Biology subjects

Marz, M.

Publications and source records attributed to Marz, M..

33 records · Page 2Linked to original sources

bioRxiv2021

The economical lifestyle of CPR bacteria in groundwater allows little preference for environmental drivers

The highly diverse Cand. Patescibacteria are predicted to have minimal biosynthetic and metabolic pathways, which hinders understanding of how their populations differentiate to environmental drivers or host organisms. Their metabolic traits to cope with oxidative stress are largely unknown. Here, we utilized genome-resolved metagenomics to investigate the adaptive genome repertoire of Patescibacteria in oxic and anoxic groundwaters, and to infer putative host ranges. Within six groundwater wells, Cand. Patescibacteria was the most dominant (up to 79%) super-phylum across 32 metagenomes obtained from sequential 0.2 and 0.1 {micro}m filtration. Of the reconstructed 1275 metagenome-assembled genomes (MAGs), 291 high-quality MAGs were classified as Cand. Patescibacteria. Cand. Paceibacteria and Cand. Microgenomates were enriched exclusively in the 0.1 {micro}m fractions, whereas candidate division ABY1 and Cand. Gracilibacteria were enriched in the 0.2 {micro}m fractions. Patescibacteria enriched in the smaller 0.1 {micro}m filter fractions had 22% smaller genomes, 13.4% lower replication measures, higher fraction of rod-shape determining proteins, and genomic features suggesting type IV pili mediated cell-cell attachments. Near-surface wells harbored Patescibacteria with higher replication rates than anoxic downstream wells characterized by longer water residence time. Except prevalence of superoxide dismutase genes in Patescibacteria MAGs enriched in oxic groundwaters (83%), no major metabolic or phylogenetic differences were observed based on oxygen concentrations. The most abundant Patescibacteria MAG in oxic groundwater encoded a nitrate transporter, nitrite reductase, and F-type ATPase, suggesting an alternative energy conservation mechanism. Patescibacteria consistently co-occurred with one another or with members of phyla Nanoarchaeota, Bacteroidota, Nitrospirota, and Omnitrophota. However, only 8% of MAGs showed highly significant one-to-one association, mostly with Omnitrophota. Genes coding for motility and transport functions in certain Patescibacteria were highly similar to genes from other phyla (Omnitrophota, Proteobacteria and Nanoarchaeota). Other than genes to cope with oxidative stress, we found little genomic evidence for niche adaptation of Patescibacteria to oxic or anoxic groundwaters. Given that we could detect specific host preference only for a few MAGs, we propose that the majority of Patescibacteria can attach to multiple hosts just long enough to loot or exchange supplies with an economic lifestyle of little preference for geochemical conditions.

microbiology↗

bioRxiv2021

BERTax: taxonomic classification of DNA sequences with Deep Neural Networks

Taxonomic classification, i.e., the identification and assignment to groups of biological organisms with the same origin and characteristics, is a common task in genetics. Nowadays, taxonomic classification is mainly based on genome similarity search to large genome databases. In this process, the classification quality depends heavily on the database since representative relatives have to be known already. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a program that uses a deep neural network to pre-cisely classify the superkingdom, phylum, and genus of DNA sequences taxonomically without the need for a known representative relative from a database. For this, BERTax uses the natural language processing model BERT trained to represent DNA. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. In case of an entirely novel organism, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality. Since BERTax is not based on homologous entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences. This leads to a higher number of correctly classified sequences and thus increases the overall information gain.

bioinformatics↗

bioRxiv2020

PoSeiDon: a Nextflow pipeline for the detection of evolutionary recombination events and positive selection

SummaryPoSeiDon is an easy-to-use pipeline that helps researchers to find recombination events and sites under positive selection in protein-coding sequences. By entering homologous sequences, PoSeiDon builds an alignment, estimates a best-fitting substitution model, and performs a recombination analysis followed by the construction of all corresponding phylogenies. Finally, significantly positive selected sites are detected according to different models for the full alignment and possible recombination fragments. The results of PoSeiDon are summarized in a user-friendly HTML page providing all intermediate results and the graphical representation of recombination events and positively selected sites. Availability and implementationPoSeiDon is freely available at https://github.com/hoelzer/poseidon. The pipeline is implemented in Nextflow with Docker support and processes the output of various tools. Contacthoelzer.martin@gmail.com

bioinformatics↗

bioRxiv2020

EpiDope: A Deep neural network for linear B-cell epitope prediction

By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly shortened by using in silico prediction. Here we present EpiDope, a python tool which uses a deep neural network to detect B-cell epitope regions on individual protein sequences (github.com/mcollatz/EpiDope). With an area under the curve (AUC) between 0.67 {+/-} 0.07 in the ROC curve, EpiDope exceeds all other currently used B-cell prediction tools. Moreover, for AUC10% (AUC for a false-positive rate < 0.1), EpiDope improves the prediction accuracy in comparison to other state-of-the-art methods. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach.

bioinformatics↗

bioRxiv2019

Inclusion of Oxford Nanopore long reads improves all microbial and phage metagenome-assembled genomes from a complex aquifer system

Assembling microbial and phage genomes from metagenomes is a powerful and appealing method to understand structure-function relationships in complex environments. In order to compare the recovery of genomes from microorganisms and their phages from groundwater, we generated shotgun metagenomes with Illumina sequencing accompanied by long reads derived from the Oxford Nanopore sequencing platform. Assembly and metagenome-assembled genome (MAG) metrics for both microbes and viruses were determined from Illumina-only assemblies and a hybrid assembly approach. Strikingly, the hybrid approach more than doubled the number of mid to high-quality MAGs (> 50% completion, < 10% redundancy), generated nearly four-fold more phage genomes, and improved all associated genome metrics relative to the Illumina only method. The hybrid assemblies yielded MAGs that were on average 7.8% more complete, with 133 fewer contigs and a 14 kbp greater N50. Furthermore, the longer contigs from the hybrid approach generated microbial MAGs that had a higher proportion of rRNA genes. We demonstrate this usefulness by linking microbial MAGs containing 16S rRNA genes with extensive amplicon dataset. This work provides quantitative data to inform a cost-benefit analysis on the decision to supplement shotgun metagenomic projects with long reads towards the goal of recovering genomes from environmentally abundant groups.

microbiology↗

bioRxiv2019

A comprehensive annotation and differential expression analysis of short and long non-coding RNAs in 16 bat genomes

Although bats are increasingly becoming the focus of scientific studies due to their unique properties, these exceptional animals are still among the least studied mammals. Assembly quality and completeness of bat genomes vary a lot and especially non-coding RNA (ncRNA) annotations are incomplete or simply missing. Accordingly, standard bioinformatics pipelines for gene expression analysis often ignore ncRNAs such as microRNAs or long antisense RNAs. The main cause of this problem is the use of incomplete genome annotations. We present a complete screening for ncRNAs within 16 bat genomes. NcRNAs affect a remarkable variety of vital biological functions, including gene expression regulation, RNA processing, RNA interference and, as recently described, regulatory processes in viral infections. Within all investigated bat assemblies we annotated 667 ncRNA families including 162 snoRNAs and 193 miRNAs as well as rRNAs, tRNAs, several snRNAs and IncRNAs, and other structural ncRNA elements. We validated our ncRNA candidates by six RNA-Seq data sets and show significant expression patterns that have never been described before in a bat species on such a large scale. Our annotations will be usable as a resource (Electronic Supplement) for deeper studying of bat evolution, ncRNAs repertoire, gene expression and regulation, ecology, and important host-virus interactions. Supplementary informationis available at rna.uni-jena.de/supplements/bats, the Open Science Framework (doi.org/10.17605/OSF.IO/4CMDN), and GitHub (github.com/rnajena/bats_ncrna).

bioinformatics↗

bioRxiv2019

Viral host prediction with Deep Learning

MotivationZoonosis, the natural transmission of infections from animals to humans, is a far-reaching global problem. The recent outbreaks of Zika virus, Ebola virus and Corona virus are examples of viral zoonosis, which occur more frequently due to globalization. In the case of a virus outbreak, it is helpful to know which host organism was the original carrier of the virus. Once the reservoir or intermediate host is known, it can be isolated to prevent further spreading of the viral infection. Recent approaches aim to predict a viral host based on the viral genome, often in combination with the potential host genome and arbitrarily selected features. These methods have a clear limitation in either the number of different hosts they can predict or the accuracy of their prediction. ResultsHere, we present a fast and accurate deep learning approach for viral host prediction, which is based on the viral genome sequence only. To ensure a high prediction accuracy, we developed an effective selection approach for the training data to avoid biases due to a highly unbalanced number of known sequences per virus-host combinations. We tested our deep neural network on three different virus species (influenza A, rabies lyssavirus, rotavirus A). We reached for each virus species an AUG between 0.93 and 0.98, outperforming previous approaches and allowing highly accurate predictions while only using fractions (100-400 bp) of the viral genome sequences. We show that deep neural networks are suitable to predict the host of a virus, even with a limited amount of sequences and highly unbalanced available data. The deep neural networks trained for this approach build the core of the virus-host predicting tool VIDHOP (Virus Deep learning HOst Prediction). AvailabilityThe trained models for the prediction of the host for the viruses influenza A, rabies lyssavirus, rotavirus A are implemented in the tool VIDHOP. This tool is freely available under https://github.com/flomock/vidhop. Supplementary informationSupplementary data are available at DOI 10.17605/OSF.IO/UXT7N

bioinformatics↗

bioRxiv2019

The landscape of the alternatively spliced transcriptome remains stable during aging across different species and tissues

Aging is characterized by a decline of cellular homeostasis over time, leading to various severe disorders and death. Alternative splicing is an important regulatory level of gene expression and thus takes on a key role in the maintenance of accurate cell and tissue function. Missplicing of certain genes has already been linked to several age-associated diseases, such as progeria, Alzheimers disease, Parkinsons disease and cancer. Nevertheless, many studies focus only on transcriptional variations of single genes or the expression changes of spliceosomal genes, coding for the proteins that aggregate to the spliceosomal machinery. Little is known on the general change of present and switching isoforms in different tissues over time. In this descriptive RNA-Seq study, we report differences and commonalities of isoform usage during aging among different tissues within one species and compare changes of alternative splicing among different, evolutionarily distinct species. Although we identified a multitude of differntially spliced genes among different time points, we observed little to no general changes in the transcriptomic landscape of the investigated samples. Although there is undoubtedly considerable influence of specifically spliced genes on certain age-associated processes, this work shows that alternative splicing remains stable for the majority of genes with aging.

bioinformatics↗

bioRxiv2019

Distributed representations of protein domains and genomes and their compositionality

Learning algorithms have at their disposal an ever-growing number of metagenomes for biomining and the study of microbial functions. We propose a novel representation of function called nanotext that scales to very large data sets while capturing precise functional relationships.\n\nThese relationships are learned from a corpus of 32 thousand genome assemblies with 145 million protein domains. We treat the protein domains in a genome like words in a document, assuming that protein domains in a similar context have similar \"meaning\". This meaning can be distributed by the Word2Vec embedding algorithm over a vector of numbers. These vectors not only encode function but can be used to predict even complex genomic features and phenotypes.\n\nWe apply nanotext to data from the Tara ocean expedition to predict plausible culture media and growth temperatures for microorganisms from their metagenome assembled genomes (MAGs) alone. nanotext is freely released under a BSD licence (https://github.com/phiweger/nanotext).

genomics↗

bioRxiv2018

Nanopore direct RNA sequencing reveals modification in full-length coronavirus genomes

Sequence analyses of RNA virus genomes remain challenging due to the exceptional genetic plasticity of these viruses. Because of high mutation and recombination rates, genome replication by viral RNA-dependent RNA polymerases leads to populations of closely related viruses, so-called quasispecies. Standard (short-read) sequencing technologies are ill-suited to reconstruct large numbers of full-length haplotypes of (i) RNA virus genomes and (ii) subgenome-length (sg) RNAs comprised of noncontiguous genome regions. Here, we used a full-length, direct RNA sequencing (DRS) approach based on nanopores to characterize viral RNAs produced in cells infected with a human coronavirus.\n\nUsing DRS, we were able to map the longest ([~]26 kb) contiguous read to the viral reference genome. By combining Illumina and nanopore sequencing, we reconstructed a highly accurate consensus sequence of the human coronavirus (HCoV) 229E genome (27.3 kb). Furthermore, using long reads that did not require an assembly step, we were able to identify, in infected cells, diverse and novel HCoV-229E sg RNAs that remain to be characterized. Also, the DRS approach, which circumvents reverse transcription and amplification of RNA, allowed us to detect methylation sites in viral RNAs. Our work paves the way for haplotype-based analyses of viral quasispecies by demonstrating the feasibility of intra-sample haplotype separation.\n\nEven though several technical challenges remain to be addressed to exploit the potential of the nanopore technology fully, our work illustrates that direct RNA sequencing may significantly advance genomic studies of complex virus populations, including predictions on long-range interactions in individual full-length viral RNA haplotypes.

genomics↗

bioRxiv2018

Peripheral blood exosomes pass blood-brain-barrier and induce glial cell activation

BackgroundExosomes are involved in intracellular communication and contain proteins, mRNAs, miRNAs, and signaling molecules. Exosomes were shown to act as neuroinflammatory mediators involved in neurodegenerative diseases including Alzheimers disease (AD), Parkinsons disease (PD), amyotrophic lateral sclerosis (ALS). Brain aging has been associated to increased neuroinflammation. In addition, a decreased extracellular vesicle concentration was observed in aging tissues. The specific mechanisms how exosomes and aging are connected are not known yet.\n\nResultsHere we have shown that peripheral injection had almost no effect on selected gene expression in the liver. However, exosome injection has led to changes in the specific markers of glial cell activation (CD68, Iba1, GFAP). Interestingly, only injection of exosomes isolated from aged mice induced significant activation of astrocyte cells, as shown by increased GFAP expression.\n\nConclusionTranscription levels of genes GFAP, TGF-{beta}, CD68, Iba1 known to be involved in glial cell function are significantly changing after introduction of peripheral extracellular vesicles. Exosomes were able to pass blood brain barrier and induce glial cell activation. GFAP known to be a specific astrocyte activation marker was significantly higher expressed after injection of old but not young exosomes, indicating a possible role of exosomes in the mechanisms of brain aging.

neuroscience↗

bioRxiv2018

SilentMutations (SIM): a tool for analyzing long-range RNA-RNA interactions in viral genomes and structural RNAs

BackgroundA single nucleotide change in the coding region can alter the amino acid sequence of a protein. In consequence, natural or artificial sequence changes in viral RNAs may have various effects not only on protein stability, function and structure but also on viral replication.\n\nIn recent decades, several tools have been developed to predict the effect of mutations in structured RNAs such as viral genomes or non-coding RNAs. Some tools use multiple point mutations and also take coding regions into account. However, none of these tools was designed to specifically simulate the effect of mutations on viral long-range interactions.\n\nResultsHere, we developed SilentMutations (SIM), an easy-to-use tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. The tool can simulate disruptive and compensatory mutants of two interacting single-stranded RNAs. This allows a fast and accurate assessment of key regions potentially involved in functional long-range RNA-RNA interactions and will eventually help virologists and RNA-experts to design appropriate experiments.\n\nSIM only requires two interacting single-stranded RNA regions as input. The output is a plain text file containing the most promising mutants and a graphical representation of all interactions.\n\nConclusionWe applied our tool on two experimentally validated influenza A virus and hepatitis C virus interactions and we were able to predict potential double mutants for in vitro validation experiments.\n\nAvailabilityThe source code and documentation of SIM are freely available at github.com/desiro/silentMutations.

bioinformatics↗

bioRxiv2018

Identification of potential microRNAs associated with Herpesvirus family based on bioinformatic analysis

MicroRNAs (miRNAs) are known key regulators of gene expression on posttranscriptional level in many organisms encoded in mammals, plants and also several viral families. To date, no homologous gene of a virus-originated miRNA is known in other organisms. To date, only a few homologous miRNA between two different viruses are known, however, no gene of a virus-originated miRNA is known in any organism of other kingdoms. This can be attributed to the fact that classical miRNA detection approaches such as homology-based predictions fail at viruses due to their highly diverse genomes and their high mutation rate.\n\nHere, we applied the virus-derived precursor miRNA (pre-miRNA) prediction pipeline ViMiFi, which combines information about sequence conservation and machine learning-based approaches, on Human Herpesvirus 7 (HHV7) and Epstein-Barr virus (EBV). ViMiFi was able to predict 61 candidates in EBV, which has 25 known pre-miRNAs. From these 25, ViMiFi identified 20. It was further able to predict 18 candidates in the HHV7 genome, in which no miRNA had been described yet. We also studied the undescribed candidates of both viruses for potential functions and found similarities with human snRNAs and miRNAs from mammals and plants.

bioinformatics↗

bioRxiv2018

Identification of miRNAs and associated pathways regulated by Leukemia Inhibitory Factor in trophoblastic cell lines

IntroductionLeukemia Inhibitory Factor (LIF) regulates behavior of trophoblast cells and their interaction with immune and endothelial cells. In vitro, trophoblast cell response to LIF may vary depending on the cell model. Reported differences in the miRNA profile of trophoblastic cells may be responsible for these observations. Therefore, miRNA expression was investigated in four trophoblastic cell lines under LIF stimulation followed by in silico analysis of altered miRNAs and their associated pathways.\n\nMethodsLow density TaqMan miRNA assays were used to quantify levels of 762 mature miRNAs under LIF stimulation in three choriocarcinoma-derived (JEG-3, ACH-3P and AC1-M59) and a trophoblast immortalized (HTR-8/SVneo) cell lines. Expression of selected miRNAs was confirmed in primary trophoblast cells and cell lines by qPCR. Targets and associated pathways of the differentially expressed miRNAs were inferred from the miRTarBase followed by a KEGG Pathway Enrichment Analysis. HTR-8/SVneo and JEG-3 cells were transfected with miR-21-mimics and expression of miR-21 targets was assessed by qPCR.\n\nResultsA similar number of miRNAs changed in each tested cell line upon LIF stimulation, however, low coincidence of individual miRNA species was observed and occurred more often among choriocarcinoma-derived cells (complete data set at http://www.ncbi.nlm.nih.gov/geo/ under GEO accession number GSE130489). Altered miRNAs were categorized into pathways involved in human diseases, cellular processes and signal transduction. Six cascades were identified as significantly enriched, including JAK/STAT and TGFB-SMAD. Upregulation of miR-21-3p was validated in all cell lines and primary cells and STAT3 was confirmed as its target.\n\nDiscussionDissimilar miRNA responses may be involved in differences of LIF effects on trophoblastic cell lines.

molecular biology↗

bioRxiv2017

A miRNA catalogue and ncRNA annotation of theshort-living fish Nothobranchius furzeri

Background: The short-lived fish Nothobranchius furzeri is the shortest-lived vertebrate that can be cultured in captivity and was recently established as a model organism for aging research. Small non-coding RNAs, especially miRNAs, are implicated in age-dependent control of gene expression.\n\nResults: Here, we present a comprehensive catalogue of miRNAs and several other non-coding RNA classes (ncRNAs) for Nothobranchius furzeri. Analyzing multiple small RNA-Seq libraries, we show most of these identified miRNAs are expressed in at least one of seven Nothobranchius species. Additionally, duplication and clustering of N. furzeri miRNAs was analyzed and compared to the four fish species Danio rerio, Oryzias latipes, Gasterosteus aculeatus and Takifugu rubripes. A peculiar characteristic of N. furzeri as compared to other teleosts was a duplication of the miR-29 cluster.\n\nConclusion: The completeness of the catalogue we provide is comparable to that of zebrafish. This catalogue represents a basis to investigate the role of miRNAs in aging and development in this species.\n\nAvailability: All supplementary material can be found online at http://www.rna.uni-jena.de/en/supplements/nothobranchius-furzeri-mirnome/.

genomics↗