The BioExperience Research and Entrepreneurship Challenge: An iGEM-inspired applied research program for BIOSTEM talent and skills development.

Post-secondary education is falling behind in delivering the talent and skills development needed to support the growth of biology-based economies and the demands of professional and research-based graduate degree programs. Here, we describe an applied research program, the BioExperience Research and Entrepreneurship Challenge, launched in May 2020 to mitigate the impact of the COVID-19 pandemic on undergraduate experiential learning programs at the University of Ottawa, Ontario, Canada. The program provided undergraduates with meaningful talent and skills development opportunities by implementing a student-centred, project-based learning approach inspired by the International Genetically Engineered Machine (iGEM) competition. We present evidence from participant exit surveys suggesting that the program delivers a high-quality learning environment and improves learning outcomes compared to traditional work-integrated learning. Notably, 84% of respondents reported an excellent or exceptional learning experience and significant or profound improvements in skills, such as leadership (72% of respondents), problem-solving (42% of respondents) and research preparedness (52%) that are difficult to develop in conventional academic programs. Remarkably, 60% of respondents report that the job-readiness training provided by the program is better or much better than traditional work-integrated learning. Our study demonstrates that a cost-effective and scalable alternative to the iGEM competition can improve talent and skills development in BIOSTEM fields.

Advancing undergraduate synthetic biology education: insights from a Canadian iGEM student perspective.

The last two decades have seen vigorous activity in synthetic biology research and the ever-increasing applications of these technologies. However, pedagogical research pertaining to teaching synthetic biology is scarce, especially when compared to other science and engineering disciplines. Within Canada, there are only three universities that offer synthetic biology programs, two of which are at the undergraduate level. Rather than taking place in formal academic settings, many Canadian undergraduate students are introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition. Although the iGEM competition has had a transformative impact on synthetic biology training in other nations, its impact in Canada has been relatively modest. Consequently, the iGEM competition remains a major setting for synthetic biology education in Canada. To promote further development of synthetic biology education, we surveyed undergraduate students from the Canadian iGEM design teams of 2019. We extracted insights from these data using qualitative analysis to provide recommendations for best teaching practices in synthetic biology undergraduate education, which we describe through our proposed Framework for Transdisciplinary Synthetic Biology Education (FTSBE).

Human gene expression variability and its dependence on methylation and aging.

BACKGROUND: Phenotypic variability of human populations is partly the result of gene polymorphism and differential gene expression. As such, understanding the molecular basis for diversity requires identifying genes with both high and low population expression variance and identifying the mechanisms underlying their expression control. Key issues remain unanswered with respect to expression variability in human populations. The role of gene methylation as well as the contribution that age, sex and tissue-specific factors have on expression variability are not well understood. RESULTS: Here we used a novel method that accounts for sampling error to classify human genes based on their expression variability in normal human breast and brain tissues. We find that high expression variability is almost exclusively unimodal, indicating that variance is not the result of segregation into distinct expression states. Genes with high expression variability differ markedly between tissues and we find that genes with high population expression variability are likely to have age-, but not sex-dependent expression. Lastly, we find that methylation likely has a key role in controlling expression variability insofar as genes with low expression variability are likely to be non-methylated. CONCLUSIONS: We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease.

Measuring Single-Cell Phenotypic Growth Heterogeneity Using a Microfluidic Cell Volume Sensor.

An imaging-integrated microfluidic cell volume sensor was used to evaluate the volumetric growth rate of single cells from a Saccharomyces cerevisiae population exhibiting two phenotypic expression states of the PDR5 gene. This gene grants multidrug resistance by transcribing a membrane transporter capable of pumping out cytotoxic compounds from the cell. Utilizing fluorescent markers, single cells were isolated and trapped, then their growth rates were measured in two on-chip environments: rich media and media dosed with the antibiotic cycloheximide. Approximating growth rates to first-order, we assessed the fitness of individual cells and found that those with low PDR5 expression had higher fitness in rich media whereas cells with high PDR5 expression had higher fitness in the presence of the drug. Moreover, the drug dramatically reduced the fitness of cells with low PDR5 expression but had comparatively minimal impact on the fitness of cells with high PDR5 expression. Our experiments show the utility of this imaging-integrated microfluidic cell volume sensor for high-resolution, single-cell analysis, as well as its potential application for studies that characterize and compare the fitness and morphology of individual cells from heterogeneous populations under different growth conditions.

NuA4 Lysine Acetyltransferase Complex Contributes to Phospholipid Homeostasis in Saccharomyces cerevisiae.

Actively proliferating cells constantly monitor and readjust their metabolic pathways to ensure the replenishment of phospholipids necessary for membrane biogenesis and intracellular trafficking. In Saccharomyces cerevisiae, multiple studies have suggested that the lysine acetyltransferase complex NuA4 plays a role in phospholipid homeostasis. For one, NuA4 mutants induce the expression of the inositol-3-phosphate synthase gene, INO1, which leads to excessive accumulation of inositol, a key metabolite used for phospholipid biosynthesis. Additionally, NuA4 mutants also display negative genetic interactions with sec14-1ts , a mutant of a lipid-binding gene responsible for phospholipid remodeling of the Golgi. Here, using a combination of genetics and transcriptional profiling, we explore the connections between NuA4, inositol, and Sec14 Surprisingly, we found that NuA4 mutants did not suppress but rather exacerbated the growth defects of sec14-1ts under inositol-depleted conditions. Transcriptome studies reveal that while loss of the NuA4 subunit EAF1 in sec14-1ts does derepress INO1 expression, it does not derepress all inositol/choline-responsive phospholipid genes, suggesting that the impact of Eaf1 on phospholipid homeostasis extends beyond inositol biosynthesis. In fact, we find that NuA4 mutants have impaired lipid droplet levels and through genetic and chemical approaches, we determine that the genetic interaction between sec14-1ts and NuA4 mutants potentially reflects a role for NuA4 in fatty acid biosynthesis. Altogether, our work identifies a new role for NuA4 in phospholipid homeostasis.

Improvement of the reverse tetracycline transactivator by single amino acid substitutions that reduce leaky target gene expression to undetectable levels.

Conditional gene expression systems that enable inducible and reversible transcriptional control are essential research tools and have broad applications in biomedicine and biotechnology. The reverse tetracycline transcriptional activator is a canonical system for engineered gene expression control that enables graded and gratuitous modulation of target gene transcription in eukaryotes from yeast to human cell lines and transgenic animals. However, the system has a tendency to activate transcription even in the absence of tetracycline and this leaky target gene expression impedes its use. Here, we identify single amino-acid substitutions that greatly enhance the dynamic range of the system in yeast by reducing leaky transcription to undetectable levels while retaining high expression capacity in the presence of inducer. While the mutations increase the inducer concentration required for full induction, additional sensitivity-enhancing mutations can compensate for this effect and confer a high degree of robustness to the system. The novel transactivator variants will be useful in applications where tight and tunable regulation of gene expression is paramount.

Erratum to: No training required: experimental tests support homology-based DNA assembly as a best practice in synthetic biology.

[This corrects the article DOI: 10.1186/s13036-015-0006-z.].

Genome-wide association between Six4, MyoD, and the histone demethylase Utx during myogenesis.

Adult skeletal muscles can regenerate after injury, due to the presence of satellite cells, a quiescent population of myogenic progenitor cells. Once activated, satellite cells repair the muscle damage by undergoing myogenic differentiation. The myogenic regulatory factors (MRFs) coordinate the process of progenitor differentiation in cooperation with other families of transcription factors (TFs). The Six1 and Six4 homeodomain TFs are expressed in developing and adult muscle and Six1 is critical for embryonic and adult myogenesis. However, the lack of a muscle developmental phenotype in Six4-null mice, which has been attributed to compensation by other Six family members, has discouraged further assessment of the role of Six4 during adult muscle regeneration. By employing genome-wide approaches to address the function of Six4 during adult skeletal myogenesis, we have identified a core set of muscle genes coordinately regulated in adult muscle precursors by Six4 and the MRF MyoD. Throughout the genome of differentiating adult myoblasts, the cooperation between Six4 and MyoD is associated with chromatin repressive mark removal by Utx, a demethylase of histone H3 trimethylated at lysine 27. Among the genes coordinately regulated by Six4 and MyoD are several genes critical for proper in vivo muscle regeneration, implicating a role of Six4 in this process. Using in vivo RNA interference of Six4, we expose an uncompensated function of this TF during muscle regeneration. Together, our results reveal a role for Six4 during adult muscle regeneration and suggest a widespread mechanism of cooperation between Six4 and MyoD.

No training required: experimental tests support homology-based DNA assembly as a best practice in synthetic biology.

The Registry of Standard Biological Parts imposes sequence constraints to enable DNA assembly using restriction enzymes. Alnahhas et al. (Journal of Biological Engineering 2014, 8:28) recently argued that these constraints should be revised because they impose an unnecessary burden on contributors that use homology-based assembly. To add to this debate, we tested four different homology-based methods, and found that students using these methods on their first attempt have a high probability of success. Because of their ease of use and high success rates, we believe that homology-based assembly is a best practice of Synthetic Biology, and recommend that the Registry implement the changes proposed by Alnahhas et al. to better support their use.

Microtubule disruption synergizes with oncolytic virotherapy by inhibiting interferon translation and potentiating bystander killing.

In this study, we show that several microtubule-destabilizing agents used for decades for treatment of cancer and other diseases also sensitize cancer cells to oncolytic rhabdoviruses and improve therapeutic outcomes in resistant murine cancer models. Drug-induced microtubule destabilization leads to superior viral spread in cancer cells by disrupting type I IFN mRNA translation, leading to decreased IFN protein expression and secretion. Furthermore, microtubule-destabilizing agents specifically promote cancer cell death following stimulation by a subset of infection-induced cytokines, thereby increasing viral bystander effects. This study reveals a previously unappreciated role for microtubule structures in the regulation of the innate cellular antiviral response and demonstrates that unexpected combinations of approved chemotherapeutics and biological agents can lead to improved therapeutic outcomes.

Coherent feedforward transcriptional regulatory motifs enhance drug resistance.

Fluctuations in gene expression give identical cells access to a spectrum of phenotypes that can serve as a transient, nongenetic basis for natural selection by temporarily increasing drug resistance. In this study, we demonstrate using mathematical modeling and simulation that certain gene regulatory network motifs, specifically coherent feedforward loop motifs, can facilitate the development of nongenetic resistance by increasing cell-to-cell variability and the time scale at which beneficial phenotypic states can be maintained. Our results highlight how regulatory network motifs enabling transient, nongenetic inheritance play an important role in defining reproductive fitness in adverse environments and provide a selective advantage subject to evolutionary pressure.

The circadian molecular clock regulates adult hippocampal neurogenesis by controlling the timing of cell-cycle entry and exit.

The subgranular zone (SGZ) of the adult hippocampus contains a pool of quiescent neural progenitor cells (QNPs) that are capable of entering the cell cycle and producing newborn neurons. The mechanisms that control the timing and extent of adult neurogenesis are not well understood. Here, we show that QNPs of the adult SGZ express molecular-clock components and proliferate in a rhythmic fashion. The clock proteins PERIOD2 and BMAL1 are critical for proper control of neurogenesis. The absence of PERIOD2 abolishes the gating of cell-cycle entrance of QNPs, whereas genetic ablation of bmal1 results in constitutively high levels of proliferation and delayed cell-cycle exit. We use mathematical model simulations to show that these observations may arise from clock-driven expression of a cell-cycle inhibitor that targets the cyclin D/Cdk4-6 complex. Our findings may have broad implications for the circadian clock in timing cell-cycle events of other stem cell populations throughout the body.

Identifiability and inference of pathway motifs by epistasis analysis.

The accuracy of genetic network inference is limited by the assumptions used to determine if one hypothetical model is better than another in explaining experimental observations. Most previous work on epistasis analysis-in which one attempts to infer pathway relationships by determining equivalences among traits following mutations-has been based on Boolean or linear models. Here, we delineate the ultimate limits of epistasis-based inference by systematically surveying all two-gene network motifs and use symbolic algebra with arbitrary regulation functions to examine trait equivalences. Our analysis divides the motifs into equivalence classes, where different genetic perturbations result in indistinguishable experimental outcomes. We demonstrate that this partitioning can reveal important information about network architecture, and show, using simulated data, that it greatly improves the accuracy of genetic network inference methods. Because of the minimal assumptions involved, equivalence partitioning has broad applicability for gene network inference.

Model-based rational design of an oncolytic virus with improved therapeutic potential.

Oncolytic viruses are complex biological agents that interact at multiple levels with both tumour and normal tissues. Antiviral pathways induced by interferon are known to have a critical role in determining tumour cell sensitivity and normal cell resistance to infection with oncolytic viruses. Here we pursue a synthetic biology approach to identify methods that enhance antitumour activity of oncolytic viruses through suppression of interferon signalling. On the basis of the mathematical analysis of multiple strategies, we hypothesize that a positive feedback loop, established by virus-mediated expression of a soluble interferon-binding decoy receptor, increases tumour cytotoxicity without compromising normal cells. Oncolytic rhabdoviruses engineered to express a secreted interferon antagonist have improved oncolytic potential in cellular cancer models, and display improved therapeutic potential in tumour-bearing mice. Our results demonstrate the potential of this methodology in evaluating potential caveats of viral immune-evasion strategies and improving the design oncolytic viruses.

Gene expression noise facilitates adaptation and drug resistance independently of mutation.

We show that the effect of stress on the reproductive fitness of noisy cell populations can be modeled as a first-passage time problem, and demonstrate that even relatively short-lived fluctuations in gene expression can ensure the long-term survival of a drug-resistant population. We examine how this effect contributes to the development of drug-resistant cancer cells, and demonstrate that permanent immunity can arise independently of mutations.

Quantitative epistasis analysis and pathway inference from genetic interaction data.

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ~80% of the known relationships without any false positives.

Chromosomal position effects are linked to sir2-mediated variation in transcriptional burst size.

Gene expression noise varies with genomic position and is a driving force in the evolution of chromosome organization. Nevertheless, position effects remain poorly characterized. Here, we present a systematic analysis of chromosomal position effects by characterizing single-cell gene expression from euchromatic positions spanning the length of a eukaryotic chromosome. We demonstrate that position affects gene expression by modulating the size of transcriptional bursts, rather than their frequency, and that the histone deacetylase Sir2 plays a role in this process across the chromosome.

Phenotypic impact of regulatory noise in cellular stress-response pathways.

Recent studies indicate that intrinsic promoter-mediated gene expression noise can confer a selective advantage under acute environmental stress by providing beneficial phenotypic diversity within cell populations. To investigate how extrinsic gene expression noise impacts the fitness of cell populations under stress, we engineered two nearly isogenic budding yeast strains; one carrying a two-step regulatory cascade that allows for precise control of the noise transmitted from a transcriptional regulator to a downstream stress-inducing gene, and one carrying a network with low constant upstream noise. The fitness and gene expression of these strains were compared under acute and prolonged stress exposure. Using a phenomenological modeling approach, we predicted that increased noise should confer a fitness advantage under high stress conditions, but reciprocally reduce the resistance of the population to low stress. The model also predicted that extrinsic noise might serve as a basis for phenotypic plasticity whereby gene expression distributions are modulated in response to prolonged stress. Experimentally, we confirmed the predicted differential fitness advantage of extrinsic noise under acute stress, as well as the predicted modulation of gene expression under prolonged stress. However, contrary to model predictions, strains with low and high extrinsic noise showed very similar adaptive responses to prolonged stress. This suggests that while phenotypic heterogeneity generated by noise in regulatory signals can confer increased robustness to acute stress, it is not a requirement for the observed long-term phenotypic plasticity.

Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis.

BACKGROUND: High-throughput genomics has enabled the global mapping of genetic interactions based on the phenotypic impact of combinatorial genetic perturbations. An important next step is to understand how these networks are dynamically remodelled in response to environmental stimuli. Here, we report on the development and testing of a method to identify such interactions. The method was developed from first principles by treating the impact on cellular growth of environmental perturbations equivalently to that of gene deletions. This allowed us to establish a novel neutrality function marking the absence of epistasis in terms of sensitivity phenotypes rather than fitness. We tested the method by identifying fitness- and sensitivity-based interactions involved in the response to drug-induced DNA-damage of budding yeast Saccharomyces cerevisiae using two mutant libraries - one containing transcription factor deletions, and the other containing deletions of DNA repair genes. RESULTS: Within the library of transcription factor deletion mutants, we observe significant differences in the sets of genetic interactions identified by the fitness- and sensitivity-based approaches. Notably, among the most likely interactions, only ~50% were identified by both methods. While interactions identified solely by the sensitivity-based approach are modulated in response to drug-induced DNA damage, those identified solely by the fitness-based method remained invariant to the treatment. Comparison of the identified interactions to transcriptional profiles and protein-DNA interaction data indicate that the sensitivity-based method improves the identification of interactions involved in the DNA damage response. Additionally, for the library containing DNA repair mutants, we observe that the sensitivity-based method improves the grouping of functionally related genes, as well as the identification of protein complexes, involved in DNA repair. CONCLUSION: Our results show that the identification of response-modulated genetic interactions can be improved by incorporating the effect of a changing environment directly into the neutrality function marking the absence of epistasis. We expect that this extension of conventional epistatic analysis will facilitate the development of dynamic models of gene networks from quantitative measurements of genetic interactions. While the method was developed for growth phenotype, it should apply equally well for other phenotypes, including the expression of fluorescent reporters.

Estimating the stochastic bifurcation structure of cellular networks.

High throughput measurement of gene expression at single-cell resolution, combined with systematic perturbation of environmental or cellular variables, provides information that can be used to generate novel insight into the properties of gene regulatory networks by linking cellular responses to external parameters. In dynamical systems theory, this information is the subject of bifurcation analysis, which establishes how system-level behaviour changes as a function of parameter values within a given deterministic mathematical model. Since cellular networks are inherently noisy, we generalize the traditional bifurcation diagram of deterministic systems theory to stochastic dynamical systems. We demonstrate how statistical methods for density estimation, in particular, mixture density and conditional mixture density estimators, can be employed to establish empirical bifurcation diagrams describing the bistable genetic switch network controlling galactose utilization in yeast Saccharomyces cerevisiae. These approaches allow us to make novel qualitative and quantitative observations about the switching behavior of the galactose network, and provide a framework that might be useful to extract information needed for the development of quantitative network models.