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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2015

Learning from heterogeneous data sources: an application in spatial proteomics

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.\n\nAbbreviations

Bioinformatics↗

bioRxiv2017

Statistical methods for large ensemble of super-resolution stochastic single particle trajectories

Following recent progresses in super-resolution microscopy obtained in the last decade, massive amount of redundant single stochastic trajectories are now available for statistical analysis. Flows of trajectories of molecules or proteins are sampling the cell membrane or its interior at a very high time and space resolution. Several statistical analysis were developed to extract information contained in these data, such as the biophysical parameters of the underlying stochastic motion to reveal the cellular organization. These trajectories can further reveal hidden subcellular organization. We present here the statistical analysis of these trajectories based on the classical Langevin equation, which serves as a model of trajectories. Parametric and non-parametric estimators are constructed by discretizing the stochastic equations and they allow recovering tethering forces, diffusion tensor or membrane organization from measured trajectories, that differ from physical ones by a localization noise. Modeling, data analysis and automatic detection algorithms serve extracting novel biophysical features such as potential wells and other sub-structures, such as rings at an unprecedented spatiotem-poral resolution. It is also possible to reconstruct the surface membrane of a biological cell from the statistics of projected random trajectories.

biophysics↗

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↗

bioRxiv2016

A scored human protein-protein interaction network to catalyze genomic interpretation

Human protein-protein interaction networks are critical to understanding cell biology and interpreting genetic and genomic data, but are challenging to produce in individual large-scale experiments. We describe a general computational framework that through data integration and quality control provides a scored human protein-protein interaction network (InWeb_IM). Juxtaposed with five comparable resources, InWeb_IM has 2.8 times more interactions (~585K) and a superior functional signal showing that the added interactions reflect real cellular biology. InWeb_IM is a versatile resource for accurate and cost-efficient functional interpretation of massive genomic datasets illustrated by annotating candidate genes from >4,700 cancer genomes and genes involved in neuropsychiatric diseases.

Genomics↗

bioRxiv2016

Laurdan and di-4-ANEPPDHQ probe different properties of the membrane

Lipid packing is a crucial feature of cellular membranes. Quantitative analysis of membrane lipid packing can be achieved using polarity sensitive probes whose emission spectrum depends on the lipid packing. However, detailed insight into the exact mechanism that causes the spectral shift is essential to interpret the data correctly. Here, we analysed frequently used polarity sensitive probes, Laurdan and di-4-ANEPPDHQ, to test whether the underlying physical mechanisms of their spectral shift is the same, thus whether they report on the same physico-chemical properties of the cell membrane. Their steady-state spectra as well as time-resolved emission spectra in solvents and model membranes showed that they probe different properties of the lipid membrane. Our findings are important for the application of these dyes in cell biology.

Biophysics↗

bioRxiv2016

Co-operation, competition and crowding: a discrete framework linking Allee kinetics, nonlinear diffusion, shocks and sharp-fronted travelling waves

AO_SCPLOWBSTRACTC_SCPLOWInvasion processes are ubiquitous throughout cell biology and ecology. During invasion, individuals can become isolated from the bulk population and behave differently. We present a discrete, exclusion-based description of the birth, death and movement of individuals. The model distinguishes between individuals that are part of, or are isolated from, the bulk population by imposing different rates of birth, death and movement. This enables the simulation of various co-operative or competitive mechanisms, where there is either a positive or negative benefit associated with being part of the bulk population, respectively. The mean-field approximation of the discrete process gives rise to 22 different classes of partial differential equation, which can include Allee kinetics and nonlinear diffusion. Here we examine the ability of each class of partial differential equation to support travelling wave solutions and interpret the long time behaviour in terms of the individual-level parameters. For the first time we show that the strong Allee effect and nonlinear diffusion can result in shock-fronted travelling waves. We also demonstrate how differences in group and individual motility rates can influence the persistence of a population and provide conditions for the successful invasion of a population.

Biophysics↗

bioRxiv2016

Deciphering the genic basis of environmental adaptations by simultaneous forward and reverse genetics in Saccharomyces cerevisiae

The budding yeast Saccharomyces cerevisiae is the best studied eukaryote in molecular and cell biology, but its utility for understanding the genetic basis of natural phenotypic variation is limited by the inefficiency of association mapping owing to strong and complex population structure. To facilitate association mapping, we analyzed 190 high-quality genomes of diverse strains, including 85 newly sequenced ones, to uncover yeasts population structure that varies substantially among genomic regions. We identified 181 yeast genes that are absent from the reference genome and demonstrated their expression and role in important functions such as drug resistance. We then simultaneously measured the growth rates of over 4500 lab strains each deficient of a nonessential gene and 81 natural strains across multiple environments using unique DNA barcode present in each strain. We combined the genome-wide reverse genetic information with genome-wide association analysis to determine potential genomic regions of importance to environmental adaptations, and for a subset experimentally validated their role by reciprocal hemizygosity tests. The resources provided permit efficient and reliable association mapping in yeast and significantly enhances its value as a model for understanding the genetic mechanisms of phenotypic polymorphism and evolution.

genomics↗

bioRxiv2016

Physical models of spatial genome organization

Remodeling of nuclear organization occurs during normal cell development, differentiation and cancer. One of the biggest gaps of knowledge remains how to link the information on chromatin and chromosome structural organization with genes activity. In this paper we introduce some physical ideas and a general computational method demonstrating how genome 3D architecture and its remodeling can be quantitatively modeled. We study a hypothetical scenario of alterations of chromosome territories positioning in the course of cell proliferation. On this basis we obtain quantitative information about chromosomal contacts in the nucleus. We predict changes of radial distributions of contacts between chromosomal megabase domains during proliferation. The proposed modeling approach may be helpful in integrating experimental data on nuclear reorganization associated with normal development and with various diseases. This predictive modeling may find applications in genome research of normal and cancer cells, stem cell biology, biology of aging, etc.

cancer biology↗

bioRxiv2016

Quantitative Analysis of Dopamine Neuron Subtypes Generated from Mouse Embryonic Stem Cells

Dopamine (DA) neuron subtypes modulate specific physiological functions and are involved in distinct neurological disorders. Embryonic stem cell (ESC) derived DA neurons have the potential to aid in the study of disease mechanisms, drug discovery, and possibly cell replacement therapies. DA neurons can be generated from ESCs in vitro, but the subtypes of ESC-derived DA neurons have not been investigated in detail despite the diversity of DA neurons observed in vivo. Due to cell culture heterogeneity, sampling methods applied to ESC-derived cultures can be ambiguous and potentially biased. Therefore, we developed a quantification method to capture the depth of DA neuron production in vitro by estimating the error associated with systematic random sampling. Using this method, we quantified calbindin+ and calretinin+ subtypes of DA neurons generated from mouse ESCs. We found a higher production of the calbindin+ subtype (11-27%) compared to the calretinin+ subtype (2-13%) of DA neuron; in addition, DA neurons expressing neither subtype marker were also generated. We then examined whether exogenous sonic hedgehog (SHH) and fibroblast growth factor 8 (FGF8) affected subtype generation. Our results demonstrate that exogenous SHH and FGF8 did not alter DA neuron subtype generation in vitro. These findings suggest that a deeper understanding DA neuron derivation inclusive of mechanisms that govern the in vitro subtype specification of ESC-derived DA neurons is required.\n\nNoteAll research was planned and conducted while members were at Brown University\n\nResearch fundingNIH/NCRR/NIGMS RI Hospital COBRE Center for Stem Cell Biology (8P20GM103468-04) (MZ) Brown Institute for Brain Science Pilot Grant (4-63662) (MZ/DHK)

neuroscience↗

bioRxiv2017

A new and accurate continuum description of moving fronts

Processes that involve moving fronts of populations are prevalent in ecology and cell biology. A common approach to describe these processes is a lattice-based random walk model, which can include mechanisms such as crowding, birth, death, movement and agent-agent adhesion. However, these models are generally analytically intractable and it is computationally expensive to perform sufficiently many realisations of the model to obtain an estimate of average behaviour that is not dominated by random fluctuations. To avoid these issues, both mean-field and corrected mean-field continuum descriptions of random walk models have been proposed. However, both continuum descriptions are inaccurate outside of limited parameter regimes, and corrected mean-field descriptions cannot be employed to describe moving fronts. Here we present an alternative description in terms of the dynamics of groups of contiguous occupied lattice sites and contiguous vacant lattice sites. Our description provides an accurate prediction of the average random walk behaviour in all parameter regimes. Critically, our description accurately predicts the persistence or extinction of the population in situations where previous continuum descriptions predict the opposite outcome. Furthermore, unlike traditional mean-field models, our approach provides information about the spatial clustering within the population and, subsequently, the moving front.

biophysics↗

bioRxiv2017

The Co-regulation Data Harvester for Tetrahymenathermophila: automated high-throughput geneannotation and functional inference in a microbialeukaryote

Identifying co-regulated genes can provide a useful approach for defining pathway-specific machinery in an organism. To be efficient, this approach relies on thorough genome annotation, which is not available for most organisms with sequenced genomes. Studies in Tetrahymena thermophila, the most experimentally accessible ciliate, have generated a rich transcriptomic database covering many well-defined physiological states. Genes that are involved in the same pathway show significant co-regulation, and screens based on gene co-regulation have identified novel factors in specific pathways, for example in membrane trafficking. However, a limitation has been the relatively sparse annotation of the Tetrahymena genome, making it impractical to approach genome-wide analyses. We have therefore developed an efficient approach to analyze both co-regulation and gene annotation, called the Co-regulation Data Harvester (CDH). The CDH automates identification of co-regulated genes by accessing the Tetrahymena transcriptome database, determines their orthologs in other organisms via reciprocal BLAST searches, and collates the annotations of those orthologs' functions. Inferences drawn from the CDH reproduce and expand upon experimental findings in Tetrahymena. The CDH, which is freely available, represents a powerful new tool for analyzing cell biological pathways in Tetrahymena. Moreover, to the extent that genes and pathways are conserved between organisms, the inferences obtained via the CDH should be relevant, and can be explored, in many other systems.

bioinformatics↗

bioRxiv2017

Sub-minute phosphoregulation of cell-cycle systems during Plasmodium gamete formation revealed by a high-resolution time course

Malaria parasites are protists of the genus Plasmodium, whose transmission to mosquitoes is initiated by the production of gametes. Male gametogenesis is an extremely rapid process that is tightly controlled to produce eight flagellated microgametes from a single haploid gametocyte within 10 minutes after ingestion by a mosquito. Regulation of the cell cycle is poorly understood in divergent eukaryotes like Plasmodium, where the highly synchronous response of gametocytes to defined chemical and physical stimuli from the mosquito has proved to be a powerful model to identify specific phosphorylation events critical for cell-cycle progression. To reveal the wider network of phosphorylation signalling in a systematic and unbiased manner, we have measured a high-resolution time course of the phosphoproteome of P. berghei gametocytes during the first minute of gametogenesis. The data show an extremely broad response in which distinct cell-cycle events such as initiation of DNA replication and mitosis are rapidly induced and simultaneously regulated. We identify several protein kinases and phosphatases that are likely central in the gametogenesis signalling pathway and validate our analysis by investigating the phosphoproteomes of mutants in two of them, CDPK4 and SRPK1. We show these protein kinases to have distinct influences over the phosphorylation of similar downstream targets that are consistent with their distinct cellular functions, which is revealed by a detailed phenotypic analysis of an SRPK1 mutant. Together, the results show that key cell-cycle systems in Plasmodium undergo simultaneous and rapid phosphoregulation. We demonstrate how a highly resolved time-course of dynamic phosphorylation events can generate deep insights into the unusual cell biology of a divergent eukaryote, which serves as a model for an important group of human pathogens.

microbiology↗

bioRxiv2017

Multiple Poliovirus-Induced Organelles Suggested By Comparison Of Spatiotemporal Dynamics Of Membranous Structures And Phosphoinositides

Poliovirus (PV) infection induces membranes with elevated levels of phosphatidylinositol-4-phosphate (PI4P) and invaginated, tubular structures that appear as vesicular clusters in cross section. Here, we characterize PV mutants, termed EG and GG, which exhibit aberrant proteolytic processing of the P3 precursor that can delay the onset of genome replication and/or impair virus assembly. For WT PV, changes to the PI4P pool were observed as early as 30 min post-infection. PI4P remodeling occurred even in the presence of guanidine hydrochloride, a replication inhibitor. Vesicular clusters were not apparent until 3 h post-infection, a time too slow for these structures to be responsible for genome replication. Delays in the onset of genome replication observed for EG and GG PVs were explained completely by the reduced kinetics of virus-induced remodeling of PI4P pools, consistent with PI4P serving as a marker of the genome-replication organelle. Infectious virus produced by GG PV is known to be reduced nearly 5 logs. We show that GG PV was unable to make virus-induced vesicular clusters. Instead, GG PV-infected cells accumulated elongated tubules. Our results are consistent with the existence of distinct organelles for genome-replication and virus assembly. We suggest that the pace of formation and spatiotemporal dynamics of PV induced organelles may be set by the rate of P3 precursor processing to form 3AB and/or 3CD proteins.\n\nAUTHOR SUMMARYAll positive-strand RNA viruses replicate their genomes in association with host cell membranes. PV does not just remodel existing membranes, but induces membranes with unique structure and lipid composition. There has been some suggestion that the functions of the PV induced structures observed during infection may not be those that perform genome-replication. This study uses kinetic analysis of virus-induced membrane formation and PI4P induction by two PV mutants to provide evidence for the existence of a virus-induced genome-replication organelle distinct from a second organelle, the absence of which impairs virus assembly. In addition, our studies suggest that formation of both organelles may require participation of viral proteins, 3AB and/or 3CD. Therefore, this study provides a new perspective on the cell biology of PV infection and should inspire a fresh look at picornavirus-induced organelles, their functions and the role of P3 proteins in their formation.

molecular biology↗

bioRxiv2017

An Aphrodisiac Produced By Vibrio fischeri Stimulates Mating In The Closest Living Relatives Of Animals

We serendipitously discovered that the marine bacterium Vibrio fischeri induces sexual reproduction in one of the closest living relatives of animals, the choanoflagellate Salpingoeca rosetta. Although bacteria influence everything from nutrition and metabolism to cell biology and development in eukaryotes, bacterial regulation of eukaryotic mating was unexpected. Here we show that a single V. fischeri protein, the previously uncharacterized EroS, fully recapitulates the aphrodisiac activity of live V. fischeri. EroS is a chondroitin lyase; although its substrate, chondroitin sulfate, was previously thought to be an animal synapomorphy, we demonstrate that S. rosetta produces chondroitin sulfate and thus extend the ancestry of this important glycosaminoglycan to the premetazoan era. Finally, we show that V. fischeri, purified EroS, and other bacterial chondroitin lyases induce S. rosetta mating at environmentally-relevant concentrations suggesting that bacterially-produced aphrodisiacs likely regulate choanoflagellate mating in nature.

microbiology↗

bioRxiv2017

miRNAtools: advanced training using the miRNA web of knowledge

miRNAs are small non-coding RNAs, that act as negative regulators of the genomic output. Their intrinsic importance within cell biology and human disease is well known. Their mechanism of action based on the base pairing binding to their cognate targets, have helped the development of many computer applications for the prediction of miRNA target recognition. More recently, many other computer applications have appeared with the objective of studying miRNA roles in many contexts, trying to dissect and predict their functions in a specific biological process. Learning about miRNA function needs a practical training in the use of specific computer and web-based applications that are complementary to the wet-lab studies. In the last seven years we have been involved in the organization of advanced training courses about the in silico functional analysis of miRNAs and non-coding RNAs, for students ranging from the postgraduate to the post-doctoral level. In order to guide the learning process about miRNAs, we have created miRNAtools (http://mirnatools.eu), a web repository of miRNA tools and tutorials. This page is a compilation of tools to analyze miRNAs and their regulatory action that intends to collect and organize the information that is dispersed in the web. The miRNAtools webpage is completed by a collection of tutorials that can be used by students and tutors engaged in advanced training courses. The tutorials follow the rationale of the analysis of the function of selected miRNAs, starting from their nomenclature and genomic localization and finishing by assessing their involvement in specific cellular functions.\n\nDESCRIPTION OF THE AUTHORFrancisco J. Enguita is assistant professor at the Faculty of Medicine, University of Lisbon and senior investigator at the Instituto de Medicina Molecular within the same University.\n\nKEY POINTSO_LI- We have designed a web-page, mIRNAtools3 (http://mirnatools.eu) specifically devoted for advanced teaching purposes within the miRNA field.\nC_LIO_LI- The webpage constitutes also a repository of web-tools for the analysis of miRNA function in several contexts.\nC_LIO_LI- The webpage contains tutorials that cover many aspects related with miRNAs including nomenclature, target prediction and validation, and functional analysis.\nC_LI

bioinformatics↗

bioRxiv2017

BldC delays entry into development to produce a sustained period of vegetative growth in Streptomyces venezuelae

Streptomycetes are filamentous bacteria that differentiate by producing spore-bearing reproductive structures called aerial hyphae. The transition from vegetative to reproductive growth is controlled by the bld (bald) loci, and mutations in bld genes prevent the formation of aerial hyphae, either by blocking entry into development (typically mutations in activators) or by inducing precocious sporulation in the vegetative mycelium (typically mutations in repressors). One of the bld genes, bldC, encodes a 68-residue DNA-binding protein related to the DNA-binding domain of MerR-family transcription factors. Recent work has shown that BldC binds DNA by a novel mechanism, but there is less insight into its impact on Streptomyces development. Here we used ChIP-seq coupled with RNA-seq to define the BldC regulon in the model species Streptomyces venezuelae, showing that BldC can function both as a repressor and as an activator of transcription. Using electron microscopy and time-lapse imaging, we show that bldC mutants are bald because they initiate development prematurely, bypassing the formation of aerial hyphae. This is consistent with the premature expression of BldC target genes encoding proteins with key roles in development (e.g. whiD, whiI, sigF), chromosome condensation and segregation (e.g. smeA-sffA, hupS), and sporulation-specific cell division (e.g. dynAB), suggesting that BldC-mediated repression is critical to maintain a sustained period of vegetative growth prior to sporulation. We discuss the possible significance of BldC as an evolutionary link between MerR family transcription factors and DNA architectural proteins.\n\nIMPORTANCEUnderstanding the mechanisms that drive bacterial morphogenesis depends on the dissection of the regulatory networks that underpin the cell biological processes involved. Recently, Streptomyces venezuelae has emerged as an attractive new model system for the study of morphological differentiation in Streptomyces. This has led to significant progress in identifying the genes controlled by the transcription factors that regulate aerial mycelium formation (Bld regulators) and sporulation (Whi regulators). Taking advantage of S. venezuelae, we used ChIP-seq coupled with RNA-seq to identify the genes directly under the control of BldC. Because S. venezuelae sporulates in liquid culture, the complete spore-to-spore life cycle can be examined using time-lapse microscopy, and we applied this technique to the bldC mutant. These combined approaches reveal BldC to be a member of an emerging class of Bld regulators that function principally to repress key sporulation genes, thereby extending vegetative growth and blocking the onset of morphological differentiation.

microbiology↗