Enterococcus durans Oregon-R-modENCODE strain BDGP3 was isolated from the Drosophila melanogaster gut for functional host-microbe interaction studies. The complete genome is composed of a single circular genome of 2,983,334 bp, with a G+C content of 38%, and a single plasmid of 5,594 bp. Copyright © 2017 Wan et al.
Analyzing human as well as animal microbiota composition has gained growing interest because structural components and metabolites of microorganisms fundamentally influence all aspects of host physiology. Originally dominated by culture-dependent methods for exploring these ecosystems, the development of molecular techniques such as high throughput sequencing has dramatically increased our knowledge. Because many studies of the microbiota are based on the bacterial 16S ribosomal RNA (rRNA) gene targets, they can, at least in principle, be compared to determine the role of the microbiome composition for developmental processes, host metabolism, and physiology as well as different diseases. In our review, we will summarize differences and pitfalls in current experimental protocols, including all steps from nucleic acid extraction to bioinformatical analysis which may produce variation that outweighs subtle biological differences. Future developments, such as integration of metabolomic, transcriptomic, and metagenomic data sets and standardization of the procedures, will be discussed. © The Author 2015. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Numerous methods have been developed to analyse RNA sequencing (RNA-seq) data, but most rely on the availability of a reference genome, making them unsuitable for non-model organisms. Here we present superTranscripts, a substitute for a reference genome, where each gene with multiple transcripts is represented by a single sequence. The Lace software is provided to construct superTranscripts from any set of transcripts, including de novo assemblies. We demonstrate how superTranscripts enable visualisation, variant detection and differential isoform detection in non-model organisms. We further use Lace to combine reference and assembled transcriptomes for chicken and recover hundreds of gaps in the reference genome.
The diversity and complexity of the human brain are widely assumed to be encoded within a constant genome. Somatic gene recombination, which changes germline DNA sequences to increase molecular diversity, could theoretically alter this code but has not been documented in the brain, to our knowledge. Here we describe recombination of the Alzheimer’s disease-related gene APP, which encodes amyloid precursor protein, in human neurons, occurring mosaically as thousands of variant ‘genomic cDNAs’ (gencDNAs). gencDNAs lacked introns and ranged from full-length cDNA copies of expressed, brain-specific RNA splice variants to myriad smaller forms that contained intra-exonic junctions, insertions, deletions, and/or single nucleotide variations. DNA in situ hybridization identified gencDNAs within single neurons that were distinct from wild-type loci and absent from non-neuronal cells. Mechanistic studies supported neuronal ‘retro-insertion’ of RNA to produce gencDNAs; this process involved transcription, DNA breaks, reverse transcriptase activity, and age. Neurons from individuals with sporadic Alzheimer’s disease showed increased gencDNA diversity, including eleven mutations known to be associated with familial Alzheimer’s disease that were absent from healthy neurons. Neuronal gene recombination may allow ‘recording’ of neural activity for selective ‘playback’ of preferred gene variants whose expression bypasses splicing; this has implications for cellular diversity, learning and memory, plasticity, and diseases of the human brain.
Acquisition of genes through horizontal gene transfer (HGT) allows microbes to rapidly gain new capabilities and adapt to new or changing environments. Identifying widespread HGT regions within multispecies microbiomes can pinpoint the molecular mechanisms that play key roles in microbiome assembly. We sought to identify horizontally transferred genes within a model microbiome, the cheese rind. Comparing 31 newly sequenced and 134 previously sequenced bacterial isolates from cheese rinds, we identified over 200 putative horizontally transferred genomic regions containing 4733 protein coding genes. The largest of these regions are enriched for genes involved in siderophore acquisition, and are widely distributed in cheese rinds in both Europe and the US. These results suggest that HGT is prevalent in cheese rind microbiomes, and that identification of genes that are frequently transferred in a particular environment may provide insight into the selective forces shaping microbial communities.
Human endogenous retroviruses (HERVs) are viruses that have colonized the germ line and spread through vertical passage. Only the more recently acquired HERVs, such as the HERV-K (HML-2) group, maintain coding open reading frames. Expression of HERV-Ks has been linked to different pathological conditions, including HIV infection, but our knowledge on which specific HERV-Ks are expressed in primary lymphocytes currently is very limited. To identify the most expressed HERV-Ks in an unbiased manner, we analyzed their expression patterns in peripheral blood lymphocytes using Pacific Biosciences (PacBio) single-molecule real-time (SMRT) sequencing. We observe that three HERV-Ks (KII, K102, and K18) constitute over 90% of the total HERV-K expression in primary human lymphocytes of five different donors. We also show experimentally that two of these HERV-K env sequences (K18 and K102) retain their ability to produce full-length and posttranslationally processed envelope proteins in cell culture. We show that HERV-K18 Env can be incorporated into HIV-1 but not simian immunodeficiency virus (SIV) particles. Moreover, HERV-K18 Env incorporation into HIV-1 virions is dependent on HIV-1 matrix. Taken together, we generated high-resolution HERV-K expression profiles specific for activated human lymphocytes. We found that one of the most abundantly expressed HERV-K envelopes not only makes a full-length protein but also specifically interacts with HIV-1. Our findings raise the possibility that these endogenous retroviral Env proteins could directly influence HIV-1 replication.Here, we report the HERV-K expression profile of primary lymphocytes from 5 different healthy donors. We used a novel deep-sequencing technology (PacBio SMRT) that produces the long reads necessary to discriminate the complexity of HERV-K expression. We find that primary lymphocytes express up to 32 different HERV-K envelopes, and that at least two of the most expressed Env proteins retain their ability to make a protein. Importantly, one of them, the envelope glycoprotein of HERV-K18, is incorporated into HIV-1 in an HIV matrix-specific fashion. The ramifications of such interactions are discussed, as the possibility of HIV-1 target tissue broadening and immune evasion are considered.
The genome of the wild diploid strawberry species Fragaria vesca, an ideal model system of cultivated strawberry (Fragaria × ananassa, octoploid) and other Rosaceae family crops, was first published in 2011 and followed by a new assembly (Fvb). However, the annotation for Fvb mainly relied on ab initio predictions and included only predicted coding sequences, therefore an improved annotation is highly desirable. Here, a new annotation version named v2.0.a2 was created for the Fvb genome by a pipeline utilizing one PacBio library, 90 Illumina RNA-seq libraries, and 9 small RNA-seq libraries. Altogether, 18,641 genes (55.6% out of 33,538 genes) were augmented with information on the 5′ and/or 3′ UTRs, 13,168 (39.3%) protein-coding genes were modified or newly identified, and 7,370 genes were found to possess alternative isoforms. In addition, 1,938 long non-coding RNAs, 171 miRNAs, and 51,714 small RNA clusters were integrated into the annotation. This new annotation of F. vesca is substantially improved in both accuracy and integrity of gene predictions, beneficial to the gene functional studies in strawberry and to the comparative genomic analysis of other horticultural crops in Rosaceae family.
Natural rubber is an indispensable commodity used in approximately 40,000 products and is fundamental to the tire industry. Among the species that produce latex, the rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg.], a species native to the Amazon rainforest, is the major producer of latex used worldwide. The Amazon Basin presents optimal conditions for rubber tree growth, but the occurrence of South American leaf blight, which is caused by the fungus Microcyclus ulei (P. Henn) v. Arx, limits rubber tree production. Currently, rubber tree plantations are located in scape regions that exhibit suboptimal conditions such as high winds and cold temperatures. Rubber tree breeding programs aim to identify clones that are adapted to these stress conditions. However, rubber tree breeding is time-consuming, taking more than 20 years to develop a new variety. It is also expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereby reducing the time and area required for these experiments. Transcriptome sequencing using next-generation sequencing (RNA-seq) is a powerful tool to identify a full set of transcripts and for evaluating gene expression in model and non-model species. In this study, we constructed a comprehensive transcriptome to evaluate the cold response strategies of the RRIM600 (cold-resistant) and GT1 (cold-tolerant) genotypes. Furthermore, we identified putative microsatellite (SSR) and single-nucleotide polymorphism (SNP) markers. Alternative splicing, which is an important mechanism for plant adaptation under abiotic stress, was further identified, providing an important database for further studies of cold tolerance.
Genome mining has become an increasingly powerful, scalable, and economically accessible tool for the study of natural product biosynthesis and drug discovery. However, there remain important biological and practical problems that can complicate or obscure biosynthetic analysis in genomic and metagenomic sequencing projects. Here, we focus on limitations of available technology as well as computational and experimental strategies to overcome them. We review the unique challenges and approaches in the study of symbiotic and uncultured systems, as well as those associated with biosynthetic gene cluster (BGC) assembly and product prediction. Finally, to explore sequencing parameters that affect the recovery and contiguity of large and repetitive BGCs assembled de novo, we simulate Illumina and PacBio sequencing of the Salinispora tropica genome focusing on assembly of the salinilactam (slm) BGC.
The development of multicellular organisms is coordinated by various gene regulatory mechanisms that ensure correct spatio-temporal patterns of gene expression. Recently, the role of antisense transcription in gene regulation has moved into focus of research. To characterize genome-wide patterns of antisense transcription and to study their evolutionary conservation, we sequenced a strand-specific RNA-seq library of the nematode Pristionchus pacificus.We identified 1112 antisense configurations of which the largest group represents 465 antisense transcripts (ASTs) that are fully embedded in introns of their host genes. We find that most ASTs show homology to protein-coding genes and are overrepresented in proteomic data. Together with the finding, that expression levels of ASTs and host genes are uncorrelated, this indicates that most ASTs in P. pacificus do not represent non-coding RNAs and do not exhibit regulatory functions on their host genes. We studied the evolution of antisense gene pairs across 20 nematode genomes, showing that the majority of pairs is lineage-specific and even the highly conserved vps-4, ddx-27, and sel-2 loci show abundant structural changes including duplications, deletions, intron gains and loss of antisense transcription. In contrast, host genes in general, are remarkably conserved and encode exceptionally long introns leading to unusually large blocks of conserved synteny.Our study has shown that in P. pacificus antisense transcription as such does not define non-coding RNAs but is rather a feature of highly conserved genes with long introns. We hypothesize that the presence of regulatory elements imposes evolutionary constraint on the intron length, but simultaneously, their large size makes them a likely target for translocation of genomic elements including protein-coding genes that eventually end up as ASTs.
Mastitis, which affects nearly all lactating mammals including human, is generally thought to be caused by local infection of the mammary glands. For treatment, antibiotics are commonly prescribed, which however are of concern in both treatment efficacy and neonate safety. Here, using bovine mastitis which is the most costly disease in the dairy industry as a model, we showed that intestinal microbiota alone can lead to mastitis.Fecal microbiota transplantation (FMT) from mastitis, but not healthy cows, to germ-free (GF) mice resulted in mastitis symptoms in mammary gland and inflammations in serum, spleen, and colon. Probiotic intake in parallel with FMT from diseased cows led to relieved mastitis symptoms in mice, by shifting the murine intestinal microbiota to a state that is functionally distinct from either healthy or diseased microbiota yet structurally similar to the latter. Despite conservation in mastitis symptoms, diseased cows and mice shared few mastitis-associated bacterial organismal or functional markers, suggesting striking divergence in mastitis-associated intestinal microbiota among lactating mammals. Moreover, an “amplification effect” of disease-health distinction in both microbiota structure and function was apparent during the cow-to-mouse FMT.Hence, dysbiosis of intestinal microbiota may be one cause of mastitis, and probiotics that restore intestinal microbiota function are an effective and safe strategy to treat mastitis.
The chicken has long served as an important model organism in many fields, and continues to aid our understanding of animal development. Functional genomics studies aimed at probing the mechanisms that regulate development require high-quality genomes and transcript annotations. The quality of these resources has improved dramatically over the last several years, but many isoforms and genes have yet to be identified. We hope to contribute to the process of improving these resources with the data presented here: a set of long cDNA sequencing reads, and a curated set of new genes and transcript isoforms not currently represented in the most up-to-date genome annotation currently available to the community of researchers who rely on the chicken genome.
Anaerobic fungi (phylum Neocallimastigomycota) are common inhabitants of the digestive tract of mammalian herbivores, and in the rumen, can account for up to 20% of the microbial biomass. Anaerobic fungi play a primary role in the degradation of lignocellulosic plant material. They also have a syntrophic interaction with methanogenic archaea, which increases their fiber degradation activity. To date, nine anaerobic fungal genera have been described, with further novel taxonomic groupings known to exist based on culture-independent molecular surveys. However, the true extent of their diversity may be even more extensively underestimated as anaerobic fungi continue being discovered in yet unexplored gut and non-gut environments. Additionally many studies are now known to have used primers that provide incomplete coverage of the Neocallimastigomycota. For ecological studies the internal transcribed spacer 1 region (ITS1) has been the taxonomic marker of choice, but due to various limitations the large subunit rRNA (LSU) is now being increasingly used. How the continued expansion of our knowledge regarding anaerobic fungal diversity will impact on our understanding of their biology and ecological role remains unclear; particularly as it is becoming apparent that anaerobic fungi display niche differentiation. As a consequence, there is a need to move beyond the broad generalization of anaerobic fungi as fiber-degraders, and explore the fundamental differences that underpin their ability to exist in distinct ecological niches. Application of genomics, transcriptomics, proteomics and metabolomics to their study in pure/mixed cultures and environmental samples will be invaluable in this process. To date the genomes and transcriptomes of several characterized anaerobic fungal isolates have been successfully generated. In contrast, the application of proteomics and metabolomics to anaerobic fungal analysis is still in its infancy. A central problem for all analyses, however, is the limited functional annotation of anaerobic fungal sequence data. There is therefore an urgent need to expand information held within publicly available reference databases. Once this challenge is overcome, along with improved sample collection and extraction, the application of these techniques will be key in furthering our understanding of the ecological role and impact of anaerobic fungi in the wide range of environments they inhabit.
The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically use targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome’s composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome’s taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically and clinically relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches. Copyright © 2016 Elsevier Inc. All rights reserved.
De novo assembly of short RNA-Seq reads into transcripts is challenging due to sequence similarities in transcriptomes arising from gene duplications and alternative splicing of transcripts. We present Shannon, an RNA-Seq assembler with an optimality guarantee derived from principles of information theory: Shannon reconstructs nearly all information-theoretically reconstructable transcripts. Shannon is based on a theory we develop for de novo RNA-Seq assembly that reveals differing abundances among transcripts to be the key, rather than the barrier, to effective assembly. The assembly problem is formulated as a sparsest-flow problem on a transcript graph, and the heart of Shannon is a novel iterative flow-decomposition algorithm. This algorithm provably solves the information-theoretically reconstructable instances in linear-time even though the general sparsest-flow problem is NP-hard. Shannon also incorporates several additional new algorithmic advances: a new error-correction algorithm based on successive cancelation, a multi-bridging algorithm that carefully utilizes read information in the k-mer de Bruijn graph, and an approximate graph partitioning algorithm to split the transcriptome de Bruijn graph into smaller components. In tests on large RNA-Seq datasets, Shannon obtains significant increases in sensitivity along with improvements in specificity in comparison to state-of-the-art assemblers.
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