Identification and stratification of systemic lupus erythematosus patients into two transcriptionally distinct clusters based on IFN-I signature.

OBJECTIVE: Despite the significant advancement in the understanding of the pathophysiology of systemic lupus erythematosus (SLE) variable clinical response to newer therapies remain a major concern, especially for patients with lupus nephritis and neuropsychiatric systemic lupus erythematosus (NPSLE). We performed this study with an objective to comprehensively characterize Indian SLE patients with renal and neuropsychiatric manifestation with respect to their gene signature, cytokine profile and immune cell phenotypes. METHODS: We characterized 68 Indian SLE subjects with diverse clinical profiles and disease activity and tried to identify differentially expressed genes and enriched pathways. To understand the temporal profile, same patients were followed at 6 and 12-months intervals. Additionally, auto-antibody profile, levels of various chemokines, cytokines and the proportion of different immune cells and their activation status were captured in these subjects. RESULTS: Multiple IFN-related pathways were enriched with significant increase in IFN-I gene signature in SLE patients as compared to normal healthy volunteers (NHV). We identified two transcriptionally distinct clusters within the same cohort of SLE patients with differential immune cell activation status, auto-antibody as well as plasma chemokines and cytokines profile. CONCLUSIONS: Identification of two distinct clusters of patients based on IFN-I signature provided new insights into the heterogeneity of underlying disease pathogenesis of Indian SLE cohort. Importantly, patient within those clusters retain their distinct expression dynamics of IFN-I signature over the time course of one year despite change in disease activity. This study will guide clinicians and researchers while designing future clinical trials on Indian SLE cohort.

Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.

Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data.

Functional divergence of the miRNA transcriptome at the onset of Drosophila metamorphosis.

MicroRNAs (miRNAs) are endogenous RNA molecules that regulate gene expression posttranscriptionally. To date, the emergence of miRNAs and their patterns of sequence evolution have been analyzed in great detail. However, the extent to which miRNA expression levels have evolved over time, the role different evolutionary forces play in shaping these changes, and whether this variation in miRNA expression can reveal the interplay between miRNAs and mRNAs remain poorly understood. This is especially true for miRNA expressed during key developmental transitions. Here, we assayed miRNA expression levels immediately before (≥18BPF [18 h before puparium formation]) and after (PF) the increase in the hormone ecdysone responsible for triggering metamorphosis. We did so in four strains of Drosophila melanogaster and two closely related species. In contrast to their sequence conservation, approximately 25% of miRNAs analyzed showed significant within-species variation in male expression levels at ≥18BPF and/or PF. Additionally, approximately 33% showed modifications in their pattern of expression bias between developmental timepoints. A separate analysis of the ≥18BPF and PF stages revealed that changes in miRNA abundance accumulate linearly over evolutionary time at PF but not at ≥18BPF. Importantly, ≥18BPF-enriched miRNAs showed the greatest variation in expression levels both within and between species, so are the less likely to evolve under stabilizing selection. Functional attributes, such as expression ubiquity, appeared more tightly associated with lower levels of miRNA expression polymorphism at PF than at ≥18BPF. Furthermore, ≥18BPF- and PF-enriched miRNAs showed opposite patterns of covariation in expression with mRNAs, which denoted the type of regulatory relationship between miRNAs and mRNAs. Collectively, our results show contrasting patterns of functional divergence associated with miRNA expression levels during Drosophila ontogeny.

Mixture models of nucleotide sequence evolution that account for heterogeneity in the substitution process across sites and across lineages.

Molecular phylogenetic studies of homologous sequences of nucleotides often assume that the underlying evolutionary process was globally stationary, reversible, and homogeneous (SRH), and that a model of evolution with one or more site-specific and time-reversible rate matrices (e.g., the GTR rate matrix) is enough to accurately model the evolution of data over the whole tree. However, an increasing body of data suggests that evolution under these conditions is an exception, rather than the norm. To address this issue, several non-SRH models of molecular evolution have been proposed, but they either ignore heterogeneity in the substitution process across sites (HAS) or assume it can be modeled accurately using the distribution. As an alternative to these models of evolution, we introduce a family of mixture models that approximate HAS without the assumption of an underlying predefined statistical distribution. This family of mixture models is combined with non-SRH models of evolution that account for heterogeneity in the substitution process across lineages (HAL). We also present two algorithms for searching model space and identifying an optimal model of evolution that is less likely to over- or underparameterize the data. The performance of the two new algorithms was evaluated using alignments of nucleotides with 10 000 sites simulated under complex non-SRH conditions on a 25-tipped tree. The algorithms were found to be very successful, identifying the correct HAL model with a 75% success rate (the average success rate for assigning rate matrices to the tree's 48 edges was 99.25%) and, for the correct HAL model, identifying the correct HAS model with a 98% success rate. Finally, parameter estimates obtained under the correct HAL-HAS model were found to be accurate and precise. The merits of our new algorithms were illustrated with an analysis of 42 337 second codon sites extracted from a concatenation of 106 alignments of orthologous genes encoded by the nuclear genomes of Saccharomyces cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, S. castellii, S. kluyveri, S. bayanus, and Candida albicans. Our results show that second codon sites in the ancestral genome of these species contained 49.1% invariable sites, 39.6% variable sites belonging to one rate category (V1), and 11.3% variable sites belonging to a second rate category (V2). The ancestral nucleotide content was found to differ markedly across these three sets of sites, and the evolutionary processes operating at the variable sites were found to be non-SRH and best modeled by a combination of eight edge-specific rate matrices (four for V1 and four for V2). The number of substitutions per site at the variable sites also differed markedly, with sites belonging to V1 evolving slower than those belonging to V2 along the lineages separating the seven species of Saccharomyces. Finally, sites belonging to V1 appeared to have ceased evolving along the lineages separating S. cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, and S. bayanus, implying that they might have become so selectively constrained that they could be considered invariable sites in these species.

MicroRNA-155 as an inducer of apoptosis and cell differentiation in Acute Myeloid Leukaemia.

BACKGROUND: Acute myeloid leukaemia (AML) is characterised by the halt in maturation of myeloid progenitor cells, combined with uncontrolled proliferation and abnormal survival, leading to the accumulation of immature blasts. In many subtypes of AML the underlying causative genetic insults are not fully described. MicroRNAs are known to be dysregulated during oncogenesis. Overexpression of miR-155 is associated with some cancers, including haematological malignancies, and it has been postulated that miR-155 has an oncogenic role. This study investigated the effects of modulating miR-155 expression in human AML cells, and its mechanism of action. RESULTS: Analysis of miR-155 expression patterns in AML patients found that Fms-like tyrosine kinase 3 (FLT3)-wildtype AML has the same expression level as normal bone marrow, with increased expression restricted to AML with the FLT3-ITD mutation. Induction of apoptosis by cytarabine arabinoside or myelomonocytic differentiation by 1,23-dihydroxyvitaminD3 in FLT3-wildtype AML cells led to upregulated miR-155 expression. Knockdown of miR-155 by locked nucleic acid antisense oligonucleotides in the FLT3-wildtype AML cells conferred resistance to cytarabine arabinoside induced apoptosis and suppressed the ability of cells to differentiate.Ectopic expression of miR-155 in FLT3-wildtype AML cells led to a significant gain of myelomonocytic markers (CD11b, CD14 and CD15), increase in apoptosis (AnnexinV binding), decrease in cell growth and clonogenic capacity.In silico target prediction identified a number of putative miR-155 target genes, and the expression changes of key transcription regulators of myeloid differentiation and apoptosis (MEIS1, GF1, cMYC, JARID2, cJUN, FOS, CTNNB1 and TRIB2) were confirmed by PCR. Assessment of expression of apoptosis-related proteins demonstrated a marked increase in cleaved caspase-3 expression confirming activation of the apoptosis cascade. CONCLUSIONS: This study provides evidence for an anti-leukaemic role for miR-155 in human FLT3-wildtype AML, by inducing cell apoptosis and myelomonocytic differentiation, which is in contrast to its previously hypothesized role as an oncogene. This highlights the complexity of gene regulation by microRNAs that may have tumour repressor or oncogenic effects depending on disease context or tissue type.

Fluid intake and all-cause mortality, cardiovascular mortality and kidney function: a population-based longitudinal cohort study.

BACKGROUND: Drinking eight glasses of fluid or water each day is widely believed to improve health, but evidence is sparse and conflicting. We aimed to investigate the association between fluid consumption and long-term mortality and kidney function. METHODS: We conducted a longitudinal analysis within a prospective, population-based cohort study of 3858 men and women aged 49 years or older residing in Australia. Daily fluid intake from food and beverages not including water was measured using a food frequency questionnaire. We did multivariable adjusted Cox proportional hazard models for all-cause and cardiovascular mortality and a boot-strapping procedure for estimated glomerular filtration rate (eGFR). RESULTS: Upper and lower quartiles of daily fluid intake corresponded to >3 L and <2 L, respectively. During a median follow-up of 13.1 years (total 43 093 years at risk), 1127 deaths (26.1 per 1000 years at risk) including 580 cardiovascular deaths (13.5 per 1000 years at risk) occurred. Daily fluid intake (per 250 mL increase) was not associated with all-cause [adjusted hazard ratio (HR) 0.99 (95% CI 0.98-1.01)] or cardiovascular mortality [HR 0.98 (95% CI 0.95-1.01)]. Overall, eGFR reduced by 2.2 mL/min per 1.73 m(2) (SD 10.9) in the 1207 (31%) participants who had repeat creatinine measurements and this was not associated with fluid intake [adjusted regression coefficient 0.06 mL/min/1.73 m(2) per 250 mL increase (95% CI -0.03 to 0.14)]. CONCLUSIONS: Fluid intake from food and beverages excluding water is not associated with improved kidney function or reduced mortality.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations.

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

miR-10a is aberrantly overexpressed in Nucleophosmin1 mutated acute myeloid leukaemia and its suppression induces cell death.

BACKGROUND: Acute myeloid leukaemia (AML) with nucleophosmin-1 (NPM1) mutation is a major subtype of AML. The NPM1 mutation induces a myeloproliferative disorder, but evidence indicates that other insults are necessary for the development of AML. We utilised microRNA microarrays and functional assays to determine if microRNA dysregulation could be involved in the pathogenesis of in NPM1 mutated (NPM1mut)-AML. RESULTS: We used a stringent locked nucleic acid (LNA) based microRNA microarray platform to profile bone marrow samples of patients with normal karyotype AML. A panel of five microRNAs dichotomised AML patients according to their NPM1 mutational status. miR-10a, let-7b and let-7c were significantly over-expressed, while miR-130a and miR-335 were under-expressed in NPM1mut-AML when compared to NPM1wildtype-AML. Of these, miR-10a is the most differentially expressed in NPM1mut-AML versus NPM1wildtype-AML (> 10 fold higher as confirmed by qRT-PCR). To investigate the functions of miR-10a, the OCI-AML3 cell line was utilised, which is the only commercially available cell line bearing NPM1mut. OCI-AML3 cells were firstly demonstrated to have a similarly high miR-10a expression to primary NPM1mut-AML patient samples. Inhibition of miR-10a expression by miRCURY LNA Inhibitors (Exiqon) in these cells resulted in increased cell death as assessed by MTS, cell cycle and Annexin-V assays and reduced clonogenic capacity, indicative of an involvement in leukaemic cell survival. In silico filtering of bioinformatically predicted targets of miR-10a identified a number of potential mRNA targets with annotated functions in haematopoiesis, cell growth and apoptosis. Lucferase reporter assays confirmed a number of these putative tumorogenic genes that are miR-10a suppressible including KLF4 and RB1CC1. This provides a potential mechanism for the pathogenic role of miR-10a in NPM1mut-AML. CONCLUSIONS: This study provides, for the first time, in vitro evidence of a pro-survival role of miR-10a in NPM1mut-AML, that it may contribute to the pathogenesis of NPM1mut-AML and identifies putative tumorogenic targets.

Reducing model complexity of the general Markov model of evolution.

The selection of an optimal model for data analysis is an important component of model-based molecular phylogenetic studies. Owing to the large number of Markov models that can be used for data analysis, model selection is a combinatorial problem that cannot be solved by performing an exhaustive search of all possible models. Currently, model selection is based on a small subset of the available Markov models, namely those that assume the evolutionary process to be globally stationary, reversible, and homogeneous. This forces the optimal model to be time reversible even though the actual data may not satisfy these assumptions. This problem can be alleviated by including more complex models during the model selection. We present a novel heuristic that evaluates a small fraction of these complex models and identifies the optimal model.

Two stationary nonhomogeneous Markov models of nucleotide sequence evolution.

The general Markov model (GMM) of nucleotide substitution does not assume the evolutionary process to be stationary, reversible, or homogeneous. The GMM can be simplified by assuming the evolutionary process to be stationary. A stationary GMM is appropriate for analyses of phylogenetic data sets that are compositionally homogeneous; a data set is considered to be compositionally homogeneous if a statistical test does not detect significant differences in the marginal distributions of the sequences. Though the general time-reversible (GTR) model assumes stationarity, it also assumes reversibility and homogeneity. We propose two new stationary and nonhomogeneous models--one constrains the GMM to be reversible, whereas the other does not. The two models, coupled with the GTR model, comprise a set of nested models that can be used to test the assumptions of reversibility and homogeneity for stationary processes. The two models are extended to incorporate invariable sites and used to analyze a seven-taxon hominoid data set that displays compositional homogeneity. We show that within the class of stationary models, a nonhomogeneous model fits the hominoid data better than the GTR model. We note that if one considers a wider set of models that are not constrained to be stationary, then an even better fit can be obtained for the hominoid data. However, the methods for reducing model complexity from an extremely large set of nonstationary models are yet to be developed.

Identification of microRNA-mRNA modules using microarray data.

BACKGROUND: MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression and are involved in numerous cellular processes. Consequently, miRNAs are an important component of gene regulatory networks and an improved understanding of miRNAs will further our knowledge of these networks. There is a many-to-many relationship between miRNAs and mRNAs because a single miRNA targets multiple mRNAs and a single mRNA is targeted by multiple miRNAs. However, most of the current methods for the identification of regulatory miRNAs and their target mRNAs ignore this biological observation and focus on miRNA-mRNA pairs. RESULTS: We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest. CONCLUSIONS: Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.

Expression profiling of cytogenetically normal acute myeloid leukemia identifies microRNAs that target genes involved in monocytic differentiation.

MicroRNAs are short ribonucleic acids (RNAs) that play an important role in many aspects of cellular biology such as differentiation and apoptosis, due to their role in the regulation of gene expression. Using microRNA microarrays, we characterized the microRNA gene expression of 27 patients with acute myeloid leukemia (AML) with normal cytogenetics, focusing on the microRNAs differentially expressed between the M1 and M5 French-American-British (FAB) subtypes. An accurate delineation of these two AML entities was observed based on the expression of 12 microRNAs. We hypothesized that these microRNAs may potentially be involved in the differentiation block of M1 blasts and consequently monocytic differentiation. Using publically available mRNA data and microRNA target prediction software, we identified several key myeloid factors that may be targeted by our candidate microRNAs. The expression changes of the candidate microRNAs during monocytic differentiation of AML cell lines treated with Vitamin D and phorbol 12-myristate 13-acetate were examined. All six candidate microRNAs were significantly down-regulated over the time course by quantitative reverse transcriptase polymerase chain reaction suggesting a link between these microRNAs and monocytic differentiation. To further characterize these microRNAs, we confirmed by luciferase assays that these microRNA target several key myeloid factors such as MAFB, IRF8, and KLF4 identifying a possible mechanism for the control of differentiation by these microRNAs.

Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data.

Over the past decade, a class of small RNA molecules called microRNAs (miRNAs) has been shown to regulate gene expression at the post-transcription stage. While early work focused on the identification of miRNAs using a combination of experimental and computational techniques, subsequent studies have focused on identification of miRNA-target mRNA pairs as each miRNA can have hundreds of mRNA targets. The experimental validation of some miRNAs as oncogenic has provided further motivation for research in this area. In this article we propose an odds-ratio (OR) statistic for identification of regulatory miRNAs. It is based on integrative analysis of matched miRNA and mRNA time-course microarray data. The OR-statistic was used for (i) identification of miRNAs with regulatory potential, (ii) identification of miRNA-target mRNA pairs and (iii) identification of time lags between changes in miRNA expression and those of its target mRNAs. We applied the OR-statistic to a cancer data set and identified a small set of miRNAs that were negatively correlated to mRNAs. A literature survey revealed that some of the miRNAs that were predicted to be regulatory, were indeed oncogenic or tumor suppressors. Finally, some of the predicted miRNA targets have been shown to be experimentally valid.

Nonadaptive molecular evolution of seminal fluid proteins in Drosophila.

Seminal fluid proteins (SFPs) are a group of reproductive proteins that are among the most evolutionarily divergent known. As SFPs can impact male and female fitness, these proteins have been proposed to evolve under postcopulatory sexual selection (PCSS). However, the fast change of the SFPs can also result from nonadaptive evolution, and the extent to which selective constraints prevent SFPs rapid evolution remains unknown. Using intra- and interspecific sequence information, along with genomics and functional data, we examine the molecular evolution of approximately 300 SFPs in Drosophila. We found that 50-57% of the SFP genes, depending on the population examined, are evolving under relaxed selection. Only 7-12% showed evidence of positive selection, with no evidence supporting other forms of PCSS, and 35-37% of the SFP genes were selectively constrained. Further, despite associations of positive selection with gene location on the X chromosome and protease activity, the analysis of additional genomic and functional features revealed their lack of influence on SFPs evolving under positive selection. Our results highlight a lack of sufficient evidence to claim that most SFPs are driven to evolve rapidly by PCSS while identifying genomic and functional attributes that influence different modes of SFPs evolution.

Intraspecific Transcriptome Variation and Sex-Biased Expression in Anopheles arabiensis.

The magnitude and functional patterns of intraspecific transcriptional variation in the anophelines, including those of sex-biased genes underlying sex-specific traits relevant for malaria transmission, remain understudied. As a result, how changes in expression levels drive adaptation in these species is poorly understood. We sequenced the female, male, and larval transcriptomes of three populations of Anopheles arabiensis from Burkina Faso. One-third of the genes were differentially expressed between populations, often involving insecticide resistance-related genes in a sample type-specific manner, and with the females showing the largest number of differentially expressed genes. At the genomic level, the X chromosome appears depleted of differentially expressed genes compared with the autosomes, chromosomes harboring inversions do not exhibit evidence for enrichment of such genes, and genes that are top contributors to functional enrichment patterns of population differentiation tend to be clustered in the genome. Further, the magnitude of variation for the sex expression ratio across populations did not substantially differ between male- and female-biased genes, except for some populations in which male-limited expressed genes showed more variation than their female counterparts. In fact, female-biased genes exhibited a larger level of interpopulation variation than male-biased genes, both when assayed in males and females. Beyond uncovering the extensive adaptive potential of transcriptional variation in An. Arabiensis, our findings suggest that the evolutionary rate of changes in expression levels on the X chromosome exceeds that on the autosomes, while pointing to female-biased genes as the most variable component of the An. Arabiensis transcriptome.

SeqVis: a tool for detecting compositional heterogeneity among aligned nucleotide sequences.

Compositional heterogeneity is a poorly appreciated attribute of aligned nucleotide and amino acid sequences. It is a common property of molecular phylogenetic data, and it has been found to occur across sequences and/or across sites. Most molecular phylogenetic methods assume that the sequences have evolved under globally stationary, reversible, and homogeneous conditions, implying that the sequences should be compositionally homogeneous. The presence of the above-mentioned compositional heterogeneity implies that the sequences must have evolved under more general conditions than is commonly assumed. Consequently, there is a need for reliable methods to detect under what conditions alignments of nucleotides or amino acids may have evolved. In this chapter, we describe one such program. SeqVis is designed to survey aligned nucleotide sequences. We discuss pros-et-cons of this program in the context of other methods to detect compositional heterogeneity and violated phylogenetic assumptions. The benefits provided by SeqVis are demonstrated in two studies of alignments of nucleotides, one of which contained 7542 nucleotides from 53 species.

Phylogenetic model evaluation.

Most phylogenetic methods are model-based and depend on Markov models designed to approximate the evolutionary rates between nucleotides or amino acids. When Markov models are selected for analysis of alignments of these characters, it is assumed that they are close approximations of the evolutionary processes that gave rise to the data. A variety of methods have been developed for estimating the fit of Markov models, and some of these methods are now frequently used for the selection of Markov models. In a growing number of cases, however, it appears that the investigators have used the model-selection methods without acknowledging their inherent shortcomings. This chapter reviews the issue of model selection and model evaluation.

A species-specific multigene family mediates differential sperm displacement in Drosophila melanogaster.

Sperm competition is a postcopulatory sexual selection mechanism in species in which females mate with multiple males. Despite its evolutionary relevance in shaping male traits, the genetic mechanisms underlying sperm competition are poorly understood. A recently originated multigene family specific to Drosophila melanogaster, Sdic, is important for the outcome of sperm competition in doubly mated females, although the mechanistic nature of this phenotype remained unresolved. Here, we compared doubly mated females, second mated to either Sdic knockout or nonknockout males, and directly visualize sperm dynamics in the female reproductive tract. We found that a less effective removal of first-to-mate male's sperm within the female's sperm storage organs is consistent with a reduced sperm competitive ability of the Sdic knockout males. Our results highlight the role young genes can play in driving the evolution of sperm competition.

Identifying Optimal Models of Evolution.

Most phylogenetic methods are model-based and depend on models of evolution designed to approximate the evolutionary processes. Several methods have been developed to identify suitable models of evolution for phylogenetic analysis of alignments of nucleotide or amino acid sequences and some of these methods are now firmly embedded in the phylogenetic protocol. However, in a disturbingly large number of cases, it appears that these models were used without acknowledgement of their inherent shortcomings. In this chapter, we discuss the problem of model selection and show how some of the inherent shortcomings may be identified and overcome.