Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of -spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS
Citation: Wang-Ren Qiu, Qian-Kun Wang, Meng-Yue Guan, Jian-Hua Jia, Xuan Xiao. Predicting S-nitrosylation proteins and sites by fusing multiple features[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9132-9147. doi: 10.3934/mbe.2021450
| [1] | Cíntia Sorane Good Kitzberger, David Pot, Pierre Marraccini, Luiz Filipe Protasio Pereira, Maria Brígida dos Santos Scholz . Flavor precursors and sensory attributes of coffee submitted to different post-harvest processing. AIMS Agriculture and Food, 2020, 5(4): 700-714. doi: 10.3934/agrfood.2020.4.700 |
| [2] | Ioannis K. Karabagias, Chara Papastephanou, Vassilios K. Karabagias . Geographical differentiation of Cypriot multifloral honeys through specific volatile compounds and the use of DFA. AIMS Agriculture and Food, 2019, 4(1): 149-162. doi: 10.3934/agrfood.2019.1.149 |
| [3] | Cíntia Sorane Good Kitzberger, Maria Brígida dos Santos Scholz, João Batista Gonçalves Dias da Silva, Marta de Toledo Benassi, Luiz Filipe Protasio Pereira . Free choice profiling sensory analysis to discriminate coffees. AIMS Agriculture and Food, 2016, 1(4): 455-469. doi: 10.3934/agrfood.2016.4.455 |
| [4] | Sapta Raharja, Yogi Purna Rahardjo, Samsudin, Khaswar Syamsu . Aroma precursor enhancing in dried cocoa beans fermentation using enzyme and heat addition. AIMS Agriculture and Food, 2023, 8(2): 674-686. doi: 10.3934/agrfood.2023037 |
| [5] | Dody Dwi Handoko, Anisa Maharani Kaseh, Laras Cempaka, Wahyudi David, Bram Kusbiantoro, Afifah Zahra Agista, Yusuke Ohsaki, Hitoshi Shirakawa, Ardiansyah . Effects of household-scale cooking on volatile compounds, sensory profile, and hypotensive effect of Kenikir (Cosmos caudatus). AIMS Agriculture and Food, 2023, 8(1): 198-213. doi: 10.3934/agrfood.2023011 |
| [6] | Ardiansyah, Fauziyyah Ariffa, Rizki Maryam Astuti, Wahyudi David, Dody Dwi Handoko, Slamet Budijanto, Hitoshi Shirakawa . Non-volatile compounds and blood pressure-lowering activity of Inpari 30 and Cempo Ireng fermented and non-fermented rice bran. AIMS Agriculture and Food, 2021, 6(1): 337-359. doi: 10.3934/agrfood.2021021 |
| [7] | Siska Septiana, Nancy Dewi Yuliana, Boy Muchlis Bachtiar, Christofora Hanny Wijaya . Aroma-active compounds of Melaleuca cajuputi essential oil, a potent flavor on Cajuputs Candy. AIMS Agriculture and Food, 2020, 5(2): 292-306. doi: 10.3934/agrfood.2020.2.292 |
| [8] | Esteban Largo-Avila, Carlos Hernán Suarez-Rodríguez, Jorge Latorre Montero, Madison Strong, Osorio-Arias Juan . The influence of hot-air mechanical drying on the sensory quality of specialty Colombian coffee. AIMS Agriculture and Food, 2023, 8(3): 789-803. doi: 10.3934/agrfood.2023042 |
| [9] | Eleni C. Pappa, Efthymia Kondyli, Anna-Maria Vlachou, Athanasia Kakouri, Evdokia Malamou, John Samelis . Semi industrial production of Tsalafouti dairy product. AIMS Agriculture and Food, 2022, 7(2): 444-460. doi: 10.3934/agrfood.2022028 |
| [10] | Cíntia Sorane Good Kitzberger, Maria Brígida dos Santos Scholz, Luiz Filipe Protasio Pereira, João Batista Gonçalves Dias da Silva, Marta de Toledo Benassi . Profile of the diterpenes, lipid and protein content of different coffee cultivars of three consecutive harvests. AIMS Agriculture and Food, 2016, 1(3): 254-264. doi: 10.3934/agrfood.2016.3.254 |
Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of -spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS
Abbreviations: LAB: lactic acid bacteria; bac +: bacteriocinogenic; PGI: Protected Geographical Indication; S2BLAF: Lb. plantarum ST153Ch fresh; S2BLA: Lb. plantarum ST153Ch lyophilized; S2BC non-inoculated; S2BLAF + LM: Lb. plantarum ST153Ch fresh plus cocktail of L. monocytogenes; S2BLA + LM: Lb. plantarum ST153Ch lyophilized plus cocktail of L. monocytogenes; S2BC + LM: cocktail of L. monocytogenes
"Alheira" is a traditional fermented smoked meat sausage, typical of the North of Portugal, which production dates back to the late fifteenth century. Nowadays, "Alheira" is a well-known Portuguese product with some varieties with Protected Geographical indication (PGI) status. "Alheiras" vary considerably in their final compositions and production processes. The relevant common elements in the production process are the boiling of various meats in lightly salted and spiced water; soaking the thinly sliced bread in some of the broth, formed during the boiling of the meats, until it is soft enough; adding meat in small pieces, spices and olive oil and/or fat drippings to the bread/broth mixture; there is no addition of starter cultures; stuffing the paste into pork intestinal or cellulose-based casings when everything is completely mixed and the salt and spices adjusted to the desirable taste (variable). Traditionally, "Alheiras" are smoked at low but uncontrolled temperature (generally < 37 ℃) and uncontrolled humidity [1], but industrially smoking is performed at higher temperatures, in ovens, and cooled rapidly in cooling chambers. Several authors observed that LAB constitute the predominant microbiota of "Alheira", with particular incidence of Lactobacillus spp. and Enterococcus spp. [1,2] from the early stages of fermentation process (during the smoking). However, pathogenic organisms, such as L. monocytogenes, Salmonella spp. and St. aureus have already been detected [2,3] which could affect the safe consumption of this food. "Alheira" have a pH ~5.1, 1.1% NaCl, ± 0.95 of water activity, 52.3% of moisture, 13% of lipids, 14% total protein and 13% of carbohydrates. These physicochemical parameters and composition, indicated that, and according to the accepted limits for these parameters, pH, salt content and moisture per se, do not assure the microbiological safety of this product. Furthermore, this is a product that is consumed cooked, but some studies suggested that internal temperatures may often not be sufficient to kill all of the pathogens originally present [4], which could justify the use of bioprotective cultures or other natural antimicrobial components.
One of the main challenges in the production of traditional fermented meat products is to improve competitiveness by identifying innovations, which comply with safety policies and regulations, and guarantee food safety, while at the same time meeting consumer demands and expectations [5]. There is an increasing interest in the use of LAB as natural preservatives, due to the potential production of antimicrobial metabolites such as bacteriocins [6,7,8]. So, LAB cultures and/or their bacteriocins are very important for bioprotective strategies to control spoilage and pathogenic microorganisms such as L. monocytogenes [6,7,8,9]. It is recommended to use strains that are well adapted to specific environment characteristics of the food matrices in which they are going to be used, preferably isolates recovered from these or from similar foods, for optimal performance and bacteriocin production [9,10,11].
Lactobacillus plantarum ST153Ch originally identified as Lb. sakei but subsequently re-identified as Lb. plantarum, is an autochthonous strain isolated from "Salpicão", a traditional Portuguese salami-like product [12]. Vaz-Velho et al. [13] showed the possibility of using this strain as a protective culture to improve the safety of fermented meat sausages with respect to L. monocytogenes. Todorov et al. [12] showed that this strain produces an anti-listerial bacteriocin (ST153Ch) that is heat resistant and stable at low temperatures, stable between pH 2.0 and 10.0 and produced at higher levels during the stationary phase of fermentation in the presence of 2% (w/v) D-Glucose [12].
Food safety challenges are constantly changing and require new approaches and tools. Omics methodologies are rapidly transforming our approaches to the detection, prevention, and removal of foodborne pathogens [14], but also to monitor changes in microbiota composition of these products, the interactions between new bio-control agents and microbiota, which is directly correlated to its taste, aspect, safety and functional characteristics [15,16]. This could be a very interesting approach for industrial application.
The aim of this work was to evaluate the effect of a fresh and a lyophilized bioprotective Lb. plantarum ST153Ch culture, at an industrial scale, on the microbiological characteristics of "Alheira de Vitela", during the 90 days of storage at 4 ℃. Simultaneously the antilisterial activity of Lb. plantarum ST153Ch was also evaluated. Changes in microbiota were also investigated by 16s rRNA gene analysis in "Alheira de Vitela" produced with and without the bacteriocinogenic culture.
Lactobacillus plantarum ST153Ch (formerly Lc. sakei [12] and reclassified following 16 sRNA sequencing) was grown in de Man, Rogosa Sharpe (MRS) broth (Lab M, Bury, UK) at 30 ℃ for 48 h.
Listeria monocytogenes Scott A [17] and L. monocytogenes 3701 (isolated from a fermented meat sausage (ESB-UCP) were grown in Tryptone Soy Broth (TSB; Biokar) supplemented with 0.6% (w/v) of yeast extract (LabM; TSBYE) at 37 ℃ for 18–22 h.
All bacterial strains were sub-cultured twice under appropriate conditions before use in experiments. All strains were stored at −20 ℃ in the presence of 30% (v/v) glycerol.
One liter of MRS broth was inoculated (1% v/v) with Lb. plantarum ST153Ch and after 24 h at 30 ℃, cells were centrifuged at 17200x g, for 10 minutes, at 4 ℃. Cells were resuspended in 100 mL of sterilized deionized water, in order to have 109 CFU/mL. For fresh culture, cells were kept at 4 ℃ until the inoculation procedure (fresh culture was prepared on the same day of inoculation).
For preparation of lyophilized culture, the method used was adapted from Barbosa et al. [18]: cells were initially frozen at −80 ℃ overnight, and then desiccated under vacuum (2 ATM) for 4 days in a freeze-drier (SB4 Armfield, UK) at room temperature; and the condenser was cooled at −48 ℃. Before lyophilization process, Lb. plantarum ST153Ch reached ~1010 CFU/mL and after the concentration was ~109 CFU/mL.
"Alheira" samples were manufactured in a meat plant according to traditional recipes and techniques. The ingredients used were as follows: Veal meat (35%), wheat bread (wheat flour, baking powder and salt), pork meat, rooster, cooking broth, spices, onion, olive oil and salt. The sausage mixture, before stuffing in natural casing, was divided into three similar portions, each one with 45 kg: one was inoculated with Lb. plantarum ST153Ch fresh (S2BLAF), another with Lb. plantarum ST153Ch lyophilized (S2BLA) and the other one was non-inoculated (S2BC), to act as a control. Lactobacillus plantarum ST153Ch, fresh or lyophilized (prepared as indicated in section 2.2) was added before stuffing, in order to reach ~108 CFU/g in the final product. Lyophilized culture was suspended in 3 L of water and sucrose (6%w/v) and then added to the fresh sausage mixture. Fresh culture was directly added to the sausage mixture. Sausages were smoked in ovens, with a slight smoke of holm-wood, in controlled humidity, for some hours. Then, "Alheiras" were packed (in packages composed with materials of polyolefin and polyamide), under modified atmosphere (20% CO2 and 80% N2) and stored for 90 days at 4 ℃. Three independent batches were produced.
Microbiological analyses of all the samples were performed before smoking (−1 day), immediately after smoking (0 day) and during storage at times 3, 7, 15, 21, 28, 60 and 90 days.
Listeria monocytogenes strains were sub-cultured twice (24 h at 37℃) in TSB broth using a 1% v/v inoculum. Each culture was centrifuged and mixed in Ringer's solution. An aliquot (300 µL) of the cocktail of L. monocytogenes suspension (107 CFU/mL for each strain of L. monocytogenes) was inoculated with a sterilized syringe in 300 g of each sample of "Alheira". This procedure was done after smoking, in the microbiology laboratory, in order to reach 105 CFU/g of sample of "Alheira": one batch was inoculated with Lb. plantarum ST153Ch fresh plus cocktail of L. monocytogenes (S2BLAF + LM), another batch with Lb. plantarum ST153Ch lyophilized plus cocktail of L. monocytogenes (S2BLA + LM) and the other one only with cocktail of L. monocytogenes (S2BC + LM), to act as a control.
Twenty-five gram samples were added to 225 mL of sterile buffered peptone water (Biokar Diagnostics, Beauvais, France), and homogenized in a stomacher for 2 min. Appropriate decimal dilutions were prepared in Ringer's solution for microbial enumeration according to ISO Standards: LAB on de Man, Rogosa and Sharpe Agar (MRS, Biokar Diagnostics; [19]) incubated at 30 ℃ for 72 h; Enterobacteriaceae on RAPID' Enterobacteriaceae medium (Bio-Rad, CA, USA; [20]) and St. aureus on Baird Parker Agar (BPA, Bio-Rad; [21]), with rabbit plasma fibrinogen confirmation [22], both incubated at 37 ℃ for 48 h; Escherichia coli on Tryptone Bile X-glucuronide Agar [23] incubated at 44 ℃ for 24 h; Bacillus cereus on Mannitol Egg York Polymyxin Agar Base (VWR International, Pennsylvania, USA; ISO 7932:2004/Amd 1 [24]), and yeasts and molds on Rose-Bengal Chloramphenicol Agar (Oxoid, Hampshire, UK; NP 3277-1:1987 [25]) incubated at 25 ℃ for 5 days. Also the detection of some agents was performed using the ISO methodologies: detection and enumeration of L. monocytogenes [26,27], detection of Salmonella spp. [28], sulfite- reducing Clostridium spores [29] and detection of Yersinia [30]. Every analysis was performed in duplicate.
Ten grams of "Alheira" samples were homogenized individually in a stomacher for 2 min. From this, 1 g was used for DNA extraction. The DNA was extracted according to the instructions of "The GRS Genomic DNA Kit–Tissue–for Stool" (GRISP, Porto, Portugal).
The samples used were: "Alheira" inoculated with Lb. plantarum ST153Ch lyophilized (S2BLA): before smoking (S2BLA (−1 day)), after smoking (S2BLA (0 day)) and after 60 days of storage (S2BLA (60 days)); and the control non-inoculated (S2BC): before smoking (S2BC (−1 day)), after smoking (S2BC (0 day)) and after 2 months storage (S2BC (60 days)).
Samples were prepared for Illumina Sequencing by 16S rRNA gene amplification of the bacterial community. The DNA was amplified for the hypervariable V3-V4 region with specific primers and further reamplified in a limited-cycle PCR reaction to add sequencing adapters and dual indexes. First PCR reactions were performed for each sample using KAPA HiFi HotStart PCR Kit according to the manufacturer's suggestions, 0.3 μM of each PCR primer: forward primer Bakt_341F 5'-CCTACGGGNGGCWGCAG-3' and reverse primer Bakt_805R 5'-GACTACHVGGGTATCTAATCC-3' [31,32] and 12.5 ng of template DNA in a total volume of 25 μL. The PCR conditions involved a 3 min denaturation at 95 ℃, followed by 25 cycles of 98 ℃ for 20 s, 55 ℃ for 30 s and 72 ℃ for 30 s and a final extension at 72 ℃ for 5 min. Second PCR reactions added indexes and sequencing adapters to both ends of the amplified target region according to manufacturer's recommendations [33]. Negative PCR controls were included for all amplification procedures. PCR products were then one-step purified and normalized using SequalPrep 96-well plate kit (ThermoFisher Scientific, Waltham, USA) [34], pooled and pair-end sequenced in the Illumina MiSeq® sequencer with the V3 chemistry, according to the manufacturer's instructions [33] at Genoinseq (Cantanhede, Portugal).
All 16S rRNA data were analyzed with Kraken v1 [35] using the pre-built MiniKraken 8Gb database (https://ccb.jhu.edu/software/kraken/) with default parameters. At each taxonomic rank, classification falling below a threshold of 0.1% of all data were excluded from the subsequent analysis (i.e. classification represented by less than ~100 reads).
The comparison of the growth of L. monocytogenes without and with the bacteriocinogenic culture (fresh or lyophilized) through the storage time was carried out by one-way Analysis of Variance (ANOVA). The LSD-Tukey test was used to determine the significant differences (p < 0.05) among group means. Statistical analysis was done using SPSS 24.0.0.0 software for Windows.
Since food safety has become an increasingly important international concern, the application of antimicrobial peptides from LAB, that target foodborne pathogens without toxic or other adverse effects, has received great attention. LAB that produce bacteriocins with antilisterial activity have been used in fermented meat sausages as bioprotective cultures since the beginning of the 90's [6,7,36,37,38,39,40,41]. Our work follows this trendline, and expects to validate the effect of this bioprotective culture at industrial scale and over long storage times.
The behaviour of L. monocytogenes and LAB in "Alheira" samples (control and inoculated with bacteriocinogenic culture) is presented in Figure 1 and Table 1, respectively. No differences were observed when L. monocytogenes strains were tested alone or combined in a cocktail (data not shown), showing that they don't interfere between them. In the first three days of storage, it was observed a slight increase in L. monocytogenes counts. In parallel, a more pronounced growth was registered for LAB achieving ca. 9–10 log CFU/g. Also, and as observed in Table 1, the decrease in pH occurs at the same time as the number of lactic acid bacteria increases. After this period and until day 15, counts of the pathogen remained at the same level in control samples and decreased ca. 0.5 and 1.0 log CFU/g in samples inoculated with lyophilized and fresh bacteriocinogenic cultures, respectively, while counts of LAB increased ca. 1 log cycle in all the samples. From day 15 until the end of storage, counts of LAB remained stable (varied less than < 1 log cycle) while counts of L. monocytogenes decreased in all the samples. Although there were no significant differences (p > 0.05) in the reduction of the pathogen between samples with and without bacteriocinogenic culture, there was a clear trend for a higher reduction of L. monocytogenes in the presence of the protective culture. It is important to highlight that results represent the mean of three independent batches produced at industrial level and consequently several factors, e.g. the composition of the indigenous microbiota of the raw materials and final products, were neither constant nor controlled. Most of the results published in previous articles were performed at laboratory or pilot scale [7,36,37,38,39,40,41]. The work published by Vaz Velho et al. [13] was developed at smaller industrial scale, with only fresh culture and only at 7 days of storage. Also, these authors showed a reduction of L. monocytogenes less than 1 log. This current work was performed at industrial scale, with all the variability resulting therefrom. Moreover, this work used two different forms of the starter culture: lyophilized and fresh and analyses were performed during 90 days of storage. This well demonstrates the importance of validating studies at the industrial level and the importance of following the entire processes, until the end of storage time. For the industry it is crucial to follow product alterations until the end of storage, especially if we keep in mind that consumers are increasingly looking for safe products with long shelf-life.
) S2BLAF + LM; (□) S2BLA + LM. For each time, columns with the same letter are not significantly different (p > 0.05).
| "Alheira" with Lb. plantarum | "Alheira" with Lb. plantarum plus cocktail of L. monocytogenes | |||||||||||
| Samples | S2BC (non-inoculated) | S2BLAF (fresh) | S2BLA (lyophilized) | S2BC + LM (only inoculated with L. monocytogenes) | S2BLAF + LM (Fresh) | S2BLA + LM (lyophilized) | ||||||
| Time (days) | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH |
| −1* | 4.0 ± 0.12 | 5.45 ± 0.04 | 8.3 ± 0.55 | 5.65 ± 0.03 | 7.8 ± 1.5 | 5.56 ± 0.06 | nd | nd | nd | |||
| 0 | 7.6 ± 1.10 | 4.72 ± 0.06 | 9.1 ± 1.46 | 4.75 ± 0.05 | 7.8 ± 1.9 | 4.65 ± 0.06 | 7.3 ± 0.95 | 4.70 ± 0.02 | 8.0 ± 1.66 | 4.73 ± 0.03 | 8.53 ± 1.48 | 4.80 ± 0.04 |
| 3 | 9.0 ± 0.06 | 4.62 ± 0.03 | 9.3 ± 0.59 | 4.59 ± 0.03 | 9.8 ± 0.6 | 4.60 ± 0.02 | 9.3 ± 0.81 | 4.62 ± 0.01 | 9.8 ± 0.40 | 4.65 ± 0, 03 | 9.87 ± 0.06 | 4, 65 ± 0, 04 |
| 7 | 9.7 ± 0.55 | 4.54 ± 0.09 | 9.5 ± 0.96 | 4.53 ± 0.04 | 9.2 ± 0.7 | 4.5 ± 0.07 | 10.3 ± 0.40 | 4.55 ± 0.05 | 9.4 ± 1.30 | 4.64 ± 0.05 | 10.07 ± 0.71 | 4.60 ± 0.03 |
| 15 | 10.0 ± 0.41 | 4.39 ± 0.05 | 10.2 ± 0.46 | 4.42 ± 0.07 | 10.5 ± 0.5 | 4.40 ± 0.03 | 10.4 ± 0.31 | 4.44 ± 0.08 | 10.7 ± 0.42 | 4.57 ± 0.02 | 10.53 ± 0.55 | 4.50 ± 0.07 |
| 21 | 11.0 ± 0.00 | 4.25 ± 0.06 | 10.6 ± 0.29 | 4.30 ± 0.08 | 11.1 ± 0.0 | 4.31 ± 0.10 | 10.8 ± 0.12 | 4.43 ± 0.06 | 10.4 ± 0.46 | 4.45 ± 0.06 | 11.07 ± 0.06 | 4.50 ± 0.04 |
| 28 | 10.7 ± 0.67 | 4.11 ± 0.08 | 10.7 ± 0.69 | 4.14 ± 0.04 | 10.9 ± 0.2 | 4.14 ± 0.06 | 10.6 ± 0.87 | 4.13 ± 0.02 | 11.0 ± 0.20 | 4.25 ± 0.04 | 11.03 ± 0.06 | 4.20 ± 0.06 |
| 60 | 10.9 ± 0.06 | 3.60 ± 0.04 | 10.8 ± 0.85 | 3.67 ± 0.02 | 10.6 ± 0.6 | 3.70 ± 0.08 | 10.7 ± 0.29 | 3.70 ± 0.02 | 10.7 ± 0.44 | 3.90 ± 0.07 | 10.97 ± 0.12 | 4.00 ± 0.07 |
| 90 | 10.5 ± 0.15 | 3.58 ± 0.09 | 10.4 ± 0.44 | 3.52 ± 0.06 | 10.4 ± 0.4 | 3.54 ± 0.04 | 10.4 ± 0.17 | 3.57 ± 0.04 | 10.4 ± 0.40 | 3.65 ± 0.05 | 10.60 ± 0.44 | 3.70 ± 0.05 |
| * before smoking. | ||||||||||||
DownLoad:
CSV
The pathogenic organisms investigated, (Salmonella, S. aureus, Yersinia, sulphite reducing Clostridium spores and E. coli) were not detected in any sample. Also, counts of Enterobacteriaceae and B. cereus were below the detection limit of the enumeration technique for 0, 3, 7, 15, 21, 28, 60 and 90 days of storage (Table 2).
| Enumeration (log CFU/g) | Presence in 25g | |||||||||
| "Alheira" | Bacillus cereus | Yeasts | Moulds | Enterobacteriaceae | E.coli | Coagulase positive staphylococci | Listeria spp. | Listeria monocytogenes | Salmonella spp. | SRC sporesa |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| Legend: aSRC spores; sulphite reducing clostridial spores, presence (+) or absence (−) in 1, 0.1 or 0.01 g sample. | ||||||||||
DownLoad:
CSV
Overall, for 16S rRNA metagenomic analysis almost all reads generated (~99%) were classified into a taxonomic rank for all the "Alheira de Vitela" samples, for the different stages of fermentation and with and without the lyophilized bacteriocinogenic culture, with more than 95% of them being classified at the family and genus level (Table 3). Regarding family analysis, Leuconostocaceae and Lactobacillaceae predominated in all the samples (Figure 2A). Lactobacillaceae was the predominant family in samples with addition of bacteriocinogenic culture and Leuconostocaceae was prevalent in control samples. At the genus level, the microbiome distribution was similar to the family classification, with the main genus being Lactobacillus, Weissella and Leuconostoc (Figure 2B). At both taxonomic levels, higher microbial diversity could be observed before and after smoking, as compared to 60 days after storage where samples presented two main families or genus. Of note, "Alheiras" sampled after 60 days of storage (60 days) with the addition of bacteriocinogenic culture were mostly populated by Lactobacillus, whereas without this addition, "Alheiras" were equally populated by Leuconostoc and a higher portion of Weisella.
| Sample | Total # of paired reads generated | Percentage of classified reads (%) | |||
| Total | Up to family | Up to genus | Up to species | ||
| S2BLA (−1 day) | 88285 | 99.93 | 94.94 (0.15) | 94.71 (0.35) | 30.03 (1.08) |
| S2BLA (0 day) | 77182 | 98.71 | 98.59 (0.67) | 98.02 (0.86) | 50.32 (1.71) |
| S2BLA (60 days) | 83671 | 99.77 | 97.53 (1.07) | 96.87 (1.19) | 81.41 (1.74) |
| S2BC (−1 day) | 91240 | 99.91 | 98.17 (0.23) | 98.07 (0.21) | 55.04 (0.8) |
| S2BC (0 day) | 62382 | 99.08 | 98.72 (0.69) | 98.52 (0.72) | 84.29 (1.18) |
| S2BC (60 days) | 76074 | 99.64 | 97.39 (0.14) | 96.98 (0.34) | 68.14 (0.73) |
| Percentages are relative to the total number of classified reads. Values in parenthesis refer to the percentage of reads excluded at each taxonomic rank. "Alheira de Vitela" non-inoculated as a control (S2BC) and inoculated with Lb. plantarum ST153Ch lyophilized (S2BLA): before smoking (−1 day), after smoking (0 day) and 60 days of storage (60 days). | |||||
DownLoad:
CSV
At the species level, it was possible to observe lower microbial diversity in the samples before smoking (labeled as −1 day), with the sample without culture presenting higher diversity than the sample with culture (Figure 2C). After smoking, the microbial diversity of the inoculated sample increased, especially when compared to the control, with a more pronounced trend after 60 days of storage, when microbial diversity was greater. Through time, in both tested conditions, a decrease in Leuconostoc and Weisella species could be observed, although this was more accentuated in samples with bacteriocinogenic culture, as only traces of Weisella species were observed and Leuconostoc species were only present below 7%. After 60 days of storage, the most predominant specie identified was Lb. sakei that, as mentioned above, was the previous classification of inoculated Lb. plantarum strain, which is likely due to the characteristics of the database used. Also, it is interesting to observe the change in microbiome of samples before and after fermentation, showing the role of LAB in this process. Although there are differences in the microbiota, these are slight, which makes it impossible to draw final conclusions. So, we believed that further studies are still needed to explain the observed differences.
Studying the changes in microbial populations can provide useful information to follow natural fermentation dynamics, particularly when using bioprotective cultures. Many different researches have been carried out unravelling the structure of the microbial consortia in dairy [42,43], meat [44] and vegetable foods [45].
Lactobacillus plantarum ST153Ch showed a slight antilisterial activity in situ, with a reduction of ~0.5–1 log until 15 days of storage of "Alheira de Vitela". However, until the end of storage, counts of L. monocytogenes decreased in all the samples, while counts of LAB remained stable. Additionally, no significant differences were found between application methodologies (fresh or lyophilized LABs), therefore industry might be able to choose the most appropriate technique according to their manufacturing process. From the analysis of the microbial communities it was confirmed the absence of the pathogens investigated by culture dependent methods and it was demonstrated that the addition of the bioprotective culture altered the microbiota of "Alheira". At different stages of fermentation with and without the bac + culture, Leuconostocaceae and Lactobacillaceae predominated in all samples and, as expected, Lactobacillus was the genus more prevalent in "Alheiras" sampled after 60 days of storage with the addition of the bacteriocinogenic culture.
Nevertheless, the few differences observed, there was a reduction in the L. monocytogenes counts in the early storage days, which could justify the use of these cultures. For the industry it is very important to control the development of a pathogen at the beginning of storage time when products are already available to consumers.
All authors declare no conflicts of interest in this paper.
This work was supported by funding from the National Funds from the Fundação para a Ciência e a Tecnologia (FCT) through project UID/Multi/50016/2013, by the project "Biological tools for adding and defending value in key agro-food chains (bio-n2-value)", nº NORTE-01-0145-FEDER-000030, funded by Fundo Europeu de Desenvolvimento Regional (FEDER), under Programa Operacional Regional do Norte-Norte2020" and a post-doctoral fellowship SFRH/BPD/113303/2015 (author J. Barbosa), and by DEM@BIOFUMADOS-Demonstrador do Biofumados Tradição vs Qualidade-Produção de Enchidos e Fumados Tradicionais Portugueses (NORTE-01-0247- FEDER-017634), co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), under Programa Operacional Regional do Norte (PO Norte).
We would like to thank Dr. João Paulo Gomes and Dr. Vitor Borges from the Bioinformatics Unit at Department of Infectious Diseases of the Portuguese National Institute of Health for their support in the bioinformatics analysis of this study.
| [1] |
I. Gusarov, E. Nudler, Protein S-nitrosylation: enzymatically controlled, but intrinsically unstable, post-translational modification, Mol. Cell, 69 (2018), 351-353. doi: 10.1016/j.molcel.2018.01.022
|
| [2] | W. Deng, Y. Wang, L. Ma, Y. Zhang, S. Ullah, Y. Xue, Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins, Briefings Bioinf., 18 (2016), 647-658. |
| [3] |
L. Kiemer, J. D. Bendtsen, N. Blom, NetAcet: prediction of N-terminal acetylation sites, Bioinformatics, 21 (2005), 1269-1270. doi: 10.1093/bioinformatics/bti130
|
| [4] |
Y. D. Khan, N. Rasool, W. Hussain, S. A. Khan, K. C. Chou, iPhosT-PseAAC: identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC, Anal. Biochem., 550 (2018), 109-116. doi: 10.1016/j.ab.2018.04.021
|
| [5] |
M. M. Hasan, B. Manavalan, M. S. Khatun, H. Kurata, Prediction of S-nitrosylation sites by integrating support vector machines and Random Forest, Mol. Omics, 15 (2019), 451-458. doi: 10.1039/C9MO00098D
|
| [6] |
V. Fernando, X. Zheng, Y. Walia, V. Sharma, J. Letson, S. Furuta, S-Nitrosylation: an emerging paradigm of redox signaling, Antioxid. (Basel), 8 (2019), 404. doi: 10.3390/antiox8090404
|
| [7] |
H. Hayashi, D. T. Hess, R. Zhang, K. Sugi, H. Gao, B. L. Tan, et al., S-nitrosylation of β-arrestins biases receptor signaling and confers ligand independence, Mol. Cell, 70 (2018), 473-487. doi: 10.1016/j.molcel.2018.03.034
|
| [8] | S. Rizza, S. Cardaci, C. Montagna, G. Di Giacomo, D. De Zio, M. Bordi, et al., S-nitrosylation drives cell senescence and aging in mammals by controlling mitochondrial dynamics and mitophagy, Proc. Natl. Acad. Sci., 115 (2018), 3388-3397. |
| [9] | F. Li, P. Sonveaux, Z. N. Rabbani, S. Liu, B. Yan, Q. Huang, et al., Regulation of HIF-1α stability through S-nitrosylation, Mol. Cell, 26 (2017), 63-74. |
| [10] |
Z. Wang, Protein S-nitrosylation and cancer, Cancer Lett., 320 (2012), 123-129. doi: 10.1016/j.canlet.2012.03.009
|
| [11] | T. S. Wijasa, M. Sylvester, N. Brocke-Ahmadinejad, S. Schwartz, F. Santarelli, V. Gieselmann, et al., Quantitative proteomics of synaptosome S-nitrosylation in Alzheimer's disease, J. Neurochem., 152 (2020), 710-726. |
| [12] |
M. Piroddi, A. Palmese, F. Pilolli, A. Amoresano, P. Pucci, C. Ronco, F. Galli, Plasma nitroproteome of kidney disease patients, Amino Acids, 40 (2011), 653-667. doi: 10.1007/s00726-010-0693-1
|
| [13] |
A. Nott, P. M. Watson, J. D. Robinson, L. Crepaldi, A. Riccio, S-nitrosylation of histone deacetylase 2 induces chromatin remodelling in neurons, Nature, 455 (2008), 411-415. doi: 10.1038/nature07238
|
| [14] |
G. Huang, J. Li, C. Zhao, Computational prediction and analysis of associations between small molecules and binding-associated S-nitrosylation sites, Molecules, 23 (2018), 954. doi: 10.3390/molecules23040954
|
| [15] | J. R. Burgoyne, P. Eaton, A rapid approach for the detection, quantification, and discovery of novel sulfenic acid or S-nitrosothiol modified proteins using a biotin-switch method, Methods Enzymol., 473 (2010), 281-303. |
| [16] | G. Hao, B. Derakhshan, L. Shi, F. Campagne, S. S. Gross, SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures, Proc. Natl. Acad. Sci. U. S. A., 103 (2006), 1012-1017. |
| [17] |
W. R. Qiu, B. Q. Sun, X. Xiao, D. Xu, K. C. Chou, iPhos-PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory, Mol. Inf., 36 (2017), 1600010. doi: 10.1002/minf.201600010
|
| [18] |
W. R. Qiu, A. Xu, Z. C. Xu, C. H. Zhang, X. Xiao, Identifying acetylation protein by fusing its PseAAC and functional domain annotation, Front. Bioeng. Biotechnol., 7 (2019), 311. doi: 10.3389/fbioe.2019.00311
|
| [19] |
Y. Xue, Z. Liu, X. Gao, C. Jin, L. Wen, X. Yao, J. Ren, GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm, PLoS One, 5 (2010), e11290. doi: 10.1371/journal.pone.0011290
|
| [20] |
T. Y. Lee, Y. J. Chen, T. C. Lu, H. D. Huang, Y. J. Chen, SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity, PLoS One, 6 (2011), e21849. doi: 10.1371/journal.pone.0021849
|
| [21] | Y. Xu, J. Ding, L. Y. Wu, K. C. Chou, iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition, PLoS One, 8 (2010), e55844. |
| [22] |
A. Siraj, T. Chantsalnyam, H. Tayara, K. T. Chong, RecSNO: prediction of protein S-nitrosylation sites using a recurrent neural network, IEEE Access, 9 (2021), 6674-6682. doi: 10.1109/ACCESS.2021.3096125
|
| [23] |
X. Cheng, X. Xiao, K. C. Chou, pLoc_bal-mGneg: predict subcellular localization of gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC, J. Theor. Biol., 458 (2018), 92-102. doi: 10.1016/j.jtbi.2018.09.005
|
| [24] |
K. C. Chou, Some remarks on protein attribute prediction and pseudo amino acid composition, J. Theor. Biol., 273 (2011), 236-247. doi: 10.1016/j.jtbi.2010.12.024
|
| [25] |
Y. Z. Chen, Y. R. Tang, Z. Y. Sheng, Z. Zhang, Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs, BMC Bioinf., 9 (2008), 101. doi: 10.1186/1471-2105-9-101
|
| [26] |
F. N. Auliah, A. N. Nilamyani, W. Shoombuatong, M. A. Alam, M. M. Hasan, H. Kurata, PUP-fuse: prediction of protein pupylation sites by integrating multiple sequence representations, Int. J. Mol. Sci., 22 (2021), 2120. doi: 10.3390/ijms22042120
|
| [27] |
Y. Liu, Z. Yu, C. Chen, Y. Han, B. Yu, Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net, Anal. Biochem., 609 (2020), 113903. doi: 10.1016/j.ab.2020.113903
|
| [28] |
Y. Xie, X. Luo, Y. Li, L. Chen, W. Ma, J. Huang, et al., DeepNitro: prediction of protein nitration and nitrosylation sites by deep learning, Genomics, Proteomics Bioinf., 16 (2018), 294-306. doi: 10.1016/j.gpb.2018.04.007
|
| [29] |
W. Qiu, Z. Lv, Y. Hong, J. Jia, X. Xiao, BOW-GBDT: a GBDT classifier combining with artificial neural network for identifying GPCR-drug interaction based on wordbook learning from sequences, Front. Cell Dev. Biol., 8 (2021), 623858. doi: 10.3389/fcell.2020.623858
|
| [30] |
S. Kawashima, M. Kanehisa, AAindex: amino acid index database, Nucleic Acids Res., 28 (2000), 374. doi: 10.1093/nar/28.1.374
|
| [31] |
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16 (2002), 321-357. doi: 10.1613/jair.953
|
| [32] |
M. J. Kim, D. K. Kang, H. B. Kim, Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction, Expert Syst. Appl., 42 (2015), 1074-1082. doi: 10.1016/j.eswa.2014.08.025
|
| [33] |
S. Yan, W. Qian, Y. Guan, B. Zheng, Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method, Med. Phys., 43 (2016), 2694-2703. doi: 10.1118/1.4948499
|
| [34] |
N. Habib, M. M. Hasan, M. M. Reza, M. M. Rahman, Ensemble of CheXNet and VGG-19 feature extractor with Random Forest classifier for pediatric pneumonia detection, SN Comput. Sci., 1 (2020), 359. doi: 10.1007/s42979-020-00373-y
|
| [35] | K. Ghosh, A. Sarkar, A. Banerjee, S. Chatterjee. Performance improvement of convolutional neural network using random under sampling, in Advances in Smart Communication Technology and Information Processing, Springer, (2021), 207-217. |
| [36] |
S. Hua, Z. Sun, Support vector machine approach for protein subcellular localization prediction, Bioinformatics, 17 (2001), 721-728. doi: 10.1093/bioinformatics/17.8.721
|
| [37] |
H. Bian, M. Guo, J. Wang, Recognition of mitochondrial proteins in plasmodium based on the tripeptide composition, Front. Cell Dev. Biol., 8 (2020), 578901. doi: 10.3389/fcell.2020.578901
|
| [38] |
H. Zou, T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B (Stat. Methodol.), 67 (2005), 301-320. doi: 10.1111/j.1467-9868.2005.00503.x
|
| [39] |
G. Chen, M. Cao, J. Yu, X. Guo, S. Shi, Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC, J. Theor. Biol., 461 (2019), 92-101. doi: 10.1016/j.jtbi.2018.10.047
|
| [40] |
W. R. Qiu, B. Q. Sun, X. Xiao, Z. C. Xu, J. H. Jia, K. C. Chou, iKcr-PseEns: identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier, Genomics, 110 (2018), 239-246. doi: 10.1016/j.ygeno.2017.10.008
|
| [41] |
L. Breiman, Random Forests. Mach. Learn., 45 (2001), 5-32. doi: 10.1023/A:1010933404324
|
| [42] | H. Talebi, L. J. M. Peeters, A. Otto, R. Tolosana-Delgado, A truly spatial Random Forests algorithm for geoscience data analysis and modelling, Math. Geosci., 2021. |
| [43] |
Z. Qiu, Q. Liu, Protein-protein interaction site prediction using random forest proximity distance, J. Bioinf. Comput. Biol., 19 (2021), 2050042. doi: 10.1142/S0219720020500420
|
| [44] |
S. Cabras, M. E. Castellanos, E. Staffetti, A Random Forest application to contact-state classification for robot programming by human demonstration, Appl. Stochastic Models Bus. Ind., 32 (2016), 209-227. doi: 10.1002/asmb.2145
|
| [45] |
N. Friedman, D. Geiger, M. Goldszmidt, Bayesian network classifiers, Mach. Learn., 29 (1997), 131-163. doi: 10.1023/A:1007465528199
|
| [46] | R. Queiroz, T. Berger, K. Czarnecki, Towards predicting feature defects in software product lines, in Proceedings of the 7th International Workshop on Feature-Oriented Software Development, Amsterdam, Netherlands, Association for Computing Machinery, (2016), 58-62. |
| [47] | H. Bohra, A. Arora, P. Gaikwad, R. Bhand, M. R. Patil, Health prediction and medical diagnosis using Naive Bayes, Ijarcce, 6 (2017), 32-35. |
| [48] |
M. S. Ahmed, M. Shahjaman, E. Kabir, M. Kamruzzaman, Prediction of protein acetylation sites using kernel Naive Bayes classifier based on protein sequences profiling, Bioinformation, 14 (2018), 213-218. doi: 10.6026/97320630014213
|
| [49] |
F. Nigsch, A. Bender, B. van Buuren, J. Tissen, E. Nigsch, J. B. Mitchell, Melting point prediction employing K-Nearest Neighbor algorithms and genetic parameter optimization, J. Chem. Inf. Model., 46 (2006), 2412-2422. doi: 10.1021/ci060149f
|
| [50] | A. Wirdiani, P. Hridayami, A. Widiari, K. Rismawan, P. Candradinata, I. Jayantha, Face identification based on K-Nearest Neighbor, Sci. J. Inf., 6 (2019), 150-159. |
| [51] | O. Borgohain, M. Dasgupta, P. Kumar, G. Talukdar, Performance analysis of nearest neighbor, K-Nearest Neighbor and weighted K-Nearest Neighbor for the cassification of Alzheimer disease, in Soft Computing Techniques and Applications, S. Borah, R. Pradhan, N. Dey, P. Gupta, Ed., Springer, Singapore, (2021), 295-304. |
| [52] |
S. Huang, M. Huang, Y. Lyu, A novel approach for sand liquefaction prediction via local mean-based pseudo nearest neighbor algorithm and its engineering application, Adv. Eng. Inf., 41 (2019), 100918. doi: 10.1016/j.aei.2019.04.008
|
| [53] | T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, Association for Computing Machinery, (2016), 785-794. |
| [54] |
I. Babajide Mustapha, F. Saeed, Bioactive molecule prediction using extreme gradient boosting, Molecules, 21 (2016), 983. doi: 10.3390/molecules21080983
|
| [55] |
J. H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Stat., 29 (2001), 1189-1232. doi: 10.1214/aos/1013203450
|
Figures(5) / Tables(7)
Wang-Ren Qiu, Qian-Kun Wang, Meng-Yue Guan, Jian-Hua Jia, Xuan Xiao. Predicting S-nitrosylation proteins and sites by fusing multiple features[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9132-9147. doi: 10.3934/mbe.2021450
| "Alheira" with Lb. plantarum | "Alheira" with Lb. plantarum plus cocktail of L. monocytogenes | |||||||||||
| Samples | S2BC (non-inoculated) | S2BLAF (fresh) | S2BLA (lyophilized) | S2BC + LM (only inoculated with L. monocytogenes) | S2BLAF + LM (Fresh) | S2BLA + LM (lyophilized) | ||||||
| Time (days) | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH |
| −1* | 4.0 ± 0.12 | 5.45 ± 0.04 | 8.3 ± 0.55 | 5.65 ± 0.03 | 7.8 ± 1.5 | 5.56 ± 0.06 | nd | nd | nd | |||
| 0 | 7.6 ± 1.10 | 4.72 ± 0.06 | 9.1 ± 1.46 | 4.75 ± 0.05 | 7.8 ± 1.9 | 4.65 ± 0.06 | 7.3 ± 0.95 | 4.70 ± 0.02 | 8.0 ± 1.66 | 4.73 ± 0.03 | 8.53 ± 1.48 | 4.80 ± 0.04 |
| 3 | 9.0 ± 0.06 | 4.62 ± 0.03 | 9.3 ± 0.59 | 4.59 ± 0.03 | 9.8 ± 0.6 | 4.60 ± 0.02 | 9.3 ± 0.81 | 4.62 ± 0.01 | 9.8 ± 0.40 | 4.65 ± 0, 03 | 9.87 ± 0.06 | 4, 65 ± 0, 04 |
| 7 | 9.7 ± 0.55 | 4.54 ± 0.09 | 9.5 ± 0.96 | 4.53 ± 0.04 | 9.2 ± 0.7 | 4.5 ± 0.07 | 10.3 ± 0.40 | 4.55 ± 0.05 | 9.4 ± 1.30 | 4.64 ± 0.05 | 10.07 ± 0.71 | 4.60 ± 0.03 |
| 15 | 10.0 ± 0.41 | 4.39 ± 0.05 | 10.2 ± 0.46 | 4.42 ± 0.07 | 10.5 ± 0.5 | 4.40 ± 0.03 | 10.4 ± 0.31 | 4.44 ± 0.08 | 10.7 ± 0.42 | 4.57 ± 0.02 | 10.53 ± 0.55 | 4.50 ± 0.07 |
| 21 | 11.0 ± 0.00 | 4.25 ± 0.06 | 10.6 ± 0.29 | 4.30 ± 0.08 | 11.1 ± 0.0 | 4.31 ± 0.10 | 10.8 ± 0.12 | 4.43 ± 0.06 | 10.4 ± 0.46 | 4.45 ± 0.06 | 11.07 ± 0.06 | 4.50 ± 0.04 |
| 28 | 10.7 ± 0.67 | 4.11 ± 0.08 | 10.7 ± 0.69 | 4.14 ± 0.04 | 10.9 ± 0.2 | 4.14 ± 0.06 | 10.6 ± 0.87 | 4.13 ± 0.02 | 11.0 ± 0.20 | 4.25 ± 0.04 | 11.03 ± 0.06 | 4.20 ± 0.06 |
| 60 | 10.9 ± 0.06 | 3.60 ± 0.04 | 10.8 ± 0.85 | 3.67 ± 0.02 | 10.6 ± 0.6 | 3.70 ± 0.08 | 10.7 ± 0.29 | 3.70 ± 0.02 | 10.7 ± 0.44 | 3.90 ± 0.07 | 10.97 ± 0.12 | 4.00 ± 0.07 |
| 90 | 10.5 ± 0.15 | 3.58 ± 0.09 | 10.4 ± 0.44 | 3.52 ± 0.06 | 10.4 ± 0.4 | 3.54 ± 0.04 | 10.4 ± 0.17 | 3.57 ± 0.04 | 10.4 ± 0.40 | 3.65 ± 0.05 | 10.60 ± 0.44 | 3.70 ± 0.05 |
| * before smoking. | ||||||||||||
DownLoad:
CSV
| Enumeration (log CFU/g) | Presence in 25g | |||||||||
| "Alheira" | Bacillus cereus | Yeasts | Moulds | Enterobacteriaceae | E.coli | Coagulase positive staphylococci | Listeria spp. | Listeria monocytogenes | Salmonella spp. | SRC sporesa |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| Legend: aSRC spores; sulphite reducing clostridial spores, presence (+) or absence (−) in 1, 0.1 or 0.01 g sample. | ||||||||||
DownLoad:
CSV
| Sample | Total # of paired reads generated | Percentage of classified reads (%) | |||
| Total | Up to family | Up to genus | Up to species | ||
| S2BLA (−1 day) | 88285 | 99.93 | 94.94 (0.15) | 94.71 (0.35) | 30.03 (1.08) |
| S2BLA (0 day) | 77182 | 98.71 | 98.59 (0.67) | 98.02 (0.86) | 50.32 (1.71) |
| S2BLA (60 days) | 83671 | 99.77 | 97.53 (1.07) | 96.87 (1.19) | 81.41 (1.74) |
| S2BC (−1 day) | 91240 | 99.91 | 98.17 (0.23) | 98.07 (0.21) | 55.04 (0.8) |
| S2BC (0 day) | 62382 | 99.08 | 98.72 (0.69) | 98.52 (0.72) | 84.29 (1.18) |
| S2BC (60 days) | 76074 | 99.64 | 97.39 (0.14) | 96.98 (0.34) | 68.14 (0.73) |
| Percentages are relative to the total number of classified reads. Values in parenthesis refer to the percentage of reads excluded at each taxonomic rank. "Alheira de Vitela" non-inoculated as a control (S2BC) and inoculated with Lb. plantarum ST153Ch lyophilized (S2BLA): before smoking (−1 day), after smoking (0 day) and 60 days of storage (60 days). | |||||
DownLoad:
CSV
| "Alheira" with Lb. plantarum | "Alheira" with Lb. plantarum plus cocktail of L. monocytogenes | |||||||||||
| Samples | S2BC (non-inoculated) | S2BLAF (fresh) | S2BLA (lyophilized) | S2BC + LM (only inoculated with L. monocytogenes) | S2BLAF + LM (Fresh) | S2BLA + LM (lyophilized) | ||||||
| Time (days) | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH | Log (CFU/g) | pH |
| −1* | 4.0 ± 0.12 | 5.45 ± 0.04 | 8.3 ± 0.55 | 5.65 ± 0.03 | 7.8 ± 1.5 | 5.56 ± 0.06 | nd | nd | nd | |||
| 0 | 7.6 ± 1.10 | 4.72 ± 0.06 | 9.1 ± 1.46 | 4.75 ± 0.05 | 7.8 ± 1.9 | 4.65 ± 0.06 | 7.3 ± 0.95 | 4.70 ± 0.02 | 8.0 ± 1.66 | 4.73 ± 0.03 | 8.53 ± 1.48 | 4.80 ± 0.04 |
| 3 | 9.0 ± 0.06 | 4.62 ± 0.03 | 9.3 ± 0.59 | 4.59 ± 0.03 | 9.8 ± 0.6 | 4.60 ± 0.02 | 9.3 ± 0.81 | 4.62 ± 0.01 | 9.8 ± 0.40 | 4.65 ± 0, 03 | 9.87 ± 0.06 | 4, 65 ± 0, 04 |
| 7 | 9.7 ± 0.55 | 4.54 ± 0.09 | 9.5 ± 0.96 | 4.53 ± 0.04 | 9.2 ± 0.7 | 4.5 ± 0.07 | 10.3 ± 0.40 | 4.55 ± 0.05 | 9.4 ± 1.30 | 4.64 ± 0.05 | 10.07 ± 0.71 | 4.60 ± 0.03 |
| 15 | 10.0 ± 0.41 | 4.39 ± 0.05 | 10.2 ± 0.46 | 4.42 ± 0.07 | 10.5 ± 0.5 | 4.40 ± 0.03 | 10.4 ± 0.31 | 4.44 ± 0.08 | 10.7 ± 0.42 | 4.57 ± 0.02 | 10.53 ± 0.55 | 4.50 ± 0.07 |
| 21 | 11.0 ± 0.00 | 4.25 ± 0.06 | 10.6 ± 0.29 | 4.30 ± 0.08 | 11.1 ± 0.0 | 4.31 ± 0.10 | 10.8 ± 0.12 | 4.43 ± 0.06 | 10.4 ± 0.46 | 4.45 ± 0.06 | 11.07 ± 0.06 | 4.50 ± 0.04 |
| 28 | 10.7 ± 0.67 | 4.11 ± 0.08 | 10.7 ± 0.69 | 4.14 ± 0.04 | 10.9 ± 0.2 | 4.14 ± 0.06 | 10.6 ± 0.87 | 4.13 ± 0.02 | 11.0 ± 0.20 | 4.25 ± 0.04 | 11.03 ± 0.06 | 4.20 ± 0.06 |
| 60 | 10.9 ± 0.06 | 3.60 ± 0.04 | 10.8 ± 0.85 | 3.67 ± 0.02 | 10.6 ± 0.6 | 3.70 ± 0.08 | 10.7 ± 0.29 | 3.70 ± 0.02 | 10.7 ± 0.44 | 3.90 ± 0.07 | 10.97 ± 0.12 | 4.00 ± 0.07 |
| 90 | 10.5 ± 0.15 | 3.58 ± 0.09 | 10.4 ± 0.44 | 3.52 ± 0.06 | 10.4 ± 0.4 | 3.54 ± 0.04 | 10.4 ± 0.17 | 3.57 ± 0.04 | 10.4 ± 0.40 | 3.65 ± 0.05 | 10.60 ± 0.44 | 3.70 ± 0.05 |
| * before smoking. | ||||||||||||
| Enumeration (log CFU/g) | Presence in 25g | |||||||||
| "Alheira" | Bacillus cereus | Yeasts | Moulds | Enterobacteriaceae | E.coli | Coagulase positive staphylococci | Listeria spp. | Listeria monocytogenes | Salmonella spp. | SRC sporesa |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BC | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLAF + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| S2BLA + LM | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | < 1.0 | − | − | − | − |
| Legend: aSRC spores; sulphite reducing clostridial spores, presence (+) or absence (−) in 1, 0.1 or 0.01 g sample. | ||||||||||
| Sample | Total # of paired reads generated | Percentage of classified reads (%) | |||
| Total | Up to family | Up to genus | Up to species | ||
| S2BLA (−1 day) | 88285 | 99.93 | 94.94 (0.15) | 94.71 (0.35) | 30.03 (1.08) |
| S2BLA (0 day) | 77182 | 98.71 | 98.59 (0.67) | 98.02 (0.86) | 50.32 (1.71) |
| S2BLA (60 days) | 83671 | 99.77 | 97.53 (1.07) | 96.87 (1.19) | 81.41 (1.74) |
| S2BC (−1 day) | 91240 | 99.91 | 98.17 (0.23) | 98.07 (0.21) | 55.04 (0.8) |
| S2BC (0 day) | 62382 | 99.08 | 98.72 (0.69) | 98.52 (0.72) | 84.29 (1.18) |
| S2BC (60 days) | 76074 | 99.64 | 97.39 (0.14) | 96.98 (0.34) | 68.14 (0.73) |
| Percentages are relative to the total number of classified reads. Values in parenthesis refer to the percentage of reads excluded at each taxonomic rank. "Alheira de Vitela" non-inoculated as a control (S2BC) and inoculated with Lb. plantarum ST153Ch lyophilized (S2BLA): before smoking (−1 day), after smoking (0 day) and 60 days of storage (60 days). | |||||
