Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2836 2023-02-16 15:22:59 |
2 Reference format revised. Meta information modification 2836 2023-02-17 06:18:53 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Júnior, R.A.; Poloni, J.D.F.; Pinto, �.S.M.; Dorn, M. Lipopeptide and Protein-Containing Biosurfactants. Encyclopedia. Available online: https://encyclopedia.pub/entry/41310 (accessed on 15 June 2024).
Júnior RA, Poloni JDF, Pinto �SM, Dorn M. Lipopeptide and Protein-Containing Biosurfactants. Encyclopedia. Available at: https://encyclopedia.pub/entry/41310. Accessed June 15, 2024.
Júnior, Régis Antonioli, Joice De Faria Poloni, Éderson Sales Moreira Pinto, Márcio Dorn. "Lipopeptide and Protein-Containing Biosurfactants" Encyclopedia, https://encyclopedia.pub/entry/41310 (accessed June 15, 2024).
Júnior, R.A., Poloni, J.D.F., Pinto, �.S.M., & Dorn, M. (2023, February 16). Lipopeptide and Protein-Containing Biosurfactants. In Encyclopedia. https://encyclopedia.pub/entry/41310
Júnior, Régis Antonioli, et al. "Lipopeptide and Protein-Containing Biosurfactants." Encyclopedia. Web. 16 February, 2023.
Lipopeptide and Protein-Containing Biosurfactants
Edit

Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known.

biosurfactant lipopeptide protein-containing biosurfactant biosurfactant physicochemical properties

1. Introduction

Biosurfactants are amphipathic molecules synthesized by plants, animals, and microbes, that reduce interfacial and superficial tensions in aqueous solutions and hydrocarbon mixtures [1]. Due to these properties, biosurfactants alter how other molecules interact, increasing the solubility of substances [2][3]. The hydrophilic portion—the head—will usually be a hydrocarbon, while the hydrophobic portion—the tail—can be non-ionic, positively or negatively charged, or amphoteric [4]. These characteristics allow biosurfactants to form aggregates called micelles, which gather when there is an increase in amphiphilic concentration in a liquid beyond a limit, known as critical micelle concentration (CMC) [5].
In contrast to the artificial surfactants, biosurfactants are eco-friendly alternatives that offer many advantages when compared to synthetic surfactants, such as surface and interfacial activity, resistance to temperature, pH and ionic force, low toxicity, availability, specificity, biocompatibility, and biodegradability [6][7][8]. Due to their varied structural diversity and composition, the biosurfactants themselves are used in various applications. Applicable areas include bioremediation, medicine, food industry, and industrial processes [9]. Though biosurfactants possess valuable capabilities, large-scale industrial applications are hindered by high costs and low efficiency of their production and recovery processes [10]
There are several classes of compounds among biosurfactants, as they have a vast structural diversity. They can be categorized as glycolipids, lipopeptides and lipoproteins, fatty acids, phospholipids, natural lipids, polymeric and particulate [11][12][13][14].
Regarding lipopeptides and lipoprotein biosurfactants, the leading producers are fungi, bacteria, and yeast [15] They act to enhance mobility, decrease viscosity, facilitate solubilization, and act as metal-sequestering agents [15]. Additionally, they can disrupt biological membranes, making them potential agents to be used as hemolytic, antiviral, antibacterial, and anti-carcinogenic molecules [15]. One of the main interests in biosurfactant research is employing them as surface-active compounds in bioremediation strategies as alternatives for traditional chemical methods.

2. Biosurfactant-Producing Microorganisms

2.1. Lipopeptides

Lipopeptides are a class of biosurfactants with high industrial interest. They are linear or cyclic oligopeptides acylated with fatty acids of different length and composition which are synthesized by the nonribosomal peptide synthetases (NRPSs) [16] and commonly secreted by bacterial genres such as Bacillus, Streptomyces and Pseudomonas [14]. Microorganisms of the genus Bacillus are some of the more well-known lipopeptide producers, and their genome contains a large operon (srfA) composed of four open reading frames which encode a peptide synthetase responsible for the surfactin biosynthesis (Figure 1A,B) [17][18]. Lipopeptides have varying emulsification properties according to temperature, pressure, pH, structure and stability of the solution [19]. For example, the emulsifying activity of surfactin produced by B. subtilis can change based on pH. With a pH above 7, it forms a stable emulsion with kerosene, but when the pH drops to 3 the emulsion does not form [20].
Figure 1. (A) NRPS modules of surfactin, iturin, and fengycin. (B) Simplified model of transcriptional regulation of srfA gene from Bacillus.

2.2. Protein-Containing Biosurfactants

Surface-active proteins are a lesser-studied class of biosurfactants that still has some relevance for industrial applications. Depending on the scholars, lipopeptides will occasionally be grouped with lipoproteins [21]. Considered as high molecular mass bioemulsifiers, they are complex structures with multiple reactive groups exposed, turning them into effective emulsifiers as they bind tightly to hydrophobic molecules [17]. Surface tension at liquid-air interfaces is a significant barrier encountered by distinct organisms. Conquering the surface tension is an essential mechanism in several species, i.e., as sporulation of bacteria and fungi, foaming in frog nests, and evaporative cooling in horses [14][21]. These biosurfactants are divided into different groups according to structure: lipid-associated proteins, such as pulmonary surfactants [22], and non-lipid-associated globular proteins, such as hydrophobins [21]. Polymeric biosurfactants are a different category of high molecular weight biosurfactants containing protein in their composition [15]. Alasan is the best-known biopolymer of this group and consists of a high molecular weight polysaccharide and protein complex with solubilizing and emulsifying activity [15]. Another effective emulsifier is liposan, composed of 87% carbohydrates and 17% proteins, produced by Candida lipolytica [15][23]. It was first described in 1985 by Cirigliano and Carman [23], who showed that it is not able to reduce the surface tension of water but effectively emulsified and stabilized water-in-oil emulsion [15].

3. Lipopeptides Synthesis and Regulation

NRPSs are multidomain mega-enzymes that synthesize NRPs without using the ribosomal machinery [24]. They have a modular structure in which each part incorporates an amino acid to the peptide moiety of the lipopeptide (Figure 1B). Another characteristic is that the NRPS obeys the colinearity rule; that is, the modules are colinear with the amino acid sequence of the peptide [16]. There are two types of modules: initiation and elongation modules. Each of these modules also contains domains that perform specific tasks [16]. While typically, initiation modules have domains responsible for the amino acid election, activation, and thioesterification of the activated amino acid, the first module also contains a condensation domain in lipopeptide biosynthesis. This domain is responsible for catalyzing the N-acylation of the first amino acid of the lipopeptide, thus linking the lipid moiety to the oligopeptide [16][25]. Elongation modules contain the same domains, but this time the condensation domain is responsible for catalyzing the peptide bond between two amino acids. The condensation domain from the elongation module will generate a lipopeptide that, by the end of the assembly line, will be cleaved by a thioesterase [16].

It has been shown via analyses of the metabolic profiles of Pseudomonas and Bacillus species that a single strain may be able to produce different forms of the same biosurfactant [16]. Examples include B. subtilis strain OKB 105, which can produce 12 different surfactin analogs [26] and P. fluorescens strain SS101 that can produce up to eight analogs of the lipopeptide massetolide A [27]. These analogs are thought to be the product of flexibility in the amino acid selection and activation by the adenylation domain of the initiation module in NRPSs. Said flexibility is common in nonribosomal peptide synthesis, which may have biological purposes for the producers [16].

Iturin production has a series of regulation factors. For example, the methylation of tyrosine residues in iturin can decerease yield and antibacterial activity [28][29]. Furthermore, iturin can also be regulated by controlling the expression of sigma factor A and the transcription factor ComA. Overexpressing the genes sigA and comA increased iturin yield [30].

As mentioned, the biosynthesis of surfactin is coordinated by the srfA operon, which contains four open reading frames (srfAA, srfAB, srfAC, and srfAD) as seen in Figure 1A [31]. These open reading frames are responsible for the peptide chain extension, a key step in the surfactin synthesis regulation [29]. Surfactin, much like iturin, is also strongly regulated by the transcription factor ComA, which binds to srfA, and thus controls its transcription and regulates srfA expression (Figure 1B) [29]. ComA phosphorylation is the key to activating the srfA operon transcription by two different pathways. The first involves the ComX peptide modified by ComQ that stimulates ComP autophosphorylation, triggering ComA phosphorylation (Figure 1A) [32]. Activated ComA translocates to the nucleus and promotes srfA transcription. PhrC importation is mediated by Spo0K, which interacts with Rap protein and inhibits its phosphatase activity, thus, preventing ComA dephosphorylation and facilitating ComA-induced srfA transcription (Figure 1A) [32][33].

4. Biosurfactant Toxicity

Most chemically synthesized surfactants widely available on the market resist biodegradation and accumulate in nature. On the other hand, Biosurfactants are, like all-natural products, susceptible to degradation in water and soil [6]. Other than that, they are known for being less toxic or even non-toxic to the environment when compared to their synthetic counterparts and, thus, being the better choice when degrading pollutants. Some studies were already published comparing synthetic and natural surfactants and show how biosurfactants can be less toxic or even non-toxic at all [34][35]. However, Edwards et al. (2003) determined that the difference may not be significant [36]. Still, there are issues regarding the conditions in nature and the concentration and availability of biosurfactants in said situations, as they can vary between species and compounds, and lab conditions cannot properly mimic that.

5. Emerging Strategies for Biosurfactant Production

Being able to implement the newly discovered compounds is needed. As mentioned before, biosurfactants still struggle to compete with synthetic surfactants on large-scale applications globally. With that in mind, recent strategies have been developed to turn biosurfactants into competitive alternatives for industrial applications.
One such application is solid-state fermentation (SSF). A strategy for biosurfactant production that is geared towards overcoming the foaming encountered in the more popular submerged fermentation (SmF), this is used not only for biosurfactants but also other bioproducts [37][38][39]. This methodology has already been applied to the peptidic biosurfactant surfactin,. By using a medium based on okara, with the addition of sugarcane bagasse as a bulking agent, SSF was employed for surfactin production by B. pumilus UFPEDA 448 [40]. Other examples include SSF of soybean flour and rice straw as a substrate for the production of an antimicrobial lipopeptide by B. amyloliquefaciens XZ-173 [41], lipopeptide production by B. subtilis SPB1 grown on a mixture of olive leaf residue flour and olive cake flour [42] and surfactin production via SSF with rapeseed cake mixed with bacterial solution [43].
The market always favors lucrative processes, and with that in mind, another feasible option for biosurfactant production is the co-production with other economically necessary products in a single bioprocess. Microorganisms tend to synthesize biosurfactants and other compounds, which could be taken advantage of. Before, co-production of pectinase and biosurfactant was observed in a B. subtilis strain isolated from a fruit dump yard [44].

6. Physical and Chemical Properties of Biosurfactants

Biosurfactants are bioderived or biomimetic surfactants that can act as detergents, wetting agents, emulsifiers, dispersants, and foaming agents [45][46]. These molecules have the properties of reducing water surface tension and decreasing water/oil interface tension (Figure 2) [3]. This occurs because these amphiphilic molecules can dispose of the limit of surface water and assemble into micelles, which can shield hydrophobic molecules present in the solution from the unfavorable interactions with water molecules [19]. Moreover, biosurfactants present adsorptive properties on surfaces and interfaces [47].
Figure 2. (A) Relationship between biosurfactant concentration and surface tension. CMC is defined by the concentration at which biosurfactant monomers form micelles and can reduce interfacial tension. (B) Different types of biosurfactant aggregates. A spherical shape occurs when the solution reaches CMC, and may become asymmetrical (such as a worm-like shape) at higher biosurfactant concentrations. Likewise, temperature or other environmental conditions induces the transition from spherical shape micelles to micellar vesicles in biosurfactant assemblies.
Biosurfactants are molecules that present a wide range of industrial applications, such as cleaning (laundry products), biofilm prevention and disruption, biocidal activity, wound healing, and various uses in the petroleum industry and oil bioremediation [46][48]. This array of applications comes from the particular chemical features that biosurfactant molecules present. From a physicochemical perspective, (bio)surfactants are amphipathic molecules that lower surface and interfacial tensions. Consequently, water-immiscible substances will increase their solubility at the surfactant-water interface [3]. The molecules will spontaneously aggregate into micelles under non-extreme conditions and at a minimum concentration. 
The CMC and the aggregation number are the two main parameters to investigate in a new biosurfactant. When biosurfactant molecules are slowly added to water, the molecules will stay only on the surface (at a very low concentration). At this point, there are no micelles in the solution. With the slow increase of molecules in the solution, it will reach a concentration in which no micelles are yet stably assembled at a certain point. Still, more biosurfactant molecules will promote the formation of thermodynamically stable micelles. This specific concentration is called CMC (Figure 2). The number of molecules that constitute a micelle formed just as CMC has been overreached is defined as the aggregation number [49]. There are different methods to determine both the CMC and aggregation number of a given biosurfactant, such as isothermal titration calorimetry [50], THP-based determination method [51], fluorimetry, conductometry, and surface tension [52]. Furthermore, CMC depends on temperature, pressure, pH, and ionic strength; thus, investigating the relationship between CMC and those parameters is utterly essential.
An essential aspect of biosurfactants CMC and its aggregation number is how temperature affects the assembly process. Attempting to predict CMC as a function of temperature is not a straightforward process. That is because surfactants, in a general perspective, vary considerably in their intrinsic structural features, such as ionic and non-ionic nature, hydrophobic moiety size, polar moiety size, and the number of hydrogen bond possibilities. Whereas some studies predicted a monotonic behavior between CMC and temperature for different surfactants [53][54], new evidence suggests that the effect of the temperature on the CMC is non-monotonic and the monotonic behavior is observed only in a specific range of temperature [55].
As for the CMC, surface tension is also dependent on temperature. Generally, interfacial and surface tensions decrease with temperature increase [56]. This observation is due to a system’s energy compensation. When molecules are transferred from the bulk to the surface, there is an increase in the system’s energy. That is due to the balance between intermolecular attractive and repulsive forces of neighboring molecules and the energy necessary to overcome those forces for motion. Given that this is a spontaneous process, the gain of energy is compensated by reducing the surface area and, consequently, surface tension. At higher temperatures, the greater kinetic energy of the molecules results in a decrease of the attractive forces, exacerbating the reduction of surface tension. Experimentally, the relationship between temperature and surface tension was modeled by different scientists throughout history. 

7. Omics Technology and Bioinformatic Analysis as a Tool for Biosurfactant Identification

7.1. Network Analysis

Understanding the relationship between molecules and the organization of possible molecular pathways associated with biosurfactant production is crucial. Interaction networks are valuable tools to deal with a massive amount of information, whether from a single methodological approach, such as protein-protein interaction networks or a combination of data from multiple levels of knowledge. These networks represent direct and indirect interactions between the components of that system, which can describe genes, proteins, and any other target molecule. It is possible to build networks from multiple omics approaches, linking the results of genomics, transcriptomics, proteomics, and metabolomics within a single system through multilayer networks. Numerous works have used interaction networks to understand the relationship between genes/proteins and different compounds [57][58][59][60]. Also, metabolomic-based networks were used to elucidate metabolic profiles and chemical structures from secondary metabolites produced by S. marcescens strains, resulting in the identification of serrawettin W1 [61].

7.2. Machine Learning and Data Integration

Machine Learning (ML) is a field derived from studies of artificial intelligence, pattern recognition, statistics, and optimization, which aims to develop algorithms capable of performing functions without being explicitly controlled by a user. ML techniques “learn” how to make predictions and decisions through a given data or by integrating more than one [62][63], which could be genomic, transcriptomic, proteomic, or metabolomic. The main advantage of using ML is its ability to extract information from a massive amount of large-scale data, which can be used to create predictive models for a given situation quickly and with high precision, in addition to finding essential patterns for the description of the biological phenomenon [64].
Analyzing the omics datasets in an integrative way using ML techniques will return a complete picture of the whole system than separately analyzing them. Data integration can be defined as the integrative study of data from multiple sources to improve knowledge discovery [65][66][67]. Tarazona et al. [68] categorizes integration methods based on incorporating additional biological and using supervised or unsupervised approaches. Supervised methods can be used in two principal ways—the first is for predicting a response variable. The second way is to understand the communication between the omics layers or, more specifically, to model the potential regulations to build the regulatory network of the biological system studied [69]. Unsupervised methods are mainly applied for a preliminary exploration of datasets and sometimes also for clustering observations.

7.3. Molecular Dynamics (MD) Simulations

The principle behind MD is to represent computationally molecules and ions, which can be at an atomistic level or coarse-grained; define a volume for the experiment to take place, which will be represented as a box, and apply Newtonian physics principles in the system to calculate all bonded and non-bonded forces in motion. In other words, MD allows us to use computer processing to simulate and visualize how a set of molecules behave under specific conditions in a given time. From one typical simulation, one can obtain a myriad of information about structural features and physical-chemical properties. Moreover, a molecule, or thousands of the same molecule, such as a specific biosurfactant, can be simulated in different solvents. This way, the properties in different interfaces can be investigated [70][71][72].
Furthermore, MD can be performed at varying temperature, pressure, ionic force, solvent type, the number of particles (concentration), and other parameters that might be relevant to a specific experiment. Therefore, one can investigate structural features, such as hydrogen bonds through time, atoms position and fluctuation through time, as well as physical-chemical properties like density and free energy of solvation. A handful of free software programs for MD, such as GROMACS [73], NAMD [74] and OpenMM [75]. An easy, straightforward tutorial to learn how to simulate from scratch using GROMACS can be found in [76].

References

  1. Jahan, R.; Bodratti, A.M.; Tsianou, M.; Alexandridis, P. Biosurfactants, natural alternatives to synthetic surfactants: Physicochemical properties and applications. Adv. Colloid Interface Sci. 2020, 275, 102061.
  2. Hamme, J.D.V.; Singh, A.; Ward, O.P. Physiological aspects: Part 1 in a series of papers devoted to surfactants in microbiology and biotechnology. Biotechnol. Adv. 2006, 24, 604–620.
  3. Singh, P.; Patil, Y.; Rale, V. Biosurfactant production: Emerging trends and promising strategies. J. Appl. Microbiol. 2019, 126, 2–13.
  4. Desai, J.D.; Banat, I.M. Microbial production of surfactants and their commercial potential. Microbiol. Mol. Biol. Rev. 1997, 61, 47–64.
  5. Machlin, E.S. CHAPTER XIV—Thermodynamics of Micelles. In An Introduction to Aspects of Thermodynamics and Kinetics Relevant to Materials Science, 3rd ed.; Machlin, E.S., Ed.; Elsevier Science Ltd.: Oxford, UK, 2007; pp. 425–454.
  6. Cameotra, S.S.; Makkar, R.S.; Kaur, J.; Mehta, S.K. Synthesis of Biosurfactants and Their Advantages to Microorganisms and Mankind. In Biosurfactants; Sen, R., Ed.; Springer: New York, NY, USA, 2010; pp. 261–280.
  7. Kiran, G.S.; Sabarathnam, B.; Selvin, J. Biofilm disruption potential of a glycolipid biosurfactant from marine Brevibacterium casei. FEMS Immunol. Med. Microbiol. 2010, 59, 432–438.
  8. Franzetti, A.; Gandolfi, I.; Fracchia, L.; Van Hamme, J.; Gkorezis, P.; Marchant, R.; Banat, I.M. Biosurfactant use in heavy metal removal from industrial effluents and contaminated sites. Biosurfactants Prod. Util. Technol. Econ. 2014, 159, 361.
  9. Reis, R.; Pacheco, G.; Pereira, A.; Freire, D. Biosurfactants: Production and Applications. In Biodegradation; Chamy, R., Rosenkranz, F., Eds.; IntechOpen: Rijeka, Croatia, 2013; Chapter 2.
  10. Souza, K.S.T.; Gudiña, E.J.; Schwan, R.F.; Rodrigues, L.R.; Dias, D.R.; Teixeira, J.A. Improvement of biosurfactant production by Wickerhamomyces anomalus CCMA 0358 and its potential application in bioremediation. J. Hazard. Mater. 2018, 346, 152–158.
  11. Vijayakumar, S.; Saravanan, V. Biosurfactants-Types, Sources and Applications. Res. J. Microbiol. 2015, 10, 181–192.
  12. Roy, A. A Review on the Biosurfactants: Properties, Types and its Applications. J. Fundam. Renew. Energy Appl. 2017, 8, 1–14.
  13. Varjani, S.J.; Upasani, V.N. Critical review on biosurfactant analysis, purification and characterization using rhamnolipid as a model biosurfactant. Bioresour. Technol. 2017, 232, 389–397.
  14. Henkel, M.; Hausmann, R. Chapter 2—Diversity and Classification of Microbial Surfactants. In Biobased Surfactants, 3rd ed.; Hayes, D.G., Solaiman, D.K., Ashby, R.D., Eds.; AOCS Press: London, UK, 2019; pp. 41–63.
  15. Kashif, A.; Rehman, R.; Fuwad, A.; Shahid, M.K.; Dayarathne, H.; Jamal, A.; Aftab, M.N.; Mainali, B.; Choi, Y. Current advances in the classification, production, properties and applications of microbial biosurfactants—A critical review. Adv. Colloid Interface Sci. 2022, 2022, 102718.
  16. Raaijmakers, J.M.; De Bruijn, I.; Nybroe, O.; Ongena, M. Natural functions of lipopeptides from Bacillus and Pseudomonas: More than surfactants and antibiotics. FEMS Microbiol. Rev. 2010, 34, 1037–1062.
  17. Marchant, R.; Funston, S.; Uzoigwe, C.; Rahman, P.; Banat, I.M. Production of Biosurfactants from Nonpathogenic Bacteria. In Biosurfactants: Production and Utilization-Processes, Technologies, and Economics; Kosaric, N., Sukan, F.V., Eds.; CRC Press: Boca Raton, FL, USA, 2014; pp. 73–81.
  18. Das, P.; Mukherjee, S.; Sen, R. Genetic regulations of the biosynthesis of microbial surfactants: An overview. Biotechnol. Genet. Eng. Rev. 2008, 25, 165–186.
  19. Carolin C, F.; Kumar, P.S.; Ngueagni, P.T. A review on new aspects of lipopeptide biosurfactant: Types, production, properties and its application in the bioremediation process. J. Hazard. Mater. 2021, 407, 124827.
  20. Long, X.; He, N.; He, Y.; Jiang, J.; Wu, T. Biosurfactant surfactin with pH-regulated emulsification activity for efficient oil separation when used as emulsifier. Bioresour. Technol. 2017, 241, 200–206.
  21. Schor, M.; Reid, J.L.; MacPhee, C.E.; Stanley-Wall, N.R. The Diverse Structures and Functions of Surfactant Proteins. Trends Biochem. Sci. 2016, 41, 610–620.
  22. Whitsett, J.A.; Weaver, T.E. Hydrophobic Surfactant Proteins in Lung Function and Disease. N. Engl. J. Med. 2002, 347, 2141–2148.
  23. Cirigliano, M.C.; Carman, G.M. Purification and characterization of liposan, a bioemulsifier from Candida lipolytica. Appl. Environ. Microbiol. 1985, 50, 846–850.
  24. Soltani, J. Chapter 22—Secondary Metabolite Diversity of the Genus Aspergillus: Recent Advances. In New and Future Developments in Microbial Biotechnology and Bioengineering; Gupta, V.K., Ed.; Elsevier: Amsterdam, The Netherlands, 2016; pp. 275–292.
  25. Roongsawang, N.; Lim, S.P.; Washio, K.; Takano, K.; Kanaya, S.; Morikawa, M. Phylogenetic analysis of condensation domains in the nonribosomal peptide synthetases. FEMS Microbiol. Lett. 2005, 252, 143–151.
  26. Kowall, M.; Vater, J.; Kluge, B.; Stein, T.; Franke, P.; Ziessow, D. Separation and Characterization of Surfactin Isoforms Produced by B. subtilis OKB 105. J. Colloid Interface Sci. 1998, 204, 1–8.
  27. de Bruijn, I.; de Kock, M.J.D.; de Waard, P.; van Beek, T.A.; Raaijmakers, J.M. Massetolide A Biosynthesis in Pseudomonas fluorescens. J. Bacteriol. 2008, 190, 2777–2789.
  28. Moran, S.; Rai, D.K.; Clark, B.R.; Murphy, C.D. Precursor-directed biosynthesis of fluorinated iturin A in Bacillus spp. Org. Biomol. Chem. 2009, 7, 644–646.
  29. Yang, R.; Lei, S.; Xu, X.; Jin, H.; Sun, H.; Zhao, X.; Pang, B.; Shi, J. Key elements and regulation strategies of NRPSs for biosynthesis of lipopeptides by Bacillus. Appl. Microbiol. Biotechnol. 2020, 104, 8077–8087.
  30. Zhang, Z.; Ding, Z.; Zhong, J.; Zhou, J.; Shu, D.; Luo, D.; Yang, J.; Tan, H. Improvement of iturin A production in B. subtilis ZK 0 by overexpression of the comA and sigA genes. Lett. Appl. Microbiol. 2017, 64, 452–458.
  31. Cosmina, P.; Rodriguez, F.; de Ferra, F.; Grandi, G.; Perego, M.; Venema, G.; van Sinderen, D. Sequence and analysis of the genetic locus responsible for surfactin synthesis in B. subtilis. Mol. Microbiol. 1993, 8, 821–831.
  32. Roongsawang, N.; Washio, K.; Morikawa, M. Diversity of nonribosomal peptide synthetases involved in the biosynthesis of lipopeptide biosurfactants. Int. J. Mol. Sci. 2010, 12, 141–172.
  33. Hu, F.; Liu, Y.; Li, S. Rational strain improvement for surfactin production: Enhancing the yield and generating novel structures. Microb. Cell Factories 2019, 18, 42.
  34. Poremba, K.; Gunkel, W.; Lang, S.; Wagner, F. Marine Biosurfactants, III. Toxicity Testing with Marine Microorganisms and Comparison with Synthetic Surfactants. Z. Naturforschung C 1991, 46, 210–216.
  35. Ribeiro, B.G.; Guerra, J.M.C.; Sarubbo, L.A. Biosurfactants: Production and application prospects in the food industry. Biotechnol. Prog. 2020, 36, e3030.
  36. Edwards, K.R.; Lepo, J.E.; Lewis, M.A. Toxicity comparison of biosurfactants and synthetic surfactants used in oil spill remediation to two estuarine species. Mar. Pollut. Bull. 2003, 46, 1309–1316.
  37. Velioğlu, Z.; Ozturk Urek, R. Biosurfactant production by Pleurotus ostreatus in submerged and solid-state fermentation systems. Turk. J. Biol. 2015, 39, 160–166.
  38. Magro, A.E.A.; Silva, L.C.; Rasera, G.B.; de Castro, R.J.S. Solid-state fermentation as an efficient strategy for the biotransformation of lentils: Enhancing their antioxidant and antidiabetic potentials. Bioresour. Bioprocess. 2019, 6, 38.
  39. Cerda, A.; Artola, A.; Barrena, R.; Font, X.; Gea, T.; Sánchez, A. Innovative Production of Bioproducts from Organic Waste through Solid-State Fermentation. Front. Sustain. Food Syst. 2019, 3, 63.
  40. Das, K.; Mukherjee, A. Comparison of lipopeptide biosurfactants production by B. subtilis strains in submerged and solid state fermentation systems using a cheap carbon source: Some industrial applications of biosurfactants. Process. Biochem. 2007, 42, 1191–1199.
  41. Zhu, Z.; Zhang, G.; Luo, Y.; Ran, W.; Shen, Q. Production of lipopeptides by Bacillus amyloliquefaciens XZ-173 in solid state fermentation using soybean flour and rice straw as the substrate. Bioresour. Technol. 2012, 112, 254–260.
  42. Zouari, R.; Ellouze-Chaabouni, S.; Ghribi-Aydi, D. Optimization of B. subtilis SPB1 Biosurfactant Production under Solid-state Fermentation Using By-Products of a Traditional Olive Mill Factory. Achieve Life Sci. 2014, 8, 162–169.
  43. Jajor, P.; Piłakowska-Pietras, D.; Krasowska, A.; Łukaszewicz, M. Surfactin analogues produced by B. subtilis strains grown on rapeseed cake. J. Mol. Struct. 2016, 1126, 141–146.
  44. Kavuthodi, B.; Thomas, S.K.; Sebastian, D. Co-production of Pectinase and Biosurfactant by the Newly Isolated Strain B. subtilis BKDS1. Microbiol. Res. J. Int. 2015, 10, 1–12.
  45. Dexter, A.F.; Middelberg, A.P. Peptides as functional surfactants. Ind. Eng. Chem. Res. 2008, 47, 6391–6398.
  46. Marchant, R.; Banat, I.M. Microbial biosurfactants: Challenges and opportunities for future exploitation. Trends Biotechnol. 2012, 30, 558–565.
  47. Belhaj, A.F.; Elraies, K.A.; Mahmood, S.M.; Zulkifli, N.N.; Akbari, S.; Hussien, O.S. The effect of surfactant concentration, salinity, temperature, and pH on surfactant adsorption for chemical enhanced oil recovery: A review. J. Pet. Explor. Prod. Technol. 2020, 10, 125–137.
  48. De Almeida, D.G.; Soares Da Silva, R.d.C.F.; Luna, J.M.; Rufino, R.D.; Santos, V.A.; Banat, I.M.; Sarubbo, L.A. Biosurfactants: Promising molecules for petroleum biotechnology advances. Front. Microbiol. 2016, 7, 1718.
  49. Satpute, S.K.; Banpurkar, A.G.; Dhakephalkar, P.K.; Banat, I.M.; Chopade, B.A. Methods for investigating biosurfactants and bioemulsifiers: A review. Crit. Rev. Biotechnol. 2010, 30, 127–144.
  50. Bouchemal, K.; Agnely, F.; Koffi, A.; Djabourov, M.; Ponchel, G. What can isothermal titration microcalorimetry experiments tell us about the self-organization of surfactants into micelles? J. Mol. Recognit. 2010, 23, 335–342.
  51. Wu, S.; Liang, F.; Hu, D.; Li, H.; Yang, W.; Zhu, Q. Determining the critical micelle concentration of surfactants by a simple and fast titration method. Anal. Chem. 2019, 92, 4259–4265.
  52. Scholz, N.; Behnke, T.; Resch-Genger, U. Determination of the critical micelle concentration of neutral and ionic surfactants with fluorometry, conductometry, and surface tension—A method comparison. J. Fluoresc. 2018, 28, 465–476.
  53. Puvvada, S.; Blankschtein, D. Molecular-thermodynamic approach to predict micellization, phase behavior and phase separation of micellar solutions. I. Application to nonionic surfactants. J. Chem. Phys. 1990, 92, 3710–3724.
  54. Nagarajan, R.; Ruckenstein, E. Theory of surfactant self-assembly: A predictive molecular thermodynamic approach. Langmuir 1991, 7, 2934–2969.
  55. Khoshnood, A.; Lukanov, B.; Firoozabadi, A. Temperature effect on micelle formation: Molecular thermodynamic model revisited. Langmuir 2016, 32, 2175–2183.
  56. Glasser, L. Volume-based thermodynamics of organic liquids: Surface tension and the Eötvös equation. J. Chem. Thermodyn. 2021, 157, 106391.
  57. Floros, D.J.; Jensen, P.R.; Dorrestein, P.C.; Koyama, N. A metabolomics guided exploration of marine natural product chemical space. Metabolomics 2016, 12, 145.
  58. de Faria Poloni, J.; Bonatto, D. Systems Chemo-Biology and Transcriptomic Meta-Analysis Reveal the Molecular Roles of Bioactive Lipids in Cardiomyocyte Differentiation. J. Cell. Biochem. 2015, 116, 2018–2031.
  59. Feltes, B.C.; Poloni, J.d.F.; Notari, D.L.; Bonatto, D. Toxicological effects of the different substances in tobacco smoke on human embryonic development by a systems chemo-biology approach. PLoS ONE 2013, 8, e61743.
  60. Feltes, B.C.; de Faria Poloni, J.; Nunes, I.J.G.; Bonatto, D. Fetal alcohol syndrome, chemo-biology and OMICS: Ethanol effects on vitamin metabolism during neurodevelopment as measured by systems biology analysis. Omics J. Integr. Biol. 2014, 18, 344–363.
  61. Clements, T.; Rautenbach, M.; Ndlovu, T.; Khan, S.; Khan, W. A metabolomics and molecular networking approach to elucidate the structures of secondary metabolites produced by S. marcescens strains. Front. Chem. 2021, 9, 72.
  62. Zitnik, M.; Nguyen, F.; Wang, B.; Leskovec, J.; Goldenberg, A.; Hoffman, M.M. Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities. Inf. Fusion 2019, 50, 71–91.
  63. Hudson, I.L. Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology. In Artificial Neural Networks; Cartwright, H., Ed.; Springer Science+Business Media: Oxford, UK, 2021; pp. 167–184.
  64. Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J.; Data, M. Data Mining: Practical Machine Learning Tools and Techniques; Morgan Kaufmann: Elsevier: Cambridge, MA, USA, 2016.
  65. Next-generation genomics: An integrative approach. Nat. Rev. Genet. 2010, 11, 476–486.
  66. Gomez-Cabrero, D.; Abugessaisa, I.; Maier, D.; Teschendorff, A.; Merkenschlager, M.; Gisel, A.; Ballestar, E.; Bongcam-Rudloff, E.; Conesa, A.; Tegnér, J. Data integration in the era of omics: Current and future challenges. BMC Syst. Biol. 2014, 8, I1.
  67. Cavill, R.; Jennen, D.; Kleinjans, J.; Briedé, J.J. Transcriptomic and metabolomic data integration. Briefings Bioinform. 2015, 17, 891–901.
  68. Tarazona, S.; Balzano-Nogueira, L.; Conesa, A. Chapter Eighteen—Multiomics Data Integration in Time Series Experiments. In Data Analysis for Omic Sciences: Methods and Applications; Comprehensive Analytical Chemistry; Jaumot, J., Bedia, C., Tauler, R., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; Volume 82, pp. 505–532.
  69. Padayachee, T.; Khamiakova, T.; Shkedy, Z.; Perola, M.; Salo, P.; Burzykowski, T. The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models. PLoS ONE 2016, 11, e0150257.
  70. Nicolas, J. Molecular dynamics simulation of surfactin molecules at the water-hexane interface. Biophys. J. 2003, 85, 1377–1391.
  71. Gang, H.Z.; Liu, J.F.; Mu, B.Z. Molecular dynamics study of surfactin monolayer at the air/water interface. J. Phys. Chem. B 2011, 115, 12770–12777.
  72. Asadi, A.; Abdolmaleki, A.; Azizi-Shalbaf, S.; Gurushankar, K. Molecular Dynamics Study of Surfactin Interaction with Lipid Bilayer Membranes. Gene Cell Tissue 2021, 8, e112646.
  73. Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005, 26, 1701–1718.
  74. Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kale, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781–1802.
  75. Eastman, P.; Swails, J.; Chodera, J.D.; McGibbon, R.T.; Zhao, Y.; Beauchamp, K.A.; Wang, L.P.; Simmonett, A.C.; Harrigan, M.P.; Stern, C.D.; et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 2017, 13, e1005659.
  76. Lemkul, J. From proteins to perturbed hamiltonians: A suite of tutorials for the gromacs-2018 molecular simulation package . Living J. Comput. Mol. Sci. 2018, 1, 5068.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , ,
View Times: 658
Revisions: 2 times (View History)
Update Date: 17 Feb 2023
1000/1000
Video Production Service