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bioRxiv2019

What is that molecular machine really doing? Automated exploration of alternative transporter mechanisms

Motivated by growing evidence for pathway heterogeneity and alternative functions of molecular machines, we demonstrate a computational approach for investigating two questions: (1) Are there multiple mechanisms (state-space pathways) by which a machine can perform a given function, such as cotransport across a membrane? (2) How can additional functionality, such as proofreading/error-correction, be built into machine function using standard biochemical processes? Answers to these questions will aid both the understanding of molecular-scale cell biology and the design of synthetic machines. Focusing on transport in this initial study, we sample a variety of mechanisms by employing Metropolis Markov chain Monte Carlo. Trial moves adjust transition rates among an automatically generated set of conformational and binding states while maintaining fidelity to thermodynamic principles and a user-supplied fitness/functionality goal. Each accepted move generates a new model. The simulations yield both single and mixed reaction pathways for cotransport in a simple environment with a single substrate along with a driving ion. In a "competitive" environment including an additional decoy substrate, several qualitatively distinct reaction pathways are found which are capable of extremely high discrimination coupled to a leak of the driving ion, akin to proofreading. The array of functional models would be difficult to find by intuition alone in the complex state-spaces of interest. Author summaryMolecular machines, which operate on the nanoscale, are proteins/complexes that perform remarkable tasks such as the selective absorption of nutrients into the cell by transporters. These complex machines are often described using a fairly simple set of states and transitions that may not account for the stochasticity and heterogeneity generally expected at the nanoscale at body temperature. New tools are needed to study the full array of possibilities. This study presents a novel in silico method to systematically generate testable molecular-machine kinetic models and explore alternative mechanisms, applied first to membrane transport proteins. Our initial results suggest these transport machines may contain mechanisms which detoxify the cell of an unwanted toxin, as well as significantly discriminate against the import of the toxin. This novel approach should aid the experimental study of key physiological processes such as renal glucose re-absorption, rational drug design, and potentially the development of synthetic machines.

biophysics↗
bioRxiv2025

Physiological and functional characterization for high-throughput optogenetic skeletal muscle exercise assays

Exercise has long been considered an essential part of human health and longevity. Recent physiological studies have expanded muscles role beyond simply acting as an actuator, revealing muscles exercise-mediated paracrine and endocrine relationships with other organ systems. In vitro engineered skeletal muscle models can address physiological questions about exercise adaptation with the precision of cell biology. Optogenetic tools have enabled a noninvasive approach to stimulating muscle contraction that avoids the potential off-target effects of electrical stimulation techniques. In this article we propose high-throughput culture and optical exercise protocols to generate statistically robust cellular exercise response datasets. We characterize optical rheobase for 2D muscle tissue morphology, finding that optical intensities as low as 5 W mm-2 can trigger contraction. We then analyze bulk RNA sequencing data collected from high throughput, acute exercise protocols and find a rich display of transcriptional behavior that is consistent with experimental observations. The spontaneous contractility of our tissue constructs introduced oxygen diffusion challenges when maintained in a 24 well plate, and our analysis shows divergent myogenic and pathological transcriptional consequences of hypoxia. We believe our techniques provide a practical foundation for conducting future high-precision in vitro exercise studies of skeletal muscle. Translational impactHigh-fidelity, engineered skeletal muscle has potential to elucidate exercise-mediated response pathways at the cell and tissue level. We leverage optogenetic techniques to develop a high-throughput assay that optically stimulates 2D muscle monolayers, avoiding potential cell damage from electrical stimulation. Our culture and exercise protocol generates statistically robust RNA sequencing datasets which reveal myogenic and pathological responses to exercise and in vitro culture conditions, informing practical next steps to cultivate stronger, more physiologically relevant muscle models.

bioengineering↗
bioRxiv2026

TurboID proximity labeling reveals protein interaction networks for germline Argonautes

In the germline, small RNA-mediated gene regulatory pathways are associated with phase separated condensates known as germ granules across species. Germ granules can be challenging to purify based on their biophysical properties, therefore we used an in vivo proximity labeling approach, TurboID, to define the proximal interaction networks of the germ granule-localized Argonaute proteins: CSR-1, PRG-1/Piwi, PPW-2/WAGO-3, WAGO-1 and WAGO-4, and the nuclear-enriched Argonaute HRDE-1 as a comparator, in C. elegans. We found that although CSR-1 and WAGO-4 bind to the same set of 22G- small RNAs and target an overlapping set of germline genes, they possess distinct proximal interaction networks. Displacement of WAGO-4 from the germ granules highlights distinct interactors involved in chromosome biology and points to a potential nuclear role for WAGO-4. PRG-1/Piwi, WAGO-1, and PPW-2/WAGO-3, act in a common pathway and share an interaction network, while HRDE-1 displays a distinct interaction network that highlights its potential to influence splicing, rather than its widely accepted role in histone modification. Performing TurboID on other sRNA pathway factors, including the RdRP RRF-1 led us to uncover a potential nuclear role for this protein, and overlapping our TurboID data with published proximity labeling data enabled us to identify a core set of eleven mostly uncharacterized proteins that play roles in fertility, germ granule organization, and transgenerational epigenetic inheritance. One of these factors, D2005.4, appears to be a heme-binding germ granule-localized protein with a Tetratricopeptide Repeat Domain that interacts with all germ granule AGOs and may play scaffolding or enzymatic roles in germ granules. Collectively, our work highlights the utility of proximity labelling to uncover insights into AGO and sRNA pathway factor function and identify new factors that could be of importance in germ cell biology.

developmental biology↗
bioRxiv2021

Cbp1, a rapidly evolving fungal virulence factor, forms an effector complex that drives macrophage lysis

Intracellular pathogens secrete effectors to manipulate their host cells. Histoplasma capsulatum (Hc) is a fungal intracellular pathogen of humans that grows in a yeast form in the host. Hc yeasts are phagocytosed by macrophages, where fungal intracellular replication precedes macrophage lysis. The most abundant virulence factor secreted by Hc yeast cells is Calcium Binding Protein 1 (Cbp1), which is absolutely required for macrophage lysis. Here we take an evolutionary, structural, and cell biological approach to understand Cbp1 function. We find that Cbp1 is present only in the genomes of closely related dimorphic fungal species of the Ajellomycetaceae family that lead primarily intracellular lifestyles in their mammalian hosts (Histoplasma, Paracoccidioides, and Emergomyces), but not conserved in the extracellular fungal pathogen Blastomyces dermatitidis. We determine the de novo structures of Hc H88 Cbp1 and the Paracoccidioides americana (Pb03) Cbp1, revealing a novel "binocular" fold consisting of a helical dimer arrangement wherein two helices from each monomer contribute to a four-helix bundle. In contrast to Pb03 Cbp1, we show that Emergomyces Cbp1 orthologs are unable to stimulate macrophage lysis when expressed in the Hc cbp1 mutant. Consistent with this result, we find that wild-type Emergomyces africanus yeast are able to grow within primary macrophages but are incapable of lysing them. Finally, we use subcellular fractionation of infected macrophages and indirect immunofluorescence to show that Cbp1 localizes to the macrophage cytosol during Hc infection, making this the first instance of a phagosomal human fungal pathogen directing an effector into the cytosol of the host cell. We additionally show that Cbp1 forms a complex with Yps-3, another known Hc virulence factor that accesses the cytosol. Taken together, these data imply that Cbp1 is a rapidly evolving fungal virulence factor that localizes to the cytosol to trigger host cell lysis. Author SummaryThe members of the Ajellomycetaceae fungal family are human pathogens that are responsible for a rising number of mycoses around the world. Calcium binding protein 1 (Cbp1) is a rapidly evolving virulence factor that is present in the genomes of the Ajellomycetaceae species that lead primarily intracellular lifestyles, including Histoplasma, Paracoccidioides, and Emergomyces but not Blastomyces, which remains largely extracellular during infection. Both Paracoccidioides and Histoplasma Cbp1 homologs are able to cause lysis of macrophages whereas Emergomyces homologs cannot. This result is consistent with Emergomyces africanus natural infection of macrophages, during which the yeast cells can replicate but cannot actively lyse the host cell. Despite divergence of the primary sequence of Histoplasma and Paracoccidioides Cbp1 homologs, their protein structures are remarkably similar and reveal a novel fold. During infection, Cbp1 enters the cytosol of the host macrophage, making it the first known virulence factor from an intracellular human fungal pathogen that localizes to the cytosol of the host cell. We also show that Cbp1 forms a complex with another cytosolic virulence factor, Yps-3. Taken together, these studies significantly advance our understanding of Histoplasma virulence.

microbiology↗
bioRxiv2024

Modified histone peptides uniquely tune the material properties of HP1α condensates.

Biomolecular condensates have emerged as a powerful new paradigm in cell biology with broad implications to human health and disease, particularly in the nucleus where phase separation is thought to underly elements of chromatin organization and regulation. Specifically, it has been recently reported that phase separation of heterochromatin protein 1alpha (HP1) with DNA contributes to the formation of condensed chromatin states. HP1 localization to heterochromatic regions is mediated by its binding to specific repressive marks on the tail of histone H3, such as trimethylated lysine 9 on histone H3 (H3K9me3). However, whether epigenetic marks play an active role in modulating the material properties of HP1 and dictating emergent functions of its condensates, remains only partially understood. Here, we leverage a reductionist system, comprised of modified and unmodified histone H3 peptides, HP1 and DNA to examine the contribution of specific epigenetic marks to phase behavior of HP1. We show that the presence of histone peptides bearing the repressive H3K9me3 is compatible with HP1 condensates, while peptides containing unmodified residues or bearing the transcriptional activation mark H3K4me3 are incompatible with HP1 phase separation. In addition, inspired by the decreased ratio of nuclear H3K9me3 to HP1 detected in cells exposed to uniaxial strain, using fluorescence microscopy and rheological approaches we demonstrate that H3K9me3 histone peptides modulate the dynamics and network properties of HP1 condensates in a concentration dependent manner. These data suggest that HP1-DNA condensates are viscoelastic materials, whose properties may provide an explanation for the dynamic behavior of heterochromatin in cells in response to mechanostimulation. Statement of significanceThe organization of genomic information in eukaryotic cells necessitates compartmentalization into functional domains allowing for the expression of cell identity-specific genes, while repressing genes related to alternative fates. Heterochromatin hosts these transcriptionally silent regions of the genome - which ensure the stability of cell identity -and is characterized by repressive histone marks (H3K9m3) and other specialized proteins (HP1a), recently shown to phase separate with DNA. We show that HP1a forms condensates with DNA which persist in the presence of H3K9me3 peptides. The viscoelastic nature of these condensates depend on H3K9me3:HP1 ratios, which are modulated by mechanical strain in cells. Thus, phase separation may explain the dynamic behavior of heterochromatin in cells, in response to mechanostimulation.

biophysics↗
bioRxiv2022

Dynamics and structural changes of calmodulin upon interaction with its potent antagonist calmidazolium

Calmodulin (CaM) is a eukaryotic multifunctional, calcium-modulated protein that regulates the activity of numerous effector proteins involved in a variety of physiological processes. Calmidazolium (CDZ) is a potent small molecule antagonist of CaM and one the most widely used inhibitors of CaM in cell biology. Here, we report the structural characterization of CaM:CDZ complexes using combined SAXS, X-ray crystallography, HDX-MS and NMR approaches. Our results provide molecular insights into the CDZ-induced dynamics and structural changes of CaM leading to its inhibition. CDZ-binding induces an open-to-closed conformational change of CaM and results in a strong stabilization of its structural elements associated with a reduction of protein dynamics over a large time range. These CDZ-triggered CaM changes mimic those induced by CaM-binding peptides derived from protein targets, despite their distant chemical nature. CaM residues in close contact with CDZ and involved in the stabilization of the CaM:CDZ complex have been identified. These results open the way to rationally design new CaM-selective drugs. Figure and text for the Table of Contents (ToC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/474921v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@b0c76org.highwire.dtl.DTLVardef@15f462forg.highwire.dtl.DTLVardef@1f8e57forg.highwire.dtl.DTLVardef@1a33575_HPS_FORMAT_FIGEXP M_FIG C_FIG Calmidazolium is a potent and widely used inhibitor of calmodulin, a major mediator of calcium-signaling in eukaryotic cells. Structural characterization of calmidazolium-binding to calmodulin reveals that it triggers open-to-closed conformational changes similar to those induced by calmodulin-binding peptides derived from enzyme targets. These results open the way to rationally design new and more selective inhibitors of calmodulin.

biophysics↗
bioRxiv2022

The growth rate of DNA condensate droplets increases with the size of participating subunits

Liquid-liquid phase separation (LLPS) is a common phenomenon underlying the formation of dynamic membraneless organelles in biological cells, which are emerging as major players in controlling cellular functions and health. The bottom-up synthesis of biomolecular liquid systems with simple constituents, like nucleic acids and peptides, is useful to understand LLPS in nature as well as to develop programmable means to build new amorphous materials with properties matching or surpassing those observed in natural condensates. In particular, understanding which parameters determine condensate growth kinetics is essential for the synthesis of condensates with the capacity for active, dynamic behaviors. Here we use DNA nanotechnology to study artificial liquid condensates through programmable star-shaped subunits, focusing on the effects of changing subunit size. First, we show that LLPS is achieved in a six-fold range of subunit size. Second, we demonstrate that the rate of growth of condensate droplets scales with subunit size. Our investigation is supported by a general model that describes how coarsening and coalescence are expected to scale with subunit size under ideal assumptions. Beyond suggesting a route toward achieving control of LLPS kinetics via design of subunit size in synthetic liquids, our work suggests that particle size may be a key parameter in biological condensation processes.

bioengineering↗
bioRxiv2024

Aggregation and disaggregation of red blood cells: depletion versus bridging

The aggregation of red blood cells (RBCs) is a complex phenomenon that strongly impacts blood flow and tissue perfusion. Despite extensive research for more than 50 years, physical mechanisms that govern RBC aggregation are still under debate. Two proposed mechanisms are based on bridging and depletion interactions between RBCs due to the presence of macromolecules in blood plasma. The bridging hypothesis assumes the formation of bonds between RBCs through adsorbing macromolecules, while the depletion mechanism results from the exclusion of macromolecules from the inter-cellular space, leading to effective attraction. Existing experimental studies generally cannot differentiate between these two aggregation mechanisms, though several recent investigations suggest concurrent involvement of the both mechanisms. We explore dynamic aggregation and disaggregation of two RBCs using three simulation models: a potential-based model mimicking depletion interactions, a bridging model with immobile bonds, and a new bridging model with mobile bonds which can slide along RBC membranes. Simulation results indicate that dynamic aggregation of RBCs primarily arises from depletion interactions, while disaggregation of RBCs involves both mechanisms. The bridging model with mobile bonds reproduces well the corresponding experimental data, offering insights into the interplay between bridging and depletion interactions and providing a framework for studying similar interactions between other biological cells.

biophysics↗
bioRxiv2022

Noise source importance in linear stochastic models of biological systems that grow, shrink, wander, or persist

While noise is an important factor in biology, biological processes often involve multiple noise sources, whose relative importance can be unclear. Here we develop tools that quantify the importance of noise sources in a network based on their contributions to variability in a quantity of interest. We generalize the edge importance measures proposed by Schmidt and Thomas [1] for first-order reaction networks whose steady-state variance is a linear combination of variance produced by each directed edge. We show that the same additive property extends to a general family of stochastic processes subject to a set of linearity assumptions, whether in discrete or continuous state or time. Our analysis applies to both expanding and contracting populations, as well as populations obeying a martingale ("wandering") at long times. We show that the original Schmidt-Thomas edge importance measure is a special case of our more general measure, and is recovered when the model satisfies a conservation constraint ("persists"). In the growing and wandering cases we show that the choice of observables (measurements) used to monitor the process does not influence which noise sources are important at long times. In contrast, in the shrinking or persisting case, which noise sources are important depends on what is measured. We also generalize our measures to admit models with affine moment update equations, which admit additional limiting scenarios, and arise naturally after linearization. We illustrate our results using examples from cell biology and ecology: (i) a model for the dynamics of the inositol trisphospate receptor, (ii) a model for an endangered population of white-tailed eagles, and (iii) a model for wood frog dispersal. Author summaryBiological processes are frequently subject to an ensemble of independent noise sources. Noise sources produce fluctuations that propagate through the system, driving fluctuations in quantities of interest such as population size or ion channel configuration. We introduce a measure that quantifies how much variability each noise source contributes to any given quantity of interest. Using these methods, we identify which binding events contribute significantly to fluctuations in the state of a molecular signalling channel, which life history events contribute the most variability to an eagle population before and after a successful conservation effort rescued the population from the brink of extinction, and which dispersal events, at what times, matter most to variability in the recolonization of a series of ponds by wood frogs after a drought.

ecology↗
bioRxiv2019

Kinetochore capture by spindle microtubules: why fission yeast may prefer pivoting to search-and-capture

The mechanism by which microtubules find kinetochores during spindle formation is a key question in cell biology. Previous experimental studies have shown that although search-and-capture of kinetochores by dynamic microtubules is a dominant mechanism in many organisms, several other capture mechanisms are also possible. One such mechanism reported in Schizosaccharomyces pombe shows that microtubules can exhibit a prolonged pause between growth and shrinkage. During the pause, the microtubules pivoted at the spindle pole body search for the kinetochores by performing an angular diffusion. Is the latter mechanism purely accidental, or could there be any physical advantage underlying its selection? To compare the efficiency of these two mechanisms, we numerically study distinct models and compute the timescales of kinetochore capture as a function of microtubule number N. We find that the capture timescales have non-trivial dependences on microtubule number, and one mechanism may be preferred over the other depending on this number. While for small N (as in fission yeast), the typical capture times due to rotational diffusion are lesser than those for search-and-capture, the situation is reversed beyond a certain N. The capture times for rotational diffusion tend to saturate due to geometrical constraints, while those for search-and-capture reduce monotonically with increasing N making it physically more efficient. The results provide a rationale for the common occurrence of classic search-and-capture process in many eukaryotes which have few hundreds of dynamic microtubules, as well as justify exceptions in cells with fewer microtubules.

biophysics↗
bioRxiv2023

Geckopy 3.0: enzyme constraints, thermodynamics constraints and omics integration in python

GEnome-scale Metabolic (GEM) models are knowledge bases of the reactions and metabolites of a particular organism. These GEM models allow for the simulation of the metabolism - e.g. calculating growth and production yields - based on the stoichiometry, reaction directionality and uptake rates of the metabolic network. Over the years, several extensions have been added to take into account other actors in metabolism, going beyond pure stoichiometry. One such extension is enzyme-constraint models, which enable the integration of kinetic data and proteomics data into GEM models. Given its relatively recent formulation, there are still challenges in standardization and data reconciliation between the model and the experimental measurements. In this work, we present geckopy 3.0 (Genome-scale model Enzyme Constraints, using Kinetics and Omics in python), an actualization from scratch of the previous python implementation of the same name. This update tackles the aforementioned challenges, in an effort to reach maturity in enzyme-constraint modeling. With the new geckopy, proteins are typed in the SBML document, taking advantage of the SBML Groups extension, in compliance with community standards. Additionally, a suite of relaxation algorithms - in the form of linear and mixed-integer linear programming problems - has been added to facilitate reconciliation of raw proteomics data with the metabolic model. Several functionalities to integrate experimental data were implemented, including an interface layer with pytfa for the usage of thermodynamics and metabolomics constraints. Finally, the relaxation algorithms were benchmarked against public proteomics datasets in Escherichia coli for different conditions, revealing targets for improving the enzyme constrained model and/or the proteomics pipeline. IMPORTANCEThe metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model which is able to efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxations algorithms in a package called geckopy. Additionally, to ensure that enzyme-constraint models follow the community standards, a format for the proteins is postulated. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. We hope that the package and algorithms presented here will serve useful for the constraint-based modeling community.

systems biology↗
bioRxiv2026

Atg8 orchestrates stress-responsive chromatin programs across immunity and metabolism

Organisms must coordinate transcriptional responses to immune and metabolic stress, often within the same tissue. In Drosophila and mammals, adipose tissue integrates these signals by mounting antimicrobial defense during acute infection and remodeling lipid metabolism under chronic nutrient surplus. How one cell-biological system supports both functions, and through what molecular machinery, remains incompletely understood. Atg8/LC3, classically defined by canonical autophagy, has emerging non-canonical roles in nuclear gene regulation, raising the possibility that it contributes to stress-coordinated transcription beyond cargo turnover. Using unbiased CUT&RUN in adult Drosophila nuclei, we find that endogenous Atg8 exhibits broad chromatin occupancy at immune, metabolic, and autophagy loci, and accumulates in nuclei under prolonged high-sugar diet (HSD) and acute Gram-positive infection. We identify two conserved Atg8-interacting motifs (AIMs) within the Rel homology domain of NF-{kappa}B/Dif. Flies carrying CRISPR-engineered AIM-mutant Dif are highly susceptible to both infection and chronic HSD, establishing a physiological requirement for intact Dif AIMs. AIM-mutant Dif shows impaired infection-induced nuclear accumulation, suggesting that Atg8 contributes to both Dif cytoplasmic-to-nuclear shuttling and nuclear function. Unbiased comparison of Atg8 chromatin occupancy across HSD and infection further reveals shared and divergent motif grammar, positioning Atg8 as a stress-responsive chromatin cofactor for immune and metabolic transcription. Together, these findings expand the functional landscape of Atg8/LC3 beyond canonical autophagy and reveal that autophagy machinery contributes to stress-specific transcriptional complex assembly. AIM/LIR-mediated interactions, exemplified by Dif, represent one such interface, while additional mechanisms likely underlie Atg8s broader chromatin engagement at loci enriched for transcription factor motifs whose cognate factors lack known AIM/LIRs. We propose that Atg8/LC3-mediated coordination of immune and metabolic transcription is a general principle by which cells integrate diverse stress signals, with implications for obesity, chronic inflammation, and other disease states in which immune and metabolic dysregulation converge. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/727304v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@15c056dorg.highwire.dtl.DTLVardef@68493dorg.highwire.dtl.DTLVardef@a07c6corg.highwire.dtl.DTLVardef@4896c6_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIStress drives Atg8 into nuclei, where it occupies immune and metabolic chromatin. C_LIO_LITwo conserved AIMs in NF-{kappa}B/Dif bind Atg8 and enable Dif nuclear entry. C_LIO_LIAIM-mutant Dif flies are highly susceptible to infection and chronic high-sugar diet. C_LIO_LIAtg8 occupies stress-related motifs on prolonged HSD and acute infection. C_LI

genomics↗
bioRxiv2020

Kinetics of self-assembly of inclusions due to lipid membrane thickness interactions

Self-assembly of proteins on lipid membranes underlies many important processes in cell biology, such as, exo- and endo-cytosis, assembly of viruses, etc. An attractive force that can cause self-assembly is mediated by membrane thickness interactions between proteins. The free energy profile associated with this attractive force is a result of the overlap of thickness deformation fields around the proteins. The thickness deformation field around proteins of various shapes can be calculated from the solution of a boundary value problem and is relatively well understood. Yet, the time scales over which self-assembly occurs has not been explored. In this paper we compute this time scale as a function of the initial distance between two inclusions by viewing their coalescence as a first passage time problem. The first passage time is computed using both Langevin dynamics and a partial differential equation, and both methods are found to be in excellent agreement. Inclusions of three different shapes are studied and it is found that for two inclusions separated by about hundred nanometers the time to coalescence is hundreds of milliseconds irrespective of shape. Our Langevin dynamics simulation of self-assembly required an efficient computation of the interaction energy of inclusions which was accomplished using a finite difference technique. The interaction energy profiles obtained using this numerical technique were in excellent agreement with those from a previously proposed semi-analytical method based on Fourier-Bessel series. The computational strategies described in this paper could potentially lead to efficient methods to explore the kinetics of self-assembly of proteins on lipid membranes. Author summarySelf-assembly of proteins on lipid membranes occurs during exo- and endo-cytosis and also when viruses exit an infected cell. The forces mediating self-assembly of inclusions on membranes have therefore been of long standing interest. However, the kinetics of self-assembly has received much less attention. As a first step in discerning the kinetics, we examine the time to coalescence of two inclusions on a membrane as a function of the distance separating them. We use both Langevin dynamics simulations and a partial differential equation to compute this time scale. We predict that the time to coalescence is on the scale of hundreds of milliseconds for two inclusions separated by about hundred nanometers. The deformation moduli of the lipid membrane and the membrane tension can affect this time scale.

biophysics↗
bioRxiv2019

Tetraploidy in rodent cardiac stem cells confers enhanced biological properties

Ploidy for cardiomyocytes is well described but remains obscure in cardiac interstitial cells (CICs). Ploidy of c-kit+CICs were assessed using a combination of confocal, karyotypic, and flow cytometric assessments coupled with molecular and bioinformatic analyses. Fundamental differences were found between cultured rodent (rat, mouse) c-kit+CICs possessing mononuclear tetraploid (4n) content versus large mammal (human, swine) with mononuclear diploid (2n) content. In-situ analysis, confirmed with fresh isolates, revealed diploid content in c-kit+CICs from human and a mixture of diploid and tetraploid nuclei in mouse. Molecular assessment of the p53 signaling pathway provides a plausible explanation for escape from replicative senescence in rodent but not human ckit+CICs. Single cell transcriptional profiling reveals distinctions between diploid versus tetraploid populations in mouse ckit+CICs, alluding to functional divergences. Collectively, these data reveal fundamental species-specific biological differences in c-kit+CICs that could account for challenges in extrapolation of myocardial preclinical studies from rodent to large animal models.

cell biology↗
bioRxiv2020

Ribosomal proteins could explain the phylogeny of Bacillus species

Protein translation is a highly conserved process in biology. As participants of translation, ribosomal proteins in the large and small subunits of the ribosomes are likely to be highly conserved; thus, could they be endowed with sufficient sequence diversity to chronicle the evolutionary history of different species in the same or different genus? Using different Bacillus species as a model system, this study sought to examine if ribosomal proteins could reproduce the maximum likelihood phylogeny described by 16S rRNA of the investigated Bacillus species. Bacillus species investigated were Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacilluspumilus, Bacillus subtilis, and Bacillus thuringiensis. Results revealed that ribosomal proteins could be categorized into four different groups depending on their extent in reproducing the 16S rRNA phylogeny of the different Bacillus species. The first group comprises ribosomal protein that could reproduce all the phylogenetic positions of the Bacillus species accurately. These ribosomal proteins were ribosomal protein L6, L7/12, L9, L13, L24, L32, S3, S9, S12, S15, S16, S17, and S18. Ribosomal proteins that hold partial phylogenetic significance constitutes the second group where the ribosomal proteins could reproduce the major branches of the 16S rRNA phylogenetic tree but differ in the placement of one or two Bacillus species. In general, this group of ribosomal proteins had difficulty differentiating B. licheniformis and B. pumilus at the sequence level. Members of this group of ribosomal protein include ribosomal protein L22, L29, L30, L31 Type B, L33, L35, S1, S4, S5, S6, S7, S8, S11, S13, S19, and S20. The third group of ribosomal proteins were those which were highly conserved at the sequence level, and which could not differentiate the different Bacillus species. These ribosomal proteins were ribosomal protein L5, L36, S2, S10, and S21. Finally, there were also ribosomal proteins that randomly placed the different Bacillus species into phylogenetic positions not in sync with those depicted by the 16S rRNA phylogenetic tree. These ribosomal proteins were ribosomal protein L7Ae, L17, L20, L23, L27, L28, L31, L34 and S14 Type Z. Overall, members of all four groups of ribosomal proteins came from both the large and small ribosome subunits which meant that evolutionary forces exerted selective pressure on both subunits but at differing extents. Collectively, specific ribosomal proteins could reproduce the phylogeny of different Bacillus species as described by the gold standard phylogenetic marker, 16S rRNA, which highlighted that co-evolutionary processes could be at work in shaping the evolution of ribosomal proteins and rRNA in close contact with each other in the ribosome. Subject areasecology, biochemistry, biotechnology, microbiology, cell biology Significance of the work16S rRNA is the gold standard phylogenetic marker used to inform the evolutionary relationships between different species across the three domains of life. Given that 16S rRNA is nestled in the ribosomes together with a consortium of ribosomal proteins each with unique structural and enzymatic functions, could ribosomal proteins be used similarly as phylogenetic markers for informing species divergence and relationships? Specifically, as part of the highly conserved ribosome important to protein translation, do ribosomal proteins possess sufficient sequence diversity to help chronicle the evolutionary relationships between different species? By reconstructing the maximum likelihood phylogenetic tree of different Bacillus species, this study revealed that ribosomal proteins fall into four categories concerning their utility for informing phylogeny between different species of the same genus. Specifically, there existed ribosomal proteins able to accurately reproduce the phylogenetic tree described by 16S rRNA. On the other hand, there were ribosomal proteins that hold only partial phylogenetic significance where they could reproduce the major branches of the reference phylogenetic tree but differ in the placement of one or two species along the tree. Besides the above two categories, they were also ribosomal proteins whose sequence diversity was not sufficient to help differentiate between different Bacillus species. Finally, another class of ribosomal proteins did not chronicle the evolutionary trajectories of the different species resulting in phylogenetic tree with random placement of the different species. Overall, evolutionary forces likely exerted different selection forces on different ribosome subunits as well as individual ribosomal protein that resulted in the differentiation of their utility as phylogenetic markers of different species of the same genus. Co-evolution between ribosomal proteins as well as between ribosomal proteins and rRNA might underpin part of the evolutionary history chronicled by individual ribosomal proteins, thereby, endowing them with phylogenetic significance. HighlightsO_LIRibosomal proteins were found to be useful in describing the phylogeny of different Bacillus species compared to the gold standard phylogenetic marker, 16S rRNA. C_LIO_LIBy examining the maximum likelihood phylogenetic tree reconstructed, ribosomal proteins could be categorized into four groups with differing phylogenetic significance. C_LIO_LIThe first group comprises ribosomal proteins able to accurately reproduce all the phylogenetic positions of different Bacillus species relative to 16S rRNA phylogenetic tree. This group include ribosomal protein L6, L7/12, L9, L13, L24, L32, S3, S9, S12, S15, S16, S17, and S18. C_LIO_LIThe second group refers to ribosomal proteins able to reproduce the major branches of the 16S rRNA phylogenetic tree but lacks in the correct placement of one or two Bacillus species. These ribosomal proteins were L22, L29, L30, L31 Type B, L33, L35, SI, S4, S5, S6, S7, S8, Sll, S13, S19, and S20. C_LIO_LIThe third group of ribosomal proteins are ones with highly conserved sequence unable to differentiate between different Bacillus species. It comprised ribosomal proteins L5, L36, S2, S10, and S21. C_LIO_LIThe final group of ribosomal proteins did not chronicle the evolutionary forces acting on the different Bacillus species and generated phylogenetic trees with random placement of the different Bacillus species. These ribosomal proteins were L7Ae, L17, L20, L23, L27, L28, L31, L34 and S14 Type Z. C_LI

evolutionary biology↗
bioRxiv2014

Preparation of Synaptosomes from Postmortem Human Prefrontal Cortex

Synaptosomes are a popular type of isolated synaptic fraction intensively used in neuroscience and cell biology. They are prepared by layering on density gradients and thought to consist largely of axonal endings with attached postsynaptic structures (Morgan, 1976), in contrast to synaptoneurosomes (Hollingsworth et al, 1985) which are prepared by filtration and are thought to consist largely of pinched-off dendritic spines with attached presynaptic structures. Although most studies of synaptosomes have utilized rodent or primate tissue, a score of studies have employed human samples derived from surgical specimens or postmortem brain. We recently described the isolation of synaptosomes from human postmortem prefrontal cortex to study the expression of synaptic microRNAs and other small RNAs in depression, schizophrenia and bipolar disorder (Smalheiser et al, 2014). This protocol alluded to methods and modifications that are scattered among several publications, and did not explain the reasons for the procedures chosen. Because our protocol differs from other published human synaptosome protocols in a variety of respects, we present here a detailed description of synaptosome preparation that should facilitate the use of this standardized synaptic fraction by other workers.

Neuroscience↗
bioRxiv2015

Cancer Classification by Correntropy-Based Sparse Compact Incremental Learning Machine

Cancer prediction is of great importance and significance and it is crucial to provide researchers and scientists with novel, accurate and robust computational tools for this issue. Recent technologies such as Microarray and Next Generation Sequencing have paved the way for computational methods and techniques to play critical roles in this regard. Many important problems in cell biology require the dense nonlinear interactions between functional modules to be considered. The importance of computer simulation in understanding cellular processes is now widely accepted, and a variety of simulation algorithms useful for studying certain subsystems have been designed. In this article, a Sparse Compact Incremental Learning Machine (SCILM) is proposed for cancer classification problem on microarray gene expression data which take advantage of Correntropy cost that makes it robust against diverse noises and outliers. Moreover, since SCILM uses l1-norm of the weights, it has sparseness which can be applied for gene selection purposes as well. Finally, due to compact structure, the proposed method is capable of performing classification tasks in all of the cases with only one neuron in its hidden layer. The experimental analysis is performed on 26 well known microarray datasets regarding diverse kinds of cancers and the results show that the proposed method not only achieved significantly high accuracy but also because of its sparseness, final connectivity weights determined the value and effectivity of each gene regarding the corresponding cancer.

Bioinformatics↗
bioRxiv2016

SourceData - a semantic platform for curating and searching figures

Here we present SourceData (http://sourcedata.embo.org), a platform that allows researchers and publishers to share scientific figures and, when available, the underlying source data in a way that is machine-readable and findable. SourceData is unique in its focus on the core of scientific evidence--data presented in figures--and its capability to make papers searchable based on their data content and hence directly couple data to improved discoverability. SourceData aims at establishing a selfreinforcing data ecosystem that bridges the conventional visual and narrative description of research findings with a machine-readable representation of data and hypotheses.\n\nIn molecular and cell biology, most of the data that result from hypothesis-driven research are exclusively available in the form of figures or tables in published papers. In spite of their importance for the understanding of biological processes and human di ...

Bioinformatics↗