Tamock: simulation of habitat-specific benchmark data in metagenomics.

BACKGROUND: Simulated metagenomic reads are widely used to benchmark software and workflows for metagenome interpretation. The results of metagenomic benchmarks depend on the assumptions about their underlying ecosystems. Conclusions from benchmark studies are therefore limited to the ecosystems they mimic. Ideally, simulations are therefore based on genomes, which resemble particular metagenomic communities realistically. RESULTS: We developed Tamock to facilitate the realistic simulation of metagenomic reads according to a metagenomic community, based on real sequence data. Benchmarks samples can be created from all genomes and taxonomic domains present in NCBI RefSeq. Tamock automatically determines taxonomic profiles from shotgun sequence data, selects reference genomes accordingly and uses them to simulate metagenomic reads. We present an example use case for Tamock by assessing assembly and binning method performance for selected microbiomes. CONCLUSIONS: Tamock facilitates automated simulation of habitat-specific benchmark metagenomic data based on real sequence data and is implemented as a user-friendly command-line application, providing extensive additional information along with the simulated benchmark data. Resulting benchmarks enable an assessment of computational methods, workflows, and parameters specifically for a metagenomic habitat or ecosystem of a metagenomic study. AVAILABILITY: Source code, documentation and install instructions are freely available at GitHub ( https://github.com/gerners/tamock ).

A Bioinformatics Guide to Plant Microbiome Analysis.

Recent evidence for intimate relationship of plants with their microbiota shows that plants host individual and diverse microbial communities that are essential for their survival. Understanding their relatedness using genome-based and high-throughput techniques remains a hot topic in microbiome research. Molecular analysis of the plant holobiont necessitates the application of specific sampling and preparatory steps that also consider sources of unwanted information, such as soil, co-amplified plant organelles, human DNA, and other contaminations. Here, we review state-of-the-art and present practical guidelines regarding experimental and computational aspects to be considered in molecular plant-microbiome studies. We discuss sequencing and "omics" techniques with a focus on the requirements needed to adapt these methods to individual research approaches. The choice of primers and sequence databases is of utmost importance for amplicon sequencing, while the assembly and binning of shotgun metagenomic sequences is crucial to obtain quality data. We discuss specific bioinformatic workflows to overcome the limitation of genome database resources and for covering large eukaryotic genomes such as fungi. In transcriptomics, it is necessary to account for the separation of host mRNA or dual-RNAseq data. Metaproteomics approaches provide a snapshot of the protein abundances within a plant tissue which requires the knowledge of complete and well-annotated plant genomes, as well as microbial genomes. Metabolomics offers a powerful tool to detect and quantify small molecules and molecular changes at the plant-bacteria interface if the necessary requirements with regard to (secondary) metabolite databases are considered. We highlight data integration and complementarity which should help to widen our understanding of the interactions among individual players of the plant holobiont in the future.

Assessment of urban microbiome assemblies with the help of targeted in silico gold standards.

BACKGROUND: Microbial communities play a crucial role in our environment and may influence human health tremendously. Despite being the place where human interaction is most abundant we still know little about the urban microbiome. This is highlighted by the large amount of unclassified DNA reads found in urban metagenome samples. The only in silico approach that allows us to find unknown species, is the assembly and classification of draft genomes from a metagenomic dataset. In this study we (1) investigate the applicability of an assembly and binning approach for urban metagenome datasets, and (2) develop a new method for the generation of in silico gold standards to better understand the specific challenges of such datasets and provide a guide in the selection of available software. RESULTS: We applied combinations of three assembly (Megahit, SPAdes and MetaSPAdes) and three binning tools (MaxBin, MetaBAT and CONCOCT) to whole genome shotgun datasets from the CAMDA 2017 Challenge. Complex in silico gold standards with a simulated bacterial fraction were generated for representative samples of each surface type and city. Using these gold standards, we found the combination of SPAdes and MetaBAT to be optimal for urban metagenome datasets by providing the best trade-off between the number of high-quality genome draft bins (MIMAG standards) retrieved, the least amount of misassemblies and contamination. The assembled draft genomes included known species like Propionibacterium acnes but also novel species according to respective ANI values. CONCLUSIONS: In our work, we showed that, even for datasets with high diversity and low sequencing depth from urban environments, assembly and binning-based methods can provide high-quality genome drafts. Of vital importance to retrieve high-quality genome drafts is sequence depth but even more so a high proportion of the bacterial sequence fraction too achieve high coverage for bacterial genomes. In contrast to read-based methods relying on database knowledge, genome-centric methods as applied in this study can provide valuable information about unknown species and strains as well as functional contributions of single community members within a sample. Furthermore, we present a method for the generation of sample-specific highly complex in silico gold standards. REVIEWERS: This article was reviewed by Craig Herbold, Serghei Mangul and Yana Bromberg.

Proteomics and metabolomics identify molecular mechanisms of aging potentially predisposing for chronic lymphocytic leukemia.

B cell chronic lymphocytic leukemia (B-CLL), the most common type of leukemia in adults, is still essentially incurable despite the development of novel therapeutic strategies. This reflects the incomplete understanding of the pathophysiology of this disease. A comprehensive proteome analysis of primary human B-CLL cells and B cells from younger as well as elderly healthy donors was performed. For comparison, the chronic B cell leukemia cell line JVM-13 was also included. A principal component analysis comprising 6,945 proteins separated these four groups, placing B cells of aged-matched controls between those of young donors and B-CLL patients, while identifying JVM-13 as poorly related cells. Mass spectrometric proteomics data have been made fully accessible via ProteomeXchange with identifier PXD006570-PXD006572, PXD006576, PXD006578, and PXD006589-PXD006591. Remarkably, B cells from aged controls displayed significant regulation of proteins related to stress management in mitochondria and ROS stress such as DLAT, FIS1, and NDUFAB1, and DNA repair, including RAD9A, MGMT, and XPA. ROS levels were indeed found significantly increased in B cells but not in T cells or monocytes from aged individuals. These alterations may be relevant for tumorigenesis and were observed similarly in B-CLL cells. In B-CLL cells, some remarkable unique features like the loss of tumor suppressor molecules PNN and JARID2, the stress-related serotonin transporter SLC6A4, and high expression of ZNF207, CCDC88A, PIGR and ID3, otherwise associated with stem cell phenotype, were determined. Alterations of metabolic enzymes were another outstanding feature in comparison to normal B cells, indicating increased beta-oxidation of fatty acids and increased consumption of glutamine. Targeted metabolomics assays corroborated these results. The present findings identify a potential proteome signature for immune senescence in addition to previously unrecognized features of B-CLL cells and suggest that aging may be accompanied by cellular reprogramming functionally relevant for predisposing B cells to transform to B-CLL cells.

Implications of evolutionary engineering for growth and recombinant protein production in methanol-based growth media in the yeast Pichia pastoris.

BACKGROUND: Pichia pastoris is a widely used eukaryotic expression host for recombinant protein production. Adaptive laboratory evolution (ALE) has been applied in a wide range of studies in order to improve strains for biotechnological purposes. In this context, the impact of long-term carbon source adaptation in P. pastoris has not been addressed so far. Thus, we performed a pilot experiment in order to analyze the applicability and potential benefits of ALE towards improved growth and recombinant protein production in P. pastoris. RESULTS: Adaptation towards growth on methanol was performed in replicate cultures in rich and minimal growth medium for 250 generations. Increased growth rates on these growth media were observed at the population and single clone level. Evolved populations showed various degrees of growth advantages and trade-offs in non-evolutionary growth conditions. Genome resequencing revealed a wide variety of potential genetic targets associated with improved growth performance on methanol-based growth media. Alcohol oxidase represented a mutational hotspot since four out of seven evolved P. pastoris clones harbored mutations in this gene, resulting in decreased Aox activity, despite increased growth rates. Selected clones displayed strain-dependent variations for AOX-promoter based recombinant protein expression yield. One particularly interesting clone showed increased product titers ranging from a 2.5-fold increase in shake flask batch culture to a 1.8-fold increase during fed batch cultivation. CONCLUSIONS: Our data indicate a complex correlation of carbon source, growth context and recombinant protein production. While similar experiments have already shown their potential in other biotechnological areas where microbes were evolutionary engineered for improved stress resistance and growth, the current dataset encourages the analysis of the potential of ALE for improved protein production in P. pastoris on a broader scale.