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.

Metaproteomics-An Advantageous Option in Studies of Host-Microbiota Interaction.

Gut microbiome contributes to host health by maintaining homeostasis, increasing digestive efficiency, and facilitating the development of the immune system. Manipulating gut microbiota is being recognized as a therapeutic target to manage various chronic diseases. The therapeutic manipulation of the intestinal microbiome is achieved through diet modification, the administration of prebiotics, probiotics, or antibiotics, and more recently, fecal microbiome transplantation (FMT). In this opinion paper, we give a perspective on the current status of application of multi-omics technologies in the analysis of host-microbiota interactions. The aim of this paper was to highlight the strengths of metaproteomics, which integrates with and often relies on other approaches.

Resistant starch slows the progression of CKD in the 5/6 nephrectomy mouse model.

BACKGROUND: Resistant Starch (RS) improves CKD outcomes. In this report, we study how RS modulates host-microbiome interactions in CKD by measuring changes in the abundance of proteins and bacteria in the gut. In addition, we demonstrate RS-mediated reduction in CKD-induced kidney damage. METHODS: Eight mice underwent 5/6 nephrectomy to induce CKD and eight served as healthy controls. CKD and Healthy (H) groups were further split into those receiving RS (CKDRS, n = 4; HRS, n = 4) and those on normal diet (CKD, n = 4, H, n = 4). Kidney injury was evaluated by measuring BUN/creatinine and by histopathological evaluation. Cecal contents were analyzed using mass spectrometry-based metaproteomics and de novo sequencing using PEAKS. All the data were analyzed using R/Bioconductor packages. RESULTS: The 5/6 nephrectomy compromised kidney function as seen by an increase in BUN/creatinine compared to healthy groups. Histopathology of kidney sections showed reduced tubulointerstitial injury in the CKDRS versus CKD group; while no significant difference in BUN/creatinine was observed between the two CKD groups. Identified proteins point toward a higher population of butyrate-producing bacteria, reduced abundance of mucin-degrading bacteria in the RS fed groups, and to the downregulation of indole metabolism in CKD groups. CONCLUSION: RS slows the progression of chronic kidney disease. Resistant starch supplementation leads to active bacterial proliferation and the reduction of harmful bacterial metabolites.

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.

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study.

Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.

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.

Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.

The analysis of gene sets (in a form of functionally related genes or pathways) has become the method of choice for extracting the strongest signals from omics data. The motivation behind using gene sets instead of individual genes is two-fold. First, this approach incorporates pre-existing biological knowledge into the analysis and facilitates the interpretation of experimental results. Second, it employs a statistical hypotheses testing framework. Here, we briefly review main Gene Set Analysis (GSA) approaches for testing differential expression of gene sets and several GSA approaches for testing statistical hypotheses beyond differential expression that allow extracting additional biological information from the data. We distinguish three major types of GSA approaches testing: (1) differential expression (DE), (2) differential variability (DV), and (3) differential co-expression (DC) of gene sets between two phenotypes. We also present comparative power analysis and Type I error rates for different approaches in each major type of GSA on simulated data. Our evaluation presents a concise guideline for selecting GSA approaches best performing under particular experimental settings. The value of the three major types of GSA approaches is illustrated with real data example. While being applied to the same data set, major types of GSA approaches result in complementary biological information.

GSAR: Bioconductor package for Gene Set analysis in R.

BACKGROUND: Gene set analysis (in a form of functionally related genes or pathways) has become the method of choice for analyzing omics data in general and gene expression data in particular. There are many statistical methods that either summarize gene-level statistics for a gene set or apply a multivariate statistic that accounts for intergene correlations. Most available methods detect complex departures from the null hypothesis but lack the ability to identify the specific alternative hypothesis that rejects the null. RESULTS: GSAR (Gene Set Analysis in R) is an open-source R/Bioconductor software package for gene set analysis (GSA). It implements self-contained multivariate non-parametric statistical methods testing a complex null hypothesis against specific alternatives, such as differences in mean (shift), variance (scale), or net correlation structure. The package also provides a graphical visualization tool, based on the union of two minimum spanning trees, for correlation networks to examine the change in the correlation structures of a gene set between two conditions and highlight influential genes (hubs). CONCLUSIONS: Package GSAR provides a set of multivariate non-parametric statistical methods that test a complex null hypothesis against specific alternatives. The methods in package GSAR are applicable to any type of omics data that can be represented in a matrix format. The package, with detailed instructions and examples, is freely available under the GPL (> = 2) license from the Bioconductor web site.

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.

Analysis of Protein-protein Interaction Interface between Yeast Mitochondrial Proteins Rim1 and Pif1 Using Chemical Cross-linking Mass Spectrometry.

Defining protein-protein contacts is a challenging problem and cross-linking is a promising solution. Here, we present a case of mitochondrial single strand binding protein Rim1 and helicase Pif1, an interaction first observed in immuno-affinity pull-down from yeast cells using Pif1 bait. We found that only the short succinimidyl-diazirine cross-linker or formaldehyde captured the interaction between recombinant Rim1 and Pif1. In addition, Pif1 needed to be stripped of its N-terminal and C-terminal domains, and Rim1's C-terminus needed to be modified for the cross-linked product to become visible. Our report is an example of a non-trivial analysis, where a previously identified stable interaction escapes initial capture with cross-linking agents and requires substantial modification to recombinant proteins and fine-tuning of the mass spectrometry-based methods for the cross-links to become detectable. We used high resolution mass spectrometry to detect the cross-linked peptides. A 1:1 mixture of 15N and 14N-labeled Rim1 was used to validate the cross-links by their mass shift in the LC-MS profiles. Two sites on Rim1 were confirmed: 1) the N-terminus, and 2) the K29 residue. Performing cross-linking with a K29A variant visibly reduced the cross-linked product. Further, K29A-Rim1 showed a five-fold lower affinity to single stranded DNA compared to wild-type Rim1. Both the K29A variant and wild type Rim1 showed similar degrees of stimulation of Pif1 helicase activity. We propose structural models of the Pif1-Rim1 interaction and discuss its functional significance. Our work represents a non-trivial protein-protein interface analysis and demonstrates utility of short and non-specific cross-linkers.

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.

The functional network of the Arabidopsis plastoglobule proteome based on quantitative proteomics and genome-wide coexpression analysis.

Plastoglobules (PGs) in chloroplasts are thylakoid-associated monolayer lipoprotein particles containing prenyl and neutral lipids and several dozen proteins mostly with unknown functions. An integrated view of the role of the PG is lacking. Here, we better define the PG proteome and provide a conceptual framework for further studies. The PG proteome from Arabidopsis (Arabidopsis thaliana) leaf chloroplasts was determined by mass spectrometry of isolated PGs and quantitative comparison with the proteomes of unfractionated leaves, thylakoids, and stroma. Scanning electron microscopy showed the purity and size distribution of the isolated PGs. Compared with previous PG proteome analyses, we excluded several proteins and identified six new PG proteins, including an M48 metallopeptidase and two Absence of bc1 complex (ABC1) atypical kinases, confirmed by immunoblotting. This refined PG proteome consisted of 30 proteins, including six ABC1 kinases and seven fibrillins together comprising more than 70% of the PG protein mass. Other fibrillins were located predominantly in the stroma or thylakoid and not in PGs; we discovered that this partitioning can be predicted by their isoelectric point and hydrophobicity. A genome-wide coexpression network for the PG genes was then constructed from mRNA expression data. This revealed a modular network with four distinct modules that each contained at least one ABC1K and/or fibrillin gene. Each module showed clear enrichment in specific functions, including chlorophyll degradation/senescence, isoprenoid biosynthesis, plastid proteolysis, and redox regulators and phosphoregulators of electron flow. We propose a new testable model for the PGs, in which sets of genes are associated with specific PG functions.

Workflow for large scale detection and validation of peptide modifications by RPLC-LTQ-Orbitrap: application to the Arabidopsis thaliana leaf proteome and an online modified peptide library.

Post-translational modifications (PTMs) of proteins add to the complexity of proteomes, thereby complicating the task of proteome characterization. An efficient strategy to identify this peptide heterogeneity is important for determination of protein function, as well as for mass spectrometry-based protein quantification. Furthermore, studies of allelic variation or single nucleotide polymorphisms (SNPs) at the proteome level, as well as mRNA editing, are increasingly relevant, but validation and determination of false positive rates are challenging. Here we describe an effective workflow for large scale PTM and amino acid substitution identification based on high resolution and high mass accuracy RPLC-MS data sets. A systematic validation strategy of PTMs using RPLC retention time shifts was implemented, and a decision tree for validation is presented. This workflow was applied to Arabidopsis proteome preparations; 1.5 million MS/MS spectra were processed resulting in 20% sequence assignments, with 5% from modified sequences and matching to 2904 proteins; this high assignment rate is in part due to the high quality spectral data. A searchable modified peptide library for Arabidopsis is available online at http://ppdb.tc.cornell.edu/. We discuss confidence in peptide and PTM assignment based on the acquired data set, as well as implications for quantitative analysis of physiologically induced and preparation-related modifications.

Subunits of the plastid ClpPR protease complex have differential contributions to embryogenesis, plastid biogenesis, and plant development in Arabidopsis.

The plastid ClpPR protease complex in Arabidopsis thaliana consists of five catalytic ClpP and four noncatalytic ClpR subunits. An extensive analysis of the CLPR family and CLPP5 is presented to address this complexity. Null alleles for CLPR2 and CLPR4 showed delayed embryogenesis and albino embryos, with seedling development blocked in the cotyledon stage; this developmental block was overcome under heterotrophic conditions, and seedlings developed into small albino to virescent seedlings. By contrast, null alleles for CLPP5 were embryo lethal. Thus, the ClpPR proteins make different functional contributions. To further test for redundancies and functional differences between the ClpR proteins, we overexpressed full-length cDNAs for ClpR1, R2, R3, R4 in clpr1, clpr2 and clpr4 mutants. This showed that overexpression of ClpR3 can complement for the loss of ClpR1, but not for the loss of ClpR2 or ClpR4, indicating that ClpR3 can functionally substitute ClpR1. By contrast, ClpR1, R2 and R4 could not substitute each other. Double mutants of weak CLPR1 and 2 alleles were seedling lethal, showing that a minimum concentration of different ClpR proteins is essential for Clp function. Microscopy and large-scale comparative leaf proteome analyses of a CLPR4 null allele demonstrate a central role of Clp protease in chloroplast biogenesis and protein homeostasis; substrates are discussed. Lack of transcriptional and translational feedback regulation within the CLPPR gene family indicates that regulation of Clp activity occurs through Clp complex assembly and substrate delivery.