Correlated miR-mRNA Expression Signatures of Mouse Hematopoietic Stem and Progenitor Cell Subsets Predict “Stemness” and “Myeloid” Interaction Networks

Several individual miRNAs (miRs) have been implicated as potent regulators of important processes during normal and malignant hematopoiesis. In addition, many miRs have been shown to fine-tune intricate molecular networks, in concert with other regulatory elements. In order to study hematopoietic networks as a whole, we first created a map of global miR expression during early murine hematopoiesis. Next, we determined the copy number per cell for each miR in each of the examined stem and progenitor cell types. As data is emerging indicating that miRs function robustly mainly when they are expressed above a certain threshold (∼100 copies per cell), our database provides a resource for determining which miRs are expressed at a potentially functional level in each cell type. Finally, we combine our miR expression map with matched mRNA expression data and external prediction algorithms, using a Bayesian modeling approach to create a global landscape of predicted miR-mRNA interactions within each of these hematopoietic stem and progenitor cell subsets. This approach implicates several interaction networks comprising a “stemness” signature in the most primitive hematopoietic stem cell (HSC) populations, as well as “myeloid” patterns associated with two branches of myeloid development.     

Mechanisms of Self-Organization of Cortical Microtubules in Plants Revealed by Computational Simulations

Microtubules confined to the two-dimensional cortex of elongating plant cells must form a parallel yet dispersed array transverse to the elongation axis for proper cell wall expansion. Some of these microtubules exhibit free minus-ends, leading to migration at the cortex by hybrid treadmilling. Collisions between microtubules can result in plus-end entrainment (“zippering”) or rapid depolymerization. Here, we present a computational model of cortical microtubule organization. We find that plus-end entrainment leads to self-organization of microtubules into parallel arrays, whereas catastrophe-inducing collisions do not. Catastrophe-inducing boundaries (e.g., upper and lower cross-walls) can tune the orientation of an ordered array to a direction transverse to elongation. We also find that changes in dynamic instability parameters, such as in mor1-1 mutants, can impede self-organization, in agreement with experimental data. Increased entrainment, as seen in clasp-1 mutants, conserves self-organization, but delays its onset and fails to demonstrate increased ordering. We find that branched nucleation at acute angles off existing microtubules results in distinctive sparse arrays and infer either that microtubule-independent or coparallel nucleation must dominate. Our simulations lead to several testable predictions, including the effects of reduced microtubule severing in katanin mutants.     

An effective data mining technique for reconstructing gene regulatory networks from time series expression data

Recent development in DNA microarray technologies has made the reconstruction of gene regulatory networks (GRNs) feasible. To infer the overall structure of a GRN, there is a need to find out how the expression of each gene can be affected by the others. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene in an unseen sample has any interactions with other genes, statistical verification of the reliability of the discovered interactions can be difficult. Furthermore, since the temporal ordering of the data is not taken into consideration, the directionality of regulation cannot be established using these existing techniques. To tackle these problems, we propose a data mining technique here. This technique makes use of a probabilistic inference approach to uncover interesting dependency relationships in noisy, high-dimensional time series expression data. It is not only able to determine if a gene is dependent on another but also whether or not it is activated or inhibited. In addition, it can predict how a gene would be affected by other genes even in unseen samples. For performance evaluation, the proposed technique has been tested with real expression data. Experimental results show that it can be very effective. The discovered dependency relationships can reveal gene regulatory relationships that could be used to infer the structures of GRNs.     

MiR‐140 modulates the inflammatory responses of Mycobacterium tuberculosis‐infected macrophages by targeting TRAF6

This study aimed to examine miR‐140 expression in clinical samples from tuberculosis (TB) patients and to explore the molecular mechanisms of miR‐140 in host‐bacterial interactions during Mycobacterium tuberculosis (M tb) infections. The miR‐140 expression and relevant mRNA expression were detected by quantitative real‐time PCR (qRT‐PCR); the protein expression levels were analysed by ELISA and western blot; M tb survival was measured by colony formation unit assay; potential interactions between miR‐140 and the 3′ untranslated region (UTR) of tumour necrosis factor receptor‐associated factor 6 (TRAF6) was confirmed by luciferase reporter assay. MiR‐140 was up‐regulated in the human peripheral blood mononuclear cells (PBMCs) from TB patients and in THP‐1 and U937 cells with M tb infection. Overexpression of miR‐140 promoted M tb survival; on the other hand, miR‐140 knockdown attenuated M tb survival. The pro‐inflammatory cytokines including interleukin 6, tumour necrosis‐α, interleukin‐1β and interferon‐γ were enhanced by M tb infection in THP‐1 and U937 cells. MiR‐140 overexpression reduced these pro‐inflammatory cytokines levels in THP‐1 and U937 cells with M tb infection; while knockdown of miR‐140 exerted the opposite actions. TRAF6 was identified to be a downstream target of miR‐140 and was negatively modulated by miR‐140. TRAF6 overexpression increased the pro‐inflammatory cytokines levels and partially restored the suppressive effects of miR‐140 overexpression on pro‐inflammatory cytokines levels in THP‐1 and U937 cells with M tb infection. In conclusion, our results implied that miR‐140 promoted M tb survival and reduced the pro‐inflammatory cytokines levels in macrophages with M tb infection partially via modulating TRAF6 expression.     

Impact of Natural Genetic Variation on Gene Expression Dynamics

DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.     

Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling

Quantifying heterogeneity in gene expression among single cells can reveal information inaccessible to cell-population averaged measurements. However, the expression level of many genes in single cells fall below the detection limit of even the most sensitive technologies currently available. One proposed approach to overcome this challenge is to measure random pools of k cells (e.g., 10) to increase sensitivity, followed by computational “deconvolution” of cellular heterogeneity parameters (CHPs), such as the biological variance of single-cell expression levels. Existing approaches infer CHPs using either single-cell or k-cell data alone, and typically within a single population of cells. However, integrating both single- and k-cell data may reap additional benefits, and quantifying differences in CHPs across cell populations or conditions could reveal novel biological information. Here we present a Bayesian approach that can utilize single-cell, k-cell, or both simultaneously to infer CHPs within a single condition or their differences across two conditions. Using simulated as well as experimentally generated single- and k-cell data, we found situations where each data type would offer advantages, but using both together can improve precision and better reconcile CHP information contained in single- and k-cell data. We illustrate the utility of our approach by applying it to jointly generated single- and k-cell data to reveal CHP differences in several key inflammatory genes between resting and inflammatory cytokine-activated human macrophages, delineating differences in the distribution of ‘ON’ versus ‘OFF’ cells and in continuous variation of expression level among cells. Our approach thus offers a practical and robust framework to assess and compare cellular heterogeneity within and across biological conditions using modern multiplexed technologies.     

Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.     

Extracting gene networks for low-dose radiation using graph theoretical algorithms

Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., “guilt-by-association”). We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response.     

Circulating MicroRNAs as Easy-to-Measure Aging Biomarkers in Older Breast Cancer Patients: Correlation with Chronological Age but Not with Fitness/Frailty Status

Circulating microRNAs (miRNAs) hold great promise as easily accessible biomarkers for diverse (patho)physiological processes, including aging. We have compared miRNA expression profiles in cell-free blood from older versus young breast cancer patients, in order to identify “aging miRNAs” that can be used in the future to monitor the impact of chemotherapy on the patient’s biological age. First, we assessed 175 miRNAs that may possibly be present in serum/plasma in an exploratory screening in 10 young and 10 older patients. The top-15 ranking miRNAs showing differential expression between young and older subjects were further investigated in an independent cohort consisting of another 10 young and 20 older subjects. Plasma levels of miR-20a-3p, miR-30b-5p, miR106b, miR191 and miR-301a were confirmed to show significant age-related decreases (all p≤0.004). The remaining miRNAs included in the validation study (miR-21, miR-210, miR-320b, miR-378, miR-423-5p, let-7d, miR-140-5p, miR-200c, miR-374a, miR376a) all showed similar trends as observed in the exploratory screening but these differences did not reach statistical significance. Interestingly, the age-associated miRNAs did not show differential expression between fit/healthy and non-fit/frail subjects within the older breast cancer cohort of the validation study and thus merit further investigation as true aging markers that not merely reflect frailty.     

MicroRNA-449a/b抑制人结肠癌细胞内Fra-1的表达

MicroRNAs (miRNAs) 是一类长度约为22 nt的非编码的调控性小RNA,它们在诸多的生命活动中发挥重要作用,如参与调控细胞的增殖、分化、凋亡以及肿瘤的发生发展. MicroRNA-449a/b (miR 449a/b) 是脊椎动物中进化保守的miRNA,作为抑癌基因,参与了许多癌症的发生过程,但其在结肠癌中的作用尚不清楚. 本文利用实时荧光定量技术研究了miR-449a/b在结肠癌组织中的表达. 利用双荧光素酶报告基因检测系统及Western印迹鉴定miR-449a/b的靶基因. 应用MTS法和Transwell分别检测miR-449a/b对结肠癌细胞增殖和迁移的影响. 检测组蛋白乙酰化酶抑制剂曲古菌素A (trichostatin A, TSA) 对结肠癌细胞中miR-449a/b表达的影响. 研究结果表明：与正常结肠组织相比,miR-449a/b在结肠癌组织中低表达;miR 449a/b能够结合到FRA-1 mRNA 3′-非翻译区 (3′-untranslated region, 3′-UTR),从而抑制结肠癌细胞HCT116内源Fra 1的表达;外源转染miR-449a/b明显抑制结肠癌细胞HCT116的增殖和迁移;并且TSA处理能够诱导结肠癌细胞HCT116中miR-449a/b的表达. 以上结果提示: miR-449a/b可能通过抑制靶基因Fra-1的表达,进而抑制结肠癌细胞的增殖和迁移．