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Results for “Biophysics”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

bioRxiv2016

Coral life history differences determine the refugium potential of a remote Caribbean reef

Remote populations can influence connectivity and may serve as refugia from climate change. Here, we investigated two reef-building corals (Pseudodiploria strigosa and Orbicella franksi) from the Flower Garden Banks (FGB) - the most isolated, high-latitude Caribbean reef system that retains high coral cover. We characterized coral size frequency distributions, quantified larval mortality rates and onset of competence, estimated larval production, and created detailed biophysical models incorporating these parameters to evaluate source-sink dynamics from 2009 to 2012. Mortality rates were similar across species but competency differed dramatically: P. strigosa was capable of metamorphosis within 2.5 days post fertilization (dpf), while O. franksi were not competent until >20dpf and remained competent up to 120dpf Despite these differences, models demonstrated that larvae of both types were similarly successful in reseeding the FGB. Nevertheless, corals with shorter pelagic larval durations (PLD), such as P. strigosa, were highly isolated from the rest of the Caribbean, while long PLD corals, such as O. franksi, could export larvae to distant northern Caribbean reefs. These results suggest that FGB coral populations are self-sustaining and highlight the potential of long PLD corals, such as endangered Orbicella, to act as larval sources for other degraded Caribbean reefs.

Ecology↗

bioRxiv2016

Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites.

Synaptic plasticity is thought to be the principal mechanism underlying learning in the brain. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the complexity of dendrites, which allow non-linear local processing of the synaptic inputs. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. Implementing biophysically realistic neuron models, we studied how dendrites allow for multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compared the conditions for STDP and for the synaptic strengthening by local dendritic spikes. We further explored how the connectivity between two cells is affected by these plasticity rules and the synaptic distributions. Finally, we show how memory retention in associative learning can be prolonged in networks of neurons with dendrites.

Neuroscience↗

bioRxiv2016

Is the Statistic Value All We Should Care about in Neuroimaging?

Here we address an important issue that has been embedded within the neuroimaging community for a long time: the absence of effect estimates in results reporting in the literature. The statistic value itself, as a dimensionless measure, does not provide information on the biophysical interpretation of a study, and it certainly does not represent the whole picture of a study. Unfortunately, in contrast to standard practice in most scientific fields, effect (or amplitude) estimates are usually not provided in most results reporting in the current neuroimaging publications and presentations. Possible reasons underlying this general trend include: 1) lack of general awareness, 2) software limitations, 3) inaccurate estimation of the BOLD response, and 4) poor modeling due to our relatively limited understanding of FMRI signal components. However, as we discuss here, such reporting damages the reliability and interpretability of the scientific findings themselves, and there is in fact no overwhelming reason for such a practice to persist. In order to promote meaningful interpretation, cross validation, reproducibility, meta and power analyses in neuroimaging, we strongly suggest that, as part of good scientific practice, effect estimates should be reported together with their corresponding statistic values. We provide several easily adaptable recommendations for facilitating this process.

Neuroscience↗

bioRxiv2016

A Multi-Scale Computational Model of the effects of TMS on Motor Cortex

The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

Neuroscience↗

bioRxiv2016

Ephemeral protein binding to DNA shapes stable nuclear bodies and chromatin domains

Fluorescence microscopy reveals that the contents of many (membrane-free) nuclear \"bodies\" exchange rapidly with the soluble pool whilst the underlying structure persists; such observations await a satisfactory biophysical explanation. To shed light on this, we perform large-scale Brownian dynamics simulations of a chromatin fiber interacting with an ensemble of (multivalent) DNA-binding proteins; these proteins switch between two states - active (binding) and inactive (non-binding). This system provides a model for any DNA-binding protein that can be modified post-translationally to change its affinity for DNA (e.g., like the phosphorylation of a transcription factor). Due to this out-of-equilibrium process, proteins spontaneously assemble into clusters of self-limiting size, as individual proteins in a cluster exchange with the soluble pool with kinetics like those seen in photo-bleaching experiments. This behavior contrasts sharply with that exhibited by \"equilibrium\", or non-switching, proteins that exist only in the binding state; when these bind to DNA non-specifically, they form clusters that grow indefinitely in size. Our results point to post-translational modification of chromatin-bridging proteins as a generic mechanism driving the self-assembly of highly dynamic, non-equilibrium, protein clusters with the properties of nuclear bodies. Such active modification also reshapes intra-chromatin contacts to give networks resembling those seen in topologically-associating domains, as switching markedly favors local (short-range) contacts over distant ones.

Preprint↗

bioRxiv2016

Neuronal gamma-band synchronization regulated by instantaneous modulations of the oscillation frequency

Neuronal gamma-band synchronization shapes information flow during sensory and cognitive processing. A common view is that a stable and shared frequency over time is required for robust and functional synchronization. To the contrary, we found that non-stationary instantaneous frequency modulations were essential for synchronization. First, we recorded gamma rhythms in monkey visual area V1, and found that they synchronized by continuously modulating their frequency difference in a phase-dependent manner. The frequency modulation properties regulated both the phase-locking and the preferred phase-relation between gamma rhythms. Second, our experimental observations were in agreement with a biophysical model of gamma rhythms and were accurately predicted by the theory of weakly coupled oscillators revealing the underlying theoretical principles that govern gamma synchronization. Thus, synchronization through instantaneous frequency modulations represents a fundamental principle of gamma-band neural coordination that is likely generalizable to other brain rhythms.

Neuroscience↗

bioRxiv2016

The visual white matter: The application of diffusion MRI and fiber tractography to vision science

Visual neuroscience has traditionally focused much of its attention on understanding the response properties of neurons along the visual pathways. This review focuses instead on the properties of the white matter connections between these neurons. Specifically, we provide an introduction to methods to study the human visual white matter using diffusion MRI (dMRI). This method allows us to measure the white matter connections in individual visual systems in vivo, allows us to trace long-range connections between different parts of the visual system, and to measure the biophysical properties of these connections. We explain the principles underlying dMRI measurements and the basics of modeling these data. We review a range of findings from recent studies on connections between different visual field maps, on the effects of visual impairment on the white matter, and on the properties underlying networks that process visual information that supports visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open data-sets that are becoming available to study brain connectivity and white matter properties, and open-source software for the analysis of these data.

Neuroscience↗

bioRxiv2017

mTORC1 controls cytoplasmic crowding by regulating ribosome concentration

Summary (Abstract)Macromolecular crowding has a profound impact on reaction rates and the physical properties of the cell interior, but the mechanisms that regulate crowding are poorly understood. We developed Genetically Encoded Multimeric nanoparticles (GEMs) to dissect these mechanisms. GEMs are homomultimeric scaffolds fused to a fluorescent protein. GEMs self-assemble into bright, stable fluorescent particles of defined size and shape. By combining tracking of GEMs with genetic and pharmacological approaches, we discovered that the mTORC1 pathway can tune the effective diffusion coefficient of macromolecules [≥]15 nm in diameter more than 2-fold without any discernable effect on the motion of molecules [≥]5 nm. These mTORCI-dependent changes in crowding and rheology affect phase-separation both in vitro and in vivo. Together, these results establish a role for mTORCI in controlling both the biophysical properties of the cytoplasm and the phase-separation of biopolymers.

cell biology↗

bioRxiv2016

Automated Incorporation of Pairwise Dependency in Transcription Factor Binding Site Prediction Using Dinucleotide Weight Tensors

Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts.\n\nHere we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites.\n\nWe make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform motif finding, i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT dilogo motifs.\n\nAuthor SummaryGene regulatory networks are ultimately encoded in constellations of short binding sites in the DNA and RNA that are recognized by regulatory factors such as transcription factors (TFs). For several decades, computational analysis of regulatory networks has relied on a model of TF sequence-specificity, the position-specific weight-matrix (PSWM), that assumes different positions in a binding site contribute independently to the total binding energy of the TF. However, in recent years evidence has been accumulating that, at least for some TFs, this assumption does not hold. Here we present a new model for the sequence-specificity of TFs, the dinucleotide weight tensor (DWT), that takes arbitrary dependencies between positions in binding sites into account and show that it consistently outperforms PSWMs on high-throughput datasets on TF binding. Moreover, in contrast to previous approaches, DWTs are directly derived from first principles within a Bayesian framework, and contain no tunable parameters. This allows them to be easily applied in practice and we make a suite of tools available for computational analysis with DWTs.

Bioinformatics↗

bioRxiv2016

Crystal structure and RNA-binding properties of an Hfq homolog from the deep-branching Aquificae: Conservation of the lateral RNA-binding mode

SynopsisThe structure of an Hfq homolog from the deep-branching thermophilic bacterium Aquifex aeolicus, determined to 1.5-[A] resolution both in apo form and bound to a uridine-rich RNA, reveals a conserved, pre-organized RNA-binding pocket on the lateral rim of the Hfq hexamer.\n\nAbstractThe host factor Hfq, as the bacterial branch of the Sm family, is an RNA-binding protein involved in post-transcriptional regulation of mRNA expression and turnover. Hfq facilitates pairing between small regulatory RNAs (sRNA) and their corresponding mRNA targets by binding both RNAs and bringing them into close proximity. Hfq homologs self-assemble into homo-hexameric rings, with at least two distinct surfaces that bind RNA. Recently, another binding site--dubbed the lateral rim--has been implicated in sRNA*mRNA annealing; the RNA-binding properties of this site appear to be rather subtle, and its degree of evolutionary conservation is unknown. An Hfq homolog has been identified in the phylogenetically deep-branching thermophile Aquifex aeolicus (Aae), but little is known about the structures and functions of Hfq from basal bacterial lineages such as the Aquificae. Thus, we have cloned, overexpressed, purified, crystallized, and biochemically characterized Aae Hfq. We have determined the structures of Aae Hfq in space-groups P1 and P6, both to 1.5 [A] resolution, and we have discovered nanomolar-scale binding affinities for uridine- and adenosine-rich RNAs. Co-crystallization with U6 RNA reveals that the outer rim of the Aae Hfq hexamer features a well-defined binding pocket that is selective for uracil. This Aae Hfq structure, combined with biochemical and biophysical characterization of the homolog, reveals deep evolutionary conservation of the lateral RNA-binding mode, and lays a foundation for further studies of Hfq-associated RNA biology in ancient bacterial phyla.

Biochemistry↗

bioRxiv2016

Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

Proteins evolve through two primary mechanisms: substitution, where mutations alter a proteins amino-acid sequence, and insertions and deletions (indels), where amino acids are either added to or removed from the sequence. Protein structure has been shown to influence the rate at which substitutions accumulate across sites in proteins, but whether structure similarly constrains the occurrence of indels has not been rigorously studied. Here, we investigate the extent to which structural properties known to covary with protein evolutionary rates might also predict protein tolerance to indels. Specifically, we analyze a publicly available dataset of single-amino-acid deletion mutations in enhanced green fluorescent protein (eGFP) to assess how well the functional effect of deletions can be predicted from protein structure. We find that weighted contact number (WCN), which measures how densely packed a residue is within the proteins three-dimensional structure, provides the best single predictor for whether eGFP will tolerate a given deletion. We additionally find that using protein design to explicitly model deletions results in improved predictions of functional status when combined with other structural predictors. Our work suggests that structure plays fundamental role in constraining deletions at sites in proteins, and further that similar biophysical constraints influence both substitutions and deletions. This study therefore provides a solid foundation for future work to examine how protein structure influences tolerance of more complex indel events, such as insertions or large deletions.

Bioinformatics↗

bioRxiv2016

Modelling avoidance in pathologically anxious humans using reinforcement-learning

Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behaviour -avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms, the biological mechanisms underlying avoidance remain unclear. Reinforcement-learning models provide formal and testable characterizations of the mechanisms of decision-making; here, we examine avoidance in these terms. One hundred and one healthy and pathologically anxious individuals completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the anxious group on a parameter of our reinforcement-learning model that characterizes a prepotent (Pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the anxious individuals were under stress. This formal description of avoidance within the reinforcement-learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of pathological anxiety.

animal behavior and cognition↗

bioRxiv2016

Experimental and Computational Investigation of the Structure of Peptide Monolayers on Gold Nanoparticles

The self-assembly and self-organization of small molecules at the surface of nanoparticles constitute a potential route towards the preparation of advanced protein-like nanosystems. However, their structural characterization, critical to the design of bio-nanomaterials with well-defined biophysical and biochemical properties, remains highly challenging. Here, a computational model for peptide-capped gold nanoparticles is developed using experimentally characterized CALNN-and CFGAILSS-capped gold nanoparticles as a benchmark. The structure of CALNN and CFGAILSS monolayers is investigated by both structural biology techniques and molecular dynamics simulations. The calculations reproduce the experimentally observed dependence of the monolayer secondary structure on peptide capping density and on nanoparticle size, thus giving us confidence in the model. Furthermore, the computational results reveal a number of new features of peptide-capped monolayers, including the importance of sulfur movement for the formation of secondary structure motifs, the presence of water close to the gold surface even in tightly packed peptide monolayers, and the existence of extended 2D parallel {beta}-sheet domains in CFGAILSS monolayers. The model developed here provides a predictive tool that may assist in the design of further bio-nanomaterials.

biochemistry↗

bioRxiv2016

Structural and Functional analysis of the GABARAP Interaction Motif (GIM).

Through the canonical LC3 interaction motif (LIR), [W/F/Y]-X1-X2-[I/L/V], protein complexes are recruited to autophagosomes to perform their functions as either autophagy adaptors or receptors. How these adaptors/receptors selectively interact with either LC3 or GABARAP families remains unclear. Herein, we determine the range of selectivity of 30 known core LIR motifs towards LC3s and GABARAPs. From these, we define a GABARAP Interaction Motif (GIM) sequence (W/F-V-X2-V) that the adaptor protein PLEKHM1 tightly conforms to. Using biophysical and structural approaches, we show that the PLEKHM1-LIR is indeed eleven-fold more specific for GABARAP than LC3B. Selective mutation of the X1 and X2 positions either completely abolished the interaction with all LC3 and GABARAPs or increased PLEKHM1-GIM selectivity 20-fold towards LC3B. Finally, we show that conversion of the canonical p62/SQSTM1-LIR into our newly defined GIM, by introducing two valine residues, enhances p62/SQSTM1 interaction with endogenous GABARAP over LC3B. The identification of a GABARAP-specific interaction motif will aid the identification and characterization of the continually expanding array of autophagy receptor and adaptor proteins and their in vivo functions.

biochemistry↗

bioRxiv2016

Bridging multiple scales in the human brain using computational modelling

Brain dynamics span multiple spatial and temporal scales, from fast spiking neurons to slow fluctuations over distributed areas. No single experimental method links data across scales. Here, we bridge this gap using The Virtual Brain connectome-based modelling platform to integrate multimodal data with biophysical models and support neurophysiological inference. Simulated cell populations were linked with subject-specific white-matter connectivity estimates and driven by electroencephalography-derived electric source activity. The models were fit to subject-specific resting-state functional magnetic resonance imaging data, and overfitting was excluded using 5-fold cross-validation. Further evaluation of the models show how balancing excitation with feedback inhibition generates an inverse relationship between -rhythms and population firing on a faster time scale and resting-state network oscillations on a slower time scale. Lastly, large-scale interactions in the model lead to the emergence of scale-free power-law spectra. Our novel findings underscore the integrative role for computational modelling to complement empirical studies.

neuroscience↗

bioRxiv2016

Optimization of lag phase shapes the evolution of a bacterial enzyme

Mutations provide the variation that drives evolution, yet their effects on fitness remain poorly understood. Here we explore how mutations in the essential enzyme Adenylate Kinase (Adk) of E. coli affect multiple phases of population growth. We introduce a biophysical fitness landscape for these phases, showing how they depend on molecular and cellular properties of Adk. We find that Adk catalytic capacity in the cell (product of activity and abundance) is the major determinant of mutational fitness effects. We show that bacterial lag times are at a well-defined optimum with respect to Adks catalytic capacity, while exponential growth rates are only weakly affected by variation in Adk. Direct pairwise competitions between strains show how environmental conditions modulate the outcome of a competition where growth rates and lag times have a tradeoff, altogether shedding light on the multidimensional nature of fitness and its importance in the evolutionary optimization of enzymes.

evolutionary biology↗

bioRxiv2016

New single nucleotide polymorphisms (SNPs) in homologous recombination repair genes detected by microarray analysis in Polish breast cancer patients

Purpose of the studyBreast cancer is the most common cause of malignancy mortality in women worldwide. This study aimed at localising homologous recombination repair (HR) genes and their chromosomal loci and correlating their nucleotide variants with susceptibility to breast cancer. In this study authors analysed the association between single nucleotide polymorphisms (SNPs) in homologous recombination repair genes and the incidence of breast cancer in the population of Polish women.\n\nMethodsBlood samples from 94 breast cancer patients were analysed as test group. Individuals were recruited into the study at the Department of Oncological Surgery and Breast Diseases of the Institute of the Polish Mothers Memorial Hospital in Lodz, Poland. Healthy controls (n=500) were obtained from the Biobank Laboratory, Department of Molecular Biophysics, University of Lodz. Then, DNA of breast cancer patients was compared with one of disease-free women. The test was supported by microarray analysis.\n\nResultsStatistically significant correlations were identified between breast cancer and 3 not described previously single nucleotide polymorphisms (SNPs) of homologous recombination repair genes BRCA1 and BRCA2: rs59004709, rs4986852 and rs1799950.\n\nConclusionsFurther studies on larger groups are warranted to support the hypothesis of correlation between the above-mentioned genetic variants and breast cancer risk.

genetics↗

bioRxiv2016

Identification and quantitative analysis of the major determinants of translation elongation rate variation

Previous studies have shown that translation elongation is regulated by multiple factors, but the observed heterogeneity remains only partially explained. To dissect quantitatively the different determinants of elongation speed, we use probabilistic modeling to estimate initiation and local elongation rates from ribosome profiling data. This model-based approach allows us to quantify the extent of interference between ribosomes on the same transcript. We show that neither interference nor the distribution of slow codons is sufficient to explain the observed heterogeneity. Instead, we find that electrostatic interactions between the ribosomal exit tunnel and specific parts of the nascent polypeptide govern the elongation rate variation as the polypeptide makes its initial pass through the tunnel. Once the N-terminus has escaped the tunnel, the hydropathy of the nascent polypeptide within the ribosome plays a major role in modulating the speed. We show that our results are consistent with the biophysical properties of the tunnel.

genomics↗