This study evaluated the use of acerola (Malpighia glabra L., CACE), cashew (Anacardium occidentale L., CCAS), and guava (Psidium guayaba L., CGUA) fruit processing coproducts as substrates to ...promote the growth, metabolite production, and maintenance of the viability/metabolic activity of the probiotics Lactobacillus acidophilus LA-05 and Lacticaseibacillus paracasei L-10 during cultivation, freeze-drying, storage, and exposure to simulated gastrointestinal digestion. Probiotic lactobacilli presented high viable counts (≥8.8 log colony-forming units (CFU)/mL) and a short lag phase during 24 h of cultivation in CACE, CCAS, and CGUA. Cultivation of probiotic lactobacilli in fruit coproducts promoted sugar consumption, medium acidification, and production of organic acids over time, besides increasing the of several phenolic compounds and antioxidant activity. Probiotic lactobacilli cultivated in fruit coproducts had increased survival percentages after freeze-drying and during 120 days of refrigerated storage. Moreover, probiotic lactobacilli cultivated and freeze-dried in fruit coproducts had larger subpopulations of live and metabolically active cells when exposed to simulated gastrointestinal digestion. The results showed that fruit coproducts not only improved the growth and helped to maintain the viability and metabolic activity of probiotic strains but also enriched the final fermented products with bioactive compounds, being an innovative circular strategy for producing high-quality probiotic cultures.
•Probiotic lactobacilli had high viable counts when grown on fruit coproducts.•Fermented coproducts had enhanced bioactive compound content and antioxidant activity.•Probiotic lactobacilli in fruit coproducts retained culturability after freeze-drying.•Probiotic lactobacilli freeze-dried in fruit coproducts had better survival during storage.•Fruit coproducts maintained physiological status of lactobacilli during digestion.
Abstract
The Clusters of Orthologous Genes (COG) database, also referred to as the Clusters of Orthologous Groups of proteins, was created in 1997 and went through several rounds of updates, most ...recently, in 2014. The current update, available at https://www.ncbi.nlm.nih.gov/research/COG, substantially expands the scope of the database to include complete genomes of 1187 bacteria and 122 archaea, typically, with a single genome per genus. In addition, the current version of the COGs includes the following new features: (i) the recently deprecated NCBI’s gene index (gi) numbers for the encoded proteins are replaced with stable RefSeq or GenBank\ENA\DDBJ coding sequence (CDS) accession numbers; (ii) COG annotations are updated for >200 newly characterized protein families with corresponding references and PDB links, where available; (iii) lists of COGs grouped by pathways and functional systems are added; (iv) 266 new COGs for proteins involved in CRISPR-Cas immunity, sporulation in Firmicutes and photosynthesis in cyanobacteria are included; and (v) the database is made available as a web page, in addition to FTP. The current release includes 4877 COGs. Future plans include further expansion of the COG collection by adding archaeal COGs (arCOGs), splitting the COGs containing multiple paralogs, and continued refinement of COG annotations.
•Kinetic growth model: Monod, Blackman, Haldane, Tessier, Moser, Contois, Logarithmic, Powell, Han and Levenspiel, Logistic, Luong, Webb, Yano and Koga, and Aiba-Edwards.•The Monod, Moser, Tesseir, ...Contois, and Blackman model do not account for inhibition of cell growth.•The Monod model does not account for the lag and death phase during the growth phase.•The Haldane model is capable of handling both toxic and non-toxic substrate.•The Monod model is the most widely used kinetic growth model.
Bacteria in biological wastewater treatment process play an important role in the removal of substrate concentration. When bacteria removes the substrate, they continuously growth until such time when the substrate concentration is depleted. This paper aims to review the performance of kinetic growth rate models that describe specific bacteria growth rate. The kinetic growth rate models reviewed were: Monod, Blackman, Haldane, Teissier, Moser, Contois, Logarithmic, Powell, Han and Levenspiel, Logistic, Luong, Webb, Yano and Koga, and Aiba-Edwards. The performance review of the models were based on coefficient of determination (R²). Another statistical measure of performance was root mean square error (RMSE). All kinetic growth rate models performed well when subjected to both toxic and non-toxic substrate concentration. The most notable kinetic growth rate model was the Haldane model which was well represented in the literature.
Biological stability of drinking water refers to the concept of providing consumers with drinking water of same microbial quality at the tap as produced at the water treatment facility. However, ...uncontrolled growth of bacteria can occur during distribution in water mains and premise plumbing, and can lead to hygienic (e.g., development of opportunistic pathogens), aesthetic (e.g., deterioration of taste, odor, color) or operational (e.g., fouling or biocorrosion of pipes) problems. Drinking water contains diverse microorganisms competing for limited available nutrients for growth. Bacterial growth and interactions are regulated by factors, such as (i) type and concentration of available organic and inorganic nutrients, (ii) type and concentration of residual disinfectant, (iii) presence of predators, such as protozoa and invertebrates, (iv) environmental conditions, such as water temperature, and (v) spatial location of microorganisms (bulk water, sediment, or biofilm). Water treatment and distribution conditions in water mains and premise plumbing affect each of these factors and shape bacterial community characteristics (abundance, composition, viability) in distribution systems. Improved understanding of bacterial interactions in distribution systems and of environmental conditions impact is needed for better control of bacterial communities during drinking water production and distribution. This article reviews (i) existing knowledge on biological stability controlling factors and (ii) how these factors are affected by drinking water production and distribution conditions. In addition, (iii) the concept of biological stability is discussed in light of experience with well-established and new analytical methods, enabling high throughput analysis and in-depth characterization of bacterial communities in drinking water. We discussed, how knowledge gained from novel techniques will improve design and monitoring of water treatment and distribution systems in order to maintain good drinking water microbial quality up to consumer's tap. A new definition and methodological approach for biological stability is proposed.
There has been a resurgence of interest in non-equilibrium stochastic processes in recent years, driven in part by the observation that the number of molecules (genes, mRNA, proteins) involved in ...gene expression are often of order 1-1000. This means that deterministic mass-action kinetics tends to break down, and one needs to take into account the discrete, stochastic nature of biochemical reactions. One of the major consequences of molecular noise is the occurrence of stochastic biological switching at both the genotypic and phenotypic levels. For example, individual gene regulatory networks can switch between graded and binary responses, exhibit translational/transcriptional bursting, and support metastability (noise-induced switching between states that are stable in the deterministic limit). If random switching persists at the phenotypic level then this can confer certain advantages to cell populations growing in a changing environment, as exemplified by bacterial persistence in response to antibiotics. Gene expression at the single-cell level can also be regulated by changes in cell density at the population level, a process known as quorum sensing. In contrast to noise-driven phenotypic switching, the switching mechanism in quorum sensing is stimulus-driven and thus noise tends to have a detrimental effect. A common approach to modeling stochastic gene expression is to assume a large but finite system and to approximate the discrete processes by continuous processes using a system-size expansion. However, there is a growing need to have some familiarity with the theory of stochastic processes that goes beyond the standard topics of chemical master equations, the system-size expansion, Langevin equations and the Fokker-Planck equation. Examples include stochastic hybrid systems (piecewise deterministic Markov processes), large deviations and the Wentzel-Kramers-Brillouin (WKB) method, adiabatic reductions, and queuing/renewal theory. The major aim of this review is to provide a self-contained survey of these mathematical methods, mainly within the context of biological switching processes at both the genotypic and phenotypic levels. However, applications to other examples of biological switching are also discussed, including stochastic ion channels, diffusion in randomly switching environments, bacterial chemotaxis, and stochastic neural networks.
This study presents a detailed investigation into conformational, physicochemical and adhesive characteristics of biopolymers on the surfaces of Escherichia coli and potential probiotic Bacillus ...subtilis harvested from middle-exponential phase (mid-EP), late-exponential phase (late-EP) and early-stationary phase (early-SP) of growth. The lengths to which bacterial surface biopolymers extend (biopolymer brush lengths), densities of grafted bacterial surface biopolymers indicating the amounts of molecules covering the bacterial surfaces (biopolymer grafting densities), adhesion forces of bacterial surface biopolymers to the model inert surfaces of silicon nitride (Si3N4), and the pull-off distances of biopolymers from Si3N4 were measured in water by atomic force microscopy (AFM). The Weibull analysis of AFM adhesion data showed that as the culture aged, the adhesive bonds between Si3N4 AFM tips and surface molecules of E. coli harvested from the culture were broken with a higher applied force. However, the highest applied force to break the bonds was required for B. subtilis in late-EP, followed by those required for cells in early-SP and mid-EP, respectively. The results of a steric model fitting to AFM approach force-distance (FD) curves and analysis of the pull-off distances in the AFM retraction FD curves showed higher biopolymer grafting density for E. coli in early-SP and longer biopolymer brush layer for B. subtilis in late-EP, which were associated with stronger adhesion to Si3N4 in water. The results of thermodynamic adhesion energy calculations based on the Wu model showed that polar interaction energy dominated the bacterial adhesion at the macroscale, the strength of which varied as a function of the growth phase for both E. coli and B. subtilis. The growth phase-dependent variation in polar components of thermodynamic adhesion energies between the bacteria and Si3N4 in water was consistent with the growth phase-dependent variation in the bond strengths between the bacteria and Si3N4 in water as revealed by the Weibull analysis of AFM adhesion data. Therefore, information obtained by Weibull analysis of nanoscale AFM bacterial adhesion data can be used to predict macroscale bacterial adhesion to the model inert Si3N4 surface in water.
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•Higher biopolymer grafting density of E. coli in early-stationary phase led to stronger adhesion.•Longer biopolymer brush length of B. subtilis in late-exponential phase led to stronger adhesion.•Adhesion energy predictions of the Wu model agreed well with nanoscale adhesion measurements.•Weibull analysis of AFM adhesion data can be used to predict macroscale bacterial adhesion.
In article number 2007993, Haeshin Lee, Hyun Jung Chung, and co‐workers demonstrate the polydopamine–oxygen relationship using a fluorescence coupling strategy during bacterial growth‐induced ...hypoxia. With the exponential growth of bacteria, oxygen is consumed and leads to the inhibition of dopamine polymerization, which is directly measured by fluorescent nanoparticle sensors. The current method can be applied as a simple diagnostic assay to detect bacterial growth and antibiotic resistance.
Droplet microfluidics has revolutionized single-cell analysis, allowing for ultra-high-throughput sorting of droplets to meet the demands of single-cell sorting. However, accurately and efficiently ...dispensing target droplets into specific containers for downstream analysis has remained a challenge. Here, we present an integrated microfluidic droplet dispenser based on the electrohydrodynamic (EHD) principle, capable of on-demand single droplet dispensing with high accuracy. Our system uses optical signal activation and EHD actuation to isolate and dispense droplets. The microfluidic droplet auto dispenser consists of four main modules: a pump-drive chip module, an optical automatic signal recognition module, a high voltage control module, and an automated X-Y translation stage module. By optimizing the chip structure and voltage parameter, this platform successfully isolates single Escherichia coli (E. coli) cells onto a Petri dish for downstream cultivation and heterogeneity analysis. In addition, the dispenser microfluidic system dispenses the sorted droplets in a "one droplet, one well" manner, providing an automated method for droplet interface from the micro- to macro- platform. This dispenser system provides a platform for phenotypic screening of single colony pick and single cell analysis
•An integrated microfluidic droplet dispenser based on the electrohydrodynamic (EHD) principle.•Realize efficient single droplet dispensing with optical signal activation and EHD-based drive.•Provide an automated method for transferring droplets from the microchip to the macroscopic world.
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