It is well known that cheese yield and quality are affected by animal genetics, milk quality (chemical, physical, and microbiological), production technology, and the type of rennet and dairy cultures used in production. Major differences in the same type of cheese (i.e., hard cheese) are caused by the rennet and dairy cultures, which affect the ripening process. This cited study ”Challenging Sustainable and Innovative Technologies in Cheese Production: A Review”-Fabijan Ostarc, Neven Antunac, Vlatka Cubric-Curik, Ino Curik, Slaven Juri , Snježana Kazazi, Marta Kis, Marko Vincekovi, Nevijo Zdolec, Jasminka Spoljari and Natasa Mikulec-Zagreb University) aims to explore current technological advancements in animal genetics, methods for the isolation and production of rennet and dairy cultures, along with possible applications of microencapsulation in rennet and dairy culture production, as well as the challenge posed to current dairy technologies by the preservation of biodiversity. 

Cheese Quality and Bovine Genotype

Cheese production is a process that dates back thousands of years, and cheese, found in every part of the world, has earned its place among the top food products due to its nutritional value and rich diversity. The earliest evidence of cheese production can be found in cave paintings around 5000 BC.

During that time, intentional conversion of milk into cheese became possible, making it safer and more durable. Since its beginnings, cheese production technology has changed due to scientific advancements in materials and production processes. Currently, cheese production has reached a very high level thanks to modern materials and incorporated technology.

The quality of cheese is affected by the genotypic and phenotypic characteristics of the animals, the chemical and microbiological properties of the milk, and the production technology. An important factor in the diversity of milk within the same breed comes from milk cultures and the type of rennet used in cheese production, as they affect the chemical processes during production and maturation.

Currently, cheese quality is still affected by the same factors, but due to globalization and industrial progress, originality and differences in certain regions have been lost. Large-scale cheese production relies mainly on the use of pasteurized milk, and cheese producers are supplied by a few manufacturers of dairy cultures and rennet from around the world.

A Complex Process

When considering the same type of matured cheese production, this means that the differences between producers are based solely on the quality of the milk and the selection of rennet and dairy cultures. Milk characteristics that affect cheese quality are regulated by animal genetics, which create the possibility of using quantitative genetics for biodiversity conservation and indigenous traits.

Raw milk and cheeses made from raw, unpasteurized milk provide rich sources of beneficial microbiota, such as lactic acid bacteria (LAB) with probiotic properties. Isolating indigenous LAB cultures and using them in cheese production can lead to biodiversity conservation and better diversification among cheese producers at a global and regional level.

Microencapsulation is seen as a novel approach for biodiversity conservation and the delivery of important ingredients in cheese. However, it should be noted that successfully encapsulating relevant beneficial components for cheese production, such as microorganisms, enzymes, peptides, aromatic compounds, chemical agents (Ca2+), and even essential oils, can be extremely challenging.

Furthermore, combinations of more than one active ingredient can make the encapsulation process even more complex. Considering that limited research has been conducted on this topic, current progress has been investigated in DNA characterization and the use of quantitative genetics to improve desired traits in dairy animals, rennet production, the analysis, isolation, and production of lactic acid bacteria, and the potential applications of microencapsulation in developing new, innovative, and sustainable technologies in cheese production, with a focus on indigenous forms and biodiversity conservation.

Genetics Impact on Milk Quality

Cheese production is a complex process, and its quality and uniqueness depend on a variety of factors. The composition and characteristics of raw milk, which are largely determined by the genetics of the animals involved in its production, are certainly among the important factors that contribute to the uniqueness of cheese as the final product.

Therefore, the genetics of an individual or a specific population (breeds) is one of the crucial elements for the successful production of indigenous dairy products, which are often commercially associated with Protected Designation of Origin (PDO).

At the same time, the influence of animal genetics on the quality and distinctive character of cheese aligns with the concept of sustainable animal breeding and the protection of farm animal diversity and dairy products.

Polygenic Inheritance

It was recognized early on that milk secretion is influenced by heredity. Later, it was clearly defined that the composition and characteristics of raw milk are continuously measured and, like most economically relevant production traits, are inherited as quantitative or complex genetic traits.

For a long time, the inheritance of quantitative traits was successfully modeled by an infinitesimal model, in which an infinite number of loci, each with an infinitesimal effect (polygenic component), and environmental influences are responsible for the measured variations (phenotypic).

Therefore, in many cases, the inheritance of milk quantity and composition has been quantified through parameters such as heritability, specifically narrow-sense heritability (h2), which represents the proportion of phenotypic variation explained by the effects of additive genes.

Milk yield, milk fat yield, and later, protein and lactose yield, have had the longest tradition of measurement and have been recognized as the most important factors in milk production for a century. Therefore, it is not surprising that the quantitative inheritance of these traits is among the most well-studied, with a large number of studies providing estimates of heritability and genetic correlations.

It is important to emphasize that traditional milk production traits (milk yield, fat yield, and protein yield) have been successfully incorporated into breeding programs because they meet the desirable characteristics of selection programs. According to Shook, successful breeding programs are based on preferred traits that should meet the following selection criteria:

(1) They should have relatively high genetic variability and heritability. (2) They should have economic value that increases production profitability. (3) They should be positively correlated with other traits used in the breeding program. (4) They should be clearly defined and measurable at low costs.

Analyses of the Genome and Genomic Selection

Recent developments in molecular genetics have allowed for genotyping a large number of markers distributed throughout the genome, significantly improving our understanding of the variation in complex traits.

Based on new evidence from Genome-Wide Association Studies (GWAS), the infinite-locus inheritance concept has been revised to a mixed inheritance model, where the variability of complex traits is caused by a polygenic component (many genes with small effects), as well as the existence of a set of candidate genes with moderate to large effects that rarely explain more than 5% of the phenotypic variation in humans.

In human populations, genes with moderate to large effects occur at low frequencies as rare or private mutations, while the occurrence of mutations with large effects at moderate or high frequencies has been more documented in animal populations. This difference is considered to be the result of artificial selection, specific breeding pressures, and objectives.

In general, the invention of the concept of genomic selection and the availability of cost-effective high-throughput genotyping data have revolutionized animal breeding in the past two decades. As a result, numerous GWAS studies have been conducted on milk production and technological traits in cattle, sheep, and goats, identifying a number of influential causal genes (mutations) and/or statistically associated markers.

While some of these analyses have confirmed previous study results, they have also successfully identified genes that have shed light on the biological understanding of milk as a raw material for cheese production. Overall, genomic selection significantly accelerates annual genetic gain by shortening the generation interval, enabling more efficient and faster achievement of breeding objectives.

Nomenclature and Function of Rennet

Peptidases are a large and important group of enzymes, many of which have been applied in various technological processes involving food, beverages, animal feed, pharmaceuticals, detergents, chemical production, leather processing, paper, and textiles.

The active coagulation of milk with enzymes used in cheese production is achieved by aspartic proteinases (EC 3.4.23), which are so named because the aspartic residues (Asp) act as ligands to water molecules in their active sites, mediating nucleophilic attacks on peptide bonds.

In the majority of known aspartic peptidases, two Asp residues work together to bind and activate the catalytic water molecules, but in some cases, residues of other amino acids substitute the second Asp. A notable characteristic of aspartic peptidases is that all enzymes described so far are endopeptidases. Endopeptidases cleave peptide bonds in the interior parts of their polypeptide chains, far away from the N- and C-terminal ends.

Peptidases are distinguished by the functional group in their active site:

(A) for aspartate, (C) for cysteine, (G) for glutamic acid, (M) for metallo, (P) for mixed, (S) for serine, (T) for threonine, (N) for asparagine lyase, and (U) for unclassified peptidases of unknown catalytic type.

Based on statistically significant similarities in their primary structures, peptidases are classified into families (approximately 268), identified by a letter representing their catalytic type. Additionally, some families may contain subfamilies.

Subfamilies

Some families are divided into subfamilies based on evidence of ancient divergence within the family (e.g., S1A, S1B). The families are then grouped into approximately 62 clans based on similarity in their 3D structures. Some clans are further divided into subclans based on evidence of ancient divergence within the clan.

The clan name is composed of two letters. The first letter represents the catalytic type, and the second letter is sequentially assigned to identify each clan. This classification method is used in the MEROPS database for peptidases and the proteins that inhibit them.

In addition to this classification, based on the functional group in the active site of the peptidase, peptidases are further classified according to the specificity for certain amino acids (sequence specificity) that form susceptible peptide bonds. For example, aspartic endopeptidases, from the pepsin family, hydrolyze peptide bonds with large hydrophobic amino acids at P1 or P10.

Aspartic peptidases are assigned to clans AA, AC, AD, AE, and AF. These are a group of peptidases from the pepsin family (A1), sharing the same catalytic mechanism, and typically functioning in acidic solutions, hence called acid peptidases.

Aspartic peptidases have a long history and are found in animals, fungi, plants, protozoa, bacteria, and viruses. Their specific action in acidic environments limits their functions in living organisms, and they are less abundant than other groups of peptidases, but due to their physiological and commercial importance, this class of peptidases is unique.

Most aspartic proteases show maximum activity at low pH (pH 3 to 4) and have isoelectric points in the pH range of 3.0 to 4.5. Their molecular weights range from 30 to 45 kDa. According to its classification in the MEROPS database, the peptidase family A1 (pepsin family) is the largest of all, containing 246 homologous proteins as of 2021. This family is mainly composed of mammalian and fungal aspartic peptidases.

Deschamps performed the first isolation of this enzyme in 1840 and suggested the name chymosin (Gr. chyme, gastric juice). In 1890, Lea and Dickinson suggested the name rennin (derived from clot), but due to confusion with the related proteolytic enzyme, rennin, from the kidney, in 1970, Foltman suggested Procese 2022, a return to the first name chymosin, which was accepted by the International Union of Biochemistry and Molecular Biology (IUBMB).

In 1872, Olof Hammarsten showed that clot (chymosin) was synthesized and stored in an inactive form and activated by contact with stomach acid. This was the first in-depth study of the effects of rennet on casein.

Pepsin

According to the MEROPS data banks, pepsin A is a member of the AA clan, (pepsin) A1 family, A1A peptidase subfamily of the third class of hydrolases [127,132]. Pepsin A (EC 3.4.23.1), known simply as pepsin, was discovered and recognized in the 18th century as the first enzyme that begins the digestion of food proteins in the stomach.

Pepsin was originally named by Schwann in 1825. Pepsin is the predominant gastric peptidase in the bottom of adult mammals and is exceptionally stable and active under acidic conditions. It is an endopeptidase characterized by specific lower activity, overall high proteolytic activity and a high pH dependence.

Pepsin B (EC 3.4.23.2) is a minor proteinase found in porcine stomachs. Plant aspartic peptidases are members of the AA and AD clans. In the AA clan, they are distributed among families A1, A3, A11 and A12. Most plant aspartic peptidases, together with pepsin-like enzymes, belong to the A1 family [133].

Rennet

The rennet (a mixture of chymosin and pepsin) is produced in the fourth stomach (abomasum) of lactating ruminants (calves, lambs, kids/goats, etc.). Chymosin is the neonatal peptidase that has a postnatal uptake of immunoglobulins and can be found in fetuses as early as the sixth month of gestation, with the greatest increase between the ninth month and the third or fourth day postpartum.

The reason for the secretion of chymosin in the stomach of newborn ruminants is to coagulate the milk to increase the nutritional value during retention in the intestine, allowing the young animals to use more nutrients.

Both peptidases (chymosin and pepsin) are secreted in their inactive form as zymogens (prochymosin and pepsinogen) in the duct with a direct connection to the lumen of the stomach and are activated by the low pH in the abomasum chymosin and pepsin by removing the N-pro segment terminal. The pro segment is responsible for the stability of inactive forms of zymogens and prevents substrate binding in the active site.

Chymosin is a polypeptide made of 323 amino acids, with a molecular weight of 35.6 kDa. In general, chymosin has a low proteolytic activity level but high milk coagulation activity.

Calf chymosin has narrow substrate specificity and cleaves the specific single peptide bond in -casein between Phe 105 and Met 106, converting it to an insoluble calcium para-casein clot form. According to Harboe et al., chymosin was found to have high activity against milk of its own species.

Calf chymosin is found in three allelic forms: A, B, and C. Chymosin B is the most abundant clot. The main difference between variants A and B is that variant A has an aspartic acid (Asp) at position 244 and variant B has a glycine (Gly) at the same position.

Variant A has 50% more proteolytic activity than form B. Variant C is genetically distinct and is the product of a different allele. Due to its use in industry, calf chymosin is well investigated and characterized at the enzymatic and molecular level.

As a conclusion, we can say that, along with animal genetics, the use of different curds also contributes to the originality of matured cheeses obtained from milk processing.