Universality and flexibility in gene expression from bacteria to human

Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.

Power law in a bounded range: Estimating the lower and upper bounds from sample data

Power law distributions are widely observed in chemical physics, geophysics, biology, and beyond. The independent variable x of these distributions has an obligatory lower bound and, in many cases, also an upper bound. Estimating these bounds from sample data is notoriously difficult, with a recent method involving O(N3) operations, where N denotes sample size. Here I develop an approach for estimating the lower and upper bounds that involve O(N) operations. The approach centers on calculating the mean values, x̂min and x̂max, of the smallest x and the largest x in N-point samples. A fit of x̂min or x̂max as a function of N yields the estimate for the lower or upper bound. Application to synthetic data demonstrates the accuracy and reliability of this approach.

Emergent statistical laws in single-cell transcriptomic data

Large-scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology, or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique.

Phenotype-specific estimation of metabolic fluxes using gene expression data

A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.

Gene product diversity: adaptive or not?

One gene does not equal one RNA or protein. The genomic revolution has revealed numerous different RNA and protein molecules that can be produced from one gene, such as circular RNAs generated by back-splicing, proteins with residues mismatching the genomic encoding because of RNA editing, and proteins extended in the C terminus via stop codon readthrough in translation. Are these diverse products results of exquisite gene regulations or imprecise biological processes? While there are cases where the gene product diversity appears beneficial, genome-scale patterns suggest that much of this diversity arises from nonadaptive, molecular errors. This finding has important implications for studying the functions of diverse gene products and for understanding the fundamental properties and evolution of cellular life.

Identifying toggle genes from transcriptome-wide scatter: A new perspective for biological regulation

The study of gene expression variability, especially for cancer and cell differentiation studies, has become important. Here, we investigate transcriptome-wide scatter of 23 cell types and conditions across different levels of biological complexity. We focused on genes that act like toggle switches between pairwise replicates of the same cell type, i.e. genes expressed in one replicate and not expressed in the other, sometimes also referred as ON/OFF genes. The proportion of these toggle genes dramatically increases from unicellular to multicellular organization, especially for development and cancer cells. A relevant portion of toggle switches are non-coding genes: in unicellular systems the most represented classes are tRNA and rRNA, while multicellular systems more frequently show lncRNA, sncRNA and pseudogenes. Notably, disease associated microRNAs (miRNAs), pseudogenes and numerous uncharacterized transcripts are present in both development and cancer cells. On top of the known intrinsic and extrinsic factors, our work indicates toggle genes as a novel collective component creating transcriptome-wide variability. This requires further investigation for elucidating both evolutionary and disease processes.

PsiNorm: a scalable normalization for single-cell RNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable.

RESULTS

Here, we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here, we show that the Pareto distribution well resembles scRNA-seq data, especially those coming from platforms that use unique molecular identifiers. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm against seven other methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover, PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized.

AVAILABILITY AND IMPLEMENTATION

PsiNorm is implemented in the scone Bioconductor package and available at https://bioconductor.org/packages/scone/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-throughput transcriptome sequencing and comparative analysis of Escherichia coli and Schizosaccharomyces pombe in respiratory and fermentative growth

In spite of increased complexity in eukaryotes compared to prokaryotes, several basic metabolic and regulatory processes are conserved. Here we explored analogies in the eubacteria Escherichia coli and the unicellular fission yeast Schizosaccharomyces pombe transcriptomes under two carbon sources: 2% glucose; or a mix of 2% glycerol and 0.2% sodium acetate using the same growth media and growth phase. Overall, twelve RNA-seq libraries were constructed. A total of 593 and 860 genes were detected as differentially expressed for E. coli and S. pombe, respectively, with a log2 of the Fold Change ≥ 1 and False Discovery Rate ≤ 0.05. In aerobic glycolysis, most of the expressed genes were associated with cell proliferation in both organisms, including amino acid metabolism and glycolysis. In contrast in glycerol/acetate condition, genes related to flagellar assembly and membrane proteins were differentially expressed such as the general transcription factors fliA, flhD, flhC, and flagellum assembly genes were detected in E. coli, whereas in S. pombe genes for hexose transporters, integral membrane proteins, galactose metabolism, and ncRNAs related to cellular stress were overexpressed. In general, our study shows that a conserved "foraging behavior" response is observed in these eukaryotic and eubacterial organisms in gluconeogenic carbon sources.

ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes

Differential expressed (DE) genes analysis is valuable for understanding comparative transcriptomics between cells, conditions or time evolution. However, the predominant way of identifying DE genes is to use arbitrary threshold fold or expression changes as cutoff. Here, we developed a more objective method, Scatter Overlay or ScatLay, to extract and graphically visualize DE genes across any two samples by utilizing their pair-wise scatter or transcriptome-wide noise, while factoring replicate variabilities. We tested ScatLay for 3 cell types: between time points for Escherichia coli aerobiosis and Saccharomyces cerevisiae hypoxia, and between untreated and Etomoxir treated Mus Musculus embryonic stem cell. As a result, we obtain 1194, 2061 and 2932 DE genes, respectively. Next, we compared these data with two widely used current approaches (DESeq2 and NOISeq) with typical twofold expression changes threshold, and show that ScatLay reveals significantly larger number of DE genes. Hence, our method provides a wider coverage of DE genes, and will likely pave way for finding more novel regulatory genes in future works.

Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlights the power of large data sets to identify the diversity of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages, functions, or subpopulations.

Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.

The highly conserved chromosomal periodicity of transcriptomes and the correlation of its amplitude with the growth rate in Escherichia coli

The growth rate, representing the fitness of a bacterial population, is determined by the transcriptome. Chromosomal periodicity, which is known as the periodic spatial pattern of a preferred chromosomal distance in microbial genomes, is a representative overall feature of the transcriptome; however, whether and how it is associated with the bacterial growth rate are unknown. To address these questions, we analysed a total of 213 transcriptomes of multiple Escherichia coli strains growing in an assortment of culture conditions varying in terms of temperature, nutrition level and osmotic pressure. Intriguingly, Fourier transform analyses of the transcriptome identified a common chromosomal periodicity of transcriptomes, which was independent of the variation in genomes and environments. In addition, fitting of the data to a theoretical model, we found that the amplitudes of the periodic transcriptomes were significantly correlated with the growth rates. These results indicated that the amplitude of periodic transcriptomes is a parameter representing the global pattern of gene expression in correlation with the bacterial growth rate. Thus, our study provides a novel parameter for evaluating the adaptiveness of a growing bacterial population and quantitatively predicting the growth dynamics according to the global expression pattern.

Diel Oscillation of Microbial Gene Transcripts Declines With Depth in Oligotrophic Ocean Waters

Diel oscillations in primary and secondary production, growth, metabolic activity, and gene expression commonly occur in marine microbial communities in ocean surface waters. Diel periodicity of gene transcription has been demonstrated in photoautotrophic and heterotrophic microbes in both coastal and open ocean environments. To better define the spatiotemporal distribution and patterns of these daily oscillations, we investigated how diel periodicity in gene transcripts changed with depth from the surface waters to the upper mesopelagic. We postulated that diel oscillation of transcript abundances would diminish at greater depths across the collective microbial community due to decreasing light availability. The results showed that the number and total proportion of gene transcripts and taxa exhibiting diel periodicity were greatest in the shallow sunlit mixed layer, diminished rapidly with increasing depth to the base of the euphotic zone, and could not be detected in the mesopelagic. The results confirmed an overall decrease in microbial diel transcript oscillation with depth through the euphotic zone and suggested a relationship between abundance of diel oscillating transcripts and the daily integrated light exposure experienced by planktonic microbes in the water column. Local dissolved macronutrient concentration also appeared to influence the diel transcriptional patterns of specific microbial genes. The diminishing diel transcript oscillations found at increasing depths suggest that diel patterns of other microbial processes and interactions may likewise be attenuated at depth.

Advances in Entamoeba histolytica Biology Through Transcriptomic Analysis

A large number of transcriptome-level studies in Entamoeba histolytica, the protozoan parasite that causes amoebiasis, have investigated gene expression patterns to help understand the pathology and biology of the organism. They have compared virulent and avirulent strains in lab culture and after tissue invasion, cells grown under different stress conditions, response to anti-amoebic drug treatments, and gene expression changes during the process of encystation. These studies have revealed interesting molecules/pathways that will help increase our mechanistic understanding of differentially expressed genes during growth perturbations and tissue invasion. Some of the important insights obtained from transcriptome studies include the observations that regulation of carbohydrate metabolism may be an important determinant for tissue invasion, while the novel up-regulated genes during encystation include phospholipase D, and meiotic genes, suggesting the possibility of meiosis during the process. Classification of genes according to expression levels showed that amongst the highly transcribed genes in cultured E. histolytica trophozoites were some virulence factors, raising the question of the role of these factors in normal parasite growth. Promoter motifs associated with differential gene expression and regulation were identified. Some of these motifs associated with high gene expression were located downstream of start codon, and were required for efficient transcription. The listing of E. histolytica genes according to transcript expression levels will help us determine the scale of post-transcriptional regulation, and the possible roles of predicted promoter motifs. The small RNA transcriptome is a valuable resource for detailed structural and functional analysis of these molecules and their regulatory roles. These studies provide new drug targets and enhance our understanding of gene regulation in E. histolytica.

Self-organizing scale-free patterns in a phase-modulated periodic connecting system

OBJECTIVE

The regularity of scale-free patterns in rank-size relations has been observed in word frequency, city size distribution, firm size distribution, and gene expression. Because of the common emergence of this regularity, understanding its mechanisms has been of great interest. For obtaining the scale-free pattern regularity, various models based on the rich-get-richer mechanism have been proposed; however, the overarching procedure of searching for the "rich" is in disagreement with the locally interacting behaviors seen in the aforementioned natural and social phenomena.

RESULTS

We implemented a computational model of a resource distribution system inspired by observations of word connectivity, which is created by local constraints with periodic and phase modulatory features. Here, we empirically demonstrated that a phase-modulated periodic connecting system can reach a dynamic equilibrium state as the most probable case, with the self-organizing scale-free patterns. The regularity could be a result of the configurational balance in spatiotemporal inequity during the resource distribution process with an adaptive constrained connectivity. Our results suggest that investigations of interferences of oscillating fluctuations in the system will elucidate the autoregulatory dynamic behavior.

Gene Expression Order Attributed to Genome Reduction and the Steady Cellular State in Escherichia coli

Transcriptomes not only reflect the growth status but also link to the genome in bacteria. To investigate if and how genome or cellular state changes contribute to the gene expression order, the growth profile-associated transcriptomes of an assortment of genetically differentiated Escherichia coli either exponentially growing under varied conditions or in response to environmental disturbance were analyzed. A total of 168 microarray data sets representing 56 transcriptome variations, were categorized by genome size (full length or reduced) and cellular state (steady or unsteady). At the genome-wide level, the power-law distribution of gene expression was found to be significantly disturbed by the genome size but not the cellular state. At the regulatory network level, more networks with improved coordination of growth rates were observed in genome reduction than at the steady state. At the single-gene level, both genome reduction and steady state increased the correlation of gene expression to growth rate, but the enriched gene categories with improved correlations were different. These findings not only illustrate the order of gene expression attributed to genome reduction and steady cellular state but also indicate that the accessory sequences acquired during genome evolution largely participated in the coordination of transcriptomes to growth fitness.

A power law distribution of metabolite abundance levels in mice regardless of the time and spatial scale of analysis

Biomolecule abundance levels change with the environment and enable a living system to adapt to the new conditions. Although, the living system maintains at least some characteristics, e.g. homeostasis. One of the characteristics maintained by a living system is a power law distribution of biomolecule abundance levels. Previous studies have pointed to a universal characteristic of biochemical reaction networks, with data obtained from lysates of multiple cells. As a result, the spatial scale of the data related to the power law distribution of biomolecule abundance levels is not clear. In this study, we researched the scaling law of metabolites in mouse tissue with a spatial scale of quantification that was changed stepwise between a whole-tissue section and a single-point analysis (25 μm). As a result, metabolites in mouse tissues were found to follow the power law distribution independently of the spatial scale of analysis. Additionally, we tested the temporal changes by comparing data from younger and older mice. Both followed similar power law distributions, indicating that metabolite composition is not diversified by aging to disrupt the power law distribution. The power law distribution of metabolite abundance is thus a robust characteristic of a living system regardless of time and space.

Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes downstream normalization and comparative analysis

BACKGROUND

Though earlier works on modelling transcript abundance from vertebrates to lower eukaroytes have specifically singled out the Zip's law, the observed distributions often deviate from a single power-law slope. In hindsight, while power-laws of critical phenomena are derived asymptotically under the conditions of infinite observations, real world observations are finite where the finite-size effects will set in to force a power-law distribution into an exponential decay and consequently, manifests as a curvature (i.e., varying exponent values) in a log-log plot. If transcript abundance is truly power-law distributed, the varying exponent signifies changing mathematical moments (e.g., mean, variance) and creates heteroskedasticity which compromises statistical rigor in analysis. The impact of this deviation from the asymptotic power-law on sequencing count data has never truly been examined and quantified.

RESULTS

The anecdotal description of transcript abundance being almost Zipf's law-like distributed can be conceptualized as the imperfect mathematical rendition of the Pareto power-law distribution when subjected to the finite-size effects in the real world; This is regardless of the advancement in sequencing technology since sampling is finite in practice. Our conceptualization agrees well with our empirical analysis of two modern day NGS (Next-generation sequencing) datasets: an in-house generated dilution miRNA study of two gastric cancer cell lines (NUGC3 and AGS) and a publicly available spike-in miRNA data; Firstly, the finite-size effects causes the deviations of sequencing count data from Zipf's law and issues of reproducibility in sequencing experiments. Secondly, it manifests as heteroskedasticity among experimental replicates to bring about statistical woes. Surprisingly, a straightforward power-law correction that restores the distribution distortion to a single exponent value can dramatically reduce data heteroskedasticity to invoke an instant increase in signal-to-noise ratio by 50% and the statistical/detection sensitivity by as high as 30% regardless of the downstream mapping and normalization methods. Most importantly, the power-law correction improves concordance in significant calls among different normalization methods of a data series averagely by 22%. When presented with a higher sequence depth (4 times difference), the improvement in concordance is asymmetrical (32% for the higher sequencing depth instance versus 13% for the lower instance) and demonstrates that the simple power-law correction can increase significant detection with higher sequencing depths. Finally, the correction dramatically enhances the statistical conclusions and eludes the metastasis potential of the NUGC3 cell line against AGS of our dilution analysis.

CONCLUSIONS

The finite-size effects due to undersampling generally plagues transcript count data with reproducibility issues but can be minimized through a simple power-law correction of the count distribution. This distribution correction has direct implication on the biological interpretation of the study and the rigor of the scientific findings.

REVIEWERS

This article was reviewed by Oliviero Carugo, Thomas Dandekar and Sandor Pongor.

Hsp70-associated chaperones have a critical role in buffering protein production costs

Proteins are necessary for cellular growth. Concurrently, however, protein production has high energetic demands associated with transcription and translation. Here, we propose that activity of molecular chaperones shape protein burden, that is the fitness costs associated with expression of unneeded proteins. To test this hypothesis, we performed a genome-wide genetic interaction screen in baker's yeast. Impairment of transcription, translation, and protein folding rendered cells hypersensitive to protein burden. Specifically, deletion of specific regulators of the Hsp70-associated chaperone network increased protein burden. In agreement with expectation, temperature stress, increased mistranslation and a chemical misfolding agent all substantially enhanced protein burden. Finally, unneeded protein perturbed interactions between key components of the Hsp70-Hsp90 network involved in folding of native proteins. We conclude that specific chaperones contribute to protein burden. Our work indicates that by minimizing the damaging impact of gratuitous protein overproduction, chaperones enable tolerance to massive changes in genomic expression.

Effects of substrate stiffness and cell-cell contact on mesenchymal stem cell differentiation

The mechanical properties of the microenvironment and direct contact-mediated cell-cell interactions are two variables known to be important in the determination of stem cell differentiation fate, but little is known about the interplay of these cues. Here, we use a micropatterning approach on polyacrylamide gels of tunable stiffnesses to study how homotypic cell-cell contacts and mechanical stiffness affect different stages of osteogenesis of mesenchymal stem cells (MSCs). Nuclear localization of transcription factors associated with osteogenesis depended on substrate stiffness and was independent of the degree of cell-cell contact. However, expression of alkaline phosphatase, an early protein marker for osteogenesis, increased only in cells with both direct contact with neighboring cells and adhesion to stiffer substrates. Finally, mature osteogenesis, as assessed by calcium deposition, was low in micropatterned cells, even on stiff substrates and in multicellular clusters. These results indicate that substrate stiffness and the presence of neighboring cells regulate osteogenesis in MSCs.

Cellular economy in fission yeast cells continuously cultured with limited nitrogen resources

In ribosome biogenesis, a large fraction of ribosomes is used for producing ribosomal proteins themselves. Here, we applied simulation and experimentation to determine what fraction of ribosomes should be allocated for the synthesis of ribosomal proteins to optimize cellular economy for growth. We define the “r-fraction” as the fraction of mRNA of the ribosomal protein genes out of the total mRNA, and we simulated the effect of the r-fraction on the number of ribosomes. We then empirically measured the amount of protein and RNA in fission yeast cells cultured with high and low nitrogen sources. In the cells cultured with a low nitrogen source, the r-fraction decreased from 0.46 to 0.42 with a 40% reduction of rRNA, but the reduction of the total protein was smaller at 30%. These results indicate that the r-fraction is internally controlled to optimize the efficiency of protein synthesis at a limited cellular cost.

Tracking global gene expression responses in T cell differentiation

Upon receiving antigens from the innate immune cells, CD4(+) T cells differentiate into distinct effector cells. To probe the global responses of distinct effector cells, we analyzed transcriptome-wide expressions of Th1, Th2, Treg and Th17 using Pearson correlation, entropy and principal component analyses, with Th0 as a control. Although the global response of Th0 was quite distinct from Th17, surprisingly, it was highly similar to Th1, Th2 and Treg. Moreover, 8 major temporal groups consisting of 5704 differentially expressed genes were revealed for both Th0 and Th17. Gene functional enrichment analysis showed immune responses and metabolic processes were mainly activated between Th0 and Th17, while genes related to cell cycle and replication were differentially regulated. Moreover, we found the upregulation of several novel genes for Th0 and Th17. Overall, we deduce that Th0 is globally similar to Th1, Th2 and Treg. Our results indicate that Th0 is a differentiated state and, therefore, may not be used as a control cell type.

Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment

Background

To understand the heterogeneous behaviors of individual cancer cells, it is essential to investigate gene expression levels as well as their divergence between different individual cells. Recent advances in next-generation sequencing-related technologies have enabled us to conduct a single-cell RNA-Seq analysis of a series of lung adenocarcinoma cell lines.

Results

We analyze a total of 336 single-cell RNA-Seq libraries from seven cell lines. The results are highly robust regarding both average expression levels and the relative gene expression differences between individual cells. Gene expression diversity is characteristic depending on genes and pathways. Analyses of individual cells treated with the multi-tyrosine kinase inhibitor vandetanib reveal that, while the ribosomal genes and many other so-called house-keeping genes reduce their relative expression diversity during the drug treatment, the genes that are directly targeted by vandetanib, the EGFR and RET genes, remain constant. Rigid transcriptional control of these genes may not allow plastic changes of their expression with the drug treatment or during the cellular acquisition of drug resistance. Additionally, we find that the gene expression patterns of cancer-related genes are sometimes more diverse than expected based on the founder cells. Furthermore, we find that this diversity is occasionally latent in a normal state and initially becomes apparent after the drug treatment.

Conclusions

Characteristic patterns in gene expression divergence, which would not be revealed by transcriptome analysis of bulk cells, may also play important roles when cells acquire drug resistance, perhaps by providing a cellular reservoir for gene expression programs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0636-y) contains supplementary material, which is available to authorized users.

Bidirectional promoters of insects: genome-wide comparison, evolutionary implication and influence on gene expression

Bidirectional promoters are widespread in insect genomes. By analyzing 23 insect genomes we show that the frequency of bidirectional gene pairs varies according to genome compactness and density of genes among the species. The density of bidirectional genes expected based on number of genes per megabase of genome explains the observed density suggesting that bidirectional pairing of genes may be due to random event. We identified specific transcription factor binding motifs that are enriched in bidirectional promoters across insect species. Furthermore, we observed that bidirectional promoters may act as transcriptional hotspots in insect genomes where protein coding genes tend to aggregate in significantly biased (p < 0.001) manner compared to unidirectional promoters. Natural selection seems to have an association with the extent of bidirectionality of genes among the species. The rate of non-synonymous-to-synonymous changes (dN/dS) shows a second-order polynomial distribution with bidirectionality between species indicating that bidirectionality is dependent upon evolutionary pressure acting on the genomes. Analysis of genome-wide microarray expression data of multiple insect species suggested that bidirectionality has a similar association with transcriptome variation across species. Furthermore, bidirectional promoters show significant association with correlated expression of the divergent gene pairs depending upon their motif composition. Analysis of gene ontology showed that bidirectional genes tend to have a common association with functions related to "binding" (including ion binding, nucleotide binding and protein binding) across genomes. Such functional constraint of bidirectional genes may explain their widespread persistence in genome of diverse insect species.

Metatranscriptomes from diverse microbial communities: assessment of data reduction techniques for rigorous annotation

Background

Metatranscriptome sequence data can contain highly redundant sequences from diverse populations of microbes and so data reduction techniques are often applied before taxonomic and functional annotation. For metagenomic data, it has been observed that the variable coverage and presence of closely related organisms can lead to fragmented assemblies containing chimeric contigs that may reduce the accuracy of downstream analyses and some advocate the use of alternate data reduction techniques. However, it is unclear how such data reduction techniques impact the annotation of metatranscriptome data and thus affect the interpretation of the results.

Results

To investigate the effect of such techniques on the annotation of metatranscriptome data we assess two commonly employed methods: clustering and de-novo assembly. To do this, we also developed an approach to simulate 454 and Illumina metatranscriptome data sets with varying degrees of taxonomic diversity. For the Illumina simulations, we found that a two-step approach of assembly followed by clustering of contigs and unassembled sequences produced the most accurate reflection of the real protein domain content of the sample. For the 454 simulations, the combined annotation of contigs and unassembled reads produced the most accurate protein domain annotations.

Conclusions

Based on these data we recommend that assembly be attempted, and that unassembled reads be included in the final annotation for metatranscriptome data, even from highly diverse environments as the resulting annotations should lead to a more accurate reflection of the transcriptional behaviour of the microbial population under investigation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-901) contains supplementary material, which is available to authorized users.

The Babesia bovis gene and promoter model: an update from full-length EST analysis

BACKGROUND

Babesia bovis is an apicomplexan parasite that causes babesiosis in infected cattle. Genomes of pathogens contain promising information that can facilitate the development of methods for controlling infections. Although the genome of B. bovis is publically available, annotated gene models are not highly reliable prior to experimental validation. Therefore, we validated a preproposed gene model of B. bovis and extended the associated annotations on the basis of experimentally obtained full-length expressed sequence tags (ESTs).

RESULTS

From in vitro cultured merozoites, 12,286 clones harboring full-length cDNAs were sequenced from both ends using the Sanger method, and 6,787 full-length cDNAs were assembled. These were then clustered, and a nonredundant referential data set of 2,115 full-length cDNA sequences was constructed. The comparison of the preproposed gene model with our data set identified 310 identical genes, 342 almost identical genes, 1,054 genes with potential structural inconsistencies, and 409 novel genes. The median length of 5' untranslated regions (UTRs) was 152 nt. Subsequently, we identified 4,086 transcription start sites (TSSs) and 2,023 transcriptionally active regions (TARs) by examining 5' ESTs. We identified ATGGGG and CCCCAT sites as consensus motifs in TARs that were distributed around -50 bp from TSSs. In addition, we found ACACA, TGTGT, and TATAT sites, which were distributed periodically around TSSs in cycles of approximately 150 bp. Moreover, related periodical distributions were not observed in mammalian promoter regions.

CONCLUSIONS

The observations in this study indicate the utility of integrated bioinformatics and experimental data for improving genome annotations. In particular, full-length cDNAs with one-base resolution for TSSs enabled the identification of consensus motifs in promoter sequences and demonstrated clear distributions of identified motifs. These observations allowed the illustration of a model promoter composition, which supports the differences in transcriptional regulation frameworks between apicomplexan parasites and mammals.

Transcriptome profiling of human hippocampus dentate gyrus granule cells in mental illness

This study is, to the best of our knowledge, the first application of whole transcriptome sequencing (RNA-seq) to cells isolated from postmortem human brain by laser capture microdissection. We investigated the transcriptome of dentate gyrus (DG) granule cells in postmortem human hippocampus in 79 subjects with mental illness (schizophrenia, bipolar disorder, major depression) and nonpsychiatric controls. We show that the choice of normalization approach for analysis of RNA-seq data had a strong effect on results; under our experimental conditions a nonstandard normalization method gave superior results. We found evidence of disrupted signaling by miR-182 in mental illness. This was confirmed using a novel method of leveraging microRNA genetic variant information to indicate active targeting. In healthy subjects and those with bipolar disorder, carriers of a high- vs those with a low-expressing genotype of miR-182 had different levels of miR-182 target gene expression, indicating an active role of miR-182 in shaping the DG transcriptome for those subject groups. By contrast, comparing the transcriptome between carriers of different genotypes among subjects with major depression and schizophrenia suggested a loss of DG miR-182 signaling in these conditions.

Technical variations in low-input RNA-seq methodologies

Recent advances in RNA-seq methodologies from limiting amounts of mRNA have facilitated the characterization of rare cell-types in various biological systems. So far, however, technical variations in these methods have not been adequately characterized, vis-à-vis sensitivity, starting with reduced levels of mRNA. Here, we generated sequencing libraries from limiting amounts of mRNA using three amplification-based methods, viz. Smart-seq, DP-seq and CEL-seq, and demonstrated significant technical variations in these libraries. Reduction in mRNA levels led to inefficient amplification of the majority of low to moderately expressed transcripts. Furthermore, noise in primer hybridization and/or enzyme incorporation was magnified during the amplification step resulting in significant distortions in fold changes of the transcripts. Consequently, the majority of the differentially expressed transcripts identified were either high-expressed and/or exhibited high fold changes. High technical variations ultimately masked subtle biological differences mandating the development of improved amplification-based strategies for quantitative transcriptomics from limiting amounts of mRNA.

Fungal gene expression levels do not display a common mode of distribution

Background

RNA-seq studies in metazoa have revealed a distinct, double-peaked (bimodal) distribution of gene expression independent of species and cell type. However, two studies in filamentous fungi yielded conflicting results, with a bimodal distribution in Pyronema confluens and varying distributions in Sordaria macrospora. To obtain a broader overview of global gene expression distributions in fungi, an additional 60 publicly available RNA-seq data sets from six ascomycetes and one basidiomycete were analyzed with respect to gene expression distributions.

Results

Clustering of normalized, log₂-transformed gene expression levels for each RNA-seq data set yielded distributions with one to five peaks. When only major peaks comprising at least 15% of all analyzed genes were considered, distributions ranged from one to three major peaks, suggesting that fungal gene expression is not generally bimodal. The number of peaks was not correlated with the phylogenetic position of a species; however, higher filamentous asco- and basidiomycetes showed up to three major peaks, whereas gene expression levels in the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe had only one to two major peaks, with one predominant peak containing at least 70% of all expressed genes. In several species, the number of peaks varied even within a single species, e.g. depending on the growth conditions as evidenced in the one to three major peaks in different samples from Neurospora crassa. Earlier studies based on microarray and SAGE data revealed distributions of gene expression level that followed Zipf’s law, i.e. log-transformed gene expression levels were inversely proportional to the log-transformed expression rank of a gene. However, analyses of the fungal RNA-seq data sets could not identify any that confirmed to Zipf’s law.

Conclusions

Fungal gene expression patterns cannot generally be described by a single type of distribution (bimodal or Zipf’s law). One hypothesis to explain this finding might be that gene expression in fungi is highly dynamic, and fine-tuned at the level of transcription not only for individual genes, but also at a global level.

Whole transcriptome organisation in the dehydrated supraoptic nucleus

The supraoptic nucleus (SON) is part of the central osmotic circuitry that synthesises the hormone vasopressin (Avp) and transports it to terminals in the posterior lobe of the pituitary. Following osmotic stress such as dehydration, this tissue undergoes morphological, electrical and transcriptional changes to facilitate the appropriate regulation and release of Avp into the circulation where it conserves water at the level of the kidney. Here, the organisation of the whole transcriptome following dehydration is modelled to fit Zipf's law, a natural power law that holds true for all natural languages, that states if the frequency of word usage is plotted against its rank, then the log linear regression of this is -1. We have applied this model to our previously published euhydrated and dehydrated SON data to observe this trend and how it changes following dehydration. In accordance with other studies, our whole transcriptome data fit well with this model in the euhydrated SON microarrays, but interestingly, fit better in the dehydrated arrays. This trend was observed in a subset of differentially regulated genes and also following network reconstruction using a third-party database that mines public data. We make use of language as a metaphor that helps us philosophise about the role of the whole transcriptome in providing a suitable environment for the delivery of Avp following a survival threat like dehydration.

Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution

BACKGROUND

The genetic network involved in the bacterial cell cycle is poorly understood even though it underpins the remarkable ability of bacteria to proliferate. How such network evolves is even less clear. The major aims of this work were to identify and examine the genes and pathways that are differentially expressed during the Caulobacter crescentus cell cycle, and to analyze the evolutionary features of the cell cycle network.

RESULTS

We used deep RNA sequencing to obtain high coverage RNA-Seq data of five C. crescentus cell cycle stages, each with three biological replicates. We found that 1,586 genes (over a third of the genome) display significant differential expression between stages. This gene list, which contains many genes previously unknown for their cell cycle regulation, includes almost half of the genes involved in primary metabolism, suggesting that these "house-keeping" genes are not constitutively transcribed during the cell cycle, as often assumed. Gene and module co-expression clustering reveal co-regulated pathways and suggest functionally coupled genes. In addition, an evolutionary analysis of the cell cycle network shows a high correlation between co-expression and co-evolution. Most co-expression modules have strong phylogenetic signals, with broadly conserved genes and clade-specific genes predominating different substructures of the cell cycle co-expression network. We also found that conserved genes tend to determine the expression profile of their module.

CONCLUSION

We describe the first phylogenetic and single-nucleotide-resolution transcriptomic analysis of a bacterial cell cycle network. In addition, the study suggests how evolution has shaped this network and provides direct biological network support that selective pressure is not on individual genes but rather on the relationship between genes, which highlights the importance of integrating phylogenetic analysis into biological network studies.

Nanostructured surfaces and detection instrumentation for photonic crystal enhanced fluorescence

Photonic crystal (PC) surfaces have been demonstrated as a compelling platform for improving the sensitivity of surface-based fluorescent assays used in disease diagnostics and life science research. PCs can be engineered to support optical resonances at specific wavelengths at which strong electromagnetic fields are utilized to enhance the intensity of surface-bound fluorophore excitation. Meanwhile, the leaky resonant modes of PCs can be used to direct emitted photons within a narrow range of angles for more efficient collection by a fluorescence detection system. The multiplicative effects of enhanced excitation combined with enhanced photon extraction combine to provide improved signal-to-noise ratios for detection of fluorescent emitters, which in turn can be used to reduce the limits of detection of low concentration analytes, such as disease biomarker proteins. Fabrication of PCs using inexpensive manufacturing methods and materials that include replica molding on plastic, nano-imprint lithography on quartz substrates result in devices that are practical for single-use disposable applications. In this review, we will describe the motivation for implementing high-sensitivity fluorescence detection in the context of molecular diagnosis and gene expression analysis though the use of PC surfaces. Recent efforts to improve the design and fabrication of PCs and their associated detection instrumentation are summarized, including the use of PCs coupled with Fabry-Perot cavities and external cavity lasers.

Fluorescence laser microdissection reveals a distinct pattern of gene activation in the mouse hippocampal region

A histoanatomical context is imperative in an analysis of gene expression in a cell in a tissue to elucidate physiological function of the cell. In this study, we made technical advances in fluorescence laser microdissection (LMD) in combination with the absolute quantification of small amounts of mRNAs from a region of interest (ROI) in fluorescence-labeled tissue sections. We demonstrate that our fluorescence LMD-RTqPCR method has three orders of dynamic range, with the lower limit of ROI-size corresponding to a single cell. The absolute quantification of the expression levels of the immediate early genes in an ROI equivalent to a few hundred neurons in the hippocampus revealed that mice transferred from their home cage to a novel environment have distinct activation profiles in the hippocampal regions (CA1, CA3, and DG) and that the gene expression pattern in CA1, but not in the other regions, follows a power law distribution.

Mining SOM expression portraits: feature selection and integrating concepts of molecular function

Background

Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function.

Results

Different expression scores based either on simple fold change-measures or on regularized Student’s t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and high expressed genes using SOM data filtering.

Conclusions

The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.

Gene-sharing networks reveal organizing principles of transcriptomes in Arabidopsis and other multicellular organisms

Understanding tissue-related gene expression patterns can provide important insights into gene, tissue, and organ function. Transcriptome analyses often have focused on housekeeping or tissue-specific genes or on gene coexpression. However, by analyzing thousands of single-gene expression distributions in multiple tissues of Arabidopsis thaliana, rice (Oryza sativa), human (Homo sapiens), and mouse (Mus musculus), we found that these organisms primarily operate by gene sharing, a phenomenon where, in each organism, most genes exhibit a high expression level in a few key tissues. We designed an analytical pipeline to characterize this phenomenon and then derived Arabidopsis and human gene-sharing networks, in which tissues are connected solely based on the extent of shared preferentially expressed genes. The results show that tissues or cell types from the same organ system tend to group together to form network modules. Tissues that are in consecutive developmental stages or have common physiological functions are connected in these networks, revealing the importance of shared preferentially expressed genes in conferring specialized functions of each tissue type. The networks provide predictive power for each tissue type regarding gene functions of both known and heretofore unknown genes, as shown by the identification of four new genes with functions in guard cell and abscisic acid response. We provide a Web interface that enables, based on the extent of gene sharing, both prediction of tissue-related functions for any Arabidopsis gene of interest and predictions concerning the relatedness of tissues. Common gene-sharing patterns observed in the four model organisms suggest that gene sharing evolved as a fundamental organizing principle of gene expression in diverse multicellular eukaryotes.

Protein misinteraction avoidance causes highly expressed proteins to evolve slowly

The tempo and mode of protein evolution have been central questions in biology. Genomic data have shown a strong influence of the expression level of a protein on its rate of sequence evolution (E-R anticorrelation), which is currently explained by the protein misfolding avoidance hypothesis. Here, we show that this hypothesis does not fully explain the E-R anticorrelation, especially for protein surface residues. We propose that natural selection against protein-protein misinteraction, which wastes functional molecules and is potentially toxic, constrains the evolution of surface residues. Because highly expressed proteins are under stronger pressures to avoid misinteraction, surface residues are expected to show an E-R anticorrelation. Our molecular-level evolutionary simulation and yeast genomic analysis confirm multiple predictions of the hypothesis. These findings show a pluralistic origin of the E-R anticorrelation and reveal the role of protein misinteraction, an inherent property of complex cellular systems, in constraining protein evolution.

Isoform diversity and regulation in peripheral and central neurons revealed through RNA-Seq

To fully understand cell type identity and function in the nervous system there is a need to understand neuronal gene expression at the level of isoform diversity. Here we applied Next Generation Sequencing of the transcriptome (RNA-Seq) to purified sensory neurons and cerebellar granular neurons (CGNs) grown on an axonal growth permissive substrate. The goal of the analysis was to uncover neuronal type specific isoforms as a prelude to understanding patterns of gene expression underlying their intrinsic growth abilities. Global gene expression patterns were comparable to those found for other cell types, in that a vast majority of genes were expressed at low abundance. Nearly 18% of gene loci produced more than one transcript. More than 8000 isoforms were differentially expressed, either to different degrees in different neuronal types or uniquely expressed in one or the other. Sensory neurons expressed a larger number of genes and gene isoforms than did CGNs. To begin to understand the mechanisms responsible for the differential gene/isoform expression we identified transcription factor binding sites present specifically in the upstream genomic sequences of differentially expressed isoforms, and analyzed the 3′ untranslated regions (3′ UTRs) for microRNA (miRNA) target sites. Our analysis defines isoform diversity for two neuronal types with diverse axon growth capabilities and begins to elucidate the complex transcriptional landscape in two neuronal populations.

PAPD5, a noncanonical poly(A) polymerase with an unusual RNA-binding motif

PAPD5 is one of the seven members of the family of noncanonical poly(A) polymerases in human cells. PAPD5 was shown to polyadenylate aberrant pre-ribosomal RNAs in vivo, similar to degradation-mediating polyadenylation by the noncanonical poly(A) polymerase Trf4p in yeast. PAPD5 has been reported to be also involved in the uridylation-dependent degradation of histone mRNAs. To test whether PAPD5 indeed catalyzes adenylation as well as uridylation of RNA substrates, we analyzed the in vitro properties of recombinant PAPD5 expressed in mammalian cells as well as in bacteria. Our results show that PAPD5 catalyzes the polyadenylation of different types of RNA substrates in vitro. Interestingly, PAPD5 is active without a protein cofactor, whereas its yeast homolog Trf4p is the catalytic subunit of a bipartite poly(A) polymerase in which a separate RNA-binding subunit is needed for activity. In contrast to the yeast protein, the C terminus of PAPD5 contains a stretch of basic amino acids that is involved in binding the RNA substrate.

Nonlinear response of gene expression to chemical perturbations: a noise-detector model and its predictions

The widespread use of microarrays provided a first glimpse at some simple laws and organizing principles that govern the transcriptome. Previous analyses have shown that the transcriptional organization is very heterogeneous and characterized by a power-law decay for gene expression levels. Moreover, a simple law was unveiled suggesting that gene expression dynamic changes under stress are proportional to their initial expression values. However, to elucidate and assess the underlying governing principles of transcriptional organization, we do not only need to identify them, but also provide theoretical models that are able to faithfully capture and reproduce them. Here we present a method to investigate the gene expression dynamics inspired by the theory of nonlinear transformation of random signals and noise. The model is able to explain not only the well-known power-law decay for abundance of expression levels, but also to reproduce the linear dependence of the standard deviation of gene expression change with respect to the initial expression value (also known as rich-travels-more dynamics). To our knowledge, this is the first model applied to gene expression dynamics that is able to simultaneously predict both statistical features. The theoretical framework derives an indicator to measure the coupling between gene expression and specific perturbations. Using genome-wide transcriptional data, this indicator identifies genes strongly coupled to specific inflammatory responses to different pathogens. The novel application of signal and noise theory to study intracellular responses and gene expression changes offers not only a new theoretical avenue to study transcriptional responses to environmental stresses and chemical signals but also provides predictive capability at the genome scale.

Analysis and simulation of gene expression profiles in pure and mixed cell populations

For analysis and interpretation of data obtained from experimental readouts of gene expression, such as microarrays and RNA-sequencing, log transformation is routinely applied. This is because expression data, like many biological parameters, are strongly skewed. We show here that gene expression levels in multicellular organisms often deviate from simple (log) normal distributions and instead exhibit shouldered or bimodal distributions. Based on a mathematical model and numerical simulations, we demonstrate that many observed distributions can be explained as mixtures of bimodal two-component lognormal models. This is due to the fact that after log-transformation, the resulting distributions display reductions in the first peak rather than increasing overlaps over a wide range of parameter values. By comparing the theoretical results with biological datasets, our findings suggest that the distributions are generally bimodal for single cell types and get obscured by the different cell types that are present in tissue samples. Our analysis thus provides an initial explanation for the various types of expression level distributions that are found for different datasets. This will be important for the interpretation of next-generation sequencing data such as transcriptomics by mRNA-sequencing and ChIP-sequencing of epigenetic marks.

Conserved toxic responses across divergent phylogenetic lineages: a meta-analysis of the neurotoxic effects of RDX among multiple species using toxicogenomics

At military training sites, a variety of pollutants such as hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), may contaminate the area originating from used munitions. Studies investigating the mechanism of toxicity of RDX have shown that it affects the central nervous system causing seizures in humans and animals. Environmental pollutants such as RDX have the potential to affect many different species, therefore it is important to establish how phylogenetically distant species may respond to these types of emerging pollutants. In this paper, we have used a transcriptional network approach to compare and contrast the neurotoxic effects of RDX among five phylogenetically disparate species: rat (Sprague-Dawley), Northern bobwhite quail (Colinus virginianus), fathead minnow (Pimephales promelas), earthworm (Eisenia fetida), and coral (Acropora formosa). Pathway enrichment analysis indicated a conservation of RDX impacts on pathways related to neuronal function in rat, Northern bobwhite quail, fathead minnows and earthworm, but not in coral. As evolutionary distance increased common responses decreased with impacts on energy and metabolism dominating effects in coral. A neurotransmission related transcriptional network based on whole rat brain responses to RDX exposure was used to identify functionally related modules of genes, components of which were conserved across species depending upon evolutionary distance. Overall, the meta-analysis using genomic data of the effects of RDX on several species suggested a common and conserved mode of action of the chemical throughout phylogenetically remote organisms.

Design and validation issues in RNA-seq experiments

The next-generation sequencing technologies are being rapidly applied in biological research. Tens of millions of short sequences generated in a single experiment provide us enormous information on genome composition, genetic variants, gene expression levels and protein binding sites depending on the applications. Various methods are being developed for analyzing the data generated by these technologies. However, the relevant experimental design issues have rarely been discussed. In this review, we use RNA-seq as an example to bring this topic into focus and to discuss experimental design and validation issues pertaining to next-generation sequencing in the quantification of transcripts.

Genome-wide characterization of transcriptional start sites in humans by integrative transcriptome analysis

We performed a genome-wide analysis of transcriptional start sites (TSSs) in human genes by multifaceted use of a massively parallel sequencer. By analyzing 800 million sequences that were obtained from various types of transcriptome analyses, we characterized 140 million TSS tags in 12 human cell types. Despite the large number of TSS clusters (TSCs), the number of TSCs was observed to decrease sharply with increasing expression levels. Highly expressed TSCs exhibited several characteristic features: Nucleosome-seq analysis revealed highly ordered nucleosome structures, ChIP-seq analysis detected clear RNA polymerase II binding signals in their surrounding regions, evaluations of previously sequenced and newly shotgun-sequenced complete cDNA sequences showed that they encode preferable transcripts for protein translation, and RNA-seq analysis of polysome-incorporated RNAs yielded direct evidence that those transcripts are actually translated into proteins. We also demonstrate that integrative interpretation of transcriptome data is essential for the selection of putative alternative promoter TSCs, two of which also have protein consequences. Furthermore, discriminative chromatin features that separate TSCs at different expression levels were found for both genic TSCs and intergenic TSCs. The collected integrative information should provide a useful basis for future biological characterization of TSCs.

A systems approach to bone pathophysiology

With evolving interest in multiscalar biological systems one could assume that reductionist approaches may not fully describe biological complexity. Instead, tools such as mathematical modeling, network analysis, and other multiplexed clinical- and research-oriented tests enable rapid analyses of high-throughput data parsed at the genomic, proteomic, metabolomic, and physiomic levels. A physiomic-level approach allows for recursive horizontal and vertical integration of subsystem coupling across and within spatiotemporal scales. Additionally, this methodology recognizes previously ignored subsystems and the strong, nonintuitively obvious and indirect connections among physiological events that potentially account for the uncertainties in medicine. In this review, we flip the reductionist research paradigm and review the concept of systems biology and its applications to bone pathophysiology. Specifically, a bone-centric physiome model is presented that incorporates systemic-level processes with their respective therapeutic implications.

The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics

Peptide detectability is defined as the probability that a peptide is identified in an LC-MS/MS experiment and has been useful in providing solutions to protein inference and label-free quantification. Previously, predictors for peptide detectability trained on standard or complex samples were proposed. Although the models trained on complex samples may benefit from the large training data sets, it is unclear to what extent they are affected by the unequal abundances of identified proteins. To address this challenge and improve detectability prediction, we present a new algorithm for the iterative learning of peptide detectability from complex mixtures. We provide evidence that the new method approximates detectability with useful accuracy and, based on its design, can be used to interpret the outcome of other learning strategies. We studied the properties of peptides from the bacterium Deinococcus radiodurans and found that at standard quantities, its tryptic peptides can be roughly classified as either detectable or undetectable, with a relatively small fraction having medium detectability. We extend the concept of detectability from peptides to proteins and apply the model to predict the behavior of a replicate LC-MS/MS experiment from a single analysis. Finally, our study summarizes a theoretical framework for peptide/protein identification and label-free quantification.

Statistical analysis of global connectivity and activity distributions in cellular networks

Various molecular interaction networks have been claimed to follow power-law decay for their global connectivity distribution. It has been proposed that there may be underlying generative models that explain this heavy-tailed behavior by self-reinforcement processes such as classical or hierarchical scale-free network models. Here, we analyze a comprehensive data set of protein-protein and transcriptional regulatory interaction networks in yeast, an Escherichia coli metabolic network, and gene activity profiles for different metabolic states in both organisms. We show that in all cases the networks have a heavy-tailed distribution, but most of them present significant differences from a power-law model according to a stringent statistical test. Those few data sets that have a statistically significant fit with a power-law model follow other distributions equally well. Thus, while our analysis supports that both global connectivity interaction networks and activity distributions are heavy-tailed, they are not generally described by any specific distribution model, leaving space for further inferences on generative models. Supplementary Material is available online at www.liebertonline.com.

Improved sensitivity of DNA microarrays using photonic crystal enhanced fluorescence

DNA microarrays are used to profile changes in gene expression between samples in a high-throughput manner, but measurements of genes with low expression levels can be problematic with standard microarray substrates. In this work, we expand the detection capabilities of a standard microarray experiment using a photonic crystal (PC) surface that enhances fluorescence observed from microarray spots. This PC is inexpensively and uniformly fabricated using a nanoreplica molding technique, with very little variation in its optical properties within- and between-devices. By using standard protocols to process glass microarray substrates in parallel with PCs, we evaluated the impact of this substrate on a one-color microarray experiment comparing gene expression in two developmental stages of Glycine max. The PCs enhanced the signal-to-noise ratio observed from microarray spots by 1 order of magnitude, significantly increasing the number of genes detected above substrate fluorescence noise. PC substrates more than double the number of genes classified as differentially expressed, detecting changes in expression even for low expression genes. This approach increases the dynamic range of a surface-bound fluorescence-based assay to reliably quantify small quantities of DNA that would be impossible with standard substrates.