrast, loss of the cell adhesion molecule (CAM) CDH-4/Fat-like cadherin causes dendrites to be ordered randomly, despite remaining bundled. Loss of the CAMs PTP-3/LAR or SAX-7/L1CAM causes dendrites to adopt an altered order, which becomes increasingly random as animals grow. Misexpression of SAX-7 leads to subtle but reproducible changes in dendrite order. Our results suggest that differential expression of CAMs allows dendrites to self-organize into a stereotyped arrangement that readily gives rise to new patterns when perturbed.

developmental biology↗

bioRxiv2018

Simvastatin mediates inhibition of exosome synthesis, localization, and secretion via multicomponent interventions.

Discovery of exosomes as modulator of cellular communication has added a new dimension to our understanding of biological processes. Exosomes influence the biological systems by mediating trans-communication across tissues and cells, which has important implication for health and disease. Identification of strategies for exosome modulation may pave the way towards better understanding of exosome biology and development of novel therapeutics. In absence of well-characterized modulators of exosome biogenesis, an alternative option is to target pathways generating important exosomal components. Cholesterol represents one such essential component required for exosomal biogenesis. We initiated this study to test the hypothesis that owing to its cholesterol lowering effect, simvastatin, a HMG CoA inhibitor, might be able to alter exosome formation and secretion. Using previously established protocols for detecting secreted exosomes in biological fluids, simvastatin was tested for its effect on exosome secretion under various in-vitro and in-vivo settings. Murine model of AAI was used for further validation of our findings. Utilizing aforementioned systems, we demonstrate exosome-lowering potential of simvastatin in various in-vivo and in-vitro models, of AAI and atherosclerosis. We believe that the knowledge acquired in this study holds potential for extension to other exosome dominated pathologies and model systems.

cell biology↗

bioRxiv2018

LION/web: a web-based ontology enrichment tool for lipidomic data analysis

A major challenge for lipidomic analyses is the handling of the large amounts of data and the translation of results to interpret the involvement of lipids in biological systems. We built a new lipid ontology (LION) that associates over 50,000 lipid species to biophysical, chemical and cell biological features. By making use of enrichment algorithms, we used LION to develop a web-based interface (LION/web, www.lipidontology.com) that allows identification of lipid-associated terms in lipidomes. LION/web was validated by analyzing a lipidomic dataset derived from well-characterized sub-cellular fractions of RAW 264.7 macrophages. Comparison of isolated plasma membranes with the microsomal fraction showed a significant enrichment of relevant LION-terms including plasma membrane, headgroup with negative charge, glycerophosphoserines, above average bilayer thickness, and below average lateral diffusion. A second validation was performed by analyzing the membrane fluidity of CHO cells incubated with arachidonic acid. An increase in membrane fluidity was observed both experimentally by using pyrene decanoic acid and by using LION/web, showing significant enrichment of terms associated with high membrane fluidity ('above average, 'very high and 'high lateral diffusion, and 'below average transition temperature). The results demonstrate the functionality of LION/web, which is freely accessible in a platform-independent way.

biochemistry↗

bioRxiv2018

Long read sequencing reveals poxvirus evolution through rapid homogenization of gene arrays

Large DNA viruses rapidly evolve to defeat host defenses. Poxvirus adaptation can involve combinations of recombination-driven gene copy number variation and beneficial single nucleotide variants (SNVs) at the same locus, yet how these distinct mechanisms of genetic diversification might simultaneously facilitate adaptation to immune blocks is unknown. We performed experimental evolution with a vaccinia virus population harboring a SNV in a gene actively undergoing copy number amplification. Comparisons of virus genomes using the Oxford Nanopore Technologies sequencing platform allowed us to phase SNVs within large gene copy arrays for the first time, and uncovered a mechanism of adaptive SNV homogenization reminiscent of gene conversion, which is actively driven by selection. Our work reveals a new mechanism for the fluid gain of beneficial mutations in genetic regions undergoing active recombination in viruses, and illustrates the value of long read sequencing technologies for investigating complex genome dynamics in diverse biological systems.

evolutionary biology↗

bioRxiv2016

Predicting gene expression level in E. coli from mRNA sequence information

MotivationThe accurate characterization of the translational mechanism is crucial for enhancing our understanding of the relationship between genotype and phenotype. In particular, predicting the impact of the genetic variants on gene expression will allow to optimize specific pathways and functions for engineering new biological systems. In this context, the development of accurate methods for predicting translation efficiency from the nucleotide sequence is a key challenge in computational biology.\n\nMethodsIn this work we present PGExpress, a binary classifier to discriminate between mRNA sequences with low and high translation efficiency in E. coli. PGExpress algorithm takes as input 12 features corresponding to RNA folding and anti-Shine-Dalgarno hybridization free energies. The method was trained on a set of 1,772 sequence variants (WT-High) of 137 essential E. coli genes. For each gene, we considered 13 sequence variants of the first 33 nucleotides encoding for the same amino acids followed by the superfolder GFP. Each gene variant is represented sequence blocks that include the Ribosome Binding Site (RBS), the first 33 nucleotides of the coding region (C33), the remaining part of the coding region (CC), and their combinations.\n\nResultsOur logistic regression-based tool (PGExpress) was trained using a 20-fold gene-based cross-validation procedure on the WT-High dataset. In this test PGExpress achieved an overall accuracy of 74%, a Matthews correlation coefficient 0.49 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.81. Tested on 3 sets of sequences with different Ribosome Binding Sites, PGExpress reaches similar AUC. Finally, we validated our method by performing in-house experiments on five newly generated mRNA sequence variants. The predictions of the expression level of the new variants are in agreement with our experimental results in E. coli.\n\nAvailabilityhttp://folding.biofold.org/pgexpress\n\nContactmarkus.kollmann@hhu.de, emidio.capriotti@unibo.it

bioinformatics↗

bioRxiv2018

Embryo timelapses can be compiled and quantified to understand canonical histone dynamics across multiple cell cycles.

In the last decade, computational analysis of big datasets has facilitated the processing of unprecedented quantities of collected biological data. Thus, automations and big data analyses have been revolutionary in detecting and quantifying subtle phenotypes in cell biological contexts. Analyzing similar quantities of data in larger and more complicated biological systems such as live embryos has been more challenging due to experimental necessities impeding both compilations of data collection and informative analysis. Here we present a streamlined workflow that can quantify cell cycle dynamics in early developing embryos using fluorescently labeled proteins. We benchmark this pipeline using Caenorhabditis elegans (nematode) embryonic development and a fluorescently labeled histone. Using our pipeline, we find that histone proteins are broadly stable in early embryonic development. In sum, we have utilized the large biological and experimental variation associated with quantification of fluorescent proteins in embryonic systems, to quantify nuclear accumulation rate, chromatin incorporation, and turnover/stability of canonical histones during early development.

developmental biology↗

bioRxiv2018

Linear-double-stranded DNA (ldsDNA) based AND logic computation in mammalian cells

Synthetic biology employs engineering principles to redesign biological system for clinical or industrial purposes. The development and application of novel genetic devices for genetic circuits construction will facilitate the rapid development of synthetic biology. Here we demonstrate that mammalian cells could perform two- and three-input linear-double-stranded DNA (ldsDNA) based Boolean AND logic computation. Through hydrodynamic ldsDNA delivery, two-input ldsDNA-base AND-gate computation could be achieved in vivo. Inhibition of DNA-PKcs expression, a key enzyme in non-homologous end joining (NHEJ), could significantly downregulate the intensity of output signals from ldsDNA-based AND-gate. We further reveal that in mammalian cells ldsDNAs could undergo end processing and then perform AND-gate calculation to generate in-frame output proteins. Moreover, we show that ldsDNAs or plasmids with identical overlapping sequences could also serve as inputs of AND-gate computation. Our work establishes novel genetic devices and principles for genetic circuits construction, thus may open a new gate for the development of new disease targeting strategies and new protein genesis methodologies.

synthetic biology↗

bioRxiv2018

Quantitative network organization of interactions emerging from the evolution of sequence and structure space of antibodies: the RADARS model

Adaptive immunity in vertebrates represents a complex self-organizing network of protein interactions that develops throughout the lifetime of an individual. While deep sequencing of the immune-receptor repertoire may reveal clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function. In this paper a novel model of antibody interaction space and network, termed radial adjustment of system resolution, RADARS, is proposed. The model is based on the radial growth of interaction affinity of antibodies towards an infinity of directions in structure space, each direction representing particular shapes of antigen epitopes. Levels of interaction affinity appear as free energy shells of the system, where hierarchical B-cell development and differentiation takes place. Equilibrium in this immunological thermodynamic system can be described by a power-law distribution of antibody free energies with an ideal network degree exponent of phi square, representing a scale-free fractal network of antibody interactions. Plasma cells are network hubs, memory B cells are nodes with intermediate degrees and B1 cells represent nodes with minimal degree. Thus, the RADARS model implies that antibody structure space develops against an infinite antigen structure space via interactions that are individually immunologically controlled, but on a systems level are organized by thermodynamic probability distributions. The network of interactions, which control B-cell development and differentiation, represent pathways of antigen removal on systems level. Understanding such quantitative network properties of the system should help the organization of sequence-derived structural data, offering the possibility to relate sequence to function in a complex, self-organizing biological system. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/438804v7_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@883eb7org.highwire.dtl.DTLVardef@cd7d5forg.highwire.dtl.DTLVardef@e225eeorg.highwire.dtl.DTLVardef@1286b49_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

immunology↗

bioRxiv2017

Promoter and transcription factor dynamics tune protein mean and noise strength in a quorum sensing-based feedback synthetic circuit

Gene expression is a fundamental cellular process. Its stochastic fluctuations due to intrinsic and extrinsic sources, known generically as gene expression noise, trigger both beneficial and harmful consequences for the cell behavior.\n\nControlling gene expression noise is of interest in many applications in biotechnology, biomedicine and others. Yet, control of the mean expression level is an equally desirable goal. Here, we analyze a gene synthetic network designed to reduce gene expression noise while achieving a desired mean expression level. The circuit combines a negative feedback loop over the gene of interest, and a cell-to-cell communication mechanism based on quorum sensing. We analyze the ability of the circuit to reduce noise as a function of parameters that can be tuned in the wet-lab, and the role quorum sensing plays. Intrinsic noise is generated by the inherent stochasticity of biochemical reactions. On the other hand, extrinsic noise is due to variability in the cell environment and the amounts of cellular components that affect gene expression. We develop a realistic model of the gene synthetic circuit over the population of cells using mass action kinetics and the stochastic Chemical Langevin Equation to include intrinsic noise, with parameters drawn from a distribution to account for extrinsic noise. Stochastic simulations allow us to quantify the mean expression level and noise strength of all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in E. coli. Our in silico experiments reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. These in silico conclusions are validated by preliminary experimental results. This gene network could have important implications as a robust protein production system in industrial biotechnology.\n\nAuthor SummaryControlling gene expression level is of interest in many applications in biotechnology, biomedicine and others. Yet, the stochastic nature of biochemical reactions plays an important role in biological systems, and cannot be disregarded. Gene expression noise resulting from this stochasticity has been studied over the past years both in vivo, and in silico using mathematical models. Nowadays, synthetic biology approaches allow to design novel biological circuits, drawing on principles elucidated from biology and engineering, for the purpose of decoupled control of mean gene expression and its variance. We propose a gene synthetic circuit with these characteristics, using negative feedback and quorum sensing based cell-to-cell communication to induce population consensus. Our in silico analysis using stochastic simulations with a realistic model reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. Preliminary in vivo results fully agree with the computational ones.

synthetic biology↗

bioRxiv2016

Electroceutical disinfection strategies impair the motility of pathogenic Pseudomonas aeruginosa and Escherichia coli

Electrotaxis or galvanotaxis refers to the migration pattern of cells induced in response to electrical potential. Although it has been extensively studied in mammalian cells, electrotaxis has not been explored in detail in bacterial cells; information regarding the impact of current on pathogenic bacteria is severely lacking. Therefore, we designed a series of single and multi-cue experiments to assess the impact of varying currents on bacterial motility dynamics in pathogenic multi-drug resistant (MDR) strains of Pseudomonas aeruginosa and Escherichia coli using a microfluidic platform. Motility plays key roles in bacterial migration and the colonization of surfaces during the formation of biofilms, which are inherently recalcitrant to removal and resistant to traditional disinfection strategies (e.g. antibiotics). Use of the microfluidic platform allows for exposure to current, which can be supplied at a range that is biocidal to bacteria, yet physiologically safe in humans (single cue). This system also allows for multi-cue experiments where acetic acid, a relatively safe compound with anti-fouling/antimicrobial properties, can be combined with current to enhance disinfection. These strategies may offer substantial therapeutic benefits, specifically for the treatment of biofilm infections, such as those found in the wound environment. Furthermore, microfluidic systems have been successfully used to model the unique microfluidic dynamics present in the wound environment, suggesting that these investigations could be extended to more complex biological systems. Our results showed that the application of current in combination with acetic acid has profound inhibitory effects on MDR strains of P. aeruginosa and E. coli, even with brief applications. Specifically, E. coli motility dynamics and cell survival were significantly impaired starting at a concentration of 125 A DC and 0.31% acetic acid, while P. aeruginosa was impaired at 70 A and 0.31% acetic acid. As these strains are relevant wound pathogens, it is likely that this strategy would be effective against similar strains in vivo and could represent a new approach to hasten wound healing.

bioengineering↗

bioRxiv2018

Ancestral Reconstruction of Protein Interaction Networks

The molecular and cellular basis of novelty is a major open question in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term \"phylogenetic latent variable models\" (PLVMs, pronounced \"plums\") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

evolutionary biology↗

bioRxiv2016

Quantitative Modeling and Automated Analysis of Meiotic Recombination

Many morphological features, in both physical and biological systems, exhibit spatial patterns that are specifically characterized by a tendency to occur with even spacing (in one, two or three dimensions). The positions of crossover (CO) recombination events along meiotic chromosomes provides an interesting biological example of such an effect (1-3). In general, mechanisms that explain such patterns may (a) be mechanically-based, (b) occur by a reaction-diffusion mechanism in which macroscopic mechanical effects are irrelevant, or (c) involve a combination of both types of effects. We have proposed that meiotic CO patterns arise by a mechanical mechanism, have developed mathematical expressions for such a process based on a particular physical system with analogous properties (the so-called \"beam-film model\"), and have shown that the beam-film model can very accurately explain experimental CO patterns as a function of the values of specific defined parameters (4-7). Importantly, the mathematical expressions of the beam-film model can apply quite generally to any mechanism, whether it involves mechanical components or not, as long as its logic and component features correspond to those of the beam-film system (3; below). Furthermore, via its various parameters, the beam-film model discretizes the patterning process into specific components. Thus, the model can be used to explore the theoretically predicted effects of various types of changes in the patterning process. Such predictions can expand detailed understanding of the bases for various biological effects (e.g. 2, 5). We present here a new MATLAB program that implements the mathematical expressions of the beam-film model with increased robustness and accessibility as compared to programs presented previously. As in previous versions, the presented program permits both: (i) simulation of predicted CO positions along chromosomes of a test population; and (ii) easy analysis of CO positions, both for experimental data sets and for data sets resulting from simulations. The goal of the current presentation is to make these approaches more readily accessible to a wider audience of researchers. Also, the program is easily modified, and we encourage interested users to make changes to suit their specific needs. A link to the program is available on the Kleckner laboratory web site: http://projects.iq.harvard.edu/kleckner_lab.

genetics↗

bioRxiv2015

Replication is Recursion; or, Lambda: the Biological Imperative

There is a striking visual symmetry between the 'paradoxical combinator' (which implements recursion in the mathematical theory of computation) and the biological replication fork (which implements reproduction in the living cell). Can this mean anything? Living organisms are information processors - the genome encodes instructions for the process of life much as computer programs encode instructions for a computer. Replication in biological systems is intuitively similar to recursion in computational systems. The following discussion will present enough of the mathematics to allow biologists to make sense of the symmetries in the logo. Mathematicians will learn nothing new about biology, but may be encouraged to look at biological processes from a new perspective.\n\nComputational biology: a flight of fancy Computational biology: a flight... Mathematics of computation Semant ...

Bioinformatics↗

bioRxiv2017

System-level studies of a cell-free transcription-translation platform for metabolic engineering

Current methods for assembling biosynthetic pathways in microorganisms require a process of repeated trial and error and have long design-build-test cycles. We describe the use of a cell-free transcription-translation (TX-TL) system as a biomolecular breadboard for the rapid engineering of the 1,4-butanediol (BDO) pathway. We demonstrate the reliability of TX-TL as a platform for engineering biological systems by undertaking a careful characterization of its transcription and translation capabilities and provide a detailed analysis of its metabolic output. Using TX-TL to survey the design space of the BDO pathway enables rapid tuning of pathway enzyme expression levels for improved product yield. Leveraging TX-TL to screen enzyme variants for improved catalytic activity accelerates design iterations that can be directly applied to in vivo strain development.

bioengineering↗

bioRxiv2016

Measurements of translation initiation from all 64 codons in E. coli

Our understanding of translation is one cornerstone of molecular biology that underpins our capacity to engineer living matter. The canonical start codon (AUG) and a few near-cognates (GUG, UUG) are typically considered as the \"start codons\" for translation initiation in Escherichia coli (E. coli). Translation is typically not thought to initiate from the 61 remaining codons. Here, we systematically quantified translation initiation in E. coli from all 64 triplet codons. We detected protein synthesis above background initiating from at least 46 codons. Translation initiated from these non-canonical start codons at levels ranging from 0.01% to 2% relative to AUG. Translation initiation from non-canonical start codons may contribute to the synthesis of peptides in both natural and synthetic biological systems

Synthetic Biology↗

bioRxiv2016

The value of prior knowledge in machine learning of complex network systems

Our overall goal is to develop machine learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing, and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks - directed graphs with 0/1 node states and logical node update rules - which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics, and as such provide a useful framework for developing machine learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine learning algorithms to predict network steady state node values (\"phenotypes\") and perturbation responses (\"drug effects\").

bioinformatics↗

bioRxiv2017

With Our Powers Combined: Integrating Behavioral And Genetic Data To Estimate Mating Success And Sexual Selection

The analysis of sexual selection classically relies on the regression of individual phenotypes against the marginal sums of a males x females matrix of pairwise reproductive success, assessed by genetic parentage analysis. When the matrix is binarized, the marginal sums give the individual mating success. Because such analysis treats male and female mating/reproductive success independently, it ignores that the success of a male x female sexual interaction can be attributable to the phenotype of both individuals. Also, because it is based on genetic data only, it is oblivious to costly yet unproductive matings, which may be documented by behavioral observations. To solve these problems, we propose a statistical model which combines matrices of offspring numbers and behavioral observations. It models reproduction on each mating occasion of a mating season as three stochastic and interdependent pairwise processes, each potentially affected by the phenotype of both individuals and by random individual effect: encounter (Bernoulli), concomitant gamete emission (Bernoulli), and offspring production (Poisson). Applied to data from a mating experiment on brown trout, the model yielded different results from the classical regression analysis, with only a limited effect of male body size on the probability of gamete release and a negative effect of female body size on the probability of encounter and gamete release. Because the general structure of the model can be adapted to other partitioning of the reproductive process, it can be used for a variety of biological systems where behavioral and genetic data are available.

evolutionary biology↗

bioRxiv2018

Differences between the de novo proteome and its non-functional precursor can result from neutral constraints on its birth process, not necessarily from natural selection alone

Proteins are among the most important constituents of biological systems. Because all proteins ultimately evolved from previously non-coding DNA, the properties of these non-coding sequences and how they shape the birth of novel proteins are also expected to influence the organization of biological networks. When trying to explain and predict the properties of novel proteins, it is of particular importance to distinguish the contributions of natural selection and other evolutionary forces. Studies in the field typically use non-coding DNA and GC-content-based random-sequence models to generate random expectations for the properties of novel functional proteins. Deviations from these expectations have been interpreted as the result of natural selection. However, interpreting such deviations requires a yet-unattained understanding of the raw material of de novo gene birth and its relation to novel functional proteins. We mathematically show how the importance of the \"junk\" polypeptides that make up this raw material goes beyond their average properties and their filtering by natural selection. We find that the mean of any property among novel functional proteins also depends on its variance among junk polypeptides and its correlation with their rate of evolutionary turnover. In order to exemplify the use of our general theoretical results, we combine them with a simple model that predicts the means and variances of the properties of junk polypeptides from the genomic GC content alone. Under this model, we predict the effect of GC content on the mean length and mean intrinsic disorder of novel functional proteins as a function of evolutionary parameters. We use these predictions to formulate new evolutionary interpretations of published data on the length and intrinsic disorder of novel functional proteins. This work provides a theoretical framework that can serve as a guide for the prediction and interpretation of past and future results in the study of novel proteins and their properties under various evolutionary models. Our results provide the foundation for a better understanding of the properties of cellular networks through the evolutionary origin of their components.

evolutionary biology↗