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- Theodor-Boveri-Institut für Biowissenschaften (88) (remove)
Quantitative high-confidence human mitochondrial proteome and its dynamics in cellular context
(2021)
Mitochondria are key organelles for cellular energetics, metabolism, signaling, and quality control and have been linked to various diseases. Different views exist on the composition of the human mitochondrial proteome. We classified >8,000 proteins in mitochondrial preparations of human cells and defined a mitochondrial high-confidence proteome of >1,100 proteins (MitoCoP). We identified interactors of translocases, respiratory chain, and ATP synthase assembly factors. The abundance of MitoCoP proteins covers six orders of magnitude and amounts to 7% of the cellular proteome with the chaperones HSP60-HSP10 being the most abundant mitochondrial proteins. MitoCoP dynamics spans three orders of magnitudes, with half-lives from hours to months, and suggests a rapid regulation of biosynthesis and assembly processes. 460 MitoCoP genes are linked to human diseases with a strong prevalence for the central nervous system and metabolism. MitoCoP will provide a high-confidence resource for placing dynamics, functions, and dysfunctions of mitochondria into the cellular context.
A phosphoproteomic approach reveals that PKD3 controls PKA-mediated glucose and tyrosine metabolism
(2021)
Members of the protein kinase D (PKD) family (PKD1, 2, and 3) integrate hormonal and nutritional inputs to regulate complex cellular metabolism. Despite the fact that a number of functions have been annotated to particular PKDs, their molecular targets are relatively poorly explored. PKD3 promotes insulin sensitivity and suppresses lipogenesis in the liver of animals fed a high-fat diet. However, its substrates are largely unknown. Here we applied proteomic approaches to determine PKD3 targets. We identified more than 300 putative targets of PKD3. Furthermore, biochemical analysis revealed that PKD3 regulates cAMP-dependent PKA activity, a master regulator of the hepatic response to glucagon and fasting. PKA regulates glucose, lipid, and amino acid metabolism in the liver, by targeting key enzymes in the respective processes. Among them the PKA targets phenylalanine hydroxylase (PAH) catalyzes the conversion of phenylalanine to tyrosine. Consistently, we showed that PKD3 is activated by glucagon and promotes glucose and tyrosine levels in hepatocytes. Therefore, our data indicate that PKD3 might play a role in the hepatic response to glucagon.
Protein–metabolite interactions play an important role in the cell’s metabolism and many methods have been developed to screen them in vitro. However, few methods can be applied at a large scale and not alter biological state. Here we describe a proteometabolomic approach, using chromatography to generate cell fractions which are then analyzed with mass spectrometry for both protein and metabolite identification. Integrating the proteomic and metabolomic analyses makes it possible to identify protein-bound metabolites. Applying the concept to the thermophilic fungus Chaetomium thermophilum, we predict 461 likely protein-metabolite interactions, most of them novel. As a proof of principle, we experimentally validate a predicted interaction between the ribosome and isopentenyl adenine.
Aim
Plant functional groups are widely used in community ecology and earth system modelling to describe trait variation within and across plant communities. However, this approach rests on the assumption that functional groups explain a large proportion of trait variation among species. We test whether four commonly used plant functional groups represent variation in six ecologically important plant traits.
Location
Tundra biome.
Time period
Data collected between 1964 and 2016.
Major taxa studied
295 tundra vascular plant species.
Methods
We compiled a database of six plant traits (plant height, leaf area, specific leaf area, leaf dry matter content, leaf nitrogen, seed mass) for tundra species. We examined the variation in species-level trait expression explained by four traditional functional groups (evergreen shrubs, deciduous shrubs, graminoids, forbs), and whether variation explained was dependent upon the traits included in analysis. We further compared the explanatory power and species composition of functional groups to alternative classifications generated using post hoc clustering of species-level traits.
Results
Traditional functional groups explained significant differences in trait expression, particularly amongst traits associated with resource economics, which were consistent across sites and at the biome scale. However, functional groups explained 19% of overall trait variation and poorly represented differences in traits associated with plant size. Post hoc classification of species did not correspond well with traditional functional groups, and explained twice as much variation in species-level trait expression.
Main conclusions
Traditional functional groups only coarsely represent variation in well-measured traits within tundra plant communities, and better explain resource economic traits than size-related traits. We recommend caution when using functional group approaches to predict tundra vegetation change, or ecosystem functions relating to plant size, such as albedo or carbon storage. We argue that alternative classifications or direct use of specific plant traits could provide new insights for ecological prediction and modelling.
Motivation
The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
Time period and grain
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format
.csv and .SQL.
High-throughput studies of microbial communities suggest that Archaea are a widespread component of microbial diversity in various ecosystems. However, proper quantification of archaeal diversity and community ecology remains limited, as sequence coverage of Archaea is usually low owing to the inability of available prokaryotic primers to efficiently amplify archaeal compared to bacterial rRNA genes. To improve identification and quantification of Archaea, we designed and validated the utility of several primer pairs to efficiently amplify archaeal 16S rRNA genes based on up-to-date reference genes. We demonstrate that several of these primer pairs amplify phylogenetically diverse Archaea with high sequencing coverage, outperforming commonly used primers. Based on comparing the resulting long 16S rRNA gene fragments with public databases from all habitats, we found several novel family- to phylum-level archaeal taxa from topsoil and surface water. Our results suggest that archaeal diversity has been largely overlooked due to the limitations of available primers, and that improved primer pairs enable to estimate archaeal diversity more accurately.
The MYC oncoprotein binds to promoter-proximal regions of virtually all transcribed genes and enhances RNA polymerase II (Pol II) function, but its precise mode of action is poorly understood. Using mass spectrometry of both MYC and Pol II complexes, we show here that MYC controls the assembly of Pol II with a small set of transcription elongation factors that includes SPT5, a subunit of the elongation factor DSIF. MYC directly binds SPT5, recruits SPT5 to promoters, and enables the CDK7-dependent transfer of SPT5 onto Pol II. Consistent with known functions of SPT5, MYC is required for fast and processive transcription elongation. Intriguingly, the high levels of MYC that are expressed in tumors sequester SPT5 into non-functional complexes, thereby decreasing the expression of growth-suppressive genes. Altogether, these results argue that MYC controls the productive assembly of processive Pol II elongation complexes and provide insight into how oncogenic levels of MYC permit uncontrolled cellular growth.
Ambrosia beetles farm ascomycetous fungi in tunnels within wood. These ambrosia fungi are regarded asexual, although population genetic proof is missing. Here we explored the intraspecific genetic diversity of Ambrosiella grosmanniae and Ambrosiella hartigii (Ascomycota: Microascales), the mutualists of the beetles Xylosandrus germanus and Anisandrus dispar. By sequencing five markers (ITS, LSU, TEF1α, RPB2, β-tubulin) from several fungal strains, we show that X. germanus cultivates the same two clones of A. grosmanniae in the USA and in Europe, whereas A. dispar is associated with a single A. hartigii clone across Europe. This low genetic diversity is consistent with predominantly asexual vertical transmission of Ambrosiella cultivars between beetle generations. This clonal agriculture is a remarkable case of convergence with fungus-farming ants, given that both groups have a completely different ecology and evolutionary history.
Quantitative mass spectrometry has established proteome-wide regulation of protein abundance and post-translational modifications in various biological processes. Here, we used quantitative mass spectrometry to systematically analyze the thermal stability and solubility of proteins on a proteome-wide scale during the eukaryotic cell cycle. We demonstrate pervasive variation of these biophysical parameters with most changes occurring in mitosis and G1. Various cellular pathways and components vary in thermal stability, such as cell-cycle factors, polymerases, and chromatin remodelers. We demonstrate that protein thermal stability serves as a proxy for enzyme activity, DNA binding, and complex formation in situ. Strikingly, a large cohort of intrinsically disordered and mitotically phosphorylated proteins is stabilized and solubilized in mitosis, suggesting a fundamental remodeling of the biophysical environment of the mitotic cell. Our data represent a rich resource for cell, structural, and systems biologists interested in proteome regulation during biological transitions.
Metagenomic sequencing has greatly improved our ability to profile the composition of environmental and host-associated microbial communities. However, the dependency of most methods on reference genomes, which are currently unavailable for a substantial fraction of microbial species, introduces estimation biases. We present an updated and functionally extended tool based on universal (i.e., reference-independent), phylogenetic marker gene (MG)-based operational taxonomic units (mOTUs) enabling the profiling of >7700 microbial species. As more than 30% of them could not previously be quantified at this taxonomic resolution, relative abundance estimates based on mOTUs are more accurate compared to other methods. As a new feature, we show that mOTUs, which are based on essential housekeeping genes, are demonstrably well-suited for quantification of basal transcriptional activity of community members. Furthermore, single nucleotide variation profiles estimated using mOTUs reflect those from whole genomes, which allows for comparing microbial strain populations (e.g., across different human body sites).