Patterns of syntrophic interactions in methanogenic conversion of propionate.

Methanogenesis is central to anaerobic digestion processes. The conversion of propionate as a key intermediate for methanogenesis requires syntrophic interactions between bacterial and archaeal partners. In this study, a series of methanogenic enrichments with propionate as the sole substrate were developed to identify microbial populations specifically involved in syntrophic propionate conversion. These rigorously controlled propionate enrichments exhibited functional stability with consistent propionate conversion and methane production; yet, the methanogenic microbial communities experienced substantial temporal dynamics, which has important implications on the understanding of mechanisms involved in microbial community assembly in anaerobic digestion. Syntrophobacter was identified as the most abundant and consistent bacterial partner in syntrophic propionate conversion regardless of the origin of the source culture, the concentration of propionate, or the temporal dynamics of the culture. In contrast, the methanogen partners involved in syntrophic propionate conversion lacked consistency, as the dominant methanogens varied as a function of process condition and temporal dynamics. Methanoculleus populations were specifically enriched as the syntrophic partner at inhibitory levels of propionate, likely due to the ability to function under unfavorable environmental conditions. Syntrophic propionate conversion was carried out exclusively via transformation of propionate into acetate and hydrogen in enrichments established in this study. Microbial populations highly tolerant of elevated propionate, represented by Syntrophobacter and Methanoculleus, are of great significance in understanding methanogenic activities during process perturbations when propionate accumulation is frequently encountered. Key points • Syntrophobacter was the most consistent bacterial partner in propionate metabolism. • Diverse hydrogenotrophic methanogen populations could serve as syntrophic partners. • Methanoculleus emerged as a methanogen partner tolerant of elevated propionate.

The LacI family protein GlyR3 co-regulates the celC operon and manB in Clostridium thermocellum.

BACKGROUND: Clostridium thermocellum utilizes a wide variety of free and cellulosomal cellulases and accessory enzymes to hydrolyze polysaccharides present in complex substrates. To date only a few studies have unveiled the details by which the expression of these cellulases are regulated. Recent studies have described the auto regulation of the celC operon and determined that the celC-glyR3-licA gene cluster and nearby manB-celT gene cluster are co-transcribed as polycistronic mRNA. RESULTS: In this paper, we demonstrate that the GlyR3 protein mediates the regulation of manB. We first identify putative GlyR3 binding sites within or just upstream of the coding regions of manB and celT. Using an electrophoretic mobility shift assay (EMSA), we determined that a higher concentration of GlyR3 is required to effectively bind to the putative manB site in comparison to the celC site. Neither the putative celT site nor random DNA significantly binds GlyR3. While laminaribiose interfered with GlyR3 binding to the celC binding site, binding to the manB site was unaffected. In the presence of laminaribiose, in vivo transcription of the celC-glyR3-licA gene cluster increases, while manB expression is repressed, compared to in the absence of laminaribiose, consistent with the results from the EMSA. An in vitro transcription assay demonstrated that GlyR3 and laminaribiose interactions were responsible for the observed patters of in vivo transcription. CONCLUSIONS: Together these results reveal a mechanism by which manB is expressed at low concentrations of GlyR3 but repressed at high concentrations. In this way, C. thermocellum is able to co-regulate both the celC and manB gene clusters in response to the availability of ß-1,3-polysaccharides in its environment.

The Low Noise Limit in Gene Expression.

Protein noise measurements are increasingly used to elucidate biophysical parameters. Unfortunately noise analyses are often at odds with directly measured parameters. Here we show that these inconsistencies arise from two problematic analytical choices: (i) the assumption that protein translation rate is invariant for different proteins of different abundances, which has inadvertently led to (ii) the assumption that a large constitutive extrinsic noise sets the low noise limit in gene expression. While growing evidence suggests that transcriptional bursting may set the low noise limit, variability in translational bursting has been largely ignored. We show that genome-wide systematic variation in translational efficiency can-and in the case of E. coli does-control the low noise limit in gene expression. Therefore constitutive extrinsic noise is small and only plays a role in the absence of a systematic variation in translational efficiency. These results show the existence of two distinct expression noise patterns: (1) a global noise floor uniformly imposed on all genes by expression bursting; and (2) high noise distributed to only a select group of genes.

Transcriptomic analysis of Clostridium thermocellum Populus hydrolysate-tolerant mutant strain shows increased cellular efficiency in response to Populus hydrolysate compared to the wild type strain.

BACKGROUND: The thermophilic, anaerobic bacterium, Clostridium thermocellum is a model organism for consolidated processing due to its efficient fermentation of cellulose. Constituents of dilute acid pretreatment hydrolysate are known to inhibit C. thermocellum and other microorganisms. To evaluate the biological impact of this type of hydrolysate, a transcriptomic analysis of growth in hydrolysate-containing medium was conducted on 17.5% v/v Populus hydrolysate-tolerant mutant (PM) and wild type (WT) strains of C. thermocellum. RESULTS: In two levels of Populus hydrolysate medium (0% and 10% v/v), the PM showed both gene specific increases and decreases of gene expression compared to the wild-type strain. The PM had increased expression of genes in energy production and conversion, and amino acid transport and metabolism in both standard and 10% v/v Populus hydrolysate media. In particular, expression of the histidine metabolism increased up to 100 fold. In contrast, the PM decreased gene expression in cell division and sporulation (standard medium only), cell defense mechanisms, cell envelope, cell motility, and cellulosome in both media. The PM downregulated inorganic ion transport and metabolism in standard medium but upregulated it in the hydrolysate media when compared to the WT. The WT differentially expressed 1072 genes in response to the hydrolysate medium which included increased transcription of cell defense mechanisms, cell motility, and cellulosome, and decreased expression in cell envelope, amino acid transport and metabolism, inorganic ion transport and metabolism, and lipid metabolism, while the PM only differentially expressed 92 genes. The PM tolerates up to 17.5% v/v Populus hydrolysate and growth in it elicited 489 genes with differential expression, which included increased expression in energy production and conversion, cellulosome production, and inorganic ion transport and metabolism and decreased expression in transcription and cell defense mechanisms. CONCLUSION: These results suggest the mechanisms of tolerance for the Populus hydrolysate-tolerant mutant strain of C. thermocellum are based on increased cellular efficiency caused apparently by downregulation of non-critical genes and increasing the expression of genes in energy production and conversion rather than tolerance to specific hydrolysate components. The wild type, conversely, responds to hydrolysate media by down-regulating growth genes and up-regulating stress response genes.

Industrial robustness: understanding the mechanism of tolerance for the Populus hydrolysate-tolerant mutant strain of Clostridium thermocellum.

BACKGROUND: An industrially robust microorganism that can efficiently degrade and convert lignocellulosic biomass into ethanol and next-generation fuels is required to economically produce future sustainable liquid transportation fuels. The anaerobic, thermophilic, cellulolytic bacterium Clostridium thermocellum is a candidate microorganism for such conversions but it, like many bacteria, is sensitive to potential toxic inhibitors developed in the liquid hydrolysate produced during biomass processing. Microbial processes leading to tolerance of these inhibitory compounds found in the pretreated biomass hydrolysate are likely complex and involve multiple genes. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we developed a 17.5% v/v Populus hydrolysate tolerant mutant strain of C. thermocellum by directed evolution. The genome of the wild type strain, six intermediate population samples and seven single colony isolates were sequenced to elucidate the mechanism of tolerance. Analysis of the 224 putative mutations revealed 73 high confidence mutations. A longitudinal analysis of the intermediate population samples, a pan-genomic analysis of the isolates, and a hotspot analysis revealed 24 core genes common to all seven isolates and 8 hotspots. Genetic mutations were matched with the observed phenotype through comparison of RNA expression levels during fermentation by the wild type strain and mutant isolate 6 in various concentrations of Populus hydrolysate (0%, 10%, and 17.5% v/v). CONCLUSION/SIGNIFICANCE: The findings suggest that there are multiple mutations responsible for the Populus hydrolysate tolerant phenotype resulting in several simultaneous mechanisms of action, including increases in cellular repair, and altered energy metabolism. To date, this study provides the most comprehensive elucidation of the mechanism of tolerance to a pretreated biomass hydrolysate by C. thermocellum. These findings make important contributions to the development of industrially robust strains of consolidated bioprocessing microorganisms.

Kinetic modeling of batch fermentation for Populus hydrolysate tolerant mutant and wild type strains of Clostridium thermocellum.

The extent of inhibition of two strains of Clostridium thermocellum by a Populus hydrolysate was investigated. A Monod-based model of wild type (WT) and Populus hydrolysate tolerant mutant (PM) strains of the cellulolytic bacterium C. thermocellum was developed to quantify growth kinetics in standard media and the extent of inhibition to a Populus hydrolysate. The PM was characterized by a higher growth rate (µmax=1.223 vs. 0.571 h(-1)) and less inhibition (KI,gen=0.991 vs. 0.757) in 10% v/v Populus hydrolysate compared to the WT. In 17.5% v/v Populus hydrolysate inhibition of PM increased slightly (KI,gen=0.888), whereas the WT was strongly inhibited and did not grow in a reproducible manner. Of the individual inhibitors tested, 4-hydroxybenzoic acid was the most inhibitory, followed by galacturonic acid. The PM did not have a greater ability to detoxify the hydrolysate than the WT.

Transcriptional burst frequency and burst size are equally modulated across the human genome.

Gene expression occurs either as an episodic process, characterized by pulsatile bursts, or as a constitutive process, characterized by a Poisson-like accumulation of gene products. It is not clear which mode of gene expression (constitutive versus bursty) predominates across a genome or how transcriptional dynamics are influenced by genomic position and promoter sequence. Here, we use time-lapse fluorescence microscopy to analyze 8,000 individual human genomic loci and find that at virtually all loci, episodic bursting--as opposed to constitutive expression--is the predominant mode of expression. Quantitative analysis of the expression dynamics at these 8,000 loci indicates that both the frequency and size of the transcriptional bursts varies equally across the human genome, independent of promoter sequence. Strikingly, weaker expression loci modulate burst frequency to increase activity, whereas stronger expression loci modulate burst size to increase activity. Transcriptional activators such as trichostatin A (TSA) and tumor necrosis factor α (TNF) only modulate burst size and frequency along a constrained trend line governed by the promoter. In summary, transcriptional bursting dominates across the human genome, both burst frequency and burst size vary by chromosomal location, and transcriptional activators alter burst frequency and burst size, depending on the expression level of the locus.

Transcriptional bursting from the HIV-1 promoter is a significant source of stochastic noise in HIV-1 gene expression.

Analysis of noise in gene expression has proven a powerful approach for analyzing gene regulatory architecture. To probe the regulatory mechanisms controlling expression of HIV-1, we analyze noise in gene-expression from HIV-1's long terminal repeat (LTR) promoter at different HIV-1 integration sites across the human genome. Flow cytometry analysis of GFP expression from the HIV-1 LTR shows high variability (noise) at each integration site. Notably, the measured noise levels are inconsistent with constitutive gene expression models. Instead, quantification of expression noise indicates that HIV-1 gene expression occurs through randomly timed bursts of activity from the LTR and that each burst generates an average of 2-10 mRNA transcripts before the promoter returns to an inactive state. These data indicate that transcriptional bursting can generate high variability in HIV-1 early gene products, which may critically influence the viral fate-decision between active replication and proviral latency.

Using risk analysis to predict design flow exceedance of decentralized wastewater management systems.

In small communities, the number of residential units is a more stable indicator of wastewater volume than population. Large communities benefit from averaging because the likelihood of having concurrent large flows from all users is small. In contrast, a single septic system connected to a single residence must be designed to accommodate large flow variations. The objective of this study was to determine the risk of assigning various daily wastewater volumes to residential units. Risk was based on the probability of underestimating daily volume and, therefore, exceeding the capacity of the system. This study concluded that assigning a value of 950 L/d per residence (250 gpd per residence) is appropriate for communities with 15 or more residences, and that assigning a value of 850 L/d per residence (225 gpd per residence) is appropriate for communities with 30 or more residential connections.

Noise in biological circuits.

Noise biology focuses on the sources, processing, and biological consequences of the inherent stochastic fluctuations in molecular transitions or interactions that control cellular behavior. These fluctuations are especially pronounced in small systems where the magnitudes of the fluctuations approach or exceed the mean value of the molecular population. Noise biology is an essential component of nanomedicine where the communication of information is across a boundary that separates small synthetic and biological systems that are bound by their size to reside in environments of large fluctuations. Here we review the fundamentals of the computational, analytical, and experimental approaches to noise biology. We review results that show that the competition between the benefits of low noise and those of low population has resulted in the evolution of genetic system architectures that produce an uneven distribution of stochasticity across the molecular components of cells and, in some cases, use noise to drive biological function. We review the exact and approximate approaches to gene circuit noise analysis and simulation, and review many of the key experimental results obtained using flow cytometry and time-lapse fluorescent microscopy. In addition, we consider the probative value of noise with a discussion of using measured noise properties to elucidate the structure and function of the underlying gene circuit. We conclude with a discussion of the frontiers of and significant future challenges for noise biology.

Using noise to probe and characterize gene circuits.

Stochastic fluctuations (or "noise") in the single-cell populations of molecular species are shaped by the structure and biokinetic rates of the underlying gene circuit. The structure of the noise is summarized by its autocorrelation function. In this article, we introduce the noise regulatory vector as a generalized framework for making inferences concerning the structure and biokinetic rates of a gene circuit from its noise autocorrelation function. Although most previous studies have focused primarily on the magnitude component of the noise (given by the zero-lag autocorrelation function), our approach also considers the correlation component, which encodes additional information concerning the circuit. Theoretical analyses and simulations of various gene circuits show that the noise regulatory vector is characteristic of the composition of the circuit. Although a particular noise regulatory vector does not map uniquely to a single underlying circuit, it does suggest possible candidate circuits, while excluding others, thereby demonstrating the probative value of noise in gene circuit analysis.

Direct quantification of N-(3-oxo-hexanoyl)-L-homoserine lactone in culture supernatant using a whole-cell bioreporter.

The autoinducer N-(3-oxo-hexanoyl)-L-homoserine lactone (3-oxo-C6-HSL) plays a significant role in the quorum-sensing system of the marine bacterium Vibrio fischeri. Upon forming a transcriptional activation complex with LuxR, 3-oxo-C6-HSL induces transcription of the luxICDABEG operon, leading to the increased production of both the 3-oxo-C6-HSL synthase (LuxI) and the bioluminescent proteins. In order to quantitatively analyze this regulatory mechanism, a novel approach was developed to measure 3-oxo-C6-HSL concentrations in V. fischeri cell culture supernatant. A bioluminescent strain of Escherichia coli that responds to 3-oxo-C6-HSL was used as a bioreporter. Although a linear response of the bioreporter to exogenously added synthetic 3-oxo-C6-HSL was found over several orders of magnitude, we show that bioreporter performance was dramatically impacted by variations in the supernatants using samples from a V. fischeri LuxI- strain. However, when maintained in the same supernatant background, the normalized peak bioluminescence maintained a linear response to 3-oxo-C6-HSL concentrations. Therefore, a standard additions technique was developed in which a known concentration of 3-oxo-C6-HSL was added to supernatant samples from wild-type V. fischeri cultures, and the incremental increase of the normalized peak bioluminescence relative to the untreated sample was determined. The concentration of 3-oxo-C6-HSL in the supernatant of the unknown sample was then quantified from the slope of the response between the normalized bioluminescent peaks with and without the addition of 3-oxo-C6-HSL. Advantages of this method are that it is rapid, does not require concentration or extraction, uses a small sample volume (ca. 2 ml), and accounts for effects caused by the composition of the supernatant. Furthermore, the findings can be broadly applicable to other bioreporter systems involving variable background conditions.

Frequency domain analysis of noise in simple gene circuits.

Recent advances in single cell methods have spurred progress in quantifying and analyzing stochastic fluctuations, or noise, in genetic networks. Many of these studies have focused on identifying the sources of noise and quantifying its magnitude, and at the same time, paying less attention to the frequency content of the noise. We have developed a frequency domain approach to extract the information contained in the frequency content of the noise. In this article we review our work in this area and extend it to explicitly consider sources of extrinsic and intrinsic noise. First we review applications of the frequency domain approach to several simple circuits, including a constitutively expressed gene, a gene regulated by transitions in its operator state, and a negatively autoregulated gene. We then review our recent experimental study, in which time-lapse microscopy was used to measure noise in the expression of green fluorescent protein in individual cells. The results demonstrate how changes in rate constants within the gene circuit are reflected in the spectral content of the noise in a manner consistent with the predictions derived through frequency domain analysis. The experimental results confirm our earlier theoretical prediction that negative autoregulation not only reduces the magnitude of the noise but shifts its content out to higher frequency. Finally, we develop a frequency domain model of gene expression that explicitly accounts for extrinsic noise at the transcriptional and translational levels. We apply the model to interpret a shift in the autocorrelation function of green fluorescent protein induced by perturbations of the translational process as a shift in the frequency spectrum of extrinsic noise and a decrease in its weighting relative to intrinsic noise.

The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior.

A key to advancing the understanding of molecular biology in the post-genomic age is the development of accurate predictive models for genetic regulation, protein interaction, metabolism, and other biochemical processes. To facilitate model development, simulation algorithms must provide an accurate representation of the system, while performing the simulation in a reasonable amount of time. Gillespie's stochastic simulation algorithm (SSA) accurately depicts spatially homogeneous models with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity. In this work, we examine the performance of different versions of the SSA when applied to several biochemical models. Through our analysis, we discover that transient changes in reaction execution frequencies, which are typical of biochemical models with gene induction and repression, can dramatically affect simulator performance. To account for these shifts, we propose a new algorithm called the sorting direct method that maintains a loosely sorted order of the reactions as the simulation executes. Our measurements show that the sorting direct method performs favorably when compared to other well-known exact stochastic simulation algorithms.

Frequency domain chemical Langevin analysis of stochasticity in gene transcriptional regulation.

We present a frequency domain Langevin approach for stochastic analysis that remains valid for many important gene circuit elements even as molecular populations approach zero. We begin by considering the case of low-rate transcription and show that the previously reported shot noise representation is exact at all mRNA population levels for a constant transcription rate. Next, we consider transcriptional control through protein-DNA interactions at an operator site within the gene promoter region. This analysis results in expressions for the dynamics and noise behavior of this important gene sub-circuit, including the spectral density of the intrinsic operator noise and the processing of extrinsic noise by this transcriptional regulation system. This analysis shows that mRNA synthesis noise is composed of wideband shot noise and band-limited operator binding generated noise components. We find that the bandwidth of operator noise and its ultimate effect on total mRNA and protein noise is controlled by operator binding and unbinding dynamics. The most substantial impact of the operator noise is seen at transcription rates just above basal expression. This analysis captures the full behavior of this transcriptional regulation system, and points to potentially serious flaws in simplified mathematical relationships often used to model transcriptional regulation.

Emerging foundations: nano-engineering and bio-microelectronics for environmental biotechnology.

The growth of nanotechnology, the emergence of 'nanobiotechnology', and the incorporation of living organisms in biomicroelectronic devices are revolutionizing the interdisciplinary opportunities for microbiologists to participate in understanding, developing and exploiting microbial processes in and from the environment.

Statistical distributions of uncertainty and variability in activated sludge model parameters.

All models used in activated sludge design and analysis use parameters to characterize process performance. The values of these parameters are often assumed based on default values recommended in the literature, but to date, no quantitative estimates of the parameter uncertainties have been published. Similarly, little attention has been given to quantifying site-specific parameter variability, even though its occurrence has been observed several times in the literature. In this paper, universal uncertainty distributions of the model parameters from Activated Sludge Model No. 1 are developed from a database of parameter values reported in the literature using Bayesian statistics. Site-specific distributions of parameter variability were developed using the same techniques. All parameter distributions developed demonstrated that significant uncertainty and variability exist, which could lead to overdesign or plant failure if not considered during the design process.

Analysis of noise in quorum sensing.

Noise may play a pivotal role in gene circuit functionality, as demonstrated for the genetic switch in the bacterial phage lambda. Like the lambda switch, bacterial quorum sensing (QS) systems operate within a population and contain a bistable switching element, making it likely that noise plays a functional role in QS circuit operation. Therefore, a detailed analysis of the noise behavior of QS systems is needed. We have developed a set of tools generally applicable to the analysis of gene circuits, with an emphasis on investigations in the frequency domain (FD), that we apply here to the QS system in the marine bacterium Vibrio fischeri. We demonstrate that a tight coupling between exact stochastic simulation and FD analysis provides insights into the structure/function relationships in the QS circuit. Furthermore, we argue that a noise analysis is incomplete without consideration of the power spectral densities (PSDs) of the important molecular output signals. As an example we consider reversible reactions in the QS circuit, and show through analysis and exact stochastic simulation that these circuits make significant and dynamic modifications to the noise spectra. In particular, we demonstrate a "whitening" effect, which occurs as the noise is processed through these reversible reactions.

Frequency domain analysis of noise in autoregulated gene circuits.

We describe a frequency domain technique for the analysis of intrinsic noise within negatively autoregulated gene circuits. This approach is based on the transfer function around the feedback loop (loop transmission) and the equivalent noise bandwidth of the system. The loop transmission, T, is shown to be a determining factor of the dynamics and the noise behavior of autoregulated gene circuits, and this T-based technique provides a simple and flexible method for the analysis of noise arising from any source within the gene circuit. We show that negative feedback not only reduces the variance of the noise in the protein concentration, but also shifts this noise to higher frequencies where it may have a negligible effect on the noise behavior of following gene circuits within a cascade. This predicted effect is demonstrated through the exact stochastic simulation of a two-gene cascade. The analysis elucidates important aspects of gene circuit structure that control functionality, and may provide some insights into selective pressures leading to this structure. The resulting analytical relationships have a simple form, making them especially useful as synthetic gene circuit design equations. With the exception of the linearization of Hill kinetics, this technique is general and may be applied to the analysis or design of networks of higher complexity. This utility is demonstrated through the exact stochastic simulation of an autoregulated two-gene cascade operating near instability.