The 29-nucleotide deletion in SARS-CoV: truncated versions of ORF8 are under purifying selection.

BACKGROUND: Accessory proteins have diverse roles in coronavirus pathobiology. One of them in SARS-CoV (the causative agent of the severe acute respiratory syndrome outbreak in 2002-2003) is encoded by the open reading frame 8 (ORF8). Among the most dramatic genomic changes observed in SARS-CoV isolated from patients during the peak of the pandemic in 2003 was the acquisition of a characteristic 29-nucleotide deletion in ORF8. This deletion cause splitting of ORF8 into two smaller ORFs, namely ORF8a and ORF8b. Functional consequences of this event are not entirely clear. RESULTS: Here, we performed evolutionary analyses of ORF8a and ORF8b genes and documented that in both cases the frequency of synonymous mutations was greater than that of nonsynonymous ones. These results suggest that ORF8a and ORF8b are under purifying selection, thus proteins translated from these ORFs are likely to be functionally important. Comparisons with several other SARS-CoV genes revealed that another accessory gene, ORF7a, has a similar ratio of nonsynonymous to synonymous mutations suggesting that ORF8a, ORF8b, and ORF7a are under similar selection pressure. CONCLUSIONS: Our results for SARS-CoV echo the known excess of deletions in the ORF7a-ORF7b-ORF8 complex of accessory genes in SARS-CoV-2. A high frequency of deletions in this gene complex might reflect recurrent searches in "functional space" of various accessory protein combinations that may eventually produce more advantageous configurations of accessory proteins similar to the fixed deletion in the SARS-CoV ORF8 gene.

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.

Leukocytosis and Expression of Bovine Leukemia Virus microRNAs in Cattle.

Bovine Leukemia Virus (BLV) is an established model for studying retroviral infections, in particular the infection by the human T-cell leukemia type 1 (HTLV-1) virus. Here, we quantified gene expression of several BLV-related genes: effector protein of T and NK-killer cells NK-lysin (Nklys), reverse BLV transcriptase pol, BLV receptor (blvr), and also key enzymes of the microRNA maturation, Dicer (dc1) and Argonaut (ago2). The differences in the expression of the above genes were compared between five groups: (1) BLV infected cows with high and (2) low lymphocyte count, (3) with and (4) without BLV microRNA expressions, and (5) cows without BLV infections (control group). As compared to control, infected cows with high lymphocyte count and BLV microRNA expression had significantly decreased Nklys gene expression and increased dc1 and ago2 gene expressions. Few infected animals without pol gene expression nevertheless transcribed BLV microRNA, while others with pol gene expression didn't transcribe BLV microRNA. Notably, Pol expression significantly (P < 0.05) correlated with dc1 expression. For infected animals, there were no direct correlations between the number of leukocytes and pol, Nklys, and BLV microRNA gene expressions. Blvr gene expression is typical for juvenile lymphocytes and decreases during terminal differentiation. Our data suggest that BLV infects primarily juvenile lymphocytes, which further divide into two groups. One group expresses BLV DNA and another one expressed BLV microRNA that decreases host immune response against cells, expressing BLV proteins. It is suspected that regulatory microRNAs play a significant role in the bovine leukemia infections, yet the precise mechanisms and targets of the microRNAs remain poorly defined. Vaccines that are currently in use have a low response rate. Understanding of microRNA regulatory mechanisms and targets would allow to develop more effective vaccines for retroviral infections.

Nucleotide Weight Matrices Reveal Ubiquitous Mutational Footprints of AID/APOBEC Deaminases in Human Cancer Genomes.

Cancer genomes accumulate nucleotide sequence variations that number in the tens of thousands per genome. A prominent fraction of these mutations is thought to arise as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases. These enzymes, collectively called activation induced deaminase (AID)/APOBECs, deaminate cytosines located within defined DNA sequence contexts. The resulting changes of the original C:G pair in these contexts (mutational signatures) provide indirect evidence for the participation of specific cytosine deaminases in a given cancer type. The conventional method used for the analysis of mutable motifs is the consensus approach. Here, for the first time, we have adopted the frequently used weight matrix (sequence profile) approach for the analysis of mutagenesis and provide evidence for this method being a more precise descriptor of mutations than the sequence consensus approach. We confirm that while mutational footprints of APOBEC1, APOBEC3A, APOBEC3B, and APOBEC3G are prominent in many cancers, mutable motifs characteristic of the action of the humoral immune response somatic hypermutation enzyme, AID, are the most widespread feature of somatic mutation spectra attributable to deaminases in cancer genomes. Overall, the weight matrix approach reveals that somatic mutations are significantly associated with at least one AID/APOBEC mutable motif in all studied cancers.

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.

Evasion of regulatory phosphorylation by an alternatively spliced isoform of Musashi2.

The Musashi family of RNA binding proteins act to promote stem cell self-renewal and oppose cell differentiation predominantly through translational repression of mRNAs encoding pro-differentiation factors and inhibitors of cell cycle progression. During tissue development and repair however, Musashi repressor function must be dynamically regulated to allow cell cycle exit and differentiation. The mechanism by which Musashi repressor function is attenuated has not been fully established. Our prior work indicated that the Musashi1 isoform undergoes site-specific regulatory phosphorylation. Here, we demonstrate that the canonical Musashi2 isoform is subject to similar regulated site-specific phosphorylation, converting Musashi2 from a repressor to an activator of target mRNA translation. We have also characterized a novel alternatively spliced, truncated isoform of human Musashi2 (variant 2) that lacks the sites of regulatory phosphorylation and fails to promote translation of target mRNAs. Consistent with a role in opposing cell cycle exit and differentiation, upregulation of Musashi2 variant 2 was observed in a number of cancers and overexpression of the Musashi2 variant 2 isoform promoted cell transformation. These findings indicate that alternately spliced isoforms of the Musashi protein family possess distinct functional and regulatory properties and suggest that differential expression of Musashi isoforms may influence cell fate decisions.

Conservation of the Exon-Intron Structure of Long Intergenic Non-Coding RNA Genes in Eutherian Mammals.

The abundance of mammalian long intergenic non-coding RNA (lincRNA) genes is high, yet their functions remain largely unknown. One possible way to study this important question is to use large-scale comparisons of various characteristics of lincRNA with those of protein-coding genes for which a large body of functional information is available. A prominent feature of mammalian protein-coding genes is the high evolutionary conservation of the exon-intron structure. Comparative analysis of putative intron positions in lincRNA genes from various mammalian genomes suggests that some lincRNA introns have been conserved for over 100 million years, thus the primary and/or secondary structure of these molecules is likely to be functionally important.

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.

Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients.

BACKGROUND: Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism. However, the analysis of such data is very demanding. In this study, we aimed to establish robust analysis procedures that can be used in clinical practice. METHODS: We studied RNA-seq data from triple-negative breast cancer patients. Specifically, we investigated the subsampling of RNA-seq data. RESULTS: The main results of our investigations are as follows: (1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices; (2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads; and (3) for an abrogated feature selection, higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values. CONCLUSIONS: Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.

A stress-activated, p38 mitogen-activated protein kinase-ATF/CREB pathway regulates posttranscriptional, sequence-dependent decay of target RNAs.

Broadly conserved, mitogen-activated/stress-activated protein kinases (MAPK/SAPK) of the p38 family regulate multiple cellular processes. They transduce signals via dimeric, basic leucine zipper (bZIP) transcription factors of the ATF/CREB family (such as Atf2, Fos, and Jun) to regulate the transcription of target genes. We report additional mechanisms for gene regulation by such pathways exerted through RNA stability controls. The Spc1 (Sty1/Phh1) kinase-regulated Atf1-Pcr1 (Mts1-Mts2) heterodimer of the fission yeast Schizosaccharomyces pombe controls the stress-induced, posttranscriptional stability and decay of sets of target RNAs. Whole transcriptome RNA sequencing data revealed that decay is associated nonrandomly with transcripts that contain an M26 sequence motif. Moreover, the ablation of an M26 sequence motif in a target mRNA is sufficient to block its stress-induced loss. Conversely, engineered M26 motifs can render a stable mRNA into one that is targeted for decay. This stress-activated RNA decay (SARD) provides a mechanism for reducing the expression of target genes without shutting off transcription itself. Thus, a single p38-ATF/CREB signal transduction pathway can coordinately induce (promote transcription and RNA stability) and repress (promote RNA decay) transcript levels for distinct sets of genes, as is required for developmental decisions in response to stress and other stimuli.

Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential.

In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using competitive gene set methods, it is imperative to apply a stringent gene filtering criterion. However, even when genes are filtered appropriately, for gene expression data from chips that do not provide a genome-scale coverage of the expression values of all mRNAs, this is not enough for GSEA, GSEArot and GAGE to ensure the statistical soundness of the applied procedure. For this reason, for biomedical and clinical studies, we strongly advice not to use GSEA, GSEArot and GAGE for such data sets.

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.

A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data.

In this paper, we present a Bayesian approach to estimate a chromosome and a disorder network from the Online Mendelian Inheritance in Man (OMIM) database. In contrast to other approaches, we obtain statistic rather than deterministic networks enabling a parametric control in the uncertainty of the underlying disorder-disease gene associations contained in the OMIM, on which the networks are based. From a structural investigation of the chromosome network, we identify three chromosome subgroups that reflect architectural differences in chromosome-disorder associations that are predictively exploitable for a functional analysis of diseases.

Evolution of gene expression and expression plasticity in long-term experimental populations of Drosophila melanogaster maintained under constant and variable ethanol stress.

Gene expression responds to the environment and can also evolve rapidly in response to altered selection regimes. Little is known, however, about the extent to which evolutionary adaptation to a particular type of stress involves changes in the within-generation ('plastic') responses of gene expression to the stress. We used microarrays to quantify gene expression plasticity in response to ethanol in laboratory populations of Drosophila melanogaster differing in their history of ethanol exposure. Two populations ('R' populations) were maintained on regular medium, two ('E') were maintained on medium supplemented with ethanol, and two ('M') were maintained in a mixed regime in which half of the population was reared on one medium type, and half on the other, each generation. After more than 300 generations, embryos from each population were collected and exposed to either ethanol or water as a control, and RNA was extracted from the larvae shortly after hatching. Nearly 2000 transcripts showed significant within-generation responses to ethanol exposure. Evolutionary history also affected gene expression: the E and M populations were largely indistinguishable in expression, but differed significantly in expression from the R populations for over 100 transcripts, the majority of which did not show plastic responses. Notably, in no case was the interaction between selection regime and ethanol exposure significant after controlling for multiple comparisons, indicating that adaptation to ethanol in the E and M populations did not involve substantial changes in gene expression plasticity. The results give evidence that expression plasticity evolves considerably more slowly than mean expression.

Statistical inference and reverse engineering of gene regulatory networks from observational expression data.

In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.

Network biology: a direct approach to study biological function.

In this paper we discuss the dualism of gene networks and their role in systems biology. We argue that gene networks (1) can serve as a conceptual framework, forming a fundamental level of a phenomenological description, and (2) are a means to represent and analyze data. The latter point does not only allow a systems analysis but is even amenable for a direct approach to study biological function. Here we focus on the clarity of our main arguments and conceptual meaning of gene networks, rather than the causal inference of gene networks from data. WIREs Syst Biol Med 2011 3 379-391 DOI: 10.1002/wsbm.134 For further resources related to this article, please visit the WIREs website.

Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets.

MOTIVATION: Recently, many univariate and several multivariate approaches have been suggested for testing differential expression of gene sets between different phenotypes. However, despite a wealth of literature studying their performance on simulated and real biological data, still there is a need to quantify their relative performance when they are testing different null hypotheses. RESULTS: In this article, we compare the performance of univariate and multivariate tests on both simulated and biological data. In the simulation study we demonstrate that high correlations equally affect the power of both, univariate as well as multivariate tests. In addition, for most of them the power is similarly affected by the dimensionality of the gene set and by the percentage of genes in the set, for which expression is changing between two phenotypes. The application of different test statistics to biological data reveals that three statistics (sum of squared t-tests, Hotelling's T(2), N-statistic), testing different null hypotheses, find some common but also some complementing differentially expressed gene sets under specific settings. This demonstrates that due to complementing null hypotheses each test projects on different aspects of the data and for the analysis of biological data it is beneficial to use all three tests simultaneously instead of focusing exclusively on just one.

Evolution and diversification of lamprey antigen receptors: evidence for involvement of an AID-APOBEC family cytosine deaminase.

The variable lymphocyte receptors (VLRs) of jawless vertebrates such as lamprey and hagfish are composed of highly diverse modular leucine-rich repeats. Each lymphocyte assembles a unique VLR by rearrangement of the germline gene. In the lamprey genome, we identify here about 850 distinct cassettes encoding leucine-rich repeat modules that serve as sequence templates for the hypervariable VLR repertoires. The data indicate a gene conversion-like process in VLR diversification. Genomic analysis suggested a link between the VLR and platelet glycoprotein receptors. Lamprey lymphocytes express two putative deaminases of the AID-APOBEC family that may be involved in VLR diversification, as indicated by in vitro mutagenesis and recombination assays. Vertebrate acquired immunity could have therefore originated from lymphocyte receptor diversification by an ancestral AID-like DNA cytosine deaminase.