Teasing apart translational and transcriptional components of stochastic variations in eukaryotic gene expression

Transcription factor binding process is the primary driver of noise in gene expression

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.

Preimplantation factor modulates trophoblastic invasion throughout the decidualization of human endometrial stromal cells

BACKGROUND

Successful human embryo implantation requires the differentiation of endometrial stromal cells (ESCs) into decidual cells during a process called decidualization. ESCs express specific markers of decidualization, including prolactin, insulin-like growth factor-binding protein-1 (IGFBP-1), and connexin-43. Decidual cells also control of trophoblast invasion by secreting various factors, such as matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases. Preimplantation factor (PIF) is a recently identified, embryo-derived peptide with activities at the fetal-maternal interface. It creates a favorable pro-inflammatory environment in human endometrium and directly controls placental development by increasing the human trophoblastic cells' ability to invade the endometrium. We hypothesized that PIF's effects on the endometrium counteract its pro-invasive effects.

METHODS

We tested sPIF effect on the expression of three decidualization markers by RT-qPCR and/or immunochemiluminescence assay. We examined sPIF effect on human ESC migration by performing an in vitro wound healing assay. We analyzed sPIF effect on endometrial control of human trophoblast invasion by performing a zymography and an invasion assay.

RESULTS

Firstly, we found that a synthetic analog of PIF (sPIF) significantly upregulates the mRNA expression of IGFBP-1 and connexin-43, and prolactin secretion in ESCs - suggesting a pro-differentiation effect. Secondly, we showed that the HTR-8/SVneo trophoblastic cell line's invasive ability was low in the presence of conditioned media from ESCs cultured with sPIF. Thirdly, this PIF's anti-invasive action was associated with a specifically decrease in MMP-9 activity.

CONCLUSION

Taken as a whole, our results suggest that PIF accentuates the decidualization process and the production of endometrial factors that limit trophoblast invasion. By controlling both trophoblast and endometrial cells, PIF therefore appears to be a pivotal player in the human embryo implantation process.

Controlling cell-to-cell variability with synthetic gene circuits

Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.

RNA Regulatory Networks as a Control of Stochasticity in Biological Systems

The discovery that the non-protein coding part of human genome, dismissed as "junk DNA," is actively transcripted and carries out crucial functions is probably one of the most important discoveries of the past decades. These transcripts are becoming the rising stars of modern biology. In this review, we have casted a new light on RNAs. We have placed these molecules in the context of life origins, evolution with a big emphasize on the "RNA networks" concept. We discuss how this view can help us to understand the global role of RNA networks in modern cells, and can change our perception of the cell biology and therapy. Finally, although high-throughput methods as well as traditional case-to-case studies have laid the groundwork for our current knowledge of transcriptomes, we would like to discuss new strategies that are better suited to uncover and tackle these integrated and complex RNA networks.

Dosage-Dependent Expression Variation Suppressed on the Drosophila Male X Chromosome

DNA copy number variation is associated with many high phenotypic heterogeneity disorders. We systematically examined the impact of Drosophila melanogaster deletions on gene expression profiles to ask whether increased expression variability owing to reduced gene dose might underlie this phenotypic heterogeneity. Indeed, we found that one-dose genes have higher gene expression variability relative to two-dose genes. We then asked whether this increase in variability could be explained by intrinsic noise within cells due to stochastic biochemical events, or whether expression variability is due to extrinsic noise arising from more complex interactions. Our modeling showed that intrinsic gene expression noise averages at the organism level and thus cannot explain increased variation in one-dose gene expression. Interestingly, expression variability was related to the magnitude of expression compensation, suggesting that regulation, induced by gene dose reduction, is noisy. In a remarkable exception to this rule, the single X chromosome of males showed reduced expression variability, even compared with two-dose genes. Analysis of sex-transformed flies indicates that X expression variability is independent of the male differentiation program. Instead, we uncovered a correlation between occupancy of the chromatin-modifying protein encoded by males absent on the first (mof) and expression variability, linking noise suppression to the specialized X chromosome dosage compensation system. MOF occupancy on autosomes in both sexes also lowered transcriptional noise. Our results demonstrate that gene dose reduction can lead to heterogeneous responses, which are often noisy. This has implications for understanding gene network regulatory interactions and phenotypic heterogeneity. Additionally, chromatin modification appears to play a role in dampening transcriptional noise.

Translation initiation events on structured eukaryotic mRNAs generate gene expression noise

Gene expression stochasticity plays a major role in biology, creating non-genetic cellular individuality and influencing multiple processes, including differentiation and stress responses. We have addressed the lack of knowledge about posttranscriptional contributions to noise by determining cell-to-cell variations in the abundance of mRNA and reporter protein in yeast. Two types of structural element, a stem–loop and a poly(G) motif, not only inhibit translation initiation when inserted into an mRNA 5΄ untranslated region, but also generate noise. The noise-enhancing effect of the stem–loop structure also remains operational when combined with an upstream open reading frame. This has broad significance, since these elements are known to modulate the expression of a diversity of eukaryotic genes. Our findings suggest a mechanism for posttranscriptional noise generation that will contribute to understanding of the generally poor correlation between protein-level stochasticity and transcriptional bursting. We propose that posttranscriptional stochasticity can be linked to cycles of folding/unfolding of a stem–loop structure, or to interconversion between higher-order structural conformations of a G-rich motif, and have created a correspondingly configured computational model that generates fits to the experimental data. Stochastic events occurring during the ribosomal scanning process can therefore feature alongside transcriptional bursting as a source of noise.

Adiponectin limits differentiation and trophoblast invasion in human endometrial cells

Successful human embryo implantation requires a proper differentiation of endometrial stromal cells (ESCs) into decidual cells, during a process called decidualization. ESCs express specific molecules, such as prolactin, insulin-like growth factor-binding protein-1 (IGFBP-1) and connexin-43. Decidual cells are also involved in the control of trophoblast invasion, by secreting various factors, such as matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs). Adiponectin is an adipokine with insulin-sensitizing, anti-inflammatory and anti-proliferative effects. At the embryo-maternal interface, adiponectin promotes differentiation and invasion of human trophoblastic cells. We hypothesize that the effects of adiponectin on endometrium could counteract its pro-invasive effects previously described in the human trophoblast. In this context, we have firstly demonstrated that adiponectin downregulates IGFBP-1 and connexin-43 mRNA expressions, as well as prolactin secretion in ESCs, suggesting an anti-differentiative effect of adiponectin. Secondly, we found that invasive capacities of trophoblastic cell line HTR-8/SVneo are reduced in the presence of conditioned media from ESC cultured in the presence of adiponectin. Adiponectin's anti-invasive action is associated with a decreased activity of MMP-2 and MMP-9, and an increased TIMP-3 mRNA expression in ESCs. Finally, adiponectin receptors (ADIPOR1 and ADIPOR2) knockdown abolishes the anti-differentiative and anti-invasive effects of adiponectin in human ESCs. Altogether, our results suggest that adiponectin reduces the decidualization process and inversely induces the production of endometrial factors that limit trophoblast invasion. Thus, through a dual control in trophoblast and endometrial cells, adiponectin appears as a pivotal actor of the embryo implantation process.

Minimum-noise production of translation factor eIF4G maps to a mechanistically determined optimal rate control window for protein synthesis

Gene expression noise influences organism evolution and fitness. The mechanisms determining the relationship between stochasticity and the functional role of translation machinery components are critical to viability. eIF4G is an essential translation factor that exerts strong control over protein synthesis. We observe an asymmetric, approximately bell-shaped, relationship between the average intracellular abundance of eIF4G and rates of cell population growth and global mRNA translation, with peak rates occurring at normal physiological abundance. This relationship fits a computational model in which eIF4G is at the core of a multi-component–complex assembly pathway. This model also correctly predicts a plateau-like response of translation to super-physiological increases in abundance of the other cap-complex factors, eIF4E and eIF4A. Engineered changes in eIF4G abundance amplify noise, demonstrating that minimum stochasticity coincides with physiological abundance of this factor. Noise is not increased when eIF4E is overproduced. Plasmid-mediated synthesis of eIF4G imposes increased global gene expression stochasticity and reduced viability because the intrinsic noise for this factor influences total cellular gene noise. The naturally evolved eIF4G gene expression noise minimum maps within the optimal activity zone dictated by eIF4G's mechanistic role. Rate control and noise are therefore interdependent and have co-evolved to share an optimal physiological abundance point.

Expression and Localization of Lung Surfactant Proteins in Human Testis

Background

Surfactant proteins (SPs) have been described in various tissues and fluids including tissues of the nasolacrimal apparatus, airways and digestive tract. Human testis have a glandular function as a part of the reproductive and the endocrine system, but no data are available on SPs in human testis and prostate under healthy and pathologic conditions.

Objective

The aim of the study was the detection and characterization of the surfactant proteins A, B, C and D (SP-A, SP-B, SP-C, SP-D) in human testis. Additionally tissue samples affected by testicular cancer were investigated.

Results

Surfactant proteins A, B, C and D were detected using RT-PCR in healthy testis. By means of Western blot analysis, these SPs were detected at the protein level in normal testis, seminoma and seminal fluid, but not in spermatozoa. Expression of SPs was weaker in seminoma compared to normal testicular tissue. SPs were localized in combination with vimentin immunohistochemically in cells of Sertoli and Leydig.

Conclusion

Surfactant proteins seem to be inherent part of the human testis. By means of physicochemical properties the proteins appear to play a role during immunological and rheological process of the testicular tissue. The presence of SP-B and SP-C in cells of Sertoli correlates with their function of fluid secretion and may support transportation of spermatozoa. In seminoma the expression of all SP's was generally weaker compared to normal germ cells. This could lead to a reduction of immunomodulatory and rheology processes in the germ cell tumor.

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C13-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.

Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia

Background

Chronic lymphocytic leukemia (CLL) presents two subtypes which have drastically different clinical outcomes, IgVH mutated (M-CLL) and IgVH unmutated (U-CLL). So far, these two subtypes are not associated to clear differences in gene expression profiles. Interestingly, recent results have highlighted important roles for heterogeneity, both at the genetic and at the epigenetic level in CLL progression.

Methods

We analyzed gene expression data of two large cohorts of CLL patients and quantified expression variability across individuals to investigate differences between the two subtypes using different measures and statistical tests. Functional significance was explored by pathway enrichment and network analyses. Furthermore, we implemented a random forest approach based on expression variability to classify patients into disease subtypes.

Results

We found that U-CLL, the more aggressive type of the disease, shows significantly increased variability of gene expression across patients and that, overall, genes that show higher variability in the aggressive subtype are related to cell cycle, development and inter-cellular communication. These functions indicate a potential relation between gene expression variability and the faster progression of this CLL subtype. Finally, a classifier based on gene expression variability was able to correctly predict the disease subtype of CLL patients.

Conclusions

There are strong relations between gene expression variability and disease subtype linking significantly increased expression variability to phenotypes such as aggressiveness and resistance to therapy in CLL.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0125-z) contains supplementary material, which is available to authorized users.

Transcript level and sequence determinants of protein abundance and noise in Escherichia coli

The range over which a protein is expressed, and its cell-to-cell variability, is often thought to be linked to the demand for its activity. Steady-state protein level is determined by multiple mechanisms controlling transcription and translation, many of which are limited by DNA- and RNA-encoded signals that affect initiation, elongation and termination of polymerases and ribosomes. We performed a comprehensive analysis of >100 sequence features to derive a predictive model composed of a minimal non-redundant set of factors explaining 66% of the total variation of protein abundance observed in >800 genes in Escherichia coli. The model suggests that protein abundance is primarily determined by the transcript level (53%) and by effectors of translation elongation (12%), whereas only a small fraction of the variation is explained by translational initiation (1%). Our analyses uncover a new sequence determinant, not previously described, affecting translation initiation and suggest that elongation rate is affected by both codon biases and specific amino acid composition. We also show that transcription and translation efficiency may have an effect on expression noise, which is more similar than previously assumed.

Mosaic physiology from developmental noise: within-organism physiological diversity as an alternative to phenotypic plasticity and phenotypic flexibility

A key problem in organismal biology is to explain the origins of functional diversity. In the context of organismal biology, functional diversity describes the set of phenotypes, across scales of biological organization and through time, that a single genotype, or genome, or organism, can produce. Functional diversity encompasses many phenomena: differences in cell types within organisms; physiological and morphological differences among tissues and organs; differences in performance; morphological shifts in external phenotype; and changes in behavior. How can single genomes produce so many different phenotypes? Modern biology proposes two general mechanisms. The first is developmental programs, by which single cells and their single genomes diversify, via relatively deterministic processes, into the sets of cell types, tissues and organs that we see in most multicellular organisms. The second general mechanism is phenotypic modification stemming from interactions between organisms and their environments – modifications known either as phenotypic plasticity or as phenotypic flexibility, depending on the time scale of the response and the degree of reversibility. These two diversity-generating mechanisms are related because phenotypic modifications may sometimes arise as a consequence of environments influencing developmental programs. Here, I propose that functional diversity also arises via a third fundamental mechanism: stochastic developmental events giving rise to mosaics of physiological diversity within individual organisms. In biological systems, stochasticity stems from the inherently random actions of small numbers of molecules interacting with one another. Although stochastic effects occur in many biological contexts, available evidence suggests that they can be especially important in gene networks, specifically as a consequence of low transcript numbers in individual cells. I briefly review known mechanisms by which organisms control such stochasticity, and how they may use it to create adaptive functional diversity. I then fold this idea into modern thinking on phenotypic plasticity and flexibility, proposing that multicellular organisms exhibit ‘mosaic physiology’. Mosaic physiology refers to sets of diversified phenotypes, within individual organisms, that carry out related functions at the same time, but that are distributed in space. Mosaic physiology arises from stochasticity-driven differentiation of cells, early during cell diversification, which is then amplified by cell division and growth into macroscopic phenotypic modules (cells, tissues, organs) making up the physiological systems of later life stages. Mosaic physiology provides a set of standing, diversified phenotypes, within single organisms, that raise the likelihood of the organism coping well with novel environmental challenges. These diversified phenotypes can be distinct, akin to polyphenisms at the organismal level; or they can be continuously distributed, creating a kind of standing, simultaneously expressed reaction norm of physiological capacities.

Down-regulation of Filamin A interacting protein 1-like Is associated with promoter methylation and an invasive phenotype in breast, colon, lung and pancreatic cancers [corrected]

Identifying key mediators of cancer cell invasion and metastasis is critical to the development of more effective cancer therapies. We previously identified Filamin A interacting protein 1-like (FILIP1L) as an important inhibitor of cell migration and invasion in ovarian cancer. FILIP1L expression was inversely correlated with the invasive potential of ovarian cancer cell lines and ovarian cancer specimens. We also demonstrated that DNA methylation in the FILIP1L promoter was a mechanism by which FILIP1L was down-regulated in ovarian cancer. In our present study, we tested this observation in other cancer histologies: breast, colon, lung and pancreatic cancers. Both mRNA and protein expression of FILIP1L were down-regulated in these cancer cells compared with their normal epithelial cells. As in ovarian cancer, DNA methylation is a mechanism by which FILIP1L is down-regulated in these cancer histologies. Methylation status of the FILIP1L promoter was inversely correlated with FILIP1L expression. Reduced methylation in the FILIP1L promoter following treatment with a DNA demethylating agent was associated with restoration of FILIP1L expression in these cancer cells. Further, FILIP1L expression was inversely correlated with the invasive potential of these cancer cells. Re-expression of FILIP1L in FILIP1L-low expressing, highly-invasive cancer cell lines resulted in inhibition of cell invasion. Correspondingly, knockdown of FILIP1L in FILIP1L-high expressing, low-invasive cancer cell lines resulted in increase of cell invasion. Overall, these findings suggest that down-regulation of FILIP1L associated with DNA methylation is related with the invasive phenotype in various cancers. Thus, modulation of FILIP1L expression has the potential to be a target for cancer therapy.