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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Computing Osmotic Permeabilities of Aquaporins AQP4, AQP5, and GlpF from Near-Equilibrium Simulations

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC=\"FIGDIR/small/102343_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@c57ff0org.highwire.dtl.DTLVardef@527a79org.highwire.dtl.DTLVardef@e6bde6org.highwire.dtl.DTLVardef@bd53cf_HPS_FORMAT_FIGEXP M_FIG C_FIG Measuring or computing the single-channel permeability of aquaporins/aquaglyceroporins (AQPs) has long been a challenge. The measured values scatter over an order of magnitude but the corresponding Arrhenius activation energies converge in the current literature. Osmotic flux through an AQP was simulated as water current forced through the channel by kilobar hydraulic pressure or theoretically approximated as single-file diffusion. In this paper, we report large scale simulations of osmotic current under sub M gradient through three water channels (the water-specific AQP4 and AQP5 along with aquaglyceroporin GlpF) using the mature particle mesh Ewald technique (PME) for which the established force fields have been optimized with known accuracy. These simulations were implemented with hybrid periodic boundary conditions devised to avoid the artifactitious mixing across the membrane in a regular PME simulation. The computed single-channel permeabilities at 5{degrees}C and 25{degrees}C are in agreement with recently refined experiments on GlpF. The Arrhenius activation energies extracted from our simulations for all the three AQPs agree with the in vitro measurements. The single-file diffusion approximations from our large-scale simulations are consistent with the current literature on smaller systems. From these unambiguous agreements among the in vitro and in silico studies, we observe the quantitative accuracy of the all-atom force fields of the current literature for water-channel biology. We also observe that AQP4, that is particularly rich in the central nervous system, is more efficient in water conduction and more temperature-sensitive than other water-specific channel proteins.

biophysics↗

bioRxiv2016

A two-state photoconversion model predicts the spectral response dynamics of optogenetic systems

In optogenetics, light signals are used to control genetically engineered photoreceptors, and in turn manipulate biological pathways with unmatched precision. Recently, evolved photoreceptors with diverse in vitro-measured wavelength and intensity-dependent photoswitching properties have been repurposed for synthetic control of gene expression, proteolysis, and numerous other cellular processes. However, the relationship between the input light spectrum and in vivo photoreceptor response dynamics is poorly understood, restricting the utility of these optogenetic tools. Here, we advance a classic in vitro two-state photoreceptor model to reflect the in vivo environment, and combine it with simplified mathematical descriptions of signal transduction and output gene expression through our previously engineered green/red and red/far red photoreversible bacterial two-component systems (TCSs). Additionally, we leverage our recent open-source optical instrument to develop a workflow of spectral and dynamical characterization experiments to parameterize the model for both TCSs. To validate our approach, we challenge the model to predict experimental responses to a series of complex light signals very different from those used during parameterization. We find that the model generalizes remarkably well, predicting the results of all categories of experiments with high quantitative accuracy for both systems. Finally, we exploit this predictive power to program two simultaneous and independent dynamical gene expression signals in bacteria expressing both TCSs. This multiplexed gene expression programming approach will enable entirely new studies of how metabolic, signaling, and decision-making pathways integrate multiple gene expression signals. Additionally, our approach should be compatible with a wide range of optogenetic tools and model organisms.\n\nSignificance statementLight-switchable signaling pathways (optogenetic tools) enable precision studies of how biochemical networks underlie cellular behaviors. We have developed a versatile mathematical model based on a two-state photoconversion mechanism that we have applied to the E. coli CcaSR and Cph8-OmpR optogenetic tools. This model enables accurate prediction of the gene expression response to virtually any light source or mixture of light sources. We express both optogenetic tools in the same cell and apply our model to program two simultaneous and independent gene expression signals in the same cell. This method can be used to study how biological pathways integrate multiple inputs and should be extensible to other optogenetic tools and host organisms.

synthetic biology↗

bioRxiv2016

Analysis of Antibody Hybridization and Autofluorescence in Touch Samples by Flow Cytometry: Implications for Front End Separation of Trace Mixture Evidence

The goal of this study was to survey optical and biochemical variation in cell populations deposited onto a surface through touch or contact and identify specific features that may be used to differentially label and then sort cell populations from separate contributors in a trace biological mixture. Cell characterizations initially focused on two different protein systems, Human Leukocyte Antigen (HLA) complex and cytokeratin (CK) filaments. Hybridization experiments using pan and allele-specific HLA antibody probes showed that surface antigens on cells transferred from the palmar surface of volunteers are largely unreactive, suggesting that they cannot be used to differentiate cell populations in a touch mixture. Touch samples were also hybridized with the pan-CK probe AE1, which targets CK proteins 10, 14, 15, 16 and 19. Fluorescence levels of AE1 hybridized cells were observed to vary across donors, although these differences were not consistent across all sampling days. We then investigated variations in red autofluorescence profiles (650-670nm) as a potential signature for distinguishing contributor cell populations. Although distinct differences in red autofluorescence profiles were observed -- with one donor consistently exhibiting higher levels of fluorescence than others -- some variation was also observed in touch samples collected from the same individual on different days. While this suggests that contributor touch samples cannot be defined by a discrete level of autofluorescence, this attribute may still be a useful means of isolating contributors to some touch mixtures. To test whether these observed optical differences could potentially be used as the basis for a cell separation workflow, a controlled two person touch mixture was separated into two fractions via Fluorescence Activated Cell Sorting (FACS) using gating criteria based on intensity of 650-670nm emissions, and then subjected to DNA analysis. STR typing of the sorted fractions provided partial profiles that were consistent with separation of individual contributors from the mixture.

Genetics↗

bioRxiv2017

Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

Genetics can provide a systematic approach to discovering the tissues and cell types relevant for a complex disease or trait. Identifying these tissues and cell types is critical for following up on non-coding allelic function, developing ex-vivo models, and identifying therapeutic targets. Here, we analyze gene expression data from several sources, including the GTEx and PsychENCODE consortia, together with genome-wide association study (GWAS) summary statistics for 48 diseases and traits with an average sample size of 169,331, to identify disease-relevant tissues and cell types. We develop and apply an approach that uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We detect tissue-specific enrichments at FDR < 5% for 34 diseases and traits across a broad range of tissues that recapitulate known biology. In our analysis of traits with observed central nervous system enrichment, we detect an enrichment of neurons over other brain cell types for several brain-related traits, enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index, and enrichments in the cortex for schizophrenia and in the striatum for migraine. In our analysis of traits with observed immunological enrichment, we identify enrichments of T cells for asthma and eczema, B cells for primary biliary cirrhosis, and myeloid cells for Alzheimer's disease, which we validated with independent chromatin data. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signal.

genetics↗

bioRxiv2018

Aperture Phase Modulation with Adaptive Optics: A Novel Approach for Speckle Reduction and Structure Extraction in Optical Coherence Tomography

AbstractSpeckle is an inevitable consequence of the use of coherent light in optical coherence tomography (OCT), and often acts as noise that obscures micro-structures of biological tissue. We here present a novel method of suppressing speckle noise intrinsically compatible with adaptive optics (AO) in OCT system: by modulating the phase inside the imaging system pupil aperture with a segmented deformable mirror, thus producing minor perturbations in the point spread function (PSF) to create un-correlated speckle pattern between B-scans, and further averaging to wash out the speckle but maintain the structures. It is a well-controlled and universal method which can efficiently determine the optimal range of phase modulation that minimizing speckle noise while maximizing image resolution and signal strength for different systems and/or samples. As an active method, its effectiveness and efficiency were demonstrated by both ex-vivo non-biological and in-vivo biological applications.

bioengineering↗

bioRxiv2018

Deconstructing cell-free extract preparation for in vitro activation of transcriptional genetic circuitry

Recent advances in cell-free gene expression (CFE) systems have enabled their use for a host of synthetic biology applications, particularly for rapid prototyping of genetic circuits designed as biosensors. Despite the proliferation of cell-free protein synthesis platforms, the large number of currently existing protocols for making CFE extracts muddles the collective understanding of how the method by which an extract is prepared affects its functionality. Specifically, a key goal toward developing cell-free biosensors based on native genetic regulators is activating the transcriptional machinery present in bacterial extracts for protein synthesis. However, protein yields from genes transcribed in vitro by the native Escherichia coli RNA polymerase are quite low in conventional crude extracts originally optimized for expression by the bacteriophage transcriptional machinery. Here, we show that cell-free expression of genes under bacterial {sigma}70 promoters is constrained by the rate of transcription in crude extracts and that processing the extract with a ribosomal run-off reaction and subsequent dialysis can alleviate this constraint. Surprisingly, these processing steps only enhance protein synthesis in genes under native regulation, indicating that the translation rate is unaffected. We further investigate the role of other common process variants on extract performance and demonstrate that bacterial transcription is inhibited by including glucose in the growth culture, but is unaffected by flash-freezing the cell pellet prior to lysis. Our final streamlined protocol for preparing extract by sonication generates extract that facilitates expression from a diverse set of sensing modalities including protein and RNA regulators. We anticipate that this work will clarify the methodology for generating CFE extracts that are active for biosensing and will encourage the further proliferation of cell-free gene expression technology for new applications.

synthetic biology↗

bioRxiv2017

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Aging-related physiological changes are systemic and, at least in humans, are linearly associated with age. Therefore, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. Aging acceleration, defined as the difference between the predicted and chronological age was found to be elevated in patients with major diseases and is predictive of mortality. In this work, we compare three increasingly accurate biological age models: metrics derived from unsupervised Principal Components Analysis (PCA), alongside two supervised biological age models; a multivariate linear regression and a state-of-the-art deep convolution neural network (CNN). All predictions were made using one-week long locomotor activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) dataset. We found that application of the supervised approaches improves the accuracy of the chronological age estimation at the expense of a loss of the association between the aging acceleration predicted by the model and all-cause mortality. Instead, we turned to the NHANES death register and introduced a novel way to train parametric proportional hazards models in a form suitable for out-of-the-box implementation with any modern machine learning software. Finally, we characterized a proof-of-concept example, a separate deep CNN trained to predict mortality risks that outperformed any of the biological age or simple linear proportional hazards models. Our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

bioinformatics↗

bioRxiv2015

GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown roots systems

Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals. This complex multidimensional amalgam of inputs enables continuous adjustment of root growth rates, direction and metabolic activity that define a dynamic physical network. Current methods for analyzing root biology balance physiological relevance with imaging capability. To bridge this divide, we developed an integrated imaging system called Growth and Luminescence Observatory for Roots (GLO-Roots) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown, light-shielded roots. We have developed image analysis algorithms that allow the spatial integration of soil properties, gene expression and root system architecture traits. We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes.

Plant Biology↗

bioRxiv2018

Proteome thermal stability reflects organ physiology and identifies drug-target engagement in vivo

Studying biological processes at a molecular level and monitoring drug-target interactions is established for simple cell systems but challenging in vivo. We introduce and apply a methodology for proteome-wide thermal stability measurements to characterize organ physiology and activity of many fundamental biological processes across tissues, such as energy metabolism and protein homeostasis. This method, termed tissue thermal proteome profiling (tissue-TPP), also enabled target and off-target identification and occupancy measurements in tissues derived from animals dosed with the non-covalent histone deacetylase inhibitor, panobinostat. Finally, we devised blood-CETSA, a thermal stability-based method to monitor target engagement in whole blood. Our study generates the first proteome-wide map of protein thermal stability in tissue and provides tools that will be of great impact for translational research.

cell biology↗

bioRxiv2015

Systems medicine links microbial inflammatory response with glycoprotein-associated mortality risk

Integrative analyses of high-throughput omics data have elucidated the aetiology and pathogenesis for complex traits and diseases1-4, and the linking of omics information to electronic health records promises new insights into human health and disease. Recent nuclear magnetic resonance (NMR) spectroscopy biomarker profiling has implicated glycoprotein acetyls (GlycA) as a biomarker for cardiovascular risk5 and all-cause mortality6. To elucidate biological processes contributing to GlycA-associated mortality risk, we leveraged human omics data from three population-based cohorts together with nation-wide Finnish hospital and mortality records. Elevated GlycA was associated with myriad infection-related inflammatory processes. Within individuals, elevated GlycA levels were stable over long time periods, up to a decade, and chronically elevated GlycA was also associated with modest elevation of numerous cytokines. Individuals with elevated GlycA also showed increased expression of a transcriptional sub-network, the Neutrophil Degranulation Module (NDM), suggesting an increased activity of microbe-driven immune response. Subsequent analysis of nation-wide hospitalisation and death records was consistent with a microbial basis for GlycA-associated mortality, with each standard deviation increase in GlycA raising an individuals future risk of hospitalization and death from non-localized infection by 40% and 136%, respectively. These results show that, beyond its established role in acute-phase response7-9, elevated GlycA is more broadly a biomarker for low-grade chronic inflammation and increased neutrophil activity. Further, increased risk of susceptibility to severe microbial-infection events in healthy individuals suggests this inflammation is a contributor to mortality risk. Taken together, this study demonstrates the power of an integrative approach that combines omics data and health records to delineate the biological processes underlying a newly discovered biomarker, providing a model strategy for future systems medicine studies.

Genomics↗

bioRxiv2015

Repeatability of evolution on epistatic landscapes

Evolution is a dynamic process. The two classical forces of evolution are mutation and selection. Assuming small mutation rates, evolution can be predicted based solely on the fitness differences between phenotypes. Predicting an evolutionary process under varying mutation rates as well as varying fitness is still an open question. Experimental procedures, however, do include these complexities along with fluctuating population sizes and stochastic events such as extinctions. We investigate the mutational path probabilities of systems having epistatic effects on both fitness and mutation rates using a theoretical and computational framework. In contrast to previous models, we do not limit ourselves to the typical strong selection, weak mutation (SSWM)-regime or to fixed population sizes. Rather we allow epistatic interactions to also affect mutation rates. This can lead to qualitatively non-trivial dynamics. Pathways, that are negligible in the SSWM-regime, can overcome fitness valleys and become accessible. This finding has the potential to extend the traditional predictions based on the SSWM foundation and bring us closer to what is observed in experimental systems.

Evolutionary Biology↗

bioRxiv2016

The innate immune systems of malacostracan crustaceans exhibit both conserved and evolutionarily distinct components

Growing demands for aquatic sources of animal proteins have attracted significant investments in aquaculture research in recent years. The crustacean aquaculture industry has undergone substantial growth to accommodate a rising global demand, however such large-scale production is susceptible to pathogen-mediated destruction. It is clear that a thorough understanding of the crustacean innate immune system is imperative for future research into combating current and future pathogens of the main food crop species. Through a comparative genomics approach utilising extant data from 55 species, we describe the innate immune system of crustaceans from the Malacostraca class. We identify 7407 malacostracan genes from 39 gene families implicated in different aspects of host defence and demonstrate dynamic evolution of innate immunity components within this group. Malacostracans have achieved flexibility in recognising infectious agents through divergent evolution and expansion of pathogen recognition receptors genes. Antiviral RNAi, Toll and JAK-STAT signal transduction pathways have remained conserved within Malacostraca, although the Imd pathway appears to lack several key components. Immune effectors such as the antimicrobial peptides (AMPs) have unique evolutionary profiles, with many malacostracan AMPs not found in other arthropod groups. Lastly, we describe four putative novel immune gene families, characterised by distinct protein domains, potentially representing important evolutionary novelties of the malacostracan immune system.

evolutionary biology↗

bioRxiv2018

Disentangling eco-evolutionary effects on trait fixation

In population genetics, fixation of traits in a demographically changing population under frequency-independent selection has been extensively analysed. In evolutionary game theory, models of fixation have typically focused on fixed population sizes and frequency-dependent selection. A combination of demographic fluctuations with frequency-dependent interactions such as Lotka-Volterra dynamics has received comparatively little attention. We consider a stochastic, competitive Lotka-Volterra model with higher order interactions between two traits. The emerging individual based model allows for stochastic fluctuations in the frequencies of the two traits and the total population size. We calculate the fixation probability of a trait under differing competition coefficients. This fixation probability resembles qualitatively the deterministic evolutionary dynamics. Furthermore, we partially disentangle the selection effects into their ecological and evolutionary components. We find that changing the evolutionary selection strength also changes the population dynamics and vice versa. Thus, a clean separation of the ecological and evolutionary effects is not possible. The entangled eco-evolutionary processes thus cannot be ignored when determining fixation properties in a co-evolutionary system.

evolutionary biology↗

bioRxiv2018

Coevolution of structural variation and optimization in sound systems of human language

Evolution of sound systems of human language is an optimization process to improve communicative effectiveness: The intrinsic structures of sound systems are constantly organized with respect to constraints in speech production and perception. However, there lack sufficient quantitative descriptions of this process, large-scale investigations on universal tendencies in sound systems, and explicit evidence on whether demographic and/or geographic factors can influence linguistic typology of sound systems. Here, we proposed two composite parameters, namely structural variation and optimization, to capture linguistic typology of sound systems, vowel systems in particular. Synchronic comparisons based on a large-scale vowel corpus of world languages revealed a universal negative correlation between the two parameters. Phylogenetic comparative analyses identified a correlated evolution of the two, but with distinct evolutionary modes: a gradual evolution of the structural variation and a punctuated equilibrium of the optimization. Mixed-effect models also reported significant effects of speaker population size and longitude on shaping vowel systems of world languages. All these findings elaborate the intrinsic evolutionary mechanism of sound systems, clarify the extrinsic, non-linguistic effects on shaping human sound systems, and quantitatively describe and interpret the evolution and typology of sound systems.

evolutionary biology↗

bioRxiv2017

Ecological Network Metrics: Opportunities For Synthesis

Network ecology provides a systems basis for approaching ecological questions, such as factors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g. stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological complexity, these advances have come at the cost of introducing new challenges, including new theoretical concepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: integrating network metrics and their terminology across sub-disciplines; benchmarking new network algorithms and models to increase mechanistic understanding; and improving tools for sharing ecological network research, in particular \"model\" data provenance, to increase the reproducibility of network models and analyses. We propose that applying these solutions will aid in synthesizing ecological subdisciplines and allied fields by improving the accessibility of network methods and models.

ecology↗

bioRxiv2017

Common features in plastic changes rather than constructed structures in recurrent neural network prefrontal cortex models

We have flexible control over our cognition depending on the context or surrounding environments. The prefrontal cortex (PFC) controls this cognitive flexibility; however, the detailed underlying mechanisms remain unclear. Recent developments in machine learning techniques have allowed simple recurrent neural network PFC models to perform human- or animal-like behavioral tasks. These systems allow us to acquire parameters, which we could not in biological experiments, for performing the tasks. We compared four models, in which a flexible cognition task, called context-dependent integration task, was performed; subsequently, we searched for common features. In all the models, we observed that high plastic synapses were concentrated in the small neuronal population and the more concentrated neuronal units contributed further to the performance. However, there were no common properties in the constructed structures. These results suggest that plastic changes can be more general and important to accomplish cognitive tasks than features of the constructed structures.

neuroscience↗

bioRxiv2018

Ultrasensitive molecular controllers for quasi-integral feedback

Feedback control has enabled the success of automated technologies by mitigating the effects of variability, unknown disturbances, and noise. Similarly, feedback loops in biology reduce the impact of noise and help shape kinetic responses, but it is still unclear how to rationally design molecular controllers that approach the performance of controllers in traditional engineering applications, in particular the performance of integral controllers. Here, we describe a strategy to build molecular quasi-integral controllers by following two design principles: (1) a highly ultrasensitive response, which guarantees a small steady-state error, and (2) a tunable ultrasensitivity threshold, which determines the system equilibrium point (reference). We describe a molecular reaction network, which we name Brink motif, that satisfies these requirements by combining sequestration and an activation/deactivation cycle. We show that if ultrasensitivity conditions are satisfied, this motif operates as a quasi-integral controller and promotes homeostatic behavior of the closed-loop system (robust tracking of the input reference while rejecting disturbances). We propose potential biological implementations of Brink controllers and we illustrate different example applications with computational models.

synthetic biology↗

bioRxiv2015

Estimating cell cycle model parameters using systems identification

A current challenge in data-driven mathematical modeling of cancer is identifying biologically-relevant parameters of mathematical models from sparse and often noisy experimental data of mixed types. We describe a cell cycle model and outline how to use the Optimization Toolbox in Matlab to estimate its timescale parameters, given flow cytometry and cell viability (synthetic) data, and illustrate the technique with simulated data. This technique can be similarly applied to a variety of cell cycle models, particularly as more laboratories begin to use high-content, quantitative cell screening and imaging platforms. An advanced version of this work (CellPD: cell line phenotype digitizer) will be released as open source in early 2016 at MultiCellDS.org.

Cancer Biology↗