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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 343 records · Page 19Linked to original sources

bioRxiv2017

COREMIC: a web-tool to search for a root-zone associated CORE MICrobiome

Microbial diversity on earth is extraordinary, and soils alone harbor thousands of species per gram of soil. Understanding how this diversity is sorted and selected into habitat niches is a major focus of ecology and biotechnology, but remains only vaguely understood. A systems-biology approach was used to mine information from databases to show how it can be used to answer questions related to the core microbiome of habitat-microbe relationships. By making use of the burgeoning growth of information from databases, our tool \"COREMIC\" meets a great need in the search for understanding niche partitioning and habitat-function relationships. The work is unique, furthermore, because it provides a user-friendly statistically robust web-tool (http://coremic2.appspot.com), developed using Google App Engine, to help in the process of database mining to identify the \"core microbiome\" associated with a given habitat. A case study is presented using data from 31 switchgrass rhizosphere community habitats across a diverse set of soil and sampling environments. The methodology utilizes an outgroup of 28 non-switchgrass (other grasses and forbs) to identify a core switchgrass microbiome. Even across a diverse set of soils (5 environments), and conservative statistical criteria (presence in more than 90% samples and FDR q-val < 0.05% for Fishers exact test) a core set of bacteria associated with switchgrass was observed. These included, among others, closely related taxa from Lysobacter spp., Mesorhizobium spp, and Chitinophagaceae. These bacteria have been shown to have functions related to the production of bacterial and fungal antibiotics and plant growth promotion. COREMIC can be used as a hypothesis generating or confirmatory tool that shows great potential for identifying taxa that may be important to the functioning of a habitat (e.g. host plant). The case study, in conclusion, shows that COREMIC can identify key habitat-specific microbes across diverse samples, using currently available databases and a unique freely available software.

microbiology↗

bioRxiv2017

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genomescale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.

bioengineering↗

bioRxiv2017

t-Darpp is an elongated monomer that binds to calcium and is phosphorylated by cyclin-dependent kinases 1 and 5

t-Darpp is a protein encoded by the PPP1R1B gene and is expressed in breast, colon, esophageal, gastric, and prostate cancers, as well as in normal adult brain striatal cells. Overexpression of t-Darpp in cultured cells leads to increased protein kinase A activity and increased phosphorylation of AKT (protein kinase B). In HER2+ breast cancer cells t-Darpp confers resistance to the chemotherapeutic agent trastuzumab. To shed light on t-Darpp function, we studied its secondary structure, oligomerization status, metal-binding properties, and phosphorylation by cyclin dependent kinases 1 and 5. t-Darpp exhibits 12% alpha helix, 29% beta strand, 24% beta turn and 35% random coil structures. t-Darpp binds to calcium, but not to other metals commonly found in biological systems. The T39 site, critical for t-Darpp activation of the AKT signaling pathway, is a substrate for phosphorylation by cyclin-dependent kinase 1 (CDK1) and cyclin-dependent kinase 5 (CDK5). Gel filtration chromatography, sedimentation equilibrium analysis, blue native gel electrophoresis, and glutaraldehyde-mediated crosslinking experiments demonstrate that the majority of t-Darpp exists as a monomer, but forms low levels (< 3%) of hetero-oligomers with its longer isoform Darpp-32. t-Darpp has a large Stokes radius of 4.4 nm relative to its mass of 19 kDa, indicating that it has an elongated structure.

biochemistry↗

bioRxiv2017

Sex-biased transcriptomic response of the reproductive axis to stress

Stress is a well-known cause of reproductive dysfunction in many species, including birds, rodents, and humans. However, little is known of the genomic basis for this dysfunction and how it may differ between the sexes. Using the classic reproductive model of the rock dove (Columba livia), we conducted the most in-depth investigation to date of how stress affects all gene transcription of a biological system essential for facilitating reproduction - the hypothalamic-pituitary-gonadal (HPG) axis. The HPG transcriptome responded to stress in both sexes, but females exhibited more differential expression than males, and these stress responsive genes were mostly unique to females. This result may be due to 1) fluctuations in the female endocrine environment to facilitate ovulation and follicle maturation, and 2) their evolutionary history. We offer a vital genomic foundation on which sex-specific reproductive dysfunction can be studied, as well as novel gene targets for genetic intervention and therapy investigations.

neuroscience↗

bioRxiv2017

Twelve Elements of Visualization and Analysis for Tertiary and Quaternary Structure of Biological Molecules

During the last decades, 3D Molecular Graphics in Life Sciences has been used almost exclusively by experts through complex software and applications ranging from Structural Biology to Computer Aided Drug Design. The emergence of JavaScript and WebGL as a viable platform has enabled 3D visualization of biomolecular structures through Web browsers, without any need for specialized software. Although still in its infancy, Web Molecular Graphics opens new perspectives. This white paper, proposes a set of Twelve Elements to consider to enable 3D visualization and structural analyses of biological systems in Web molecular viewers. The Elements go beyond 3D graphics and propose an integrated approach to visualize and analyze molecular entities and their interactions in multiple dimensions, at multiple levels of details, for diverse users. The bridging of 1D sequence browsers and 3D structure viewers, possible under a Web browser, enables information flow where molecular biologists can use structural information directly at the sequence level. Given the tsunami of sequence information linked to diseases from next generation sequencing - in need for interpretation - making structural information readily available to research scientists is a tremendous opportunity for medical discovery. The Twelve Elements are conceptual and are intended to entice developers to architect software components and APIs, and to gather together as a community around common goals and open source software. A few features of emerging viewers, all available as open source, are highlighted. Speed and quality of 3D graphics for large molecular systems, the interoperability of Web components, and the instantaneous sharing of annotated visualizations through the Web, are some of the most amazing and promising capabilities of 3D Web viewing, opening bright perspectives for Life Sciences research.

bioinformatics↗

bioRxiv2017

Synaptic homeostasis at the Drosophila neuromuscular junction is a reversible signaling process that is sensitive to high temperature

Homeostasis is a vital mode of biological self-regulation. The hallmarks of homeostasis for any biological system are a baseline set point of physiological activity, detection of unacceptable deviations from the set point, and effective corrective measures to counteract deviations. Homeostatic synaptic plasticity (HSP) is a form of neuroplasticity in which neurons and circuits resist environmental perturbations in order to maintain appropriate levels of activity. One assumption is that if an environmental perturbation triggers homeostatic corrective changes in neuronal properties, those corrective measures should be reversed upon removal of the perturbation. We test the reversibility and limits of HSP at a well-studied model synapse, the Drosophila melanogaster neuromuscular junction (NMJ). At the Drosophila NMJ, impairment of glutamate receptors causes a decrease in quantal size, which is offset by a corrective, homeostatic increase in the number of vesicles released per evoked presynaptic stimulus, or quantal content. This process has been termed presynaptic homeostatic potentiation (PHP). Taking advantage of a GAL4/GAL80TS/UAS expression system, we triggered PHP by expressing a dominant-negative glutamate receptor subunit at the NMJ. We then reversed PHP by halting expression of the dominant-negative receptor. Our data show that PHP is fully reversible over a time course of 48-72 hours after the dominant-negative glutamate receptor stops being genetically expressed. Additionally, we found that the PHP response triggered by the dominant-negative subunit was ablated at high temperatures. Our data show that the long-term maintenance of PHP at the Drosophila NMJ is a reversible regulatory process that is sensitive to temperature.\n\nSIGNIFICANCE STATEMENTBiological homeostatic systems must upregulate or downregulate cellular parameters in order to maintain appropriate set points of physiological activity. Homeostasis is a well-documented mode of regulation in metazoan nervous systems. True homeostatic control should be a reversible process - but due to technical difficulties of presenting and removing functional challenges to living synapses, the reversibility of homeostatic forms of synapse regulation has not been rigorously examined in vivo over extended periods of developmental time. Here we formally demonstrate that homeostatic regulation of Drosophila melanogaster neuromuscular synapse function is reversible and temperature-labile. This is significant because developing methods to study how homeostatic regulatory systems are turned on and off could lead to fundamental new insights about control of synaptic output.

neuroscience↗

bioRxiv2017

Combining multiple functional annotation tools increases completeness of metabolic annotation

Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30-50% of genes without functional annotation, severely limiting our knowledge of the \"parts lists\" that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes. Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3-8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are more phylogenetically distant from well-studied model organisms such as E. coli.

bioinformatics↗

bioRxiv2017

pyABC: distributed, likelihood-free inference

Likelihood-free methods are often required for inference in systems biology. While Approximate Bayesian Computation (ABC) provides a theoretical solution, its practical application has often been challenging due to its high computational demands. To scale likelihood-free inference to computationally demanding stochastic models we developed pyABC: a distributed and scalable ABC-Sequential Monte Carlo (ABC-SMC) framework. It implements computation-minimizing and scalable, runtime-minimizing parallelization strategies for multi-core and distributed environments scaling to thousands of cores. The framework is accessible to non-expert users and also enables advanced users to experiment with and to custom implement many options of ABC-SMC schemes, such as acceptance threshold schedules, transition kernels and distance functions without alteration of pyABCs source code. pyABC includes a web interface to visualize ongoing and 1nished ABC-SMC runs and exposes an API for data querying and post-processing.\n\nAvailability and ImplementationpyABC is written in Python 3 and is released under the GPLv3 license. The source code is hosted on https://github.com/neuralyzer/pyabc and the documentation on http://pyabc.readthedocs.io. It can be installed from the Python Package Index (PyPI).

bioinformatics↗

bioRxiv2017

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network (SCN) from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we use this to define, transcriptomic brain networks (TBN) by estimating gene co-expression between pairs of cortical regions. Finally, we explore the hypothesis that TBN and the SCN are coupled.\n\nTBN and SCN were correlated across connection weights and showed qualitatively similar complex topological properties. There were differences between networks in degree and distance distributions. However, cortical areas connected to each other within modules of the SCN network had significantly higher levels of whole genome co-expression than expected by chance.\n\nNodes connected in the SCN had significantly higher levels of expression and co-expression of a Human Supragranular Enriched (HSE) gene set that are known to be important for large-scale cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not completely related to the common constraint of physical distance on both networks.

neuroscience↗

bioRxiv2017

Objective, Quantitative, Data-Driven Assessment of Chemical Probes

Chemical probes are essential tools for understanding biological systems and for target validation, yet selecting tools for biomedical research is largely biased and subjective. Here we describe the Probe Miner: Chemical Probes Objective Assessment resource - capitalising on the plethora of public medicinal chemistry data to empower quantitative, objective, Big Data-driven assessment of chemical probes. We assess >1.8m compounds for their suitability as chemical tools against 2,220 human targets and dissect their biases and limitations.

bioinformatics↗

bioRxiv2017

Effects of mutation and selection on plasticity of TDH3 promoter activity in Saccharomyces cerevisiae

Phenotypic plasticity is an evolvable property of biological systems that can arise from environment-specific regulation of gene expression. To better understand the evolutionary and molecular mechanisms that give rise to plasticity in gene expression, we quantified the effects of 235 single nucleotide mutations in the Saccharomyces cerevisiae TDH3 promoter (PTDH3) on the activity of this promoter in media containing glucose, galactose, or glycerol as a carbon source. We found that the distributions of mutational effects differed among environments because many mutations altered the plastic response exhibited by the wild type allele. Comparing the effects of these mutations to the effects of 30 PTDH3 polymorphisms on expression plasticity in the same environments provided evidence of natural selection acting to prevent the plastic response in PTDH3 activity between glucose and galactose from becoming larger. The largest changes in expression plasticity were observed between fermentable (glucose or galactose) and nonfermentable (glycerol) carbon sources and were caused by mutations located in the RAP1 and GCR1 transcription factor binding sites. Mutations altered expression plasticity most frequently between the two fermentable environments, however, with mutations causing significant changes in plasticity between glucose and galactose distributed throughout the promoter, suggesting they might affect chromatin structure. Taken together, these results provide insight into the molecular mechanisms underlying gene-by-environment interactions affecting gene expression as well as the evolutionary dynamics affecting natural variation in plasticity of gene expression.\n\nSignificance StatementFrom seasonal variation in the color of butterfly wings to trees bending toward the light, organisms often change in response to their environment. These changes, known as phenotypic plasticity, can result from differences in how genes are expressed among environments. Mutations causing environment-specific changes in gene expression provide raw material for phenotypic plasticity, but their frequency, effect size and direction of effects among environments are not well understood. This study shows that mutations in the promoter of a yeast metabolic gene often display environment-dependent effects on gene expression and that these environment-dependent effects have been shaped by selection in natural populations.

genetics↗

bioRxiv2017

Nanopore sequencing enables near-complete de novo assembly of Saccharomyces cerevisiae reference strain CEN.PK113-7D

The haploid Saccharomyces cerevisiae strain CEN.PK113-7D is a popular model system for metabolic engineering and systems biology research. Current genome assemblies are based on short-read sequencing data scaffolded based on homology to strain S288C. However, these assemblies contain large sequence gaps, particularly in subtelomeric regions, and the assumption of perfect homology to S288C for scaffolding introduces bias.\n\nIn this study, we obtained a near-complete genome assembly of CEN.PK113-7D using only Oxford Nanopore Technologys MinION sequencing platform. 15 of the 16 chromosomes, the mitochondrial genome, and the 2-micron plasmid are assembled in single contigs and all but one chromosome starts or ends in a telomere cap. This improved genome assembly contains 770 Kbp of added sequence containing 248 gene annotations in comparison to the previous assembly of CEN.PK113-7D. Many of these genes encode functions determining fitness in specific growth conditions and are therefore highly relevant for various industrial applications. Furthermore, we discovered a translocation between chromosomes III and VIII which caused misidentification of a MAL locus in the previous CEN.PK113-7D assembly. This study demonstrates the power of long-read sequencing by providing a high-quality reference assembly and annotation of CEN.PK113-7D and places a caveat on assumed genome stability of microorganisms.

genomics↗

bioRxiv2017

Correlations, interactions, and predictability in virus-microbe networks

1Microbes are present in high abundances in the environment and in human-associated microbiomes, often exceeding one million per milliliter. Viruses of microbes are present in even higher abundances and are important in shaping microbial populations, communities, and ecosystems. Given the relative specificity of viral infection, it is essential to identify the functional linkages between viruses and their microbial hosts, particularly given dynamic changes in virus and host abundances. Multiple approaches have been proposed to infer infection networks from time-series of in situ communities, among which correlation-based approaches have emerged as the de facto standard. In this work, we evaluate the accuracy of correlation-based inference methods using an in silico approach. In doing so, we compare predicted networks to actual networks to assess the self-consistency of correlation-based inference. At odds with assumptions underlying its widespread use, we find that correlation is a poor predictor of interactions in the context of viral infection and lysis of microbial hosts. The failure to predict interactions holds for methods which leverage product-moment, time-lagged, and relative-abundance based correlations. In closing, we discuss alternative inference methods, particularly model-based methods, as a means to infer interactions in complex microbial communities with viruses.\n\n2 ImportanceInferring interactions from population time-series is an active and ongoing area of research. It is relevant across many biological systems - in particular in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference - using correlations to predict interactions - is widespread. However, it is well known that \"correlation does not imply causation\". Despite this, many studies apply correlation-based inference methods to experimental time-series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silica virus-microbe communities, raising questions on their relevance when applied in situ.

ecology↗

bioRxiv2017

Optical Coherence Tomography Reveals Mechanobiologically Stable Self-Organizing Di-Fork Architecture of Mice Cutaneous Scars

Scientific studies report crucial impacts of biomechanical effectors to modulate wound healing either by scarring or regeneration. Further, the biological decision to predominantly favor the former is still cryptic. Real-time visualization of biomechanical manifestations in situ in scarring is hence necessary. Endorsed by nanostructural testing, synthetic phantom analysis, and computational simulations, we found strong mechanobiological correlates for Swept Source Optical Coherence Tomography (SS-OCT) speckles in mice cutaneous repair (full-thickness) up to 10 months. The theoretical basis of the optomechanics to provide insights into scar form-factor and evolution is proposed. Optomechanical changes have been considered as the resultant of intrinsic (e.g. fiber elastic modulus) and gross tissue mechanics (extracellular matrix (ECM)) in maturing scars. Non-invasive optomechanics supported with microscopic findings reveal scars cross-sectional self-organizing di-fork architecture. Dual-compartment heterogeneity of di-fork exhibits stress-evading features with a dichotomy in inhabitant cellular stress-fiber distributions. This differential interactivity of scar with adjoining tissues reflects its architectural intelligence to compensate tissue loss (hypodermis/muscle) by assembling into a di-fork. Gradual establishment of baseline shifted lasting mechanobiological steady-state, later in scarring, expose scar as an alternate stable state within the skin. Significance StatementWound repair in mammals, predominantly culminates into function compromising scar that is occasionally fatal in vital organs. How the biological system often adopts scarring over a restorative regeneration is yet a conundrum. Wound and ambient mechanics play a pivotal role in deciding the healing fate. SS-OCT is hence demonstrated here as a non-invasive window to such mechanical manifestations during skin wound healing. This exposed gradual emergence of temporally maintained and stress-resilient di-fork architecture of the scar with differential neighborhood interfaces. Accommodation of such an alternate self-organizing steady-state of scar sheds light on its sustenance and paradoxical selection.

biophysics↗

bioRxiv2017

canvasXpress: A versatile interactive high-resolution scientific multi-panel visualization toolkit

To the Editor: CanvasXpress (https://canvasxpress.org) was developed as the core visualization component for bioinformatics and systems biology analysis at Bristol-Myers Squibb and further enhanced by scientists around the world and served as a key visualization engine for many popular bioinformatics tools1,2,3,4,5,6. It offers a rich set of interactive plots to display scientific and genomics data, such as oncoprint of cancer mutations, heatmap, 3D scatter, violin, radar, and profile plots (Figure 1, canvasXpress plots arranged by canvasDesigner https://baohongz.github.io/canvasDesigner). Recently, the reproducibility and usability of the package in real world bioinformatics and clinical use cases have been improved significantly witnessed by continuous add-on features and wide adoption of the toolkit in the scientific communities. Furthermore, It is the first noteworthy package harmonizing real time interactive exploring and analyzing of big data, full-fledged customizing of look-n-feel, and producing multi-panel publication-ready figures in PDF format simultaneously.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC=\"FIGDIR/small/186213_fig1.gif\" ALT=\"Figure 1\">\nView larger version (36K):\norg.highwire.dtl.DTLVardef@1d8392borg.highwire.dtl.DTLVardef@916a49org.highwire.dtl.DTLVardef@d90b05org.highwire.dtl.DTLVardef@162a8fc_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Versatile plots generated by canvasXpress. A) Oncoprint of cancer mutations; B) Gene expression heatmap with comprehensive annotations; C) 3D scatter plot; D) Violin plot; E) Radar plot with annotations; F) Profile plot of gene expression.\n\nC_FIG

bioinformatics↗

bioRxiv2017

Reactive astrocytes - comprehending when neurons play 4’33”

Homeostatic regulation is a powerful tool utilized by virtually all biological systems, brain included. Broadly speaking, each homeostatic process embodies two components: the sensing component, whereby a deviation is detected and quantified, and the effecting component that executes the homeostatic adjustment with the goal of alleviating the deviation. In the central nervous system, homeostatic plasticity has been suggested to play an important role in shaping the dynamics of single neurons and neuronal networks. However, existing \"biophysical\" models of homeostatic plasticity are exceedingly simplistic. These models usually describe the sensor component in terms of simple averaging over neuronal activity and offer no explanation of the relevant biochemical pathways. Here, we attempt to fill this gap in our understanding of homeostatic plasticity by proposing a biophysical framework to explain detection of prolonged synaptic inactivity that may occur in some scenarios of brain injury. We propose that sensing of, and response to, synaptic inactivity involves detection of the extracellular glutamate level and occurs via the activation of metabotropic glutamate receptors (mGluRs), while the inactivity-induced synthesis of one of the homeostatic plasticity effectors, tumor necrosis factor alpha (TNF), serves as an effecting component of the system. This model can help to explain the experimental observations linking prolonged neuronal inactivity to TNF signaling. Importantly, the proposed signaling scheme is not limited to mGluRs and astrocytes, but rather is potentially applicable to any cells expressing receptors that activate the relevant G protein units. The proposed signaling scheme is likely to be useful for developing pharmacological interventions targeting homeostatic plasticity pathways.\n\nAuthor summaryHomeostatic regulation refers to the self-regulation ability of a system aimed at remaining in the same (or nearly the same) state. Homeostatic plasticity, a form of homeostatic regulation that arises in the context of neural dynamics, has been shown to shape neuronal and network dynamics by attempting to maintain physiological levels of neuronal activity in brain networks. Thus, it changes the excitation-inhibition balance in a network when sufficiently large and long-lasting deviations from physiological levels of activity are detected. However, the biophysical mechanisms of homeostatic plasticity and in particular those related to the ability to sense neuronal inactivity, remain elusive. We propose a feasible biophysical model of a homeostatic sensor, in which the sensing of inactivity depends on the presence and activation of G-protein-coupled receptors with different activation thresholds. In this model, the activation of the higher-threshold receptor suppresses the detection of inactivity, while the activation of the lower-threshold receptor promotes the detection of inactivity. The proposed model helps to explain a growing body of experimental data relating synaptic inactivity to production of homeostatic plasticity effectors such as tumor necrosis factor alpha. Although we consider a specific case study of metabotropic glutamate receptors on astrocytes, the model conclusions are likely to be applicable to any cells expressing receptors that activate the relevant G protein units.

neuroscience↗

bioRxiv2017

Dynamic and Depth Dependent Nanomechanical Properties of Dorsal Ruffles in Live Cells and Biopolymeric Hydrogels

The nanomechanical properties of various biological and cellular surfaces are increasingly investigated with Scanning Probe Microscopy. Surface stiffness measurements are currently being used to define metastatic properties of various cancerous cell lines and other related biological tissues. Here we present a unique methodology to understand depth dependent nanomechanical variations in stiffness in biopolymers and live cells. In this study we have used A2780 & NIH3T3 cell lines and 0.5% & 1% Agarose to investigate depth dependent stiffness and porosity on nanomechanical properties in different biological systems. This analytical methodology can circumvent the issue associated with the contribution of substrates on cell stiffness. Here we demonstrate that by calculating continuous-step-wise-modulus on force vs. distance curves one can observe minute variation as function of depth. Due to the presence of different kinds of cytoskeletal filament, dissipation of contact force might vary from one portion of a cell to another. On NIH3T3 cell lines, stiffness profile of Circular Dorsal Ruffles could be observed in form of large parabolic feature with changes in stiffness at different depth. In biopolymers like agarose, depending upon the extent of polymerization in there can be increase or decrease in stiffness due variations in pore size and extent to which crosslinking is taking place at different depths. 0.5% agarose showed gradual decrease in stiffness whereas with 1% agarose there was slight increase in stiffness as one indents deeper into its surface.

biophysics↗

bioRxiv2017

Continuous rearrangement of the postsynaptic gephyrin scaffolding domain: a super-resolution quantified and energetic approach

Synaptic function and plasticity requires a delicate balance between overall structural stability and the continuous rearrangement of the components that make up the presynaptic active zone and the postsynaptic density (PSD). Photoactivated localization microscopy (PALM) has provided a detailed view of the nanoscopic structure and organization of some of these synaptic elements. Still lacking, are tools to address the morphing and stability of such complexes at super-resolution. We describe an approach to quantify morphological changes and energetic states of multimolecular assemblies over time. With this method, we studied the scaffold protein gephyrin, which forms postsynaptic clusters that play a key role in the stabilization of receptors at inhibitory synapses. Postsynaptic gephyrin clusters exhibit an internal microstructure composed of nanodomains. We found, that within the PSD, gephyrin molecules continuously undergo spatial reorganization. This dynamic behavior depends on neuronal activity and cytoskeleton integrity. The proposed approach also allowed access to the effective energy responsible for the tenacity of the PSD despite molecular instability.\n\nSignificant statementSuper-resolution microscopy has become an important tool for the study of biological systems, allowing detailed, nano-scale structural reconstruction, single molecule tracking, particle counting, and interaction studies. However, quantification tools that take full advantage of the information provided by this technology are still lacking. We describe a novel quantification method to obtain information related to the size, directionality, dynamics, and stability of clustered structures from super-resolution microscopy. With this method, we studied the stability of gephyrin clusters, the main inhibitory scaffold protein. We found that gephyrin molecules continuously undergo reorganization based on neuronal activity and changes in the cytoskeleton.

biophysics↗