Biology subjects

Reiter, T.

Publications and source records attributed to Reiter, T..

10 recordsLinked to original sources

bioRxiv2025

Genome-wide cline analysis identifies new locus contributing to a barrier to gene flow across an Antirrhinum hybrid zone

Identification of the genomic regions that contribute to reproductive isolation and how they interact is a major goal of evolutionary genetics. Much effort has focused on locating candidate genes and potential barrier loci by scanning genomes for regions of excess differentiation (FST). An alternative, and perhaps more robust approach, would be to scan for genomic regions exhibiting steep clines in allele frequency across a hybrid zone. Here we develop this approach and apply it to genomic data from across a hybrid zone between varieties of Antirrhinum majus subspecies majus with different flower colour patterns (A. m. m var. pseudomajus and A. m. m var. striatum). Most steep clines are clustered in seven genomic regions, only four of which were present from FST scans between all pair-wise comparisons. Six of these regions carry previously identified loci that influence flower colour in the hybrid zone. The seventh region harbours a novel locus, RUBIA, modifying magenta intensity. Clines at RUBIA approached towards fixation on magenta side of the hybrid zone, whilst remaining polymorphic on the yellow side. This polymorphism on the yellow side may reflect a smaller phenotypic effect of RUBIA in yellow compared to magenta genetic backgrounds. Our findings illustrate how whole-genome cline scans in hybrid zones can robustly detect genomic regions contributing to phenotypic differences and highlight how different reproductive barrier loci interact across the genome.

evolutionary biology↗

bioRxiv2021

Nuclear mRNA metabolism drives selective basket assembly on a subset of nuclear pores in budding yeast

To determine which transcripts should reach the cytoplasm for translation, eukaryotic cells have established mechanisms to regulate selective mRNA export through the nuclear pore complex (NPC). The nuclear basket, a substructure of the NPC protruding into the nucleoplasm, is thought to function as a stable platform where mRNA-protein complexes (mRNPs) are rearranged and undergo quality control (QC) prior to export, ensuring that only mature mRNAs reach the cytoplasm. Here, we use proteomic, genetic, live-cell, and single-molecule resolution microscopy approaches in budding yeast to demonstrate that baskets assemble only on a subset of NPCs and that basket formation is dependent on RNA polymerase II (Pol II) transcription and subsequent mRNP processing. Specifically, we observe that the cleavage and polyadenylation machinery, the poly(A)-binding protein Pab1, and pre-mRNA-leakage factor Pml39 are required for basket assembly. We further show that while all nuclear pores can bind Mlp1, baskets assemble only on a subset of nucleoplasmic NPCs, and these basket-containing pores associate a distinct protein and RNA interactome. Taken together, our data points towards nuclear pore heterogeneity and an RNA-dependent mechanism for functionalization of nuclear pores in budding yeast through nuclear basket assembly.

cell biology↗

bioRxiv2021

Eukaryotic genomes from a global metagenomic dataset illuminate trophic modes and biogeography of ocean plankton

Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are not designed to consider eukaryotes, nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome assembled genomes (MAGs) from large-scale metagenomic datasets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8-2000{micro}m) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally-relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand the known marine phylogenetic diversity through the increase in coverage of likely particle- and host-associated taxa. We also demonstrate an approach to infer the putative functional mode of the recovered eukaryotic MAGs. A global survey of the TOPAZ MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. ImportanceDespite the ecological importance of single-celled eukaryotic organisms in marine environments, the majority are difficult to cultivate in the lab. Sequencing genetic material extracted from environmental samples enables researchers to document naturally-occurring protistan communities. However, conventional sequencing methodologies cannot separate out the genomes of individual organisms. To more completely capture the entire genomic content of mixed protistan community, we can create bins of sequences that represent the same organism. We developed a pipeline that enables scientists to bin individual organisms out of metagenomic reads, and show results that provide exciting insights into what protistan communities are present in the ocean and what roles they play in the ecosystem. Here, a global survey of both eukaryotic and prokaryotic MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. Accessible and scalable computational tools, such as EukHeist, are likely to accelerate the identification of meaningful genetic signatures from large datasets, ultimately expanding the eukaryotic tree of life.

microbiology↗

bioRxiv2021

Mapping global shifts in Saccharomyces cerevisiae gene expression across asynchronous time trajectories with diffusion maps

Grapes grown in a particular geographic region often produce wines with consistent characteristics, suggesting there are site-specific factors driving recurrent fermentation outcomes. However, our understanding of the relationship between site-specific factors, microbial metabolism, and wine fermentation outcomes are not well understood. Here, we used differences in Saccharomyces cerevisiae gene expression as a biosensor for differences among Pinot noir fermentations from 15 vineyard sites. We profiled time series gene expression patterns of primary fermentations, but fermentations proceeded at different rates, making analyzes of these data with conventional differential expression tools difficult. This led us to develop a novel approach that combines diffusion mapping with continuous differential expression analysis. Using this method, we identified vineyard specific deviations in gene expression, including changes in gene expression correlated with the activity of the non-Saccharomyces yeast Hanseniaspora uvarum, as well as with initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated wine fermentation outcomes. In addition, we demonstrate that our analysis approach can extract biologically relevant gene expression patterns in other contexts (e.g., hypoxic response of Saccharomyces cerevisiae), indicating that this approach offers a general method for investigating asynchronous time series gene expression data. ImportanceWhile it is generally accepted that foods, in particular wine, possess sensory characteristics associated with or derived from their place of origin, we lack knowledge of the biotic and abiotic factors central to this phenomenon. We have used Saccharomyces cerevisiae gene expression as a biosensor to capture differences in fermentations of Pinot noir grapes from 15 vineyards across two vintages. We find that gene expression by non-Saccharomyces yeasts and initial nitrogen content in the grape must correlates with differences in gene expression among fermentations from these vintages. These findings highlight important relationships between site-specific variables and gene expression that can be used to understand, or possibly modify, wine fermentation outcomes. Our work also provides a novel analysis method for investigating asynchronous gene expression data sets that is able to reveal both global shifts and subtle differences in gene expression due to varied cell - environment interactions.

microbiology↗

bioRxiv2021

Saccharomyces cerevisiae gene expression during fermentation of Pinot noir wines at industrially relevant scale

During a wine fermentation, Saccharomyces cerevisiae transforms grape must through metabolic activities that generate ethanol and other compounds. Thousands of genes change expression over the course of a wine fermentation to allow S. cerevisiae to adapt to and dominate the fermentation environment. Investigations into these gene expression patterns have previously revealed genes that underlie cellular adaptation to the grape must and wine environment involving metabolic specialization and ethanol tolerance. However, the vast majority of studies detailing gene expression patterns have occurred in controlled environments that do not recapitulate the biological and chemical complexity of fermentations performed at production scale. Here, we present an analysis of the S. cerevisiae RC212 gene expression program across 40 pilot-scale fermentations (150 liters) using Pinot noir grapes from 10 California vineyards across two vintages. We observe a core gene expression program across all fermentations irrespective of vintage similar to that of laboratory fermentations, in addition to novel gene expression patterns likely related to the presence of non-Saccharomyces microorganisms and oxygen availability during fermentation. These gene expression patterns, both common and diverse, provide insight into Saccharomyces cerevisiae biology critical to fermentation outcomes at industry-relevant scales. ImportanceThis study characterized Saccharomyces cerevisiae RC212 gene expression during Pinot noir fermentation at pilot scale (150 liters) using production-relevant conditions. The reported gene expression patterns of RC212 is generally similar to that observed in laboratory fermentation conditions, but also contains gene expression signatures related to yeast-environment interactions found in a production setting (e.g., presence of non-Saccharomyces microorganisms). Key genes and pathways highlighted by this work remain under-characterized, raising the need for further research to understand the roles of these genes and their impact on industrial wine fermentation outcomes.

microbiology↗

bioRxiv2021

Transcriptomics provides a genetic signature of vineyard site with insight into vintage-independent regional wine characteristics

In wine fermentations, the metabolic activity of both Saccharomyces cerevisiae and non-Saccharomyces organisms impact wine chemistry. Ribosomal DNA amplicon sequencing of grape musts has demonstrated that microorganisms occur non-randomly and are associated with the vineyard of origin, suggesting a role for the vineyard, grape, and wine microbiome in shaping wine fermentation outcomes. We used ribosomal DNA amplicon sequencing of grape must and RNA sequencing of primary fermentations to profile fermentations from 15 vineyards in California and Oregon across two vintages. We find that the relative abundance of fungal organisms detected by ribosomal DNA amplicon sequencing did not correlate with transcript abundance from those organisms within the RNA sequencing data, suggesting that the majority of the fungi detected in must by ribosomal DNA amplicon sequencing are not active during these inoculated fermentations. Additionally, we detect genetic signatures of vineyard site and region during fermentation that are predictive for each vineyard site, identifying nitrogen, sulfur, and thiamine metabolism as important factors for distinguishing vineyard site and region. ImportanceThe wine industry generates billions of dollars of revenue annually, and economic productivity is in part associated with regional distinctiveness of wine sensory attributes. Microorganisms associated with grapes and wineries are influenced by region of origin, and given that some microorganisms play a role in fermentation, it is thought that microbes may contribute to the regional distinctiveness of wine. We show that while the presence of microbial DNA is associated with wine region and vineyard site, the presence of microbial DNA is not associated with gene expression of those microorganisms during fermentation. We further show that detected gene expression signatures associated with wine region and vineyard site provide a means to address differences in fermentations that may drive regional distinctiveness.

microbiology↗

bioRxiv2020

Streamlining Data-Intensive Biology With Workflow Systems

As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter modifications. These workflows can produce hundreds to thousands of intermediate files and results that must be integrated for biological insight. Data-centric workflow systems that internally manage computational resources, software, and conditional execution of analysis steps are reshaping the landscape of biological data analysis, and empowering researchers to conduct reproducible analyses at scale. Adoption of these tools can facilitate and expedite robust data analysis, but knowledge of these techniques is still lacking. Here, we provide a series of practices and strategies for leveraging workflow systems with structured project, data, and resource management to streamline large-scale biological analysis. We present these strategies in the context of high-throughput sequencing data analysis, but the principles are broadly applicable to biologists working beyond this field. Author SummaryWe present a guide for workflow-enabled biological sequence data analysis, developed through our own teaching, training and analysis projects. We recognize that this is based on our own use cases and experiences, but we hope that our guide will contribute to a larger discussion within the open source and open science communities and lead to more comprehensive resources. Our main goal is to accelerate the research of scientists conducting sequence analyses by introducing them to organized workflow practices that not only benefit their own research but also facilitate open and reproducible science.

bioinformatics↗

bioRxiv2019

Large-scale sequence comparisons with sourmash

The sourmash software package uses MinHash-based sketching to create \"signatures\", compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.

bioinformatics↗

bioRxiv2018

Microbial contamination in the genome of the domesticated olive

Draft genomes of both the wild and domesticated olive were recently published. While working with these genomes, we identified contamination in the domesticated olive genome representing .06% of basepairs and 1.3% of scaffolds. We used targeted and untargeted approaches to identify the contaminating sequences, the majority of which were Aureobasidium pullulans. We applied the same method to the wild olive genome and did not find evidence of contamination. Although contamination in the domesticated olive genome was not prolific, it could lead to biased results either from functional content encoded in the contaminant sequences, or from using it to separate microbial reads from olive reads in microbiome studies.

genomics↗

bioRxiv2018

Exploring neighborhoods in large metagenome assembly graphs reveals hidden sequence diversity

Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation. We introduce an information retrieval system for large metagenomic data sets that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an inferred genome. We apply this system to recover missing content from genome bins and show that substantial genomic sequence variation is present in a real metagenome. Our software implementation is available at https://github.com/spacegraphcats/ spacegraphcats under the 3-Clause BSD License.

bioinformatics↗