Novel Sarcopenia-related Alterations in Sarcomeric Protein Post-translational Modifications (PTMs) in Skeletal Muscles Identified by Top-down Proteomics

Sarcopenia, the age-related loss of skeletal muscle mass and strength, is a significant cause of morbidity in the elderly and is a major burden on health care systems. Unfortunately, the underlying molecular mechanisms in sarcopenia remain poorly understood. Herein, we utilized top-down proteomics to elucidate sarcopenia-related changes in the fast- and slow-twitch skeletal muscles of aging rats with a focus on the sarcomeric proteome, which includes both myofilament and Z-disc proteins-the proteins that constitute the contractile apparatuses. Top-down quantitative proteomics identified significant changes in the post-translational modifications (PTMs) of critical myofilament proteins in the fast-twitch skeletal muscles of aging rats, in accordance with the vulnerability of fast-twitch muscles to sarcopenia. Surprisingly, age-related alterations in the phosphorylation of Cypher isoforms, proteins that localize to the Z-discs in striated muscles, were also noted in the fast-twitch skeletal muscle of aging rats. This represents the first report of changes in the phosphorylation of Z-disc proteins in skeletal muscle during aging. In addition, increased glutathionylation of slow skeletal troponin I, a novel modification that may help protect against oxidative damage, was observed in slow-twitch skeletal muscles. Furthermore, we have identified and characterized novel muscle type-specific proteoforms of myofilament proteins and Z-disc proteins, including a novel isoform of the Z-disc protein Enigma. The finding that the phosphorylation of Z-disc proteins is altered in response to aging in the fast-twitch skeletal muscles of aging rats opens new avenues for the investigation of the role of Z-discs in age-related muscle dysfunction.

The Impact of Phosphorylation on Electron Capture Dissociation of Proteins: A Top-Down Perspective

Electron capture dissociation (ECD) is well suited for the characterization of phosphoproteins, with which labile phosphate groups are generally preserved during the fragmentation process. However, the impact of phosphorylation on ECD fragmentation of intact proteins remains unclear. Here, we have performed a systematic investigation of the phosphorylation effect on ECD of intact proteins by comparing the ECD cleavages of mono-phosphorylated α-casein, multi-phosphorylated β-casein, and immunoaffinity-purified phosphorylated cardiac troponin I with those of their unphosphorylated counterparts, respectively. In contrast to phosphopeptides, phosphorylation has significantly reduced deleterious effects on the fragmentation of intact proteins during ECD. On a global scale, the fragmentation patterns are highly comparable between unphosphorylated and phosphorylated precursors under the same ECD conditions, despite a slight decrease in the number of fragment ions observed for the phosphorylated forms. On a local scale, single phosphorylation of intact proteins imposes minimal effects on fragmentation near the phosphorylation sites, but multiple phosphorylations in close proximity result in a significant reduction of ECD bond cleavages. Graphical Abstract ᅟ.

Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy

Mass spectrometry (MS)-based top-down proteomics is a powerful method for the comprehensive analysis of proteoforms that arise from genetic variations and post-translational modifications (PTMs). However, top-down MS analysis of high molecular weight (MW) proteins remains challenging mainly due to the exponential decay of signal-to-noise ratio with increasing MW. Size exclusion chromatography (SEC) is a favored method for size-based separation of biomacromolecules but typically suffers from low resolution. Herein, we developed a serial size exclusion chromatography (sSEC) strategy to enable high-resolution size-based fractionation of intact proteins (10-223 kDa) from complex protein mixtures. The sSEC fractions could be further separated by reverse phase chromatography (RPC) coupled online with high-resolution MS. We have shown that two-dimensional (2D) sSEC-RPC allowed for the identification of 4044 more unique proteoforms and a 15-fold increase in the detection of proteins above 60 kDa, compared to one-dimensional (1D) RPC. Notably, effective sSEC-RPC separation of proteins significantly enhanced the detection of high MW proteins up to 223 kDa and also revealed low abundance proteoforms that are post-translationally modified. This sSEC method is MS-friendly, robust, and reproducible and, thus, can be applied to both high-efficiency protein purification and large-scale proteomics analysis of cell or tissue lysate for enhanced proteome coverage, particularly for low abundance and high MW proteoforms.

Mechanism of imidazolium ionic liquids toxicity in Saccharomyces cerevisiae and rational engineering of a tolerant, xylose-fermenting strain

BACKGROUND

Imidazolium ionic liquids (IILs) underpin promising technologies that generate fermentable sugars from lignocellulose for future biorefineries. However, residual IILs are toxic to fermentative microbes such as Saccharomyces cerevisiae, making IIL-tolerance a key property for strain engineering. To enable rational engineering, we used chemical genomic profiling to understand the effects of IILs on S. cerevisiae.

RESULTS

We found that IILs likely target mitochondria as their chemical genomic profiles closely resembled that of the mitochondrial membrane disrupting agent valinomycin. Further, several deletions of genes encoding mitochondrial proteins exhibited increased sensitivity to IIL. High-throughput chemical proteomics confirmed effects of IILs on mitochondrial protein levels. IILs induced abnormal mitochondrial morphology, as well as altered polarization of mitochondrial membrane potential similar to valinomycin. Deletion of the putative serine/threonine kinase PTK2 thought to activate the plasma-membrane proton efflux pump Pma1p conferred a significant IIL-fitness advantage. Conversely, overexpression of PMA1 conferred sensitivity to IILs, suggesting that hydrogen ion efflux may be coupled to influx of the toxic imidazolium cation. PTK2 deletion conferred resistance to multiple IILs, including [EMIM]Cl, [BMIM]Cl, and [EMIM]Ac. An engineered, xylose-converting ptk2∆ S. cerevisiae (Y133-IIL) strain consumed glucose and xylose faster and produced more ethanol in the presence of 1 % [BMIM]Cl than the wild-type PTK2 strain. We propose a model of IIL toxicity and resistance.

CONCLUSIONS

This work demonstrates the utility of chemical genomics-guided biodesign for development of superior microbial biocatalysts for the ever-changing landscape of fermentation inhibitors.

Plant-derived antifungal agent poacic acid targets β-1,3-glucan

A rise in resistance to current antifungals necessitates strategies to identify alternative sources of effective fungicides. We report the discovery of poacic acid, a potent antifungal compound found in lignocellulosic hydrolysates of grasses. Chemical genomics using Saccharomyces cerevisiae showed that loss of cell wall synthesis and maintenance genes conferred increased sensitivity to poacic acid. Morphological analysis revealed that cells treated with poacic acid behaved similarly to cells treated with other cell wall-targeting drugs and mutants with deletions in genes involved in processes related to cell wall biogenesis. Poacic acid causes rapid cell lysis and is synergistic with caspofungin and fluconazole. The cellular target was identified; poacic acid localized to the cell wall and inhibited β-1,3-glucan synthesis in vivo and in vitro, apparently by directly binding β-1,3-glucan. Through its activity on the glucan layer, poacic acid inhibits growth of the fungi Sclerotinia sclerotiorum and Alternaria solani as well as the oomycete Phytophthora sojae. A single application of poacic acid to leaves infected with the broad-range fungal pathogen S. sclerotiorum substantially reduced lesion development. The discovery of poacic acid as a natural antifungal agent targeting β-1,3-glucan highlights the potential side use of products generated in the processing of renewable biomass toward biofuels as a source of valuable bioactive compounds and further clarifies the nature and mechanism of fermentation inhibitors found in lignocellulosic hydrolysates.

Crux: rapid open source protein tandem mass spectrometry analysis

Efficiently and accurately analyzing big protein tandem mass spectrometry data sets requires robust software that incorporates state-of-the-art computational, machine learning, and statistical methods. The Crux mass spectrometry analysis software toolkit ( http://cruxtoolkit.sourceforge.net ) is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data.

Forazoline A: marine-derived polyketide with antifungal in vivo efficacy

Forazoline A, a novel antifungal polyketide with in vivo efficacy against Candida albicans, was discovered using LCMS-based metabolomics to investigate marine-invertebrate-associated bacteria. Forazoline A had a highly unusual and unprecedented skeleton. Acquisition of (13)C-(13)C gCOSY and (13)C-(15)N HMQC NMR data provided the direct carbon-carbon and carbon-nitrogen connectivity, respectively. This approach represents the first example of determining direct (13)C-(15)N connectivity for a natural product. Using yeast chemical genomics, we propose that forazoline A operated through a new mechanism of action with a phenotypic outcome of disrupting membrane integrity.

Aromatic inhibitors derived from ammonia-pretreated lignocellulose hinder bacterial ethanologenesis by activating regulatory circuits controlling inhibitor efflux and detoxification

Efficient microbial conversion of lignocellulosic hydrolysates to biofuels is a key barrier to the economically viable deployment of lignocellulosic biofuels. A chief contributor to this barrier is the impact on microbial processes and energy metabolism of lignocellulose-derived inhibitors, including phenolic carboxylates, phenolic amides (for ammonia-pretreated biomass), phenolic aldehydes, and furfurals. To understand the bacterial pathways induced by inhibitors present in ammonia-pretreated biomass hydrolysates, which are less well studied than acid-pretreated biomass hydrolysates, we developed and exploited synthetic mimics of ammonia-pretreated corn stover hydrolysate (ACSH). To determine regulatory responses to the inhibitors normally present in ACSH, we measured transcript and protein levels in an Escherichia coli ethanologen using RNA-seq and quantitative proteomics during fermentation to ethanol of synthetic hydrolysates containing or lacking the inhibitors. Our study identified four major regulators mediating these responses, the MarA/SoxS/Rob network, AaeR, FrmR, and YqhC. Induction of these regulons was correlated with a reduced rate of ethanol production, buildup of pyruvate, depletion of ATP and NAD(P)H, and an inhibition of xylose conversion. The aromatic aldehyde inhibitor 5-hydroxymethylfurfural appeared to be reduced to its alcohol form by the ethanologen during fermentation, whereas phenolic acid and amide inhibitors were not metabolized. Together, our findings establish that the major regulatory responses to lignocellulose-derived inhibitors are mediated by transcriptional rather than translational regulators, suggest that energy consumed for inhibitor efflux and detoxification may limit biofuel production, and identify a network of regulators for future synthetic biology efforts.

Analysis of secondary structure in proteins by chemical cross-linking coupled to MS

Chemical cross-linking is an attractive technique for the study of the structure of protein complexes due to its low sample consumption and short analysis time. Furthermore, distance constraints obtained from the identification of cross-linked peptides by MS can be used to construct and validate protein models. If a sufficient number of distance constraints are obtained, then determining the secondary structure of a protein can allow inference of the protein's fold. In this work, we show how the distance constraints obtained from cross-linking experiments can identify secondary structures within the protein sequence. Molecular modeling of alpha helices and beta sheets reveals that each secondary structure presents different cross-linking possibilities due to the topological distances between reactive residues. Cross-linking experiments performed with amine reactive cross-linkers with model alpha helix containing proteins corroborated the molecular modeling predictions. The cross-linking patterns established here can be extended to other cross-linkers with known lengths for the determination of secondary structures in proteins.

Estimating relative abundances of proteins from shotgun proteomics data

Background

Spectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. The crux spectral-counts command, implemented as part of the Crux software toolkit, implements four previously reported spectral counting methods, the spectral index (SI_N), the exponentially modified protein abundance index (emPAI), the normalized spectral abundance factor (NSAF), and the distributed normalized spectral abundance factor (dNSAF).

Results

We compared the reproducibility and the linearity relative to each protein’s abundance of the four spectral counting metrics. Our analysis suggests that NSAF yields the most reproducible counts across technical and biological replicates, and both SI_N and NSAF achieve the best linearity.

Conclusions

With the crux spectral-counts command, Crux provides open-source modular methods to analyze mass spectrometry data for identifying and now quantifying peptides and proteins. The C++ source code, compiled binaries, spectra and sequence databases are available at http://noble.gs.washington.edu/proj/crux-spectral-counts.

Detecting cross-linked peptides by searching against a database of cross-linked peptide pairs

Mass spectrometric identification of cross-linked peptides can provide valuable information about the structure of protein complexes. We describe a straightforward database search scheme that identifies and assigns statistical confidence estimates to spectra from cross-linked peptides. The method is well suited to targeted analysis of a single protein complex, without requiring an isotope labeling strategy. Our approach uses a SEQUEST-style search procedure in which the database is comprised of a mixture of single peptides with and without linkers attached and cross-linked products. In contrast to several previous approaches, we generate theoretical spectra that account for all of the expected peaks from a cross-linked product, and we employ an empirical curve-fitting procedure to estimate statistical confidence measures. We show that our fully automated procedure successfully reidentifies spectra from a previous study, and we provide evidence that our statistical confidence estimates are accurate.

Prion disease diagnosis by proteomic profiling

Definitive prion disease diagnosis is currently limited to postmortem assay for the presence of the disease-associated proteinase K-resistant prion protein. Using cerebrospinal fluid (CSF) from prion-infected hamsters, matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS), and support vector machines (SVM), we have identified peptide profiles characteristic of disease state. Using 10-fold leave-one-out cross-validation, we report a predictive accuracy of 72% with a true positive rate of 73% and a false positive rate of 27% demonstrating the suitability of using proteomic profiling and CSF for the development of multiple marker diagnostics of prion disease.

Matching isotopic distributions from metabolically labeled samples

MOTIVATION

In recent years stable isotopic labeling has become a standard approach for quantitative proteomic analyses. Among the many available isotopic labeling strategies, metabolic labeling is attractive for the excellent internal control it provides. However, analysis of data from metabolic labeling experiments can be complicated because the spacing between labeled and unlabeled forms of each peptide depends on its sequence, and is thus variable from analyte to analyte. As a result, one generally needs to know the sequence of a peptide to identify its matching isotopic distributions in an automated fashion. In some experimental situations it would be necessary or desirable to match pairs of labeled and unlabeled peaks from peptides of unknown sequence. This article addresses this largely overlooked problem in the analysis of quantitative mass spectrometry data by presenting an algorithm that not only identifies isotopic distributions within a mass spectrum, but also annotates matches between natural abundance light isotopic distributions and their metabolically labeled counterparts. This algorithm is designed in two stages: first we annotate the isotopic peaks using a modified version of the IDM algorithm described last year; then we use a probabilistic classifier that is supplemented by dynamic programming to find the metabolically labeled matched isotopic pairs. Such a method is needed for high-throughput quantitative proteomic metabolomic experiments measured via mass spectrometry.

RESULTS

The primary result of this article is that the dynamic programming approach performs well given perfect isotopic distribution annotations. Our algorithm achieves a true positive rate of 99% and a false positive rate of 1% using perfect isotopic distribution annotations. When the isotopic distributions are annotated given 'expert' selected peaks, the same algorithm gets a true positive rate of 77% and a false positive rate of 1%. Finally, when annotating using machine selected peaks, which may contain noise, the dynamic programming algorithm gives a true positive rate of 36% and a false positive rate of 1%. It is important to mention that these rates arise from the requirement of exact annotations of both the light and heavy isotopic distributions. In our evaluations, a match is considered 'entirely incorrect' if it is missing even one peak or containing an extraneous peak. If we only require that the 'monoisotopic' peaks exist within the two matched distributions, our algorithm obtains a positive rate of 45% and a false positive rate of 1% on the 'machine' selected data. Changes to the algorithm's scoring function and training example generation improves our 'monoisotopic' peak score true positive rate to 65% while obtaining a false positive rate of 2%. All results were obtained within 10-fold cross-validation of 41 mass spectra with a mass-to-charge range of 800-4000 m/z. There are a total of 713 isotopic distributions and 255 matched isotopic pairs that are hand-annotated for this study.

AVAILABILITY

Programs are available via http://www.cs.wisc.edu/~mcilwain/IDM/.

Combining MALDI-FTMS and bioinformatics for rapid peptidomic comparisons

Increasing research efforts in large-scale mass spectral analyses of peptides and proteins have led to many advances in technology and method development for collecting data and improving the quality of data. However, the resultant large data sets often pose significant challenges in extracting useful information in a high-throughput manner. Here, we describe one such method where we analyzed a large mass spectral data set collected using decapod crustacean nervous tissue extracts separated via high-performance liquid chromatography (HPLC) coupled to high-resolution matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS). Following their acquisition, the data collected from discrete LC fractions was compiled and analyzed using an in-house developed software package that deisotoped, compressed, calibrated, and matched peaks to a list of known crustacean neuropeptides. By processing these data via bioinformatics tools such as hierarchical clustering, more than 110 neuropeptides that belong to 14 peptide families were mapped in five crustacean species. Overall, we demonstrate the utility of MALDI-FTMS in combination with a bioinformatics software package for the elucidation and comparison of peptidomes of varying crustacean species. This study established an effective methodology and will provide the basis for future investigations into more comprehensive comparative peptidomics with larger collection of species and phyla in order to gain a deeper understanding of the evolution and diversification of peptide families.

Using dynamic programming to create isotopic distribution maps from mass spectra

MOTIVATION

This article presents a method to identify the isotopic distributions within a mass spectrum using a probabilistic classifier supplemented with dynamic programming. Such a system is needed for a variety of purposes, including generating robust and meaningful features from mass spectra to be used in classification.

RESULTS

The primary result of this article is that the dynamic programming approach significantly improves sensitivity, without harming specificity, of a probabilistic classifier for identifying the isotopic distributions. When annotating isotopic distributions where an expert has performed the initial 'peak-picking' (removal of noise peaks), the dynamic programming approach gives a true positive rate of 96% and a false positive rate of 0.0%, whereas the classifier alone has a true positive rate of only 47% when the false positive rate is 0.0%. When annotating isotopic distributions in machine peak-picked spectra, which may contain many noise peaks, the dynamic programming approach gives a true positive rate of only 22.0%, but it still keeps a low false positive rate of 1.0% and still outperforms the classifier alone. It is important to note that all these rates are when we require exact matches with the distributions in annotated spectra; in our evaluation a distribution is considered 'entirely incorrect' if it is missing even one peak or contains even one extraneous peak. We compared to the THRASH and AID-MS systems using a looser requirement: correctly identifying the distribution that contains the mono-isotopic mass. Under this measure, our dynamic programming approach achieves a true positive rate of 82% and a false positive rate of 1%, which again outperforms the classifier alone. The dynamic programming approach ends up being more conservative than THRASH and AID-MS, yielding both fewer true and false peaks, but the F-score of the dynamic programming approach is significantly better than those of THRASH and AID-MS. All results were obtained with 10-fold cross-validation of 99 sections of mass spectra with a total of 214 hand-annotated isotopic distributions.

AVAILABILITY

Programs are available via http://www.cs.wisc.edu/~mcilwain/IDM.

Susceptibility to Taura Syndrome Virus of Some Penaeid Shrimp Species Native to the Gulf of Mexico and the Southeastern United States

Experimental studies demonstrated that Penaeus setiferus, but not Penaeus aztecus or Penaeus duorarum, could be killed by Taura syndrome virus (TSV). However, specimens of P. setiferus that survived infection and both P. aztecus and P. duorarum at least 79 days postexposure that did not demonstrate gross signs of infection were shown to harbor virus by bioassay using Penaeus vannamei, a highly susceptible target host. Consequently, all three of those penaeids native to the southeast United States can serve as carriers or reservoir hosts of TSV without necessarily exhibiting disease. Infections in P. setiferus took longer to cause mortality than in P. vannamei and killed a smaller percentage of that host. Also, histological lesions diagnostic of TSV infection were not always evident in sectioned tissue of infected P. setiferus, and they generally were more conspicuous during Days 4-7 postexposure compared with lesions that also occurred at both earlier and later days in tissues of P. vannamei. Infections could be produced by injection, ingestion, and incorporation of the infective material into dietary brine shrimp. There appeared to be a difference in susceptibility to TSV disease by different stocks of P. setiferus, but different experiments produced conflicting evidence regarding a relationship between age and predilection to mortality. Large and small specimens of equal-aged shrimp succumbed similarly to TSV infections for both P. vannamei and P. setiferus. The nonnative species P. chinensis demonstrated a high susceptibility to experimental TSV disease.

Influenza B virus infections in the community and the family. The epidemics of 1976-1977 and 1979-1980 in Houston, Texas

Influenza B virus epidemics occurred in Houston, Texas, in 1976-1977 and 1979-1980. Among families with young children followed longitudinally in the Houston Family Study, 112 infections were detected during 511 person-years of observation. The infection rates for the two epidemics were similar--24 per cent and 20 per cent--although the two epidemics differed greatly in the community. The first epidemic was much more intense with a mid-winter peak that produced school absentee rates above 12 per cent for four consecutive weeks. The indolent epidemic of 1979-1980 smoldered from late September to mid-April with a peak during the second week of March for which school absenteeism did not exceed 8 per cent. In the Houston Family Study population, the combined infection rate for the two outbreaks was highest at 35 per 100 person-years for school children aged 6-19 years. Preschool children aged 7 months-5 years and adults had infection rates of 31 and 16 per 100 person-years, respectively. Preexisting neutralizing antibody titers greater than or equal to 3.5 log2 protected against influenza B infection and illness. Preschool children above 6 months of age, school age children, and parents introduced infection into the family at rates of 15, 15, and 9 per 100 person-years, respectively. Three second introductions were observed. The secondary infection rate was highest among school aged children at 61 per 100 persons at risk.