Discussion

The current study analysed the omics studies on mastitis by using text mining and topic analysis techniques. The omics studies on mastitis were initiated in the 1990s and have shown to increase over the years. This trend is attributed to the increasing accessibility of next-generation sequencing (NGS) technologies, driven by their growing popularity and decreasing costs (Shendure and Ji, Reference Shendure and Ji2008). Furthermore, the growing expertise in bioinformatics within veterinary science may have also contributed to this outcome. The increased trend of omics studies in veterinary medicine was seen in previous studies as well (Bourganou et al., Reference Bourganou, Chatzopoulos, Lianou and Tsangaris2024; Esener, Reference Esener2025).

The most frequent words included ‘gene’, ‘protein’, ‘cow’, ‘milk’, ‘dairi’, ‘isol’ and ‘aureus’. The term gene must have been related to genomics and transcriptomics, as these are the most frequently used omics studies. Protein must show the importance of proteomics. As stated earlier, bovine mastitis is the biggest issue in the dairy industry; hence, ‘dairi’ and ‘cow’ must have been that frequent. Terms like ‘isol’, ‘strain’ and ‘aureus’ must be related to mastitis agents as they are mainly bacteria. The virulence and antimicrobial resistance genes of mastitis pathogens such as Staphylococcus aureus might have been studied intensively.

Omics approaches beyond genomics, transcriptomics, and proteomics appear to be underrepresented in the current literature on mastitis. For instance, Luo et al. (Reference Luo, Kong, Yang, He, Huan, Li and Yi2023) employed an integrated analysis combining 16S rRNA sequencing and lipidomics to investigate microbiota diversity and milk quality, demonstrating that specific microbial populations are associated with the regulation of certain lipid profiles. Similarly, Hasankhani et al. (Reference Hasankhani, Bakherad, Bahrami, Shahrbabak, Pecho and Shahrbabak2023) explored the molecular interactome involved in intramammary inflammation caused by Streptococcus uberis in dairy cows. However, research explicitly addressing other omics disciplines – such as glycomics, pharmacogenomics, foodomics and exposomics – in the context of mastitis was not identified, aside from a few instances where these terms appeared as keywords without direct investigation.

The keywords such as ‘protein’, ‘proteom’, ‘peptid’, ‘mass’ and ‘spectrometri’ showed that Topic 1 was related to ‘Proteomics’ (see Fig. 2). The studies found in this topic were mainly related to inflammatory responses in cows (Danielsen et al., Reference Danielsen, Codrea, Ingvartsen, Friggens, Bendixen and Røntved2010), goats (Olumee-Shabon et al., Reference Olumee-Shabon, Swain, Smith, Tall and Boehmer2013), buffalos (Das et al., Reference Das, Swamy, Sahoo, Ahmed and More2008) and yaks (Li et al., Reference Li, Ding, Wan, Liu and Du2014) milk proteome. These studies mainly targeted to discover potential vaccine antigens for intramammary infections such as Streptococcus uberis (Collado et al., Reference Collado, Prenafeta, González-González, Pérez-Pons and Sitjà2016), S. aureus and Escherichia coli. Moreover, regulation of the proteins during antimicrobial-resistant pathogen infections such as Tylosin resistance of Staphylococcus xylosus (Liu et al., Reference Liu, Wang, Chen, Che, Ding, Yu, Zhou, Cui, Xiaoxu and God'spower2019), during the treatment of the intramammary infection (Kang et al., Reference Kang, Cho, Kim and Cho2016) was also studied. This topic was the only one where China was not the most prolific country, but the USA. This also accords with the previous scientometrics of proteomic technologies on ruminant mastitis (Bourganou et al., Reference Bourganou, Chatzopoulos, Lianou and Tsangaris2024).

Topic 2 had keywords such as ‘strain’, ‘isol’, ‘genom’, ‘type’ and ‘sequence’ that are terms related to mastitis pathogen genomics (see Fig. 2). Draft genomes of Streptococcus agalactiae (Murungi et al., Reference Murungi, Masila, Ogali, Langat, Onywera, Malonza, Inguyesi, Onyambu, Wesonga and Maichomo2022) and E. coli (Rahman Mohammad et al., Reference Rahman Mohammad, El Zowalaty Mohamed, Falgenhauer, Khan Mohammad Ferdousur, Alam, Popy Najmun and Rahman2023) were among the studies on this topic. Moreover, whole genome sequencing (WGS) studies of S. aureus (Greening et al., Reference Greening, Zhang, Midwinter, Wilkinson, McDougall, Gates and French2021), Mycoplasma arginini (Gioia et al., Reference Gioia, Severgnini, Cremonesi, Castiglioni, Freeman, Sipka, Santisteban, Wieland, Gallardo, Scott, Moroni and Addis2023), S. agalactiae (Oliveira et al., Reference Oliveira, Simões, Crestani, Costa, Pantoja, Rabello, Teixeira, Khan, Bentley, Jamrozy, Pinto and Zadoks2024), E. coli, S. uberis (Silva et al., Reference Silva, Yang, Rodrigues, Tomazi and Bicalho2021), Actinobaculum suis (Amigo et al., Reference Amigo, Moreno, Ferreira, Gomes, Poor, de Oliveira, Parra, Silva and Moreno2019) were also listed in this topic. Park et al. (Reference Park, Jung, O'Brien, Ruffini, Dussault, Dube-Duquette, É, Lucier, Malouin and Dufour2022) studied pangenome analysis of S. aureus. Furthermore, comparative genomic studies of Enterococcus faecium and Enterococcus faecalis (Apostolakos et al., Reference Apostolakos, Tsigkrimani, Paramithiotis and Mataragas2022) were performed. Nonetheless, this group was characterised by the presence of not only pathogenic bacterial genomics but also the potential probiotic Pediococcus pentosaceus (Rahman et al., Reference Rahman, Siddique, Gilman, Hasnat, Haider, Talukder, Rahman, Islam, Das, Hossain and Hoque2025) and the zoonotic fungal species Candida tropicalis (Jiang et al., Reference Jiang, Fang, Chen, Guo, Gao, Liu, Hu, Li, Mai and Liu2024). This topic has the highest number of papers (181 articles). This is plausible because genomics represents the foundational milestone of the omics era and remains the most extensively developed and established discipline within the omics sciences (Nehme et al., Reference Nehme, Awada, Kobeissy, Mazurier, Zibara, Rajewsky, Jurga and Barciszewski2018). Moreover, genomics for mastitis can be studied from two perspectives, either host or pathogen; the pathogen genome is more convenient (i.e. smaller genomes and lower costs) to study (Donkor, Reference Donkor2013).

Topic 9 was found to be overlapping with Topic 2 on the intertopic distance map, where keywords such as ‘aureus’, ‘resist’, ‘isol’, ‘gene’, ‘staphylococcus’, ‘antibiot’, ‘antimicrobial’ and ‘virul’ were the main terms (see Fig. 2). Therefore, it was defined as ‘Antimicrobial resistance’. The observed overlap between Topic 2 (‘pathogen genomics’) and Topic 9 (‘antimicrobial resistance’) in the intertopic distance map (see Fig. 3) is expected, as both topics are intrinsically connected through the genomic study of pathogenic microorganisms. Research efforts have predominantly concentrated on antimicrobial resistance and virulence genes in Staphylococcus spp. (Rodríguez et al., Reference Rodríguez, Gomez and Ceballos-Garzon2023), especially S. aureus (Zaatout et al., Reference Zaatout, Ayachi, Kecha and Kadlec2019) and Streptococcus spp. including S. pyogenes, S. agalactiae, S. porcinus and S. uberis (Vidal Amaral et al., Reference Vidal Amaral, Jucá Ramos, Almeida Araújo, Bentes Kato, Figueira Aburjaile, de Castro Soares, Góes-Neto, Matiuzzi da Costa, Azevedo, Brenig, de Oliveira S and Soares Rosado2022). Other studies screened the antimicrobial resistance genes in E. coli (Alawneh et al., Reference Alawneh, Vezina, Ramay, Al-Harbi, James, Soust, Moore and Olchowy2020), Klebsiella pneumaniae (Y Yang et al., Reference Yang, Higgins Catherine, Rehman, Galvao Klibs, Brito Ilana, Bicalho Marcela, Song, Wang and Bicalho Rodrigo2019) and Brucella spp. (Grützke et al., Reference Grützke, Gwida, Deneke, Brendebach, Projahn, Schattschneider, Hofreuter, El-Ashker, Malorny and Al Dahouk2021). Hoque et al. (Reference Hoque, Istiaq, Rahman, Islam, Anwar, Siddiki, Sultana, Crandall and Hossain2020) screened the antimicrobial resistance profiles of different pathological conditions of mastitis at the microbiome level.

Topic 3 included studies that explored differentially expressed genes and proteins. According to the intertopic distance map (see Fig. 3), this topic appeared to be the most distant from the others, indicating the most unique terms in this topic. In a study with ewes, different somatic cell scores (high and low) and energy balance groups (negative and positive) were compared by using RNA-seq data in terms of mastitis resistance (Bouvier-Muller et al., Reference Bouvier-Muller, Allain, Enjalbert, Farizon, Portes, Foucras and Rupp2018). There were examples of studies comparing the expression differences of both genes and proteins between healthy and mastitic dairy cows (Olumee-Shabon et al., Reference Olumee-Shabon, Swain, Smith, Tall and Boehmer2013; Sun et al., Reference Sun, Li, Li, Wang and Xing2019). Although these studies were mainly with E. coli infection (Moyes et al., Reference Moyes, Sørensen and Bionaz2016), there were also other mastitis pathogens such as Prototheca spp. and S. agalactiae (Bisutti et al., Reference Bisutti, Mach, Giannuzzi, Vanzin, Capra, Negrini, Gelain, Cecchinato, Ajmone-Marsan and Pegolo2023; Zhang et al., Reference Zhang, Jiang, Fan, Chen, Li, Mao, Karrow, Loor, Moore and Yang2018). Moreover, (Chen et al., Reference Chen, Xia, Shen, Xu, Arbab, Li, Zhang, Mao and Yang2018) compared the differentially expressed proteins from the healthy and S. aureus-infected tissues of the same individual dairy cows. In another study, Wang et al. (Reference Wang, Liang, Ibeagha-Awemu, Li, Zhang, Chen, Sun, Karrow, Yang and Mao2020) compared the epigenetic changes in the expression of S. aureus-induced and healthy mammary gland quarters. In this topic, there were also studies enabling to understand mastitis resistance in depth, such as detecting the differentially expressed long non-coding RNAs in dairy cows (Asselstine et al., Reference Asselstine, Medrano, Muniz, Mallard, Karrow and Cánovas2024). Furthermore, low susceptibility to intramammary infections and low prevalence of mastitis in donkeys were also studied by using RNA-seq (Wang et al., Reference Wang, Fei, Shao, Liao, Meng, Chen and Deng2024).

The relevant terms in Topic 4 suggest that this topic is best described under the umbrella of ‘metabolism’ (see Fig. 2). The studies related to mammary gland metabolism of both ovine (Chiaradia et al., Reference Chiaradia, Valiani, Tartaglia, Scoppetta, Renzone, Arena, Avellini, Benda, Gaiti and Scaloni2013) and bovine (Ying et al., Reference Ying, Yang, Tan, Liu, Zhuang, Xu and Ren2021) regarding mastitis were listed in this topic. A study on Canadian dairy cows demonstrated distinct differences in blood metabolomic profiles between mastitic and healthy individuals, highlighting their potential application in the early identification of animals susceptible to mastitis (Haxhiaj et al., Reference Haxhiaj, Li, Johnson and Dunn2022). Furthermore, metabolomic differences by applying alternative treatment techniques for women with mastitis were also shown (Vázquez-Fresno et al., Reference Vázquez-Fresno, Llorach, Marinic, Tulipani, Garcia-Aloy, Espinosa-Martos, Jiménez, Rodríguez and Andres-Lacueva2014). There were also studies related to the carbon and energy metabolism of mastitis pathogens listed in this topic (Li et al., Reference Li, Chen, Ma, Huang, Li, He, Kong, Tang, Chen, Ding and Zhang2024). This topic appeared to be the most recent, as indicated by the timing of the first publications, suggesting that metabolite-focused studies on mastitis were initiated later compared to other omics disciplines. This aligns with the historical timeline of omics technologies, as the term ‘metabolome’ was first introduced in 1998 (Arıkan and Muth, Reference Arıkan and Muth2023).

In Topic 5, researches performed with techniques such as GWAS and WGS of cattle were included. These studies revealed the genes related to milk quality and production (Liu et al., Reference Liu, Lund, Boichard, Karaman, Fritz, Aamand, Nielsen, Wang and Su2020), mastitis resistance (F Yang et al., Reference Yang, Chen, Li, Yan, Badri, Lv, Yu, Zhang, Jang and Li2019) and dairy-related other traits (Marete et al., Reference Marete, Lund, Boichard and Ramayo-Caldas2018). Local cattle breeds were also studied based on production-wise important traits (Schmidtmann et al., Reference Schmidtmann, Schönherz, Guldbrandtsen, Marjanovic, Calus, Hinrichs and Thaller2021). A study conducted on the Holstein breed compared mastitis-resistant and susceptible dairy cows, identifying 117 candidate SNPs and 27 QTLs that may serve as potential markers for selective breeding programs (Kurz et al., Reference Kurz, Yang, Weiss, Wilson, Rood, Liu and Wang2019). In a comprehensive study with 16 US dairy farms, breeding values of dairy-related diseases, including mastitis were predicted by using the Bayesian regression model (Lopes et al., Reference Lopes, Rosa, Pinedo, Santos, Chebel, Galvao, Schuenemann, Bicalho, Gilbert, Rodrigez-Zas, Seabury and Thatcher2020).

Topic 6 was found to be slightly overlapping with Topic 1, as shown on the intertopic distance map (see Fig. 3). Relevant keywords for this topic, such as ‘disease’, ‘anim’ and ‘pathogen’ etc. might explain mastitis and its agents. Keywords such as ‘loss’, ‘econom’ and ‘industry’ might be related to the cost of the disease in the dairy industry. The articles clustered on this topic support this claim as there were mainly studies mentioning the economic losses in dairy farms due to mastitis (Fenske et al., Reference Fenske, Noll, Blom, Ewers, Semmler, Fawzy and Eisenberg2022; Goldstone et al., Reference Goldstone, Harris and Smith2016; Opsal et al., Reference Opsal, Våge, Hayes, Berget and Lien2006; Taverna et al., Reference Taverna, Negri, Piccinini, Zecconi, Nonnis, Ronchi and Tedeschi2007). Another study, which offered potential Multiple Loci Variable Number of Tandem Repeats Analysis for S. uberis as a low-cost typing technique was located on this topic (Gilbert et al., Reference Gilbert, Fromageau, Lamoureux and Poutrel2006). Topic 6 has the least number of papers (60 articles) among all these topics. The reason for this might be that although mastitis is a well-known issue for the dairy sector, there were limited omics studies combined with its economy.

Topic 7 was named as ‘Molecular Diagnostic’ based on the frequent use of ‘dna’, ‘method’, ‘detect’, ‘pcr’, ‘assay’ and ‘molecular’. In this topic, researchers have used several molecular techniques, such as restriction fragment length polymorphism (RFLP) (Musser et al., Reference Musser, Schlievert, Chow, Ewan, Kreiswirth, Rosdahl, Naidu, Witte and Selander1990), pulsed-field gel electrophoresis (Fijałkowski et al., Reference Fijałkowski, Masiuk, Czernomysy-Furowicz, Karakulska, Nawrotek, Paszkowska, Struk and Giedrys-Kalemba2013), polymerase chain reaction (PCR; Matthews et al., Reference Matthews, Kumar, O'Conner, Harmon, Pankey, Fox and Oliver1994), short (Rahman et al., Reference Rahman, El Zowalaty, Falgenhauer, Khan, Alam, Popy and Rahman2024) and long-read sequencing technologies (White et al., Reference White, Bakker, Burton, Castro, Couldrey, Dyet, Eustace, Harland, Hutton and Macartney-Coxson2024), for the detection, identification and typing of mastitis pathogens.

The frequent keywords such as ‘milk’, ‘dairy’, ‘bacteria’, ‘microbiota’ and ‘composition’ in Topic 8 lead to ‘Microbiome’. Some examples of research related to this topic were milk microbiome characterisation for dairy ewes (Castro et al., Reference Castro, Alba, Aparicio, Arroyo, Jiménez, Fernández, Arias and Rodríguez2019) and cows (Gao et al., Reference Gao, Liu, Wang, Li, Wang, Wu, Zhang, Gao and Qi2020). Moreover, some studies screened specific mastitis agents in the bulk milk samples (Patel et al., Reference Patel, Joshi, Nauriyal and Kunjadiya2017; Xu et al., Reference Xu, Liu, Gao, Zhou, Yang, He, Kastelic, Deng and Han2021). A few examples of hindgut (Vasco et al., Reference Vasco, Hansen, Schilmiller, Bowcutt, Carbonell, Ruegg, Quinn, Zhang and Manning2024) and ruminal (Chang et al., Reference Chang, Wang, Zeng, Zhai, Huang, Wang and Han2023) microbiota studies were also found on this topic. Moreover, there were not only animal microbiome studies but also breast microbiota that were compared between non-puerperal mastitis patients and healthy females (Zhu et al., Reference Zhu, Wu, Liang, Mo, Qi, Liang, Lian, Qiu, Yu, Tang and Wu2022).

Topic 10 was related to polymorphism studies. Unlike Topic 5, there were mainly studies related to mastitis pathogens clustered. Some of the studies were as follows: S. aureus was characterised by random amplified polymorphic DNA-PCR (RAPD-PCR) (Myllys et al., Reference Myllys, Ridell, Björkroth, Biese and Pyörälä1997), fluorescence amplified fragment length polymorphism (fAFLP) (Trovó Fabiano et al., Reference Trovó Fabiano, Lemos and Givisiez2005) and other typing techniques (Vautor et al., Reference Vautor, Cockfield, Le Marechal, Le Loir, Chevalier, Robinson, Thiery and Lindsay2009), S. uberis was differentiated by RFLP (Jayarao et al., Reference Jayarao, Doré, Baumbach, Matthews and Oliver1991), Coagulase-Negative Staphylococci was isolated by RFLP-PCR (Melo et al., Reference Melo, Motta, Rojas, Soares, Coelho, Coelho and Souza2018). Studies using SNPs for S. aureus (Huang et al., Reference Huang, Zhu, Yan, Lin, Ding, He, Li, Ying, Shen and Jiang2024), S. agalactiae (Wang et al., Reference Wang, Li, Huang, Luo, Liang, Gan, Huang, Lei, Chen and Chen2015) and Mycoplasma bovis (Kumar et al., Reference Kumar, Register, Christopher-Hennings, Moroni, Gioia, Garcia-Fernandez, Nelson, Jelinski, Lysnyansky and Bayles2020) were also among the clustered articles on this topic.

Although two major bibliographic databases, Scopus and Web of Science, were utilised in this study, a key limitation was the exclusion of other relevant databases, such as PubMed and Cochrane, which may have resulted in the omission of omics-related mastitis studies indexed exclusively in those sources. Another constraint was that only articles published in English were considered, potentially limiting the inclusion of valuable regional research published in other languages. Despite a meticulous manual screening process, it is possible that some studies that did not fully meet the inclusion criteria – such as review articles or papers not directly addressing the omics-based investigation of mastitis (e.g. studies related to dairy products rather than mastitis) – remained in the dataset. Moreover, a critical limitation stems from the broader issue of publication bias, where studies reporting statistically significant findings are more likely to be published, while non-significant results are underreported (Rosenthal, Reference Rosenthal1979), potentially skewing the thematic distribution observed in this type of analysis.

In conclusion, this study provides a comprehensive overview of omics-related research on mastitis through the application of bibliometric analysis integrated with text-mining techniques. The use of LDA enabled the classification of omics approaches applied to mastitis into ten distinct thematic topics. The quantitative findings offer valuable insights into current research trends and highlight existing gaps in the literature. The results suggest that advancements in high-throughput technologies, such as NGS, have contributed to the growing prevalence of omics studies in this field. As the costs associated with sequencing technologies continue to stabilise, the accessibility of such methods is likely to increase, facilitating broader research efforts, particularly in developing countries. Furthermore, there remains a need for additional omics research focusing on mastitis in livestock species beyond dairy cattle, such as sheep and goats, as well as in the context of human mastitis.