Milk Formula Diet Alters Bacterial and Host Protein Profile in Comparison to Human Milk Diet in Neonatal Piglet Model.

The metaproteome profiling of cecal contents collected from neonatal piglets fed pasteurized human milk (HM) or a dairy-based infant formula (MF) from postnatal day (PND) 2 to 21 were assessed. At PND 21, a subset of piglets from each group (n = 11/group) were euthanized, and cecal contents were collected for further metaproteome analysis. Cecal microbiota composition showed predominantly more Firmicutes phyla and Lachnospiraceae family in the lumen of cecum of HM-fed piglets in comparison to the MF-fed group. Ruminococcus gnavus was the most abundant species from the Firmicutes phyla in the cecal contents of the HM-fed piglets at 21 days of age. A greater number of expressed proteins were identified in the cecal contents of the HM-fed piglets relative to the MF-fed piglets. Greater abundances of proteins potentially expressed by Bacteroides spp. such as glycoside enzymes were noted in the cecal lumen of HM-fed piglets relative to the MF. Additionally, lyases associated with Lachnospiraceae family were abundant in the cecum of the HM group relative to the MF group. Overall, our findings indicate that neonatal diet impacts the gut bacterial taxa and microbial proteins prior to weaning. The metaproteomics data were deposited into PRIDE, PXD025432 and 10.6019/PXD025432.

Integration of Flow Cytometry and Single Cell Sequencing.

Integrating cytometric analysis of cells, mitochondria, and other polynucleotide-containing biological particles with high-throughput single particle sequencing would provide an ultimate bioanalytical tool, simultaneously assessing phenotype, functionality, genome, and transcriptome of each particle in a large population. Here, we describe how such integration could be performed by adapting existing, well-established technologies.

DEAD-box RNA helicases Dbp2, Ded1 and Mss116 bind to G-quadruplex nucleic acids and destabilize G-quadruplex RNA.

We identified 29 G-quadruplex binding proteins by affinity purification and quantitative LC-MS/MS. We demonstrated that the DEAD-box RNA helicases Dbp2, Ded1 and Mss116 preferentially bind to G-quadruplex nucleic acids in vitro and destabilize RNA quadruplexes, suggesting new potential roles for these helicases in disruption of quadruplex structures in RNA.

Chronic kidney disease and the gut microbiome.

The gut microbiome is composed of a diverse population of bacteria that have beneficial and adverse effects on human health. The microbiome has recently gained attention and is increasingly noted to play a significant role in health and a number of disease states. Increasing urea concentration during chronic kidney disease (CKD) leads to alterations in the intestinal flora that can increase production of gut-derived toxins and alter the intestinal epithelial barrier. These changes can lead to an acceleration of the process of kidney injury. A number of strategies have been proposed to interrupt this pathway of injury in CKD. The purpose of this review is to summarize the role of the gut microbiome in CKD, tools used to study this microbial population, and attempts to alter its composition for therapeutic purposes.

Metaproteomics reveals potential mechanisms by which dietary resistant starch supplementation attenuates chronic kidney disease progression in rats.

BACKGROUND: Resistant starch is a prebiotic metabolized by the gut bacteria. It has been shown to attenuate chronic kidney disease (CKD) progression in rats. Previous studies employed taxonomic analysis using 16S rRNA sequencing and untargeted metabolomics profiling. Here we expand these studies by metaproteomics, gaining new insight into the host-microbiome interaction. METHODS: Differences between cecum contents in CKD rats fed a diet containing resistant starch with those fed a diet containing digestible starch were examined by comparative metaproteomics analysis. Taxonomic information was obtained using unique protein sequences. Our methodology results in quantitative data covering both host and bacterial proteins. RESULTS: 5,834 proteins were quantified, with 947 proteins originating from the host organism. Taxonomic information derived from metaproteomics data surpassed previous 16S RNA analysis, and reached species resolutions for moderately abundant taxonomic groups. In particular, the Ruminococcaceae family becomes well resolved-with butyrate producers and amylolytic species such as R. bromii clearly visible and significantly higher while fibrolytic species such as R. flavefaciens are significantly lower with resistant starch feeding. The observed changes in protein patterns are consistent with fiber-associated improvement in CKD phenotype. Several known host CKD-associated proteins and biomarkers of impaired kidney function were significantly reduced with resistant starch supplementation. Data are available via ProteomeXchange with identifier PXD008845. CONCLUSIONS: Metaproteomics analysis of cecum contents of CKD rats with and without resistant starch supplementation reveals changes within gut microbiota at unprecedented resolution, providing both functional and taxonomic information. Proteins and organisms differentially abundant with RS supplementation point toward a shift from mucin degraders to butyrate producers.

Evidence That G-quadruplex DNA Accumulates in the Cytoplasm and Participates in Stress Granule Assembly in Response to Oxidative Stress.

Cells engage numerous signaling pathways in response to oxidative stress that together repair macromolecular damage or direct the cell toward apoptosis. As a result of DNA damage, mitochondrial DNA or nuclear DNA has been shown to enter the cytoplasm where it binds to "DNA sensors," which in turn initiate signaling cascades. Here we report data that support a novel signaling pathway in response to oxidative stress mediated by specific guanine-rich sequences that can fold into G-quadruplex DNA (G4DNA). In response to oxidative stress, we demonstrate that sequences capable of forming G4DNA appear at increasing levels in the cytoplasm and participate in assembly of stress granules. Identified proteins that bind to endogenous G4DNA in the cytoplasm are known to modulate mRNA translation and participate in stress granule formation. Consistent with these findings, stress granule formation is known to regulate mRNA translation during oxidative stress. We propose a signaling pathway whereby cells can rapidly respond to DNA damage caused by oxidative stress. Guanine-rich sequences that are excised from damaged genomic DNA are proposed to enter the cytoplasm where they can regulate translation through stress granule formation. This newly proposed role for G4DNA provides an additional molecular explanation for why such sequences are prevalent in the human genome.

Protein-protein interaction analysis for functional characterization of helicases.

Helicases are enzymes involved in nucleic acid metabolism, playing major roles in replication, transcription, and repair. Defining helicases oligomerization state and transient and persistent protein interactions is essential for understanding of their function. In this article we review current methods for the protein-protein interaction analysis, and discuss examples of its application to the study of helicases: Pif1 and DDX3. Proteomics methods are our main focus - affinity pull-downs and chemical cross-linking followed by mass spectrometry. We review advantages and limitations of these methods and provide general guidelines for their implementation in the functional analysis of helicases.

Yeast transcription co-activator Sub1 and its human homolog PC4 preferentially bind to G-quadruplex DNA.

Using a G-quadruplex bait, we identified the transcription co-activator Sub1 as a G-quadruplex binding protein by quantitative LC-MS/MS and demonstrated in vivo G-quadruplex binding by ChIP. In vitro, Sub1, and its human homolog PC4, bind preferentially to G-quadruplexes. This provides a possible mechanism by which G-quadruplexes can influence gene transcription.

XLPM: efficient algorithm for the analysis of protein-protein contacts using chemical cross-linking mass spectrometry.

BACKGROUND: Chemical cross-linking is used for protein-protein contacts mapping and for structural analysis. One of the difficulties in cross-linking studies is the analysis of mass-spectrometry data and the assignment of the site of cross-link incorporation. The difficulties are due to higher charges of fragment ions, and to the overall low-abundance of cross-link species in the background of linear peptides. Cross-linkers non-specific at one end, such as photo-inducible diazirines, may complicate the analysis further. In this report, we design and validate a novel cross-linked peptide mapping algorithm (XLPM) and compare it to StavroX, which is currently one of the best algorithms in this class. RESULTS: We have designed a novel cross-link search algorithm -XLPM - and implemented it both as an online tool and as a downloadable archive of scripts. We designed a filter based on an observation that observation of a b-ion implies observation of a complimentary y-ion with high probability (b-y filter). We validated the b-y filter on the set of linear peptides from NIST library, and demonstrate that it is an effective way to find high-quality mass spectra. Next, we generated cross-linked data from an ssDNA binding protein, Rim1with a specific cross-linker disuccinimidyl suberate, and a semi-specific cross-linker NHS-Diazirine, followed by analysis of the cross-linked products by nanoLC-LTQ-Orbitrap mass spectrometry. The cross-linked data were searched by XLPM and StavroX and the performance of the two algorithms was compared. The cross-links were mapped to the X-ray structure of Rim1 tetramer. Analysis of the mixture of NHS-Diazirine cross-linked ¹5N and ¹4N-labeled Rim1 tetramers yielded ¹5N-labeled to ¹4N-labeled cross-linked peptide pairs, corresponding to C-terminus-to-N-terminus cross-linking, demonstrating interaction between different two Rim1 tetramers. Both XLPM and StavroX were successful in identification of this interaction, with XLPM leading to a better annotation of higher-charged fragments. We also put forward a new method of estimating specificity and sensitivity of identification of a cross-linked residue in the case of a non-specific cross-linker. CONCLUSIONS: The novel cross-link mapping algorithm, XLPM, considerably improves the speed and accuracy of the analysis compared to other methods. The quality selection filter based on b-to-y ions ratio proved to be an effective way to select high quality cross-linked spectra.

Large Scale Chemical Cross-linking Mass Spectrometry Perspectives.

The spectacular heterogeneity of a complex protein mixture from biological samples becomes even more difficult to tackle when one's attention is shifted towards different protein complex topologies, transient interactions, or localization of PPIs. Meticulous protein-by-protein affinity pull-downs and yeast-two-hybrid screens are the two approaches currently used to decipher proteome-wide interaction networks. Another method is to employ chemical cross-linking, which gives not only identities of interactors, but could also provide information on the sites of interactions and interaction interfaces. Despite significant advances in mass spectrometry instrumentation over the last decade, mapping Protein-Protein Interactions (PPIs) using chemical cross-linking remains time consuming and requires substantial expertise, even in the simplest of systems. While robust methodologies and software exist for the analysis of binary PPIs and also for the single protein structure refinement using cross-linking-derived constraints, undertaking a proteome-wide cross-linking study is highly complex. Difficulties include i) identifying cross-linkers of the right length and selectivity that could capture interactions of interest; ii) enrichment of the cross-linked species; iii) identification and validation of the cross-linked peptides and cross-linked sites. In this review we examine existing literature aimed at the large-scale protein cross-linking and discuss possible paths for improvement. We also discuss short-length cross-linkers of broad specificity such as formaldehyde and diazirine-based photo-cross-linkers. These cross-linkers could potentially capture many types of interactions, without strict requirement for a particular amino-acid to be present at a given protein-protein interface. How these shortlength, broad specificity cross-linkers be applied to proteome-wide studies? We will suggest specific advances in methodology, instrumentation and software that are needed to make such a leap.

Computational prediction of polycomb-associated long non-coding RNAs.

Among thousands of long non-coding RNAs (lncRNAs) only a small subset is functionally characterized and the functional annotation of lncRNAs on the genomic scale remains inadequate. In this study we computationally characterized two functionally different parts of human lncRNAs transcriptome based on their ability to bind the polycomb repressive complex, PRC2. This classification is enabled by the fact that while all lncRNAs constitute a diverse set of sequences, the classes of PRC2-binding and PRC2 non-binding lncRNAs possess characteristic combinations of sequence-structure patterns and, therefore, can be separated within the feature space. Based on the specific combination of features, we built several machine-learning classifiers and identified the SVM-based classifier as the best performing. We further showed that the SVM-based classifier is able to generalize on the independent data sets. We observed that this classifier, trained on the human lncRNAs, can predict up to 59.4% of PRC2-binding lncRNAs in mice. This suggests that, despite the low degree of sequence conservation, many lncRNAs play functionally conserved biological roles.

Quantitative shotgun proteomics using a protease with broad specificity and normalized spectral abundance factors.

Non-specific proteases are rarely used in quantitative shotgun proteomics due to potentially high false discovery rates. Yet, there are instances when application of a non-specific protease is desirable to obtain sufficient sequence coverage of otherwise poorly accessible proteins or structural domains. Using the non-specific protease, proteinase K, we analyzed Saccharomyces cerevisiae preparations grown in (14)N rich media and (15)N minimal media and obtained relative quantitation from the dataset using normalized spectral abundance factors (NSAFs). A critical step in using a spectral counting based approach for quantitative proteomics is ensuring the inclusion of high quality spectra in the dataset. One way to do this is to minimize the false discovery rate, which can be accomplished by applying different filters to a searched dataset. Natural log transformation of proteinase K derived NSAF values followed a normal distribution and allowed for statistical analysis by the t-test. Using this approach, we generated a dataset of 719 unique proteins found in each of the three independent biological replicates, of which 84 showed a statistically significant difference in expression levels between the two growth conditions.