Rapid DNA, RNA and protein extraction protocols optimized for slow continuously growing yeast cultures

Nucleic acids enrichment of fungal pathogens to study host-pathogen interactions

Fungal infections, ranging from superficial to life-threatening infections, represent a major public health problem that affects 25% of the worldwide population. In this context, the study of host-pathogen interactions within the host is crucial to advance antifungal therapy. However, since fungal cells are usually outnumbered by host cells, the fungal transcriptome frequently remains uncovered. We compared three different methods to selectively lyse human cells from in vitro mixes, composed of Candida cells and peripheral blood mononuclear cells. In order to prevent transcriptional modification, the mixes were stored in RNAlater. We evaluated the enrichment of fungal cells through cell counting using microscopy and aimed to further enrich fungal nucleic acids by centrifugation and by reducing contaminant nucleic acids from the host. We verified the enrichment of fungal DNA and RNA through qPCR and RT-qPCR respectively and confirmed that the resulting RNA has high integrity scores, suitable for downstream applications. The enrichment method provided here, i.e., lysis with Buffer RLT followed by centrifugation, may contribute to increase the proportion of nucleic acids from fungi in clinical samples, thus promoting more comprehensive analysis of fungal transcriptional profiles. Although we focused on C. albicans, the enrichment may be applicable to other fungal pathogens.

Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm

The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previously done for prokaryote cells (E. coli) but not for eukaryote cells such as yeast as a model organism. Yeast proteomics datasets vary widely in terms of cell growth conditions, the technique used to determine protein composition, the reported relative abundance of proteins, and the units in which abundances are reported. We determined that the gene ontology profiles of the most abundant proteins across these datasets are similar, but their abundances vary greatly. To overcome this problem, we chose five mass spectrometry proteomics datasets that fulfilled the following criteria: high internal consistency, consistency with published experimental data, and freedom from GFP-tagging artifacts. Using these datasets, the contents of a simulation cell containing a single 80S ribosome were defined, such that the macromolecular density and the mass ratio of ribosomal-to-cytoplasmic proteins were consistent with experiment and chosen datasets. Finally, multiple tRNAs were added, consistent with their experimentally-determined number in the yeast cell. The resulting composition can be readily used in molecular simulations representative of yeast cytoplasmic macromolecular crowding conditions to characterize a variety of phenomena, such as protein diffusion, protein-protein interactions and biological processes such as protein translation.

Evaluation of Sample Preparation Methods for Fast Proteotyping of Microorganisms by Tandem Mass Spectrometry

Tandem mass spectrometry-based proteotyping allows characterizing microorganisms in terms of taxonomy and is becoming an important tool for investigating microbial diversity from several ecosystems. Fast and automatable sample preparation for obtaining peptide pools amenable to tandem mass spectrometry is necessary for enabling proteotyping as a high-throughput method. First, the protocol to increase the yield of lysis of several representative bacterial and eukaryotic microorganisms was optimized by using a long and drastic bead-beating setting with 0.1 mm silica beads, 0.1 and 0.5 mm glass beads, in presence of detergents. Then, three different methods to obtain greater digestion yield from these extracts were tested and optimized for improve efficiency and reduce application time: denaturing electrophoresis of proteins and in-gel proteolysis, suspension-trapping filter-based approach (S-Trap) and, solid-phase-enhanced sample preparation named SP3. The latter method outperforms the other two in terms of speed and delivers also more peptides and proteins than with the in-gel proteolysis (2.2 fold for both) and S-trap approaches (1.3 and 1.2 fold, respectively). Thus, SP3 directly improves tandem mass spectrometry proteotyping.

Similarity-Based Segmentation of Multi-Dimensional Signals

The segmentation of time series and genomic data is a common problem in computational biology. With increasingly complex measurement procedures individual data points are often not just numbers or simple vectors in which all components are of the same kind. Analysis methods that capitalize on slopes in a single real-valued data track or that make explicit use of the vectorial nature of the data are not applicable in such scenaria. We develop here a framework for segmentation in arbitrary data domains that only requires a minimal notion of similarity. Using unsupervised clustering of (a sample of) the input yields an approximate segmentation algorithm that is efficient enough for genome-wide applications. As a showcase application we segment a time-series of transcriptome sequencing data from budding yeast, in high temporal resolution over ca. 2.5 cycles of the short-period respiratory oscillation. The algorithm is used with a similarity measure focussing on periodic expression profiles across the metabolic cycle rather than coverage per time point.

Investigation of an optimal cell lysis method for the study of the zinc metalloproteome of Histoplasma capsulatum

This work sought to assess optimal extraction conditions in the study of the metalloproteome of the dimorphic fungus Histoplasma capsulatum. One of the body's responses to H. capsulatum infection is sequestration of zinc within host macrophage (MØ), as reported by Vignesh et al. (Immunity 39:697-710, 2013) and Vignesh et al. (PLOS Pathog 9:E1003815, 2013). Thus, metalloproteins containing zinc were of greatest interest as it plays a critical role in survival of the fungus. One challenge in metalloproteomics is the preservation of the native structure of proteins to retain non-covalently bound metals. Many of the conventional cell lysis, separation, and identification techniques in proteomics are carried out under conditions that could lead to protein denaturation. Various cell lysis techniques were investigated in an effort to both maintain the metalloproteins during lysis and subsequent analysis while, at the same time, serving to be strong enough to break the cell wall, allowing access to cytosolic metalloproteins. The addition of 1% Triton x-100, a non-ionic detergent, to the lysis buffer was also studied. Seven lysis methods were considered and these included: Glass Homogenizer (H), Bead Beater (BB), Sonication Probe (SP), Vortex with 1% Triton x-100 (V, T), Vortex with no Triton x-100 (V, NT), Sonication Bath, Vortex, and 1% Triton x-100 (SB, V, T) and Sonication Bath, Vortex, and no Triton x-100 (SB, V, NT). A Qubit® Assay was used to compare total protein concentration and inductively coupled plasma-mass spectrometry (ICP-MS) was utilized for total metal analysis of cell lysates. Size exclusion chromatography coupled to ICP-MS (SEC-HPLC-ICP-MS) was used for separation of the metalloproteins in the cell lysate and the concentration of Zn over a wide molecular weight range was examined. Additional factors such as potential contamination sources were also considered. A cell lysis method involving vortexing H. capsulatum yeast cells with 500 μm glass beads in a 1% Triton x-100 lysis buffer (V, T) was found to be most advantageous to extract intact zinc metalloproteins as demonstrated by the highest Zn to protein ratio, 1.030 ng Zn/μg protein, and Zn distribution among high, mid, and low molecular weights suggesting the least amount of protein denaturation. Graphical abstract In this work, several cell lysis techniques and two lysis buffers were investigated to evaluate the preservation of the zinc metalloproteome of H. capsulatum while maintaining compatibility with the analytical techniques employed.

A straightforward and efficient analytical pipeline for metaproteome characterization

BACKGROUND

The massive characterization of host-associated and environmental microbial communities has represented a real breakthrough in the life sciences in the last years. In this context, metaproteomics specifically enables the transition from assessing the genomic potential to actually measuring the functional expression of a microbiome. However, significant research efforts are still required to develop analysis pipelines optimized for metaproteome characterization.

RESULTS

This work presents an efficient analytical pipeline for shotgun metaproteomic analysis, combining bead-beating/freeze-thawing for protein extraction, filter-aided sample preparation for cleanup and digestion, and single-run liquid chromatography-tandem mass spectrometry for peptide separation and identification. The overall procedure is more time-effective and less labor-intensive when compared to state-of-the-art metaproteomic techniques. The pipeline was first evaluated using mock microbial mixtures containing different types of bacteria and yeasts, enabling the identification of up to over 15,000 non-redundant peptide sequences per run with a linear dynamic range from 10(4) to 10(8) colony-forming units. The pipeline was then applied to the mouse fecal metaproteome, leading to the overall identification of over 13,000 non-redundant microbial peptides with a false discovery rate of <1%, belonging to over 600 different microbial species and 250 functionally relevant protein families. An extensive mapping of the main microbial metabolic pathways actively functioning in the gut microbiome was also achieved.

CONCLUSIONS

The analytical pipeline presented here may be successfully used for the in-depth and time-effective characterization of complex microbial communities, such as the gut microbiome, and represents a useful tool for the microbiome research community.

Quantifying periodicity in omics data

Oscillations play a significant role in biological systems, with many examples in the fast, ultradian, circadian, circalunar, and yearly time domains. However, determining periodicity in such data can be problematic. There are a number of computational methods to identify the periodic components in large datasets, such as signal-to-noise based Fourier decomposition, Fisher's g-test and autocorrelation. However, the available methods assume a sinusoidal model and do not attempt to quantify the waveform shape and the presence of multiple periodicities, which provide vital clues in determining the underlying dynamics. Here, we developed a Fourier based measure that generates a de-noised waveform from multiple significant frequencies. This waveform is then correlated with the raw data from the respiratory oscillation found in yeast, to provide oscillation statistics including waveform metrics and multi-periods. The method is compared and contrasted to commonly used statistics. Moreover, we show the utility of the program in the analysis of noisy datasets and other high-throughput analyses, such as metabolomics and flow cytometry, respectively.

A yeast metabolite extraction protocol optimised for time-series analyses

There is an increasing call for the absolute quantification of time-resolved metabolite data. However, a number of technical issues exist, such as metabolites being modified/degraded either chemically or enzymatically during the extraction process. Additionally, capillary electrophoresis mass spectrometry (CE-MS) is incompatible with high salt concentrations often used in extraction protocols. In microbial systems, metabolite yield is influenced by the extraction protocol used and the cell disruption rate. Here we present a method that rapidly quenches metabolism using dry-ice ethanol bath and methanol N-ethylmaleimide solution (thus stabilising thiols), disrupts cells efficiently using bead-beating and avoids artefacts created by live-cell pelleting. Rapid sample processing minimised metabolite leaching. Cell weight, number and size distribution was used to calculate metabolites to an attomol/cell level. We apply this method to samples obtained from the respiratory oscillation that occurs when yeast are grown continuously.