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Butyric acid is one of the volatile organic compounds that significantly contribute to malodour emission from pit latrines. The purpose of this work is to isolate and identify bacterial strains that have the capability to degrade butyric acid, determine their butyric acid degradation efficiencies and estimate their growth pattern parameters of microbiological relevance. Pure cultures of bacterial strains capable of degrading butyric acid were isolated from pit latrine faecal sludge using an enrichment technique and were identified based on 16S rRNA analysis. The bacterial strains were cultured in mineral salt medium (MSM) supplemented with −1 1000 mg L butyric acid, as a sole carbon and energy source, at 30 ± 1 °C, pH 7 and 110 rpm under aerobic growth conditions. The modified Gompertz model was used to estimate growth pattern parameters of microbiological relevance. Bacterial strains were phylogenetically identified as Alcaligenes sp. strain SY1, Achromobacter animicus, Pseudomonas aeruginosa, Serratia marcescens, Achromobacter xylosoxidans, Bacillus cereus, Lysinibacillus fusiformis, Bacillus methylotrophicus and Bacillus −1 subtilis. The bacterial strains in pure cultures degraded butyric acid of 1000 mg L within 20–24 h. The growth kinetics of the bacterial strains in pure culture utilising butyric acid were well described by the modified Gompertz model. This work highlights the potential for use of these bacterial strains in microbial degradation of butyric acid for deodorisation of pit latrine faecal sludge. This work also contributes significantly to our understanding of bioremediation of faecal sludge odours and informs the development of appropriate odour control technologies that may improve odour emissions from pit latrines. . . . . Keywords Biodegradation Butyric acid Growth kinetics Odour Pit latrine Introduction meeting the sanitation-related target of achieving Sustainable Development Goal (SDG) 6.2: universal access to safe sani- Simple latrines that safely contain faeces have been used in tation by 2030 (Ravenscroft et al. 2017). While high pit latrine essence to eliminate open defecation. Pit latrines are the pre- coverage levels are realised, of great concern is the fact that dominant means of human excreta collection for an estimated open defecation is obstinately continuing either by preference 1.77 billion people in low-income communities in the devel- or necessity (Mara 2017). Surprisingly, some individuals even oping world (Graham and Polizzotto 2013). It is expected that in households that own a working latrine, nevertheless, prefer there will be a burgeoning use of pit latrines in response to to defecate in the open. Open defecation has long-since been implicated in the transmission of numerous infectious diseases and adverse health effects such as small-intestine bacterial Electronic supplementary material The online version of this article overgrowth, diarrhoea, typhoid, giardiasis, soil-transmitted (https://doi.org/10.1007/s13213-018-1408-1) contains supplementary helminthiases, anaemia, environmental enteropathy and chol- material, which is available to authorized users. era. These are in addition to life threatening violence against women and girls (Jadhav et al. 2016;O’Reilly 2016). * John Bright J. Njalam’mano Statistics in South Africa indicate that 4% of households chinamvuu@yahoo.co.uk still practise open defecation with the majority of the house- holds living in the rural and informal settlements (STATSA Water Utilisation and Environmental Engineering Division, 2016). Hutton and Chase (2016) found that this is due to Department of Chemical Engineering, University of Pretoria, Private Bag X20, Hatfield, Pretoria, South Africa contextual, technological and behavioural factors that are 108 Ann Microbiol (2019) 69:107–122 associated with sanitation adoption. Malodours that emanate methods. Biological treatment is relatively efficient and cost- from latrines are reported to be one of the impediments to effective technology for environmental pollution attenuation, investment, adoption and consistent use of pit latrines as and uses microorganisms to reduce, oxidise or eliminate pol- shown by experiences in sanitation promotion in the develop- lutants (Sheridan et al. 2003). Microorganisms capable of ing countries (Rheinländer et al. 2013). Several studies degrading malodorous compounds may be an attractive alter- (Grimason et al. 2000; Lundblad and Hellström 2005;Diallo native to the existing odour control techniques and strategies et al. 2007;Le et al. 2012; Tsinda et al. 2013; Obeng et al. currently used in low income settings in the developing world. 2015) have alluded to the same. Moreover, while malodours However, detailed information on microorganisms that de- are intrinsically not noxious, they can cause nausea, stress and grade odour-causing compounds, including butyric acid in annoyance to communities; in addition to its adverse effects the pit latrines, is scarce and very little is known about their on aesthetics and property values (Mills 1995; Rappert and degradation performance and growth behaviours. Muller 2005). Malodours can also attract flies, which are the In view of the above background, the objective of this work most important water- and excreta-related diseases carriers was to enrich, isolate and phylogenetically identify the indig- and spreaders (Morgan 2014; Nakagiri et al. 2016). In re- enous bacterial strains from South Africa that have capabilities sponse to concerns about the detrimental effects of the offen- to utilise butyric acid as a sole source of carbon and energy sive smells, which emanate from the pit latrines, people, in- and further determine their butyric acid degradation efficien- cluding children and adults, abandon them in favour of alter- cies. Also, the growth behaviour of the identified bacterial natives to latrines including open defecation (Rheinländer strains under studied environmental conditions was described et al. 2013). by estimating their maximum specific growth rates, lag times Studies conducted by Chappuis et al. (2016)showed that and asymptotic values. To the best of our knowledge and after butyric acid (C H O ) is one of the four key odorants that a thorough search in the literature, the use of aerobic bacteria 4 8 2 significantly contribute to human faecal odour. Butyric acid isolated from pit latrine faecal sludge for degradation of bu- is a four carbon short-chain fatty acid, which is one of the tyric acid has not been reported in the literature yet. intermediate products of anaerobic digestion, in which com- plex soluble organic materials are reduced to a methane (CH ) and carbon dioxide (CO ) mixture as the main final products Materials and methods (Siegert and Banks 2005). This process comprises of a con- tinuum of metabolic reactions (hydrolysis, acidogenesis and Chemicals and reagents methanogenesis) as a result of a complex intimate relationship between the acid-forming species and the methane-producing Analytical grade butyric acid (≥ 99% purity) was purchased species of bacteria (Lee et al. 2015). Butyric acid, in its pure from Sigma Aldrich Inc., St Louis, MO, USA. HPLC grade state as an individual compound, exhibits an idiosyncratic sulphuric acid (H SO ) (98% purity) was purchased from 2 4 smell of sweet rancid (Sheridan et al. 2003; Otten et al. Glassworld, South Africa. Other chemicals and reagents used 2004), which makes it offensive to handle. It is one of the in this study were of analytical grade and were locally pur- volatile compounds that have a very low human odour detec- chased from Merck Chemicals (Pty) Ltd., Gauteng, South tion threshold (Sheridan et al. 2003). Africa. Over the years, there are many technologies and strategies that have been developed by the users as well as scientists to Medium preparation avert and mitigate malodours emission from the latrines. These include the following: use of naturally fragrance occur- The mineral salt medium (MSM) consisted of the following: ring substances, addition of wood ash, antiseptics, insecti- 2.722 g KH PO ;0.535 gNH Cl; 0.049 g MgSO ;4.259 g 2 4 4 4 cides, lubricants, laundry and soapy water, motor-battery Na HPO ; 0.114 g Na SO per litre of 18.2 MΩ deionised 2 4 2 4 acids, detergents and modified latrine designs such as venti- water. The MSM was supplemented with 1 mL of trace ele- lated improved pit (VIP) latrine, urine-diverting dry and eco- ment solution per litre of MSM solution. The trace element logical sanitation toilets and pour flush latrines (Rheinländer solution consisted of 0.0128 g NiCl , 0.549 g CaCl ,0.0124 g 2 2 et al. 2013). However, these strategies and technologies to a H BO ,6.9505 gFeSO , 0.0347 g CuCl , 0.0136 g ZnCl , 3 3 4 2 2 greater extent have not provided the desired results as they are 0.0103 g NaBr, 0.0121 g NaMoO , 0.0198 g MnCl ,0.0166g 2 2 associated with their own social, economic, institutional and KI and 0.0238 g CoCl per litre of 18.2 MΩ deionised water technological challenges. The use of organisms for bioreme- (Roslev et al. 1998). For degradation and cell growth studies, −1 diation of environmental pollutants, including odour-causing MSM was supplemented with 1000 mg L butyric acid. The compounds, either in situ or ex situ, has lately been a subject pH of the medium was adjusted to 7.0, by titration with 6.0 M of much research due to certain competitive advantages of- NaOH, which was prepared with 18.2 MΩ deionised water fered over the conventional physical and chemical treatment and sterilised by autoclaving at 121 °C for 15 min. Ann Microbiol (2019) 69:107–122 109 Faecal sludge sample collection and preparation and compared with other genes available through the GenBank database using a basic BLAST of the National Faecal sludge samples were collected from pit latrines in the Center for Biotechnology Information (NCBI) (http:// semi-rural mining area of Kendal, in Mpumalanga Province, www.ncbi.nlm.nih.gov). PCR amplification of the 16S South Africa at 26°5′24″S, 28°58′17 E. Pit latrines are the rRNA yielded single fragments of 700 bootstraps based common means of human waste disposal, for the residents on 100 pseudo replicates. Phylogenetic dendograms were of the area. Faecal sludge samples were collected from a depth assembled based on the 16S rRNA gene sequence of of 0 (surface) to 10 cm using pre-sterilised auger-like equip- isolates and closely related strains by neighbour-joining ment. All non-faecal wastes (such as diapers, stones, clothes, method using MEGA 6.0 (Tamura et al. 2013). metals, plastic bags, etc.) were removed. The samples were immediately transported to the laboratory and preserved at Degradation and bacterial growth conditions 4 °C prior to use. A mass of approximately 100 g of faecal sludge sample To investigate the degradation of butyric acid as well as the was suspended in a pre-sterilised 2 L Schott bottle with 1 L growth of bacterial strains with butyric acid as a carbon and of sterile 18.2 MΩ deionised water prepared by Purelab Flex energy source, 1 mL of bacterial strain pure seed culture −1 purification system (ELGA Lab Water Ltd., UK). The mixture (OD = 2.0) (equivalent biomass, mg L , for each of the was vigorously vortexed for 5 min and the suspended solids bacterial strains are provided in Table S1 of the supplemen- were allowed to settle down for 10 min. The supernatant was tary material (SM) was inoculated into 150 mL each of the −1 subsequently filtered through sterilised cotton wool (Dischem, MSM supplemented with 1000 mg L butyric acid in South Africa) in a sterilised funnel for complete removal of 250 mL Erlenmeyer flask in triplicates. The experiments the top layer (scum). The cotton wool was replaced after every were aseptically conducted. Likewise, abiotic MSM with 100 mL of the supernatant is filtered to avoid cotton wool the same concentration of butyric acid was used as a control compacting when wet. The aliquot of the filtrate obtained in triplicates. All the reactors were incubated at 30 ± 1 °C therefrom was preserved at 4 °C prior to use for bacterial on a temperature-controlled rotary shaker in the dark at isolation. 110 rpm for 24 h. The samples were aseptically withdrawn at regular time intervals of 4 h to determine both butyric Isolation and molecular identification of bacterial acid concentration and optical density (OD). Samples for strains determination of bacterial growth were withdrawn from the reactor before and at 4, 8, 12, 16, 20 and 24 h after starting A1000 μL of an aliquot of the filtrate obtained from a mixture incubation while for determination of butyric acid concen- of faecal sludge and deionised water was subsequently inoc- tration, samples were withdrawn at 4, 8, 12, 16, 20 and 24 h −1 ulated into MSM supplemented with 500 mg L butyric acid after starting the incubation. From this procedure, the deg- was incubated at 30 ± 1 °C on a temperature controlled rotary radation efficiencies of the bacterial strains were deter- shaker at 110 rpm for 24 h in the dark. The procedure was minedwithEq.(1) (Gutarowska et al. 2014): repeated thrice to enrich microbial cultures and increase pop- A −A c s ulation density. Then, 100 μL of each resulting culture was D ¼ 100% ð1Þ serially diluted and was spread onto nutrient agar plate media and incubated for 24–48 h in the static incubator at 30 ± 1 °C where D , A and A are the degradation efficiency of bu- e c s in the dark. The strains were purified by streaking agar plates. −1 tyric acid (%), the concentration of butyric acid (mg L )in Morphologically distinct colonies were streaked at least three the abiotic culture at t (0, 4, 8, 12, 16, 20 and 24 h) and the times on fresh agar plates and incubated as above to obtain −1 concentration of butyric acid (mg L ) in the biotic culture pure cultures in preparation for 16S rRNA sequence at t (0, 4, 8, 12, 16, 20 and 24 h), respectively. identification. Bacterial genomic DNA was extracted using the boiling Determination of bacterial growth kinetic parameters method from a 24–48-h pre-grown cell suspensions of the pure cultures. The 16S rRNA genes of isolates were am- Bacterial growth curve analysis was performed based on the plified by a reverse transcriptase-polymerase chain reac- modified Gompertz model to estimate the bacterial growth tion (RT-PCR). The amplification and sequencing was con- kinetic parameters of each of the bacterial strains. The modi- ducted by using universal forward primer (27F: 5′ GAG fied Gompertz model has the form expressed according to the TTT GAT CCT GGC TCA G 3′) and reverse primer Eq. (2) (Gibson et al. 1988): (1492R: 5′ GGT TAC CTT GTT ACG ACT T-3′). The RNA sequence analyses of the PCR products from the LogN ¼ A þ C:expfg −exp½ −btðÞ −m ð2Þ ðÞ t 16S rRNA gene of the isolates were obtained, submitted 110 Ann Microbiol (2019) 69:107–122 where Log N is the decimal logarithm of optical density By substituting the biological parameters in Eq. (5), the re- (t) at time, t (h), A is the optical density value of the lower parameterised modified Gompertz models can be written as asymptote (dimesionless), C is the difference in optical (Zwietering et al. 1990; Mytilinaios 2013): density between inoculum and the stationary phase no hi μ −e y ¼ A:exp −exp ðÞ λ−t þ 1 ð9Þ (dimensionless), m is thetimeatwhichtheabsolutegrowth rate is maximal (time at inflexion) (h), and b is the relative −1 maximum growth rate determined at time, m.(h )(the The model was iteratively best fitted to the experimental slope of tangent to the curve at m). data by Levenberg Marquardt based on non-linear least- In this study, the relative optical density N /N or A − squares algorithms through minimisation of the sum of the (t) (0) (t) A was used for the densimetric assay. In view of this, the squares of the errors between the model and the experimental (0) parameter, A,inEq. (2) is equal to zero. Equation (2)subse- data points by adaptively varying the parameter values be- quently changes to: tween the Gauss-Newton update and the gradient descent up- date (Garvin 2017). The non-linear curve fitting was success- ðÞ t fully achieved using Origin 2018 data analysis and graphing In ¼ C:expfg −exp½ −btðÞ −m ð3Þ ðÞ 0 software (Originlab Corporation, Northampton, MA, USA) with α = 0.005 for all the parameters. Each growth curve where N is initial optical density at the time of (0) was generated based on the average of experiments carried inoculation, t(h) = 0 and C is the upper asymptotic value out in triplicates. The fitted curves were statistically evaluated (dimensionless). using the coefficient of determination (R ) and root mean Similarly, Zwietering et al. (1990) described the modified square error (RMSE) as expressed by Eq. (10)and Eq.(11), Gompertz function as: respectively: Y ¼ a:exp½ −expðÞ b−ct ð4Þ ðÞ t ∑e 2 i R ¼ 1− ð10Þ ðÞ t ∑ y −y where Y is In (t) i ðÞ 0 : The Zweitering’sparameter, a, is the same as the Gibson’s where e is an error of the predictive values, y is the pre- i i parameter, C; therefore, the Eq. (3) can be written as dicted values and y is mean of the predicted values. (Garthright 1991): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Y ¼ a:expfg −exp½ −btðÞ −m ð5Þ ðÞ t RMSE ¼ ∑ðÞ E −O ð11Þ i i i¼1 The Gompertz function does contain mathematical param- eters; a, b and m rather than parameters of biological meaning where E is the predicted value and O is the observed value. asymptote value, A (dimesionless), maximum growth rate, μ −1 (h ), and lag time, λ (h). Additionally, it is easy to compute Analytical procedures the biological parameters with 95% confidence intervals if they are directly computed from the mathematical parameters The aliquot (6 mL) of culture medium was withdrawn from in the Eq. (5). Hence, an expression of biological parameters the enrichment flasks at 4 h time intervals and was centrifuged was derived as a function of the parameters of the basic func- at 9000 rpm for 10 min at room temperature, using a Minispin tion as follows: centrifuge of Eppendorf AG type (Hamburg, German). The The maximum specific growth rate (μ ), Eq. (6) was de- supernatant was subsequently filtered through Milipore rived as follows: Millex-GV Hydrophilic PVDF 0.22 μm membrane and dis- persed into 2 mL HPLC vial prior to analysis. b:a All analyses in this work were performed on a Waters μ ¼ ð6Þ Alliance 2695 Separation Module HPLC system (Waters Corporation, Milford, MA, USA) in triplicates to determine where asymptote value, ɑ, in Eq. (7)is reached fortime is the residual butyric acid concentration. The system was approaching infinity and is expressed as: equipped with a low-pressure mixing pump, an inline A ¼ a ð7Þ degasser, an auto-sampler with programmable temperature control (samples held at 5 °C) and a Waters 2998 and the lag time, λ,Eq. (8) was derived as follows: Photodiode array detector (PAD) equipped with micro UV cell (Waters Corporation, Milford, MA, USA). An HPLC mobile λ ¼ m− ð8Þ phase of 0.02 M sulphuric acid (H SO ) was used. The mobile 2 4 phase was prepared by diluting 1.1 mL of 18.4 M H SO with 2 4 Ann Microbiol (2019) 69:107–122 111 18.2 MΩ deionised water to a final volume of 1.0 L. This was (Garcha et al. 2016). Further, the identification is more objec- filtered through a Nylon 5-μm membrane before injection into tive as optimal growth and microbial viability are not the the HPLC. Sample injection volume of 10 μL was used for all prerequisites (Reller et al. 2007). Comparative phylogenetic analyses. The stationery phase was an Aminex HPX-87H87H dendrograms generated based on 16S rRNA gene sequences ion-exclusion organic acid, 300 mm × 7.8 mm, 9 μmparticle of the isolates with closely related species revealed that the size column (Bio-Rad Laboratories, Berkeley, CA, USA) ran bacterial isolates Ba, B1a, B1b, B6a, B5a, B7a, C4c, CrNb −1 with an isocratic flow rate of 1 mL min at a column temper- and CrNc clearly marched with Alcaligenes sp. strain SY1, ature of 60 °C. The detection of the peaks was achieved at a Achromobacter animicus, Pseudomonas aeruginosa, Serratia wavelength of 210 nm. Retention time for butyric acid was marcescens, Achromobacter xylosoxidans, Bacillus cereus, 12.2 min and the total run time was set at 15 min. Lysinibacillus fusiformis, Bacillus methylotrophicus and Chromatographic data were processed by Empower2 Build Bacillus subtilis, respectively. Their phylogenetic dendro- 2154 software (Waters Corporation, Milford, MA, USA). grams showing the closest NCBI (BLASTn) relatives based Qualitative and quantitative data were obtained by comparing on the 16S rRNA gene sequence were constructed by the the peak area and peak height to butyric acid standard com- neighbour-joining method as shown in Fig. 1. pound with known concentration. The concentration of butyr- The highest sequence homology (% identity) of each bac- ic acid was deduced from an external calibration curve. terial strain and their closely related strains are also presented Quantitative determination of bacterial growth yields that in Table 1. The identification of the high percentage of was determined in the medium was spectrophotometrically Bacillus sp. related strains is probably because Bacillus strains monitored by measuring the OD at a single wavelength λ = are not difficult to cultivate in the medium used in this study, 600 nm using a UV Lightwave II spectrophotometer (Labotec, or environmental conditions in the pit latrines in Kendal, Gauteng, South Africa). The quartz cuvette of 10 mm optical South Africa are favourable for their survival and growth path length was used to carry the aliquots in the sample cham- (Zhang et al. 2010). To the best of our knowledge and after ber of the spectrophotometer. The measurements were thorough search in the literature, this is the first time all these blanked to zero using the same MSM without inoculum as a bacterial strains but members of genus Pseudomonas have reference. All the experiments (both biotic and abiotic) were been reported to utilise butyric acid as the sole carbon and performed in triplicates. The dry weight method was applied energy source (Sheridan et al. 2003) and Bacillus sp. in a to estimate biomass in milligram per litre. The generated cal- mixture of other volatile fatty acids (VFAs) (Yun and Ohta ibration equations of each bacterial strain are listed in Table S1 1997). Since they are indigenous organisms, they are more of the SM. likely to survive and to be active than exogenous bacterial strains when introduced into pit latrine environments in South Africa or similar environments. The introduced exoge- Results and discussion nous bacterial strains are more likely to be subjected to intense competition, predation or parasitism after their release into the Isolation and molecular identification of the bacterial targetenvironment(Hanetal. 2015), in this case the pit strains latrine. In this study, indigenous aerobic bacterial strains capable of utilising butyric acid as a sole carbon and energy source were Butyric acid degradation by pure bacterial cultures successfully isolated from pit latrine faecal sludge. There were a total of 24 morphologically distinct bacterial colonies that The ability of the bacterial strains to utilise butyric acid as a were isolated. The isolates were further screened for their bu- sole source of carbon and energy was investigated. As shown −1 tyric acid-degrading ability using MSM supplemented with in Fig. 2, the initial 1000 mg L of butyric acid can be butyric acid. Of the 24 bacterial isolates tested, 9 bacterial biodegraded effectively by the indigenous pure bacterial isolates demonstrated pronounced growth in butyric acid- strains as it can be observed that it was completely degraded supplemented MSM as pure cultures after enrichment and within 20–24 h. However, the degradation rates varied from purification. The bacterial isolates were designated as Ba, one bacterial strain to another. The bacterial strains B1a, B1b, B6a, B5a, B7a, C4c, CrNb and CrNc for identifi- Achromobacter xylosoxidans, Bacillus subtilis, cation purposes. The RNA sequence analyses of the PCR Lysinibacillus fusiformis, Bacillus cereus, Pseudomonas products from the 16S rRNA gene of the isolates were obtain- aeruginosa and Bacillus methylotrophicus completely degrad- −1 ed, submitted and compared with other genes in GenBank ed 1000 mg L butyric acid within 20 h while Achromobacter using a basic BLAST of the NCBI. The 16S rRNA gene se- animicus, Serratia marcescens and Alcaligenes sp. strain SY1 quencing was used for identification because it is present in completely degraded butyric acid within 24 h. The reason for virtually all bacteria and its role has not temporarily changed the differences in degradation efficiencies is unclear. 112 Ann Microbiol (2019) 69:107–122 Fig. 1 a The phylogenetic tree for Alcaligenes sp. SY1, 100 replicates. d The phylogenetic tree for Bacillus cereus and related Achromobacter animicus and Achromobacter xylosoxidans and strains based on 16S rRNA gene sequences. Bootstrap values were related strains based on 16S rRNA gene sequences. Bootstrap values based on 100 replicates. e The phylogenetic tree for Bacillus were based on 100 replicates. b The phylogenetic tree for methylotrophicus,and Bacillus subtilis and related strains based on Pseudomonas aeruginosa and related strains based on 16S rRNA 16S rRNA gene sequences. Bootstrap values were based on 100 gene sequences. Bootstrap values were based on 100 replicates. c replicates. f The phylogenetic tree for Lysinibacillus fusiformis and The phylogenetic tree for Serratia marcescens and related strains related strains based on 16S rRNA gene sequences. Bootstrap values based on 16S rRNA gene sequences. Bootstrap values were based on were basedon100 replicates Previous studies (Bourque et al. 1987;Yun andOhta 1997; butyric acid in the presence of other VFAs after incubation of Chin et al. 2010) have found that many bacterial strains can 37 °C and medium pH of 8.0 for 2 days. Conversely, in these degrade butyric acid. For instance, Bourque et al. (1987) isolat- previous studies, butyric acid was not the sole source of carbon. ed Acinetobacter calcoaceticus, Alcaligenes faecalis and Only Chin et al. (2010) isolated bacterial strains identified as Arthrobacter flavescens from swine waste that was able to aer- Acinetobacter calcoaceticus, Wautersia paucula, Burkholdeira obically degrade butyric acid completely in the presence of cepacia which have the ability to completely degrade −1 other VFAs such as acetic acid, propionic acid, isobutyric acid 1000 mg L butyric acid as a sole source of carbon and energy. and valeric acid and phenol and p-cresol after incubation at The complete degradation of butyric acid was achieved within 29 °C and 200 rpm within 3 to 5 days. Yun and Ohta (1997) 18hfor Acinetobacter calcoaceticus while the other strains it isolated bacterial strains identified as Bacillus sp., Rhodococcus was achieved within 30–55 h at 30 ± 1 °C and pH 7.0. sp. and Staphylococcus sp. from seed culture which was used The complete degradation of butyric acid in this work is for the treatment of animal faeces which exhibited growth on important. This is primarily due to the fact that even at low Ann Microbiol (2019) 69:107–122 113 Fig. 1 continued. concentrations, butyric acid is one of the VFAs that has high The butyric acid degradation and growth potential of the bac- odour nuisance index. Its odour can even create problems at a terial strains were investigated in detail. Although it was not receptor of odour nuisance at distances far away from the known that these are their optimal growth conditions, all the points of emission. This is attributed to its very low odour strains showed remarkable ability to grow well at pH 7.0, 30 ± detection threshold (Sheridan et al. 2003). Butyric acid is 1 °C and agitation rate of 110 rpm and butyric acid concentra- −1 one of the short-chain volatile fatty acids (SVFAs) which in- tions 1000 mg L utilising butyric acid as the growth substrate finitely dissolves in aqueous solution (Hughes 1934). Hence, as provided as a sole source of carbon and energy with initial the high degradation of butyric acid could be attributed to its seed culture of 2.0. The increase in cell density of each bacterial high rates of dissolution and solubility in water which deter- strain as expressed by its absorbance value measured at 600 nm mines its bioavailability (Kristiansen et al. 2011). was positively correlated to degradation efficiency of butyric acid As shown in Fig. 2, in the control experiments, the concen- as illustrated in Fig. 2. The Pearson correlation coefficients were tration of butyric acid remained almost stable from 1000 to in the range of 0.990 (Achromobacter animicus) to 0.999 −1 996.99 mg L during the incubation for 24 h. The loss of (Lysinibacillus fusiformis)at p < 0.01. Bacterial cell density butyric that resulted from abiotic process was insignificant. was increased with incubation time in all the bacterial strains, This could be attributed to either surface volatilisation losses reaching the maximal density at different times that ranged from or photo-degradation due to exposure to light during sample 0.990 ± 0.01 to 1.25 ± 0.004 within 20 to 24 h dependant on the withdrawals that was inevitable. bacterial strain as can be seen in Fig. 3. 114 Ann Microbiol (2019) 69:107–122 Fig. 1 continued. Butyric acid was degraded by all the bacteria strains as efficiencies were observed in the exponential phase of shown in Fig. 2 and Fig. 3. However, during the lag phase growth for all the bacterial strains. Thus, 95 to 100% of particularly 4 h after incubation, all the bacterial strains the butyric acid degradation occurred in this phase. but Achromobacter xylosoxidans and Bacillus cereus did Generally, there was a very high increase in butyric acid not manifest the degradation of butyric acid as determined degradation efficiencies of the bacterial strains near the by the HPLC. It is assumed that this lag phase allows the mid-exponential growth phase and decreased as the bacteria to adapt to the new environmental conditions re- cultures aged towards the early stationary phase. This is quired for bacterial cells to begin cell division (Baranyi consistent with Kotler et al. (1993) previous observations et al. 1993). Although there were variations in degrada- that bacterial cells in their exponential growth phase rap- tion efficiencies of butyric acid between bacterial strains idly consume the available nutrients in most nutritionally during the duration of the lag phase, the high degradation defined media and then ceases to grow exponentially. Ann Microbiol (2019) 69:107–122 115 Table 1 Closest relatives of the Isolate designation Closest hit Accession no. Homology (%) 16S rRNA gene sequences of bacterial isolates in this study 1Ba Alcaligenes sp. strain SY1 99 2B1a Achromobacter animicus LMG26690 HE613448 99 3B1b Pseudomonas aeruginosa LMG 1224 Z76651 100 4B5a Achromobacter xylosoxidans LMG 26686 FM999735 93 5B6a Serratia marcescens DMS 30121 AJ233431 100 6B7a Bacillus cereus ATCC14579 AE016877 100 7C4c Lysinibacillus fusiformis NRS-350 AF169537 100 8CrNb Bacillus methylotrophicus CBMB205 EU194897 100 9CrNc Bacillus subtilis DSM10 AJ276351 100 Kinetics of bacterial growth Gompertz, Logistic, Richards, Stannard, Schnute models, etc. (Longhi et al. 2017). These models are numerically easier The biodegradation of butyric acid in batch reactors led to the to handle as opposed to mechanistic models (Thakur 1991), formation of biomass. The amount of biomass formed in- for instance, the Monod and Michaelis-Menten based models creased with the degradation of butyric acid as observed in which are preferred for systems to be scaled-up consistently. Fig. 3 but increased exponentially with respect to time during In this work, based on a modified Gompertz model (Eq. (5)) the log phase. Further, the increase in biomass concentration mathematical parameters, ɑ, b and m for bacterial growth were was dependent on the concentration of butyric acid remaining predicted. The model described the growth kinetics of all the in the solution. Due to inadequate knowledge about the struc- bacterial strains individually as pure cultures from the lag phase tural connectivity and functional mechanisms of the systems to the stationary phase (Baty and Delignette-Muller 2004). The of the bacterial strains at the physiological level, an empirical parameters of biological meaning such as lag time (λ), maximum model was used to understand the primary system purely specific growth rate (μ ) and asymptotic growth level (A)as based on its extrinsic behaviour. Numerous mathematical showninTable 2 were also calculated by fitting the model pa- models and equations that describe microbial growth in cul- rameters to the experimental data. This was founded on Eq. (2)to ture media have been developed and used. These include Eq. (9) as derived by Zwietering et al. (1990)aspreviously Fig. 2 Butyric acid degradation kinetics by different bacterial isolates; Achromobacter xylosoxidans (AX), Bacillus cereus (BC), Lysinibacillus Alcaligenes sp. strain SY1 (AS), Achromobacter animicus (AA), fusiformis (LF), Bacillus methylotrophicus (BM) and Bacillus subtilis Pseudomonas aeruginosa (PA), Serratia marcescens (SM), (BS) 116 Ann Microbiol (2019) 69:107–122 discussed in the materials and methods section above. The model Fig. 3 a Butyric acid degradation and bacterial growth under pH 7, 30 ± 1 °C and 110 rpm against incubation time of Alcaligenes sp. strain SY1: was chosen because it has a term of time delay introduced which butyric acid degradation efficiency, bar graphs; bacterial growth, line allows it to fit a sigmoidal pattern of growth, which is an analo- graph. b Butyric acid degradation and bacterial growth under pH 7, 30 gous pattern most bacteria follow as noted in most published ± 1 °C and 110 rpm against incubation time of Pseudomonas aeruginosa: research work. This is unlike the classical Gompertz model which butyric acid degradation efficiency, bar graphs; bacterial growth, line graph. c Butyric acid degradation and bacterial growth under pH 7, 30 does not take into consideration the delay time (Mytilinaios et al. ± 1 °C and 110 rpm against incubation time of Achromobacter animicus: 2012). Further, the model was re-parameterised in such a way that butyric acid degradation efficiency, bar graphs; bacterial growth, line those parameters such as μ , λ and A, that are microbiologically graph. d Butyric acid degradation and bacterial growth under pH 7, 30 significant, can be more suitably estimated (Zwietering et al. ± 1 °C and 110 rpm against incubation time of Achromobacter xylosoxidans: butyric acid degradation efficiency, bar graphs; bacterial 1990). It is, therefore, viewed as the best sigmoidal model that growth, line graph. e Butyric acid degradation and bacterial growth describes bacterial growth data both in terms of statistical accura- under pH 7, 30 ± 1 °C and 110 rpm against incubation time of Serratia cy and simplicity in use as opposed to analogous sigmoidal marcescens: butyric acid degradation efficiency, bar graphs; bacterial models (Baty and Delignette-Muller 2004). growth, line graph. f Butyric acid degradation and bacterial growth under pH 7, 30 ± 1 °C and 110 rpm against incubation time of Bacillus The OD measurements were used for estimation of growth methylotrophicus: butyric acid degradation efficiency, bar graphs; parameters due to the merits of the method over conventional bacterial growth, line graph. g Butyric acid degradation and bacterial viable counts methods. It is considered to be rapid, non-destruc- growth under pH 7, 30 ± 1 °C and 110 rpm against incubation time of tive, relatively inexpensive and easy to automate method to mon- Bacillus cereus: butyric acid degradation efficiency, bar graphs; bacterial growth, line graph. h Butyric acid degradation and bacterial growth under itor bacterial growth (Dalgaard and Koutsoumanis 2001;Perni pH 7, 30 ± 1 °C and 110 rpm against incubation time of Lysinibacillus et al. 2005). Actually, the OD measurements have recently been fusiformis: butyric acid degradation rate, bar graphs; bacterial growth, line used to accurately derive growth parameters using numerous graph. i Butyric acid degradation and bacterial growth under pH 7, 30 ± techniques and mathematical models (Dalgaard and 1 °C and 110 rpm against incubation time of Bacillus subtilis: butyric acid degradation rate, bar graphs; bacterial growth, line graph Koutsoumanis 2001; Koseki and Nonaka 2012; Pla et al. 2015). However, growth rates of relatively high cell density cul- tures are those that can be determined directly from the changes prediction adequately described the bacterial strains’ growth in OD measurements (Dalgaard and Koutsoumanis 2001). curves of the observed experimental data. Root mean square The average growth kinetic parameters for each of the nine error, RMSE (Eq.(11)), is a standard statistical measure of the bacterial strains exposed to the same experimental conditions and precision of a predictive model, and gives an explanation for the with the same preculture history and standardised inoculum are differences between predicted and observed values (Sant’Ana showninTable 2. When the growth parameters were compared, et al. 2012). The RMSE values in Table 2 to validate the the lag time was in the range of 5.54 h (Achromobacter animicus) model’s performance revealed that it provided a reliably better and8.47h(Alcaligenes sp. SY1). The maximum specific growth goodness-of-fit to the observed experimental data for all bacte- −1 −1 rate was between 0.07 h (Serratia marcescens) and 0.15 h rial strains. By comparing the statistical criterion of RMSE (Achromobacter xylosoxidans). The values of the parameters values of the modified Gompertz for all bacterial strains’ might be overestimated as the model is known for overestimation growth curves, the results show that Achromobacter animicus of lag time and maximum specific growth rate as one of the major growth curve had the smallest RMSE value (0.0002) while drawbacks to its use (Baty and Delignette-Muller 2004). The Bacillus methylotrophicus had the highest RMSE value maximum biomass concentration was between 1.06 (Bacillus (0.004). This demonstrated that of all the bacterial strains in this subtilis) and 1.59 (Alcaligenes sp. SY1). It is worth stating that work, the modified Gompertz model adequately described the the growth curves for Alcaligenes sp. SY1, Achromobacter growth of Achromobacter animicus at the set environmental animicus,and Serratia marcescens did not reach the stationary conditions. Comparatively, these RMSE values are significant- phase; therefore, their asymptotic growth levels predicted by the ly smaller than some that have been reported in the literature. model could be estimated with uncertainty which might affect the For instance, Pla et al. (2015) checked the performance of dif- values of the other parameters (Longhi et al. 2017). ferent primary models (three-phase linear and non-linear; The model estimated the expected values for the growth Gompertz, Logistic, Richards and Baranyi) in their modified parameters and fitted the data well, as demonstrated by the forms to describe OD growth curves of Bacillus cereus, analysed statistics. As can be seen in Table 2, according to Listeria monocytogenes and Escherichia coli. Although their 2 2 goodness-of-fit criterion, the coefficient of determination, R R (0.939–0.999) were close to those calculated in this work, (Eq. (10)), to evaluate fitting of the modified Gompertz model, their RMSE values (0.007–0.061) were much higher than those was found to be high ranging between 0.986 and 0.999. The R calculated in this work. The smaller RMSE values obtained is a statistical measure of the proportion of the variability in the essentially reveal the suitableness of the model in this work. data set, which is used to predict a response using the model This connotes that the estimated growth parameters for the (Sant’Ana et al. 2012). The high R values for the model ob- bacterial strains estimated on the basis of OD measurements tained in this work suggest that the modified Gompertz model in this work can be more appropriately evaluated. Ann Microbiol (2019) 69:107–122 117 The modified Gompertz model used in this study appears to of-fit in predicting its growth parameters using optical density satisfactorily fit the bacterial strains’ growth curves as shown in growth curves. When these authors (Pla et al. 2015)inthesame Fig. S1 of the SM. However, in contrast with other or analogous study used plate count growth curves, the observations were empirical sigmoidal growth models reported in literature, differ- dissimilar, the Baranyi model was the best fitting model. ent studies have reached different conclusions. Pla et al. (2015) Similarly, in their study, Tarlak and colleagues (Tarlak et al. studied the growth of Bacillus cereus at 30 °C in brain heart 2018) concluded that modified Baranyi model gave better infusion (BHI) medium with different inoculum concentrations goodness-of-fit than the modified logistic and Gompertz and shown that the Richards model had best statistical goodness- models in describing the growth behaviour of Pseudomonas 118 Ann Microbiol (2019) 69:107–122 Fig. 3 continued. spp. on the sliced mushroom at different isothermal storage a consistently better goodness-of-fit over all the growth curves at temperatures. In contrast, Li et al. (2013) reported that the mean all different temperatures. Also, Zwietering et al. (1990)reported values of four statistical criteria showed that the modified that the growth data of Lactobacillus plantarum incubated at Gompertz model adequately described the growth of different temperatures in MRS medium were better fitted with Pseudomonas spp. in pallet-package pork under isothermal con- the Gompertz model compared to linear, quadratic, exponential, th ditions at different temperatures, although they noted that the logistic and t power models. Furthermore, George et al. (1996) modified Gompertz, Baranyi and Huang models could not give studied the combined effect of different temperatures, pH values Ann Microbiol (2019) 69:107–122 119 Fig. 3 continued. and acetic and lactic acids on the growth of Listeria The values of asymptote value, A, maximum growth rate, μ , monocytogenes. They concluded that the Baranyi model and lag time, λ, calculated by the modified Gompertz model provided the best fitting for the growth data. Qi et al. (2006) were very close to that calculated by the modified Richards mod- indicated that both the Richards and Gompertz models success- el. However, the authors preferred the modified Gompertz model fully described the growth curves of microencapsulated and non- because it has three parameters that make it simpler and easier to encapsulated free E. coli and Saccharomyces cerevisiae cultures. use in addition to more robust as the parameters are less 120 Ann Microbiol (2019) 69:107–122 Table 2 Growth parameters and −1 −1 2 Bacterial strain λ [h] μ [h ]a m [h] b [h ] R RMSE their α = 0.005 limits, R and m RMSE of the fit generated by Alcaligenes sp. SY1 8.47 0.09 1.59 14.88 0.16 0.997 0.003 modified Gompertz model for the average OD growth curves of the (± 0.13) (± 0.76) (± 0.03) identified bacterial strains at Pseudomonas aeruginosa 5.72 0.10 1.31 10.49 0.21 0.987 0.005 pH 7.0, 30 ± 1 °C, 110 rpm in (± 0.11) (± 0.72) (± 0.05) MSM supplemented with Achromobacter animicus 5.54 0.08 1.40 11.87 0.16 0.999 0.0002 −1 1000 mg L butyric acid with (± 0.36) (± 0.23) (± 0.01) 1mLof2.0, OD inoculum Achromobacter xylosoxidans 6.71 0.15 1.41 10.24 0.28 0.986 0.007 (± 0.15) (± 0.53) (± 0.08) Serratia marcescens 5.75 0.07 1.55 13.54 0.23 0.999 0.0003 (± 0.07) (± 0.46) (± 0.05) Bacillus methylotrophicus 7.31 0.11 1.34 11.61 0.43 0.982 0.004 (± 0.09) (± 0.55) (± 0.07) Bacillus cereus 5.90 0.09 1.37 11.61 0.17 0.987 0.003 (± 0.13) (± 0.79) (± 0.04) Lysinibacillus fusiformis 5.73 0.10 1.25 10.11 0.38 0.995 0.002 (± 0.05) (± 0.39) (± 0.05) Bacillus subtilis 5.62 0.11 1.06 9.32 0.27 0.992 0.003 (± 0.08) (± 0.51) (± 0.06) correlated. Moreover, the shape parameter in the Richards model bioremediation for the control of the odour as a result of butyric is difficult to explain biologically (Bahçeci and Acar 2007). The acid in the pit latrines. Nevertheless, the mechanisms involved in contrasts in the best fitting model conclusions reached by the the degradation of butyric acid for each bacterial strain need to be authors could be to some extent explained by the use of different further investigated. It is likely that the results of this work have microorganisms grown under different environmental conditions provided a basis upon which further investigations of butyric as well as the use of different biomass concentrations, measure- acid-degrading bacteria from pit latrine faecal sludge can be car- ment methods and the number of experimental data points. ried out for microbial deodorization of pit latrines stench using in situ microbial community. This could also be used to develop other microbial-based pit latrine faecal sludge deodorization tech- nologies and strategies. Conclusions Acknowledgements We would like to thank Professor Fanus Venter for In the present work, 9 out of a total of 24 isolated indigenous his excellent assistance in purification and identification of the bacterial bacterial strains that were screened for the capability to utilise strains. This research was supported in part by a Water Chair from butyric acid as a sole source of carbon and energy could be Sedibeng Water and the National Research Commission Project NRF adapted to perform butyric acid degradation. Based on 16S Competitive Programme for Rated Researchers Grant No. CSUR180215313534 awarded to Prof Evans Chirwa of the University rRNA gene analysis, these strains were identified as Alcaligenes of Pretoria. We are also grateful to the Department of Research and sp. strain SY1, Achromobacter animicus, Pseudomonas Innovation at the University of Pretoria for awarding the postgraduate aeruginosa, Serratia marcescens, Achromobacter xylosoxidans, fellowship through the UP-Commonwealth programme to the co-author Bacillus cereus, Lysinibacillus fusiformis, Bacillus Mr. John Njala’mmano. methylotrophicus and Bacillus subtilis. The bacterial strains were −1 Funding This work was funded by Sediberg Water, South Africa and the capable of degrading 1000 mg L butyric acid within 20 to 24 h National Research Commission Project NRF Competitive Programme for at an incubation temperature of 30 ± 1 °C, agitation rate of Rated Researchers Grant No. CSUR180215313534 awarded to Prof 110 rpm and pH 7. The growth patterns of the bacterial strains Evans Chirwa of the University of Pretoria. in pure culture utilising butyric acid as the sole source of carbon and energy was well described by the modified Gompertz model. Compliance with ethical standards Prediction from primary models such as the modified Gompertz model is a useful tool to predict the behaviour of the bacterial Conflict of interest The authors declare that they have no conflicts of strains isolated in this work in real pit latrine environmental con- interest. ditions. However, this model has to be investigated under a range Human or animal participants No humans or animals were used in this of environmental conditions (inter alia; temperature, medium and work. pH) to demonstrate its validity. 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Annals of Microbiology – Springer Journals
Published: Nov 27, 2018
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