A Microfluidic Platform for Screening Gene Expression Dynamics across Yeast Strain Libraries

The relative ease of genetic manipulation in S. cerevisiae is one of its greatest strengths as a model eukaryotic organism. Researchers have leveraged this quality of the budding yeast to study the effects of a variety of genetic perturbations, such as deletion or overexpression, in a high-throughput manner. This has been accomplished by producing a number of strain libraries that can contain hundreds or even thousands of distinct yeast strains with unique genetic alterations. While these strategies have led to enormous increases in our understanding of the functions and roles that genes play within cells, the techniques used to screen genetically modified libraries of yeast strains typically rely on plate or sequencing-based assays that make it difficult to analyze gene expression changes over time. Microfluidic devices, combined with fluorescence microscopy, can allow gene expression dynamics of different strains to be captured in a continuous culture environment; however, these approaches often have significantly lower throughput compared to traditional techniques. To address these limitations, we have developed a microfluidic platform that uses an array pinning robot to allow for up to 48 different yeast strains to be transferred onto a single device. Here, we detail a validated methodology for constructing and setting up this microfluidic device, starting with the photolithography steps for constructing the wafer, then the soft lithography steps for making polydimethylsiloxane (PDMS) microfluidic devices, and finally the robotic arraying of strains onto the device for experiments. We have applied this device for dynamic screens of a protein aggregation library; however, this methodology has the potential to enable complex and dynamic screens of yeast libraries for a wide range of applications. Key features • Major steps of this protocol require access to specialized equipment (i.e., microfabrication tools typically found in a cleanroom facility and an array pinning robot). • Construction of microfluidic devices with multiple different feature heights using photolithography and soft lithography with PDMS. • Robotic spotting of up to 48 different yeast strains onto microfluidic devices.

Systematic temporal analysis of peripheral blood transcriptomes using TrendCatcher identifies early and persistent neutrophil activation as a hallmark of severe COVID-19

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there is a lack of methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic datasets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal datasets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk RNA-seq and scRNA-seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways as well as impaired type I interferon (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

Expression dynamics of pregnane X receptor-controlled genes in 3D primary human hepatocyte spheroids

The pregnane X receptor (PXR) is a ligand-activated nuclear receptor controlling hepatocyte expression of numerous genes. Although expression changes in xenobiotic-metabolizing, lipogenic, gluconeogenic and bile acid synthetic genes have been described after PXR activation, the temporal dynamics of their expression is largely unknown. Recently, 3D spheroids of primary human hepatocytes (PHHs) have been characterized as the most phenotypically relevant hepatocyte model. We used 3D PHHs to assess time-dependent expression profiles of 12 prototypic PXR-controlled genes in the time course of 168 h of rifampicin treatment (1 or 10 µM). We observed a similar bell-shaped time-induction pattern for xenobiotic-handling genes (CYP3A4, CYP2C9, CYP2B6, and MDR1). However, we observed either biphasic profiles for genes involved in endogenous metabolism (FASN, GLUT2, G6PC, PCK1, and CYP7A1), a decrease for SHP or oscillation for PDK4 and PXR. The rifampicin concentration determined the expression profiles for some genes. Moreover, we calculated half-lives of CYP3A4 and CYP2C9 mRNA under induced or basal conditions and we used a mathematical model to describe PXR-mediated regulation of CYP3A4 expression employing 3D PHHs. The study shows the importance of long-term time-expression profiling of PXR target genes in phenotypically stable 3D PHHs and provides insight into PXR function in liver beyond our knowledge from conventional 2D in vitro models.

Human protein-RNA interaction network is highly stable across mammals

BACKGROUND

RNA-binding proteins (RBPs) are crucial in modulating RNA metabolism in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Although previous studies on the conservation of RBP targets have been carried out in lower eukaryotes such as yeast, relatively little is known about the extent of conservation of the binding sites of RBPs across mammalian species.

RESULTS

In this study, we employ CLIP-seq datasets for 60 human RBPs and demonstrate that most binding sites for a third of these RBPs are conserved in at least 50% of the studied vertebrate species. Across the studied RBPs, binding sites were found to exhibit a median conservation of 58%, ~ 20% higher than random genomic locations, suggesting a significantly higher preservation of RBP-RNA interaction networks across vertebrates. RBP binding sites were highly conserved across primates with weak conservation profiles in birds and fishes. We also note that phylogenetic relationship between members of an RBP family does not explain the extent of conservation of their binding sites across species. Multivariate analysis to uncover features contributing to differences in the extents of conservation of binding sites across RBPs revealed RBP expression level and number of post-transcriptional targets to be the most prominent factors. Examination of the location of binding sites at the gene level confirmed that binding sites occurring on the 3' region of a gene are highly conserved across species with 90% of the RBPs exhibiting a significantly higher conservation of binding sites in 3' regions of a gene than those occurring in the 5'. Gene set enrichment analysis on the extent of conservation of binding sites to identify significantly associated human phenotypes revealed an enrichment for multiple developmental abnormalities.

CONCLUSIONS

Our results suggest that binding sites of human RBPs are highly conserved across primates with weak conservation profiles in lower vertebrates and evolutionary relationship between members of an RBP family does not explain the extent of conservation of their binding sites. Expression level and number of targets of an RBP are important factors contributing to the differences in the extent of conservation of binding sites. RBP binding sites on 3' ends of a gene are the most conserved across species. Phenotypic analysis on the extent of conservation of binding sites revealed the importance of lineage-specific developmental events in post-transcriptional regulatory network evolution.

Transcriptome resilience predicts thermotolerance in Caenorhabditis elegans

BACKGROUND

The detrimental effects of a short bout of stress can persist and potentially turn lethal, long after the return to normal conditions. Thermotolerance, which is the capacity of an organism to withstand relatively extreme temperatures, is influenced by the response during stress exposure, as well as the recovery process afterwards. While heat-shock response mechanisms have been studied intensively, predicting thermal tolerance remains a challenge.

RESULTS

Here, we use the nematode Caenorhabditis elegans to measure transcriptional resilience to heat stress and predict thermotolerance. Using principal component analysis in combination with genome-wide gene expression profiles collected in three high-resolution time series during control, heat stress, and recovery conditions, we infer a quantitative scale capturing the extent of stress-induced transcriptome dynamics in a single value. This scale provides a basis for evaluating transcriptome resilience, defined here as the ability to depart from stress-expression dynamics during recovery. Independent replication across multiple highly divergent genotypes reveals that the transcriptional resilience parameter measured after a spike in temperature is quantitatively linked to long-term survival after heat stress.

CONCLUSION

Our findings imply that thermotolerance is an intrinsic property that pre-determines long-term outcome of stress and can be predicted by the transcriptional resilience parameter. Inferring the transcriptional resilience parameters of higher organisms could aid in evaluating rehabilitation strategies after stresses such as disease and trauma.

Understanding the Biology and Pathogenesis of the Kidney by Single-Cell Transcriptomic Analysis

Background

Single-cell RNA-seq (scRNA-seq) has recently emerged as a revolutionary and powerful tool for biomedical research. However, there have been relatively few studies using scRNA-seq in the field of kidney study.

Summary

scRNA-seq achieves gene expression profiling at single-cell resolution in contrast with the conventional methods of gene expression profiling, which are based on cell population and give averaged values of gene expression of the cells. Single-cell transcriptomic analysis is crucial because individual cells of the same type are highly heterogeneous in gene expression, which reflects the existence of subpopulations, different cellular states, or molecular dynamics, of the cells, and should be resolved for further insights. In addition, gene expression analysis of tissues or organs that usually comprise multiple cell types or subtypes results in data that are not fully applicable to any given cell type. scRNA-seq is capable of identifying all cell types and subtypes in a tissue, including those that are new or present in small quantity. With these unique capabilities, scRNA-seq has been used to dissect molecular processes in cell differentiation and to trace cell lineages in development. It is also used to analyze the cells in a lesion of disease to identify the cell types and molecular dynamics implicated in the injury. With continuous technical improvement, scRNA-seq has become extremely high throughput and cost effective, making it accessible to all laboratories. In the present review article, we provide an overall review of scRNA-seq concerning its history, improvements, and applications. In addition, we describe the available studies in which scRNA-seq was employed in the field of kidney research. Lastly, we discuss other potential uses of scRNA-seq for kidney research.

Key Message

This review article provides general information on scRNA-seq and its various uses. Particularly, we summarize the studies in the field of kidney diseases in which scRNA-seq was used and discuss potential additional uses of scRNA-seq for kidney research.

A broad-host range dual-fluorescence reporter system for gene expression analysis in Gram-negative bacteria

Fluorescence-based reporter systems are valuable tools for studying gene expression dynamics in living cells. Here we describe a dual-fluorescence reporter system carrying the red fluorescent marker mCherry and the blue fluorescent protein EBFP2 enabling the simultaneous analysis of two promoters in broad-host range autofluorescent Gram-negative bacteria.

Understanding key features of bacterial restriction-modification systems through quantitative modeling

Background

Restriction-modification (R-M) systems are rudimentary bacterial immune systems. The main components include restriction enzyme (R), which cuts specific unmethylated DNA sequences, and the methyltransferase (M), which protects the same DNA sequences. The expression of R-M system components is considered to be tightly regulated, to ensure successful establishment in a naïve bacterial host. R-M systems are organized in different architectures (convergent or divergent) and are characterized by different features, i.e. binding cooperativities, dissociation constants of dimerization, translation rates, which ensure this tight regulation. It has been proposed that R-M systems should exhibit certain dynamical properties during the system establishment, such as: i) a delayed expression of R with respect to M, ii) fast transition of R from “OFF” to “ON” state, iii) increased stability of the toxic molecule (R) steady-state levels. It is however unclear how different R-M system features and architectures ensure these dynamical properties, particularly since it is hard to address this question experimentally.

Results

To understand design of different R-M systems, we computationally analyze two R-M systems, representative of the subset controlled by small regulators called ‘C proteins’, and differing in having convergent or divergent promoter architecture. We show that, in the convergent system, abolishing any of the characteristic system features adversely affects the dynamical properties outlined above. Moreover, an extreme binding cooperativity, accompanied by a very high dissociation constant of dimerization, observed in the convergent system, but absent from other R-M systems, can be explained in terms of the same properties. Furthermore, we develop the first theoretical model for dynamics of a divergent R-M system, which does not share any of the convergent system features, but has overlapping promoters. We show that i) the system dynamics exhibits the same three dynamical properties, ii) introducing any of the convergent system features to the divergent system actually diminishes these properties.

Conclusions

Our results suggest that different R-M architectures and features may be understood in terms of constraints imposed by few simple dynamical properties of the system, providing a unifying framework for understanding these seemingly diverse systems. We also provided predictions for the perturbed R-M systems dynamics, which may in future be tested through increasingly available experimental techniques, such as re-engineering R-M systems and single-cell experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0377-x) contains supplementary material, which is available to authorized users.

Synergistic Activation of RD29A Via Integration of Salinity Stress and Abscisic Acid in Arabidopsis thaliana

Plants perceive information from the surroundings and elicit appropriate molecular responses. How plants dynamically respond to combinations of external inputs is yet to be revealed, despite the detailed current knowledge of intracellular signaling pathways. We measured dynamics of Response-to-Dehydration 29A (RD29A) expression induced by single or combined NaCl and ABA treatments in Arabidopsis thaliana. RD29A expression in response to a combination of NaCl and ABA leads to unique dynamic behavior that cannot be explained by the sum of responses to individual NaCl and ABA. To explore the potential mechanisms responsible for the observed synergistic response, we developed a mathematical model of the DREB2 and AREB pathways based on existing knowledge, where NaCl and ABA act as the cognate inputs, respectively, and examined various system structures with cross-input modulation, where non-cognate input affects expression of the genes involved in adjacent signaling pathways. The results from the analysis of system structures, combined with the insights from microarray expression profiles and model-guided experiments, predicted that synergistic activation of RD29A originates from enhancement of DREB2 activity by ABA. Our analysis of RD29A expression profiles demonstrates that a simple mathematical model can be used to extract information from temporal dynamics induced by combinatorial stimuli and produce experimentally testable hypotheses.