Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics

Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling

Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.

Computer simulation study of nutrient-driven bacterial biofilm stratification

Here, employing computer simulation tools, we present a study on the development of a bacterial biofilm from a single starter cell on a flat inert surface overlaid by an aqueous solution containing nutrients. In our simulations, surface colonization involves an initial stage of two-dimensional cell proliferation to eventually transition to three-dimensional growth leading to the formation of biofilm colonies with characteristic three-dimensional semi-ellipsoids shapes. Thus, we have introduced the influence of the nutrient concentration on bacterial growth, and calculated the cell growth rate as a function of nutrient uptake, which in turn depends on local nutrient concentration in the vicinity of each bacterial cell. Our results show that the combination of cell growth and nutrient uptake and diffusion leads to the formation of stratified colonies containing an inner core in which nutrients are depleted and cells cannot grow or divide, surrounded by an outer, shallow crust in which cells have access to nutrients from the bulk medium and continue growing. This phenomenon is more apparent at high uptake rates that enable fast nutrient depletion. Our simulations also predict that the shape and internal structure of the biofilm are largely conditioned by the balance between nutrient diffusion and uptake.

Development of a hybrid bio-purification process of lactic acid solutions employing an engineered E. coli strain in a membrane bioreactor

BACKGROUND

A potential alternative to lactic acid production through sugar fermentation is its recovery from grass silage leachate. The separation and purification of lactic acid from fermentation broths remain a key issue, as it amounts to up to 80% of its industrial production cost. In this study, a genetically engineered E. coli strain (A1:ldhA), that cannot catabolize lactic acid, has been used to selectively remove impurities from a synthetic medium comprising typical components (i.e., glucose and acetic acid) of green grass silage leachate. A systematic approach has been followed to provide a proof-of-concept for a bio-purification process of lactic acid solutions in a membrane bioreactor operating in semi-continuous mode.

RESULTS

The synthetic medium composition was initially optimized in shake-flasks experiments, followed by scale-up in bench-scale bioreactor. Complete (i.e., 100%) and 60.4% removal for glucose and acetic acid, respectively, has been achieved in batch bioreactor experiments with a synthetic medium comprising 0.5 g/L glucose and 0.5 g/L acetic acid as carbon sources, and 10 g/L lactic acid; no lactic acid catabolism was observed in all batch fermentation tests. Afterwards, a hybrid biotechnological process combining semi-continuous bioreactor fermentation and ultrafiltration membrane separation (membrane bioreactor) was applied to in-situ separate purified medium from the active cells. The process was assessed under different semi-continuous operating conditions, resulting in a bacteria-free effluent and 100% glucose and acetic acid depletion, with no lactic acid catabolism, thus increasing the purity of the synthetic lactic acid solution.

CONCLUSIONS

The study clearly demonstrated that a bio-purification process for lactic acid employing the engineered E. coli strain cultivated in a membrane bioreactor is a technically feasible concept, paving the way for further technological advancement.

Regulation of Aerobic Succinate Transporter dctA of E. coli by cAMP-CRP, DcuS-DcuR, and EIIAGlc: Succinate as a Carbon Substrate and Signaling Molecule

INTRODUCTION

C4-dicarboxylates (C4-DC) have emerged as significant growth substrates and signaling molecules for various Enterobacteriaceae during their colonization of mammalian hosts. Particularly noteworthy is the essential role of fumarate respiration during colonization of pathogenic bacteria. To investigate the regulation of aerobic C4-DC metabolism, the study explored the transcriptional control of the main aerobic C4-DC transporter, dctA, under different carbohydrate conditions. In addition, mutants related to carbon catabolite repression (CCR) and C4-DC regulation (DcuS-DcuR) were examined to better understand the regulatory integration of aerobic C4-DC metabolism into CCR. For initial insight into posttranslational regulation, the interaction between the aerobic C4-DC transporter DctA and EIIAGlc from the glucose-specific phosphotransferase system was investigated.

METHODS

The expression of dctA was characterized in the presence of various carbohydrates and regulatory mutants affecting CCR. This was accomplished by fusing the dctA promoter (PdctA) to the lacZ reporter gene. Additionally, the interaction between DctA and EIIAGlc of the glucose-specific phosphotransferase system was examined in vivo using a bacterial two-hybrid system.

RESULTS

The dctA promoter region contains a class I cAMP-CRP-binding site at position -81.5 and a DcuR-binding site at position -105.5. DcuR, the response regulator of the C4-DC-activated DcuS-DcuR two-component system, and cAMP-CRP stimulate dctA expression. The expression of dctA is subject to the influence of various carbohydrates via cAMP-CRP, which differently modulate cAMP levels. Here we show that EIIAGlc of the glucose-specific phosphotransferase system strongly interacts with DctA, potentially resulting in the exclusion of C4-DCs when preferred carbon substrates, such as sugars, are present. In contrast to the classical inducer exclusion known for lactose permease LacY, inhibition of C4-DC uptake into the cytoplasm affects only its role as a substrate, but not as an inducer since DcuS detects C4-DCs in the periplasmic space ("substrate exclusion"). The work shows an interplay between cAMP-CRP and the DcuS-DcuR regulatory system for the regulation of dctA at both transcriptional and posttranslational levels.

CONCLUSION

The study highlights a hierarchical interplay between global (cAMP-CRP) and specific (DcuS-DcuR) regulation of dctA at the transcriptional and posttranslational levels. The integration of global and specific transcriptional regulation of dctA, along with the influence of EIIAGlc on DctA, fine-tunes C4-DC catabolism in response to the availability of other preferred carbon sources. It attributes DctA a central role in the control of aerobic C4-DC catabolism and suggests a new role to EIIAGlc on transporters (control of substrate uptake by substrate exclusion).

Marine Microbiota Responses to Shipping Scrubber Effluent Assessed at Community Structure and Function Endpoints

The use of novel high-throughput sequencing (HTS) technologies to examine the responses of natural multidomain microbial communities to scrubber effluent discharges to the marine environment is still limited. Thus, we applied metabarcoding sequencing targeting the planktonic unicellular eukaryotic and prokaryotic fraction (phytoplankton, bacterioplankton, and protozooplankton) in mesocosm experiments with natural microbial communities from a polluted and an unpolluted site. Furthermore, metagenomic analysis revealed changes in the taxonomic and functional dominance of multidomain marine microbial communities after scrubber effluent additions. The results indicated a clear shift in the microbial communities after such additions, which favored bacterial taxa with known oil and polycyclic aromatic hydrocarbons (PAHs) biodegradation capacities. These bacteria exhibited high connectedness with planktonic unicellular eukaryotes employing variable trophic strategies, suggesting that environmentally relevant bacteria can influence eukaryotic community structure. Furthermore, Clusters of Orthologous Genes associated with pathways of PAHs and monocyclic hydrocarbon degradation increased in numbers at treatments with high scrubber effluent additions acutely. These genes are known to express enzymes acting at various substrates including PAHs. These indications, in combination with the abrupt decrease in the most abundant PAHs in the scrubber effluent below the limit of detection-much faster than their known half-lives-could point toward a bacterioplankton-initiated rapid ultimate biodegradation of the most abundant toxic contaminants of the scrubber effluent. The implementation of HTS could be a valuable tool to develop multilevel biodiversity indicators of the scrubber effluent impacts on the marine environment, which could lead to improved impact assessment. Environ Toxicol Chem 2024;00:1-18. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

An Automatic Method for Generation of CFD-Based 3D Compartment Models: Towards Real-Time Mixing Simulations

This article aims to develop a method to automatically generate CFD-based compartment models. This effort to simplify mixing models aims at capturing the interactions between material transport and chemical/biochemical conversions in large-scale reactors. The proposed method converts the CFD results into a system of mass balance equations for each defined component. The compartmentalization method is applied to two bioreactor geometries and was able to replicate tracer mixing profiles observed in CFD simulations. The generated compartment models were successfully coupled with, a simple Monod-type biokinetic model describing microbial growth, substrate consumption and product formation. The coupled model was used to simulate a four-hour fermentation in a 190 L reactor and a 10 m3 reactor. Resolving the substrate gradients had a clear impact on the biokinetics, increasing with the scale of the reactor. Moreover, the coupled model could simulate the fermentation faster than real-time. Having a real-time-solvable model is essential for implementations in digital twins and other real-time applications using the models as predictive tools.

Estimating the contribution of the porcine fecal core microbiota to metabolite production via mathematical modeling and in vitro fermentation

The swine gut microbiota is a complex ecosystem found throughout the gastrointestinal tract, with multiple exchanges with the host and whose composition is linked to both external and internal factors, such as diet or breed. Diet, probiotic, or prebiotic interventions have been designed to boost beneficial host-microbiota interactions, such as the production of anti-inflammatory molecules, or the fermentation of otherwise undigested resources. In parallel, a smaller microbial population, shared among the same host species, independent of external or internal factors, has been described and defined as the "core microbiota." Therapies targeting the core microbiota could possibly lead to more precise and long-lasting effects. However, the metabolic role of the porcine core microbiota, especially in relation to the rest of the microbial community, is currently missing. We present here the first dynamic model of the porcine core microbiota, which we used to estimate the core-microbiota metabolite production and to forecast the effect of a synbiotic intervention targeting the core genera of the core microbiota. We developed a community model in which a total of 17 microbial groups were established based on culture-based information of representative species. First, the model parameters were estimated, and the resulting model simulations were compared favorably with in vitro experimentation. The model was then used to predict the microbial dynamics of the core and non-core members under different experimental conditions. Therefore, it was able to theorize the main-metabolite core microbiota contribution, hypothesizing that it could be mainly responsible for acetate and propionate, but not for butyrate production.IMPORTANCECurrently, little information is present in the literature to describe the generic metabolic role of the porcine core microbiota or to inform on the effect of interventions targeting the core genera. Moreover, both in vitro and in vivo experimentations aiming to explore the core microbiota dynamics are technically demanding, expensive, or restricted by ethical considerations. Modeling approaches can be used as an initial exploratory tool to develop hypotheses for targeted experimentation. Our mathematical model provides initial information on the microbial and metabolite dynamics of the core microbiota in relation to diet and therapeutic intervention.

The dynamics of phage predation on a microcolony

Phage predation is an important factor for controlling the bacterial biomass. At face value, dense microbial habitats are expected to be vulnerable to phage epidemics due to the abundance of fresh hosts immediately next to any infected bacteria. Despite this, the bacterial microcolony is a common habitat for bacteria in nature. Here, we experimentally quantify the fate of microcolonies of Escherichia coli exposed to virulent phage T4. It has been proposed that the outer bacterial layers of the colony will shield the inner layers from the phage invasion and thereby constrain the phage to the colony's surface. We develop a dynamical model that incorporates this shielding mechanism and fit the results with experimental measurements to extract important phage-bacteria interaction parameters. The analysis suggests that, while the shielding mechanism delays phage attack, T4 phage are able to diffuse so deep into the dense bacterial environment that colony-level survival of the bacterial community is challenged.

Nickel-iron doped on granular activated carbon for efficient immobilization in biohydrogen production

Nickel-iron doped granular activated carbon (GAC-N) was used to enhance immobilization in biohydrogen production. The effect of the sludge ratio to GAC-N, ranged 1:0.5-4, was studied. The optimum hydrogen yield (HY) of 1.64 ± 0.04 mol H2/mol sugar consumed and hydrogen production rate (HPR) of 45.67 ± 1.00 ml H2/L.h was achieved at a ratio of 1:1. Immobilization study was performed at 2 d HRT with a stable HY of 2.94 ± 0.16 mol H2/mol sugar consumed (HPR of 83.10 ± 4.61 ml H2/L.h), shorten biohydrogen production from 66 d to 26 d, incrementing HY by 57.30 %. The Monod model resulted in the optimum initial sugar, maximum specific growth rate, specific growth rate, and cell growth saturation coefficient at 20 g/L, 2.05 h-1, 1.98 h-1 and 6.96 g/L, respectively. The dominant bacteria identified was Thermoanaerobacterium spp. The GAC-N showed potential as a medium for immobilization to improve biohydrogen production.

Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics

The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.

Esterase and ALDH dehydrogenase-based pesticide degradation by Bacillus brevis 1B from a contaminated environment

The isolated bacterial strain (Bacillus brevis strain 1 B) showed a maximum tolerated level of 450 mg L^-1 of the selected pesticides namely: imidacloprid, fipronil, cypermethrin, and sulfosulfuron. Within 15 days of the experiment, strain 1 B was able to reduce up to 95% of a pesticide mixture (20 mg L^-1) in a carbon-deficient medium (minimal medium). The optimal conditions obtained using Response Surface Methodology (RSM) were: inoculums; 2.0 × 10⁷ CFU mL^-1, shaking speed; 120 rpm, and pesticide concentration; 80 mg L^-1. After 15 days of soil-based bioremediation using strain 1 B, the degradation pattern for imidacloprid, fipronil, cypermethrin, sulfosulfuron, and control was 99, 98.5, 94, 91.67, and 7%, respectively. Gas chromatography-mass spectrometry (GC-MS) analysis was used to determine the intermediate metabolites of cypermethrin with bacterial 1 B as 2-cyclopenten-1-one, 2-methylpyrrolidine, 2-oxonanone, 2-pentenoic acid, 2-penten-1-ol, hexadecanoic acid or palmitic acid, pentadecanoic acid, 3-cyclopentylpropionic acid, and 2-dimethyl. Furthermore, genes encoding aldehyde dehydrogenase (ALDH) and esterase were expressed under stress conditions and connected to pesticide bioremediation. Hence the efficacy of Bacillus brevis (1 B) could be employed for the bioremediation of pesticide mixtures and other toxic substances (dye, polyaromatic hydrocarbon, etc.) from contaminated sites.

Application of Immersed Membrane Bioreactor for Semi-Continuous Production of Polyhydroxyalkanoates from Organic Waste-Based Volatile Fatty Acids

Volatile fatty acids (VFAs) appear to be an economical carbon feedstock for the cost-effective production of polyhydroxyalkanoates (PHAs). The use of VFAs, however, could impose a drawback of substrate inhibition at high concentrations, resulting in low microbial PHA productivity in batch cultivations. In this regard, retaining high cell density using immersed membrane bioreactor (iMBR) in a (semi-) continuous process could enhance production yields. In this study, an iMBR with a flat-sheet membrane was applied for semi-continuous cultivation and recovery of Cupriavidus necator in a bench-scale bioreactor using VFAs as the sole carbon source. The cultivation was prolonged up to 128 h under an interval feed of 5 g/L VFAs at a dilution rate of 0.15 (d^-1), yielding a maximum biomass and PHA production of 6.6 and 2.8 g/L, respectively. Potato liquor and apple pomace-based VFAs with a total concentration of 8.8 g/L were also successfully used in the iMBR, rendering the highest PHA content of 1.3 g/L after 128 h of cultivation. The PHAs obtained from both synthetic and real VFA effluents were affirmed to be poly(3-hydroxybutyrate-co-3-hydroxyvalerate) with a crystallinity degree of 23.8 and 9.6%, respectively. The application of iMBR could open an opportunity for semi-continuous production of PHA, increasing the feasibility of upscaling PHA production using waste-based VFAs.

Spatial structure and benefits to hosts allow plasmids with and without post-segregational killing systems to coexist

To persist, a plasmid relies on being passed on to a daughter cell, but this does not always occur. Plasmids with post-segregational killing (PSK) systems kill a daughter cell if the plasmid has not been passed on. By killing the host, it also kills competing plasmids in the same host, something competing plasmids without a similar system cannot do. Accordingly, plasmids with PSK systems can displace other plasmids. In nature, plasmids with and without PSK systems coexist and prior theory has suggested this is expected to be very rare or unstable, such that one or the other type of plasmid eventually takes over. Here, we show that if there is spatial structure and plasmids confer benefits to hosts, coexistence of plasmids occurs broadly. Often plasmids confer benefits (even ones with a PSK system) and bacteria are often spatially structured. So, our results may be generally applicable.

The Slowdown of Growth Rate Controls the Single-Cell Distribution of Biofilm Matrix Production via an SinI-SinR-SlrR Network

In Bacillus subtilis, master regulator Spo0A controls several cell-differentiation pathways. Under moderate starvation, phosphorylated Spo0A (Spo0A~P) induces biofilm formation by indirectly activating genes controlling matrix production in a subpopulation of cells via an SinI-SinR-SlrR network. Under severe starvation, Spo0A~P induces sporulation by directly and indirectly regulating sporulation gene expression. However, what determines the heterogeneity of individual cell fates is not fully understood. In particular, it is still unclear why, despite being controlled by a single master regulator, biofilm matrix production and sporulation seem mutually exclusive on a single-cell level. In this work, with mathematical modeling, we showed that the fluctuations in the growth rate and the intrinsic noise amplified by the bistability in the SinI-SinR-SlrR network could explain the single-cell distribution of matrix production. Moreover, we predicted an incoherent feed-forward loop; the decrease in the cellular growth rate first activates matrix production by increasing in Spo0A phosphorylation level but then represses it via changing the relative concentrations of SinR and SlrR. Experimental data provide evidence to support model predictions. In particular, we demonstrate how the degree to which matrix production and sporulation appear mutually exclusive is affected by genetic perturbations. IMPORTANCE The mechanisms of cell-fate decisions are fundamental to our understanding of multicellular organisms and bacterial communities. However, even for the best-studied model systems we still lack a complete picture of how phenotypic heterogeneity of genetically identical cells is controlled. Here, using B. subtilis as a model system, we employ a combination of mathematical modeling and experiments to explain the population-level dynamics and single-cell level heterogeneity of matrix gene expression. The results demonstrate how the two cell fates, biofilm matrix production and sporulation, can appear mutually exclusive without explicitly inhibiting one another. Such a mechanism could be used in a wide range of other biological systems.

Experimental kS estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale

Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as k_S , is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate k_S . In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of k_S values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity k_S of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A k_S of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial k_S estimation, sl-MSCC allows an affinity estimation by determining t_D at concentrations less or equal to the k_S value. Thus, sl-MSCC lays the foundation for strain-specific k_S estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity.

Controlled Production of Carnosic Acid and Carnosol in Cell Suspensions of Lepechinia meyenii Treated with Different Elicitors and Biosynthetic Precursors

Lepechinia meyenii is a medicinal plant specialized in the biosynthesis of different types of antioxidants including the diterpenes carnosic (CA) acid and carnosol (CS). Herein we present the results of plant tissue culture approaches performed in this medicinal plant with particular emphasis on the generation and evaluation of a cell suspension system for CA and CS production. The effect of sucrose concentration, temperature, pH, and UV-light exposure was explored. In addition, diverse concentrations of microbial elicitors (salicylic acid, pyocyanin, Glucanex, and chitin), simulators of abiotic elicitors (polyethylene glycol and NaCl), and biosynthetic precursors (mevalonolactone, geranylgeraniol, and miltiradiene/abietatriene) were evaluated on batch cultures for 20 days. Miltiradiene/abietatriene obtainment was achieved through a metabolic engineering approach using a recombinant strain of Saccharomyces cerevisiae. Our results suggested that the maximum accumulation (Acc_max ) of CA and CS was mainly conferred to stimuli associated with oxidative stress such as UV-light exposure (Acc_max , 6.2 mg L^-1 ) polyethylene glycol (Acc_max , 6.5 mg L^-1 ) NaCl (Acc_max , 5.9 mg L^-1 ) which simulated drought and saline stress, respectively. Nevertheless the bacterial elicitor pyocyanin was also effective to increase the production of both diterpenes (Acc_max , 6.4 mg L^-1 ). Outstandingly, the incorporation of upstream biosynthetic precursors such as geranylgeraniol and miltiradiene/abietatriene, generated the best results with Acc_max of 8.6 and 16.7 mg L^-1 , respectively. Optimized batch cultures containing 100 mg L^-1 geranylgeraniol, 50 mg L^-1 miltiradiene/abietatriene (95 : 5 %) and 5 g L^-1 polyethylene glycol treated with 6 min UV light pulse during 30 days resulted in Acc_max of 26.7 mg L^-1 for CA and 17.3 mg L^-1 for CS on days 18-24. This strategy allowed to increase seven folds the amounts of CA and CS in comparison with batch cultures without elicitation (Acc_max , 4.3 mg L^-1 ).

Reactive transport of micropollutants in laboratory aquifers undergoing transient exposure periods

Groundwater quality is of increasing concern due to the ubiquitous occurrence of micropollutant mixtures. Stream-groundwater interactions near agricultural and urban areas represent an important entry pathway of micropollutants into shallow aquifers. Here, we evaluated the biotransformation of a micropollutant mixture (i.e., caffeine, metformin, atrazine, terbutryn, S-metolachlor and metalaxyl) during lateral stream water flow to adjacent groundwater. We used an integrative approach combining concentrations and transformation products (TPs) of the micropollutants, compound-specific isotope analysis (δ¹³C and δ¹⁵N), sequencing of 16S rRNA gene amplicons and reactive transport modeling. Duplicate laboratory aquifers (160 cm × 80 cm × 7 cm) were fed with stream water and subjected over 140 d to three successive periods of micropollutant exposures as pulse-like (6000 μg L^-1) and constant (600 μg L^-1) injections under steady-state conditions. Atrazine, terbutryn, S-metolachlor and metalaxyl persisted in both aquifers during all periods (<10 % attenuation). Metformin attenuation (up to 14 %) was only observed from 90 d onwards, suggesting enhanced degradation over time. In contrast, caffeine dissipated during all injection periods (>90 %), agreeing with fast degradation rates (t_1/2 < 3 d) in parallel microcosm experiments and detection of TPs (theobromine and xanthine). Significant stable carbon isotope fractionation (Δδ¹³C ≥ 6.6 ‰) was observed for caffeine in both aquifers, whereas no enrichment in ¹⁵N occurred. A concentration dependence of caffeine biotransformation in the aquifers was further suggested by model simulations following Michaelis-Menten kinetics. Changes in bacterial community composition reflected long-term bacterial adaptation to micropollutant exposures. Altogether, the use of an integrative approach can help to understand the interplay of subsurface hydrochemistry, bacterial adaptations and micropollutants biotransformation during stream-groundwater interactions.

Microbial population dynamics decouple growth response from environmental nutrient concentration

How the growth rate of a microbial population responds to the environmental availability of chemical nutrients and other resources is a fundamental question in microbiology. Models of this response, such as the widely used Monod model, are generally characterized by a maximum growth rate and a half-saturation concentration of the resource. What values should we expect for these half-saturation concentrations, and how should they depend on the environmental concentration of the resource? We survey growth response data across a wide range of organisms and resources. We find that the half-saturation concentrations vary across orders of magnitude, even for the same organism and resource. To explain this variation, we develop an evolutionary model to show that demographic fluctuations (genetic drift) can constrain the adaptation of half-saturation concentrations. We find that this effect fundamentally differs depending on the type of population dynamics: Populations undergoing periodic bottlenecks of fixed size will adapt their half-saturation concentrations in proportion to the environmental resource concentrations, but populations undergoing periodic dilutions of fixed size will evolve half-saturation concentrations that are largely decoupled from the environmental concentrations. Our model not only provides testable predictions for laboratory evolution experiments, but it also reveals how an evolved half-saturation concentration may not reflect the organism's environment. In particular, this explains how organisms in resource-rich environments can still evolve fast growth at low resource concentrations. Altogether, our results demonstrate the critical role of population dynamics in shaping fundamental ecological traits.

Effects of Nutrient Level and Growth Rate on the Conjugation Process That Transfers Mobile Antibiotic Resistance Genes in Continuous Cultures

Bacteria in the effluent of wastewater treatment plants (WWTPs) can transfer antibiotic resistance genes (ARGs) to the bacteria in receiving water through conjugation; however, there is a lack of quantitative assessment of this phenomenon in continuous cultures. Our objective was to determine the effects of background nutrient levels in river water column and growth rates of bacteria on the conjugation frequency of ARGs from effluent bacteria to river bacteria, as well as on the resulting resistance level (i.e., MICs) of the river bacteria. Chemostats were employed to simulate the discharge points of WWTPs into rivers, where effluent bacteria (donor cells) meet river bacteria (recipient cells). Both donor and recipient cells were Escherichia coli cells, and the donor cells were constructed by filter mating with bacteria in the effluent of a local WWTP. Results showed that higher bacterial growth rate (0.45 h^-1 versus 0.15 h^-1) led to higher conjugation frequencies (10^-4 versus 10^-6 transconjugant per recipient). The nutrient level also significantly affected the conjugation frequency, albeit to a lesser extent than the growth rate. The MIC against tetracycline increased from 2 mg/L in the recipient to 64 to 128 mg/L in transconjugants. In comparison, the MIC only increased to as high as 8 mg/L in mutants. Whole-genome sequencing showed that the tet-containing plasmid in both the donor and the transconjugant cells also occur in other fecal bacterial genera. The quantitative information obtained from this study can inform hazard identification related to the proliferation of wastewater-associated ARGs in surface water. IMPORTANCE WWTPs have been regarded as an important hot spot of ARGs. The discharge point of WWTP effluent, where ARGs may be horizontally transferred from bacteria of treated wastewater to bacteria of receiving water, is an important interface between the human-dominated ecosystem and the natural environment. The use of batch cultures in previous studies cannot adequately simulate the nutrient conditions and growth rates in receiving water. In this study, chemostats were employed to simulate the continuous growth of bacteria in receiving water. Furthermore, the experimental setup allowed for separate investigations on the effects of nutrient levels (i.e., simulating background nutrients in river water) and bacterial growth rates on conjugation frequencies and resulting resistance levels. The study generates statistically sound ecological data that can be used to estimate the risk of wastewater-originated ARGs as part of the One Health framework.

Even allocation of benefits stabilizes microbial community engaged in metabolic division of labor

Microbial communities execute metabolic pathways to drive global nutrient cycles. Within a community, functionally specialized strains can perform different yet complementary steps within a linear pathway, a phenomenon termed metabolic division of labor (MDOL). However, little is known about how such metabolic behaviors shape microbial communities. Here, we derive a theoretical framework to define the assembly of a community that degrades an organic compound through MDOL. The framework indicates that to ensure community stability, the strains performing the initial steps should hold a growth advantage (m) over the "private benefit" (n) of the strain performing the last step. The steady-state frequency of the last strain is then determined by the quotient of n and m. Our experiments show that the framework accurately predicts the assembly of our synthetic consortia that degrade naphthalene through MDOL. Our results provide insights for designing and managing stable microbial systems for metabolic pathway optimization.

The inhibitory effects and underlying mechanism of high ammonia stress on sulfide-driven denitrification process

Sulfide-driven denitrification (SD) process has been widely studied for treating wastewater containing sulfate and ammonia in recent years. But influence of high ammonia stress on the SD process and microbial community remained unclear. In this work, a series of tests were conducted to investigate effects of different ammonia stress (200-3000 mg-total ammonia nitrogen (TAN)/L) on denitrification efficiency, byproduct accumulation and microbial community of the SD process. According to our results, the SD process was severely inhibited, and 32.67 ± 5.15 mg/L NO₂^--N was accumulated when ammonia stress reached 3000 mg TAN/L. But the inhibited SD process could recover in about 40 days when ammonia stress was decreased to 200 mg TAN/L. After analyzing the microbial community, Thiobacillus sp. (Thiobacillus sp. 65-29, Thiobacillus sp. SCN 64-317, Thiobacillus sp. 63-78 and Thiobacillus denitrificans) was confirmed as dominant bacteria responsible for the SD process. Further, expression of narG, napA, nirK and nirS were inhibited under high ammonia stress, thus making the SD process stuck in NO₃^- and NO₂^- reduction step. This study reveals the inhibitory effects of high ammonia stress on the SD process and its possible underlying mechanism with discussion in gene level.

Effect of Nitrogen, Phosphorous, and Light Colimitation on Amphidinol Production and Growth in the Marine Dinoflagellate Microalga Amphidinium carterae

The marine dinoflagellate microalga Amphidinium carterae is a source of amphidinols, a fascinating group of polyketide metabolites potentially useful in drug design. However, Amphidinium carterae grows slowly and produces these toxins in tiny amounts, representing a hurdle for large-scale production. Understanding dinoflagellate growth kinetics under different photobioreactor conditions is imperative for promoting the successful implementation of a full-scale integrated bioproduct production system. This study evaluates the feasibility of growing Amphidinium carterae under different ranges of nitrogen concentration (NO₃⁻ = 882–2646 µM), phosphorus concentration (PO₃³⁻ = 181–529 µM), and light intensity (Y₀ = 286–573 µE m⁻² s⁻¹) to produce amphidinols. A mathematical colimitation kinetic model based on the “cell quota” concept is developed to predict both algal growth and nutrient drawdown, assuming that all three variables (nitrogen, phosphorous and light) can simultaneously colimit microalgal growth. The model was applied to the semicontinuous culture of the marine microalgae Amphidinium carterae in an indoor LED-lit raceway photobioreactor. The results show that both growth and amphidinol production strongly depend on nutrient concentrations and light intensity. Nonetheless, it was possible to increase Amphidinium carterae growth while simultaneously promoting the overproduction of amphidinols. The proposed model adequately describes Amphidinium carterae growth, nitrate and phosphate concentrations, and intracellular nitrogen and phosphorus storage, and has therefore the potential to be extended to other systems used in dinoflagellate cultivation and the production of bioproducts obtained therein.

Microfluidic single-cell scale-down bioreactors: A proof-of-concept for the growth of Corynebacterium glutamicum at oscillating pH values

In large-scale bioreactors, gradients in cultivation parameter such as oxygen, substrate and pH result in fluctuating cell environments. pH fluctuations were identified as a critical parameter for bioprocess performance. Traditionally, scale-down systems at the laboratory scale are used to analyze the effects of fluctuating pH values on strain and thus process performance. Here, we demonstrate the application of dynamic microfluidic single-cell cultivation (dMSCC) as a novel scale-down system for the characterization of Corynebacterium glutamicum growth using oscillating pH conditions as a model stress-factor. A detailed comparison between two-compartment reactor (two-CR) scale-down experiments and dMSCC was performed for one specific pH oscillation between reference pH 7 (~ 8 min) and disturbed pH 6 (~2 min). Similar reductions in growth rates were observed in both systems (dMSCC 21% and two-CR 27%) compared to undisturbed cultivation at pH 7. Afterwards, systematic experiments at symmetric and asymmetric pH oscillations between pH ranges of 4-6 and 8-11 and different intervals from 1 minute to 20 minutes, were performed to demonstrate the unique application range and throughput of the dMSCC system. Finally, the strength of the dMSCC application was demonstrated by mimicking fluctuating environmental conditions of a putative large-scale bioprocesse, which is difficult to conduct using two-CRs. This article is protected by copyright. All rights reserved.

Dynamic utilization of low-molecular-weight organic substrates across a microbial growth rate gradient

AIM

Low-molecular-weight organic substances (LMWOSs) are at the nexus between microorganisms, plant roots, detritus, and the soil mineral matrix. Nominal oxidation state of carbon (NOSC) has been suggested a potential parameter for modeling microbial uptake rates of LMWOSs and the efficiency of carbon incorporation into new biomass.

METHODS AND RESULTS

In this study, we assessed the role of compound class and oxidation state on uptake kinetics and substrate-specific carbon use efficiency (SUE) during the growth of three model soil microorganisms, a fungal isolate (Penicillium spinulosum) and two bacterial isolates (Paraburkholderia solitsugae, and Ralstonia pickettii). Isolates were chosen that spanned a growth rate gradient (0.046-0.316 h^-1 ) in media containing 34 common LMWOSs at realistically low initial concentrations (25 μM each). Clustered, co-utilization of LMWOSs occurred for all three organisms. Potential trends (p < 0.05) for early utilization of more oxidized substrates were present for the two bacterial isolates (P. solitsugae and R. pickettii), but high variability (R² < 0.15) and a small effect of NOSC indicate these relationships are not useful for prediction. The SUEs of selected substrates ranged from 0.16-0.99 and there was no observed relationship between NOSC and SUE.

CONCLUSION

Our results do not provide compelling population-level support for NOSC as a predictive tool for either uptake kinetics or the efficiency of use of LMWOS in soil solution.

SIGNIFICANCE AND IMPACT OF THE STUDY

Metabolic strategies of organisms are likely more important than chemical identity in determining LMWOS cycling in soils. Previous community-level observations may be biased towards fast-responding bacterial community members.

Evaluating the relative adsorption and biodegradation of 2-methylisoborneol and geosmin across granular activated carbon filter-adsorbers

This study investigated the relative contributions of adsorption vs. biodegradation towards 2-methylisoborneol (MIB) and geosmin removal in the granular activated carbon (GAC) harvested from six filter-adsorbers in three drinking water treatment plants in the Great Lakes region. Column tests using azide-treated (sterilized) and untreated GAC in parallel were used to isolate the two effects. It was identified that substantial MIB and geosmin biodegradation in the GAC was occurring in one location, and that GAC in some cases had significant adsorption capacity after as much as 9 years of operation. Four alternative biological parameters (adenosine triphosphate, esterase activity, phosphatase activity, and ¹⁴C-glucose respiration rate) were measured to quantify the biological activity of the GAC, and ¹⁴C-glucose respiration rate was identified to be a potential indicator for GAC biodegradative capacity in terms of MIB, geosmin, and dissolved organic carbon. Several potential MIB and geosmin biodegradation products were also identified using non-targeted screening analysis. By using the new tools identified in this study, we can begin to better understand where adsorption vs. biodegradation may predominate under real-world conditions (e.g., different temperatures, influent concentrations, and empty bed contact time), leading ultimately to more cost-effective use of GAC.

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production

A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction. GBR-based growth kinetics was found to be matching well with the results of the conventional experimental approach while significantly reducing the experimental effort, time, and resources by ∼ 50%. Further, the activity and enzyme kinetics of lipase produced in this study was investigated on hydrolysis of p-nitrophenyl butyrate resulting in a maximum lipase activity of 24.07 U at 44 h. The robustness and significance of developed kinetic models were ensured through detailed statistical analysis. The application of the proposed hybrid approach can be extended to any other microbial process.

Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling

The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen's metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth.

Physiological Acclimation Extrapolates the Kinetics and Thermodynamics of Methanogenesis From Laboratory Experiments to Natural Environments

Chemotrophic microorganisms face the steep challenge of limited energy resources in natural environments. This observation has important implications for interpreting and modeling the kinetics and thermodynamics of microbial reactions. Current modeling frameworks treat microbes as autocatalysts, and simulate microbial energy conservation and growth with fixed kinetic and thermodynamic parameters. However, microbes are capable of acclimating to the environment and modulating their parameters in order to gain competitive fitness. Here we constructed an optimization model and described microbes as self-adapting catalysts by linking microbial parameters to intracellular metabolic resources. From the optimization results, we related microbial parameters to the substrate concentration and the energy available in the environment, and simplified the relationship between the kinetics and the thermodynamics of microbial reactions. We took as examples Methanosarcina and Methanosaeta – the methanogens that produce methane from acetate – and showed how the acclimation model extrapolated laboratory observations to natural environments and improved the simulation of methanogenesis and the dominance of Methanosaeta over Methanosarcina in lake sediments. These results highlight the importance of physiological acclimation in shaping the kinetics and thermodynamics of microbial reactions and in determining the outcome of microbial interactions.

Microfluidic Reproduction of Dynamic Bioreactor Environment Based on Computational Lifelines

The biotechnological production of fine chemicals, proteins and pharmaceuticals is usually hampered by loss of microbial performance during scale-up. This challenge is mainly caused by discrepancies between homogeneous environmental conditions at laboratory scale, where bioprocesses are optimized, and inhomogeneous conditions in large-scale bioreactors, where production takes place. Therefore, to improve strain selection and process development, it is of great interest to characterize these fluctuating conditions at large-scale and to study their effects on microbial cells. In this paper, we demonstrate the potential of computational fluid dynamics (CFD) simulation of large-scale bioreactors combined with dynamic microfluidic single-cell cultivation (dMSCC). Environmental conditions in a 200 L bioreactor were characterized with CFD simulations. Computational lifelines were determined by combining simulated turbulent multiphase flow, mass transport and particle tracing. Glucose availability for Corynebacterium glutamicum cells was determined. The reactor was simulated with average glucose concentrations of 6 g m−3, 10 g m−3 and 16 g m−3. The resulting computational lifelines, discretized into starvation and abundance regimes, were used as feed profiles for the dMSCC to investigate how varying glucose concentration affects cell physiology and growth rate. In this study, each colony in the dMSCC device represents a single cell as it travels through the reactor. Under oscillating conditions reproduced in the dMSCC device, a decrease in growth rate of about 40% was observed compared to continuous supply with the same average glucose availability. The presented approach provides insights into environmental conditions observed by microorganisms in large-scale bioreactors. It also paves the way for an improved understanding of how inhomogeneous environmental conditions influence cellular physiology, growth and production.

Insights into the Antibacterial Activity of Prolactin-Inducible Protein against the Standard and Environmental MDR Bacterial Strains

Background: Prolactin inducible protein (PIP) is a small secretary glycoprotein present in most biological fluids and contributes to various cellular functions, including cell growth, fertility, antitumor, and antifungal activities. Objectives: The present study evaluated the antibacterial activities of recombinant PIP against multiple broad-spectrum MDR bacterial strains. Methods: The PIP gene was cloned, expressed and purified using affinity chromatography. Disk diffusion, broth microdilution, and growth kinetic assays were used to determine the antibacterial activities of PIP. Results: Disk diffusion assay showed that PIP has a minimum and maximum zone of inhibition against E. coli and P. aeruginosa, respectively, compared to the reference drug ampicillin. Furthermore, growth kinetics studies also suggested that PIP significantly inhibited the growth of E. coli and P. aeruginosa. The minimum inhibitory concentration of PIP was 32 µg/mL for E. coli (443), a standard bacterial strain, and 64 µg/mL for Bacillus sp. (LG1), an environmental multidrug-resistant (MDR) strain. The synergistic studies of PIP with ampicillin showed better efficacies towards selected bacterial strains having MDR properties. Conclusion: Our findings suggest that PIP has a broad range of antibacterial activities with important implications in alleviating MDR problems.

Bacillus subtilis Histidine Kinase KinC Activates Biofilm Formation by Controlling Heterogeneity of Single-Cell Responses

In Bacillus subtilis, biofilm and sporulation pathways are both controlled by a master regulator, Spo0A, which is activated by phosphorylation via a phosphorelay-a cascade of phosphotransfer reactions commencing with autophosphorylation of histidine kinases KinA, KinB, KinC, KinD, and KinE. However, it is unclear how the kinases, despite acting via the same regulator, Spo0A, differentially regulate downstream pathways, i.e., how KinA mainly activates sporulation genes and KinC mainly activates biofilm genes. In this work, we found that KinC also downregulates sporulation genes, suggesting that KinC has a negative effect on Spo0A activity. To explain this effect, with a mathematical model of the phosphorelay, we revealed that unlike KinA, which always activates Spo0A, KinC has distinct effects on Spo0A at different growth stages: during fast growth, KinC acts as a phosphate source and activates Spo0A, whereas during slow growth, KinC becomes a phosphate sink and contributes to decreasing Spo0A activity. However, under these conditions, KinC can still increase the population-mean biofilm matrix production activity. In a population, individual cells grow at different rates, and KinC would increase the Spo0A activity in the fast-growing cells but reduce the Spo0A activity in the slow-growing cells. This mechanism reduces single-cell heterogeneity of Spo0A activity, thereby increasing the fraction of cells that activate biofilm matrix production. Thus, KinC activates biofilm formation by controlling the fraction of cells activating biofilm gene expression. IMPORTANCE In many bacterial and eukaryotic systems, multiple cell fate decisions are activated by a single master regulator. Typically, the activities of the regulators are controlled posttranslationally in response to different environmental stimuli. The mechanisms underlying the ability of these regulators to control multiple outcomes are not understood in many systems. By investigating the regulation of Bacillus subtilis master regulator Spo0A, we show that sensor kinases can use a novel mechanism to control cell fate decisions. By acting as a phosphate source or sink, kinases can interact with one another and provide accurate regulation of the phosphorylation level. Moreover, this mechanism affects the cell-to-cell heterogeneity of the transcription factor activity and eventually determines the fraction of different cell types in the population. These results demonstrate the importance of intercellular heterogeneity for understanding the effects of genetic perturbations on cell fate decisions. Such effects can be applicable to a wide range of cellular systems.

Novel mechanism and degradation kinetics of pesticides mixture using Bacillus sp. strain 3C in contaminated sites

The present study has investigated the potential of Bacillus sp. strain 3C able to degrade mixture of pesticides from the environment. It showed maximum tolerance up to 450 mg·L^-1 for cypermethrin, fipronil, imidacloprid and sulfosulfuron. The strain 3C was able to degrade up to the 94% of mixture of pesticides (20 mg·L^-1) within 15 days of experiment. The Box-Behnken design of Response Surface Methodology (RSM) determined the optimized conditions as; inoculum size 3.0 × 10⁷ CFU·mL^-1, shaking speed 120 rpm, and pesticides concentration 80 mg·L^-1. In soil-based bioremediation with strain 3C after 15 days degradation pattern was; 99, 94, 92, 92 and 7% for the imidacloprid, sulfosulfuron, fipronil, cypermethrin and control respectively. The novel intermediate metabolites for cypermethrin degradation were investigated as decyl isobutyl ester, phthalic acid, cyclopropane carboxylic acid tri dec-2-ynyl ester, 9- octadecanal, tridecane, propanoic acid, cyclohexene, bicyclo[2.2.1] heptan-2-ol, and acetic acid were identified using Gas chromatography Mass Spectrometry (GC-MS) with strain 3C. Moreover, the results of the laccase based enzymatic kinetics suggested that the rate of production was maximum in pesticides stress (94 μg·μL^-1) whereas, in normal condition 51 μg·μL^-1. The K_m value found to be decreased in pesticides stress condition 12.25 and increment in K_m 13.58 mM was observed without stress. Furthermore, aldehyde dehydrogenase (ALDH) and laccase encoding genes were amplified and linked with mixture of pesticides bioremediation. The efficiency of bacterial strain 3C, could be used for bioremediation of mixture of pesticides, and other xenobiotic compounds from the contaminated environments.

Two-stage integrated process for bio-methanol production coupled with methane and carbon dioxide sequestration: Kinetic modelling and experimental validation

The study demonstrates a two-stage integrated process for bio-methanol production using Methylosinus trichosporium NCIMB 11131, coupled with sequestration of methane and carbon dioxide. The first stage involved generation of methanotrophic biomass via sequestration of methane; which was used as biocatalyst to reduce carbon dioxide into methanol in the second stage. Maximum biomass titer of 3.39 g L^-1 and productivity of 0.60 g L^-1 d^-1 were achieved in semi-batch stirred tank reactor with methane concentration in the inlet gas mixture of 2.5% v/v and gas flow rate of 0.5 vvm. Methane fixation rate was estimated to be 0.32 g L^-1 d^-1. Maximum methanol titer of 0.58 g L^-1 was achieved at headspace carbon dioxide concentration of 50% v/v and liquid to headspace volume ratio 10:90. Subsequently, a kinetic model was developed to predict and understand the system behaviour in terms of dynamic profile of growth, methanol formation, concentration of dissolved methane or carbon dioxide in the aqueous phase and headspace carbon dioxide concentration, in response to varying process parameters. The model can serve as a tool for estimation of process parameters and aid in overall production optimization.

Enzyme regulation and mutation in a model serial-dilution ecosystem

Microbial communities are ubiquitous in nature and come in a multitude of forms, ranging from communities dominated by a handful of species to communities containing a wide variety of metabolically distinct organisms. This huge range in diversity is not a curiosity-microbial diversity has been linked to outcomes of substantial ecological and medical importance. However, the mechanisms underlying microbial diversity are still under debate, as simple mathematical models only permit as many species to coexist as there are resources. A plethora of mechanisms have been proposed to explain the origins of microbial diversity, but many of these analyses omit a key property of real microbial ecosystems: the propensity of the microbes themselves to change their growth properties within and across generations. In order to explore the impact of this key property on microbial diversity, we expand upon a recently developed model of microbial diversity in fluctuating environments. We implement changes in growth strategy in two distinct ways. First, we consider the regulation of a cell's enzyme levels within short, ecological times, and second we consider evolutionary changes driven by mutations across generations. Interestingly, we find that these two types of microbial responses to the environment can have drastically different outcomes. Enzyme regulation may collapse diversity over long enough times while, conversely, strategy-randomizing mutations can produce a "rich-get-poorer" effect that promotes diversity. This paper makes explicit, using a simple serial-dilutions framework, the conflicting ways that microbial adaptation and evolution can affect community diversity.

An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes

The cell cultivation process in a bioreactor is a high-value manufacturing process that requires excessive monitoring and control compatibility. The specific cell growth rate is a crucial parameter that describes the online quality of the cultivation process. Most methods and algorithms developed for online estimations of the specific growth rate controls in batch and fed-batch microbial cultivation processes rely on biomass growth models. In this paper, we present a soft sensor – a specific growth rate estimator that does not require a particular bioprocess model. The approach for online estimation of the specific growth rate is based on an online measurement of the oxygen uptake rate. The feasibility of the estimator developed in this study was determined in two ways. First, we used numerical simulations on a virtual platform, where the cell culture processes were theoretically modeled. Next, we performed experimental validation based on laboratory-scale (7, 12, 15 L) bioreactor experiments with three different Escherichia coli BL21 cell strains.

Diversity begets diversity during community assembly until ecological limits impose a diversity ceiling

Microbial communities are hugely diverse, but we do not yet understand how species invasions and extinctions drive and limit their diversity. On the one hand, the ecological limits hypothesis posits that diversity is primarily limited by environmental resources. On the other hand, the diversity‐begets‐diversity hypothesis posits that such limits can be easily lifted when new ecological niches are created by biotic interactions. To find out which hypothesis better explains the assembly of microbial communities, we used metabolic modelling. We represent each microbial species by a metabolic network that harbours thousands of biochemical reactions. Together, these reactions determine which carbon and energy sources a species can use, and which metabolic by‐products—potential nutrients for other species—it can excrete in a given environment. We assemble communities by modelling thousands of species invasions in a chemostat‐like environment. We find that early during the assembly process, diversity begets diversity. By‐product excretion transforms a simple environment into one that can sustain dozens of species. During later assembly stages, the creation of new niches slows down, existing niches become filled, successful invasions become rare, and species diversity plateaus. Thus, ecological limitations dominate the late assembly process. We conclude that each hypothesis captures a different stage of the assembly process. Species interactions can raise a community's diversity ceiling dramatically, but only within limits imposed by the environment.

Multiple thresholds and trajectories of microbial biodiversity predicted across browning gradients by neural networks and decision tree learning

Ecological association studies often assume monotonicity such as between biodiversity and environmental properties although there is growing evidence that nonmonotonic relations dominate in nature. Here, we apply machine-learning algorithms to reveal the nonmonotonic association between microbial diversity and an anthropogenic-induced large-scale change, the browning of freshwaters, along a longitudinal gradient covering 70 boreal lakes in Scandinavia. Measures of bacterial richness and evenness (alpha-diversity) showed nonmonotonic trends in relation to environmental gradients, peaking at intermediate levels of browning. Depending on the statistical methods, variables indicative for browning could explain 5% of the variance in bacterial community composition (beta-diversity) when applying standard methods assuming monotonic relations and up to 45% with machine-learning methods taking non-monotonicity into account. This non-monotonicity observed at the community level was explained by the complex interchangeable nature of individual taxa responses as shown by a high degree of nonmonotonic responses of individual bacterial sequence variants to browning. Furthermore, the nonmonotonic models provide the position of thresholds and predict alternative bacterial diversity trajectories in boreal freshwater as a result of ongoing climate and land-use changes, which in turn will affect entire ecosystem metabolism and likely greenhouse gas production.

Differential proteomic analysis under pesticides stress and normal conditions in Bacillus cereus 2D

A potential pesticide degrading bacterial isolate (2D), showing maximum tolerance (450 mg∙L^-1) for cypermethrin, fipronil, imidacloprid and sulfosulfuron was recovered from a pesticide contaminated agricultural field. The isolate degraded cypermethrin, imidacloprid, fipronil and sulfosulfuron in minimal salt medium with 94, 91, 89 and 86% respectively as revealed by high performance liquid chromatography (HPLC) and gas chromatography (GC) analysis after 15 days of incubation. Presence of cyclobutane, pyrrolidine, chloroacetic acid, formic acid and decyl ester as major intermediate metabolites of cypermethrin biodegradation was observed in gas chromatography mass spectrometry (GC-MS) analysis. Results based on 16S rDNA sequencing, and phylogenetic analysis showed maximum similarity of 2D with Bacillus cereus (Accession ID: MH341691). Stress responsive and catabolic/pesticide degrading proteins were over expressed in the presence of cypermethrin in bacteria. Enzymatic kinetics of laccase was deduced in the test isolate under normal and pesticide stress conditions which suggested that the production of enzyme was induced significantly in pesticide stress (163 μg.μL^-1) as compare to normal conditions(29 μg.μL^-1) while the K_m value was decreased in pesticides stress condition (K_m = 10.57 mM) and increases in normal condition (K_m = 14.33 mM).Amplification of laccase gene showed a major band of 1200bp. The present study highlights on the potential of 2D bacterial strain i.e., high tolerance level of pesticide, effective biodegradation rate, and presence of laccase gene in bacterial strain 2D, could become a potential biological agent for large-scale treatment of mixture of pesticide (cypermethrin, fipronil, imidacloprid and sulfosulfuron) in natural environment (soil and water).

Bio-Electrochemical System Depollution Capabilities and Monitoring Applications: Models, Applicability, Advanced Bio-Based Concept for Predicting Pollutant Degradation and Microbial Growth Kinetics via Gene Regulation Modelling

Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored.

Genomic and kinetic analysis of novel Nitrospinae enriched by cell sorting

Chemolithoautotrophic nitrite-oxidizing bacteria (NOB) are key players in global nitrogen and carbon cycling. Members of the phylum Nitrospinae are the most abundant, known NOB in the oceans. To date, only two closely affiliated Nitrospinae species have been isolated, which are only distantly related to the environmentally abundant uncultured Nitrospinae clades. Here, we applied live cell sorting, activity screening, and subcultivation on marine nitrite-oxidizing enrichments to obtain novel marine Nitrospinae. Two binary cultures were obtained, each containing one Nitrospinae strain and one alphaproteobacterial heterotroph. The Nitrospinae strains represent two new genera, and one strain is more closely related to environmentally abundant Nitrospinae than previously cultured NOB. With an apparent half-saturation constant of 8.7 ± 2.5 µM, this strain has the highest affinity for nitrite among characterized marine NOB, while the other strain (16.2 ± 1.6 µM) and Nitrospina gracilis (20.1 ± 2.1 µM) displayed slightly lower nitrite affinities. The new strains and N. gracilis share core metabolic pathways for nitrite oxidation and CO2 fixation but differ remarkably in their genomic repertoires of terminal oxidases, use of organic N sources, alternative energy metabolisms, osmotic stress and phage defense. The new strains, tentatively named "Candidatus Nitrohelix vancouverensis" and "Candidatus Nitronauta litoralis", shed light on the niche differentiation and potential ecological roles of Nitrospinae.

A computational framework for finding parameter sets associated with chaotic dynamics

Many biological ecosystems exhibit chaotic behavior, demonstrated either analytically using parameter choices in an associated dynamical systems model or empirically through analysis of experimental data. In this paper, we use existing software tools (COPASI, R) to explore dynamical systems and uncover regions with positive Lyapunov exponents where thus chaos exists. We evaluate the ability of the software's optimization algorithms to find these positive values with several dynamical systems used to model biological populations. The algorithms have been able to identify parameter sets which lead to positive Lyapunov exponents, even when those exponents lie in regions with small support. For one of the examined systems, we observed that positive Lyapunov exponents were not uncovered when executing a search over the parameter space with small spacings between values of the independent variables.

A review of the biotransformations of priority pharmaceuticals in biological wastewater treatment processes

Wastewater effluent discharges have been considered as one of the main sources of synthetic chemicals entering into the aquatic environment. Even though they occur at low concentrations, pharmaceutically active compounds (PhACs) can have an impact on ecological toxicity that affects aquatic organisms. Moreover, new regulations in development toward preserving water quality reinforces the increasing need to monitor and abate some PhACs in wastewater treatment plants (WWTPs), where they are typically only partially eliminated. Unlike most previous reviews, we have focussed on how the main biological and chemical molecular factors impact the biotransformations of key PhACs in biological WWTP processes. Biotransformations have been found to be an important contributor towards the removal of PhACs from WWTP effluents. This review paper critically assesses these aspects and the recent advances that have been achieved in wastewater treatment processes for biodegradation of 7 PhACs; namely the non-steroidal anti-inflammatory drug (NSAID) diclofenac (DCF); the macrolide antibiotics azithromycin (AZM), erythromycin (ERY) and clarithromycin (CLR); the two natural estrogens estrone (E1) and 17β-estradiol (E2), and the synthetic estrogen 17α-ethinylesradiol (EE2). These represent the micropollutants of the EU Watch list in Decision 2015/495/EU that are most relevant to WWTPs due to their frequent detection. The metabolic pathways, transformation products and impact of relevant factors to biological WWTP processes is addressed in this review. The biokinetics of PhAC biodegradation in different engineered bioprocesses is also discussed. Promising technologies and operational strategies that are likely to have a high impact on controlling PhAC releases are highlighted and future research needs are also proposed.