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Results for “Molecular Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems

Advances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.

neuroscience↗

bioRxiv2017

Symbiotic root infections in Medicago truncatula require remorin-mediated receptor stabilization in membrane nanodomains

Plant cell infection is tightly controlled by cell surface receptor-like kinases (RLKs) Alike other RLKs the Medicago truncatula entry receptor LYK3 laterally segregates into membrane nanodomains in a stimulus-dependent manner. Although nanodomain localization arises as a generic feature of plant membrane proteins, molecular mechanisms underlying such dynamic transitions and their functional relevance remained poorly understood. Here, we demonstrate that actin and the flotillin protein FLOT4 form the primary and indispensable core of a specific nanodomain. Infection-dependent induction of the remorin protein and secondary molecular scaffold SYMREM1 results in subsequent recruitment of ligand-activated LYK3 and its stabilization within these membrane subcompartments. Reciprocally, the majority of this LYK3 receptor pool is destabilized at the plasma membrane and undergoes rapid endocytosis in symrem1 mutants upon rhizobial inoculation resulting in premature abortion of host cell infections. These data reveal that receptor recruitment into nanodomains is indispensable for their function during host cell infection.\n\nSIGNIFICANCE STATEMENTPattern recognition receptors control the cellular entry of pathogenic as well as symbiotic microbes. While ligand-induced changes in receptor mobility at the plasma membrane and their localization in membrane nanodomains appears as a general feature, the molecular mechanism and the biological relevance of this phenomenon remained unknown. Here, we show that immobilization of the symbiotic cell entry receptor LYK3 in nanodomains requires the presence of actin and the two molecular scaffold proteins FLOT4 and SYMREM1. While FLOT4 forms the initial core structure, infection-induced expression and subsequent physical interaction of SYMREM1 with LYK3 stabilizes the activated receptors in membrane nanodomains. This recruitment prevents its stimulus-dependent endocytosis and ensures progression of the primary infection thread into root cortical cells.

plant biology↗

bioRxiv2018

Network Enhancement: a general method to denoise weighted biological networks

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of technology used to generate them as well as inherent variation within samples. The presence of high levels of noise can hamper discovery of patterns and dynamics encapsulated by these networks. Here we propose Network Enhancement (NE), a novel method for improving the signal-to-noise ratio of undirected, weighted networks, and thereby improving the performance of downstream analysis. NE applies a novel operator that induces sparsity and leverages higher-order network structures to remove weak edges and enhance real connections. This iterative approach has a closed-form solution at convergence with desirable performance properties. We demonstrate the effectiveness of NE in denoising biological networks for several challenging yet important problems. Our experiments show that NE improves gene function prediction by denoising interaction networks from 22 human tissues. Further, we use NE to interpret noisy Hi-C contact maps from the human genome and demonstrate its utility across varying degrees of data quality. Finally, when applied to fine-grained species identification, NE outperforms alternative approaches by a significant margin. Taken together, our results indicate that NE is widely applicable for denoising weighted biological networks, especially when they contain high levels of noise.

bioinformatics↗

bioRxiv2017

Dynamic expression of tRNA-derived small RNAs define cellular states

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) have recently emerged as important regulators of protein translation and shown to have diverse biological functions. However, the underlying cellular and molecular mechanisms of tsRNA function in the context of dynamic cell-state transitions remain unclear. Here we report the identification of a set of tsRNAs upregulated in differentiating mouse embryonic stem cells (mESCs). Mechanistic analyses revealed primary functions of tsRNAs in regulating polysome assembly and translation. Notably, interactome studies with differentially-enriched tsRNAs revealed a switch in associations with effector RNPs and target mRNAs in different cell-states. We also demonstrate that a specific pool of tsRNAs can interact with Igf2bp1, an RNA-binding protein, to influence the expression of the pluripotency-promoting factor-c-Myc, thereby providing evidence for tsRNAs in modulating stem cell-states in mESCs. Finally, tsRNA expression analyses in distinct, heterologous cell and tissue models of stem/transformed versus differentiated/normal states reveal that tsRNA-mediated regulation of protein translation may represent a global biological phenomenon associated with cell-state transitions.\n\nOne Sentence SummaryIdentification and functional characterization of tRNA-derived small RNAs (tsRNAs) in cell state switches.

molecular biology↗

bioRxiv2017

Signaling Networks and Dynamics of Allosteric Transitions in Bacterial Chaperonin GroEL: Implications for Iterative Annealing of Misfolded Proteins

Signal transmission at the molecular level in many biological complexes occurs through allosteric transitions. They describe the response a complex to binding of ligands at sites that are spatially well separated from the binding region. We describe the Structural Perturbation Method (SPM), based on phonon propagation in solids, that can be used to determine the signal transmitting allostery wiring diagram (AWD) in large but finite-sized biological complexes. Applications to the bacterial chaperonin GroEL-GroES complex shows that the AWD determined from structures also drive the allosteric transitions dynamically. Both from a structural and dynamical perspective these transitions are largely determined by formation and rupture of salt-bridges. The molecular description of allostery in GroEL provides insights into its function, which is quantitatively described by the Iterative Annealing Mechanism. Remarkably, in this complex molecular machine, a deep connection is established between the structures, reaction cycle during which GroEL undergoes a sequence of allosteric transitions, and function in a self-consistent manner.

biophysics↗

bioRxiv2017

CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

BackgroundOver the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.\n\nResultsCrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.\n\nConclusionCrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.\n\nAvailabilityThe web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.\n\nContactbhaibeka@uhnresearch.ca

systems biology↗

bioRxiv2018

An algorithm-centric Monte Carlo method to empiricallyquantify motion type estimation uncertainty in single-particle tracking

Quantitative analysis of microscopy images is ideally suited for understanding the functional biological correlates of individual molecular species identified by one of the several available \"omics\" techniques. Due to advances in fluorescent labeling, microscopy engineering and image processing, it is now possible to routinely observe and quantitatively analyze at high temporal and spatial resolution the real-time behavior of thousands of individual cellular structures as they perform their functional task inside living systems. Despite the central role of microscopic imaging in modern biology, unbiased inference, valid interpretation, scientific reproducibility and results dissemination are hampered by the still prevalent need for subjective interpretation of image data and by the limited attention given to the quantitative assessment and reporting of the error associated with each measurement or calculation, and on its effect on downstream analysis steps (i.e., error propagation). One of the mainstays of bioimage analysis is represented by single-particle tracking (SPT)1-5, which coupled with the mathematical analysis of trajectories and with the interpretative modelling of motion modalities, is of key importance for the quantitative understanding of the heterogeneous intracellular dynamic behavior of fluorescently-labeled individual cellular structures, vesicles, virions and single-molecules. Despite substantial advances, the evaluation of analytical error propagation through SPT and motion analysis pipelines is absent from most available tools 6. This severely hinders the critical evaluation, comparison, reproducibility and integration of results emerging from different laboratories, at different times, under different experimental conditions and using different model systems. Here we describe a novel, algorithmic-centric, Monte Carlo method to assess the effect of experimental parameters such as signal to noise ratio (SNR), particle detection error, trajectory length, and the diffusivity characteristics of the moving particle on the uncertainty associated with motion type classification The method is easily extensible to a wide variety of SPT algorithms, is made widely available via its implementation in our Open Microscopy Environment inteGrated Analysis (OMEGA) software tool for the management and analysis of tracking data 7, and forms an integral part of our Minimum Information About Particle Tracking Experiments (MIAPTE) data model 8.

bioinformatics↗

bioRxiv2017

Simple rules for an efficient use of Geographic Information Systems in molecular ecology

Geographic Information Systems (GIS) are becoming increasingly popular in the context of molecular ecology and conservation biology thanks to their display options efficiency, flexibility and management of geodata. Indeed, spatial data for wildlife and livestock species is becoming a trend with many researchers publishing genomic data that is specifically suitable for landscape studies. GIS uniquely reveal the possibility to overlay genetic information with environmental data and, as such, allow us to locate and analyze genetic boundaries of various plant and animal species or to study gene-environment associations (GEA). This means that, using GIS, we can potentially identify the genetic bases of species adaptation to particular geographic conditions or to climate change. However, many biologists are not familiar with the use of GIS and underlying concepts and thus experience difficulties in finding relevant information and instructions on how to use them. In this paper, we illustrate the power of free and open source GIS approaches and provide essential information for their successful application in molecular ecology. First, we introduce key concepts related to GIS than are too often overlooked in the literature, for example coordinate systems, GPS accuracy and scale. We then provide an overview of the most employed open-source GIS-related software, file formats and refer to major environmental databases. We also reconsider sampling strategies as high costs of Next Generation Sequencing (NGS) data currently diminish the number of samples that can be sequenced per location. Thereafter, we detail methods of data exploration and spatial statistics suited for the analysis of large genetic datasets. Finally, we provide suggestions to properly edit maps and to make them as comprehensive as possible, either manually or trough programming languages.

ecology↗

bioRxiv2026

Molecular basis of AMPA receptor labeling by ligand-directed acyl imidazole chemistry in living neurons

Rational design of covalent protein-labeling reagents in complex biological environments requires a molecular-level understanding of how the protein microenvironment governs chemical reactivity; yet, such mechanistic details remain inaccessible to experimental methods alone. In living neurons, Ligand-Directed Acyl Imidazole (LDAI) chemistry has been used to label AMPA receptors as a traceless, affinity-based protein labeling method. Although LDAI labeling reagents have been optimized in the lab, the atomic details of their interactions with the protein and the underlying mechanism remain elusive. In this work, we combined Quantum Mechanical (QM) calculations and molecular dynamics (MD) simulations to propose a detailed reaction mechanism for AMPAR labeling by LDAI reagents and to clarify how the protein microenvironment governs reactivity. Although Lys residues are usually protonated at physiological pH and therefore less nucleophilic in water, our QM results show that Lys labeling is energetically more favorable than competing reactions with Ser or water. MD simulations reveal that PFQX ---the LDAI reagent precursor--- binds dynamically to the GluA2 AMPAR as an antagonist, inducing conformational changes that reshape the local environment of the acyl imidazole (AI) warhead, underscoring that ligand identity strongly affects labeling outcomes. We also identified intra and intermolecular hydrogen bond networks that may contribute to further immobilize and pre-organize the LDAI reagent. Moreover, the probe's chemical nature shapes its interactions with the Ligand Binding Domain (LBD), offering a plausible rationale for the previously experimentally observed ligand-dependent fluorescent response. Taken together, our results establish design principles for exploiting the reagent geometry and binding pocket hydrogen-bonding networks for the rational design of LDAI reagents.

biophysics↗

bioRxiv2018

Single-Nucleotide-Resolution Computing and Memory in Living Cells

Computing and memory in living cells are central to encoding next-generation therapies and studying in situ biology, but existing strategies have limited encoding capacity and are challenging to scale. To overcome this bottleneck, we developed a highly scalable, robust and compact platform for encoding logic and memory operations in living bacterial and human cells. This platform, named DOMINO for DNA-based Ordered Memory and Iteration Network Operator, converts DNA in living cells into an addressable, readable, and writable computation and storage medium via a single-nucleotide resolution read-write head that enables dynamic and highly efficient DNA manipulation. We demonstrate that the order and combination of DNA writing events can be programmed by biological cues and multiple molecular recorders can be coordinated to encode a wide range of order-independent, sequential, and temporal logic and memory operations. Furthermore, we show that these operators can be used to perform both digital and analog computation, and record signaling dynamics and cellular states in a long-term, autonomous, and minimally disruptive fashion. Finally, we show that the platform can be functionalized with gene regulatory modules and interfaced with cellular circuits to continuously monitor cellular phenotypes and engineer gene circuits with artificial learning capacities. We envision that highly scalable, compact, and modular DOMINO operators will lay the foundation for building robust and sophisticated synthetic gene circuits for numerous biotechnological and biomedical applications.\n\nOne Sentence SummaryA programmable read-write head with single-nucleotide-resolution for genomic DNA enables robust and scalable computing and memory operations in living cells.

synthetic biology↗

bioRxiv2018

Satb1 integrates DNA sequence, shape, motif density and torsional stress to differentially bind targets in nucleosome-dense regions.

Satb1 is a genome organizer that regulates multiple cellular and developmental processes. It is not yet clear how Satb1 selects different sets of targets throughout the genome. We used live-cell single molecule imaging and deep sequencing to assess determinants of Satb1 binding-site selectivity. We found that Satb1 preferentially targets nucleosome-dense regions and can directly bind consensus motifs within nucleosomes. Some genomic regions harbor multiple regularly spaced Satb1 binding motifs (typical separation [~]1 turn of the DNA helix), characterized by highly cooperative binding. The Satb1 homeodomain is dispensable for high-affinity binding but is essential for specificity. Finally, Satb1{Leftrightarrow}DNA interactions are mechanosensitive: increasing negative torsional stress in DNA enhances Satb1 binding and Satb1 stabilizes base unpairing regions (BURs) against melting by molecular machines. The ability of Satb1 to control diverse biological programs may reflect its ability to combinatorially use multiple site selection criteria.

molecular biology↗

bioRxiv2015

Transcriptomic Characterization of Soybean Roots in Response to Bradyrhizobium Infection by RNA Sequencing

Legumes interact with rhizobium convert N2 into ammonia for plant use. To investigate the plant basal nitrogen fixation mechanisms induced in response to Bradyrhizobium, differential gene expression in root of inoculated and mock-inoculated soybean was analysed by RNA-Seq. A total of 55787 transcripts were aligned to soybean genome reference sequences, 280 and 316 transcripts were found to be up-and down-regulated, respectively, in inoculated relative to mock-inoculated soybeans root at V1 stage. Gene ontology (GO) analyses detected 104, 182 and 178 genes associated with cell component category, molecular function category and biological process category, respectively. Pathway analysis revealed that 98 differentiallly expressed genes (115 transcripts) involved in 169 biological pathways. We selected 19 differentially expressed genes and analyzed their expressions in mock-inoculated, inoculated USDA110 and CCBAU45436 using qRT-PCR. The results were consistent with those obtained from rhizobia infected RNA-Seq data. These showed that the results of RNA-Seq have reliability and universality. Additionally, this study showed some novel genes associated with nitrogen fixation process comparison with the previously identified QTLs.

Genomics↗

bioRxiv2016

Rapidly evolving homing CRISPR barcodes

We present here an approach for engineering evolving DNA barcodes in living cells. The methodology entails using a homing guide RNA (hgRNA) scaffold that directs the Cas9-hgRNA complex to target the DNA locus of the hgRNA itself. We show that this homing CRISPR-Cas9 system acts as an expressed genetic barcode that diversifies its sequence and that the rate of diversification can be controlled in cultured cells. We further evaluate these barcodes in cultured cell populations and show that they can record lineage history and and that their RNA can be assayed as single molecules in situ. This integrated approach will have wide ranging applications, such as in deep lineage tracing, cellular barcoding, molecular recording, dissecting cancer biology, and connectome mapping.

Synthetic Biology↗

bioRxiv2016

Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories

The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression trajectory from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves. SCIMITAR yields rich models from the data that highlight genes with expression and co-expression patterns that are associated with the inferred progression. Further, SCIMITAR extracts regulatory states from the implicated trajectory-evolving co-expression networks. We benchmark the method on simulated data to show that it yields accurate cell ordering and gene network inferences. Applied to the interpretation of a single-cell human fetal neuron dataset, SCIMITAR finds progression-associated genes in cornerstone neural differentiation pathways missed by standard differential expression tests. Finally, by leveraging the rewiring of gene-gene co-expression relations across the progression, the method reveals the rise and fall of co-regulatory states and trajectory-dependent gene modules. These analyses implicate new transcription factors in neural differentiation including putative co-factors for the multi-functional NFAT pathway.

Bioinformatics↗

bioRxiv2017

De novo discovery of structural motifs in RNA 3D structures through clustering

As functional components in three-dimensional conformation of an RNA, the RNA structural motifs provide an easy way to associate the molecular architectures with their biological mechanisms. In the past years, many computational tools have been developed to search motif instances by using the existing knowledge of well-studied families. Recently, with the rapidly increasing number of resolved RNA 3D structures, there is an urgent need to discover novel motifs with the newly presented information. In this work, we classify all the loops in non-redundant RNA 3D structures to detect plausible RNA structural motif families by using a clustering pipeline. Compared with other clustering approaches, our method has two benefits: first, the underlying alignment algorithm is tolerant to the variations in 3D structures; second, sophisticated downstream analysis has been performed to ensure the clusters are valid and easily applied to further research. The final clustering results contain many interesting new variants of known motif families, such as GNAA tetraloop, kink-turn, sarcin-ricin, and T-loop. We have also discovered potential novel functional motifs conserved in ribosomal RNA, sgRNA, SRP RNA, riboswitch, and ribozyme.

bioinformatics↗

bioRxiv2017

Optical crosstalk in SPAD arrays for high-throughput single-molecule fluorescence spectroscopy

Single-molecule fluorescence spectroscopy (SMFS), based on the detection of individual molecules freely diffusing through the excitation spot of a confocal microscope, has allowed unprecedented insights into biological processes at the molecular level, but suffers from limited throughput. We have recently introduced a multispot version of SMFS, which allows achieving high-throughput SMFS by virtue of parallelization, and relies on custom silicon single-photon avalanche diode (SPAD) detector arrays. Here, we examine the premise of this parallelization approach, which is that data acquired from different spots is uncorrelated. In particular, we measure the optical crosstalk characteristics of the two 48-SPAD arrays used in our recent SMFS studies, and demonstrate that it is negligible (crosstalk probability [≤] 1.1 10-3) and is undetectable in cross-correlation analysis of actual single-molecule fluorescence data.

biophysics↗

bioRxiv2018

Controlling load-dependent contractility of the heart at the single molecule level

Concepts in molecular tension sensing in biology are growing and have their origins in studies of muscle contraction. In the heart muscle, a key parameter of contractility is the detachment rate from actin of myosin, which determines the time that myosin is bound to actin in a force-producing state and, importantly, depends on the load (force) against which myosin works. Here, we measure the detachment rate of single molecules of human {beta}-cardiac myosin and its load dependence. We find that both can be modulated by both small molecule compounds and cardiomyopathy-causing mutations. Furthermore, effects of mutations can be reversed by introducing appropriate compounds. Our results suggest that activating vs. inhibitory perturbations of cardiac myosin are discriminated by the aggregate result on duty ratio, average force, and ultimately average power output and that cardiac contractility can be controlled by tuning the load-dependent kinetics of single myosin molecules.

biophysics↗

bioRxiv2018

Causal interactions from proteomic profiles: molecular data meets pathway knowledge

Measurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at http://causalpath.org.

systems biology↗