SEARCH · bioRxiv Science

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27Linked to original sources

bioRxiv2020

Ribosome Exit Tunnel Electrostatics

The impact of the ribosome exit tunnel electrostatics on the protein elongation rate or on the forces acting upon the nascent polypeptide chain are currently not fully elucidated. In the past, researchers have measured the electrostatic potential inside the ribosome polypeptide exit tunnel at a limited number of spatial points, at least in prokaryotes. Here, we present a basic electrostatic model of the exit tunnel of the ribosome, providing a quantitative physical description of the tunnel interaction with the nascent proteins at all centro-axial points inside the tunnel. We show how the tunnel geometry causes a positive potential difference between the tunnel exit and entry points which impedes positively charged amino acid residues from progressing through the tunnel, affecting the elongation rate in a range of minus 40% to plus 85% when compared to the average elongation rate. The time spent by the ribosome to decode the genetic encrypted message is constrained accordingly. We quantitatively derived, at single residue resolution, the axial forces acting on the nascent peptide from its particular sequence embedded in the tunnel. The model sheds light on how the experimental data point measurements of the potential are linked to the local structural chemistry of the inner wall and the shape and size of the tunnel. The model consistently connects experimental observations coming from different fields in molecular biology, structural and physical chemistry, biomechanics, synthetic and multi-omics biology. Our model should be a valuable tool to gain insight into protein synthesis dynamics, translational control and into the role of the ribosomes mechanochemistry in the co-translational protein folding.

biophysics↗

bioRxiv2020

Age, wound size and position of injury - dependent vascular regeneration assay in growing leaves

BackgroundRecurring damage to aerial organs of plants necessitates their prompt repair, particularly their vasculature. While vascular regeneration assay in aerial plant parts such as stem and inflorescence stalk are well established, those on leaf vasculature remained unexplored. Recently we established a new vascular regeneration assay in growing leaf and discovered the underlying molecular mechanism. ResultsHere we describe the detailed stepwise method of incision and the regeneration assay used for studying the leaf vascular regeneration. By using a combination of micro-surgical perturbations, brightfield microscopy and other experimental approaches, our new findings show that the regeneration efficiency decreases with aging of the leaf, and increases with the nearness of the wound towards the proximal end of the leaf. ConclusionThis easy-to-master vascular regeneration assay is an efficient and rapid method to study the mechanism of vascular regeneration in growing leaves. It can be readily adapted for other plant species and can be combined with cellular and molecular biology techniques.

plant biology↗

bioRxiv2020

Trehalose and α-glucan mediate distinct abiotic stress responses in Pseudomonas aeruginosa

An important prelude to bacterial infection is the ability of a pathogen to survive independently of the host and to withstand environmental stress. The compatible solute trehalose has previously been connected with diverse abiotic stress tolerances, particularly osmotic shock. In this study, we combine molecular biology and biochemistry to dissect the trehalose metabolic network in the opportunistic human pathogen Pseudomonas aeruginosa PA01 and define its role in abiotic stress protection. We show that trehalose metabolism in PA01 is integrated with the biosynthesis of branched -glucan (glycogen), with mutants in either biosynthetic pathway significantly compromised for survival on abiotic surfaces. While both trehalose and -glucan are important for abiotic stress tolerance, we show they counter distinct stresses. Trehalose is vital to the PA01 osmotic stress response, with trehalose synthesis mutants displaying severely compromised growth in elevated salt conditions. However, trehalose does not contribute directly to the PA01 desiccation response. Rather, desiccation tolerance is mediated directly by GlgE-derived -glucan, with deletion of the glgE synthase gene compromising PA01 survival in low humidity but having little effect on osmotic sensitivity. Desiccation tolerance is independent of trehalose concentration, marking a clear distinction between the roles of these two molecules in mediating responses to abiotic stress.

microbiology↗

bioRxiv2020

Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots

SO_SCPLOWUMMARYC_SCPLOWIn molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many measurements (rows) for a set of variables (columns). While projection methods like Principal Component Analysis or Correspondence Analysis can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question which measurements are associated to a cluster and distinguish it from other clusters. Correspondence Analysis employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific measurements in complex data. Association Plots are two-dimensional, independent of the size of data matrix or cluster, and depict the measurements associated to a cluster of variables. We demonstrate our method first on a small data set and then on a genomic example comprising more than 10,000 conditions. We will show that Association Plots can clearly highlight those measurements which characterize a cluster of variables.

bioinformatics↗

bioRxiv2020

Shannon Entropy as a metric for conditional gene expression in Neurospora crassa

Neurospora crassa has been an important model organism for molecular biology and genetics for over 60 years. N. crassa has a complex life cycle, with over 28 distinct cell types and is capable of transcriptional responses to many environmental conditions including nutrient availability, temperature, and light. To quantify variation in N. crassa gene expression, we analyzed public expression data from 97 conditions and calculated the Shannon Entropy value for Neurosporas approximately 11,000 genes. Entropy values can be used to estimate the variability in expression for a single gene over a range of conditions and be used to classify individual genes as constitutive or condition-specific. Shannon entropy has previously been used measure the degree of tissue specificity of multicellular plant or animal genes. We use this metric here to measure variable gene expression in a microbe and provide this information as a resource for the N. crassa research community. Finally, we demonstrate the utility of this approach by using entropy values to identify genes with constitutive expression across a wide range of conditions and to identify genes that are activated exclusively during sexual development.

bioinformatics↗

bioRxiv2020

Improved chromosome level genome assembly of the Glanville fritillary butterfly (Melitaea cinxia) based on SMRT Sequencing and linkage map.

The Glanville fritillary (Melitaea cinxia) butterfly is a long-term model system for metapopulation dynamics research in fragmented landscapes. Here, we provide a chromosome level assembly of the butterflys genome produced from Pacific Biosciences sequencing of a pool of males, combined with a linkage map from population crosses. The final assembly size of 484 Mb is an increase of 94 Mb on the previously published genome. Estimation of the completeness of the genome with BUSCO, indicates that the genome contains 93 - 95% of the BUSCO genes in complete and single copies. We predicted 14,830 gene models using the MAKER pipeline and manually curated 1,232 of these gene models. The genome and its annotated gene models are a valuable resource for future comparative genomics, molecular biology, transcriptome and genetics studies on this species.

genomics↗

bioRxiv2020

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades 1,2, the most dramatic advances in machine-reading have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in machine reading methodology and automated knowledge extraction systems in the same way that ImageNet 4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named-entity analysis tool for biomedicine: (a) a new, Named-Entity Recognition Ontology (NERO) developed specifically for describing entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named-entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named-entity recognition automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

systems biology↗

bioRxiv2020

Machine learning aided top-down protein identification on a microfluidic platform

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of novel method for protein identification in a microfluidic format.

biophysics↗

bioRxiv2020

K-seq, an affordable, reliable, and open Klenow NGS-based genotyping technology

K-seq, a new genotyping methodology based on the amplification of genomic regions using two steps of Klenow amplification with short oligonucleotides, followed by standard PCR and Illumina sequencing, is presented. The protocol was accompanied by software developed to aid with primer set design. As the first examples, K-seq in species as diverse as tomato, dog and wheat was developed. K-seq provided genetic distances similar to those based on WGS in dogs. Experiments comparing K-seq and GBS in tomato showed similar genetic results, although K-seq had the advantage of finding more SNPs for the same number of Illumina reads. The technology reproducibility was tested with two independent runs of the tomato samples, and the correlation coefficient of the SNP coverages between samples was 0.8 and the genotype match was above 94%. K-seq also proved to be useful in polyploid species. The wheat samples generated specific markers for all subgenomes, and the SNPs generated from the diploid ancestors were located in the expected subgenome with accuracies greater than 80%. K-seq is an open, patent-unencumbered, easy-to-set-up, cost-effective and reliable technology ready to be used by any molecular biology laboratory without special equipment in many genetic studies.

genetics↗

bioRxiv2020

Building a pathway for diversity in plant sciences in Argentina: highlighting the work of women scientists through virtual activities

Encouraging the participation of a diverse workforce in academia increases plurality as it broadens the range of skills, ways of thinking and experiences. Institutions and professional societies have been putting efforts on building plans that help make workplaces, conferences, education and extension programs more relatable to a highly diverse population. Argentina has an overall gender-balanced workforce in the sciences (~53% women/total), with an even higher representation in disciplines related to plant sciences. However, media outlets and national conferences related to genetics, botany, plant physiology, ecology and molecular biology, fail to reflect those numbers as the proportion of women invited for interviews, plenary lectures, and symposia falls below ~30%. As a way to increase the visibility of the wealth of plant science topics and experimental approaches in which Argentinian women work, and to facilitate connections among them across the country and abroad, we created the Argentinian Women in Plant Science network (https://argplantwomen.weebly.com/). This group has grown to over 200 members, representing a wide range of career stages and research topics. Since April, and taking advantage of the confinement situation, our weekly webinar series highlighting women plant scientists has reached an average audience of 60-70 participants, with a record of 100. Recently, we have begun a series of open professional development webinars to reach a wider public. Our first webinar, focused on Scientific poster design, had ~250 participants, most of them undergrad and graduate students from all over the country covering a diverse range of disciplines, including the social sciences. Even though we have immersed ourselves in the plant science community with our weekly seminars, we have expanded our goals with activities aimed to reach out to a much wider audience with webinars and teacher training workshops, hopefully making plant science more attainable to all.

scientific communication and education↗

bioRxiv2020

Effect of belt electrode-skeletal muscle electrical stimulation on immobilization-induced muscle fibrosis

PurposeMacrophage accumulation in response to decreasing myonuclei may be the major mechanism underlying immobilization-induced muscle fibrosis in muscle contracture, an intervention strategy suppressing these lesions is necessary. Therefore, this research investigated the effect of belt electrode-skeletal muscle electrical stimulation (B-SES), a new electrical stimulation device, to the macrophage accumulation via myonuclei decrease in immobilization-induced muscle fibrosis. Materials and Methods18 Wistar male rats were divided into the control group, immobilization group (with plaster cast fixation to immobilize the soleus muscles in a shortened position for 2 weeks), and B-SES group (with muscle contractile exercise through B-SES during the immobilization period). B-SES stimulation was performed at a frequency of 50 Hz and an intensity of 4.7 mA, muscle contractile exercise by B-SES was applied to the lower limb muscles for 20 minutes/session (twice a day) for 2 weeks (6 times/week). The bilateral soleus muscles were used for histological, immunohistochemical, biochemical, and molecular biological analyses. ResultsThe number of myonuclei was significantly higher in the B-SES group than in the immobilization group, and there was no significant difference between the B-SES and control groups. The cross-sectional area of type I and II myofibers in the immobilization and B-SES groups was significantly lower than that in the control group, and the cross-sectional area of type I myofibers in the B-SES group was higher than that in the immobilization group. However, Atrogin-1 and MuRF-1 mRNA expression in the immobilization and B-SES groups was significantly higher than those in the control group. Additionally, the number of macrophages, IL-1{beta}, TGF-{beta}1, and -SMA mRNA expression, and hydroxyproline expression was significantly lower in the control and B-SES groups than those in the immobilization group. ConclusionThis research surmised that muscle contractile exercise through B-SES prevented immobilization-induced muscle fibrosis, and this alteration suppressed the development of muscle contracture.

pathology↗

bioRxiv2020

Selective increase of functional network connectivity in Arc-positive neuronal engrams after long-term potentiation

New tools in optogenetics and molecular biology have culminated in recent studies which mark immediate-early gene (IEG)-expressing neurons as memory traces or engrams. Although the activity-dependent expression of IEGs has been successfully utilised to label memory traces, their roles in engram specification is incompletely understood. Outstanding questions remain as to whether expression of IEGs can interplay with network properties such as functional connectivity and also if neurons expressing different IEGs are functionally distinct. We investigated the expression of Arc and c-Fos, two commonly utilised IEGs in memory engram specification, in cultured hippocampal neurons. After pharmacological induction of long-term potentiation (LTP) in the network, we noted an emergent network property of refinement in functional connectivity between neurons, characterized by a global down-regulation of network connectivity, together with strengthening of specific connections. Subsequently, we show that Arc expression correlates with the effects of network refinement, with Arc-positive neurons being selectively strengthened. Arc positive neurons were also found to be located in closer physical proximity to each other in the network. While the expression pattern of IEGs c-Fos and Arc strongly overlaps, Arc was more selectively expressed than c-Fos. These IEGs also act together in coding information about connection strength pruning. These results demonstrate important links between IEG expression and network connectivity, which serve to bridge the gap between cellular correlates and network effects in learning and memory.

neuroscience↗

bioRxiv2020

Signaling interaction link prediction using deep graph neural networks integrating protein-protein interactions and omics data

Uncovering signaling links or cascades among proteins that potentially regulate tumor development and drug response is one of the most critical and challenging tasks in cancer molecular biology. Inhibition of the targets on the core signaling cascades can be effective as novel cancer treatment regimens. However, signaling cascades inference remains an open problem, and there is a lack of effective computational models. The widely used gene co-expression network (no-direct signaling cascades) and shortest-path based protein-protein interaction (PPI) network analysis (with too many interactions, and did not consider the sparsity of signaling cascades) were not specifically designed to predict the direct and sparse signaling cascades. To resolve the challenges, we proposed a novel deep learning model, deepSignalingLinkNet, to predict signaling cascades by integrating transcriptomics data and copy number data of a large set of cancer samples with the protein-protein interactions (PPIs) via a novel deep graph neural network model. Different from the existing models, the proposed deep learning model was trained using the curated KEGG signaling pathways to identify the informative omics and PPI topology features in the data-driven manner to predict the potential signaling cascades. The validation results indicated the feasibility of signaling cascade prediction using the proposed deep learning models. Moreover, the trained model can potentially predict the signaling cascades among the new proteins by transferring the learned patterns on the curated signaling pathways. The code was available at: https://github.com/fuhaililab/deepSignalingPathwayPrediction.

bioinformatics↗

bioRxiv2021

Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing

Over the last two decades, molecular biology has been changed by the introduction of high-throughput technologies. Data sharing requirements have prompted the establishment of persistent data archives. A standardized approach for recording and managing these data was first proposed in the Minimal Information About a Microarray Experiment (MIAME) guidelines. The Minimal Information about a high throughput nucleotide Sequencing Experiment (MINSEQE) proposal was introduced in 2008 as a logical extension of the guidelines to next-generation sequencing (NGS) technologies used for transcriptome analysis. We present a historical snapshot of the data-sharing situation focusing on transcriptomics data from both microarray and RNA-sequencing experiments published between 2009 and 2013, a period during which RNA-seq studies became increasingly popular for transcriptome analysis. We assess how much data from RNA-seq based experiments is actually available in persistent data archives, compared to data derived from microarray based experiments, and evaluate how these types of data differ. Based on this analysis, we provide recommendations to improve RNA-seq data availability, reusability, and reproducibility.

bioinformatics↗

bioRxiv2021

Beeporter: a high-throughput tool for non-invasive analysis of honey bee virus infection

Honey bees face numerous pests and pathogens but arguably none are as devastating as Deformed wing virus (DWV). Development of antiviral therapeutics and virus-resistant honey bee lines to control DWV in honey bees is slowed by the lack of a cost-effective high-throughput screening of DWV infection. Currently, analysis of virus infection and screening for antiviral treatments in bees and their colonies is tedious, requiring a well-equipped molecular biology laboratory and the use of hazardous chemicals. Here we utilize a cDNA clone of DWV tagged with green fluorescent protein (GFP) to develop the Beeporter assay, a method for detection and quantification of DWV infection in live honey bees. The assay involves infection of honey bee pupae by injecting a standardized DWV-GFP inoculum, followed by incubation for up to 44 hours. GFP fluorescence is recorded at intervals via commonly available long-wave UV light sources and a smartphone camera or a standard ultraviolet transilluminator gel imaging system. Nonlethal DWV monitoring allows high-throughput screening of antiviral candidates and a direct breeding tool for identifying honey bee parents with increased antivirus resistance. For even more rapid drug screening, we also describe a method for screening bees using 96-well trays and a spectrophotometer.

microbiology↗

bioRxiv2021

LanceOtron: a deep learning peak caller for ATAC-seq, ChIP-seq, and DNase-seq

ATAC-seq, ChIP-seq, and DNase-seq have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome-wide. Regions where these elements are found appear as peaks in the analog signal of an assays coverage track, and despite the ease with which humans can visually categorize these regions, meaningful peak calls from whole genome datasets require complex analytical techniques. Current methods focus on statistical tests to classify peaks, reducing the information-dense peak shapes to simply maximum height, and discounting that background signals do not completely follow any known probability distribution for significance testing. Deep learning has been shown to be highly accurate for image recognition, on par or exceeding human ability, providing an opportunity to reimagine and improve peak calling. We present the peak calling framework LanceOtron, which combines multifaceted enrichment measurements with deep learning image recognition techniques for assessing peak shape. In benchmarking transcription factor binding, chromatin modification, and open chromatin datasets, LanceOtron outperforms the long-standing, gold-standard peak caller MACS2 through its improved selectivity and near perfect sensitivity. In addition to command line accessibility, a graphical web application was designed to give any researcher the ability to generate optimal peak calls and interactive visualizations in a single step.

bioinformatics↗

bioRxiv2021

Mouse oocytes do not contain a conventional Balbiani body

Oocytes spend the majority of their lifetime in a primordial, dormant state. Unlike many somatic cell types and mature oocytes, the cellular and molecular biology of primordial oocytes is largely unexplored. Yet, studying their cellular biology is necessary to understand the mechanisms through which oocytes maintain cellular fitness for decades, and why they eventually fail with age. A hallmark of primordial oocytes in many species is the Balbiani body, a non-membrane bound compartment that contains the majority of mitochondria in the oocyte cytoplasm. The Balbiani body has been proposed to be essential for maintaining mitochondria in a healthy state during long-lasting dormancy, however, the architecture and function of the mammalian Balbiani body remains unknown. Here, we develop enabling methods for live-imaging based comparative characterization of Xenopus, mouse and human primordial oocytes. We show that primordial oocytes in all three vertebrate species contain active mitochondria, Golgi apparatus and lysosomes. We further demonstrate that human and Xenopus oocytes have a Balbiani body characterized by a dense accumulation of mitochondria in their cytoplasm. However, despite previous reports, we did not find a Balbiani body in mouse oocytes. Instead, we demonstrate what was previously used as a marker for the Balbiani body in mouse primordial oocytes is in fact a ring-shaped Golgi apparatus that is not functionally associated with oocyte dormancy. Our work provides the first insights into the organisation of the cytoplasm in mammalian primordial oocytes, and clarifies relative advantages and limitations of choosing different model organisms for studying oocyte dormancy.

cell biology↗

bioRxiv2021

Whole genome 6-methyladenosine sequencing (6-mA-Seq) enables bacterial epigenomics studies.

Methylation sequencing using bisulfite treatment has revolutionized the field of molecular biology for eukaryotic systems, unveiling levels of intricacy in regulation of gene expression in response to different environmental conditions. While bacteria also utilize methylation to regulate gene expression, bisulfite sequencing does not work as well in bacteria as in eukaryotes, because bacteria methylate adenosine instead of cytosine. Therefore, global bacterial methylation patterns cannot be studied using common Illumina sequencers. In this work, we demonstrate 6mA-seq, a method that can be used to identify patterns in bacteria that methylate adenosines at GATC sites. Furthermore, this method was used on Escherichia coli cultured on four different carbon sources to demonstrate different methylation patterns due to carbon utilization. 6mA-seq can increase the speed in which epigenetic research is conducted in bacteria.

genomics↗