Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

3D Hydrogel Culture System Recapitulates Key Tuberculosis Phenotypes and Demonstrates Pyrazinamide Efficacy

The mortality caused by tuberculosis (TB) infections is a global concern, and there is a need to improve understanding of the disease. Current in vitro infection models to study the disease have limitations such as short investigation durations and divergent transcriptional signatures. This study aims to overcome these limitations by developing a 3D collagen culture system that mimics the biomechanical and extracellular matrix (ECM) of lung microenvironment (collagen fibers, stiffness comparable to in vivo conditions) as the infection primarily manifests in the lungs. The system incorporates Mycobacterium tuberculosis (Mtb) infected human THP-1 or primary monocytes/macrophages. Dual RNA sequencing reveals higher mammalian gene expression similarity with patient samples than 2D macrophage infections. Similarly, bacterial gene expression more accurately recapitulates in vivo gene expression patterns compared to bacteria in 2D infection models. Key phenotypes observed in humans, such as foamy macrophages and mycobacterial cords, are reproduced in the model. This biomaterial system overcomes challenges associated with traditional platforms by modulating immune cells and closely mimicking in vivo infection conditions, including showing efficacy with clinically relevant concentrations of anti-TB drug pyrazinamide, not seen in any other in vitro infection model, making it reliable and readily adoptable for tuberculosis studies and drug screening.

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE-a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Mimicked Mixing-Induced Heterogeneities of Industrial Bioreactors Stimulate Long-Lasting Adaption Programs in Ethanol-Producing Yeasts

Commercial-scale bioreactors create an unnatural environment for microbes from an evolutionary point of view. Mixing insufficiencies expose individual cells to fluctuating nutrient concentrations on a second-to-minute scale while transcriptional and translational capacities limit the microbial adaptation time from minutes to hours. This mismatch carries the risk of inadequate adaptation effects, especially considering that nutrients are available at optimal concentrations on average. Consequently, industrial bioprocesses that strive to maintain microbes in a phenotypic sweet spot, during lab-scale development, might suffer performance losses when said adaptive misconfigurations arise during scale-up. Here, we investigated the influence of fluctuating glucose availability on the gene-expression profile in the industrial yeast Ethanol Red™. The stimulus-response experiment introduced 2 min glucose depletion phases to cells growing under glucose limitation in a chemostat. Even though Ethanol Red™ displayed robust growth and productivity, a single 2 min depletion of glucose transiently triggered the environmental stress response. Furthermore, a new growth phenotype with an increased ribosome portfolio emerged after complete adaptation to recurring glucose shortages. The results of this study serve a twofold purpose. First, it highlights the necessity to consider the large-scale environment already at the experimental development stage, even when process-related stressors are moderate. Second, it allowed the deduction of strain engineering guidelines to optimize the genetic background of large-scale production hosts.

Disrupting the ArcA Regulatory Network Amplifies the Fitness Cost of Tetracycline Resistance in Escherichia coli

There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline-resistant (TetR) Escherichia coli undergoes global transcriptional and metabolic remodeling, including downregulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Using a pooled genome-wide library of single-gene deletion strains, at least 308 genes, including four transcriptional regulators identified by our network analysis, were confirmed as essential for restoring the fitness of TetR E. coli during treatment with tetracycline. Targeted knockout of ArcA, identified by network analysis as a master regulator of this new compensatory physiological state, significantly compromised fitness of TetR E. coli during tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain, also resensitized TetR E. coli to tetracycline. We discovered that the potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of potential synergy was through action of sertraline on the tetracycline-induced ArcA network in the TetR strain. Our findings demonstrate that therapies that target mechanistic drivers of compensatory physiological states could resensitize AMR pathogens to lost antibiotics. IMPORTANCE Antimicrobial resistance (AMR) is projected to be the cause of >10 million deaths annually by 2050. While efforts to find new potent antibiotics are effective, they are expensive and outpaced by the rate at which new resistant strains emerge. There is desperate need for a rational approach to accelerate the discovery of drugs and drug combinations that effectively clear AMR pathogens and even prevent the emergence of new resistant strains. Using tetracycline-resistant (TetR) Escherichia coli, we demonstrate that gaining resistance is accompanied by loss of fitness, which is restored by compensatory physiological changes. We demonstrate that transcriptional regulators of the compensatory physiologic state are promising drug targets because their disruption increases the susceptibility of TetR E. coli to tetracycline. Thus, we describe a generalizable systems biology approach to identify new vulnerabilities within AMR strains to rationally accelerate the discovery of therapeutics that extend the life span of existing antibiotics.

Performing in spite of starvation: How Saccharomyces cerevisiae maintains robust growth when facing famine zones in industrial bioreactors

In fed-batch operated industrial bioreactors, glucose-limited feeding is commonly applied for optimal control of cell growth and product formation. Still, microbial cells such as yeasts and bacteria are frequently exposed to glucose starvation conditions in poorly mixed zones or far away from the feedstock inlet point. Despite its commonness, studies mimicking related stimuli are still underrepresented in scale-up/scale-down considerations. This may surprise as the transition from glucose limitation to starvation has the potential to provoke regulatory responses with negative consequences for production performance. In order to shed more light, we performed gene-expression analysis of Saccharomyces cerevisiae grown in intermittently fed chemostat cultures to study the effect of limitation-starvation transitions. The resulting glucose concentration gradient was representative for the commercial scale and compelled cells to tolerate about 76 s with sub-optimal substrate supply. Special attention was paid to the adaptation status of the population by discriminating between first time and repeated entry into the starvation regime. Unprepared cells reacted with a transiently reduced growth rate governed by the general stress response. Yeasts adapted to the dynamic environment by increasing internal growth capacities at the cost of rising maintenance demands by 2.7%. Evidence was found that multiple protein kinase A (PKA) and Snf1-mediated regulatory circuits were initiated and ramped down still keeping the cells in an adapted trade-off between growth optimization and down-regulation of stress response. From this finding, primary engineering guidelines are deduced to optimize both the production host's genetic background and the design of scale-down experiments.

Deletion of NGG1 in a recombinant Saccharomyces cerevisiae improved xylose utilization and affected transcription of genes related to amino acid metabolism

Production of biofuels and biochemicals from xylose using yeast cell factory is of great interest for lignocellulosic biorefinery. Our previous studies revealed that a natural yeast isolate Saccharomyces cerevisiae YB-2625 has superior xylose-fermenting ability. Through integrative omics analysis, NGG1, which encodes a transcription regulator as well as a subunit of chromatin modifying histone acetyltransferase complexes was revealed to regulate xylose metabolism. Deletion of NGG1 in S. cerevisiae YRH396h, which is the haploid version of the recombinant yeast using S. cerevisiae YB-2625 as the host strain, improved xylose consumption by 28.6%. Comparative transcriptome analysis revealed that NGG1 deletion down-regulated genes related to mitochondrial function, TCA cycle, ATP biosynthesis, respiration, as well as NADH generation. In addition, the NGG1 deletion mutant also showed transcriptional changes in amino acid biosynthesis genes. Further analysis of intracellular amino acid content confirmed the effect of NGG1 on amino acid accumulation during xylose utilization. Our results indicated that NGG1 is one of the core nodes for coordinated regulation of carbon and nitrogen metabolism in the recombinant S. cerevisiae. This work reveals novel function of Ngg1p in yeast metabolism and provides basis for developing robust yeast strains to produce ethanol and biochemicals using lignocellulosic biomass.

AI-based forecasting of ethanol fermentation using yeast morphological data

Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a nonstaining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of >0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.

Xylose Assimilation for the Efficient Production of Biofuels and Chemicals by Engineered Saccharomyces cerevisiae

Microbial conversion of plant biomass into fuels and chemicals offers a practical solution to global concerns over limited natural resources, environmental pollution, and climate change. Pursuant to these goals, researchers have put tremendous efforts and resources toward engineering the yeast Saccharomyces cerevisiae to efficiently convert xylose, the second most abundant sugar in lignocellulosic biomass, into various fuels and chemicals. Here, recent advances in metabolic engineering of yeast is summarized to address bottlenecks on xylose assimilation and to enable simultaneous co-utilization of xylose and other substrates in lignocellulosic hydrolysates. Distinct characteristics of xylose metabolism that can be harnessed to produce advanced biofuels and chemicals are also highlighted. Although many challenges remain, recent research investments have facilitated the efficient fermentation of xylose and simultaneous co-consumption of xylose and glucose. In particular, understanding xylose-induced metabolic rewiring in engineered yeast has encouraged the use of xylose as a carbon source for producing various non-ethanol bioproducts. To boost the lignocellulosic biomass-based bioeconomy, much attention is expected to promote xylose-utilizing efficiency via reprogramming cellular regulatory networks, to attain robust co-fermentation of xylose and other cellulosic carbon sources under industrial conditions, and to exploit the advantageous traits of yeast xylose metabolism for producing diverse fuels and chemicals.

Different transcriptional responses of haploid and diploid S. cerevisiae strains to changes in cofactor preference of XR

Background

Xylitol accumulation is a major barrier for efficient ethanol production through heterologous xylose reductase-xylitol dehydrogenase (XR-XDH) pathway in recombinant Saccharomyces cerevisiae. Mutated NADH-preferring XR is usually employed to alleviate xylitol accumulation. However, it remains unclear how mutated XR affects the metabolic network for xylose metabolism. In this study, haploid and diploid strains were employed to investigate the transcriptional responses to changes in cofactor preference of XR through RNA-seq analysis during xylose fermentation.

Results

For the haploid strains, genes involved in xylose-assimilation (XYL1, XYL2, XKS1), glycolysis, and alcohol fermentation had higher transcript levels in response to mutated XR, which was consistent with the improved xylose consumption rate and ethanol yield. For the diploid strains, genes related to protein biosynthesis were upregulated while genes involved in glyoxylate shunt were downregulated in response to mutated XR, which might contribute to the improved yields of biomass and ethanol. When comparing the diploids with the haploids, genes involved in glycolysis and MAPK signaling pathway were significantly downregulated, while oxidative stress related transcription factors (TFs) were significantly upregulated, irrespective of the cofactor preference of XR.

Conclusions

Our results not only revealed the differences in transcriptional responses of the diploid and haploid strains to mutated XR, but also provided underlying basis for better understanding the differences in xylose metabolism between the diploid and haploid strains.

Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses

A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.

In-Depth Two-Stage Transcriptional Reprogramming and Evolutionary Engineering of Saccharomyces cerevisiae for Efficient Bioethanol Production from Xylose with Acetate

In order to achieve rapid xylose utilization in the presence of acetate, improved yeast strains were engineered for higher bioethanol production. First, a six-gene cluster, including XYL1/XYL2/XKS1/TAL1/PYK1/MGT05196, was generated by using an in-depth two-stage (glucose and xylose) transcription reprogramming strategy in an evolutionary adapted strain of CE7, resulting in two improved engineered strains WXY46 and WXY53. Through a combined screening of xylose and glucose stage-specific promoters between tricarboxylic acid (TCA)/HSP and constitutive types, respectively, WXY46 with the constitutive promoters showed a much higher ethanol yield than that of WXY53 with the TCA/HSP promoters. Second, an optimized strain WXY74 was obtained by using more copies of a six-gene cluster, which resulted in a higher ethanol yield of 0.500 g/g total sugars with acetate conditions. At last, simultaneous saccharification and co-fermentation were performed by using the evolved WXY74 strain, which produced 58.4 g/L of ethanol from wheat straw waste and outperformed previous haploid XR-XDH strains.

d-Xylose consumption by nonrecombinant Saccharomyces cerevisiae: A review

Xylose is the second most abundant sugar in nature. Its efficient fermentation has been considered as a critical factor for a feasible conversion of renewable biomass resources into biofuels and other chemicals. The yeast Saccharomyces cerevisiae is of exceptional industrial importance due to its excellent capability to ferment sugars. However, although S. cerevisiae is able to ferment xylulose, it is considered unable to metabolize xylose, and thus, a lot of research has been directed to engineer this yeast with heterologous genes to allow xylose consumption and fermentation. The analysis of the natural genetic diversity of this yeast has also revealed some nonrecombinant S. cerevisiae strains that consume or even grow (modestly) on xylose. The genome of this yeast has all the genes required for xylose transport and metabolism through the xylose reductase, xylitol dehydrogenase, and xylulokinase pathway, but there seems to be problems in their kinetic properties and/or required expression. Self-cloning industrial S. cerevisiae strains overexpressing some of the endogenous genes have shown interesting results, and new strategies and approaches designed to improve these S. cerevisiae strains for ethanol production from xylose will also be presented in this review.

A Thi2p Regulatory Network Controls the Post-glucose Effect of Xylose Utilization in Saccharomyces cerevisiae

The complete and efficient utilization of both glucose and xylose is necessary for the economically viable production of biofuels and chemicals using lignocellulosic feedstocks. Although recently obtained recombinant Saccharomyces cerevisiae strains metabolize xylose well when xylose is the sole carbon source in the medium (henceforth referred to as "X stage"), their xylose consumption rate is significantly reduced during the xylose-only consumption phase of glucose-xylose co-fermentation ("GX stage"). This post-glucose effect seriously decreases overall fermentation efficiency. We showed in previous work that THI2 deletion can alleviate this post-glucose effect, but the underlying mechanisms were ill-defined. In the present study, we profiled the transcriptome of a thi2Δ strain growing at the GX stage. Thi2p in GX stage cells regulates genes involved in the cell cycle, stress tolerance, and cell viability. Importantly, the regulation of Thi2p differs from a previous regulatory network that functions when glucose is the sole carbon source, which suggests that the function of Thi2p depends on the carbon source. Modeling research seeking to optimize metabolic engineering via TFs should account for this important carbon source difference. Building on our initial study, we confirmed that several identified factors did indeed increase fermentation efficiency. Specifically, overexpressing STT4, RGI2, and TFC3 increases specific xylose utilization rate of the strain by 36.9, 29.7, 42.8%, respectively, in the GX stage of anaerobic fermentation. Our study thus illustrates a promising strategy for the rational engineering of yeast for lignocellulosic ethanol production.

Path-seq identifies an essential mycolate remodeling program for mycobacterial host adaptation

The success of Mycobacterium tuberculosis (MTB) stems from its ability to remain hidden from the immune system within macrophages. Here, we report a new technology (Path-seq) to sequence miniscule amounts of MTB transcripts within up to million-fold excess host RNA Using Path-seq and regulatory network analyses, we have discovered a novel transcriptional program for in vivo mycobacterial cell wall remodeling when the pathogen infects alveolar macrophages in mice. We have discovered that MadR transcriptionally modulates two mycolic acid desaturases desA1/desA2 to initially promote cell wall remodeling upon in vitro macrophage infection and, subsequently, reduces mycolate biosynthesis upon entering dormancy. We demonstrate that disrupting MadR program is lethal to diverse mycobacteria making this evolutionarily conserved regulator a prime antitubercular target for both early and late stages of infection.

Rewired cellular signaling coordinates sugar and hypoxic responses for anaerobic xylose fermentation in yeast

Microbes can be metabolically engineered to produce biofuels and biochemicals, but rerouting metabolic flux toward products is a major hurdle without a systems-level understanding of how cellular flux is controlled. To understand flux rerouting, we investigated a panel of Saccharomyces cerevisiae strains with progressive improvements in anaerobic fermentation of xylose, a sugar abundant in sustainable plant biomass used for biofuel production. We combined comparative transcriptomics, proteomics, and phosphoproteomics with network analysis to understand the physiology of improved anaerobic xylose fermentation. Our results show that upstream regulatory changes produce a suite of physiological effects that collectively impact the phenotype. Evolved strains show an unusual co-activation of Protein Kinase A (PKA) and Snf1, thus combining responses seen during feast on glucose and famine on non-preferred sugars. Surprisingly, these regulatory changes were required to mount the hypoxic response when cells were grown on xylose, revealing a previously unknown connection between sugar source and anaerobic response. Network analysis identified several downstream transcription factors that play a significant, but on their own minor, role in anaerobic xylose fermentation, consistent with the combinatorial effects of small-impact changes. We also discovered that different routes of PKA activation produce distinct phenotypes: deletion of the RAS/PKA inhibitor IRA2 promotes xylose growth and metabolism, whereas deletion of PKA inhibitor BCY1 decouples growth from metabolism to enable robust fermentation without division. Comparing phosphoproteomic changes across ira2Δ and bcy1Δ strains implicated regulatory changes linked to xylose-dependent growth versus metabolism. Together, our results present a picture of the metabolic logic behind anaerobic xylose flux and suggest that widespread cellular remodeling, rather than individual metabolic changes, is an important goal for metabolic engineering.

Pentose Metabolism in Saccharomyces cerevisiae: The Need to Engineer Global Regulatory Systems

Extending the host substrate range of industrially relevant microbes, such as Saccharomyces cerevisiae, has been a highly-active area of research since the conception of metabolic engineering. Yet, rational strategies that enable non-native substrate utilization in this yeast without the need for combinatorial and/or evolutionary techniques are underdeveloped. Herein, this review focuses on pentose metabolism in S. cerevisiae as a case study to highlight the challenges in this field. In the last three decades, work has focused on expressing exogenous pentose metabolizing enzymes as well as endogenous enzymes for effective pentose assimilation, growth, and biofuel production. The engineering strategies that are employed for pentose assimilation in this yeast are reviewed, and compared with metabolism and regulation of native sugar, galactose. In the case of galactose metabolism, multiple signals regulate and aid growth in the presence of the sugar. However, for pentoses that are non-native, it is unclear if similar growth and regulatory signals are activated. Such a comparative analysis aids in identifying missing links in xylose and arabinose utilization. While research on pentose metabolism have mostly concentrated on pathway level optimization, recent transcriptomics analyses highlight the need to consider more global regulatory, structural, and signaling components.

Integrated Epigenetic Mapping of Human and Mouse Salivary Gene Regulation

Significant effort has been applied to identify the genome-wide gene expression profiles associated with salivary gland development and pathophysiology. However, relatively little is known about the regulators that control salivary gland gene expression. We integrated data from DNase1 digital genomic footprinting, RNA-seq, and gene expression microarrays to comprehensively characterize the cis- and trans-regulatory components controlling gene expression of the healthy submandibular salivary gland. Analysis of 32 human tissues and 87 mouse tissues was performed to identify the highly expressed and tissue-enriched transcription factors driving salivary gland gene expression. Following RNA analysis, protein expression levels and subcellular localization of 39 salivary transcription factors were confirmed by immunohistochemistry. These expression analyses revealed that the salivary gland highly expresses transcription factors associated with endoplasmic reticulum stress, human T-cell lymphotrophic virus 1 expression, and Epstein-Barr virus reactivation. DNase1 digital genomic footprinting to a depth of 333,426,353 reads was performed and utilized to generate a salivary gland gene regulatory network describing the genome-wide chromatin accessibility and transcription factor binding of the salivary gland at a single-nucleotide resolution. Analysis of the DNase1 gene regulatory network identified dense interconnectivity among PLAG1, MYB, and 13 other transcription factors associated with balanced chromosomal translocations and salivary gland tumors. Collectively, these analyses provide a comprehensive atlas of the cis- and trans-regulators of the salivary gland and highlight known aberrantly regulated pathways of diseases affecting the salivary glands.

ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Background

Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data.

Results

We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads.

Conclusions

We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4943-z) contains supplementary material, which is available to authorized users.

Disruption of the transcription factors Thi2p and Nrm1p alleviates the post-glucose effect on xylose utilization in Saccharomyces cerevisiae

BACKGROUND

The recombinant Saccharomyces cerevisiae strains that acquired the ability to utilize xylose through metabolic and evolutionary engineering exhibit good performance when xylose is the sole carbon source in the medium (designated the X stage in the present work). However, the xylose consumption rate of strains is generally low after glucose depletion during glucose-xylose co-fermentation, despite the presence of xylose in the medium (designated the GX stage in the present work). Glucose fermentation appears to reduce the capacity of these strains to "recognize" xylose during the GX stage, a phenomenon termed the post-glucose effect on xylose metabolism.

RESULTS

Two independent xylose-fermenting S. cerevisiae strains derived from a haploid laboratory strain and a diploid industrial strain were used in the present study. Their common characteristics were investigated to reveal the mechanism underlying the post-glucose effect and to develop methods to alleviate this effect. Both strains showed lower growth and specific xylose consumption rates during the GX stage than during the X stage. Glycolysis, the pentose phosphate pathway, and translation-related gene expression were reduced; meanwhile, genes in the tricarboxylic acid cycle and glyoxylic acid cycle demonstrated higher expression during the GX stage than during the X stage. The effects of 11 transcription factors (TFs) whose expression levels significantly differed between the GX and X stages in both strains were investigated. Knockout of THI2 promoted ribosome synthesis, and the growth rate, specific xylose utilization rate, and specific ethanol production rate of the strain increased by 17.4, 26.8, and 32.4%, respectively, in the GX stage. Overexpression of the ribosome-related genes RPL9A, RPL7B, and RPL7A also enhanced xylose utilization in a corresponding manner. Furthermore, the overexpression of NRM1, which is related to the cell cycle, increased the growth rate by 8.7%, the xylose utilization rate by 30.0%, and the ethanol production rate by 76.6%.

CONCLUSIONS

The TFs Thi2p and Nrm1p exerted unexpected effects on the post-glucose effect, enhancing ribosome synthesis and altering the cell cycle, respectively. The results of this study will aid in maintaining highly efficient xylose metabolism during glucose-xylose co-fermentation, which is utilized for lignocellulosic bioethanol production.

A semi-synthetic regulon enables rapid growth of yeast on xylose

Nutrient assimilation is the first step that allows biological systems to proliferate and produce value-added products. Yet, implementation of heterologous catabolic pathways has so far relied on constitutive gene expression without consideration for global regulatory systems that may enhance nutrient assimilation and cell growth. In contrast, natural systems prefer nutrient-responsive gene regulation (called regulons) that control multiple cellular functions necessary for cell survival and growth. Here, in Saccharomyces cerevisiae, by partially- and fully uncoupling galactose (GAL)-responsive regulation and metabolism, we demonstrate the significant growth benefits conferred by the GAL regulon. Next, by adapting the various aspects of the GAL regulon for a non-native nutrient, xylose, we build a semi-synthetic regulon that exhibits higher growth rate, better nutrient consumption, and improved growth fitness compared to the traditional and ubiquitous constitutive expression strategy. This work provides an elegant paradigm to integrate non-native nutrient catabolism with native, global cellular responses to support fast growth.

Efficient assimilation of nutrients is essential for the production of value-added products in microbial fermentation. Here the authors design a semi-synthetic xylose regulon to improve growth characteristics of Saccharomyces cerevisiae on this non-native sugar.

Exploiting transcriptomic data for metabolic engineering: toward a systematic strain design

Transcriptomics is now recognized as a primary tool for metabolic engineering as it can be used for identifying new strain designs by diagnosing current states of microbial cells. This review summarizes current application of transcriptomic data for strain design. Along with a few successful examples, limitations of conventionally used differentially expressed gene-based strain design approaches have been discussed, which have been major reasons why transcriptomic data are considerably underutilized. Recently, integrative network-based approaches interpreting transcriptomic data in the context of biological networks were invented to provide complimentary solutions for metabolic engineering by overcoming the limitations of conventional approaches. Here, we highlight recent pioneering studies in which integrative network-based methods have been used for providing novel strain designs.

Association of improved oxidative stress tolerance and alleviation of glucose repression with superior xylose-utilization capability by a natural isolate of Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae wild strains generally have poor xylose-utilization capability, which is a major barrier for efficient bioconversion of lignocellulosic biomass. Laboratory adaption is commonly used to enhance xylose utilization of recombinant S. cerevisiae. Apparently, yeast cells could remodel the metabolic network for xylose metabolism. However, it still remains unclear why natural isolates of S. cerevisiae poorly utilize xylose. Here, we analyzed a unique S. cerevisiae natural isolate YB-2625 which has superior xylose metabolism capability in the presence of mixed-sugar. Comparative transcriptomic analysis was performed using S. cerevisiae YB-2625 grown in a mixture of glucose and xylose, and the model yeast strain S288C served as a control. Global gene transcription was compared at both the early mixed-sugar utilization stage and the latter xylose-utilization stage.

RESULTS

Genes involved in endogenous xylose-assimilation (XYL2 and XKS1), gluconeogenesis, and TCA cycle showed higher transcription levels in S. cerevisiae YB-2625 at the xylose-utilization stage, when compared to the reference strain. On the other hand, transcription factor encoding genes involved in regulation of glucose repression (MIG1, MIG2, and MIG3) as well as HXK2 displayed decreased transcriptional levels in YB-2625, suggesting the alleviation of glucose repression of S. cerevisiae YB-2625. Notably, genes encoding antioxidant enzymes (CTT1, CTA1, SOD2, and PRX1) showed higher transcription levels in S. cerevisiae YB-2625 in the xylose-utilization stage than that of the reference strain. Consistently, catalase activity of YB-2625 was 1.9-fold higher than that of S. cerevisiae S288C during the xylose-utilization stage. As a result, intracellular reactive oxygen species levels of S. cerevisiae YB-2625 were 43.3 and 58.6% lower than that of S288C at both sugar utilization stages. Overexpression of CTT1 and PRX1 in the recombinant strain S. cerevisiae YRH396 deriving from S. cerevisiae YB-2625 increased cell growth when xylose was used as the sole carbon source, leading to 13.5 and 18.1%, respectively, more xylose consumption.

CONCLUSIONS

Enhanced oxidative stress tolerance and relief of glucose repression are proposed to be two major mechanisms for superior xylose utilization by S. cerevisiae YB-2625. The present study provides insights into the innate regulatory mechanisms underlying xylose utilization in wild-type S. cerevisiae, which benefits the rapid development of robust yeast strains for lignocellulosic biorefineries.

Integrating transcriptional activity in genome-scale models of metabolism

Background

Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype.

Results

We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods.

Conclusions

Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.

Algorithm for cellular reprogramming

Reprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

The day we understand the time evolution of subcellular events at a level of detail comparable to physical systems governed by Newton’s laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology. With data-guided frameworks we can develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. Here we describe an approach for optimizing the use of transcription factors (TFs) in cellular reprogramming, based on a device commonly used in optimal control. We construct an approximate model for the natural evolution of a cell-cycle–synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points during the cell cycle. To arrive at a model of moderate complexity, we cluster gene expression based on division of the genome into topologically associating domains (TADs) and then model the dynamics of TAD expression levels. Based on this dynamical model and additional data, such as known TF binding sites and activity, we develop a methodology for identifying the top TF candidates for a specific cellular reprogramming task. Our data-guided methodology identifies a number of TFs previously validated for reprogramming and/or natural differentiation and predicts some potentially useful combinations of TFs. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes.

NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources

Motivation

Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and ‘integrative’ algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro. However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types.

Results

We present NetProphet 2.0, a ‘data light’ algorithm for TF network mapping, and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms. In particular, it improves on the accuracy of NetProphet 1.0, which used only gene expression data, by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map.

Availability and implementation

Source code and comprehensive documentation are freely available at https://github.com/yiming-kang/NetProphet_2.0.

Supplementary information

Supplementary data are available at Bioinformatics online.

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.