Abstract

Background and Objective: Yoghurt is a fermented milk product by bacteria; a process including transformation of lactose and galactose to lactic acid. In other words, milk acidification is a critical step in the industrial process to produce various dairy foods and components such as yogurt and caseinates. This study aimed to assess yoghurt effects on gene expression of human whole blood against acidified milk.

Material and Methods: Whole blood gene expression changes of yoghurt consumers against individuals that received acidified milks were retrieved from gene expression omnibus (GEO) database and pre-assessed via GEO2R program to find significant differentially expressed genes (DEG). Significant DEGs were assessed via director protein-protein interaction (PPI) network analysis and gene ontology enrichment to investigate critical genes and targeted biological processes.

Results and Conclusion: Pre-assessment analysis showed that whole blood gene expression profiles of the yoghurt group changed (characterized by 37 significant DEGs) while samples of acidified milk consumers included no significant alterations. Moreover, PPI network analyses showed that RPSA, RPS5, RPS14, PABPC1, DDX60L, FEN1, MRPL12 and KAT6A were the highlighted significant DEGs. Based on the gene ontology, enrichment biosynthesis of ceruloplasmin was addressed as the targeted biological process.

It was concluded that yoghurt included significant effects on gene expression profiles of whole blood while acidified milk did not. Downregulation of genes that were involved in ceruloplasmin production and function was highlighted as the major event in blood of yoghurt consumers.

Conflict of interest: The authors declare no conflict of interest.

1. Introduction

Lactic acid bacteria are used to convert milk to yoghurt via the fermentation process. Lactose and galactose of milk transform to lactic acid via fermentation; a process which is accompanied with nutrient, physical and chemical modifications of the milk matrix [1]. Several factors such as starter cultures, fermentation conditions and milk characteristics can affect milk fermentation for yoghurt production. Improvement of the fermentation process leads to the production of quality products [2]. There are evidence that lactic acid bacteria modify microbiota of the yoghurt consumers through fermentation [3]. Acidified milk foods are well-known products worldwide [4]. Milk acidification is an essential step of the industrial production process of various dairy products and components such as cheeses, yogurts and caseinates [5]. Investigations indicate that glucono-δ-lactone as a milk acidifier induces coagulation of milk proteins [6]. Studies have shown advantages of fermented dairy consumption such as decreased lipid parameters, decreased circulating parameters of inflammation and regulation of glycemia [7-9]. Significant characteristics of milk fermentation that affects metabolism correspond to lactose metabolism in people worldwide, who cannot eat milk due to its lactose [10].

Liver plays critical roles in copper homeostasis. Investigations indicate that most of the copper is transferred from the liver to plasma in form of ceruloplasmin [11]. Ceruloplasmin is involved in the metabolic balance of iron. Contribution of ceruloplasmin in neurodegenerative diseases such as Alzheimer’s disease, Wilson’s disease and Parkinson’s disease is verified by the researchers. Its roles in metabolic diseases such as obesity, diabetes and hyperlipidemia are reported by the researchers as well [12].

Sample collection is a critical step in analytical experiments. Whole blood is an available source of genomic DNA, which has widely been used in services worldwide [13,14]. High-throughput technologies such as genomics, proteomics and transcriptomics can provide large numbers of data on gene products [15]. Bioinformatics as an appropriate tool is close to genomics to solve numerous problems in medicine and biology [16]. The PPI network analysis as a bioinformatics technique has been interested by the scientists to analyze genomic data. Genes or proteins are included in networks and play various roles in the networks based on their characteristics. The PPI network analysis is used in various fields of medicine and nutrition [17-19]. There are investigations about the effects of yoghurt consumption on gene expression profiles of blood cells. Roles of yoghurt and acidified milk consumptions in modulation of inflammation are documented [20]. In the present study, transcriptomic data of the whole blood of seven young men, who consumed yoghurt and acidified milk, were extracted from the GEO database and compared to each other using PPI network analysis. Critical genes were identified and assessed via gene ontology to find targeted biological terms. Findings open new windows to possible advantages of yoghurt consumption as a public nutrient.

1. Materials and Methods

2.1. Data collection

To investigate effects of fermentation on the quality of milk and the consequence results on human health, GSE98645 was searched within the GEO database. Whole blood transcriptomes of seven healthy young men (mean (±SEM) age of 24.6 y ±4.7) after consuming probiotic yoghurt or acidified milk were compared with those before interventions. Data were published as an original article [21]. The yoghurt was fermented by Lactobacillus delbrueckii spp. bulgaricus, Streptococcus thermophilus and L. rhamnosus and the acidified milk was prepared using d-(+)-glucono-δ-lactone (2%). Details of the method were described by Burton et al. [21]. The acidified milk mimicked physical characteristics, pH and texture of yoghurt [22]. The dairy product was consumed once (800 g) for 15 min

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse98645). The postprandial assay was completed after an overnight fast. Venous blood sampling was completed in the fasting state and during the 6-h postprandial time following dairy intakes [21]. Then, RNA was extracted using Paxgene whole-blood samples and sequenced using Illumina HiSeq platform.

2.2. Pre-assessment analysis

Gene expression profiles of the samples linked to the consumed yoghurt and acidified milk were compared with those of control individuals using GEO2R program. Moderated t-statistic method was used in GEO2R analysis. Distribution of data was assessed via a box plot to investigate equivalence characteristics of the samples. Significant up and down-regulated DEGs were visualized using volcano plot. Significant DEGs were identified and cleaned (uncharacterized genes were removed) based on an adjusted p-value of less than 0.05.

2.3. Protein-protein interaction network analysis

Significant DEGs were included in CluePedia v.1.5.7 of Cytoscape software v.3.7.2 to form a directed PPI network to find the critical genes. Nodes were connected by the activation, inhibition, expression, reaction, catalysis and post-translation modification actions. Undirected PPI network was used to find binding actions between the recognized significant DEGs. Significant DEGs were included in the “protein query” of the STRING database to construct a PPI network using Cytoscape software. A confidence score of 0.1 was used to maximize connections between the nodes. The network was analyzed via the “Network analyzer” plugin of Cytoscape software. Element of the major connected component was visualized and sorted through the degree values. Associated biological terms were assessed via gene ontology enrichment. Significant DEGs were enriched using ClueGO v.1.5.7 plugin of Cytoscape software. Biological terms were extracted from GO_BiologicalProcess-EBI-UniProt-GOA-ACAP-ARAP_08.05.2020_00h00, GOCellularComponent-EBI-UniProt-GOA-ACAP-ARAP_08.05.2020_00h00,EACTOME_Reactions_08.05.2020 ontology sources. Clustering of biological terms was carried out using default kappa score. Corrected statistical p-values were generated via Bonferroni step-down correction method.

2.4. Statistical analysis

Significant DEGs were identified based on adjusted p-values of less than 0.05. Undirected PPI network was constructed regarding confidence scores of 0.1. Biological terms were achieved based on the corrected p-values via Bonferroni step-down correction method.

Enrichment/depletion (two-sided hypergeometric test) statistical test was used as well. A network specificity less than detailed was addressed.

1. Results and Discussion

3.1. Pre-assessment analysis

A box plot of compared whole blood gene expression profiles of the yoghurt group (seven samples) with control group is present in Figure 1. Samples were median-centric and comparable statistically. A linked volcano plot of yoghurt-control analysis is shown in Figure 2. Significant up and down-regulated DEGs are visualized in Figure 2. The box plot of compared seven whole-blood gene expression profiles of the acidified milk group with those of the control groups is illustrated in Figure 3. Samples were matched statistically. Volcano plot of the acidified milk-control analysis is shown in Figure 4. As shown in the figure, no significant DEGs were detected. This analysis was ignored for further assessments. Assessment showed 37 significant DEGs (adjusted p-value < 0.05) from separating the yoghurt group from the controls. A list of the significant DEGs is present in Table 1.

3.2. Protein-protein interaction network analysis

Significant DEGs were assessed via a directed PPI network to find prominent relationships between DEGs. The PPI network included two subnetworks and the isolated nodes. The two subnetworks of the directed PPI network of yoghurt–control analysis including the significant DEGs are shown in Figure 5. Activation, inhibition, expression, reaction, catalysis and post-translation modification actions were used to form the network. In general, activation, reaction, catalysis and post-translation modification actions included relationships between nodes of the identified subnetworks. The constructed undirected PPI network of yoghurt-control analysis is mapped in Figure 6. The 22 nodes of the major connected component of the PPI network were connected by 51 edges. Isolated and paired nodes are shown in Figure 6. However, undirected PPI network was small and was not a scale-free network, nodes were layout based on degree values to show various centrality characteristics of the nodes.

3.3. Gene ontology enrichment

Gene ontology results for yoghurt–control analysis are present in Figure 7. A total number of 54 biological terms of biological processes, cellular components and reactions were identified as the linked biological terms. Biological terms were clustered in four groups [“formation of translation initiation complexes yielding circularized ceruloplasmin mRNA in a 'closed-loop' conformation”, “positive regulation of nuclear-transcribed mRNA poly(A) tail shortening”, “rDNA heterochromatin assembly” and “protein import into mitochondrial matrix”] were as the targeted terms. List of the biological terms is shown in Table 2.

Pre-assessment analyses, including box plots and volcano plots (Figures 1, 2), indicated that whole-blood gene expression profiles of the participants who consumed yoghurt were comparable with those of the controls. Significant DEGs were visualized in the volcano plot. Rundblad et al. reported effects of fermented dairy product intakes on gene expression responses of the peripheral blood mononuclear cells associated with less inflammation [23]. As shown in Figure 1, box plot of Figure 3 corres-ponds to a similar analysis. However, no significant DEGs were reported (Figure 4). Results indicated that yoghurt included significant effects on gene expression profiles of the whole bloods while acidified milk did not.

A list of 37 significant DEGs, including 14 upregulated and 13 downregulated genes responsible for consuming yoghurt, is present in Table 1. Importance of the biomarkers of food intake is highlighted in the literature

[24]. An investigation by Rezaei et al. showed that consumption of yoghurt led to beneficial effects on blood pressure, blood glucose, serum lipid and glycated hemoglobin. However, C-reactive protein level, high-density lipoprotein and cholesterol were not affected by yoghurt consumption [25].

To screen the 37 significant DEGs, actions between the studied genes were mapped (Figure 5). Technically, RPSA, RPS5, RPS14, PABPC1, DDX60L, FEN1, MRPL12, H4C11, H4C12 and KAT6A were addressed as the interacted DEGs. From the ten highlighted DEGs, H4C11 and H4C12 were not present in the PPI network while others were appeared as the central nodes (Figure 6). Gene ontology assessment is an appropriate tool to assess a set of genes [26]. Results of gene ontology enrichment of the 37 DEGs are illustrated in Figure 7. As depicted in Figure 7, two clusters of biological processes including “rDNA heterochromatin assembly” and “formation of translation initiation complexes yielding circularized ceruloplasmin mRNA in ‘closed-loop’ conformation” were associated to the RPSA, RPS5, RPS14, PABPC1, H4C11 and H4C12. It seems that “formation of translation initiation complexes yielding circularized ceruloplasmin mRNA in ‘closed-loop’ conformation” was the major targeted group of biological processes.

The most complex characteristics of translation in eukaryotes include the established initiation process. It needs at least  11 eukaryotic initiation factors with coordinated interactions [27]. There is a model that corresponds with the formation a closed-loop structure by mRNA in the initiation of protein synthesis in eukaryotic organisms [28]. More than 95% of plasma copper is associated with ceruloplasmin.

This serum ferroxidase belongs to the multicopper oxidase family, which needs copper for its function. Investigations indicate that malfunction of ceruloplasmin leads to aceruloplasminemia; a neurodegenerative disease [29]. As depicted in Table 2 and Figure 7, “formation of translation initiation complexes yielding circularized ceruloplasmin mRNA in ‘closed-loop’ conformation” includes 16 biological processes and is associated to RPSA, RPS5, RPS14, PABPC1 and DHX30 genes. As discussed previously, RPSA, RPS5, RPS14 and PABPC1 were highlighted as critical genes. Relationships between these four critical genes are shown in Figure 5. As present in Figure 5, RPS14 is affected directly and indirectly by RPS5, RPSA and PABPC1. Downregulation of RPSA, RPS5 and RPS14 is shown in Table 1.

It could be concluded that yoghurt consumption was accompanied by decreased yield of “formation of translation initiation complexes yielding circularized ceruloplasmin mRNA in ‘closed-loop’ conformation” biological processes group, meaning that blood ceruloplasmin level decreased in the yoghurt consumers. Studies demonstrate that ceruloplasmin is the most abundant protein in milk containing β-casein variant A1A1 [30]. Raia et al. reported effects of ceruloplasmin deficiency on the impairment of  brain iron metabolisms and behaviors in mice [31]. They showed that ceruloplasmin deficiency played roles in dysregulation of lipid metabolism in the liver and adipose tissues of mice [32]. As shown in Figure 7, “formation of translation initiation complexes yielding circularized ceruloplasmin mRNA in a 'closed-loop' conformation” was connected to five associated DEGs; hence, it could be concluded that ceruloplasmin deficiency was a specific change in gene expression profiles of the yoghurt consumers. Benefits of this finding need further studies.

1. Conclusion

In conclusion, yoghurt includes significant effects on gene expression of whole bloods while acidified milk does not. Totally, 37 significant DEGs were highlighted as a result of yoghurt consumption. Downregulation of genes that are associated with the production and function of ceruloplasmin was highlighted in the blood of yoghurt consumers. Further studies, including detailed experiments on ceruloplasmin in bloods of yoghurt consumers, validation of the introduced critical DEGs via the associated methods such as quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and larger size samples, are needed to investigate complete molecular metabolisms associated to yoghurt consumption.

1. Ethical Code

This study was approved via IR.SBMU.RETECH.-REC.1403.260 ethical code

1. Acknowledgements

This study was supported by Shahid Beheshti University of Medical Sciences.

1. Conflict of Interest

Mostafa Rezaei-Tavirani contributed to the conception and design of the study and literature review. All authors participated equally in project administration and writing of the primary draft of the manuscript, providing critical revision and editing. All authors approved the final version of the manuscript.

References

1. Liu SQ. Practical implications of lactate and pyruvate metabolism by lactic acid bacteria in food and beverage fermentations. Int J Food Microbiol.2003; 83(2): 115-31.

https://doi.org/10.1016/S0168-1605(02)00366-5

1. Trimigno A, Bøge Lyndgaard C, Atladóttir GA, Aru V, Balling Engelsen S, Harder Clemmensen LK. An NMR metabolomics approach to investigate factors affecting the yoghurt fermentation process and quality. Metabolites. 2020; 10(7): 293.

https://doi.org/10.3390/metabo10070293

1. Le Roy CI, Kurilshikov A, Leeming ER, Visconti A, Bowyer RC, Menni C, Falchi M, Koutnikova H, Veiga P, Zhernakova A, Derrien M, Spector TD. Yoghurt consumption is associat-ed with changes in the composition of the human gut micr-obiome and metabolome. BMC Microbiol. 2022; 22(1): 39.

https://doi.org/10.1186/s12866-021-02364-2

1. Czaja TP, Vickovic D, Pedersen SJ, Hougaard AB, Ahrné L. Spectroscopic characterisation of acidified milk powders. Int Dairy J. 2023;142:105664.

https://doi.org/10.1016/j.idairyj.2023.105664

1. Liu D, Yu Y, Zhang J, Liu X, Wang M, Hemar Y, M. Regenstein J, Zhou P. Biochemical and physico-chemical changes of skim milk during acidification with glucono-δ-lactone and hydrogen chloride. Food Hydrocoll. 2017; 66: 99-109.

https://doi.org/10.1016/j.foodhyd.2016.12.030

1. Chen YC, Chen CC, Chen ST, Hsieh JF. Proteomic profiling of the coagulation of milk proteins induced by glucono-delta-lactone. Food Hydrocoll. 2016; 52: 137-143.

https://doi.org/10.1016/j.foodhyd.2015.06.005

1. Mohamadshahi M, Veissi M, Haidari F, Shahbazian H, Kaydani G-A, Mohammadi F. Effects of probiotic yogurt consumption on inflammatory biomarkers in patients with type 2 diabetes. BioImpacts. 2014; 4(2): 83-88.

https://doi.org/10.5681%2Fbi.2014.007

1. Nestel PJ, Mellett N, Pally S, Wong G, Barlow CK, Croft K, Trevor A M, Peter J M. Effects of low-fat or full-fat fermented and non-fermented dairy foods on selected cardiovascular biomarkers in overweight adults. Br J Nutr. 2013; 110(12): 2242-2249.

https://doi.org/10.1017/S0007114513001621

1. Ejtahed HS, Mohtadi-Nia J, Homayouni-Rad A, Niafar M, Asghari-Jafarabadi M, Mofid V. Probiotic yogurt improves antioxidant status in type 2 diabetic patients. Nutr J. 2012; 28(5): 539-543.

https://doi.org/10.1016/j.nut.2011.08.013

1. Savaiano DA, AbouElAnouar A, Smith DE, Levitt MD. Lactose malabsorption from yogurt, pasteurized yogurt, sweet acidophilus milk and cultured milk in lactase-deficient individuals. Am J Clin Nutr. 1984; 40(6): 1219-1223.

https://doi.org/10.1093/ajcn/40.6.1219

1. Linder M C. Ceruloplasmin and other copper binding components of blood plasma and their functions: An update: Metallomics. 2016; 8(9): 887-905.

https://doi.org/10.1039/c6mt00103c

1. Liu Z, Wang M, Zhang C, Zhou S, Ji G. Molecular functions of ceruloplasmin in metabolic disease pathology. Diabetes Obes Metab. 2022; 15: 695-711.

https://doi.org/10.2147/DMSO.S346648

1. Giavarina D, Lippi G. Blood venous sample collection: Recommendations overview and a checklist to improve quality. Clin Chem. 2017; 50(10-11): 568-573.

https://doi.org/10.1016/j.clinbiochem.2017.02.021

1. Chacon-Cortes D, Griffiths LR. Methods for extracting genomic DNA from whole blood samples: Current perspectives. J Biorepos Sci Appl Med. 2014; 2: 1-9

https://doi.org/10.2147/BSAM.S46573

1. Petrov PD, Bonet ML, Reynes B, Oliver P, Palou A, Ribot J. Whole blood RNA as a source of transcript-based nutrition-and metabolic health-related biomarkers. PloS one. 2016; 11(5): e0155361.

https://doi.org/10.1371/journal.pone.0155361

1. Pereira R, Oliveira J, Sousa M. Bioinformatics and computational tools for next-generation sequencing analysis in clinical genetics. J Clin Med. 2020; 9(1): 132.

https://doi.org/10.3390/jcm9010132

1. Rezaei Tavirani M, Ahmadzadeh A, Rostami Nejad M. Herbal diet and the impact of nutrition on lipogenic activity, a system biology study. Res J Pharmacogn. 2023; 10(4): 43-50.

https://doi.org/10.22127/rjp.2023.405866.2155

1. Rezaei Tavirani M, Arjmand B, Razzaghi M, Ahmadzadeh A. 50S Ribosomal proteins family is the main target of cinnamon extract: A network analysis. Res J Pharmacogn. 2021; 8(2): 63-68.

https://doi.org/10.22127/rjp.2021.265776.1659

1. Rezaei Tavirani M, Farahani M, Razzaghi Z, Arjmand B, Khodadoost M. Introducing Coffee as a Complementary Agent Beside Metformin Against Type 2 Diabetes. Res J Pharmacogn. 2024; 11(3): 31-40.

https://doi.org/10.22127/rjp.2024.418824.2239

1. Sagaya FM, Hurrell RF, Vergères G. Postprandial blood cell transcriptomics in response to the ingestion of dairy products by healthy individuals. J Nutr Biochem. 2012; 23(12): 1701-1715.

https://doi.org/10.1016/j.jnutbio.2012.01.001

1. Burton KJ, Pimentel G, Zangger N, Vionnet N, Drai J, McTernan PG, Pralong FP, Delorenzi M, Vergères G. Modulation of the peripheral blood transcriptome by the ingestion of probiotic yoghurt and acidified milk in healthy, young men. PLoS One. 2018; 13(2): e0192947.

https://doi.org/10.1371/journal.pone.0192947

1. Burton KJ, Rosikiewicz M, Pimentel G, Bütikofer U, Von Ah U, Voirol M-J, Greub G, Pralong FP, Vergères G, Vionnet N, Croxatto. A, Aeby S, Drai J, McTernan Ph. Probiotic yogurt and acidified milk similarly reduce postprandial inflammation and both alter the gut microbiota of healthy, young men. Br J Nutr. 2017; 117(9): 1312-1322.

https://doi.org/10.1017/S0007114517000885

1. Rundblad A, Holven KB, Øyri LK, Hansson P, Ivan IH, Gjevestad GO, Thoresen M, Ulven SM. Intake of fermented dairy products induces a less pro‐inflammatory postprandial peripheral blood mononuclear cell gene expression response than non‐fermented dairy products: A randomized controlled cross‐over trial. Mol Nutr Food Res. 2020; 64(21): e2000319.

https://doi.org/10.1002/mnfr.202000319

1. Münger LH, Garcia-Aloy M, Vázquez-Fresno R, Gille D, Rosana ARR, Passerini A, Soria-Florido M-T , Pimentel G, Sajed T, Wishart DS, Lacueva CA, Vergères G, Praticò G. Biomarker of food intake for assessing the consumption of dairy and egg products. Genes Nutr. 2018; 13: 1-18.

https://doi.org/10.1186/s12263-018-0615-5

1. Rezaei M, Sanagoo A, Jouybari L, Behnampoo N, Kavosi A. The effect of probiotic yogurt on blood glucose and cardiovascular biomarkers in patients with type II diabetes: A randomized controlled trial. Evid Based Care. 2017; 6(4): 26-35.

https://doi.org/10.22038/ebcj.2016.7984

1. Razzaghi Z, Arjmand B, Hamzeloo-Moghadam M, Tavirani MR. Gene ontology assessment of indirect cold physical plasma and UV-radiation molecular mechanism at the cellular level. J Lasers Med Sci. 2023; 14: e10

https://doi.org/10.34172/jlms.2023.10

1. Pisarev AV, Unbehaun A, Hellen CU, Pestova TV. Assembly and analysis of eukaryotic translation initiation complexes. Method Enzymol. 2007; 430: 147-77.

https://doi.org/10.1016/S0076-6879(07)30007-4

1. Denis CL, Laue TM, Wang X. Identification of a 57S translation complex containing closed-loop factors and the 60S ribosome subunit. Sci Rep. 2018; 8(1): 11468.

https://doi.org/10.1038/s41598-018-29832-6

1. Hellman NE, Gitlin JD. Ceruloplasmin metabolism and function. Annu Rev Nutr. 2002; 22(1): 439-458.

https://doi.org/10.1146/annurev.nutr.22.012502.114457

1. Wang X, Yu Z, Zhao X, Han R, Huang D, Yang Y, et al. Comparative proteomic characterization of bovine milk containing β‐casein variants A₁A₁ and A₂A₂ and their hetero-zygote A1A2. J Sci Food Agric. 2021; 101(2): 718-725.

https://doi.org/10.1002/jsfa.10684

1. Niu L, Zhou Y, Lu L, Su A, Guo X. Ceruloplasmin deficiency impaired brain iron metabolism and behavior in mice. Cell Biochem Biophys . 2022; 80(2): 385-393.

https://doi.org/10.1007/s12013-022-01061-9

1. Raia S, Conti A, Zanardi A, Ferrini B, Scotti GM, Gilberti E, De Palma G, David S, Alessio M. Ceruloplasmin-deficient mice show dysregulation of lipid metabolism in liver and adipose tissue reduced by a protein replacement. Int J Mol Sci. 2023; 24(2): 1150.

https://doi.org/10.3390/ijms24021150.