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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 775 records · Page 43Linked to original sources

bioRxiv2018

Kinetic Model of Translational Autoregulation

We investigate dynamics of a kinetic model of inhibitory autoregulation as exemplified when a protein inhibits its own production by interfering with its messenger RNA, known in molecular biology as translational autoregulation. We first show how linear models without feedback set the stage with a nonequilibrium steady state that constitutes the target of the regulation. However, regulation in the simple linear model is far from optimal. The negative feedback mechanism whereby the protein \"jams\" the mRNA greatly enhances the effectiveness of the control, with response to perturbation that is targeted, rapid, and metabolically efficient. Understanding the full dynamics of the system phase space is essential to understanding the autoregulation process.

systems biology↗

bioRxiv2018

Integrating Vectors for Genetic Studies in the Rare Actinomycete Amycolatopsis marina

Few natural product pathways from rare Actinomycetes have been studied due to the difficulty in applying molecular approaches in these genetically intractable organisms. In this study, we sought to identify integrating vectors, derived using phage int/attP loci, that would efficiently integrate site-specifically in the rare Actinomycete, Amycolatopsis marina DSM45569. Analysis of the genome of A. marina DSM45569 indicated the presence of attB-like sequences for TG1 and R4 integrases. The TG1 and R4 attBs were active in in vitro recombination assays with their cognate purified integrases and attP loci. Integrating vectors containing either the TG1 or R4 int/attP loci yielded exconjugants in conjugation assays from E. coli to A. marina DSM45569. Site-specific recombination of the plasmids into the host TG1 or R4 attB sites was confirmed by sequencing. The presence of homologous TG1 and R4 attB sites in other species of this genus indicates that vectors based on TG1 and R4 integrases could be widely applicable.\n\nImportanceRare Actinomycetes have the same potential of natural product discovery as Streptomyces, but the potential has not been fully explored due to the lack of efficient molecular biology tools. In this study, we identified two serine integrases, TG1 and R4, which could be used in the rare Actinomycetes species, Amycolatopsis marina, as tools for genome integration. The high level of conservation between the attB sites for TG1 and R4 in a number of Amycolatopsis species suggested that plasmids with the integration systems from these phages should be widely useful in this genus.

microbiology↗

bioRxiv2018

Draft genome of the liver fluke Fasciola gigantica

Fascioliasis is a neglected food-borne disease caused by liver flukes (genus Fasciola) and affects more than 200 million people worldwide. Despite technological advances, little is known about the molecular biology and biochemistry of the fluke. We present the draft genome of Fasciola gigantica for the first time. The assembled draft genome has a size of ~1.04 Gb with an N50 of 129 kb. A total of 20,858 genes were predicted. The de novo repeats identified in the draft genome were 46.85%. In pathway analysis, all the genes of glycolysis, Krebs cycle and fatty acid metabolism were found to be present, but the key genes for fatty acid production in fatty acid biosynthesis were missing. This indicates that the fatty acid required for the survival of the fluke may be acquired from the host bile. The genomic information will provide a comprehensive resource to facilitate the development of novel interventions for fascioliasis control.

bioinformatics↗

bioRxiv2018

Laboratory contamination over time during low-biomass sample analysis

Bacteria are not only ubiquitous on earth but can also be incredibly diverse within clean laboratories and reagents. The presence of both living and dead bacteria in laboratory environments and reagents is especially problematic when examining samples with low endogenous content (e.g. skin swabs, tissue biopsies, ice, water, degraded forensic samples, or ancient material), where contaminants can outnumber endogenous microorganisms within samples. The contribution of contaminants within high-throughput studies remains poorly understood because of the relatively low number of contaminant surveys. Here, we examined 144 negative control samples (extraction blank and no-template amplification controls) collected in both typical molecular laboratories and an ultraclean ancient DNA laboratory over five years to characterize long-term contaminant diversity. We additionally compared the contaminant content within a homemade silica-based extraction method, commonly used to analyse low-endogenous samples, with a widely used commercial DNA extraction kit. The contaminant taxonomic profile of the ultraclean ancient DNA laboratory was unique compared to the modern molecular biology laboratories, and changed over time according to researchers, month, and season. The commercial kit contained higher microbial diversity and several human-associated taxa in comparison to the homemade silica extraction protocol. We recommend a minimum of two strategies to reduce the impacts of laboratory contaminants within low-biomass metagenomic studies: 1) extraction blank controls should be included and sequenced with every batch of extractions and 2) the contributions of laboratory contamination should be assessed and reported in each high-throughput metagenomic study.

microbiology↗

bioRxiv2018

Convolutional models of RNA energetics

Nucleic acid molecular biology and synthetic biology are undergoing rapid advances with the emergence of designer riboswitches controlling living cells, CRISPR/Cas9-based genome editing, high-throughput RNA-based silencing, and reengineering of mRNA translation. Many of these efforts require the design of nucleic acid interactions, which relies on accurate models for DNA and RNA energetics. Existing models utilize nearest neighbor rules, which were parameterized through careful optical melting measurements. However, these relatively simple rules often fail to quantitatively account for the biophysical behavior of molecules even in vitro, let alone in vivo. This is due to the limited experimental throughput of optical melting experiments and the infinitely large space of possible motifs that can be formed. Here, we present a convolutional neural network architecture to model the energies of nucleic acid motifs, allowing for learning of representations of physical interactions that generalize to arbitrary unmeasured motifs. First, we used existing parameterizations of motif energies to train the model and demonstrate that our model is expressive enough to recapitulate the current model. Then, through training on optical melting datasets from the literature, we have shown that the model can accurately predict the thermodynamics of hairpins containing unmeasured motifs. This work demonstrates the utility of convolutional models for capturing the thermodynamic parameters that underlie nucleic acid interactions.

biophysics↗

bioRxiv2018

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator. AvailabilityAll code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment. Suppl. InformationSupplementary Files are available online. Contactpmoreno@ebi.ac.uk, European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, Tel: +44-1223-494267, Fax: +44-1223-484696.

bioinformatics↗

bioRxiv2018

Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention

DNA methylation has been extensively linked to alterations in gene expression, playing a key role in the manifestation of multiple diseases, most notably cancer. For this reason, researchers have long been measuring DNA methylation in living organisms. The relationship between methylation and expression, and between methylation in different genomic regions is of great theoretical interest from a molecular biology perspective. Therefore, several models have been suggested to support the prediction of methylation status in samples. These models, however, have two main limitations: (a) they heavily rely on partially measured methylation levels as input, somewhat defeating the object as one is required to collect measurements from the sample of interest before applying the model; and (b) they are largely based on human mediated feature engineering, thus preventing the model from unveiling its own representations. To address these limitations we used deep learning, with an attention mechanism, to produce a general model that predicts DNA methylation for a given sample in any CpG position based solely on the sample's gene expression profile and the sequence surrounding the CpG.\n\nWe show that our model is capable of generalizing to a completely separate test set of CpG positions and subjects. Depending on gene-CpG proximity conditions, our model can attain a Spearman correlation of up to 0.8 and MAE of 0.14 for thousands of CpG sites in the test data. We also identify and analyze several motifs and genes that our model suggests may be linked to methylation activity, such as Nodal and Hand1. Moreover, our approach, and most notably the use of attention mechanisms, offers a novel framework with which to extract valuable insights from gene expression data when combined with sequence information.\n\nThe code and trained models are available at: https://github.com/YakhiniGroup/Methylation

bioinformatics↗

bioRxiv2018

Trace impurities in test stimuli can seriously compromise chemosensory studies

The discovery of olfactory receptors and major technological advances have greatly accelerated our understanding of chemosensory mechanisms. However, some of this rapid progress may be compromised by inadequate knowledge or characterization of the purity of chemical stimuli used to challenge olfactory or other chemoreceptors when mapping their response profiles. Here, we provide strong evidence that the presence of trace impurities in test stimuli can completely obscure true ligand-receptor relationships. DmOR7a, an olfactory receptor of the vinegar fly (Drosophila melanogaster) has been reported to respond to several long-chain aliphatic ligands such as a putative Drosophila pheromone1, the pheromone of the silkworm moth Bombyx mori2, and a common fatty acid, linoleic acid3. By contrast, we show that DmOR7a responds with high sensitivity to volatile impurities and degradation products present in minute quantities in authentic standards of those compounds, but not to the standards themselves. Responses to impurities can easily go unnoticed due to two main factors. First, the sensitivity of receptors to key ligands may be greater than that of analytical chemistry instruments used to check sample purity. Second, the concentration of highly volatile impurities in an odour puff may be orders of magnitude higher than the main component of a sample, due to the large differences in vapour pressures between the impurities and the main component. Issues concerning impurities are not limited to studies on olfaction that use odour puffs to characterize receptor-ligand interactions, but may affect all studies on chemosensation, from molecular biology and in-silico predictions to behaviour. Purity, which is crucial in receptor-ligand studies, is always implied, but rarely checked rigorously. To avoid misinterpretations, a proper account of all compounds present in test stimuli and an unequivocal confirmation of ligand affinity should accompany chemosensory studies.

neuroscience↗

bioRxiv2018

High gene dosage changes transcriptional regulation of genetic structures and contributes to diversity and heterogeneity

Differences in gene dosages between and within species are widely observed. Nevertheless, our understanding of the impacts of gene dosages on transcriptional regulation is far from complete and it is largely ignored in many genetic studies. In this article, we showed that dynamic properties of three genetic structures became significantly different when gene dosages were high and, for self-inhibiting genes, high gene dosages could facilitate monoallelic expression at single cell level and heterogeneity of cell population. This shed lights on potential links between levels of ploidy and phenotypes. In addition, in the context of modern molecular biology, especially synthetic biology, gene dosage needs to be taken into consideration when designing or modifying genetic circuits.

systems biology↗

bioRxiv2019

A multi-reporter bacterial 2-hybrid assay for the fast, simple, and dynamic assay of PDZ domain - peptide interactions

The accurate determination of protein-protein interactions is an important goal of molecular biology and much progress has been made with modern methods emerging in the past decade. However, current methods have limitations, including scale and restriction to high affinity interactions. This has limited our understanding of the large subset of protein-motif interactions. Here we describe a modified bacterial-hybrid assay that employs a combined selectable and scalable reporter system that allows the screen of large protein-peptide libraries and their sort by relative strength. We have applied this tool to characterize a set of human and E. coli PDZ domains. Our results are consistent with prior characterization of these proteins and the improved sensitivity increases our ability to predict known and novel in vivo binding partners. This approach allows for the recovery of a wide range of affinities with a high throughput method that does not sacrifice the scale of the screen.

synthetic biology↗

bioRxiv2019

Ultra-fast fit-free analysis of complex fluorescence lifetime imaging via deep learning

Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies, but relies on complex data fitting techniques to derive the quantities of interest. Herein, we propose a novel fit-free approach in FLI image formation that is based on Deep Learning (DL) to quantify complex fluorescence decays simultaneously over a whole image and at ultra-fast speeds. Our deep neural network (DNN), named FLI-Net, is designed and model-based trained to provide all lifetime-based parameters that are typically employed in the field. We demonstrate the accuracy and generalizability of FLI-Net by performing quantitative microscopic and preclinical experimental lifetime-based studies across the visible and NIR spectra, as well as across the two main data acquisition technologies. Our results demonstrate that FLI-Net is well suited to quantify complex fluorescence lifetimes, accurately, in real time in cells and intact animals without any parameter settings. Hence, it paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications, especially in clinical settings.

bioengineering↗

bioRxiv2019

Identification of transgene-free CRISPR edited plants of rice and tomato by monitoring DsRED fluorescence in dry seeds

CRISPR/Cas technology for specific and precise modification of genomes has transformed molecular biology. However, quick elimination of the transgene remains a challenge in plant biotechnology after genome edition, especially for crops due to their long life cycle and multiploidy, not only to avoid transgene position effects and to minimize the probability of off-target mutation appearance, but also to deliver end users with edited plants free of the recombinant gene editing machinery. Counter selection based on resistance marker genes are inconvenient in the case of CRISPR/Cas applications because plants lacking the transgene cannot survive the selection, and thus two more generations must be screened to evaluate the presence of the transgene. In the case of some crops, generations can last between 6 months and a few years, and the workload may be a limiting factor because transgene detection by PCR requires germination of seeds, and selected plants must be grown until a new generation can be harvested. The expression of fluorescent proteins as selective marker has been successfully used in Arabidopsis thaliana (Stuitje et al., 2003) as a fast method for transgene presence detection prior to seed germination. Despite this clear advantage, it has not been tested in other species yet, because of the special requirements of in vitro transformation protocols. To overcome the above mentioned inconvenient, we have adapted fluorescence-dependent monitoring of transgene to genome editing approaches in tomato and rice with the goal of obtaining transgene-free homozygous edited crop plants in two generations.

plant biology↗

bioRxiv2019

Is understanding the structure-function of microbial community of sludge suitable using DNA-based molecular methods?

Analysis the similarities between total and active microbial community is crucial to evaluate whether understanding the structure-function of microbial community of different types sludge is suitable using DNA-based molecular methods. In this study, procaryotic communities in different types sludge samples (including primary sludge, excess sludge, mixed sludge, and digested sludge) were evaluated using DNA-based and RNA-based Illumina MiSeq. Results showed that the similarities between total and active procaryotic communities of all different types sludge were considerable high. Meanwhile, the similarity in running sludge higher than that in idle sludge, because of the Jarccards coefficient of digested sludge was the highest in all types sludge samples. This study indicates that the DNA-based molecular biology technology could be applicable to profile procaryotic community structure and function in sludge samples, and this study adds the understanding of total and active procaryotic communities of sludge samples.

ecology↗

bioRxiv2019

Nucleic Acid Cleavage with a Hyperthermophilic Cas9 from an Unculturable Ignavibacterium

CRISPR-Cas9 systems have been effectively harnessed to engineer the genomes of organisms from across the tree of life. Nearly all currently characterized Cas9 proteins are derived from mesophilic bacteria, and canonical Cas9 systems are challenged by applications requiring enhanced stability or elevated temperatures. We discovered IgnaviCas9, a Cas9 protein from a hyperthermophilic Ignavibacterium identified through mini-metagenomic sequencing of samples from a hot spring. IgnaviCas9 is active at temperatures up to 100 {degrees}C in vitro, which enables DNA cleavage beyond the 44 {degrees}C limit of Streptococcus pyogenes Cas9 (SpyCas9) and the 70 {degrees}C limit of both Geobacillus stearothermophilus Cas9 (GeoCas9) and Geobacillus thermodenitrificans T12 Cas9 (ThermoCas9). As a potential application of this enzyme, we demonstrated that IgnaviCas9 can be used in bacterial RNA-seq library preparation to remove unwanted cDNA from 16s ribosomal rRNA (rRNA) without increasing the number of steps, thus underscoring the benefits provided by its exceptional thermostability in improving molecular biology and genomic workflows. Taken together, IgnaviCas9 is an exciting addition to the CRISPR-Cas9 toolbox and expands its temperature range.

genomics↗

bioRxiv2019

Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.

genomics↗

bioRxiv2019

Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features

Untargeted mass spectrometry is a powerful method for detecting metabolites in biological samples. However, fast and accurate identification of the metabolites structures from MS/MS spectra is still a great challenge. We present a new analysis method, called SF-Matching, that is based on the hypothesis that molecules with similar structural features will exhibit similar fragmentation patterns. We combine information on fragmentation patterns of molecules with shared substructures and then use random forest models to predict whether a given structure can yield a certain fragmentation pattern. These models can then be used to score candidate molecules for a given mass spectrum. For rapid identification, we pre-compute such scores for common biological molecular structure databases. Using benchmarking datasets, we find that our method has similar performance to CSI:FingerID and that very high accuracies can be achieved by combining our method with CSI:FingerID. Rarefaction analysis of the training dataset shows that the performance of our method will increase as more experimental data become available.

bioinformatics↗

bioRxiv2019

Genomics In The Jungle: Using Portable Sequencing As A Teaching Tool In Field Courses

Genetic research is a rapidly evolving field of study that is increasingly being utilized as a tool for wildlife conservation. However, researchers and science educators in remote areas can often find it difficult to access the latest genetic technologies, often due to a combination of high costs, bulky equipment, and lack of infrastructure. Recent technological innovations are resulting in portable, low-cost instruments that enable next-generation sequencing in remote environments, offering new opportunities to generate a more widespread network of trained conservation scientists, particularly within regions of high biodiversity. What is currently lacking are formalized educational efforts to teach participants in biodiverse areas with hands-on training in molecular biology and real-time DNA sequencing techniques. To address this challenge, we report the design and summarized feedback/outcomes of a conservation genetics field course, called Genomics in the Jungle, that took place at a field research station in the Amazon rainforest of southeastern Peru. The program was established by a small US-based NGO, Field Projects International, and facilitated by a local eco-tourism company in Peru, Inkaterra. We utilized portable sequencing technologies from Oxford Nanopore Technologies, and in-kind support from the manufacturers MiniPCR, MiniOne Systems, Promega, and New England Biolabs. Participants included a mix of non-Peruvian students and local/regional students, some of which had no prior exposure to a genetics laboratory. Overall, we maintain that portable sequencing technology is democratizing scientific research and conservation efforts, and is a major step forward for science educators and conservationists.

scientific communication and education↗

bioRxiv2019

Comparative analysis of gene expression in virulent and attenuated strains of infectious bronchitis virus at sub-codon resolution

Infectious bronchitis virus (IBV) is a member of the genus Gammacoronavirus and the causative agent of avian infectious bronchitis. IBV has a single-stranded, positive-sense RNA genome ~27 kb in length and, like all coronaviruses, produces a set of sub-genomic messenger RNAs (sgmRNAs) synthesised via the viral polymerase. Here, we used RNA sequencing (RNASeq) and ribosome profiling (RiboSeq) to delineate gene expression in the IBV M41-CK and Beau-CK strains at sub-codon resolution. Quantification of reads flanking the programmed ribosomal frameshifting (PRF) signal at the genomic RNA ORF1a/ORF1b junction revealed that PRF in IBV is highly efficient (33-40%), consistent with in vitro measurements. Triplet phasing of the profiling data allowed precise determination of reading frames and revealed the translation of two intergenic genes (4b and 4c on sgmRNA4), which are widely conserved across IBV isolates. RNASeq revealed two novel transcription junction sites in the attenuated Beau-CK strain, one of which would generate a sgmRNA encoding a ribosomally occupied ORF in the viral 3 untranslated region (dORF). Within IBV transcripts, the nucleocapsid (N) protein was unexpectedly found to be inefficiently translated, despite being an abundant structural component of mature IBV virions. Finally, we demonstrate that the host cell response to IBV occurs primarily at the level of transcription, with a global up-regulation of immune-related mRNA transcripts following infection, and comparatively modest changes in the translation efficiencies of host genes.\n\nIMPORTANCEIBV is a major avian pathogen and presents a substantial economic burden to the poultry industry. Improved vaccination strategies are urgently needed to curb the global spread of this pathogen, and the development of suitable vaccine candidates will be aided by an improved understanding of IBV molecular biology. Our high-resolution data have enabled a precise study of transcription and translation in both pathogenic and attenuated forms of IBV, and expand our understanding of gammacoronaviral gene expression. We demonstrate that gene expression shows considerable intra-species variation, with single nucleotide polymorphisms associated with altered production of sgmRNA transcripts, and our RiboSeq data sets enabled us to uncover novel ribosomally occupied ORFs in both strains. We also identify numerous cellular genes and gene networks that are differentially expressed during virus infection, giving insights into the host cell reponse to IBV infection.

genomics↗