The Biological Science Research Laboratory of Kao Corporation (President: Michitaka Sawada,) analyzed ribonucleic acid (RNA) molecules in skin surface lipids (SSL-RNA) collected from infants with atopic dermatitis (AD) and reports that changes to SSL-RNA including those known to be characteristically altered in pediatric AD were observed. They also found that it is possible to distinguish healthy infants from those with AD using information derived from the RNA expression of thymus and activation-regulated chemokine (TARC). *1
Furthermore, the laboratory has developed a technology to collect both SSL-RNA and proteins in skin surface lipids (SSL-protein) simultaneously using a single oil-blotting film.
These study findings were presented at the 69th Annual Meeting of the Japanese Society of Allergology in conjunction with the World Allergy Congress 2020 that was held online from September 17 to October 20, 2020.
Early-onset AD is known to cause roughness and itchiness of the skin and is associated with subsequent development of various allergies such as food allergies and asthma (i.e., atopic march) during infancy and childhood. Therefore, AD symptoms should be detected as early as possible and be treated appropriately. However, it is difficult to distinguish AD from other rashes that frequently occur in infants. In addition, long-term observation of skin conditions is required for definitive diagnosis of AD, placing a heavy burden on physicians, infants, and their parents.
The SSL-RNA monitoring technology reported by Kao in 2019*2 allows for easy collection of SSL-RNA from facial sebum for analysis without damage to the skin. Utilizing this technology, Kao studied whether it is possible to detect changes in molecules in the skin of infants with AD.
Based on interviews and skin examinations by a dermatologist, 20 healthy infants (healthy group) and 16 infants with AD (AD group) were included in the study. SSL-RNA was analyzed using facial sebum collected from these infants. The researchers compared RNA expression of 3,217 RNAs obtained from the analysis between the healthy and AD groups and found differences in SSL-RNA expression information.
They subsequently analyzed the functions of RNAs that displayed different behaviors between the healthy and AD groups. The results of this analysis showed that the expression of RNAs associated with an inflammatory reaction increased while that of RNAs important for barrier functions decreased in the AD group. RNAs associated with the synthesis and metabolism of lipids, which have been reported to decrease specifically in pediatric AD, were included in the molecules associated with barrier functions. Therefore, it seemed that this technology may have detected changes in molecules characteristic of AD in infants (Figure 1).
Since these findings showed that SSL-RNA may be used to identify infants with AD presenting mild symptoms that are difficult to observe visually, the researchers further studied whether the skin conditions of infants with AD can be distinguished from those of healthy infants based only on the analysis of SSL-RNA. RNAs important for identifying infants with AD were selected using a machine learning technique called random forest. As a result, it was found that a molecule called TARC, which has already been used as a diagnostic and severity marker of AD, is the most important for distinguishing between healthy and AD-affected skin. Therefore, they developed a discriminant model to distinguish healthy infants from infants with AD using the information on TARC expression in SSL-RNA. The results of the analysis using this model suggested that they may be distinguished with a precision of 89% (sensitivity: 75%; specificity: 100%)*3 (Table 1).
In conventional SSL-RNA monitoring, residues remaining after extraction and purification of SSL-RNA were not used and disposed instead. Although many proteins are contained in the residues, no methods for purification and analysis of these proteins had yet been established. However, Kao considered that, if the information on the expression of these proteins can be analyzed, it would be possible to acquire a wide range of biological information including both RNA and protein expression profiles from a single oil-blotting film. Therefore, Kao explored methods to extract and analyze proteins from these residues and succeeded in developing a technology to comprehensively analyze SSL-protein (Figure 2).
Using this technology, the researchers extracted and analyzed proteins from residues obtained after extracting SSL-RNA from the sebum of infants. As a result, they could detect as many as 800 proteins. In these molecules, those that were conventionally difficult to detect by RNA analysis were also included, suggesting that analysis of proteins simultaneously with RNAs may lead to a deeper understanding of biological functions.
Moreover, in the comparison between the healthy and AD groups, AD-specific changes in expression, which have been reported in a previous study of pediatric AD, were also found in SSL-protein, suggesting that the analysis of SSL-protein may be used to monitor the state of AD in infants.
Infants often do not complain of symptoms such as itchiness because they do not have the words to describe them. Therefore, objective signs of skin and body conditions of infants are very useful for both infants and parents in the awareness of various diseases. In their study, Kao found that AD-specific changes in RNA molecules can be detected by a technique using SSL-RNA and showed that it is possible to identify AD based only on SSL-RNA information.
Moreover, Kao has newly developed a technology to analyze protein expression in sebum using residues obtained from the SSL-RNA extraction process. The company considers that the use of the information available on both SSL-RNA and SSL-protein will enable us to closely monitor AD conditions and to understand the disease mechanism of AD.
Kao will conduct a further study on the early-onset AD in infants aged 6 months or younger using SSL-RNA and SSL-protein in collaboration with the National Center for Child Health and Development.
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