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کتابچه خلاصه مقالات همایش ملی ابن سینا و دانش پزشکی
No full textBook of Abstracts: National Conference on Avicenna and Medical Scienceکتابچه خلاصه مقالات همایش ملی ابن سینا و دانش پزشک
The decision to have an only child among Iranian mothers: Reasons and Conditions
Full text linkBackground: The worldwide reduction in fertility, change in demographic policies, rise in divorce ages, urbanization, and growth of the nuclear family require greater attention. Having an only child in the context of child-rearing seems to be a strategy for creating a balance between individual, marital, family, and environmental conditions, leading Iran to become an aging country in the future.
Methods: This qualitative study is based on semi-structured interviews with 11 married women with only one child.
Results: We used the grounded theory method to investigate the factors influencing Iranian mothers' decision to have an only child. We extracted 571 initial concepts, 55 subcategories, and 23 main categories by coding the interviews. The findings are classified into five categories shaping the final theoretical model. The results show that people can fall into groups, "forced only-child" and "voluntary only-child," regarding their attitudes toward having an only child.
Conclusion: Participants' decision to have an only child is influenced by external factors related to society's economy, politics, and culture. On the other hand, this decision also reflects the individuals' circumstances shaped by internal factors that impact the participants. Furthermore, maintaining the decision is influenced by factors that directly affect the causal conditions and ultimately result in having an only child
Comparing the effectiveness of schema-based and emotionally focused couple therapies on self-differentiation and marital disillusionment in couples in Mashhad
Full text linkBackground: Reducing self-differentiation and marital frustration in couples weakens the marital bond. The aim of the study was to compare the effectiveness of schema-based couple therapy (SBCT) and emotion-focused couple therapy (EFCT) on self-differentiation and marital frustration in couples.
Methods: This study was a quasi-experimental design involving couples referred to counseling centers in Mashhad Regions 1 and 2 due to marital dissatisfaction in 2023. Of these, 45 couples were randomly selected as a sample and placed in two experimental groups and a control group. The experimental group 1 received SBCT, the experimental group 2 received EFCT, and the control group received only general counseling. The research tools included the Self-Discrimination Questionnaire (DSI) by Skowron and Friedlander (1998) and the Kaiser Marital Dissatisfaction Questionnaire (1993) and afterward, a post-test was administered. The data were analyzed using SPSS software (version 24) and a multivariate analysis of covariance (ANCOVA) test.
Results: The results of the pairwise comparison showed that there is no significant difference between the mean scores of self-differentiation as a result of SBCT and EFCT (P>0.05), while SBCT had a greater effect on marital disillusionment than EFCT (P < 0.05).
Conclusion: Based on the results, SBCT can be effective in reducing marital disillusionment among couples
Designing a competency system model with emphasis on employee dignity using the grounded theory approach
Full text linkBackground: In today’s complex and competitive organizational environments, designing competency systems that enhance performance while upholding employee dignity has become a strategic priority in HR management. Employee dignity, defined as the recognition of individual worth, respect, and fair treatment in the workplace, is a multidimensional concept influencing motivation and organizational commitment. This study, using the grounded theory approach, aimed to develop a competency system model that integrates and promotes the principles of employee dignity within organizational practices.
Methods: This research employed a mixed-methods design (qualitative–quantitative). In the qualitative phase, grounded theory (Strauss and Corbin approach) was used to analyse data from semi-structured interviews with 18 HR managers and experts. In the quantitative phase, the proposed model was tested using structural equation modelling (SEM) on a sample of 260 employees, and further validated through a nonlinear Bayesian model. Model fit was assessed using indices such as RMSEA, CFI, and TLI. Integration of qualitative and quantitative findings was performed through a comparative analysis, ensuring theoretical saturation aligned with empirical validation.
Results: The qualitative results led to the identification of seven causal categories within the competency system, including professional, cognitive, and organizational competencies. In the quantitative prioritization, professional, cognitive, and organizational competencies received the highest weights. Path analysis revealed that these competencies significantly impacted job dignity and, subsequently, organizational performance. Model fit indices confirmed the high predictive power and accuracy of the conceptual model.
Conclusion: The developed model presents a comprehensive and localized framework for a competency system that can serve as a foundation for human resource policies aimed at enhancing employee dignity and productivity
فارسی فراترکیب رابطۀ سکوهای رسانهای و سلامت معنوی
No full textخلفية البحث وأهدافه: تناولت الدراسات السابقة دور وسائل الإعلام الجماهيرية وشبكات التواصل الاجتماعي في تعزيز أو إضعاف الأبعاد النفسية والاجتماعية للصحة. ومع ذلك، لم تتناول سوى قلة من الدراسات العلاقة المباشرة بين المنصات الإعلامية الحديثة ومقوّمات الصحة الروحية، مما أحدث فجوة بحثية كانت الدافع لاقتراح موضوع الدراسة الحالية. ويهدف هذا البحث إلى تحديد العوامل المؤثرة على الصحة الروحية من خلال استخدام المنصات الإعلامية، مع التركيز على إنستغرام، ومن ثم تقديم توصيات لتعزيز الصحة الروحية في الفضاء الرقمي.
منهجية البحث: يعتمد هذه الدراسة على منهج الترکیب التحلیلي، حيث تمّ اختيار الدراسات المدرجة وفقًا لمعايير إدراج واستبعاد محدّدة بدقة. تمّت مراجعة 509 مقالة وفقًا للرتبة العلميّة المعتمدة. تمّ استخدام في الدراسة النموذج النوعي لأترايد سترلينغ، الذي يقوم على ثلاثة مستويات من المضامين: المضامين الأساسية، والمضامين التنظيمية، والمضامين الشاملة.
المعطیات: أظهرت النتائج أن المضامين الرئيسة المتعلقة بالصحة الروحية في سياق المنصات الإعلامية كانت: تعزيز التواصل الاجتماعي، والمقارنة الاجتماعية، والهوية وعرض الذات، والاستهلاك الثقافي وأسلوب الحياة، والمراقبة والخصوصية، وتحويل الحياة اليومية إلى مشهد إعلامي.
الاستنتاج: بناءً على النتائج المتحصل عليها، تُعد المنصات الإعلامية أدوات تواصلية ذات تأثيرات مزدوجة على المستخدمين. فمن ناحية، تمكّن هذه المنصات المستخدمين من تعزيز تواصلهم الاجتماعي والاستفادة من أشكال الدعم الاجتماعي. ومن ناحية أخرى، قد ينتج عن استخدامها آثار سلبية، مثل زيادة المقارنات الاجتماعية، وانخفاض تقدير الذات، والاعتماد المفرط على الحصول على إعجاب الآخرين، وما يترتب على ذلك من ضغوط نفسية. كما قد يؤدي المحتوى المنشور على هذه المنصات إلى انتشار المعلومات المضللة، وإضعاف الخصوصية، وتشكُّل معايير اجتماعية غير واقعية. بشكل عام، إن تأثير المنصات الإعلامية على المستخدمين يتوقف على نمط الاستخدام والظروف الفردية، وقد يكون إيجابيًا أو سلبيًا، مما يجعل إدارتها بوعي أمرًا ضروريًا للاستفادة من إيجابياتها والتخفيف من سلبياتها.Background and Objective: Previous studies have examined the role of mass media and social networks in strengthening or weakening the psychological and social dimensions of health. However, only a limited number of investigations have directly addressed the relationship between modern media platforms and the components of spiritual health. This research gap provided the basis for the present study. The ultimate aim of this research was to identify the factors influencing spiritual health through the use of media platforms -with a particular focus on Instagram- and to propose strategies for improving spiritual health in the digital environment.
Methods: The present study employed a qualitative meta-synthesis approach. Studies meeting predefined inclusion and exclusion criteria were analyzed, yielding a total of 509 peer-reviewed articles. The analysis followed the Attride-Stirling thematic network framework, which organizes findings across three levels: basic themes, organizing themes, and global themes. The authors reported no conflicts of interest.
Results: The results revealed that the main themes associated with spiritual health in relation to media platforms included: strengthening social connections, social comparison, self-identity and self-presentation, cultural consumption and lifestyle, surveillance and privacy, and the mediatization of everyday life.
Conclusion: Based on the findings, media platforms function as communicative tools that exert dual effects on users. On the one hand, they can enhance social interactions and provide access to social support. On the other hand, their use may generate negative consequences such as increased social comparisons, reduced self-esteem, excessive dependence on external validation, and heightened psychological pressure. Furthermore, the content shared on these platforms can contribute to the spread of misinformation, the erosion of privacy, and the formation of unrealistic social norms. Overall, the impact of media platforms on users can be both positive and negative depending on individual conditions and patterns of use. Conscious and informed management of these platforms is, therefore, essential to maximizing their benefits while minimizing their adverse effects.سابقه و هدف: در مطالعات پیشین، نقش رسانههای جمعی و شبکههای اجتماعی در تقویت یا تضعیف ابعاد روانی و اجتماعی سلامت بررسی شده است. با این حال، در پژوهشهای اندکی به پیوند مستقیم بین سکوهای رسانهای نوین و مؤلفههای سلامت معنوی پرداخته شده و این خلأ پژوهشی زمینهساز طرح موضوع حاضر است. هدف نهایی این پژوهش، تعیین عوامل مؤثر بر سلامت معنوی از طریق استفاده از سکوهای رسانهای با تأکید بر اینستاگرام بوده و بهدنبال آن، راهکارهایی برای بهبود وضعیت سلامت معنوی در فضای دیجیتال پیشنهاد شده است.
روش کار: روش پژوهش حاضر فراترکیب است و مطالعات با رعایت معیارهای ورود و خروج وارد پژوهش شدند. 509 مقاله با رتبۀ علمی مصوب بررسی شد. از الگوی آتراید استرلینگ یک فن کیفی بر اساس سه سطح مضامین اساسی، مضامین سازماندهی و مضامین فراگیر استفاده شد.
یافتهها: نتایج نشان داد مضامین اصلی مرتبط با سلامت معنوی با توجه به سکوهای رسانهای تقویت ارتباطات اجتماعی، مقایسۀ اجتماعی، هویت و نمایش خود، مصرف فرهنگی و سبک زندگی، نظارت و حریم خصوصی و رسانهایشدن زندگی روزمره بود.
نتیجهگیری: بر اساس یافتههای بهدستآمده، سکوهای رسانهای ابزاری ارتباطی هستند که تأثیرات دوگانهای بر کاربران دارند. از یک سو، کاربران با استفاده از آنها میتوانند ارتباطات اجتماعی خود را تقویت کنند و از حمایتهای اجتماعی بهرهمند شوند. از سوی دیگر، استفاده از این سکوها ممکن است پیامدهای منفی نیز به همراه داشته باشد؛ مانند افزایش مقایسههای اجتماعی، کاهش عزّت نفس، وابستگی بیش از حد به تأیید دیگران و ایجاد فشار روانی. همچنین، محتوای منتشرشده در این رسانهها میتواند موجب گسترش اطلاعات نادرست، تضعیف حریم خصوصی و شکلگیری هنجارهای اجتماعی غیرواقعی شود. بهطور کلی، تأثیر سکوهای رسانهای بر کاربران بسته به نحوۀ استفاده و شرایط فردی، میتواند هم مثبت هم منفی باشد و مدیریت آگاهانۀ آنها برای بهرهمندی از جنبههای مثبت و کاهش پیامدهای منفی ضروری است
Characterization, molecular identification and antimicrobial activity of lactic acid bacteria with potentials as halal probiotics isolated from Rinuak fish (Psilopsis sp.) in Lake Maninjau, West Sumatra, Indonesia
Full text linkBackground and Objective: The exploration of lactic acid bacteria in integration of specific halal certification is one of the major research topics in the fields of health, food, animal husbandry and agriculture. This study aimed to investigate antimicrobial potentials of probiotic lactic acid bacteria isolated from Rinuak fish (Psilopsis sp.) from Lake Maninjau, Indonesia.
Material and Methods: Totally, 15 lactic acid bacteria were isolated from four samples of Rinuak fish (Psilopsis sp.) and investigated for their characteristics as probiotic candidates using conventional laboratory assessments and 16S rRNA sequencing methods.
Results and Conclusion: Five isolates were identified as probiotic candidates, including IR2.2, IR2.4, IR4.1, IR4.3 and IR4.5 due to their good resistance of gastric pH ranging 84.24–88.01% and their survival ability against bile salts (resistance of 50.37–57.35%). The IR4.3 was identified to generate the greatest antimicrobial activity against Escherichia coli ATCC 0157, Staphylococcus aureus ATCC 25923 and Salmonella enteridis ATCC 13076 with their diameter of inhibition zone of 22.46, 19.34 and 9.41 mm, respectively. The 16S RRNA sequencing method verified that the lactic acid bacteria isolated from rinuak fish (Psilopsis sp.) included 97.69% similarity to Lactobacillus fermentum strain 4901. This strain promised as an antidiarrheal and antityphoid agent and a natural food preservative appropriate for incorporation into HALAL-compliant foods and pharmaceutical products.
Keywords: Food preservative, Endemic Fish, Microbial fermentation, Molecular characterization, Lactobacillus fermentum strain 4901، Indigenous probiotics،Food microbiology،Gastrointestinal health
Introduction
Global challenges in various aspects of life are becoming larger. This can be seen from the increasing human population and hence demands for food products are increasing as well; one of which, is fishery products. This fishery product is a basic requirement to meet the needs of protein sources. The protein must be ensured that is halal due to the tauths of the Islamic religion, because Islam emphasizes that maintaining a healthy body by consuming halal foods and drinks is an obligation for every muslim [1]. For halal food development, it is essential to ensure that all ingredients and processes comply with Islamic dietary laws. Lactic acid bacteria (LAB) sourced from permissible animals such as fish and processed without contamination from non-halal substances can be addressed as halal. However, clear certification is warranted for commercial uses. Fermented fish is one of the products from fisheries, which is rich in proteins and LAB [2]. In the fermentation process, microbes and enzymes can stimulate specific flavors, increase the digestibility of food ingredients, decrease the content of anti-nutrients and other undesirable ingredients and produce derivative products and compounds that are beneficial for human life [3]. In general, LAB are food-grade microorganisms. These bacteria can provide flavors to foods, inhibit spoilage bacteria in foods and inhibit pathogenic bacteria. The LAB can be isolated from various sources, especially from fermented foods [4]. In addition, LAB can create an acidic atmosphere, which can decrease the number of pathogen colonies [5]. The existence of selected strains of LAB has demonstrated beneficial effects, as probiotics for humans [6].
The LAB isolation is possible from plant and animal-based products; for example fish, fruits and milk [7]. Rinuak fish (Psilopsis sp.) of Lake Maninjau, Indonesia, is one of those products. Rinuak fish is an domestic fish of Lake Maninjau that includes animal proteins, which is potentially developed due to its high nutritional compounds. The flesh of rinuak fish (Psilopsis sp.) contains proteins of 14.52%, magnesium of 0.21% (in fresh rinuak), phosphorus of 2.4% (in fresh rinuak), water content of 78.62% and ash content of 6.4% (in fresh rinuak). Rinuak fish also contains calcium [8]. The mineral composition especially magnesium and calcium are able to stimulate the bacterial growth. Magnesium (especially in gluconate form) improves probiotic survival, texture, acidity and viability (> 10⁶ CFU g-1) during storage [9]. Research by [10] showed that addition of calcium (calcium carbonate) to fermented feed helped boost growth of Lactiplantibacillus plantarum, Lacticaseibacillus rhamnosus and Bacillus subtilis. Therefore, these mineral nutrients of rinuak fish support the potential of rinuak fish for LAB growth. However, there is a lack of studies that investigate its antimicrobial activity and potential characteristics as probiotics.
One of the most important characteristics of LAB is that they can produce the antimicrobial compounds of bacteriocins [11]. Bacteriocins are secondary metabolite products of LAB that include similar actions to antibiotics, being able to inhibit certain bacteria from growing [12]. Previous studies were carried out worldwide to investigate the LAB content in fish. For example, studies on swamp fish fermented with the addition of pure coconut oil resulted in the discovery of LAB with antimicrobial activity [13]. Potential antimicrobials can be achieved from LAB produced by fermentation of rinuak fish (Psilopsis sp.) in Lake Maninjau by isolating and characterizing DNA from LAB through polymerase chain reaction (PCR) analysis of 16s rRNA gene and genome sequencing. Based on the results of this molecular analysis, the phylogenetics of LAB from the fermented rinuak fish (Psilopsis sp.) of Lake Maninjau was investigated. Numerous previous studies were carried out on the nutritient of rinuak and LAB contents of rinuaks. However, study on the antimicrobial potential of fermented rinuak fish and its probiotic characteristics has not been investigated. Therefore, this study aimed to characterize the isolated LAB morphologically and biochemically and carry out theit molecular identification using 16s RNA sequence method. The probiotics effectiveness in this study was assessed on a strain spesific basis.
Materials and Methods
Research Equipment and Materials
Equipment of this study included incubator (Infors HT Ecotorn, USA), anaerobic jars, refrigerator, autoclave (ALP KT40S, Japan), hot plate (Sybron nouveau II, USA), vortex (Labmart 3000, China), analytical balance (Kern ABT 320-4M, Germany), bunsen, microtubes, hockey sticks, inoculation needles, glass slides, pipettes and micropippette tips (DragonLab, BIO-RAD, USA), light microscope (Irmeco, USA), UV-vis spectrophotometer, oven (Memmert UF 100 Plus, Germany), centrifuge (Eppendorf 5417 R, Germany), thin layer chromatography (TLC) chamber and paper chromatograph, capilary tubes, 96-well microplates, microplate reader (PR 4100, BIO-RAD, USA), pH meter, shaker waterbath (IC DK-540b, Japan), PCR equipment (Techne TC-312, UK), spinner down (BIO-RAD, USA) electrophoresis equipment (BIO-RAD, USA), gel documantation imager (BIO-RAD, USA) and labobartory standard glasswares.
Materials of this study included rinuak fish sampels collected from Lake Maninjaus, Indonesia, de Mann-Rogosa-Sharpe (MRS) broth and agar (Merck, Germany), distilled water (DW), 70 and 90% ethanol, peptone water, MRS agar (Merck, Germany), 37% HCl (Merck, Germany), ox-gall, nutrient agar (NA), penicillin, kanamycin, ampicillin, NaOH (Merck, Germany), genomic DNA mini kit (Invitrogen Pure-Link, USA) and lysozyme (Invitrogen PureLink, USA)
Fermentation of Rinuak Fish (Psilopsis sp.) from Lake Maninjau
The process of making rinuak fish fermentation (Psilopsis sp.) was as follows. The ingredients were fish, salt and rice. First, fish was washed and then salt and rice were add to nourish the taste. Samples were transferred into a jar and the lid was close tightly. Jars were stored at room temperature (RT) for 4 d and then dried for 3–5 d [14]. The flowchart of the fermentation process for rinuak fish (Psilopsis sp.) from Lake Maninjau can be seen in Figure 1.
Sampling Locations for Rinuak Fish (Psilopsis sp.)
Samples of rinuak Fish (Psilopsis sp.) were collected from fishermen in Lake Maninjau at various locations based on the easy access to collection locations, residential areas in the lake area, upstream rivers around the lake, depth of the lake and food sources for rinuak Fish. Sample 1 was collected in a dense settlement and included a traditional market in the Lake Maninjau area. Sample 2 was collected from the middle of the lake with a depth of nearly ±150 M. Sample 3 was collected in a tourist attraction area at Lake Maninjau and Sample 4 was collected in the upstream area of the river, the Batang Sri Antokan River; where, there was the Maninjau Hydroelectric Power Plant. Fish was immediately transferred into a cold box and carried to the Microbiology Laboratory of Stikes Syedza Saintika. Then, isolation and molecular characterization of DNA of LAB were carried out at the Biotechnology laboratory of Andalas University, Indonesia. The location of each sample is illustrated in Table 1 and a map showing the locations for rinuak fish in Lake Maninjau, West Sumatra, Indonesia, is demonstrated in Figure 2.
Isolation of Lactic Acid Bacteria from Rinuak Fish (Psilopsis sp) Lake Maninjau, West Sumatra, Indonesia
The LAB isolation process began with an enrichment process; 1 g of rinuak fish sample was added into 9 ml of MRS broth and then homogenized to achieve a 10-1 dilution. This was incubated at 37 oC for 24 h. After incubation, 100 µl of the enrichment or 10-1 dilution were added into a microtube with 900 µl of peptone water and then homogenized using vortex to achieve a 10-2 dilution. This procedure was repeated until 10-8 dilutions were achieved. Then, plating process was carried out and the culture from the 10-8 dilution was inoculated on MRS agar and incubated anaerobically at 37 oC for 48 h. Single LAB colonies that grew on the surface of the media were re-purified by inoculating the colony onto MRS agar, incubating anaerobically at 37 oC for 24 h [15].
Characterization of Lactic Acid Bacteria from Rinuak Fish (Psilopsis sp.) in Lake Maninjau, West Sumatra, Indonesia
Identification of lactic acid bacteria morphology
Morphological identification was carried out macroscopically on LAB cultures inoculated on MRS agar to identify the shape, color and diameter of LAB isolates. Then, Gram staining was carried out to investigate LAB morphology microscopically by verifying the color and the shape of the cells. Meanwhile, biochemical characterization was carried out using fermentation-type and catalase assays [15].
Selection of Lactic Acid Bacteria from Rinuak Fish (Psilopsis sp) from Lake Maninjau, West Sumatra, Indonesia, as Probiotic Candidates
1) Resistance of Rinuak Fish (Psilopsis sp.) from Lake Maninjau, West Sumatra, Indonesia, to Gastric pH
The resistance assessment for gastric pH was carried out based on Kim et al. [16] method with modifications. This assay used two types of media, including MRS broth with addition of 37% HCl to achieve a pH of 2.5 and MRS broth without addition of HCl to maintain a pH of 6.8 (as a control). The medium was sterilized at 121 °C for 15 min using autoclave. Moreover, 5 ml of MRS broth-HCl were added to 0.5 ml of the bacterial isolate and incubated at 37 oC for 3 and 6 h. Then, absorbance was read at 600 nm. This was carried out three times. Resistance was expressed as a percentage. According to [17], percentage of the resistance of LAB isolates can be calculated using the following formula:
2) Resistance of Rinuak Fish (Psilopsis sp.) from Lake Maninjau, West Sumatra, Indonesia, to Bile Salts
The resistance assessment of bile salt was carried out based on a method from Gotcheva V et al. [18] with modifications. Various concentrations of bile salt of 0, 0.3 and 0.5% were added to the MRS broth media. The media were autoclaved at 121 °C for 15 min. The bacterial isolate (0.5 ml) was transferred into 5 ml of MRS broth added with 0, 0.3 and 0.5% ox-gall and incubated at 37 oC for 5 h. The MRS broth containing no bile salt was set as the control and compared with the treatments. The LAB growth was assessed using UV spectrophotometry based on the absorbance at 600 nm. All assessments were carried out in three replications. Isolate resistance was expressed as a percentage. According to [19], percentage of the resistance of LAB isolates can be calculated using the following formula:
Screening of Lactic Acid Bacteria from Rinuak Fish (Psilopsis sp.) in Lake Maninjau for their Antimicrobial Potentials
The lactic acid bacteria antimicrobial activity assay
Three pathogens of Salmonella typhi, Staphylococcus aureus and Escherichia coli were assessed for antimicrobial activity of LAB using modified paper diffusion method [20]. Briefly, 1 ml of LAB culture enriched for 48 h was collected using micropipette and transferred into a sterile microtube. This was centrifuged at 10,000 rpm for 5 min and the supernatant was collected for antimicrobial resistance assessment. Then, 0.4 g of NA media was homogenized by heating using hot plate and sterilizing using autoclave. Then, 40 µl of the bacterial isolate enriched for 24 h were poured into a Petri dish containing 20 ml of NA media and cooled down until the media hardened. The paper disk was soaked in LAB isolate suspension for approximately 10 min. After the agar solidified, the paper disk was set on NA media, which contained isolates of pathogenic bacteria. Then, positive control was set by dropping LAB supernatant (20 µl) onto sterile test papers, including 10 g of penicillin, 30 g of kanamycin and 10 g of ampicillin. This was incubated at 37 °C for 24 h anaerobically. After 24 h, diameter of the inhibition zone was reported using caliper [21].
Antimicrobial Assay of Crude Bacteriocin Supernatant
Briefly, 1 ml was cultured in 9 ml of MRS solution at 37 oC and incubated for 2 d. This was centrifuged at 14,000 rpm for 5 min. Then, 0.22-µl membrane filter was used to filter the supernatant. To eliminate the barrier effect because of the presence of organic acids, 1 N NaOH solution was added to the cell-free supernatant to maintain pH 6.5 [21]. The bacterial pathogens were grown aerobically at 37 °C for 24 h. Then, 0.2% pathogenic bacteria were transferred onto 20 ml of MHA solution at 50 °C. After the gelatin was solid, a 6-mm well was made in the media using cork borer. Furthermore, 50 µl of supernatant were transferred into the wells and set 10–15 min. The incubation was carried out at 37 °C for 24 h under aerobic conditions. Antimicrobial activity of bacteriocins in the supernatant was verified by varying the time intervals of 15, 30 and 60 min at 100 ºC. The supernatant of LAB was investigated for its ability in inhibiting bacteria, including E. coli O157, S. aureus ATCC 25923 and Salmonella enteritidis ATCC 13076 via similar methods. The inhibition zone was associated with existence of bacteriocin compounds and its dimension was recorded using caliper [22].
Isolation and Characterization of 16S rRNA
Genome isolation was carried out using genomic DNA mini kit. Lysozyme was used at a concentration of 20 mg ml-1 to lyse the bacterial cell walls. The 16S rRNA gene was amplified using selected bacterial genomic DNA kit. Amplification was carried out using reverse primer 1387R (5'-GGGCGGGGTGTACAAGGC-3') and forward primer 63F (5'-CAGGCCTAACACATGCAAGTC-3'). Reactions were carried out in a total volume of 50 μl. The PCR mixture contained 25 μl of DreamTaq Green DNA polymerase (Thermo Fisher Scientific, USA), 22 μl of milli Q water (MQ), 1 μl of the template and 1 μl of each forward or reverse primers (10 μM each, IDT synthesis). Amplification conditions included preheating at 95 °C for 5 min and then denaturation at 95 °C for 30 s, primer annealing at 58 °C for 30 s, 1 min extension step at 72 °C for 35 cycles and post cycling extension for 5 min at 72 °C. The reaction was carried out in a thermal cycler (Biometra T-Personal Thermal Cycler, USA). The amplified DNA was added to GelRed nucleic acid gel stain and electrophoresed on agarose gels [1.5% (w/v) in TBE buffer] at 100 V for 60 min. The amplicons could be visualized using gel documentation imager. The PCR amplification product was purified using absolute ethanol Na-acetate method and then sequenced [23].
The BLAST and Phylogenetic Analysis
Phylogenetic analysis method was carried out based on former studies [24]. Sequence data were collected using BioEdit software and then converted to FASTA format. The BLAST was used to carry out sequence analysis (http://www.ncbi.nl-m.nih.gov/blast/cgi). Moreover, DNA sequences were imported into Clustal W2 (http://www.ebi.ac.uk/Tools/clustalw2/) to carry out phylogenetic analysis. A phylogenetic tree was created using BLAST MEGA v.6.0 (http://www.megasoftware.net) and neighbor-joining (NJ) method.
Data Analysis
Data were present as mean ±SD (standard deviation). Statistical significance was reported using one-way analysis of variance (ANOVA) and SPSS software v.26 (IBM, USA). Tukey post-hoc test was used to assess significant differences between group means, with a p-value of < 0.05 as statistically significant.
Results and Discussion
Isolation of Lactic Acid Bacteria in Rinuak Fish (Psilopsis sp.) from Lake Maninjau, West Sumatra, Indonesia
The LAB selective media (MRS broth and agar), providing appropriate nutrients and pH for LAB growth, were used in the serial dilution-agar plate method. This allowed LAB to grow and reproduce effectively. Serial dilution was necessary to decrease bacterial density, ensuring individual colonies grow separately instead of clustering together. The MRS broth and agar as the sources of nutrition and appropriate pH for LAB growth were used in serial dilution-agar plates to isolate LAB. Serial dilution was needed to decrease density of the inoculated LAB to enable the LAB to develop in colonies independently of one another, rather than in piles [19] (Figure 3). Single colonies that were round, convex, yellowish-white and shiny grew separately with various diameter sizes. These were re-inoculated on MRS agar using streak method to achieve pure isolates of LAB from rinuak fish. These findings were similar to those of Purwati study, which showed colonies of LAB on MRS agar as yellowish-white colonies. Details on the isolation and purification of 15 LAB isolates were as follow: three isolates of IR1 (IR1.1, IR1.2 and IR1.3), five isolates of IR2 (IR2.1, IR2.2, IR2.3, IR2.4 and IR2.5), one isolate of IR3 (IR3) and six isolates of IR4 (IR4.1, IR4.2, IR4.3, IR4.4, IR4.5 and IR4.6). A higher number of LAB isolates (n = 15) was achieved in this study, compared to previous reports, which achieved 9–12 isolates in Tilapia fish [25] and 10–13 isolates in Bilih fish [24].
The observed differences in the diversity of LAB types within the samples might indeed be affected by the environmental conditions at the respective sampling locations. For example, Sample IR.1 was collected from Maninjau Village, an area with high population density and a traditional market, while IR.3 originated from Duo Koto Village, a well-known tourist destination. Such anthropogenic activities in these areas might contribute to pollution of the close lake ecosystem, including household and market waste directly discharged into Lake Maninjau. In contrast, IR.2 and IR.4 were collected from relatively undisturbed mid-lake regions—Tanjung Sani Village and Koto Malintang Village respectively—where the water depth exceeded 100 m and minimal human activity was observed, potentially preserving a further appropriate aquatic environment.
However, it is acknowledged that geographic origin alone might not fully responsible for the variations in LAB diversity. Several other ecological and biological factors such as the natural diet of rinuak fish, differences in water temperature, seasonal fluctuations and post-capture handling practices (e.g. time to processing or storage conditions) could play significant roles in shaping the microbiota. These variables might affect selective pressures and survival conditions for specific LAB strains. Further studies incorporating water physicochemical analyses, fish diet profiling and standardized handling procedures are recommended to strengthen the interpretation of LAB diversity patterns. A further holistic approach is necessary to elucidate the multifactorial nature of LAB colonization and persistence in rinuak fish across various environments. Survival of microorganisms depends greatly on environmental conditions and is affected by their food sources [26].
Characterization of Lactic Acid Bacteria in Rinuak Fish (Psilopsis sp.) from Lake Maninjau, West Sumatra, Indonesia
The morphological characteristics of all isolates were carried out microscopically through Gram staining (Table 2). The LAB is classified as Gram-positive bacteria that can bind crystal violet-iodine complexes and preserve the purple color of their cells [27]. The stages in Gram staining are crystal violet as the primary dye, Lugol solution (KI-I2) as the mordant, 96% alcohol as the decolorizing agent and safranin solution as the counterstain. Crystal violet dissociates in solution into CV+ and Cl-. These ions then penetrate the bacterial membranes and cell walls. Moreover, CV+ ions react with negatively charged bacterial cell components and make bacterial cells (-) react with CV+ purple. Addition of iodine solution (I- or I3) forms a crystal violet-iodine complex (CV-I) in the inner and outer layers of the cells; thereby, strengthening purple color of the bacterial cells [28]. The cell walls of Gram-positive bacteria are in the form of thick fibers consisting of 50–90% peptidoglycan. T
Innovative Computational Design of Sustainable Milk-Clotting Peptides for Enhanced Cheese Production
Full text linkBackground and Objective: Identifying a milk-clotting enzyme (MCE) with high κ-casein specificity and heat sensitivity remains a challenge in cheese production. Current microbial, plant, and recombinant MCEs often exhibit low clotting activity, poor κ-casein specificity, and high thermostability, compromising cheese quality and increasing production costs. In this study, to address this, we developed a computational pipeline combining structural analysis, machine learning, and molecular dynamics simulation to design approximately 160,000 peptides from the Rhizomucor miehei protease–Pepstatin A complex (PDB ID: 2RMP).
Material and Methods: Single-site mutagenesis, ML-driven affinity re-prediction, and physicochemical filtering yielded 84 peptides. Their specificity as aspartic proteases were validated via predicted Pepstatin A binding and further screened for cross-reactivity with αs1-, αs2-, and β-caseins.
Results and Conclusion: Two candidates, Pep1 and Pep2, demonstrated superior κ-casein binding affinities (ΔG = -50.20 and -39.07 kcal/mol at 40 °C, respectively), lower melting indices (-4.05 and -3.09), and significantly enhanced specificity scores (-10.85) compared to Rhizomucor miehei protease (ΔG = -33.9 kcal/mol at 45 °C; melting index = 0.17; specificity score = 0.85). These peptides represent promising vegan- and halal-friendly alternatives to chymosins, pending experimental validation.
Keywords: Milk-clotting enzymes, Computational peptide design, κ-casein specificity, Thermolabile peptides, Sustainable cheese production, Molecular dynamics simulations, Machine learning, Vegan cheese production, Halal cheese production, Aspartic protease.
. Introduction
Identifying chymosin substitutes with comparable enzymatic characteristics particularly high specificity for κ-casein (reflected in a high milk-clotting activity to proteolytic activity ratio, MCA/PA) and optimal heat sensitivity remains a significant challenge in cheesemaking [1-3]. Chymosin, traditionally derived from the stomachs of calves, has long been the gold standard for milk clotting due to its high specificity for κ-casein and its ability to produce high-quality cheese [4]. However, economic, ethical, dietary, and religious concerns, coupled with the rising demand for cheese production and consumption, have spurred the search for viable alternatives [1, 5]. Various sources of milk-clotting enzymes (MCEs) have been explored, including other animal-derived rennet (from lamb, buffalo, and camel), plant-based coagulants, and microbial and recombinant enzymes. Lamb and buffalo chymosin have lower MCA/PA ratios than calf chymosin, compromising cheese quality, though buffalo rennet’s stability suits mozzarella production [6, 7]. In contrast, camel rennet, while showing desirable clotting properties, is difficult to obtain in large quantities, limiting its widespread use [8]. Rennet from other animals, such as pigs and rabbits, often shows poor specificity and thermal stability, restricting their utility in cheesemaking [1]. Recently, marine organisms such as the shrimp Pleoticus muelleri have also been investigated as novel sources of coagulants [9-11]. Nevertheless, animal-based MCEs continue to face ethical and religious constraints. Plant-based coagulants, such as chymopapain from papaya, offer promising vegan- and halal-friendly alternatives [12]. However, their lower specificity toward κ-casein and tendency to generate bitterness limit their use, especially in aged cheeses [1, 13]. Microbial coagulants, particularly from bacterial and fungal sources, are increasingly favoured due to their cost-effectiveness and suitability for vegetarian and organic cheese production [14]. Although bacterial coagulants offer functional diversity [5, 15-18], they often exhibit high proteolytic activity, necessitating the development of improved strains through mutation [1]. Fungal coagulants are generally more compatible with cheesemaking conditions, particularly in terms of pH and temperature [1, 19]. Still, their high thermostability can cause undesired proteolysis during cheese ripening, adversely affecting texture and flavour [20]. Among fungal coagulants, mucorpepsin (EC 3.4.23.23) from Rhizomucor miehei is widely used due to its relative specificity. However, it still acts on α- and β-caseins and possesses high thermal stability, which can result in residual enzymatic activity after curd formation. This residual activity often leads to bitterness, altered texture, and reduced yield during cheese production [21-25]. Several studies have attempted to address these limitations using physical, chemical, enzymatic, and genetic modifications to enhance specificity, reduce thermostability, and minimise off-target proteolysis while maintaining milk-clotting efficiency. For example, some efforts have successfully reduced thermostability by removing N-linked carbohydrates, one of the primary factors contributing to R. miehei protease’s thermal resistance without significantly diminishing clotting activity [1, 25-29]. Other studies have enhanced milk coagulation without altering thermostability [30-35]. Genetic engineering, mutagenesis [36-39], substrate modification[40-43], and enzyme immobilisation [44, 45], have all been explored to develop efficient coagulants suitable for industrial cheese production. Nevertheless, regulatory restrictions on genetically modified organisms and technical and economic challenges have limited the commercial adoption of many of these approaches [46]. A novel strategy for identifying milk-clotting enzymes involves targeting conserved motifs—namely FDTSSD or FDTGSSE, found in all currently known commercial coagulants. Using this approach, several in silico BLAST searches have identified new candidate proteases for cheese manufacturing. Moreover, degenerate primers based on these motifs can identify relevant genes even when full gene sequences are unknown [26]. Computational enzyme design and molecular modelling have shown great potential in optimizing enzyme activities [47-49]. Advances in bioinformatics, molecular dynamics, and machine learning now enable the prediction and engineering of enzymes with enhanced specificity, catalytic efficiency, and thermostability. Structural insights into active sites support rational design through targeted mutations[50-52]. These in silico methods reduce experimental cost and time by predicting enzyme performance before validation. Notably, ancestral sequence reconstruction has been used to design a novel aspartic protease with improved κ-casein affinity and thermolability for cheesemaking [53]. Although milk-clotting enzymes (MCEs) have been widely studied, the application of advanced computational tools for their rational design remains underexplored [54]. Moreover, current MCEs often exhibit limitations such as low substrate specificity and excessive thermostability, which can negatively affect cheese quality and increase commercial costs. This study introduces a computational pipeline to design thermolabile, κ-casein-specific milk-clotting peptides as sustainable, vegan-, and halal-friendly alternatives for cheese production. An integrated computational pipeline was developed, incorporating machine learning, rational design, and structure-based screening. A combinatorial library of 160,000 peptide variants was generated based on conserved catalytic motifs from the Rhizomucor miehei protease–Pepstatin A complex, linked through a rationally designed sequence. The library was screened using predictive models trained on binding affinity data, followed by structure-based refinement, physicochemical profiling, and validation through molecular docking and molecular dynamics simulations. This in silico framework provides a foundation for the experimental development of next-generation coagulants for dairy applications.
Materials and Methods
2.1. Aspartic peptide design strategy
We present a computational pipeline for the de novo design of short peptide-based milk-clotting enzymes (MCEs) with enhanced affinity for κ-casein and improved chemosensitivity. The design strategy involves identifying interface residues containing catalytic motifs from Rhizomucor miehei protease (RMP), linking them via an optimised linker, and screening the resulting peptide candidates for specific binding and functional activity. To benchmark the design process, RMP was used as a positive control and also served as the structural template, while a peptide with low affinity for Pepstatin A was employed as a negative control. The results for both controls are provided in the supplementary file on GitHub.
2.2. Interface residue identification
Interface residues from the crystal structure of 2RMP (RMP–Pepstatin A complex) were identified using the PDBePISA tool (https://www.ebi.ac.uk/pdbe/pisa/) [55] and served as the starting point for aspartic peptidase design. Two continuous interface segments containing the catalytic aspartic acid residues were selected: LFDTGSS (residues 36–42, harbouring the DTGS motif) and TIDTGTNFFI (residues 235–244, containing the DTGT motif). Although the ITYGT segment was identified among the interface residues, it was excluded from the design due to its lack of direct involvement in the conserved DTGS/DTGT catalytic motifs. Furthermore, incorporating non-catalytic segments would unnecessarily increase peptide length without functional contribution, potentially reducing the structural stability and target specificity of the designed peptides.
2.3. Continuous interface residues segment
Catalytic motif of 2RMP (38-41, 237-240)
LFDTGSS = 36-42 (7 residues)
TIDTGTNFFI = 235-244 (10 residues)
ITYGT = 80-84
2.4. Linker design and variant library construction
The designed peptide sequence comprises two continuous interface segments, LFDTGSS (residues 36–42) and TIDTGTNFFI (residues 235–244), totalling 17 residues. The spatial distance between the terminal residues of these segments (Ser42 and Thr235) was measured to be 13.8 Å using chimaera v1.17.3. The average distance between consecutive Cα atoms in a polypeptide chain is approximately 3.8Å [56-58].This distance corresponds to approximately four amino acids, indicating the optimal linker length required to bridge the two segments. Incorporating a four-residue linker (XXXX), the final peptide construct contains 21 amino acids. A saturated mutagenesis approach was applied to the linker, yielding 160,000 variants (20⁴ combinations) of the parent sequence, LFDTGSS-XXXX-TIDTGTNFFI.
2.5.Machine learning models for protein -protein interaction prediction: PDBbind+ and SKEMPI 2.0 Datasets
In this study, datasets were sourced from three major repositories: (1) 160,000 peptide variants generated through combinatorial substitution, (2) 7,086 mutants curated from the SKEMPI 2.0 database, which focuses on mutations in protein–protein interactions[59], and (3) 3,176 protein–protein complexes obtained from the PDBbind+ dataset[60]. For SKEMPI 2.0, duplicates, ambiguous entries (e.g., conflicting binding affinities or incomplete mutation annotations), and incomplete records (e.g., missing sequences or ΔΔG values) were removed. Similarly, the PDBbind+ dataset was filtered by excluding duplicate complexes, sequences with non-canonical residues or extreme lengths, entries lacking binding affinity or structural data, and those with physiologically irrelevant binding values. After filtering, the cleaned data were divided into training (70%), testing (20%), and validation (10%) sets. Two independent machine learning models XGBoost and a Convolutional Neural Network (CNN) were then employed to predict protein–protein interactions.
2.6. SKEMPI 2.0 dataset and CNN-based machine learning model
The SKEMPI 2.0 dataset, comprising 7,086 mutations from 295 studies, was used to train a deep learning model for predicting changes in protein–protein binding affinity[61]. After removing duplicates, ambiguous entries, and incomplete data, 343 high-quality entries were selected, each containing PDB IDs, chain identifiers, affinity values, and protein sequences. Sequences were normalised by peptide length for consistency. Model training was performed using the DeepPurpose framework (v0.1.5)[62], which employs convolutional neural networks (CNNs) for protein sequence encoding. Protein sequences were processed using DeepPurpose’s data_process() function, which internally encodes sequences and applies padding to handle variable lengths without manual preprocessing. The dataset was divided into training (70%), testing (20%), and validation (10%) sets. The model was trained over 100 epochs with a learning rate of 0.001 and a batch size of 32. Performance was evaluated using mean squared error (MSE), R², and Pearson correlation coefficient. The trained model was then used to predict the binding affinity between κ-casein and 160,000 designed peptide variants. Based on predicted scores, the top 21 peptides were selected for further analysis.
2.7. PDBbind+ dataset and XGBoost-based machine learning model
The PDBbind+ dataset, consisting of 3,176 protein–protein complexes [60], was curated by eliminating duplicates, invalid sequences, and missing data, resulting in 1,049 remaining complexes. Affinity values were standardised, log-transformed, and negative values were excluded. A sequence-based predictive model was developed using Conjoint Triad encoding (via DeepPurpose) and logarithmic normalisation to estimate protein–protein and protein–peptide binding affinities. Several machine learning models were tested using Scikit-learn, including Random Forest (R² = 0.67), Ridge (R² = –1.482), Gradient Boosting (R² = 0.647), and XGBoost (R² = 0.696). XGBoost was selected for its superior performance[63]. The model was optimised using the Optuna framework with 100 trials and 5-fold cross-validation, tuning hyperparameters such as estimators (100–1000), learning rate (0.01–0.3), max depth (3–10), subsample (0.6–1.0), and colsample_bytree (0.6–1.0). Outlier detection was performed using an Isolation Forest with a contamination rate of 0.15. The final model, evaluated using mean squared error (MSE), R², and Pearson correlation, was used to predict binding affinities for 160,000 κ-casein peptide variants, identifying the top 48 candidates for further investigation.
2.8. Single-site mutation and machine learning prediction
The top 69 sequences (48 from the model trained on PDBbind+ and 21 from the model trained on SKEMPI 2.0) were further refined through single-site mutations at positions Phe2 (PHE), Ile13 (ILE), and Phe19 (PHE). To preserve peptide structural integrity and avoid disrupting potential hydrogen bonding networks, mutations were excluded from adjacent serine (S) and threonine (T) residues surrounding the linker. Instead, three hydrophobic residues Phe2 (F2), Ile13 (I13), and Phe19 (F19) were carefully chosen as mutation sites due to their roles in local packing and potential linker interactions. Each position was systematically substituted with all 20 canonical amino acids, generating 4,140 unique sequences (20×3×69). The two trained models were used again to predict interactions between these sequences and κ-casein, resulting in 101 selected peptide sequences (96 from the model trained on PDBbind+ and 5 from the model trained on SKEMPI 2.0) with optimal binding affinity.
2.9. Physicochemical property assessment
The top 101 peptide sequences underwent comprehensive screening based on thermal stability, pH, solubility, and toxicity. DeepSTABp (https://csb-deepstabp.bio.rptu.de/) was used to predict melting temperature [64].
ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/multi_submit.php) evaluated pH and solubility based on isoelectric point, hydrophobicity, hydrophilicity, amphipathicity, steric hindrance, charge, and molecular weight[65]. ToxinPred2 (https://webs.iiitd.edu.in/raghava/toxinpred2/) [66] and ToxinPred3 (https://webs.iiitd.edu.in/raghava/toxinpred3/index.html) [67] assessed toxicity profiles. This screening identified 84 peptide sequences with favourable physicochemical properties.
2.10. Pepstatin A binding affinity as a proxy for aspartic protease activity
Pepstatin A binding was used as a proxy to assess aspartic protease activity. A pre-trained model, Morgan_CNN_BindingDB_IC50, from the DeepPurpose framework[68], was employed to predict interactions between Pepstatin A and the 84 selected peptides. The model utilised a multi-layer perceptron for drug representation (hidden layers: [1024, 256, 64]) and a convolutional neural network for peptide representation (convolutional layers: filters [32, 64, 96]; kernel sizes [4, 8, 12]). Based on predicted binding affinity, 17 peptides with strong interaction to Pepstatin A were identified for further analysis.
2.11. Cross-reactivity profiling with non-target casein subunits
The 17 selected peptide sequences were further evaluated for binding specificity by assessing their predicted interactions with αs1-casein, αs2-casein, and β-casein, using the same trained models and datasets. Two peptides exhibited low binding affinities to these casein subunits, suggesting a high degree of specificity toward κ-casein. These sequences hold particular promise for targeted applications in unique cheese production processes.
2.12. Predicting and evaluating the 3D structures of the peptidase
The two peptides with the highest binding affinities to Pepstatin A were predicted using PEP-FOLD4 (https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4/) [69], a free tool for predicting linear, cyclic, and chemically modified peptide structures. It uses the improved sOPEP force field and Monte Carlo simulation with Debye-Hückel-free parameters for refinement. The Best Model is based on the lowest SOPEP score. PEP-FOLD4 also provides an SA probability map showing residue conformations: red (helices), green (β-strands), and blue (coils/loops), aiding in stability and secondary structure prediction. MD simulations were run for each peptide, and final stable structures were extracted. Structural quality was then assessed using ERRAT[70], Procheck[71], and verified for further processing.
2.13. Molecular docking of peptidase with κ-casein
Since the X-ray structure of κ-casein remains undetermined, our analysis focused on the local structure surrounding the Phe105–Met106 bond, which is susceptible to aspartic enzyme cleavage in bovine κ-casein. We aimed to explore how this local fragment interacts with RMP and the designed peptides to investigate the underlying mechanism, as highlighted in earlier studies [53, 72, 73]. The same methodology was followed for ligand preparation, with some modifications. The RMP binding site in κ-casein spans residues 102 to 108 [74], while the chymosin binding site covers residues 97 to 112 [75, 76]. The shorter segment (residues 102–108, HLSFMAI) was used as the ligand for docking with RMP, as it corresponds to the P4–P4′ binding region of Pepstatin A in the Co-Crystallised complex with RMP [74], ensuring structural relevance. In contrast, the longer fragment (residues 97–112, RHPHPHLSFMAIPPKK) was used for docking with the designed peptides (Pep 1 and Pep 2) and was also utilised throughout the machine learning pipeline to capture a broader binding context, consistent with its known role in chymosin recognition. This region was predicted using AlphaFold2[77]. Molecular docking was performed using the HADDOCK v2.4-2022.08 web server[78, 79], with the peptides as receptors and κ-casein as the ligand. Asp3 and Asp14 were defined as active residues for the peptides (first molecule), and residues 102 to 108 were set as active for κ-casein (second molecule). The top docking poses were selected based on a combination of criteria, including the size of the largest cluster, the most negative Z-score, the number of hydrogen bonds, and the lowest binding energy. Interaction analyses were carried out using LigPlot+ v1.4.5 software[80].
2.14. Molecular dynamics simulation (MD)
The well-recognised and freely licensed software used in this study was GROMACS version 2024[81]. Topology parameters for the anticipated models were produced using the AMBER force field (ff99SB) in GROMACS [82] .The constructed peptides (pep 1and Pep 2) and RMP models were placed in a cubic box with 10 Å spacing, solvated with the TIP3P water model[83]. Energy minimisation was performed using the steepest descent technique[84], and long-range electrostatics were calculated using the Particle-Mesh Ewald (PME) method[85], with a 1.0 nm cut-off for van der Waals and electrostatic interactions. The maximum number of minimisation steps was 50,000. Equilibration was carried out using 5000 ps of NVT and NPT runs. The V-rescale thermostat (0.1 ps coupling) maintained temperature at 318.15 K, which is the optimal temperature for RMP, during NVT. The Berendsen barostat was employed during the NPT equilibration phase with a coupling time of 2.0 ps to allow fast and stable pressure convergence. Although the Berendsen method does not sample a true NPT ensemble, it is widely used for initial stabilisation [86]. For the subsequent 100 ns production run, the Parrinello–Rahman barostat was used to ensure thermodynamically rigorous sampling of pressure fluctuations and system behaviour [87]. Simulations were also conducted at different temperatures for comparative analysis. Analytical tools including gmx rms, gmx rmsf, gmx gyrate, gmx hbond, and gmx sasa were used to calculate RMSD, RMSF, radius of gyration, hydrogen bonds, and SASA, respectively[88]. All data were extracted from trajectories and visualised using Origin.
2.15. MM-PBSA binding energy, principal component, and free energy landscape analyses of protease complexes
MM-PBSA binding free energy and PCA ana
Microbial persistence in pasteurized milk: Biocontrol and heat treatment optimization
Full text linkBackground and Objective: Increasing interests in healthy nutrition stimulate use of lactic acid bacteria with functional characteristics in production of dairy products. Lactic acid bacteria are included in the composition of starters for cheese making as well as other dairy products. In addition to their general health benefits, they show pronounced antimicrobial activities, partly due to their metabolite complexes. The primary method of ensuring milk safety is heat treatment. Pasteurization at 72°C ±2 is commonly used in cheese production. Nevertheless, the survival of viable Pseudomonas (P.) aeruginosa cells, capable of growing at refrigeration temperatures, may lead to spoilage during storage.
Material and Methods: Antagonistic activity of industrial Lactobacillus (L.) helveticus, against P. aeruginosa strains as well as the effectiveness of pasteurization at 72 °C ±2 (common in cheese production) was assessed. L. helveticus demonstrated high antagonistic activity against the reference collection strain of P. aeruginosa ATCC 25668 and the wild-type isolate P. aeruginosa 47, verifying its potential as a biological control agent.
Results and Conclusion: Limitations of standard pasteurization have been identified. During the heat treatment of sterile milk contaminated with the wild-type strain P. aeruginosa 47, 1.9 × 10⁴ CFU ml-1 of viable cells survived after a 20-s exposure, while only 1.8 × 10¹ CFU ml-1 survived after a 40-60 s exposure. Cell growth was observed at up to 3.5 × 10⁶ CFU ml-1 during 7 d of storage. Microscopy revealed morphological changes in the cells (elongation and filamentous structures), indicating adaptive mechanisms. Similar results were detected for the reference collection strain of P. aeruginosa ATCC 25668. A 15-min pasteurization time at 72°C ±2 was effective against the two strains. The study suggests an integrated approach to ensuring the safety of dairy products by combining biological control with the optimization of heat treatment regimes.
Keywords: Microbial persistence, Heat treatment, Lactic acid bacteria, Pseudomonas aeruginosa
Introduction
The increasing interest in healthy nutrition stimulates use of lactic acid bacteria (LAB) with functional characteristics in production of dairy products. Metabolites from these cultures show pronounced antimicrobial activity in addition to their general health benefits. The LAB and their metabolic complexes may be promising agents for the study [1, 2]. Throughout their life cycle, LAB can produce a range of antimicrobial compounds, including organic acids (lactic, acetic, propionic and butyric acids), bacteriocins, hydrogen peroxide and bacteriocin-like inhibitory substances [3]. Their use can be considered as a natural strategy of pathogen control, including P. aeruginosa, and a basis for the development of innovative technologies in various industries [4, 5]. Moreover, the antagonistic activity of LAB against pathogen growth varies within species and strains. This antimicrobial effect results from the synergistic action of various metabolites [2, 3]. The primary method of ensuring milk safety is heat treatment. Pasteurization at 72 °C ±2 is commonly used in cheese production. Nevertheless, survival of viable P. aeruginosa cells, capable of growing at refrigeration temperatures, may lead to spoilage during storage. The LAB with strong antagonistic characteristics against P. aeruginosa can serve as additional barriers to ensuring the product quality and safety, when used as starter cultures.
Raw milk delivered to dairy plants is frequently contaminated with psychrotrophic bacteria. Within psychrotrophic microorganisms, P. aeruginosa is a Gram-negative pathogen with high spoilage potentials, significant resistance to antimicrobial agents and capacity to form biofilms [6]. Moreover, increase of multidrug-resistant strains due to the improper and excessive use of antibiotics increases morbidity and mortality within immuno-compromised patients. The P. aeruginosa is included in the top three causes of opportunistic infections in humans, affecting more than 2 million patients and causing approximately 90,000 deaths annually [7]. Despite being often underestimated as a foodborne pathogen, P. aeruginosa plays an increasingly significant role in contaminating industrial equipment and finished dairy products [8]. Moreover, P. aeruginosa can quickly become resistant to various stress factors, including antimicrobial agents and conventional disinfectants, which increases concerns about the potential transmission of resistant strains through the food chain [9,10]. The spoilage of products by P. aeruginosa poses a serious concern for consumers and food safety regulators. Milk is an excellent medium for the growth of various microorganisms. The psychrotrophic P. aeruginosa can continuously increase its population in milk at refrigeration temperatures (4–6 °C). Its short generation time—less than 4 h—enables it to reach cell counts exceeding 10⁶ CFU ml-1 in milk after 8 d of storage [11]. The count of psychrotrophic bacteria in raw milk delivered to dairy plants reflects the effectiveness of sanitary and hygienic measures on the farm. Contamination of the dairy plant with P. aeruginosa lessens adequate sanitation. This is due to the P. aeruginosa ability to form biofilms actively and rapidly acquire resistance to disinfectants. Moreover, strains with various susceptibility to antimicrobial agents, including potentially resistant ones, may co-exist within a single facility [12]. Based on the International Dairy Federation’s recommendations, milk should be cooled to 4–10 °C within 1.5–3 h. In some countries, milk is delivered to plants only once a day, which may promote bacterial growth due to P. aeruginosa ability to multiply under a wide temperature range. Regarding its multidrug resistance and high adaptability, there is an urgent need to develop effective strategies to control P. aeruginosa contamination in the food industry.
Currently, the primary most effective method for eliminating pathogenic and spoilage-causing bacteria in milk is heat treatment. It is used in production of almost all dairy products [13]. Milk pasteurization is achieved by heating it to a temperature less than 100 °C for a time sufficient to eliminate pathogenic bacteria that may present. There are various pasteurization regimes and the choice depends on the type of product. For example, pasteurization at 72 °C ±2 is widely used for cheesemaking. Low-temperature pasteurization was previously assumed sufficient to prevent transmission of P. aeruginosa through dairy products. This assumption was based on the premise that non-spore-forming pathogenic bacteria should be eliminated at temperatures greater than 60 °C due to the denaturation of their essential cellular proteins [14,15]. However, recent studies have reported presence of heat-resistant bacteria that can survive standard pasteurization regimes [16]. Studies on the heat resistance of pathogens such as Listeria monocytogenes have shown that short-time pasteurization of milk at 71.7 °C for 15 s is insufficient [17]. Cells may be able to survive even after exposure to 72 °C for up to 4 min [17]. Frequency of P. aeruginosa detection in milk after pasteurization can relatively be high [18]. In the Czech Republic, a study of pasteurized milk samples for a year detected that 4% of the samples included P. aeruginosa. The effectiveness of pasteurization depends on the initial microbiological contamination of milk and the strain composition of the microbial contaminants [19]. The heat resistance of microorganisms varies widely and depends on factors that affect them before, during and after heat treatment [20].
The mechanisms enabling microorganisms to adapt to high temperatures are not fully understood. Scientific evidence suggests that the cellular response to heat shock involves synthesis of various proteins known as heat shock proteins [21]. Heat shock disrupts folding of existing and newly synthesized proteins. Intermolecular interactions within misfolded proteins lead to the formation of aggregates and their number increases proportionally with the intensity of heat treatment [22]. Thus, loss of essential cellular proteins compromises cell viability and may ultimately result in cell death. To modify these processes, bacteria produce cellular disaggregases that solubilize protein aggregates, promoting their refolding and recovery; thereby, enhancing heat resistance. A stronger adaptive response at higher temperatures is associated with the accumulation of larger qiantities and/or induction of a distinct subset of heat shock proteins. Other heat protective mechanisms may exist [20].
Most studies have focused on clinical isolates. Analysis of wild-type isolates may reveal previously unknown characteristics of P. aeruginosa, including enhanced heat resistance acquired under exposure to external stressors [23, 24]. This study included a reference strain of P. aeruginosa and a wild-type strain isolated from a dairy farm. The aim of this study and its novelty included investigation of the ability of P. aeruginosa cells to survive under pasteurization regimes used in cheese making, as well as their ability for reactivation during refrigerated storage. The study assessed potential use of industrial LAB strains as protective cultures within starter compositions, serving as additional antimicrobial barriers against P. aeruginosa.
Materials and Methods
2.1. Pseudomonas aeruginosa strains used in the study
The reference strain of P. aeruginosa ATCC 25668 was provided by the State Collection of Pathogenic Microorganisms and Cell Cultures "GCPM-Obolensk", Russia. The wild-type strain of P. aeruginosa 47 was isolated during microbiological monitoring of equipment surfaces at a private dairy farm. Identification was carried out according to GOST ISO 16266-2018, "Water Quality. Detection and Enumeration of P. aeruginosa. Membrane Filtration Method." Nutrient broth and dehydrated media (nutrient broth, dehydrated nutrient agar) from NPC LLC "Biocompas-S," as well as pseudomonas agar from Himedia, India, were used to achieve 24-h cultures and to inoculate. Compared to the reference strain of P. aeruginosa ATCC 25668, the isolate of P. aeruginosa 47 produced further pigments on nutrient and pseudomonas agars. This trait is generally addressed as an indicator of higher virulence [6]. A bacterial suspension was prepared from a 24-h culture in physiological saline, adjusted to a cell count of approximately 1.5 × 10⁸ CFU ml-1, corresponding to an optical density of 0.5 of the McFarland. Experiments were carried out using sterile milk of the "Standard" brand (Complimilk, Belarus). Strains from the probiotic and lactic acid bacterial collection of the All-Russian Dairy Research Institute (FGANU “VNIIMI”) were used to assess antagonistic activity of the industrially promising LAB against P. aeruginosa 25668 and P. aeruginosa 47.
2.2. Lactic acid bacterial strains used in the study
The LAB strains were stored in lyophilized form at -50 °C ±1. Lyophilized cultures were reconstituted in sterile skim milk by incubation for 16 h. Strains were incubated at 37 °C ±1 (L. helveticus, Lacticaseibacillus rhamnosus and Streptococcus thermophilus) or at 30 °C ±1 (L. lactis).
2.3. Study of antagonistic activity using co-cultivation method
In general, P. aeruginosa and LAB cultures were prepared as described in Sections 2.1 and 2.2. Antimicrobial activity was assessed by co-cultivation of LAB with P. aeruginosa strains. Briefly, 1 ml of inocula from the selected LAB strains and P. aeruginosa strains was added to 20 ml of sterile skim milk and incubated at 37 °C ±1 for 48 h. Milk samples inoculated with 1 ml of the P. aeruginosa strain served as controls. After incubation, serial 10-fold dilutions were prepared from each sample and P. aeruginosa counts were reported by plating on cetrimide agar (Himedia, India), a selective medium for pseudomonads. Plates were incubated at 37 °C ±1 for 24 h; then, colony-forming units (CFU) were counted. The growth inhibition of P. aeruginosa was calculated regarding control sample (monoculture), which was reported 100%.
2.4 Study of the effectiveness of heat treatment against Pseudomonas aeruginosa
To assess effectiveness of the heat treatment against P. aeruginosa, sterile milk in test tubes was inoculated with a bacterial suspension of the P. aeruginosa strains to reach a final cell count of approximately n × 10⁵ CFU ml-1. The cell count was verified by plating aliquots from serial dilutions of the sample. The contaminated milk was processed using UKT-150 heat resistance control device, which simulated a pasteurizer, providing 99.99% efficiency equivalent to standard pasteurization (Figure 1) [25].
After system activation, a rack with sealed, heat-resistant test tubes containing contaminated milk was transferred into a silicone solution. The oscillatory movements of the device contributed to the uniform heating of samples within the test tubes. The pasteurization regime (72 °C ±2), with a typical exposure time of 20 s for cheesemaking, was used with various exposure times of 40 s, 60 s, 5 min, 10 min and 15 min. Temperature control was carried out using control test tube with milk and thermometer to monitor the time needed to reach the target temperature. Upon completion of heat treatment, test tubes were rapidly cooled down using ice bath. The heat-treated and cooled contaminated milk was plated onto solid nutrient media and incubated at 37 °C ±1 for 24-48 h. Results were recored based on colony counts. To simulate shelf-life and storage conditions of the pasteurized milk, the experimental test tubes were stored in a refrigerator at 6 °C ±2 for up to 14 d. To assess the potential recovery of heat-stressed P. aeruginosa cells, samples were removed from the experimental test tubes after 7 and 14 d of storage, followed by plating and colony enumeration. Microscopic analysis was carried out to detect potential phenotypic changes in the cells.
2.5. Statistics
The MS Office Excel 2016 was used for data analysis and graph construction. Experiments were carried out in three independent replicates and results were expressed as mean ±SD (standard deviation). Differences were considered statistically significant at p < 0.05 using two-tailed Student’s t-test followed by Tukey’s HSD test (for multiple group comparisons). The study was carried out using equipment from the Collaborative Center of the All-Russian Dairy Research Institute (CKP VNIMI).
Results and Discussion
3.1. Study of the antagonistic activity of LAB against P. aeruginosa using co-cultivation method
Results of the study on the antagonistic activity of industrial LAB strains are present in Table 1. As shown in Table 1, L. helveticus Bbn4 and L. helveticus NK1 were most effective in growth inhibition of the reference strain of P. aeruginosa and the wild-type isolate. After 48 h of co-cultivation, growth inhibition reached approximately 70%. Streptococcus thermophilus 16t effectively inhibited growth of the reference strain of P. aeruginosa 25668 but showed a limited activity against the wild-type isolate (growth inhibition of 24%). The antagonistic activity of L. rhamnosus F was less than that of L. helveticus strains, with growth inhibition reaching approximately 50%. In contrast, Lactococcus strains showed negligible or no inhibitory effects. These results were similar to those of previous studies reporting strong antagonistic activity of L. helveticus strains, including that against P. aeruginosa [26, 27]. Inhibitory effect of L. helveticus Bbn4 might be attributed to the synthesis of antimicrobial compounds, primarily organic acids, which effectively inhibit growth of pathogenic bacteria [28, 29]. It is known that L. helveticus strains are active acid producers [29, 30]. Additionally, L. helveticus strains can synthesize bacteriocins that inhibit the growth of pathogens, including P. aeruginosa [26]. The potential presence of antimicrobial compound-encoding genes in metagenome of L. helveticus Bbn4 warrants further investigations. The strains of L. helveticus Bbn4 and L. helveticus NK1 show potentials for use in production of fermented dairy products to decrease risks of P. aeruginosa contamination. However, further studies into the mechanisms of their antimicrobial action are necessary.
3.2. Study on the effectiveness of milk pasteurization at 72 °C ±2
Results of the study on the effectiveness of milk pasteurization at 72 °C ±2 with exposure times of 20, 40 and 60 s for samples contaminated with P. aeruginosa 47 are present in Figures 2a,b.
As shown in Figure 2a, viable cells forming colonies on nutrient agar were detected in all samples pasteurized at 72 °C ±2, regardless of exposure time. The shortest exposure time (20 s) was the least effective for P. aeruginosa 47 elimination, as the cell count decreased by only one order of magnitude from 1.8 × 10⁵ to 1.9 × 10⁴ CFU ml-1. After 7 d of refrigerated storage (Figure 2b), cell counts increased to 3.5 × 10⁶ CFU ml-1, with a slight increase to 5.4 × 10⁶ CFU ml-1 on Day 14. The cell count of P. aeruginosa 47 decreased by four orders of magnitude, reaching 1.8 × 10¹ CFU ml-1 following exposure at 72 °C ±2 for 40 and 60 s. After 7 d of refrigerated storage, the cell counts increased to 3.0 × 106 and 5.7 × 105 CFU ml-1 in samples pasteurized for 40 and 60 s, respectively. Thus, none of the tested regimes resulted in the complete elimination of P. aeruginosa 47, with the 20-s pasteurization as the least effective. An increase in viable cell counts was observed in all samples after 7 d of refrigerated storage, exceeding the initial values, followed by a gradual stabilization on Day 14. Results of the study on the effectiveness of milk pasteurization at 72 °C ±2 with exposure times of 20, 40 and 60 s for samples contaminated with P. aeruginosa 25668 are present in Figures 3a,b.
For the reference strain of P. aeruginosa 25668, viable cells were detected immediately after all the pasteurization regimes were used. After 20 s of exposure, the cell count decreased by two orders of magnitude, from 1.9 × 105 to 5.3 × 103 CFU ml-1. A 40-s pasteurization decreased the cell count by four orders of magnitude to 1.8 × 10¹ CFU ml-1. After 60 s, only single colonies were observed (Figure 3a). On Day 7 of refrigerated storage (Figure 3b), cell counts increased to 3.3 × 106, 1.8 × 105 and 7.7 × 104 CFU ml-1 for 20, 40 and 60-s pasteurization regimes, respectively. A slight decrease was observed on Day 14, with cell counts reaching 1.7 × 10⁶, 3.4 × 10⁵ and 1.6 × 10⁴ CFU ml-1 for similar pasteurization regimes. On Day 14, visible spoilage was seen in the test tubes, characterized by color change and formation of a surface film (Figure 4).
To establish the time needed for the effective elimination of P. aeruginosa during pasteurization at 72 °C ±2, contaminated milk samples were heat-treated for 5, 10 and 15 min. A 15-min pasteurization at 72 °C ±2 was sufficiently effective against the reference and wild-type strains, with initial titers of 9.5 × 10⁵ and 9.8 × 10⁵CFU ml-1, respectively. No bacterial growth was detected under this pasteurization regime through 14-day refrigerated storage time. Pasteurization regime of 72 °C ±2 with exposure times of 5 and 10 min did not guarantee the absence of forms capable of reactivation, as growth of individual colonies was seen.
This study verified high antagonistic activity of L. helveticus Bbn4 and L. helveticus NK1 against the reference strain and the wild-type isolate of P. aeruginosa from a dairy farm. Their inhibitory effects might be attributed to various metabolic products that effectively inhibited growth of the pathogen. It is known that P. aeruginosa produces siderophores, low-molecular-weight (LMW) compounds that chelate and solubilize iron [6]. Lactobacilli, unlike most microorganisms, are iron-independent, making them unaffected by siderophores produced by P. aeruginosa. Moreover, organic acids, the primary metabolites from LAB, possess chelating characteristics and can bind iron from the substrate; thereby, limiting its availability for P. aeruginosa growth. Antimicrobial activity of Lactobacillus spp. might be associated to the induction of enzymes that degraded peptidoglycan layer of the cell walls of Gram-negative bacteria. Thus, antimicrobial activity of the industrially linked LAB strains of L. helveticus NK1 and L. helveticus Bbn4 against P. aeruginosa verified their functional characteristics. These strains show promise for use in development of functional foods, especially in dairy formulations.
The study demonstrated that short-time pasteurization (20–60 s at 72 °C ±2) was insufficient to eliminate P. aeruginosa, as viable cells with an initial titer of approximately 105 CFU ml-1 were detected immediately after heat treatment. According to Sviridenko et al., the P. aeruginosa culture, known for its psychrotrophic characteristics, showed the ability of individual surviving cells to multiply after heat treatment at 72 °C ±2, with an initial contamination level of approximately 106 CFU ml-1 and to preserve ability to reactivate, similar to the present findings [19].
Pasteurization for 5 and 10 min at the highlighted temperature did not guarantee complete inactivation of P. aeruginosa, as cells preserved ability to recover and resume growth during storage. On Day 14 of storage, no growth was observed in the sample that was heat-treated for 15 min. Based on the scientific data on P. aeruginosa survival, heating milk for at 63.5 °C for 30 min with an initial contamination level of 5.8 × 10⁵ CFU ml-1 resulted in a 4-log dcrease in viable cell count. At the same time, a significant propo
Design of Statistically-Based Bioprocesses for the Enhanced Production of Moderate Thermophilic Alkaline Α-Amylase from Bacillus Subtilis Isolated From Guilan Rice Mill Wastes
Full text linkBackground and Objective: The demand for cost-effective and thermostable α-amylases for industrial applications has driven the research to discover new microbial sources. This research aimed to isolate and characterize α-amylase-producing bacteria from rice milling wastes and employ Response Surface Methodology (RSM) to improve enzyme production.
Material and Methods: Bacterial samples were collected from different agro-industrial wastes and primarily screened using Lugol's iodine method. Secondary isolation was performed by α-amylase activity assessment using the DNS assay. Enzyme production was optimized by RSM, with the temperature, pH, and starch concentration as key variables. In addition, the effect of different pH and temperatures was assessed on the α-amylase activity. 16S rRNA sequencing and phylogenetic analysis were used for bacterial identification.
Results and Conclusion: The isolate was identified as Bacillus subtilis NllST B 627. Optimum conditions for maximum enzyme production (0.21 Umg-1) were starch 5.5 gL-1, temperature 40°C, and pH 7. Temperature was the most significant factor influencing enzyme production, whereas pH and starch concentration showed weaker effects but potentially relevant interactions. The overall model based on response surface curves was statistically significant, indicating that the combination of independent variables significantly influences enzyme production. The enzyme exhibited maximum activity at pH 7, while the lowest activity was observed at pH 5. Also, the enzyme's optimal activity occurred at 40°C, while the lowest catalysis was detected at 60°C. The identified strain exhibits promising properties for application in starch hydrolysis and other industrial purposes. This highlights the potential of rice mill wastes as a sustainable and low-cost resource for microbial enzyme production, and this study is the first to explore Guilan rice mill wastes for α-amylase production.
Keywords: α-Αmylase production, Detergent industry, Enzyme optimization, Isolation and identification, RSM Method
Introduction
Producing enzymes on an industrial scale is an expensive effort. A potential approach to overcome this issue involves identifying enzyme-producing microorganisms from agricultural/industrial waste, a method that both lowers production costs and promotes waste beneficial reuse and environmental sustainability [1,2]. So, rice milling wastes are an abundant by-product found in areas where rice is cultivated and constitute a largely available material full of microbial diversity. The moist and starch-rich remnants produced during rice processing create a perfect environment for the growth of α-amylase-producing bacteria, which have potential uses in various industries [3].
α-Αmylases (EC 3.2.1.1) are extensively utilized in the food, fermentation, detergent, pharmaceutical industries, ethanol production [4], and as an antibiofilm agent [5]. These enzymes cleave α-1,4-glycosidic bonds in starch, facilitating the production of various products such as dextrose, glucose, and starch syrups [6]. Although many thermostable α-amylase-producing strains, such as Bacillus subtilis, Bacillus amyloliquefaciens, Bacillus stearothermo-philus, Bacillus licheniformis, Bacillus polymyxa, and Bacillus coagulans have been identified so far [7], discovering new strains from novel sources could result in enzymes operating in extreme conditions. This leads to increasing efficiency and reducing costs on an industrial scale [8].
Globally, α-amylases account for approximately 23-33% of the enzymatic market share [9]. However, the cost of industrial-scale enzyme production remains significantly high. It is estimated that microbial culture media formulation alone accounts for about 30-40% of the final enzyme production costs [10]. Consequently, there is a persistent need to reduce these costs by developing inexpensive culture media formulations, leading to significant efforts to identify cost-effective alternatives for industrial enzyme production.
Various studies have been conducted on the identification and isolation of α-amylase-producing strains from different sources, including agricultural soils such as potato fields [11], industrial soils such as brick kiln [12], hot spring [13], deserts [14], different wastes [15,16], etc. For example, Niyomukiza et al. isolated amylolytic bacteria from starchy food wastes (maize meal and potato peel wastes), and 16S rRNA sequencing verified them as Bacillus subtilis. The optimum temperature for the enzyme was 60 °C, and the pH was 9 [17]. Tripathi et al. used Bacillus polymyxa NCIM 2539 to produce amylase using agro-industrial byproducts. Among various substrates tested, orange peel yielded the highest enzyme activity. Supplementation of the medium significantly enhanced amylase production, with optimal levels obtained at specific concentrations of orange peel, cysteine, and thiamine [18].
Many industries produce agricultural waste, one of which is the rice factory industry located in the North of Iran. They produce waste with high organic matter content, one of which is starch. The starch contained in the liquid waste can be broken down by bacteria-producing amylase enzymes into simpler molecules. In this research, there has been scarce focus on the microbial strains that exist within the starch-rich waste produced by rice mills, especially in Guilan province (Iran), where rice farming is a significant agricultural profession. This research aimed to isolate and molecularly characterize α-amylase-producing bacteria from rice milling waste and employ Response Surface Methodology (RSM) to improve enzyme production processes. Optimization of three parameters (incubation time, starch concentration, and incubation temperature) was investigated. Using an inexpensive, starch-rich waste substrate and statistical modeling to enhance enzyme output, this study could contribute to cost-effective enzyme development and play a significant role in sustainable industrial waste management.
Materials and Methods
2.1. Primary isolation and screening of α-amylase-producing bacteria
Bacterial samples were collected from rice mill wastes, chip manufacturing wastes, bakery dusts, textile wastewater, and other industrial waste, because these sources contain a significant percentage of starch and are likely to contain more degrading bacteria. For isolating spore-forming bacteria, particularly Bacillus species, serial dilutions were prepared. Heat shock treatment was applied for 10 minutes at 80°C in a water bath. Subsequently, bacteria were isolated using the streak plate method on Nutrient Agar medium [19]. The isolated bacteria were then tested for starch hydrolysis by inoculating them onto the starch agar plates containing 2% starch and incubating for 24 hours. After incubation, the ability to hydrolyze starch was assessed by flooding the plates with Lugol's iodine solution. Bacteria showing a clear zone around colonies, indicative of starch hydrolysis, were selected for further analysis.
2.2. Secondary screening by α-amylase activity assessment
In the secondary screening phase, bacterial isolates that produced larger clear zones were selected for further enzymatic activity assessment. The α-amylase activity was measured in 250 mL flasks containing 50 mL of cultivation medium with the following composition (gL-1, Sigma-Aldrich, USA): starch 10%, peptone 5%, yeast extract 2.05%, NaCl 1.5 grL-1, KH₂PO₄ 0.5 gr/L, MgSO₄ 0.5 grL-1, CaCl₂ 0.1 grL-1, and glycerol 15% (vv-1). The flasks were incubated on a shaker at 120 rpm and 37°C for 48 hours. The initial pH of the medium was adjusted to 7.0. The inoculated bacterial strains were transferred into sterile pre-prepared medium. After incubation, samples were centrifuged at 10,000 rpm for 20 minutes, and the supernatant was collected for enzyme activity measurement.
2.3. Enzyme activity determination using the DNS method
The dinitrosalicylic Acid (DNS) method was employed to quantify α-amylase activity [20]. DNS is an alkaline reagent that reacts with reducing sugars, causing a color change from yellow to reddish-brown. A reaction mixture comprising 0.5 mL of crude enzyme and 0.5 mL of 1% starch solution was prepared. The mixture was incubated at 37°C for 30 minutes. The reaction was stopped by adding 1 mL of DNS reagent, followed by boiling for 5 minutes. Glucose concentration was measured using a spectrophoto-meter at 540 nm. A glucose standard curve was generated by plotting absorbance at 540 nm against the amounts of glucose released to define the concentration of glucose formed in each solution. One unit of enzyme activity was defined as the amount of enzyme required to liberate 1 μmol of reducing sugars per minute.
2.4. Response surface methodology for production optimization
The three strains exhibiting the highest production levels were selected from the screened bacterial isolates, and strain 2 was employed for statistical production optimization. RSM is a technique used to evaluate the influence of input parameters on responses [21]. Central Composite Design (CCD) provides an efficient approach to predict the interaction effects of influential factors on the process. In this study, optimization aimed to maximize enzyme production, using the Statistical Design-Expert 7.0 software, followed by CCD analysis [22].
In this design, the quantitative impact of the most effective variables, including starch concentration, temperature, and pH, were examined (Table 1). All experiments were conducted in triplicate. Positive and negative control strains were processed with every batch.
2.5. Evaluation of pH on the α-amylase activity
The bacteria were cultivated in production media and incubated in a shaker incubator for 24 hours, followed by centrifugation. In separate tubes, 0.5 mL of the supernatant was mixed with 0.5 mL of sodium carbonate buffers at 50 mM with pH values of 9 and 10, trisodium citrate buffers at 50 mM with pH 5 and 6, disodium hydrogen phosphate buffers at 50 mM with pH 7 and 8, and starch. The mixtures were incubated at 37°C for 30 minutes. Subsequently, 0.5 mL of DNS reagent was added, and absorbance was measured at 540 nm using a spectrophotometer.
2.6. Evaluation of temperature on the α-amylase activity
To evaluate the effect of temperature, bacteria were cultured in the production medium, centrifuged, and then 0.5 mL of the supernatant was mixed with 0.5 mL of 1% starch solution in a test tube. The test tubes were then placed in a water bath or thermostatic water bath set at temperatures of 40°, 50°, 60°, and 70°C for 30 minutes. The enzyme activity under each condition was subsequently assessed.
Identification of α-amylase-producing bacteria
α-Amylase-producing bacteria were identified through phenotypic and macroscopic characterization, followed by microscopic examination using Gram staining and sporulation tests. For definitive identification, 16S rRNA gene analysis was conducted. Bacterial DNA was extracted using a boiling method. Briefly, the bacterial cultures were centrifuged at 2000 g for 20 minutes to obtain a cell pellet. The pellet was resuspended in sterile distilled water, and the microtubes were first placed in a freezer and then boiled in a water bath to lyse the bacterial cells and release the DNA. After centrifugation at 7000 rpm for 10 minutes, the supernatant containing the DNA was collected and precipitated with cold ethanol. The DNA pellet was air-dried and dissolved in a small volume of distilled water, and its concentration and purity were measured using a NanoDrop spectrophotometer. PCR amplification was performed with universal bacterial primers 27F (5′- AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-CGGTTACCTTGTTACGACTT-3′) [23]. The PCR protocol consisted of 30 cycles involving: denaturation at 95°C for 1 minute, annealing at 56°C for 1 minute, extension at 72°C for 1 minute, and a final elongation step at 72°C for 7 minutes. The PCR products were analyzed by gel electrophoresis, and successful amplicons were sent to Pishgaman Co. (Iran, Tehran) for sequencing. The obtained sequences were compared to those in the NCBI database using the BLAST tool (https://blast.ncbi.nlm.nih-gov/Blast.cgi) [24]. The closest related species were identified based on the similarities in the 16S rRNA gene sequences of the α-amylase. For phylogenetic analysis, the sequences were searched and aligned in the NCBI GenBank database using multiple sequence alignment with the ClustalW algorithm (https://www.genome.jp/tools-bin/clustalw). A phylogeny-etic tree was constructed using the Molecular Evolution Genetic Analysis (MEGA 4) software with the Neighbor-Joining algorithm and 1000 bootstrap replicates [25].
Statistical analysis
Data were analyzed using the Design Expert 7.0 software. All the experiments were conducted in triplicate. Results are presented as mean values ± standard error (SE), with a significance threshold of P < 0.05.
Results and Discussion
3.1. Isolation and screening of α-amylase-producing bacteria
The Lugol's iodine test was performed for primary isolation of α-amylase-producing bacteria from different waste samples. Results demonstrated that three bacterial isolates, including strain #1 from the bakery's waste, strain #2 from the rice mill's waste, and strain #3 from the agricultural waste, produced extracellular α-amylase, positively (Figure 1 A to C), as evidenced by the presence of a clear zone surrounding the colonies after washing with iodine solution. The formation of a clear zone indicates that the starch present in the starch agar medium was hydrolyzed into monomers by α-amylase enzymes produced by the bacteria. In contrast, a dark blue-black zone represents an iodine-starch complex. The isolates showing clear zones were considered positive for α-amylase production (Figure 1 A to C). In contrast, the absence of a clear zone indicated a lack of extracellular α-amylase activity (Figure 1D).
3.2. Assessment of bacterial α-amylase activity
α-Amylase activity of the three selected samples was assessed for further screening of bacteria. Results revealed that the α-amylase activity of strain #2, from the rice mill waste, was significantly higher than that of others (P < 0.05) (Figure 2).
3.3. Experimental design
In this design, based on RSM and using the CCD method, 15 experimental runs were arranged, as summarized in Table 2. In this research, based on a multiple regression analysis of experimental data, the final model was presented as follows (Equation 1):
α-Amylase activity = 0.20 - 0.025A - 0.055B +0.020C + 0.030AB - 0.025AC - -5.000E-003BC - 0.046A2 - 0.076B2 - 0.031C2
Where A, B, and C are pH, starch concentration, and temperature, respectively.
The analysis of variance (ANOVA) is described in Tables 3 and 4. The larger the F-value, the greater the variation between sample means relative to the variation within the samples. The p-value is the probability of obtaining an F-ratio as large or larger than the one observed, assuming that there is no difference between the group averages. The model F value of 51.62 implies that the model is significant. Values of Prob > F less than 0.05 imply that the model terms are relevant at the 95% confidence level. The model showed a high determination coefficient (R2 = 0.9894), indicating a strong correlation between the experimental and predicted values.
Figure 3 shows the normal plots of residual (difference between the observed and predicted value). A low residual value is necessary for a good mathematical model fitted on observed data. The predicted responses and observed responses are shown in Figure 4 and the data points located close to the diagonal line, suggesting a satisfactory correlation.
To study the interaction among the different independent variables and their corresponding effect on the response, contour plots and 3-D plots were drawn. A contour plot is a graphical representation of a three-dimensional response surface based on two independent variables, helping to illustrate their main and interaction effects on the response. Figure 5 shows the amylase response and correlation between variables in plots. Optimum conditions for maximum enzyme production (0.21 Umg-1) were starch 5.5 gL-1, temperature 40°C, and pH 7.
The bell-shaped surface indicates that extreme acidic or alkaline conditions and temperatures above 45 °C negatively influence production, likely due to reduced microbial growth or enzyme instability. The nearly symmetrical surface curvature also suggests that the process remains relatively stable near the optimal point.
In Fig. 5b, the interaction between temperature and starch concentration shows that enzyme yield rose with increasing starch up to about 5–6 gL-1, but declined at higher levels, possibly because of substrate inhibition or catabolized repression. Maximum activity was achieved at a moderate starch concentration and 40 °C. Together, these plots confirm that temperature is the dominant factor, while pH and substrate concentration contribute secondary but interactive effects, defining a narrow yet stable region for optimal amylase production. According to the results of 15 experiments, the lowest activity was observed at high temperature, low starch concentration, and under alkaline pH. This suggests excessive heat or inappropriate substrate levels may negatively impact enzyme production. These data underline that interactions between parameters are critical in optimizing enzyme yield, and single-factor optimization may be insufficient. Temperature was identified as the most significant factor influencing enzyme production, whereas pH and starch concentration showed weaker individual effects but potentially relevant interactions. When compared with similar studies, some differences in optimal conditions were observed. In the work performed by Adetiloye et al., Bacillus cereus from a warm spring demonstrated a slightly higher optimal temperature at 45°C and an RSM-predicted optimal pH of 7, although OFAT analysis also indicated potent activity at pH 8 [23]. In the study performed by Sharif et al., Bacillus licheniformis exhibited an even higher optimal temperature at 55°C and a more alkaline preference at pH 9. This suggests that variations in enzyme thermostability and pH tolerance may depend on the species [26].
Regarding substrate concentration, both the current study and the work by Adetiloye et al. [23] identified 5% starch as optimal, whereas Sharif et al. reported an optimum of 1% [26], possibly due to substrate inhibition effects at higher concentrations. The differences in assay methods (µmolmin-1 vs. UmL-1) and strain-specific enzyme kinetics make direct comparison of activity values difficult; however, both Adetiloye et al. and Sharif et al. reported higher numerical activities than the current study, which could be related to strain genetics, cultivation conditions, or methodological variations in activity measurement. The emphasis is on identifying robust operating conditions within the region of interest that maximize production while remaining practically feasible in downstream processing.
3.4. The effect of pH on the α-amylase activity
As illustrated in Figure 6A, the enzyme exhibited maximum activity at pH 8, while the lowest activity was observed at pH 5 (P < 0.05). It can be concluded that the enzyme operates most efficiently under slightly alkaline conditions, which may be due to the stabilization of the enzyme’s active site and overall tertiary structure at this pH, which enhances its catalytic efficiency. Conversely, the lowest enzymatic activity at pH 5 suggests that acidic conditions lead to reduced enzyme performance, likely as a result of denaturation or alteration in the ionization state of critical amino acids involved in substrate binding and catalysis [27]. A moderate reduction in the activity at pH 9 indicates a narrow optimal pH range for the enzyme. These findings emphasize the importance of pH optimization in industrial applications of this enzyme. These findings are consistent with previously reported characteristics of bacterial α-amylases, which generally prefer neutral to slightly alkaline environments for optimal activity [14,22,28,29].
3.5. The effect of temperature on the α-amylase activity
The impact of temperature on the catalytic activity of α-amylase from Isolate 2 is presented in Figure 6B. The enzyme demonstrated optimal activity at 50°C, while the lowest enzyme activity was detected at 60°C (P < 0.05). The enzyme may retain its functional conformation at moderately high temperatures. This property makes the enzyme potentially suitable for industrial processes that require elevated temperatures, such as starch liquefaction and food processing [30]. Reduction of catalytic activity at 60°C may be due to thermal denaturation or irreversible structural changes, leading to loss of function [27]. Overall, the results confirm that temperature plays a critical role in the amylolysis function, and identifying the optimum point is crucial for maximizing enzymatic yield.
Various studies have reported varying optimal temperatures and pHs for the α-amylase activity of Bacillus species isolated from different sources. Some of them are as follows: The highest α-amylase activity of the purified Bacillus licheniformis strain LB04 isolated from Espinazo hot springs in Mexico was at pH 3 and 65 ºC [31]. Bacillus licheniformis HULUB1 and Bacillus subtilis SUNGB2 isolated from Malaysian hot spring showed the highest α-amylase activity at 65° C and pH 6.0 [13]. Bacillus cereus and Bacillus licheniformis isolated from the potato fields demonstrated α-amylase function at pH 8.0 and temperatures of 45°C and 65°C, respectively [11]. Maximum α-amylase activity of the three
Probiotics as a Therapeutic Strategy for Inflammatory Diseases: A Review on Mechanistic, Diagnostic, and Laboratory Perspectives: Probiotics as a Therapeutic Strategy for Inflammatory Diseases: A Review on Mechanistic, Diagnostic, and Laboratory Perspectives
No full textIntroduction/Background: Inflammatory diseases are among the most common chronic health conditions and are frequently linked to microbial dysbiosis and immune system dysfunction. Although corticosteroids remain a standard therapeutic option, their long-term use is associated with serious adverse effects, highlighting the need for safer alternatives. Probiotics, live beneficial microorganisms, have emerged as promising therapeutic agents due to their anti-inflammatory, immunomodulatory, antimicrobial, and antioxidant properties. They can restore gut microbial balance, enhance epithelial barrier function, and modulate host immune responses through pathways involving cytokine regulation and short-chain fatty acid production.
Purpose/Objectives: This review provides a comprehensive overview of the molecular mechanisms through which probiotics mitigate inflammation, with particular attention to their role in modulating mucosal immunity, suppressing pro-inflammatory signaling, and enhancing intestinal integrity.
Findings: Clinical studies support the use of probiotics in managing a variety of inflammation-related conditions, including inflammatory bowel disease (IBD), respiratory tract infections, allergic responses, metabolic disorders, and neuroinflammation. However, strain specificity, formulation challenges, and host-related factors continue to influence clinical outcomes.
Conclusion: Overall, probiotics represent a promising, biologically based approach for managing inflammation and improving patient outcomes across a broad spectrum of chronic diseases. In addition, advanced diagnostic and laboratory techniques play a crucial role in elucidating the molecular and functional impacts of probiotics, enabling precise evaluation of their efficacy, strain-specific effects, and mechanisms of action in both experimental and clinical settings. Future research should focus on identifying the most effective probiotic strains, understanding host–microbe interactions, and conducting long-term studies to establish safety and efficacy.
*Corresponding Author: Fatemeh Ahangari; Email: [email protected], [email protected]; ORCID iD: https://orcid.org/0000-0001-7253-6603
Please cite this article as: Haghighi M, Haghighi F, Hajiloo Z, Ahangari F. Probiotics as a Therapeutic Strategy for Inflammatory Diseases: A Review on Mechanistic, Diagnostic, and Laboratory Perspectives. Arch Med Lab Sci. 2025;11:1-15 (e3). https://doi.org/10.22037/amls.v9.4879